Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosis
Objective: The aim of this study was to construct a microRNA (miRNA)–messenger RNA (mRNA)–transcription factor (TF) regulatory network and explore underlying molecular mechanisms, effective biomarkers, and drugs in renal fibrosis (RF).Methods: A total of six datasets were downloaded from Gene Expres...
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.925097/full |
| _version_ | 1811220992726401024 |
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| author | Le Deng Gaosi Xu Qipeng Huang |
| author_facet | Le Deng Gaosi Xu Qipeng Huang |
| author_sort | Le Deng |
| collection | DOAJ |
| description | Objective: The aim of this study was to construct a microRNA (miRNA)–messenger RNA (mRNA)–transcription factor (TF) regulatory network and explore underlying molecular mechanisms, effective biomarkers, and drugs in renal fibrosis (RF).Methods: A total of six datasets were downloaded from Gene Expression Omnibus. “Limma” and “DESeq2” packages in R software and GEO2R were applied to identify the differentially expressed miRNAs and mRNAs (DEmiRNAs and DEmRNAs, respectively). The determination and verification of DEmiRNAs and DEmRNAs were performed through the integrated analysis of datasets from five mouse 7 days of unilateral ureteral obstruction datasets and one human chronic kidney disease dataset and the Human Protein Atlas (http://www.proteinatlas.org). Target mRNAs of DEmiRNAs and TFs were predicted by prediction databases and the iRegulon plugin in Cytoscape, respectively. A protein–protein interaction network was constructed using STRING, Cytoscape v3.9.1, and CytoNCA. Functional enrichment analysis was performed by DIANA-miRPath v3.0 and R package “clusterProfiler.” A miRNA–mRNA–TF network was established using Cytoscape. Receiver operating characteristic (ROC) curve analysis was used to examine the diagnostic value of the key hub genes. Finally, the Comparative Toxicogenomics Database and Drug-Gene Interaction database were applied to identify potential drugs.Results: Here, 4 DEmiRNAs and 11 hub genes were determined and confirmed in five mouse datasets, of which Bckdha and Vegfa were further verified in one human dataset and HPA, respectively. Moreover, Bckdha and Vegfa were also predicted by miR-125a-3p and miR-199a-5p, respectively, in humans as in mice. The sequences of miR-125a-3p and miR-199a-5p in mice were identical to those in humans. A total of 6 TFs were predicted to regulate Bckdha and Vegfa across mice and humans; then, a miRNA–mRNA–TF regulatory network was built. Subsequently, ROC curve analysis showed that the area under the curve value of Vegfa was 0.825 (p = 0.002). Finally, enalapril was identified to target Vegfa for RF therapy.Conclusion: Pax2, Pax5, Sp1, Sp2, Sp3, and Sp4 together with Bckdha-dependent miR-125a-3p/Vegfa-dependent miR-199a-5p formed a co-regulatory network enabling Bckdha/Vegfa to be tightly controlled in the underlying pathogenesis of RF across mice and humans. Vegfa could act as a potential novel diagnostic marker and might be targeted by enalapril for RF therapy. |
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| language | English |
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| spelling | doaj.art-7c53684f64b84178bfbee0da386142c82022-12-22T03:41:34ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-11-011310.3389/fgene.2022.925097925097Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosisLe Deng0Gaosi Xu1Qipeng Huang2Department of Nephrology, The Second Affiliated Hospital of Nanchang University, Jiangxi, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Nanchang University, Jiangxi, ChinaDepartment of Nephrology, The Fifth Affiliated Hospital of Jinan University, Heyuan, ChinaObjective: The aim of this study was to construct a microRNA (miRNA)–messenger RNA (mRNA)–transcription factor (TF) regulatory network and explore underlying molecular mechanisms, effective biomarkers, and drugs in renal fibrosis (RF).Methods: A total of six datasets were downloaded from Gene Expression Omnibus. “Limma” and “DESeq2” packages in R software and GEO2R were applied to identify the differentially expressed miRNAs and mRNAs (DEmiRNAs and DEmRNAs, respectively). The determination and verification of DEmiRNAs and DEmRNAs were performed through the integrated analysis of datasets from five mouse 7 days of unilateral ureteral obstruction datasets and one human chronic kidney disease dataset and the Human Protein Atlas (http://www.proteinatlas.org). Target mRNAs of DEmiRNAs and TFs were predicted by prediction databases and the iRegulon plugin in Cytoscape, respectively. A protein–protein interaction network was constructed using STRING, Cytoscape v3.9.1, and CytoNCA. Functional enrichment analysis was performed by DIANA-miRPath v3.0 and R package “clusterProfiler.” A miRNA–mRNA–TF network was established using Cytoscape. Receiver operating characteristic (ROC) curve analysis was used to examine the diagnostic value of the key hub genes. Finally, the Comparative Toxicogenomics Database and Drug-Gene Interaction database were applied to identify potential drugs.Results: Here, 4 DEmiRNAs and 11 hub genes were determined and confirmed in five mouse datasets, of which Bckdha and Vegfa were further verified in one human dataset and HPA, respectively. Moreover, Bckdha and Vegfa were also predicted by miR-125a-3p and miR-199a-5p, respectively, in humans as in mice. The sequences of miR-125a-3p and miR-199a-5p in mice were identical to those in humans. A total of 6 TFs were predicted to regulate Bckdha and Vegfa across mice and humans; then, a miRNA–mRNA–TF regulatory network was built. Subsequently, ROC curve analysis showed that the area under the curve value of Vegfa was 0.825 (p = 0.002). Finally, enalapril was identified to target Vegfa for RF therapy.Conclusion: Pax2, Pax5, Sp1, Sp2, Sp3, and Sp4 together with Bckdha-dependent miR-125a-3p/Vegfa-dependent miR-199a-5p formed a co-regulatory network enabling Bckdha/Vegfa to be tightly controlled in the underlying pathogenesis of RF across mice and humans. Vegfa could act as a potential novel diagnostic marker and might be targeted by enalapril for RF therapy.https://www.frontiersin.org/articles/10.3389/fgene.2022.925097/fulltherapeutic targetsmiRNA–mRNA–transcription factor regulatory networkunilateral ureteral obstructionrenal fibrosisbioinformatics analysis |
| spellingShingle | Le Deng Gaosi Xu Qipeng Huang Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosis Frontiers in Genetics therapeutic targets miRNA–mRNA–transcription factor regulatory network unilateral ureteral obstruction renal fibrosis bioinformatics analysis |
| title | Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosis |
| title_full | Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosis |
| title_fullStr | Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosis |
| title_full_unstemmed | Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosis |
| title_short | Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosis |
| title_sort | comprehensive analyses of the microrna messenger rna transcription factor regulatory network in mouse and human renal fibrosis |
| topic | therapeutic targets miRNA–mRNA–transcription factor regulatory network unilateral ureteral obstruction renal fibrosis bioinformatics analysis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.925097/full |
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