Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0011881 (diabetic nephropathy)
 10,836 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

To explore the molecular mechanisms of diabetic nephropathy (DN) progression and provide the theoretical basis for treating DN, GSE1009 microarray data were downloaded from Gene Expression Omnibus database. Microarray data were obtained from glomeruli isolated from normal kidneys (n=3) and kidneys from patients with DN (n=3). We first screened the differentially expressed genes (DEGs) in kidneys by the Linear Models for Microarray Data package in R. Then the function of DEGs in DN was explored through Gene Ontology (GO) and KEGG pathway enrichment analyses. Critical DEGs for DN progression were investigated by constructing PPI network and mining significant modules. Afterwards, enriched protein domains of modules were analyzed by Interpro and DAVID. At last, the regulatory miRNAs for DEGs were calculated by WebGestalt, and DEGs-miRNAs network was visualized with Cytoscape. A total of 666 DEGs including 384 up- and 282 down-regulated genes were screened out. The up-regulated DEGs were significantly enriched in plasma membrane and signal transmission, and mainly participated in pathways of cytokine-cytokine receptor and neuroactive ligand-receptor interaction. The down-regulated DEGs significantly enriched in extracellular region and cytoskeletal protein binding, and mainly participated in ECM-receptor interaction and dilated cardiomyopathy. 2 PPI networks were constructed with confidence score>0.4. One significant module obtained from PPI network for up-regulated DEGs mainly enriched in protein domain of rhodopsin-like G protein-coupled receptors. The down-regulated DEGs were mainly regulated by 10 miRNAs clusters. Together, we constructed a comprehensive molecular network for DN progression and miR-1 and miR-25 might be theoretical targets for DN.
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PMID:Revealing the underlying mechanism of diabetic nephropathy viewed by microarray analysis. 2591 80

Diabetic nephropathy (DN), a common diabetic microvascular complication, is characterized by progressive glomerular sclerosis and tubulointerstitial fibrosis. However, the underlying mechanisms involved in DN remain to be elucidated. We explored changes in the transcriptional profile in spontaneous type 2 diabetic db/db mice by using the cDNA microarray. Compared with control db/m mice, the db/db mice exhibited marked increases in body weight, kidney weight, and urinary albumin excretion. Renal histological analysis revealed mesangial expansion and thickness of the basement membrane in the kidney of the db/db mice. A total of 355 differentially expressed genes (DEGs) were identified by microarray analysis. Pathway enrichment analysis suggested that biological oxidation, bile acid metabolism, and steroid hormone synthesis were the 3 major significant pathways. The top 10 hub genes were selected from the constructed PPI network of DEGs, including Ccnb2 and Nr1i2, which remained largely unclear in DN. We believe that our study can help elucidate the molecular mechanisms underlying DN.
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PMID:Transcriptional Profile of Kidney from Type 2 Diabetic db/db Mice. 2823 50

The pathogenesis of diabetic nephropathy is not completely understood, and the effects of existing treatments are not satisfactory. Various public platforms already contain extensive data for deeper bioinformatics analysis. From the GSE30529 dataset based on diabetic nephropathy tubular samples, we identified 345 genes through differential expression analysis and weighted gene coexpression correlation network analysis. GO annotations mainly included neutrophil activation, regulation of immune effector process, positive regulation of cytokine production and neutrophil-mediated immunity. KEGG pathways mostly included phagosome, complement and coagulation cascades, cell adhesion molecules and the AGE-RAGE signalling pathway in diabetic complications. Additional datasets were analysed to understand the mechanisms of differential gene expression from an epigenetic perspective. Differentially expressed miRNAs were obtained to construct a miRNA-mRNA network from the miRNA profiles in the GSE57674 dataset. The miR-1237-3p/SH2B3, miR-1238-5p/ZNF652 and miR-766-3p/TGFBI axes may be involved in diabetic nephropathy. The methylation levels of the 345 genes were also tested based on the gene methylation profiles of the GSE121820 dataset. The top 20 hub genes in the PPI network were discerned using the CytoHubba tool. Correlation analysis with GFR showed that SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA, SERPING1, EGF and KNG1 may be involved in diabetic nephropathy. Eight small molecule compounds were identified as potential therapeutic drugs using Connectivity Map.
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PMID:Identification of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis. 3277 79