中药治疗人类冠状病毒 COVID-19 引起的疾病的机理研究

IF 2.4 3区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Xian-Fang Wang, Chong-Yang Ma, Zhi-Yong Du, Yi-Feng Liu, Shao-Hui Ma, Sang Yu, Rui-xia Jin, Dong-qing Wei
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The main purpose of this paper is to analyze the effective components and action targets of the Longyi Zhengqi formula and Lianhua Qingwen formula, study their mechanism of action in the treatment of new coronavirus pneumonia (new coronavirus pneumonia), compare the similarities and differences of their pharmacological effects, and obtain the pharmacodynamic mechanism of the two traditional Chinese medicine compounds. Method: Obtain the effective ingredients and targets of Longyi-Zhengqi Formula and Lianhua- Qingwen Formula from ETCM (Encyclopedia of Traditional Chinese Medicine) and other traditional Chinese medicine databases, use GeneCards database to obtain the relevant targets of COVID-19, and use Cytoscape software to build the component COVID-19 target network of Longyi-Zhengqi Formula and the component COVID-19 target network of Lianhua-Qingwen Formula. STRING was used to construct a protein interaction network and screen key targets. GO (Gene Ontology) was used for enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) was used for pathways to find out the targets and pathways related to the treatment of COVID-19. Results: In the GO enrichment analysis results, there are 106 biological processes, 31 cell localization and 28 molecular functions of the intersection PPI network targets of Longyi-Zhengqi Formula- COVID-19, 224 biological processes, 51 cell localization and 55 molecular functions of the intersection PPI network targets of Lianhua-Qingwen Formula-COVID-19. In the KEGG pathway analysis results, the number of targets of Longyi-Zhengqi Formula on the COVID-19 pathway is 7, and the number of targets of Lianhua-Qingwen Formula on the COVID-19 pathway is 19; In the regulation analysis results, Longyi-Zhengqi Formula achieves the effect of treating COVID-19 by regulating IL-6, and Lianhua-Qingwen Formula achieves the effect of treating pneumonia by regulating TLR4. 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引用次数: 0

摘要

背景:人类冠状病毒是一大类病毒,广泛存在于自然界中,通过自我复制进行繁殖。由于其突发性和变异性,它对全球人类健康构成了巨大威胁,也是医疗卫生领域目前面临的一个主要问题:人类冠状病毒是一大类病毒,广泛存在于自然界中,通过自我复制进行繁殖。由于其突发性和变异性,它对全球人类健康构成了巨大威胁,也是医学和卫生领域目前面临的主要问题。目的:COVID-19 是已知的第七种可感染人类的冠状病毒。本文的主要目的是分析龙益正气方和莲花清心方的有效成分和作用靶点,研究其治疗新型冠状病毒肺炎(新型冠状病毒肺炎)的作用机制,比较其药理作用的异同,获得两种中药复方的药效学机制。方法从ETCM(Encyclopedia of Traditional Chinese Medicine)等中药数据库中获取龙益正气方和连花清瘟方的有效成分和靶点,利用GeneCards数据库获取COVID-19的相关靶点,利用Cytoscape软件构建龙益正气方的组分COVID-19靶点网络和连花清瘟方的组分COVID-19靶点网络。STRING 用于构建蛋白质相互作用网络和筛选关键靶标。利用GO(Gene Ontology)进行富集分析,利用KEGG(Kyoto Encyclopedia of Genes and Genomes)进行通路分析,寻找与COVID-19治疗相关的靶点和通路。结果在GO富集分析结果中,龙益正气方-COVID-19的交叉PPI网络靶点有106个生物过程、31个细胞定位和28个分子功能;莲花清心方-COVID-19的交叉PPI网络靶点有224个生物过程、51个细胞定位和55个分子功能。在KEGG通路分析结果中,龙益正气方在COVID-19通路上的靶点数为7个,连花清瘟方在COVID-19通路上的靶点数为19个;在调控分析结果中,龙益正气方通过调控IL-6达到治疗COVID-19的效果,连花清瘟方通过调控TLR4达到治疗肺炎的效果。结论本文基于网络药理学的方法,探讨了龙益正气方和连花清瘟方治疗COVID-19的作用机制,从药物靶点、疾病相互作用等方面为中药治疗人类冠状病毒所致突发性疾病提供了理论依据。具有一定的现实意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Mechanism of Traditional Chinese Medicine Treatment for Diseases caused by Human Coronavirus COVID-19
Background: Human coronaviruses are a large group of viruses that exist widely in nature and multiply through self-replication. Due to its suddenness and variability, it poses a great threat to global human health and is a major problem currently faced by the medical and health fields. background: Human coronaviruses are a large group of viruses that exist widely in nature and multiply through self-replication. Due to its suddenness and variability, it poses a great threat to global human health and is a major problem currently faced by the medical and health fields. Objective: COVID-19 is the seventh known coronavirus that can infect humans. The main purpose of this paper is to analyze the effective components and action targets of the Longyi Zhengqi formula and Lianhua Qingwen formula, study their mechanism of action in the treatment of new coronavirus pneumonia (new coronavirus pneumonia), compare the similarities and differences of their pharmacological effects, and obtain the pharmacodynamic mechanism of the two traditional Chinese medicine compounds. Method: Obtain the effective ingredients and targets of Longyi-Zhengqi Formula and Lianhua- Qingwen Formula from ETCM (Encyclopedia of Traditional Chinese Medicine) and other traditional Chinese medicine databases, use GeneCards database to obtain the relevant targets of COVID-19, and use Cytoscape software to build the component COVID-19 target network of Longyi-Zhengqi Formula and the component COVID-19 target network of Lianhua-Qingwen Formula. STRING was used to construct a protein interaction network and screen key targets. GO (Gene Ontology) was used for enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) was used for pathways to find out the targets and pathways related to the treatment of COVID-19. Results: In the GO enrichment analysis results, there are 106 biological processes, 31 cell localization and 28 molecular functions of the intersection PPI network targets of Longyi-Zhengqi Formula- COVID-19, 224 biological processes, 51 cell localization and 55 molecular functions of the intersection PPI network targets of Lianhua-Qingwen Formula-COVID-19. In the KEGG pathway analysis results, the number of targets of Longyi-Zhengqi Formula on the COVID-19 pathway is 7, and the number of targets of Lianhua-Qingwen Formula on the COVID-19 pathway is 19; In the regulation analysis results, Longyi-Zhengqi Formula achieves the effect of treating COVID-19 by regulating IL-6, and Lianhua-Qingwen Formula achieves the effect of treating pneumonia by regulating TLR4. Conclusion: This paper explores the mechanism of action of Longyi-Zhengqi Formula and Lianhua-Qingwen Formula in treating COVID-19 based on the method of network pharmacology, and provides a theoretical basis for traditional Chinese medicine to treat sudden diseases caused by human coronavirus in terms of drug targets and disease interactions. It has certain practical significance.
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来源期刊
Current Bioinformatics
Current Bioinformatics 生物-生化研究方法
CiteScore
6.60
自引率
2.50%
发文量
77
审稿时长
>12 weeks
期刊介绍: Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
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