Prediction of the molecular mechanisms and potential therapeutic targets for diabetic nephropathy by bioinformatics methods.
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ABSTRACT:
SUBMITTER: Wang WN
PROVIDER: S-EPMC4829133 | biostudies-literature | 2016 May
REPOSITORIES: biostudies-literature
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Project description:In order to further elucidate the potential correlations and treatments of IgA nephropathy (IgAN) and hypertensive nephropathy (HT), bioinformatics analysis of IgAN and HT was performed. The mRNA expression profiles of human renal biopsy samples from patients with IgAN, patients with HT and pre‑transplant healthy living controls (LD) were downloaded from the Gene Expression Omnibus database. Then, the differentially expressed genes (DEGs) were identified and functions of DEGs were analyzed. Finally, the regulatory networks containing DEGs and related‑transcription factors (TFs) were constructed using Cytoscape software. When compared with the LD group, 134 and 188 DEGs were obtained in the IgAN and HT groups, respectively. A total of 39 genes were altered in the HT group when compared with the IgAN group. In addition, 66 genes were shared in the IgAN and HT groups when compared with the LD group, 6 of which [early growth response 1, activating transcription factor 3, nuclear receptor subfamily 4 group A member 2 (NR4A2), NR4A1, v‑maf avian musculoaponeurotic fibrosarcoma oncogene homolog F and Kruppel like factor 6] were identified as TFs. In addition, DEGs including interleukin (IL) 1 receptor antagonist, collagen type 4 α2 chain, IL8, FBJ murine osteosarcoma viral oncogene homolog and somatostatin were enriched in a number of inflammation‑associated biological processes, and DEGs including structural maintenance of chromosomes protein 3, v‑crk avian sarcoma virus CT10 oncogene homolog and myosin 6 were enriched in non‑inflammation‑associated biological processes. Therefore, the differentially expressed TF genes and the genes associated with inflammation may be effective as potential therapeutic targets for IgAN and HT.
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Project description:Diabetic nephropathy (DN) is one of the most common diabetic complications, which is the major course of end-stage renal disease (ESRD). However, the systematical molecular characterizations during DN pathogenesis and progression has not been not well understood. To identify the fundamental mediators of the pathogenesis and progression of DN. we performed a combination RNASeq, proteomics, and metabolomics analyses of both patients' derived kidney biopsy samples and kidneys from in vivo DN model. As a result, molecular changes of DN contain extracellular matrix accumulation, abnormal activated inflamed microenvironment, and metabolism disorders, bringing about glomerular sclerosis and tubular interstitial fibrosis. Specificity, Further integration analyses have identified that the linoleic acid metabolism and fatty-acids β-oxidation are significantly inhibited during DN pathogenesis and progression, the transporter protein ABCD3, the fatty acyl-CoA activated enzymes ACOX1, ACOX2, and ACOX3, and some corresponding metabolites such as 13'-HODE, stearidonic acid, docosahexaenoic acid, (±)10(11)-EpDPA were also significantly reduced. Our study thus provides potential molecular mechanisms for DN progression and suggests that targeting the key enzymes or supplying some lipids may be a promising avenue in the treatment of DN, especially advanced-stage DN.
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Project description:Background: Previous retrospective cohorts showed that Rehmannia-6 (R-6, Liu-wei-di-huang-wan) formulations were associated with significant kidney function preservation and mortality reduction among chronic kidney disease patients with diabetes. This study aimed to investigate the potential mechanism of action of common R-6 variations in a clinical protocol for diabetic nephropathy (DN) from a system pharmacology approach. Study Design and Methods: Disease-related genes were retrieved from GeneCards and OMIM by searching "Diabetic Nephropathy" and "Macroalbuminuria". Variations of R-6 were identified from a published existing clinical practice guideline developed from expert consensus and pilot clinical service program. The chemical compound IDs of each herb were retrieved from TCM-Mesh and PubChem. Drug targets were subsequently revealed via PharmaMapper and UniProtKB. The disease gene interactions were assessed through STRING, and disease-drug protein-protein interaction network was integrated and visualized by Cytoscape. Clusters of disease-drug protein-protein interaction were constructed by Molecular Complex Detection (MCODE) extension. Functional annotation of clusters was analyzed by DAVID and KEGG pathway enrichment. Differences among variations of R-6 were compared. Binding was verified by molecular docking with AutoDock. Results: Three hundred fifty-eight genes related to DN were identified, forming 11 clusters which corresponded to complement and coagulation cascades and signaling pathways of adipocytokine, TNF, HIF-1, and AMPK. Five variations of R-6 were analyzed. Common putative targets of the R-6 variations on DN included ACE, APOE, CCL2, CRP, EDN1, FN1, HGF, ICAM1, IL10, IL1B, IL6, INS, LEP, MMP9, PTGS2, SERPINE1, and TNF, which are related to regulation of nitric oxide biosynthesis, lipid storage, cellular response to lipopolysaccharide, inflammatory response, NF-kappa B transcription factor activity, smooth muscle cell proliferation, blood pressure, cellular response to interleukin-1, angiogenesis, cell proliferation, peptidyl-tyrosine phosphorylation, and protein kinase B signaling. TNF was identified as the seed for the most significant cluster of all R-6 variations. Targets specific to each formulation were identified. The key chemical compounds of R-6 have good binding ability to the putative protein targets. Conclusion: The mechanism of action of R-6 on DN is mostly related to the TNF signaling pathway as a core mechanism, involving amelioration of angiogenesis, fibrosis, inflammation, disease susceptibility, and oxidative stress. The putative targets identified could be validated through clinical trials.
