Bioinformatics Analysis Reveals Crosstalk Among Platelets, Immune Cells, and the Glomerulus That May Play an Important Role in the Development of Diabetic Nephropathy.
Bioinformatics Analysis Reveals Crosstalk Among Platelets, Immune Cells, and the Glomerulus That May Play an Important Role in the Development of Diabetic Nephropathy.
复制标题
生物信息学分析揭示血小板、免疫细胞和肾小球之间的串扰可能在糖尿病肾病的发展中发挥重要作用
DOI：
10.3389/fmed.2021.657918
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发表时间：
2021
影响因子：
3.9
通讯作者：
Li Z
中科院分区：
文献类型：
--
作者：
Yao X;Shen H;Cao F;He H;Li B;Zhang H;Zhang X;Li Z
糖尿病肾病（DN）是终末期肾病（ESRD）的主要病因。肾小球损害是DN的主要病理改变之一。为了揭示肾小球中参与DN发展的基因表达变化，我们筛选了截至2020年12月的基因表达综合数据库（GEO）。下载11个关于人DN肾小球及其对照的基因表达的基因表达数据集，用于进一步的生物信息学分析。使用R语言对所有表达数据进行提取，并通过Shambhala进行跨平台归一化。差异表达基因（DEG）通过Student t检验结合假发现率（FDR）（P < 0.05）和倍数变化（FC）≥1.5进行鉴定。通过注释、可视化和集成发现数据库（大卫）进一步分析DEG以丰富基因本体（GO）术语和基因和基因组京都百科全书（KEGG）途径。我们进一步构建了DEG的蛋白质-蛋白质相互作用（PPI）网络来识别核心基因。我们使用数字化细胞仪软件CIBERSORTx来分析DN中免疫细胞的浸润。在这项研究中，共有578个基因被鉴定为DEG。核心基因13个，其中LYZ、LUM和THBS 2与DN的连锁较少。基于GO、KEGG富集和CIBERSORTx免疫细胞浸润分析的结果，我们假设肾小球、血小板和免疫细胞之间可能形成正反馈。这种恶性循环可能会持续损害肾小球，即使在最初的高葡萄糖损害被删除。对这些基因和通路的研究可能为DN的发病机制提供新的认识。
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.