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基于网络药理学和动物实验探讨三阳合治法治疗流感的作用及机制

Analysis of effect and mechanism of "San-Yang" combined treatment for influenza based on network pharmacology and animal experimentation
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摘要 目的探讨基于三阳合治法的麻杏石甘汤合小柴胡汤治疗流感的治疗作用及潜在靶点、作用机制。方法基于TCMSP数据库筛选麻杏石甘汤合小柴胡汤的活性成分及靶点,通过OMIM、Genecards、DisGeNET及TTD数据库获取流感相关靶点,取二者交集得到共同靶点,导入STRING数据库,构建蛋白互作网络,筛选药物治疗流感的关键靶点。通过DAVID数据库,对共同靶点进行富集分析。基于CytoScape3.9.0平台构建成分-流感靶点-通路网络图。应用Sailvina对关键靶点与主要活性成分进行对接验证。同时构建流感病毒性肺炎动物模型,验证三阳合治法对流感小鼠肺指数、肺组织病理损伤及炎性损伤的保护作用。结果共检索并筛选到麻杏石甘汤合小柴胡汤活性成分184种,靶点316个,流感靶点2894个,二者共同靶点152个,构建PPI网络筛选关键靶点有AKT1、STAT3、TNF等。GO和KEGG富集分析提示,麻杏石甘汤合小柴胡汤可能是通过调节IL-17、TNF、Toll样受体等信号通路治疗流感。分子对接显示,关键靶点与主要活性成分具有较好的结合能力。动物实验表明,麻杏石甘汤合小柴胡汤能改善病毒性肺炎小鼠体重下降、肺指数及肺组织炎症情况,并改善肺组织病理损伤、血清CCL3、CCL5因子水平。结论三阳合治法对于流感病毒性肺炎小鼠具有较好的保护作用,可通过多靶点、多通路发挥抗病毒作用。 ObjectiveTo investigate the therapeutic effects as well as potential targets and mechanisms of Maxing Shigan decoction combined with Xiaochaihu decoction for influenza treatment based on"San-Yang"combined treatment theory.MethodsThe active components and targets of Maxing Shigan Decoction and Xiaochaihu Decoction were screened based on TCMSP database.The influenza-related targets were acquired through OMIM,Genecards,DisGeNET and TTD databases,and the common targets were obtained from their intersection.The common targets were imported into STRING database to construct a protein interaction network(PPI)for screening key targets.The enrichment of common targets were analyzed through DAVID database.The diagram of composition-influenza target-pathway network was constructed based on CytoScape3.9.0 platform to screen the main active components of the drugs.Sailvina was used to verify the docking between key targets and main active components.The animal model of influenza virus pneumonia was constructed to verify the protective effect of"San-Yang"combined treatment on lung index,lung pathological injury and inflammatory injury in influenza mice.ResultsA total of 184 active components and 316 targets in Maxing Shigan decoction and Xiaochaihu decoction,as well as 2894 targets in influenza were found in screening,and 152 were the common targets.The key targets of PPI network screening included AKT1,STAT3 and TNF,etc.The enrichment analysis of GO and KEGG suggested that Maxing Shigan Decoction and Xiaochaihu Decoction may treat influenza by regulating IL-17,TNF,Toll-like receptor signaling pathway.Molecular docking showed that the key targets have good binding ability with the main active components.Animal experiments showed that Maxing Shigan decoction combined with Xiaochaihu decoction improved the citeria,including weight loss,lung index and reduced the inflammation,with significantly improvement of pathologic injury in lung tissues and levels of serum CCL3 and CCL5 factors.Conclusions"San-Yang"combined treatment showed good protective effect on mice with influenza viral pneumonia,and may exert anti-influenza virus effects through multi-targets and multi-pathways.
作者 王明哲 张亚楠 李德莹 刘畅 季舒杨 沈冠彤 乜炜成 翟志光 宋利琼 李金桐 王成祥 张立山 班承钧 程淼 Wang Mingzhe;Zhang Yanan;Li Deying;Liu Chang;Ji Shuyang;Shen Guantong;Nie Weicheng;Zhai Zhiguang;Song Liqiong;Li Jintong;Wang Chengxiang;Zhang Lishan;Ban Chengjun;Cheng Miao(Respiratory Department,Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine,Beijing 100700,China;Respiratory Department,Henan Province Hospital of TCM,Zhengzhou 400053,China;Institute of Basic Theory for Chinese Medicine,China Academy of Chinese Medical Sciences,Beijing 100700,China;State Key Laboratory of Infectious Disease Prevention and Control,National Institute for Communicable Disease Control and Prevention,Chinese Center for Disease Control and Prevention,Beijing 102206,China;Respiratory Department,Beijing University of Chinese Medicine Third Affiliated Hospital,Beijing 100029,China)
出处 《国际病毒学杂志》 北大核心 2024年第4期285-290,共6页 International Journal of Virology
基金 东直门医院高水平医院项目——青年后备人才(DZMG-QNHB0001) 国家自然科学基金(81973784) 北京市中医药科技发展资金项目(JJ-2020-84) 北京市自然科学基金(7232293)。
关键词 麻杏石甘汤 小柴胡汤 流感 网络药理 分子对接 Maxing Shigan decoction Xiaochaihu decoction Influenza Network pharmacology Molecular docking
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