Table_2_Bioinformatics Analysis Reveals Crosstalk Among Platelets, Immune Cells, and the Glomerulus That May Play an Important Role in the Development of Diabetic Nephropathy.XLSX

Diabetic nephropathy (DN) is the main cause of end stage renal disease (ESRD). Glomerulus damage is one of the primary pathological changes in DN. To reveal the gene expression alteration in the glomerulus involved in DN development, we screened the Gene Expression Omnibus (GEO) database up to December 2020. Eleven gene expression datasets about gene expression of the human DN glomerulus and its control were downloaded for further bioinformatics analysis. By using R language, all expression data were extracted and were further cross-platform normalized by Shambhala. Differentially expressed genes (DEGs) were identified by Student's t-test coupled with false discovery rate (FDR) (P < 0.05) and fold change (FC) ≥1.5. DEGs were further analyzed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to enrich the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. We further constructed a protein-protein interaction (PPI) network of DEGs to identify the core genes. We used digital cytometry software CIBERSORTx to analyze the infiltration of immune cells in DN. A total of 578 genes were identified as DEGs in this study. Thirteen were identified as core genes, in which LYZ, LUM, and THBS2 were seldom linked with DN. Based on the result of GO, KEGG enrichment, and CIBERSORTx immune cells infiltration analysis, we hypothesize that positive feedback may form among the glomerulus, platelets, and immune cells. This vicious cycle may damage the glomerulus persistently even after the initial high glucose damage was removed. Studying the genes and pathway reported in this study may shed light on new knowledge of DN pathogenesis.

糖尿病肾病（Diabetic nephropathy，DN）是终末期肾病（end stage renal disease，ESRD）的主要致病原因。肾小球（Glomerulus）损伤是DN的核心病理改变之一。为揭示参与DN发生发展的肾小球基因表达异常，我们检索了截至2020年12月的基因表达综合数据库（Gene Expression Omnibus，GEO），共下载11组人类DN肾小球及其对照样本的基因表达数据集，用于后续生物信息学分析。借助R语言提取所有表达数据，并通过Shambhala工具完成跨平台标准化处理。采用结合错误发现率（false discovery rate，FDR）校正的学生t检验（P<0.05）及倍数变化（fold change，FC）≥1.5的筛选阈值，鉴定得到差异表达基因（differentially expressed genes，DEGs）。通过注释、可视化与综合发现数据库（Database for Annotation, Visualization, and Integrated Discovery，DAVID）对DEGs进行富集分析，以获取基因本体论（Gene Ontology，GO）功能条目及京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）通路注释。进一步构建DEGs的蛋白质-蛋白质相互作用（protein-protein interaction，PPI）网络以筛选核心基因，并使用数字细胞分析软件CIBERSORTx分析DN样本中的免疫细胞浸润情况。本研究共鉴定得到578个DEGs，其中13个被鉴定为核心基因，LYZ、LUM及THBS2这三个基因此前与DN的关联报道极少。基于GO富集、KEGG富集及CIBERSORTx免疫细胞浸润分析结果，我们推测肾小球、血小板与免疫细胞之间可能形成正反馈环路。即使初始高糖损伤已被解除，该恶性循环仍可持续损伤肾小球。对本研究鉴定的基因及通路开展后续研究，可为解析DN的发病机制提供新的理论视角。