2026 年第 4 期 第 21 卷

基孔肯雅病毒感染中微小RNA-信使RNA负调控网络构建及潜在治疗药物筛选

Construction of microRNA-messenger RNA negative regulatory network and screening of potential therapeutic drugs in Chikungunya virus infection

作者：唐育杰1赵鹏1侯传东2张昊军1孙鑫2黄磊3卢学春1,2,4

英文作者：Tang Yujie1 Zhao Peng1 Hou Chuandong2 Zhang Haojun1 Sun Xin2 Huang Lei3 Lu Xuechun124

单位：1山西医科大学管理学院，太原030600；2中国人民解放军总医院第二医学中心血液科国家老年疾病临床医学研究中心（解放军总医院），北京100853；3中国人民解放军总医院第五医学中心感染病医学部国家感染性疾病临床医学研究中心，北京100039；4临床决策研究大数据山西省重点实验室，太原030600

英文单位：1School of Management Shanxi Medical University Taiyuan 030600 China； 2Department of Hematology the Second Medical Center Chinese PLA General Hospital National Clinical Medical Research Center for Geriatric Diseases (Chinese PLA General Hospital) Beijing 100853 China; 3Department of Infectious Diseases Medicine the Fifth Medical Center Chinese PLA General Hospital National Clinical Medical Research Center for Infectious Diseases Beijing 100039 China; 4Shanxi Key Laboratory of Big Data for Clinical Decision Research Taiyuan 030600 China

关键词：基孔肯雅病毒；微小RNA-信使RNA调控网络；分子对接；药物预测；药物重定位

英文关键词：Chikungunyavirus;MicroRNA-messengerRNAregulatorynetwork;Moleculardocking; Drugprediction;Drugrepurposing

• 摘要：

• 目的 本研究基于公开的基孔肯雅病毒（CHIKV）多组学数据库，整合多组学分析方法 ，揭示CHIKV感染过程中微小RNA（miR）的调控机制，并预测CHIKV感染的潜在治疗药物。方法 通过分析基因表达综合数据库中多个数据集（GSE49985、GSE49884、GSE143390），使用R语言limma包鉴定差异表达信使RNA（DEmRNA）和差异表达miR（DEmiR）。利用miRTarBase、miRDB和TargetScanHuman数据库预测DEmiR的靶基因，构建CHIKV感染的miR-mRNA负调控网络。最后，基于EpiMed平台、Connectivity Map数据库和高通量虚拟筛选预测潜在治疗药物。结果 研究鉴定了1 134个DEmRNA和54个DEmiR；基因本体论分析结果显示，DEmRNA显著参与细胞周期、染色体分离、蛋白质折叠等生物学过程；京都基因与基因组百科全书分析结果显示，DEmRNA在病毒感染、蛋白质加工、溶酶体和神经退行性疾病等通路中富集。miR-mRNA负调控网络包含29对上调miR-下调mRNA和90对下调miR-上调mRNA；利用Cytoscape软件的cytoHubba插件中的最大团中心性算法筛选预测的靶基因，构建子调控网络。通过药物预测，EpiMed平台识别出94种候选药物，Connectivity Map数据库筛选出2 907种候选药物，经过高通量虚拟筛选进行综合评估，最终确定氯硝西泮、奈韦拉平、伊曲康唑、匹美克莫司和美洛昔康等5种与CHIKV感染典型临床症状相关的潜在治疗药物。结论 本研究构建了CHIKV感染相关miR-mRNA负调控网络并筛选关键基因，进一步结合药物重定位与虚拟筛选预测5种潜在候选药物，为CHIKV靶向干预提供了系统生物学线索与候选资源。

  目的 本研究基于公开的基孔肯雅病毒（CHIKV）多组学数据库，整合多组学分析方法 ，揭示CHIKV感染过程中微小RNA（miR）的调控机制，并预测CHIKV感染的潜在治疗药物。方法 通过分析基因表达综合数据库中多个数据集（GSE49985、GSE49884、GSE143390），使用R语言limma包鉴定差异表达信使RNA（DEmRNA）和差异表达miR（DEmiR）。利用miRTarBase、miRDB和TargetScanHuman数据库预测DEmiR的靶基因，构建CHIKV感染的miR-mRNA负调控网络。最后，基于EpiMed平台、Connectivity Map数据库和高通量虚拟筛选预测潜在治疗药物。结果 研究鉴定了1 134个DEmRNA和54个DEmiR；基因本体论分析结果显示，DEmRNA显著参与细胞周期、染色体分离、蛋白质折叠等生物学过程；京都基因与基因组百科全书分析结果显示，DEmRNA在病毒感染、蛋白质加工、溶酶体和神经退行性疾病等通路中富集。miR-mRNA负调控网络包含29对上调miR-下调mRNA和90对下调miR-上调mRNA；利用Cytoscape软件的cytoHubba插件中的最大团中心性算法筛选预测的靶基因，构建子调控网络。通过药物预测，EpiMed平台识别出94种候选药物，Connectivity Map数据库筛选出2 907种候选药物，经过高通量虚拟筛选进行综合评估，最终确定氯硝西泮、奈韦拉平、伊曲康唑、匹美克莫司和美洛昔康等5种与CHIKV感染典型临床症状相关的潜在治疗药物。结论 本研究构建了CHIKV感染相关miR-mRNA负调控网络并筛选关键基因，进一步结合药物重定位与虚拟筛选预测5种潜在候选药物，为CHIKV靶向干预提供了系统生物学线索与候选资源。

• Objective To reveal the regulatory mechanism of microRNA (miR) during Chikungunya virus (CHIKV) infection and predict potential therapeutic drugs for CHIKV infection, this study integrating multi-omics analysis methods  based on public multi-omics databases of CHIKV. Methods By analyzing multiple datasets (GSE49985, GSE49884, GSE143390) in the Gene Expression Omnibus database, the limma package of R language was used to identify differentially expressed messenger RNAs (DEmRNAs) and differentially expressed miRs (DEmiRs). The miRTarBase, miRDB and TargetScanHuman databases were applied to predict the target genes of DEmiRs, and the miR-mRNA negative regulatory network in CHIKV infection was constructed. Finally, potential therapeutic drugs were predicted based on the EpiMed platform, Connectivity Map database and high-throughput virtual screening. Results A total of 1134 DEmRNAs and 54 DEmiRs were identified in this study. Gene Ontology analysis showed that DEmRNAs were significantly involved in biological processes such as cell cycle, chromosome segregation and protein folding. Kyoto Encyclopedia of Genes and Genomes analysis indicated that DEmRNAs were enriched in pathways including viral infection, protein processing, lysosome and neurodegenerative diseases. The miR-mRNA negative regulatory network contained 29 pairs of up-regulated miR with down-regulated mRNA and 90 pairs of down-regulated miR with up-regulated mRNA. The maximal clique centrality algorithm in the cytoHubba plug-in of Cytoscape software was used to screen the predicted target genes and construct a sub-regulatory network. Through drug prediction, 94 candidate drugs were identified by the EpiMed platform and 2907 candidate drugs were screened out by the Connectivity Map database. Comprehensive evaluation was performed via high-throughput virtual screening, and finally 5 potential therapeutic drugs associated with typical clinical symptoms of CHIKV infection were determined, including clonazepam, nevirapine, itraconazole, pimecrolimus and meloxicam. ConclusionsThis study constructed the CHIKV infection-related miR-mRNA negative regulatory network and screened key genes, and further predicted 5 potential candidate drugs by combining drug repurposing and virtual screening, which provides systems biology clues and candidate resources for targeted intervention of CHIKV.