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Prediction of the molecular mechanisms and potential therapeutic targets for diabetic nephropathy by bioinformatics methods.

ABSTRACT: In this study, we aimed to explore the molecular mechanisms of and genetic factors influencing diabetic nephropathy (DN). Gene expression profiles associated with DN were obtained from the GEO database (Accession no. GSE20844). The differentially expressed genes (DEGs) between diabetic mice and non-diabetic mice were screened. Subsequently, the DEGs were subjected to functional and pathway analysis. The protein-protein interaction (PPI) network was constructed and the transcription factors (TFs) were screened among the DEGs. A total of 92 upregulated and 118 downregulated genes were screened. Pathway analysis revealed that the p53 signaling pathway, the transforming growth factor (TGF)-? signaling pathway and the mitogen-activated protein kinase (MAPK) signaling pathway were significantly enriched by upregulated genes. Serpine1 (also known as plasminogen activator inhibitor-1), early growth response 1 (Egr1) and Mdk were found to be significant nodes in the PPI network by three methods. A total of 12 TFs were found to be differentially expressed, of which nuclear receptor subfamily 4, group A, member 1 (Nr4a1) and peroxisome proliferator-activated receptor gamma (Pparg) were found to have multiple interactions with other DEGs. We demonstrated that the p53 signaling pathway, the TGF-? signaling pathway and the MAPK signaling pathway were dysregulated in the diabetic mice. The significant nodes (Serpine1, Egr1 and Mdk) and differentially expressed TFs (Nr4a1 and Pparg) may provide a novel avenue for the targeted therapy of DN.

SUBMITTER: Wang WN

PROVIDER: S-EPMC4829133 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Prediction of the molecular mechanisms and potential therapeutic targets for diabetic nephropathy by bioinformatics methods.

Wang Wan-Ning WN Zhang Wen-Long WL Zhou Guang-Yu GY Ma Fu-Zhe FZ Sun Tao T Su Sen-Sen SS Xu Zhong-Gao ZG

International journal of molecular medicine 20160315 5

In this study, we aimed to explore the molecular mechanisms of and genetic factors influencing diabetic nephropathy (DN). Gene expression profiles associated with DN were obtained from the GEO database (Accession no. GSE20844). The differentially expressed genes (DEGs) between diabetic mice and non-diabetic mice were screened. Subsequently, the DEGs were subjected to functional and pathway analysis. The protein-protein interaction (PPI) network was constructed and the transcription factors (TFs) ...[more]

PMID: 26986014

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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.

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: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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Prediction of the molecular mechanisms and potential therapeutic targets for diabetic nephropathy by bioinformatics methods.

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Prediction of the molecular mechanisms and potential therapeutic targets for diabetic nephropathy by bioinformatics methods.

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