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pmid:36246123

Identification of hub genes associated with acute kidney injury induced by renal ischemia-reperfusion injury in mice.

Sheng He · Lili He · Fangran Yan · Junda Li · Xiaoting Liao · Maoyao Ling · Ren Jing · Linghui Pan

Frontiers in physiology · 2022 doi:10.3389/fphys.2022.951855

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complete

Experiment

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Data

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title
Identification of hub genes associated with acute kidney injury induced by renal ischemia-reperfusion injury in mice.
doi
10.3389/fphys.2022.951855
pmid
36246123
authors array · 8 items
[0]
Sheng He
[1]
Lili He
[2]
Fangran Yan
[3]
Junda Li
[4]
Xiaoting Liao
[5]
Maoyao Ling
[6]
Ren Jing
[7]
Linghui Pan
journal
Frontiers in physiology
year
2022
abstract
Background: Acute kidney injury (AKI) is a severe clinical syndrome, and ischemia-reperfusion injury is an important cause of acute kidney injury. The aim of the present study was to investigate the related genes and pathways in the mouse model of acute kidney injury induced by ischemia-reperfusion injury (IRI-AKI). Method: Two public datasets (GSE39548 and GSE131288) originating from the NCBI Gene Expression Omnibus (GEO) database were analyzed using the R software limma package, and differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) and gene set enrichment analysis (GSEA) were performed using the differentially expressed genes. Furthermore, a protein-protein interaction (PPI) network was constructed to investigate hub genes, and transcription factor (TF)-hub gene and miRNA-hub gene networks were constructed. Drugs and molecular compounds that could interact with hub genes were predicted using the DGIdb. Result: A total of 323 common differentially expressed genes were identified in the renal ischemia-reperfusion injury group compared with the control group. Among these, 260 differentially expressed genes were upregulated and 66 differentially expressed genes were downregulated. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes analysis results showed that these common differentially expressed genes were enriched in positive regulation of cytokine production, muscle tissue development, and other biological processes, indicating that they were involved in mitogen-activated protein kinase (MAPK), PI3K-Akt, TNF, apoptosis, and Epstein-Barr virus infection signaling pathways. Protein-protein interaction analysis showed 10 hub genes, namely, Jun, Stat3, MYC, Cdkn1a, Hif1a, FOS, Atf3, Mdm2, Egr1, and Ddit3. Using the STRUST database, starBase database, and DGIdb database, it was predicted that 34 transcription factors, 161 mi-RNAs, and 299 drugs or molecular compounds might interact with hub genes. Conclusion: Our findings may provide novel potential biomarkers and insights into the pathogenesis of ischemia-reperfusion injury-acute kidney injury through a comprehensive analysis of Gene Expression Omnibus data, which may provide a reliable basis for early diagnosis and treatment of ischemia-reperfusion injury-acute kidney injury.
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The classical Bayesian algorithm in the “limma” package (Ritchie et al., 2015) was used to correctly identify differentially expressed genes (DEGs) in IRI-AKI samples compared with the non-injured renal samples in two datasets (GSE39548 and GES131288).
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sections[4] "Identification of differentially expressed genes" paragraph 2
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The classical Bayesian algorithm in the “limma” package (Ritchie et al., 2015) was used to correctly identify differentially expressed genes (DEGs) in IRI-AKI samples compared with the non-injured renal samples in two datasets (GSE39548 and GES131288).
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Analysis of the GSE39548 and GSE131288 mouse renal gene-expression datasets identified 323 common differentially expressed genes and 10 PPI-derived hub genes associated with ischemia–reperfusion injury–acute kidney injury.
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A total of 323 common differentially expressed genes were identified in the renal ischemia–reperfusion injury group compared with the control group.
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sections[11] "Identification of differentially expressed genes" paragraph 2
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In addition, after taking the intersection of DEGs of the two datasets, 323 genes with common DEGs (co-DEGs) were obtained, of which 260 were upregulated and 63 downregulated in the IRI group, which were visualized by the Venn diagram (Figures 3E,F); Venn diagrams for upregulated and downregulated DEGs are shown in Supplementary Tables S5–S6.
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sections[14] "Protein–protein interaction network construction and identification of hub genes" paragraph 2
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In addition, the top 10 genes were identified as core genes by calculating the tightness of the connection of the node using the cytoHubba plug-in, namely, Jun, Stat3, Myc, Cdkn1a, Hif1a, Fos, Atf3, Mdm2, Egr1, and Ddit3 (Figure 7B).
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Analysis of the GSE39548 and GSE131288 mouse renal gene-expression datasets identified 323 common differentially expressed genes and 10 PPI-derived hub genes associated with ischemia–reperfusion injury–acute kidney injury.
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induced by renal ischemia-reperfusion injury in mice.
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The aim of the present study was to investigate the related genes and pathways in the mouse model of acute kidney injury induced by ischemia-reperfusion injury (IRI-AKI). Method: Two public datasets (GSE39548 and GSE131288) originating from the NCBI Gene Expression Omnibus (GEO) database were analyzed using the R software limma package, and differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) and gene set enrichment analysis (GSEA) were performed using the differentially expressed genes. Furthermore, a protein-protein interaction (PPI) network was constructed to investigate hub genes, and transcription factor (TF)-hub gene and miRNA-hub gene networks were constructed. Drugs and molecular compounds that could interact with hub genes were predicted using the DGIdb. Result: A total of 323 common differentially expressed genes were identified in the renal ischemia-reperfusion injury group compared with the control group. Among these, 260 differentially expressed genes were upregulated and 66 differentially expressed genes were downregulated. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes analysis results showed that these common differentially expressed genes were enriched in positive regulation of cytokine production, muscle tissue development, and other biological processes, indicating that they were involved in mitogen-activated protein kinase (MAPK), PI3K-Akt, TNF, apoptosis, and Epstein-Barr virus infection signaling pathways. Protein-protein interaction analysis showed 10 hub genes, namely, Jun, Stat3, MYC, Cdkn1a, Hif1a, FOS, Atf3, Mdm2, Egr1, and Ddit3. Using the STRUST database, starBase database, and DGIdb database, it was predicted that 34 transcription factors, 161 mi-RNAs, and 299 drugs or molecular compounds might interact with hub genes. Conclusion: Our findings may provide novel potential biomarkers and insights into the pathogenesis of ischemia-reperfusion injury-acute kidney injury through a comprehensive analysis of Gene Expression Omnibus data, which may provide a reliable basis for early diagnosis and treatment of ischemia-reperfusion injury-acute kidney injury.", "claims": [{"id": "claim-method", "statement": "The classical Bayesian algorithm in the “limma” package (Ritchie et al., 2015) was used to correctly identify differentially expressed genes (DEGs) in IRI-AKI samples compared with the non-injured renal samples in two datasets (GSE39548 and GES131288).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36246123", "locator": "sections[4] \"Identification of differentially expressed genes\" paragraph 2", "quote": "The classical Bayesian algorithm in the “limma” package (Ritchie et al., 2015) was used to correctly identify differentially expressed genes (DEGs) in IRI-AKI samples compared with the non-injured renal samples in two datasets (GSE39548 and GES131288)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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