天然产物研究与开发 ›› 2022, Vol. 34 ›› Issue (10): 1774-1783.doi: 10.16333/j.1001-6880.2022.10.016

• 数据研究 • 上一篇    下一篇

生物信息学方法下糖尿病微血管并发症关键基因与通路筛选及中药预测研究

荣   旺1,朱占兰2,宋亚一3*   

  1. 1南京中医药大学附属连云港医院,连云港 222000;2南京医科大学康达学院附属连云港市立东方医院,连云港 222042;3连云港市妇幼保健院,连云港 222000
  • 出版日期:2022-10-28 发布日期:2022-10-31
  • 基金资助:
    江苏省双创博士项目(JSSCBS20211635)

Screening of key genes and pathways of microvascular complications of diabetes mellitus and prediction of traditional Chinese medicine based on bioinformatics

RONG Wang1,ZHU Zhan-lan2,SONG Ya-yi3*   

  1. 1Lianyungang Hospital Affiliated to Nanjing University of Chinese Medicine,Lianyungang 222000,China;2Lianyungang Oriental Hospital Affiliated to Kangda College of Nanjing Medical University,Lianyungang 222042,China; 3Lianyungang Maternity and Child Health Care Hospital,Lianyungang 222000,China
  • Online:2022-10-28 Published:2022-10-31

摘要:

本研究利用生物信息学方法对糖尿病微血管并发症(microvascular complications of diabetes mellitus)基因表达谱芯片进行分析,获取疾病的关键基因及相关信号通路,探索糖尿病微血管并发症的分子机制,预测治疗糖尿病微血管并发症的潜在中药。首先从GEO数据库下载关于糖尿病微血管并发症的基因表达谱芯片数据集GSE43950,利用GEO2R在线分析工具筛选糖尿病微血管并发症差异表达基因(differentially expressed genes,DEGs)并可视化,对DEGs利用DAVID在线分析数据库进行基因本体论(gene ontology,GO)和通路富集(Kyoto Encyclopedia of Genes And Genomes,KEGG)分析,通过STRING在线分析工具、Cytoscape软件及其插件cytoHubba对糖尿病微血管并发症的DEGs进行蛋白互作(protein-protein interaction,PPI)网络分析,寻找关键基因,随后将关键基因映射到医学本体信息检索平台(coremine medical),筛选治疗糖尿病微血管并发症的潜在中药,同时构建“药物-活性成分-作用靶点”网络。最后共获得692个DEGs,包括121个上调基因和571个下调基因。GO功能注释显示DEGs参与了血液凝固、止血的调节、中性粒细胞活化免疫应答、DNA的特异性结合等生物过程,KEGG信号通路分析主要涉及MAPK信号通路、NF-κB信号通路、Toll样受体信号通路、糖尿病微血管并发症中的AGE-RAGE信号通路等。利用蛋白互作数据库STRING以及Cytoscape软件中的cytoHubba分析DEGs,获取了前10位的hub基因,分别是CD4、IL1B、TLR4、TLR2、ITGAM、CD86、CSF1R、TLR8、CYBB、TLR1。通过关键基因筛选得到治疗糖尿病微血管并发症的潜在中药为黄芩、丹参、川牛膝和郁金。包括TLR2、TLR4、IL1B等在内的10个关键基因可能在糖尿病微血管并发症发生发展中起重要作用,黄芩、丹参、川牛膝和郁金等中药可能是糖尿病微血管并发症治疗的潜在分子药物来源。

关键词: 生物信息学, 糖尿病微血管并发症, 差异表达基因, 中药靶点预测

Abstract:

With analyzing the gene expression profile of microvascular complications of diabetes mellitus (MCDM) by bioinformatics methods,we may obtain its key genes and signaling pathways to explore the molecular mechanism of MCDM and predict the potential traditional Chinese medicine.The gene expression profile of MCDM was downloaded from the GSE43950 dataset.We screened the differentially expressed genes (DEGs) with GEO2R online analysis tool.Pathway and functional enrichment analyses was performed by using DAVID database online analysis tool,such as gene ontology (GO) and KEGG pathway enrichment.Protein-protein interaction (PPI) network was visualized.The key genes were identified by using STRING online analysis tool,Cytoscape software and its plug-in cytoHubba.Then the key genes and the medical ontology information retrieval platform (coremine medical) were mapped against each other to single out the Chinese medicine for the treatment of MCDM and construct the network of “drug-active constituent-target”.Finally,a total of 692 DEGs were obtained,including 121 up-regulated genes and 571 down-regulated genes.The GO function and KEGG signal pathway enrichment analysis revealed that DEGs involved in the biological processes such as blood coagulation,hemostasis,neutrophil activation immune response,core promoter sequence specific DNA binding and participated in some signaling pathways,including MAPK signaling pathway,AGE-RAGE signaling pathway,NF-κB signaling pathway and toll like receptor signaling pathway,etc.Ten hub genes were extracted from the Cytoscape software,which were CD4,IL1B,TLR4,TLR2,ITGAM,CD86,CSF1R,TLR8,CYBB,TLR1.Through screening of key genes,the potential Chinese medicine for treating MCDM including Scutellariae Radix,Salviae Miltiorrhizae Radix et Rhizoma,Cyathulae Radix,Curcumae Radix.These key genes including TLR2,TLR4 and IL1B may play an important role in the development of MCDM,Scutellariae Radix,Salviae Miltiorrhizae Radix et Rhizoma,Cyathulae Radix,Curcumae Radix may become the potential molecular medicine sources for treating MCDM.

Key words: bioinformatics, microvascular complications of diabetes mellitus, differentially expressed genes, prediction of traditional Chinese medicine

中图分类号:  R285.6