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Identification of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis.

ABSTRACT: 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.

SUBMITTER: Tang S

PROVIDER: S-EPMC7417539 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identification of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis.

Tang ShuMei S Wang XiuFen X Deng TianCi T Ge HuiPeng H Xiao XiangCheng X

Scientific reports 20200810 1

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 ef ...[more]

PMID: 32778679

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Project description:The aim of the present study was to identify genes under the effect of transforming growth factor-β (TGF-β1), high glucose (HG) and glucosamine (GlcN) in MES-13 mesangial cells and elucidate the molecular mechanisms of diabetic nephropathy (DN). The gene expression datasets GSE2557 and GSE2558 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were independently screened using the GEO2R online tool. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape software. The hub genes were identified by the NetworkAnalyzer plugin. Overlapping genes were subjected to molecular docking analysis using SystemsDock. A total of 202 upregulated and 158 downregulated DEGs from the HG-treated groups, 138 upregulated and 103 downregulated DEGs from the GlcN-treated groups, and 81 upregulated and 44 downregulated DEGs from the TGF-β1-treated groups were identified. The majority of the DEGs were independently enriched in 'nucleosome assembly', 'chromatin silencing' and 'xenobiotic glucuronidation'. In addition, KEGG pathways were significantly enriched in 'systemic lupus erythematosus', 'protein processing in endoplasmic reticulum' and 'aldarate metabolism pathway', and 'TNF signaling pathway' intersected in the TGF-β1-treated and HG-treated groups. In total, eight hub genes, Jun, prostaglandin-endoperoxide synthase 2 (Ptgs2), fibronectin 1 (Fn1), cyclin-dependent kinase (Cdk)2, Fos, heat shock protein family A (Hsp70) member 5 (Hspa5), Hsp90b1 and homo sapiens hypoxia upregulated 1 (Hyou1), and three overlapping genes, Ras homolog gene family, member B (RHOB), complement factor H (CFH) and Krüppel-like factor 15 (KLF15), were selected. Valsartan with RHOB, and fosinopril with CFH and KLF15 had preferential binding activity. In conclusion, Jun, Ptgs2, Fn1, Cdk2, Fos, Hspa5, Hsp90b1, Hyou1, RHOB, CFH and KLF15 may be potential therapeutic targets for mesangial cells associated with DN, which may provide insight into DN treatment strategies.

Project description:BackgroundBerberine is used to treat diabetes and dyslipidemia. However, the effect of berberine on specific diabetes treatment targets is unknown. In the current study, we investigated the effect of berberine on the random plasma glucose, glycated hemoglobin (HbA1C), AST, ALT, BUN and CREA levels of Zucker diabetic fatty (ZDF) rats, and we identified and verified the importance of potential therapeutic target genes to provide molecular information for further investigation of the mechanisms underlying the anti-diabetic effects of berberine.MethodsZDF rats were randomly divided into control (Con), diabetic (DM) and berberine-treated (300 mg⋅kg-1, BBR) groups. After the ZDF rats were treated with BBR for 12 weeks, its effect on the random plasma glucose and HbA1C levels was evaluated. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen (BUN), CREA and OGTT were measured from blood, respectively. The levels of gene expression in liver samples were analyzed using an Agilent rat gene expression 4x44K microarray. The differentially expressed genes (DEGs) were screened as those with log2 (Con vs DM) ≥ 1 and log2 (BBR vs DM) ≥ 1 expression levels, which were the genes with up-regulated expression, and those with log2 (Con vs DM) ≤ -1 and log2 (BBR vs DM) ≤ -1 expression levels, which were the genes with down-regulated expression; the changes in gene expression were considered significant at P<0.05. The functions of the DEGs were determined using gene ontology (GO) and pathway analysis. Furthermore, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape software. The expression levels of the key node genes in the livers of the ZDF rats were also analyzed using qRT-PCR.ResultsWe found that 12 weeks of berberine treatment significantly decreased the random plasma glucose, HbA1C levels and improved glucose tolerance. There was a tendency for berberine to reduce AST, ALT, BUN except increase CREA levels. In the livers of the BBR group, we found 154 DEGs, including 91 genes with up-regulated expression and 63 genes with down-regulated expression. In addition, GO enrichment analysis showed significant enrichment of the DEGs in the following categories: metabolic process, localization, cellular process, biological regulation and response to stimulus process. After the gene screening, KEGG pathway analysis showed that the target genes are involved in multiple pathways, including the lysine degradation, glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and pyruvate metabolism pathways. By combining the results of PPI network and KEGG pathway analyses, we identified seven key node genes. The qRT-PCR results confirmed that the expression of the RHOA, MAPK4 and DLAT genes was significantly down-regulated compared with the levels in DM group, whereas the expression of the SgK494, DOT1L, SETD2 and ME3 genes was significantly up-regulated in the BBR group.ConclusionBerberine can significantly improve glucose metabolism and has a protective effects of liver and kidney function in ZDF rats. The qRT-PCR results for the crucial DEGs validated the microarray results. These results suggested that the RHOA, MAPK4, SGK494, DOT1L, SETD2, ME3 and DLAT genes are potential therapeutic target genes for the treatment of diabetes.

