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

Shen-fu Injection Modulates HIF- 1α/BNIP3-Mediated Mitophagy to Alleviate Myocardial Ischemia-Reperfusion Injury

Coronary reperfusion therapy is the most common surgical treatment for myocardial infarction, but it can further induce myocardial ischemia-reperfusion injury (MIRI). Therefore, MIRI following coronary intervention is a challenging clinical issue. This study aims to investigate the involvement of HIF- 1α/BNIP3-mediated mitophagy in the protective effects of Shen-fu Injection (SFI) on MIRI in rats. Key targets and signaling pathways of myocardial MIRI were analyzed using high-throughput transcriptome data from the GSE240842 dataset in the GEO database.To establish the MIRI rat model, the left anterior descending coronary artery was ligated for 30 min, followed by reperfusion for 120 min. Hypoxia/reoxygenation (H/R) in neonatal rat primary cardiomyocytes was induced by oxygen-glucose deprivation for 4 h, followed by reoxygenation for 2 h. Two hours after reperfusion, assessments included myocardial infarction area, CK-MB, CTnI, HE staining, TUNEL, mitochondrial ultrastructure and autophagosomes, HIF- 1α, BNIP3, LC3B-II, LC3B-I protein expression, immunofluorescence, and qRT-PCR. Cardiac function was also evaluated using M-mode ultrasound 2 h after reperfusion. In cardiomyocytes, CCK- 8, EdU cell proliferation levels, scratch assay, mitochondrial membrane potential, ROS levels, cardiomyocyte apoptosis, protein expression levels, and immunofluorescence were assessed 2 h after reoxygenation. Our results indicate that HIF- 1α and BNIP3 are key targets in MIRI. SFI upregulates HIF- 1α expression, promoting moderate mitophagy. This process clears excessively damaged mitochondria, reduces cardiomyocyte apoptosis, and decreases myocardial injury. Additionally, SFI reduces autophagosome accumulation, lowers ROS production, and stabilizes membrane potential. Consequently, the area of myocardial infarction is reduced, and cardiac function is improved. SFI activates the HIF- 1α/BNIP3 pathway to mediate moderate mitophagy, effectively reducing cardiomyocyte apoptosis and alleviating myocardial ischemia-reperfusion injury, thereby protecting cardiomyocytes.

Identification of Key Genes and Potential Therapeutic Targets in Sepsis-Associated Acute Kidney Injury Using Transformer and Machine Learning Approaches

Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication of sepsis, characterized by high mortality and prolonged hospitalization. Early diagnosis and effective therapy remain difficult despite extensive investigation. To address this, we developed an AI-driven integrative framework that combines a Transformer-based deep learning model with established machine learning techniques (LASSO, SVM-RFE, Random Forest and neural networks) to uncover complex, nonlinear interactions among gene-expression biomarkers. Analysis of normalized microarray data from GEO (GSE95233 and GSE69063) identified differentially expressed genes (DEGs), and KEGG/GO enrichment via clusterProfiler revealed key pathways in immune response, protein synthesis, and antigen presentation. By integrating multiple transcriptomic cohorts, we pinpointed 617 SA-AKI-associated DEGs-21 of which overlapped between sepsis and AKI datasets. Our Transformer-based classifier ranked five genes (MYL12B, RPL10, PTBP1, PPIA, and TOMM7) as top diagnostic markers, with AUC values ranging from 0.9395 to 0.9996 (MYL12B yielding 0.9996). Drug-gene interaction mining using DGIdb (FDR < 0.05) nominated 19 candidate therapeutics for SA-AKI. Together, these findings demonstrate that melding deep learning with classical machine learning not only sharpens early SA-AKI detection but also systematically uncovers actionable drug targets, laying groundwork for precision intervention in critical care settings.

Metabolomic and transcriptomic insights into the mechanisms of renal ischemia-reperfusion injury progression

Renal ischemia-reperfusion injury (IRI) is an important cause of acute kidney injury (AKI). However, the pathophysiological changes and mechanisms during IRI-AKI progression remain unclear. This study aims toinvestigate the potential mechanisms in the progression of IRI-AKI by integrating metabolomics and transcriptomics data, providing a reference for the subsequent identification of biomarkers and therapeutic targets. IRI-AKI rat models with 30 min of ischemia and 24-72 h of reperfusion surgery simulating the progression of AKI were established. Compared to the control group underwent sham surgery (NC group), most of the differentially expressed metabolites (DEMs) in IRI-AKI 24 h and IRI-AKI 72 h decreased, mainly including amino acids, organic acids, and carnitines. Additionally, we found that DEMs were mainly enriched in amino acid-related pathways, among which valine, leucine, and isoleucine biosynthesis were dramatically altered in all comparisons. Transcriptomics revealed that differentially expressed genes (DEGs) were primarily involved in amino acid, lipid, and fatty acid metabolism. By integrating metabolomics and transcriptomics, we found valine, leucine, and isoleucine biosynthesis play key roles in IRI-AKI development. Our findings concluded that valine, leucine, and isoleucine pathways are hubs that potentially connect transcriptomes to metabolomes, providing new insights regarding the pathogenesis of IRI-AKI and its potential biomarkers and therapeutic strategies.

