Yu-tong Liu, Yongli Situ, Tingting Zhao, Lina Long, Hekun Zeng, Shangdong Liang, G. Schmalzing, Hong-Wei Gao, Jinbin Wei, Chuan-Hua He, Hong Nie
{"title":"网络药理学研究重楼镇痛作用机理","authors":"Yu-tong Liu, Yongli Situ, Tingting Zhao, Lina Long, Hekun Zeng, Shangdong Liang, G. Schmalzing, Hong-Wei Gao, Jinbin Wei, Chuan-Hua He, Hong Nie","doi":"10.4103/wjtcm.wjtcm_84_20","DOIUrl":null,"url":null,"abstract":"Objective: The objective of this study is to screen the therapeutic targets of pain of traditional Chinese medicine Chonglou and explore the relevant mechanism by network pharmacology techniques and methods. Materials and Methods: The chemical components of Chonglou were collected according to chemistry database and related literature. SwissADME was used to collect the potential active ingredients from all the chemical components of Chonglou and SwissTarget Prediction was utilized to predict their targets. The genes related to pain were collected from GeneCards and Online Mendelian Inheritance in Man databases. Joint genes were uploaded to the online string database for the analysis and the PPI network was constructed. The “Chonglou-active component-target-pain” network was constructed by Cytoscape 3.7.1 software, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for key target proteins. The top three active components with most targets in the network were docked with the target proteins by the molecular docking technique. Results: A total of nine potential active compounds of Chonglou, 264 potential target genes, 2385 targets of pain disorder, and 128 common targets for drug and disease were screened. One hundred and thirty-one GO items were identified by the GO enrichment analysis, and 23 related signaling pathways were identified by the KEGG pathway enrichment analysis. Molecular-docking results show that pennogenin is the optimal butt ligand of PIK3CA, STAT3, mitogen-activated protein kinase 14, and ADORA1. Conclusion: It is preliminarily revealed that Chonglou might treat pain through multiple targets, multiple biology processes and multiple pain-related signaling pathways, providing reference for the subsequent experimental research.","PeriodicalId":23692,"journal":{"name":"World Journal of Traditional Chinese Medicine","volume":"7 1","pages":"419 - 426"},"PeriodicalIF":4.3000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Mechanism research of chonglou as a pain killer by network pharmacology\",\"authors\":\"Yu-tong Liu, Yongli Situ, Tingting Zhao, Lina Long, Hekun Zeng, Shangdong Liang, G. Schmalzing, Hong-Wei Gao, Jinbin Wei, Chuan-Hua He, Hong Nie\",\"doi\":\"10.4103/wjtcm.wjtcm_84_20\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective: The objective of this study is to screen the therapeutic targets of pain of traditional Chinese medicine Chonglou and explore the relevant mechanism by network pharmacology techniques and methods. Materials and Methods: The chemical components of Chonglou were collected according to chemistry database and related literature. SwissADME was used to collect the potential active ingredients from all the chemical components of Chonglou and SwissTarget Prediction was utilized to predict their targets. The genes related to pain were collected from GeneCards and Online Mendelian Inheritance in Man databases. Joint genes were uploaded to the online string database for the analysis and the PPI network was constructed. The “Chonglou-active component-target-pain” network was constructed by Cytoscape 3.7.1 software, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for key target proteins. The top three active components with most targets in the network were docked with the target proteins by the molecular docking technique. Results: A total of nine potential active compounds of Chonglou, 264 potential target genes, 2385 targets of pain disorder, and 128 common targets for drug and disease were screened. One hundred and thirty-one GO items were identified by the GO enrichment analysis, and 23 related signaling pathways were identified by the KEGG pathway enrichment analysis. Molecular-docking results show that pennogenin is the optimal butt ligand of PIK3CA, STAT3, mitogen-activated protein kinase 14, and ADORA1. Conclusion: It is preliminarily revealed that Chonglou might treat pain through multiple targets, multiple biology processes and multiple pain-related signaling pathways, providing reference for the subsequent experimental research.\",\"PeriodicalId\":23692,\"journal\":{\"name\":\"World Journal of Traditional Chinese Medicine\",\"volume\":\"7 1\",\"pages\":\"419 - 426\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World Journal of Traditional Chinese Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.4103/wjtcm.wjtcm_84_20\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INTEGRATIVE & COMPLEMENTARY MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Traditional Chinese Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.4103/wjtcm.wjtcm_84_20","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INTEGRATIVE & COMPLEMENTARY MEDICINE","Score":null,"Total":0}
Mechanism research of chonglou as a pain killer by network pharmacology
Objective: The objective of this study is to screen the therapeutic targets of pain of traditional Chinese medicine Chonglou and explore the relevant mechanism by network pharmacology techniques and methods. Materials and Methods: The chemical components of Chonglou were collected according to chemistry database and related literature. SwissADME was used to collect the potential active ingredients from all the chemical components of Chonglou and SwissTarget Prediction was utilized to predict their targets. The genes related to pain were collected from GeneCards and Online Mendelian Inheritance in Man databases. Joint genes were uploaded to the online string database for the analysis and the PPI network was constructed. The “Chonglou-active component-target-pain” network was constructed by Cytoscape 3.7.1 software, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for key target proteins. The top three active components with most targets in the network were docked with the target proteins by the molecular docking technique. Results: A total of nine potential active compounds of Chonglou, 264 potential target genes, 2385 targets of pain disorder, and 128 common targets for drug and disease were screened. One hundred and thirty-one GO items were identified by the GO enrichment analysis, and 23 related signaling pathways were identified by the KEGG pathway enrichment analysis. Molecular-docking results show that pennogenin is the optimal butt ligand of PIK3CA, STAT3, mitogen-activated protein kinase 14, and ADORA1. Conclusion: It is preliminarily revealed that Chonglou might treat pain through multiple targets, multiple biology processes and multiple pain-related signaling pathways, providing reference for the subsequent experimental research.