{"title":"金银花木犀草素抑菌作用的蛋白网络相互作用。","authors":"Jianfeng Zhang, Mujun Chen, Dianzeng Yang, Yanjie Jia","doi":"10.3389/fbinf.2025.1637479","DOIUrl":null,"url":null,"abstract":"<p><p>A comprehensive analysis of the bacteriostatic mechanism of luteolin at the molecular level was performed. Luteolin-related targets were first retrieved from the STITCH database, followed by the acquisition of protein-protein interaction (PPI) information from the STRING database. The retrieved PPI data was subsequently imported into Cytoscape software to construct a PPI network. Finally, the Molecular Complexity Detection (MCODE) algorithm and BinGo plugin were utilized to conduct module analysis and functional annotation of the constructed network, respectively. The results showed that a total of ten targets were successfully screened from the database. Based on these targets, a PPI network consisting of 91 nodes and 332 edges was constructed. Cluster analysis identified seven distinct functional modules, and subsequent module analysis further demonstrated that luteolin was primarily involved in multiple biological processes, including pathogenic bacteria resistance, antibacterial defensive responses, pathogenic fungi resistance, and resistance to both gram-negative and gram-positive bacteria. These findings indicated that luteolin exhibits robust antibacterial and antifungal activities. By investigating the inhibitory mechanism of luteolin at the molecular-network level, this study paves the way for the development of novel bacteriostatic strategies, offering a valuable perspective for related research.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1637479"},"PeriodicalIF":3.9000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12354353/pdf/","citationCount":"0","resultStr":"{\"title\":\"The bacteriostatic regulation of luteolin from honeysuckle by protein network interaction.\",\"authors\":\"Jianfeng Zhang, Mujun Chen, Dianzeng Yang, Yanjie Jia\",\"doi\":\"10.3389/fbinf.2025.1637479\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>A comprehensive analysis of the bacteriostatic mechanism of luteolin at the molecular level was performed. Luteolin-related targets were first retrieved from the STITCH database, followed by the acquisition of protein-protein interaction (PPI) information from the STRING database. The retrieved PPI data was subsequently imported into Cytoscape software to construct a PPI network. Finally, the Molecular Complexity Detection (MCODE) algorithm and BinGo plugin were utilized to conduct module analysis and functional annotation of the constructed network, respectively. The results showed that a total of ten targets were successfully screened from the database. Based on these targets, a PPI network consisting of 91 nodes and 332 edges was constructed. Cluster analysis identified seven distinct functional modules, and subsequent module analysis further demonstrated that luteolin was primarily involved in multiple biological processes, including pathogenic bacteria resistance, antibacterial defensive responses, pathogenic fungi resistance, and resistance to both gram-negative and gram-positive bacteria. These findings indicated that luteolin exhibits robust antibacterial and antifungal activities. By investigating the inhibitory mechanism of luteolin at the molecular-network level, this study paves the way for the development of novel bacteriostatic strategies, offering a valuable perspective for related research.</p>\",\"PeriodicalId\":73066,\"journal\":{\"name\":\"Frontiers in bioinformatics\",\"volume\":\"5 \",\"pages\":\"1637479\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12354353/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fbinf.2025.1637479\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fbinf.2025.1637479","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
The bacteriostatic regulation of luteolin from honeysuckle by protein network interaction.
A comprehensive analysis of the bacteriostatic mechanism of luteolin at the molecular level was performed. Luteolin-related targets were first retrieved from the STITCH database, followed by the acquisition of protein-protein interaction (PPI) information from the STRING database. The retrieved PPI data was subsequently imported into Cytoscape software to construct a PPI network. Finally, the Molecular Complexity Detection (MCODE) algorithm and BinGo plugin were utilized to conduct module analysis and functional annotation of the constructed network, respectively. The results showed that a total of ten targets were successfully screened from the database. Based on these targets, a PPI network consisting of 91 nodes and 332 edges was constructed. Cluster analysis identified seven distinct functional modules, and subsequent module analysis further demonstrated that luteolin was primarily involved in multiple biological processes, including pathogenic bacteria resistance, antibacterial defensive responses, pathogenic fungi resistance, and resistance to both gram-negative and gram-positive bacteria. These findings indicated that luteolin exhibits robust antibacterial and antifungal activities. By investigating the inhibitory mechanism of luteolin at the molecular-network level, this study paves the way for the development of novel bacteriostatic strategies, offering a valuable perspective for related research.