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Project description:Isocitrate dehydrogenase (IDH) enzymes catalyse the oxidative decarboxylation of isocitrate and therefore play key roles in the Krebs cycle and cellular homoeostasis. Major advances in cancer genetics over the past decade have revealed that the genes encoding IDHs are frequently mutated in a variety of human malignancies, including gliomas, acute myeloid leukaemia, cholangiocarcinoma, chondrosarcoma and thyroid carcinoma. A series of seminal studies further elucidated the biological impact of the IDH mutation and uncovered the potential role of IDH mutants in oncogenesis. Notably, the neomorphic activity of the IDH mutants establishes distinctive patterns in cancer metabolism, epigenetic shift and therapy resistance. Novel molecular targeting approaches have been developed to improve the efficacy of therapeutics against IDH-mutated cancers. Here we provide an overview of the latest findings in IDH-mutated human malignancies, with a focus on glioma, discussing unique biological signatures and proceedings in translational research.
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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.
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Project description:BackgroundNumerous epidemiological studies have confirmed that diabetes can promote the development of malignant tumors. However, the relationship between renal cell carcinoma (RCC) and diabetic nephropathy (DN) is still controversial. This study aimed to investigate the genes that are co-expressed in DN and RCC in order to gain a better understanding of the relationship between these diseases, and to identify potential biomarkers and targets for the treatment of DN-related RCC.MethodsWe evaluated the differentially expressed genes (DEGs) that are co-expressed in DN and RCC using a wide range of target prediction and analysis methods. Twenty-four genes were identified by intersecting the differential genes of 3 DN datasets and 2 RCC datasets. We predicted the micro-ribonucleic acids (miRNAs) of these genes that may be controlled using the miRNA Data Integration Portal (mirDIP) database, and rated them according to each data forecast based on the Comparative Toxicogenomics Database (CTD) and the StarBase database.ResultsFour genes were associated with DN and RCC patients: the predicted miRNAs hsa-miR-200b-3p and hsa-miR-429 of fibronectin 1 (FN1); the predicted miRNA hsa-miR-29c-3p of collagen type 1 alpha 2 (COL1A2); the predicted miRNA hsa-miR-29c-3p of collagen type 3 alpha 1 (COL3A1); and the predicted miRNA hsa-miR-29a-3p and hsa-miR-200c-3p of glucose-6-phosphatase catalytic subunit (G6PC). These genes may serve as potential biomarkers or specific targets in the treatment of DN-related RCC.ConclusionsA significant correlation was identified between DN and RCC. The FN1, COL1A2, COL3A1, and G6PC genes could be novel biomarkers of DN-related RCC.
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Project description:BackgroundAstragalus Radix (AR)-Panax notoginseng (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of AR-PN treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of AR-PN treating DN based on network pharmacology combined with bioinformatics.Materials and methodsThe Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of AR-PN. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of AR-PN putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves.ResultsTwenty-two bioactive ingredients and 189 putative targets of AR-PN were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of AR-PN against DN were identified. GO and KEGG analyses revealed that candidate targets of AR-PN against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls.ConclusionsAR-PN against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.
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Project description:Diabetic nephropathy (DN) has been recognized as a severe complication of diabetes mellitus and a dominant pathogeny of end-stage kidney disease, which causes serious health problems and great financial burden to human society worldwide. Conventional strategies, such as renin-angiotensin-aldosterone system blockade, blood glucose level control, and bodyweight reduction, may not achieve satisfactory outcomes in many clinical practices for DN management. Notably, due to the multi-target function, Chinese medicine possesses promising clinical benefits as primary or alternative therapies for DN treatment. Increasing studies have emphasized identifying bioactive compounds and molecular mechanisms of reno-protective effects of Chinese medicines. Signaling pathways involved in glucose/lipid metabolism regulation, antioxidation, anti-inflammation, anti-fibrosis, and podocyte protection have been identified as crucial mechanisms of action. Herein, we summarize the clinical efficacies of Chinese medicines and their bioactive components in treating and managing DN after reviewing the results demonstrated in clinical trials, systematic reviews, and meta-analyses, with a thorough discussion on the relative underlying mechanisms and molecular targets reported in animal and cellular experiments. We aim to provide comprehensive insights into the protective effects of Chinese medicines against DN.
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Project description: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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