赤芍抗脓毒症凝血功能的分子机制:网络药理学和分子对接方法

Shan Gao, Dongsheng Wang
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引用次数: 0

摘要

基于网络药理学和分子对接方法,探讨丹芍治疗脓毒症致凝血病(SIC)的有效成分及其作用机制。目前,还没有药物被批准用于治疗SIC。从中药中鉴别SIC药物是一个令人鼓舞的研究方向。通过对TCMSP数据库的分析,确定了PRR的预测靶点和有效成分。利用生物信息学数据库确定SIC的疾病靶点。然后将这些关键靶点上传到STRING数据库,以生成蛋白质-蛋白质相互作用网络。使用R v4.1.2软件中的ORG包对关键靶点进行功能和途径富集分析。最后,利用discovery studio软件对关键目标和有效成分进行对接分析。鉴定出9种化学活性成分和84种与药物和SIC相关的共同靶点。PPI网络分析确定了几个关键目标。进一步分析发现了几个信号通路的富集;这些变化可能对许多生物过程产生影响,包括对外源刺激、氧化应激、细菌源分子的反应,从而发挥抗sic的药理作用。根据分子对接结果,这些关键靶点与活性成分具有较强的结合亲和力。PRR可通过给药核心靶基因(如CASP3、PTGS2、TP53、AKT1、MMP9、TNF、JUN、IL6、CXCL8)和调节多个关键通路(如脂质和动脉粥样硬化通路)来促进SIC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Molecular Mechanisms Underlying the Effect of Paeoniae Radix Rubra On Sepsis-Induced Coagulopathy: A Network Pharmacology and Molecular Docking Approach
To investigate the effective components and underlying mechanism of Paeoniae radix rubra (PRR) in treating sepsis-induced coagulopathy (SIC) on the basis of network pharmacology and molecular docking approaches. At present, no therapeutic agent has been approved for the treatment of SIC. Identifying drugs for SIC from Chinese medicine is an encouraging research direction. The predicted targets and effective components of PRR were identified by analysis of the TCMSP database. Bioinformatics databases were employed to identify the disease targets of SIC. These key targets were then uploaded to the STRING database to generate protein-protein interaction networks. The ORG package in R v4.1.2 software was applied for functional and pathway enrichment analyses of the key targets. Finally, discovery studio software was used to perform docking analyses of key targets and effective components. Nine chemically active components and eighty-four common targets associated with drugs and SIC were identified. PPI network analysis identified several key targets. Further analysis identified enrichment in several signaling pathways; these changes could exert influence on a number of biological processes, including responses to xenobiotic stimuli, oxidative stress, molecules of bacterial origin, thus playing an anti-SIC pharmacological role. According to molecular docking results, these key targets had strong binding affinity to the active components. PRR can contribute to SIC by medicating core target genes (e.g., CASP3, PTGS2, TP53, AKT1, MMP9, TNF, JUN, IL6, and CXCL8), and regulating multiple key pathways (e.g., the Lipid and atherosclerosis pathway).
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