Project description:Diabetic nephropathy (DN) is the most important cause of end-stage renal disease with a poorer prognosis and high economic burdens of medical treatments. It is of great research value and clinical significance to explore potential gene targets of renal tubulointerstitial lesions in DN. To properly identify key genes associated with tubulointerstitial injury of DN, we initially performed a weighted gene coexpression network analysis of the dataset to screen out two nonconserved gene modules (dark orange and dark red). The regulation of oxidative stress-induced intrinsic apoptotic signaling pathway, PI3K-Akt signaling pathway, p38MAPK cascade, and Th1 and Th2 cell differentiation were primarily included in Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of these two modules. Next, 199 differentially expressed genes (DEGs) were identified via the limma package. Then, the GO annotation and KEGG pathways of the DEGs were primarily enriched in extracellular matrix (ECM) organization, epithelial cell migration, cell adhesion molecules (CAMs), NF-kappa B signaling pathway, and ECM-receptor interaction. Gene set enrichment analysis showed that in the DN group, the interaction of ECM-receptor, CAMs, the interaction of cytokine-cytokine receptor, and complement and coagulation cascade pathways were significantly activated. Eleven key genes, including ALB, ANXA1, ANXA2, C3, CCL2, CLU, EGF, FOS, PLG, TIMP1, and VCAM1, were selected by constructing a protein-protein interaction network, and expression validation, ECM-related pathways, and glomerular filtration rate correlation analysis were performed in the validated dataset. The upregulated expression of hub genes ANXA2 and FOS was verified by real-time quantitative PCR in HK-2 cells treated with high glucose. This study revealed potential regulatory mechanisms of renal tubulointerstitial damage and highlighted the crucial role of extracellular matrix in DN, which may promote the identification of new biomarkers and therapeutic targets.

Project description:BackgroundIgA nephropathy (IgAN) is the most common primary glomerular disease and the leading cause of the end-stage renal disease in the world. The pathogenesis of IgAN has not been well elucidated, and yet treatment is limited. High-throughput microarray has been applied for elucidating molecular biomarkers and potential mechanisms involved in IgAN. This study aimed to identify the potential key genes and therapeutics associated with IgAN using integrative bioinformatics and transcriptome-based computational drug repurposing approach.MethodsThree datasets of mRNA expression profile were obtained from the gene expression omnibus database and differentially expressed genes (DEGs) between IgAN glomeruli and normal tissue were identified by integrated analysis. Gene ontology and pathway enrichment analyses of the DEGs were performed by R software, and protein-protein interaction networks were constructed using the STRING online search tool. External dataset and immunohistochemical assessment of kidney biopsy specimens were used for hub gene validation. Potential compounds for IgAN therapy were obtained by Connectivity Map (CMap) analysis and preliminarily verified in vitro. Stimulated human mesangial cells were collected for cell proliferation and cell cycle analysis using cell counting kit 8 and flow cytometry, respectively.Results134 DEGs genes were differentially expressed across kidney transcriptomic data from IgAN patients and healthy living donors. Enrichment analysis showed that the glomerular compartments underwent a wide range of interesting pathological changes during kidney injury, focused on anion transmembrane transporter activity and protein digestion and absorption mostly. Hub genes (ITGB2, FCER1G, CSF1R) were identified and verified to be significantly upregulated in IgAN patients, and associated with severity of renal lesions. Computational drug repurposing with the CMap identified tetrandrine as a candidate treatment to reverse IgAN hub gene expression. Tetrandrine administration significantly reversed mesangial cell proliferation and cell cycle transition.ConclusionThe identification of DEGs and related therapeutic strategies of IgAN through this integrated bioinformatics analysis provides a valuable resource of therapeutic targets and agents of IgAN. Especially, our findings suggest that tetrandrine might be beneficial for IgAN, which deserves future research.

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Identification of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis.

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Identification of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis.

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