基于小鼠转录组测序的糖尿病肾病关键生物标志物的生物信息学预测和实验验证。

BACKGROUND: Diabetic kidney disease (DKD) is the leading cause of death in people with type 2 diabetes mellitus (T2DM). The main objective of this study is to find the potential biomarkers for DKD. MATERIALS AND METHODS: Two datasets (GSE86300 and GSE184836) retrieved from Gene Expression Omnibus (GEO) database were used, combined with our RNA sequencing (RNA-seq) results of DKD mice (C57 BLKS-32w db/db) and non-diabetic (db/m) mice for further analysis. After processing the expression matrix of the three sets of data using R software "Limma", differential expression analysis was performed. The significantly differentially expressed genes (DEGs) (-logFC- > 1, -value < 0.05) were visualized by heatmaps and volcano plots respectively. Next, the co-expression genes expressed in the three groups of DEGs were obtained by constructing a Venn diagram. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were further analyzed the related functions and enrichment pathways of these co-expression genes. Then, qRT-PCR was used to verify the expression levels of co-expression genes in the kidney of DKD and control mice. Finally, protein-protein interaction network (PPI), GO, KEGG analysis and Pearson correlation test were performed on the experimentally validated genes, in order to clarify the possible mechanism of them in DKD. RESULTS: Our RNA-seq results identified a total of 125 DEGs, including 59 up-regulated and 66 down-regulated DEGs. At the same time, 183 up-regulated and 153 down-regulated DEGs were obtained in GEO database GSE86300, and 76 up-regulated and 117 down-regulated DEGs were obtained in GSE184836. Venn diagram showed that 13 co-expression DEGs among the three groups of DEGs. GO analysis showed that biological processes (BP) were mainly enriched inresponse to stilbenoid, response to fatty acid, response to nutrient, positive regulation of macrophage derived foam cell differentiation, triglyceride metabolic process. KEGG pathway analysis showed that the three major enriched pathways were cholesterol metabolism, drug metabolism-cytochrome P450, PPAR signaling pathway. After qRT-PCR validation, we obtained 11 genes that were significant differentially expressed in the kidney tissues of DKD mice compared with control mice. (The mRNA expression levels of Aacs, Cpe, Cd36, Slc22a7, Slc1a4, Lpl, Cyp7b1, Akr1c14 and Apoh were declined, whereas Abcc4 and Gsta2 were elevated). CONCLUSION: Our study, based on RNA-seq results, GEO databases and qRT-PCR, identified 11 significant dysregulated DEGs, which play an important role in lipid metabolism and the PPAR signaling pathway, which provide novel targets for diagnosis and treatment of DKD.

背景：糖尿病肾病（DKD）是 2 型糖尿病（T2DM）患者死亡的主要原因。本研究的主要目的是寻找 DKD 的潜在生物标志物。

材料和方法：从基因表达综合数据库（GEO）中检索了两个数据集（GSE86300 和 GSE184836），并结合我们的 DKD 小鼠（C57BLKS-32w db/db）和非糖尿病（db/m）小鼠的 RNA 测序（RNA-seq）结果进行进一步分析。使用 R 软件“Limma”处理三组数据的表达矩阵后，进行差异表达分析。通过热图和火山图分别可视化显著差异表达基因（DEGs）（-logFC- > 1，-值<0.05）。接下来，通过构建韦恩图获得三组 DEGs 中表达的共表达基因。此外，还进一步进行了基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析，以研究这些共表达基因的相关功能和富集通路。然后，使用 qRT-PCR 验证 DKD 和对照小鼠肾脏中这些共表达基因的表达水平。最后，对实验验证的基因进行蛋白质-蛋白质相互作用网络（PPI）、GO、KEGG 分析和 Pearson 相关性检验，以阐明它们在 DKD 中的可能机制。

结果：我们的 RNA-seq 结果共鉴定出 125 个 DEGs，包括 59 个上调和 66 个下调 DEGs。同时，在 GEO 数据库 GSE86300 中获得了 183 个上调和 153 个下调 DEGs，在 GSE184836 中获得了 76 个上调和 117 个下调 DEGs。韦恩图显示三组 DEGs 中有 13 个共表达 DEGs。GO 分析表明，生物过程（BP）主要富集于对芪类、脂肪酸的反应、对营养的反应、巨噬细胞衍生泡沫细胞分化的正调控、甘油三酯代谢过程。KEGG 通路分析表明，三个主要富集途径是胆固醇代谢、药物代谢-细胞色素 P450、PPAR 信号通路。经过 qRT-PCR 验证，我们在 DKD 小鼠肾脏组织中获得了 11 个差异表达显著的基因（Aacs、Cpe、Cd36、Slc22a7、Slc1a4、Lpl、Cyp7b1、Akr1c14 和 Apoh 的 mRNA 表达水平下降，而 Abcc4 和 Gsta2 则升高）。

结论：本研究基于 RNA-seq 结果、GEO 数据库和 qRT-PCR，鉴定出 11 个显著失调的 DEGs，它们在脂质代谢和 PPAR 信号通路中发挥重要作用，为 DKD 的诊断和治疗提供了新的靶点。