Identification of hub genes and transcription factor-miRNA-mRNA pathways in mice and human renal ischemia-reperfusion injury

Multi-omic analysis of human kidney tissue identified medulla-specific gene expression patterns

The kidney medulla is a specialized region with important homeostatic functions. It has been implicated in genetic and developmental disorders along with ischemic and drug-induced injuries. Despite its role in kidney function and disease, the medulla's baseline gene expression and epigenomic signatures have not been well described in the adult human kidney. Here we generated and analyzed gene expression (RNA-seq), chromatin accessibility (ATAC-seq), chromatin conformation (Hi-C) and spatial transcriptomic data from the adult human kidney cortex and medulla. Tissue samples were obtained from macroscopically dissected cortex and medulla of tumor-adjacent normal material in nephrectomy specimens from five male patients. We used these carefully annotated specimens to reassign incorrectly labeled samples in the larger public Genotype-Tissue Expression (GTEx) Project, and to extract meaningful medullary gene expression signatures. Using integrated analysis of gene expression, chromatin accessibility and conformation profiles, we found insights into medulla development and function and then validated this by spatial transcriptomics and immunohistochemistry. Thus, our datasets provide a valuable resource for functional annotation of variants from genome-wide association studies and are freely accessible through an epigenome browser portal.

Identification of biomarkers of renal ischemia-reperfusion injury by bioinformatics analysis and single-cell sequencing analysis combined with in vivo validation

BACKGROUND

Renal ischemia-reperfusion injury (IRI) is a serious clinical complication of kidney injury. This research dealt with investigating the hub genes and pathways associated with renal IRI.

METHODS

The transcriptome expression dataset of mouse renal ischemia samples (GSE39548) was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were filtered by R software for key genes utilized for gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene enrichment analysis (GSEA). The gene co-expression network was developed by WGCNA analysis to screen important modules. Hub genes from the intersection of DEGs and WGCNA were subjected to protein-protein interaction (PPI) network. The biomarkers obtained by SVM-REF and LASSO algorithm were validated by other datasets and subjected to GSEA analysis. The expression of biomarkers in renal IRI was detected by qRT-PCR and subjected to single-cell analysis.

RESULTS

A total of 157 DEGs were discovered. Biological function analysis depicted that the DEGs were primarily involved in cytokine-cytokine receptor interaction, as well as the signaling pathways IL-17, MAPK, and TNF. The intersection of DEGs and the genes obtained by WGCNA analysis yielded 149 hubs genes. Based on SVM-REF and LASSO algorithm, cyp1a1 and pdk4 were determined as potential biomarkers in individuals with renal ischemia and showed good diagnostic value. qRT-PCR results depicted that cyp1a1 and pdk4 were significantly up-regulated in renal ischemia mice (P < 0.05). Finally, the single-cell analysis identified the expression of Cyp1a1 and Pdk4 in mice kidney tissue.

CONCLUSION

cyp1a1 and pdk4 were identified to play important roles in renal IRI. This research provides a new perspective and basis for studying the pathogenesis of renal IRI and developing new treatments.

Functional mechanism of EGR3 in cerebral ischemia/reperfusion injury in rats by modulating transcription of pri-miR-146a/146b to miR-146 and suppressing SORT1 expression

OBJECTIVE

EGR3 is implicated in angiogenesis in rats with cerebral ischemia/reperfusion injury (CIRI). This research aimed to explore the effect and in vivo and ex vivo mechanisms of EGR3 in CIRI.

METHODS

CIRI rat models were established via middle cerebral artery occlusion. Cell models were established via oxygen-glucose deprivation/reoxygenation (OGD/R). Brain injury was assessed by neurological scoring, HE, and TTC staining. Inflammatory factors and oxidative stress markers were measured using corresponding kits. Mitochondrial membrane potential and mitochondrial respiration were examined by flow cytometry and respirometry. EGR3-miR-146 network was predicted on TransmiR v2.0 database. Target genes of miR-146 were screened on Starbase, Targetscan, and miRDB databases. miR-146 expression was determined by RT-qPCR. Levels of EGR3 and SORT1 were determined by Western blot. Binding relationships among EGR3, miR-146, and SORT1 were validated by dual-luciferase assay. EGR3, miR-146, and SORT1 levels were altered by injection or cell transfection to observe their functions.

RESULTS

EGR3 was poorly-expressed in CIRI rats and OGD/R-induced neurons. EGR3 overexpression reduced inflammatory factor levels and attenuated oxidative stress and mitochondrial injury in CIRI rats and OGD/R-induced neurons. EGR3 bound to miR-146b promoter region. EGR3 promoted pri-miR-146a/146b processing and stimulated miR-146 transcription. miR-146 overexpression ameliorated oxidative stress and mitochondrial injury and miR-146 downregulation abolished the effect of EGR3 overexpression in vitro. miR-146 targeted SORT1. SORT1 overexpression invalidated the protective function of miR-146 overexpression on oxidative stress and mitochondrial injury in vitro.

CONCLUSION

EGR3 protected against CIRI by mitigating oxidative stress and mitochondrial injury via the miR-146/SORT1 axis.

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

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.

Identification of functional TF-miRNA-hub gene regulatory network associated with ovarian endometriosis

Endometriosis (EMs), one of the most common gynecological diseases, seriously affects the health and wellness of women; however, the underlying pathogenesis remains unclear. This study focused on dysregulated genes and their predicted transcription factors (TFs) and miRNAs, which may provide ideas for further mechanistic research. The microarray expression dataset GSE58178, which included six ovarian endometriosis (OE) samples and six control samples, was downloaded from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to study the cellular and organism-level functions of DEGs. The protein-protein interaction (PPI) network was built and visualized using Cytoscape, and modules and hub genes were explored using various algorithms. Furthermore, we predicted miRNAs and TFs of hub genes using online databases, and constructed the TF-miRNA-hub gene network. There were 124 upregulated genes and 66 downregulated genes in EMs tissues. GO enrichment analysis showed that DEGs were concentrated in reproductive structure development and collagen-containing extracellular matrix, while KEGG pathway analysis showed that glycolysis/gluconeogenesis and central carbon metabolism in cancer require further exploration. Subsequently, HIF1A, LDHA, PGK1, TFRC, and CD9 were identified as hub genes, 22 miRNAs and 34 TFs were predicted to be upstream regulators of hub genes, and these molecules were pooled together. In addition, we found three key feedback loops in the network, MYC-miR-34a-5p-LDHA, YY1-miR-155-5p-HIF1A, and RELA-miR-93-5p-HIF1A, which may be closely related to OE development. Taken together, our study structured a TF-miRNA-hub gene network to decipher the molecular mechanism of OE, which may provide novel insights for clinical diagnosis and treatment.