生物信息学深入分析糖尿病肾脏疾病的分子作用网络  

Bioinformatics analysis of the molecular network of diabetic nephropathy

作　　者：耿晓东[1,2] 王旭 沈婉君[1] 吴镝 蔡广研[1] 陈香美[1] 洪权[1] Geng Xiaodong;Wang Xu;Shen Wanjun;Wu Di;Cai Guangyan;Chen Xiangmei;Hong Quan(Department of Nephrology,Chinese PLA General Hospital,Chinese PLA Institute of Nephrology,State Key Laboratory of Kidney Diseases,National Clinical Research Center for Kidney Diseases,Beijing 100853,China;Department of nephrology,People′s Liberation Army Beidaihe Rehabilitation Center,Qinhuangdao 066100,China)

机构地区：[1]解放军总医院肾脏病科,解放军肾脏病研究所,肾脏疾病国家重点实验室,国家慢性肾病临床医学研究中心,北京100853 [2]解放军北戴河康复疗养中心肾科,066100

出　　处：《中华糖尿病杂志》2019年第4期254-259,共6页CHINESE JOURNAL OF DIABETES MELLITUS

基　　金：国家自然科学基金(81470949,81870491).

摘　　要：目的利用糖尿病肾脏疾病(DKD)患者和小鼠模型肾脏基因组表达数据进行生物信息学分析,探寻DKD可能的致病机制及潜在治疗作用靶点。方法从NCBI-GEO数据库选取DKD患者和DKD小鼠模型肾脏基因组表达数据,通过生物信息学软件进行综合分析,查找共同差异基因,然后进行基因本体论分析及创新途径分析(IPA)生物网络功能分析,寻找潜在作用靶点。结果通过对人和小鼠DKD数据库的综合对比,筛选出了89个共同差异基因,差异基因主要富集位于细胞外区域、细胞外基质、细胞黏附、细胞浆膜等方面。通过IPA生物信息学分析,发现共同差异基因表达主要集中在补体系统通路、主要免疫缺陷信号传导通路和B细胞发育通路,其中补体系统通路是糖尿病肾病中一条重要的调节通路。疾病和生物功能富集分析显示DKD与糖尿病、葡萄糖代谢障碍、血细胞的移动、血细胞活化和白细胞迁移等存在密切的联系。IPA调控网络分析发现原癌基因ETS1和整合素β2(ITGB)两个重要作用位点。结论利用DKD不同种属即人和小鼠肾脏基因表达谱数据库,可筛选DKD潜在的作用位点,通过生物信息学分析发现转录因子ETS1和ITGB可能在糖尿病肾病的疾病进程中发挥一定的作用。Objective To investigate the potential pathogenesis and therapeutic targets for diabetic kidney disease (DKD) using renal genomic expression profile and the bioinformatics methods. Methods Genomic data of kidney from diabetic kidney disease patients and diabetic kidney disease mice model were selected from NCBI-GEO database. The data was analyzed by bioinformatics software to find differentially expressed genes (DEG). Gene ontology and ingenuity pathway analysis (IPA) were performed to find potential targets for DKD for elucidating the possible molecular mechanisms in DKD. Results Eighty-nine common DEG were identified from the human and mouse diabetic kidney disease database. Most of the DEG were mainly enriched in metabolism-related enzymes, transporters, nucleic acid binding proteins, and cell proliferation and extracellular matrix. The bioinformatics analysis of IPA showed that the common differentially expressed genes were mainly concentrated in the complement system, the primary immunodeficiency signaling and the B cell development, among them, the complement system is one of important regulatory pathways in diabetic kidney disease. Disease and biological function enrichment analysis showed that DKD was closely related to diabetes mellitu, glucose metabolism disorder, cell movement of blood cells, activation of blood cells and leukocyte migration. IPA regulatory network analysis revealed that proto-oncogene ETS1 and integrin β2 (ITGB) were two crucial sites identified by IPA bioinformatics analysis. Conclusion The potential site of diabetic kidney disease can be screened by using different species genomic data of diabetic kidney disease. Bioinformatics analysis shows that transcription factor ETS1 and ITGB might play a role in the pathogenesis of diabetic kidney disease.

关 键 词：糖尿病肾脏疾病 基因表达谱 原癌基因ETS1 整合素β2 

分 类 号：R692[医药卫生—泌尿科学] R587[医药卫生—外科学]