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Examining the effector mechanisms of Xuebijing injection on COVID-19 based on network pharmacology

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机构: [1]The First Clinical Medical School of Guangzhou University of Chinese Medicine, Guangzhou, China. [2]The Second Clinical Medical School of Guangzhou University of Chinese Medicine, Guangzhou, China. [3]The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. [4]Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China.
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关键词: Active ingredient Coronavirus disease 2019 Effector mechanism Molecular docking Network pharmacology Xuebijing

摘要:
Background Chinese medicine Xuebijing (XBJ) has proven to be effective in the treatment of mild coronavirus disease 2019 (COVID-19) cases. But the bioactive compounds and potential mechanisms of XBJ for COVID-19 prevention and treatment are unclear. This study aimed to examine the potential effector mechanisms of XBJ on COVID-19 based on network pharmacology. Methods We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ's effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a "XBJ active-compound-potential-effector target" network and protein-protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) biological process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ's effector targets. We used AutoDock vina and PyMOL software for molecular docking. Results We obtained 144 potential COVID-19 effector targets of XBJ. Fourteen of these targets-glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3),MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2),JUN, interleukin-2 (IL-2), estrogen receptor 1 (ESR1), andMAPK14had degree values > 40 and therefore could be considered key targets. They participated in extracellular signal-regulated kinase 1 and 2 (ERK1,ERK2) cascade, the T-cell receptor signaling pathway, activation ofMAPKactivity, cellular response to lipopolysaccharide, and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin-angiotensin system (RAS), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kappa B),MAPK, phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K)-protein kinase B (Akt)-vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a "drug-ingredient-target-pathway" effector network. The molecular docking results showed that the core 18 effective ingredients had a docking score of less than - 4.0 with those top 10 targets. Conclusion The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. Our study demonstrated that key ingredients and their targets have potential binding activity, the existing studies on the pharmacological mechanisms of XBJ in the treatment of sepsis and severe pneumonia, could explain the effector mechanism of XBJ in COVID-19 treatment, and those provided a preliminary examination of the potential effector mechanism in this disease.

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出版当年[2019]版:
大类 | 4 区 生物
小类 | 3 区 数学与计算生物学
最新[2025]版:
大类 | 3 区 生物学
小类 | 3 区 数学与计算生物学
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出版当年[2018]版:
Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
最新[2023]版:
Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

影响因子: 最新[2023版] 最新五年平均 出版当年[2018版] 出版当年五年平均 出版前一年[2017版] 出版后一年[2019版]

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第一作者机构: [1]The First Clinical Medical School of Guangzhou University of Chinese Medicine, Guangzhou, China.
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