{"title":"Identification of Macrophage-Associated Novel Drug Targets in Atherosclerosis Based on Integrated Transcriptome Features.","authors":"Jingzhi Wang, Sida Qin, Xiaohui Zhang, Jixin Zhi","doi":"10.1021/acs.jcim.4c01558","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This study explores the pathological mechanisms of atherosclerosis (AS), focusing on the role of macrophages in its formation and development, and potential therapeutic targets.</p><p><strong>Methods: </strong>The heterogeneity of the AS single-cell data set GSE131778 was analyzed using Seurat. Tissue sequencing data GSE28829 and GSE43292 were analyzed for immune cell abundance using CIBERSORT. Differential genes were identified, and WGCNA was used to create a coexpression network. Hub genes were identified using MCODE and CytoHubba and analyzed with GO and KEGG enrichment analysis, GSVA, and immune infiltration analysis. DrugBank identified potential drugs, and molecular docking verified drug binding to key targets. Key targets were experimentally validated.</p><p><strong>Results: </strong>Nineteen cell clusters were identified in the GSE131778 data set, classified into ten cell types. Macrophages in AS and normal tissues were identified based on cell abundance. CIBERSORT showed a significant increase in cell cluster 9 in AS samples. Thirty-two hub genes, including CD86, LILRB2, and IRF8, were validated. GO and KEGG analyses indicated Hub genes primarily affect immune functions. GSVA identified 29 significantly increased pathways in AS samples. Immune infiltration analysis revealed a positive correlation between IRF8, CD86, and LILRB2 expression and macrophage content. Molecular docking suggested CD86 as a potential drug target for AS. qRT-PCR confirmed increased IRF8 and CD86 expression.</p><p><strong>Conclusions: </strong>CD86, LILRB2, and IRF8 are highly expressed in foam cell samples, with CD86 forming hydrogen bonds with several AS drugs, indicating CD86 as a promising target for AS treatment.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"9009-9020"},"PeriodicalIF":5.6000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jcim.4c01558","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: This study explores the pathological mechanisms of atherosclerosis (AS), focusing on the role of macrophages in its formation and development, and potential therapeutic targets.
Methods: The heterogeneity of the AS single-cell data set GSE131778 was analyzed using Seurat. Tissue sequencing data GSE28829 and GSE43292 were analyzed for immune cell abundance using CIBERSORT. Differential genes were identified, and WGCNA was used to create a coexpression network. Hub genes were identified using MCODE and CytoHubba and analyzed with GO and KEGG enrichment analysis, GSVA, and immune infiltration analysis. DrugBank identified potential drugs, and molecular docking verified drug binding to key targets. Key targets were experimentally validated.
Results: Nineteen cell clusters were identified in the GSE131778 data set, classified into ten cell types. Macrophages in AS and normal tissues were identified based on cell abundance. CIBERSORT showed a significant increase in cell cluster 9 in AS samples. Thirty-two hub genes, including CD86, LILRB2, and IRF8, were validated. GO and KEGG analyses indicated Hub genes primarily affect immune functions. GSVA identified 29 significantly increased pathways in AS samples. Immune infiltration analysis revealed a positive correlation between IRF8, CD86, and LILRB2 expression and macrophage content. Molecular docking suggested CD86 as a potential drug target for AS. qRT-PCR confirmed increased IRF8 and CD86 expression.
Conclusions: CD86, LILRB2, and IRF8 are highly expressed in foam cell samples, with CD86 forming hydrogen bonds with several AS drugs, indicating CD86 as a promising target for AS treatment.
期刊介绍：
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field.
As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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