a Department of Emergency , China-Japan Union Hospital of Jilin University , Changchun , China.
b Department of Pediatrics , China-Japan Union Hospital of Jilin University , Changchun , China.
Ren Fail. 2018 Nov;40(1):136-145. doi: 10.1080/0886022X.2017.1421556. Epub 2018 Mar 13.
Recent studies have reported that mesenchymal stem cells (MSCs) exert therapeutic effects on the treatment of diabetic nephropathy (DN), but the underlying mechanisms remain unclear.
A dataset GSE65561 was obtained from Gene Expression Omnibus (GEO) database, which contained four healthy control samples (group 1), four healthy controls samples co-cultured with MSCs (group 2), five DN samples (group 3) and five DN samples co-cultured with MSCs (group 4). The differentially expressed genes (DEGs) between group 3 vs. group 1 and group 4 vs. group 2 were constructed using Linear Models for Microarray (LIMMA) package package. Then, DAVID was used to analyze the functional enrichment of DEGs. Based on STRING database the protein-protein interaction (PPI) network was visualized by the Cytoscape plug-in CytoNCA. Besides, the hub miRNAs and transcription factors (TFs) regulating DEGs were predicted using Webgestalt.
Totally, 303 up-regulated and 88 down-regulated DEGs were shared in group 3 vs. group 1 and group 4 vs. group 2. Besides, the up-regulated DEGs were mainly enriched in 'translation' and 'translational elongation', while the down-regulated genes were only enriched in 'protein kinase activity'. RPS27A and RPLP0 had a higher degree in the PPI network and they were regulated by EIF3M. In addition, ETF1 was predicted to be an important gene, which was regulated by miR-150, miR-134 and EIF2S1.
RPS27A, RPLP0 and ETF1 may be potential targets for MSCs on the treatment of DN. Highlights RPS27A and RPLP0 may be important genes in the treatment of MSCs for DN. TF EIF3M may play a key role in the treatment of MSCs for DN. MiR-150 and miR-134 may be essential microRNAs in the treatment of MSCs for DN.
最近的研究表明，间充质干细胞（MSCs）对糖尿病肾病（DN）的治疗有治疗作用，但潜在机制尚不清楚。
从基因表达综合数据库（GEO）中获取数据集 GSE65561，其中包含 4 个健康对照样本（第 1 组）、4 个与 MSCs 共培养的健康对照样本（第 2 组）、5 个 DN 样本（第 3 组）和 5 个与 MSCs 共培养的 DN 样本（第 4 组）。使用线性模型进行微阵列（LIMMA）包包构建组 3 与组 1 之间和组 4 与组 2 之间的差异表达基因（DEGs）。然后，使用 DAVID 分析 DEGs 的功能富集。基于 STRING 数据库，使用 Cytoscape 插件 CytoNCA 可视化蛋白质-蛋白质相互作用（PPI）网络。此外，使用 Webgestalt 预测调节 DEGs 的关键 miRNA 和转录因子（TFs）。
在组 3 与组 1 和组 4 与组 2 之间，共有 303 个上调和 88 个下调的 DEGs。此外，上调的 DEGs 主要富集在“翻译”和“翻译延伸”中，而下调的基因仅富集在“蛋白激酶活性”中。在 PPI 网络中，RPS27A 和 RPLP0 的程度较高，它们受 EIF3M 调控。此外，预测 ETF1 是一个重要的基因，受 miR-150、miR-134 和 EIF2S1 调节。
RPS27A、RPLP0 和 ETF1 可能是 MSCs 治疗 DN 的潜在靶点。RPS27A 和 RPLP0 可能是 MSCs 治疗 DN 的重要基因。TF EIF3M 可能在 MSCs 治疗 DN 中起关键作用。miR-150 和 miR-134 可能是 MSCs 治疗 DN 的关键 microRNAs。
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