Comprehensive Analysis of RNA Methylation-Regulated Gene Signature and Immune Infiltration in Ischemia/Reperfusion-Induced Acute Kidney Injury

INTRODUCTION

The morbidity and mortality of acute kidney injury (AKI) are increasing. Epigenetic regulation and immune cell infiltration are thought to be involved in AKI. However, the relationship between epigenetic regulation and immune cell infiltration in AKI has not been elucidated. This study was conducted to identify the differentially expressed genes (DEGs), differentially expressed RNA methylation genes (DEMGs), and infiltrated immune cells in the kidneys of ischemia-reperfusion induced-acute kidney injury (IRI-AKI) models and further explore their relationships in IRI-AKI.

METHODS

This is a bioinformatic analysis using R programming language in 3 selected IRI-AKI datasets from the Gene Expression Omnibus (GEO) database, including 16 IRI-AKI kidney tissues and 10 normal kidney tissues. The DEGs were screened, and enrichment pathways were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The DEMGs and core DEMGs were identified using the R package. The ROC curve was plotted to predict disease occurrence of 7 core DEMGs. The correlation of 7 core DEMGs and other genes was analyzed using Pearson's correlation test. The gene set enrichment analysis (GSEA) of each DEMG was conducted using the R package. The upstream miRNAs and transcript factors of 7 core DEMGs were predicted based on the RegNetwork database and Cytoscape software. The STITCH database was used to predict the possible binding compounds of the 7 core DEMGs. Immune cell infiltration in kidney tissues between the IRI-AKI group and control group was evaluated using the R package.

RESULTS

A total of 2,367 DEGs were obtained, including 1,180 upregulated and 1,187 downregulated genes in IRI-AKI kidney associated with the cell structure, proliferation, molecule binding/interaction, and signaling pathways such as the leukocyte migration and chemokine signaling pathways. Ten DEMGs were identified, with Ythdf1, Rbm15, Trmt6, Hnrnpc, and Dnmt1 being significantly upregulated, while Lrpprc, Cyfip2, Mettl3, Ncbp2, and Nudt7 were significantly downregulated in IRI-AKI tissues. The molecules interacting with 7 core DEMGs were identified. Significant changes in the infiltration of 8 types of immune cells were observed in IRI-AKI kidneys compared to normal controls. The significant correlation between 6 core DEMGs and the infiltration of immune cells was observed.

CONCLUSION

IRI may induce AKI through RNA methylation to regulate the expression of genes involved in immune cell infiltration.

Bioinformatics integration reveals key genes associated with mitophagy in myocardial ischemia-reperfusion injury

BACKGROUND

Myocardial ischemia is a prevalent cardiovascular disorder associated with significant morbidity and mortality. While prompt restoration of blood flow is essential for improving patient outcomes, the subsequent reperfusion process can result in myocardial ischemia-reperfusion injury (MIRI). Mitophagy, a specialized autophagic mechanism, has consistently been implicated in various cardiovascular disorders. However, the specific connection between ischemia-reperfusion and mitophagy remains elusive. This study aims to elucidate and validate central mitophagy-related genes associated with MIRI through comprehensive bioinformatics analysis.

METHODS

We acquired the microarray expression profile dataset (GSE108940) from the Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) using GEO2R. Subsequently, these DEGs were cross-referenced with the mitophagy database, and differential nucleotide sequence analysis was performed through enrichment analysis. Protein-protein interaction (PPI) network analysis was employed to identify hub genes, followed by clustering of these hub genes using cytoHubba and MCODE within Cytoscape software. Gene set enrichment analysis (GSEA) was conducted on central genes. Additionally, Western blotting, immunofluorescence, and quantitative polymerase chain reaction (qPCR) analyses were conducted to validate the expression patterns of pivotal genes in MIRI rat model and H9C2 cardiomyocytes.

RESULTS

A total of 2719 DEGs and 61 mitophagy-DEGs were identified, followed by enrichment analyses and the construction of a PPI network. HSP90AA1, RPS27A, EEF2, EIF4A1, EIF2S1, HIF-1α, and BNIP3 emerged as the seven hub genes identified by cytoHubba and MCODE of Cytoscape software. Functional clustering analysis of HIF-1α and BNIP3 yielded a score of 9.647, as determined by Cytoscape (MCODE). In our MIRI rat model, Western blot and immunofluorescence analyses confirmed a significant elevation in the expression of HIF-1α and BNIP3, accompanied by a notable increase in the ratio of LC3II to LC3I. Subsequently, qPCR confirmed a significant upregulation of HIF-1α, BNIP3, and LC3 mRNA in the MIRI group. Activation of the HIF-1α/BNIP3 pathway mediates the regulation of the degree of Mitophagy, thereby effectively reducing apoptosis in rat H9C2 cardiomyocytes.

CONCLUSIONS

This study has identified seven central genes among mitophagy-related DEGs that may play a pivotal role in MIRI, suggesting a correlation between the HIF-1α/BNIP3 pathway of mitophagy and the pathogenesis of MIRI. The findings highlight the potential importance of mitophagy in MIRI and provide valuable insights into underlying mechanisms and potential therapeutic targets for further exploration in future studies.

Molecular mechanism of NR4A1/MDM2/P53 signaling pathway regulation inducing ferroptosis in renal tubular epithelial cells involved in the progression of renal ischemia-reperfusion injury

Revealing the possible molecular mechanism of the NR4A1 (nuclear receptor subfamily 4 group A member 1)-MDM2 (MDM2 proto-oncogene)-P53 (tumor protein p53) signaling pathway that induces ferroptosis in renal tubular epithelial cells. Renal ischemia-reperfusion injury (RIRI) -related datasets were obtained from the GEO database. Differentially expressed genes in RIRI were analyzed using R language, intersected with RIRI-related genes in the GeneCard database, and retrieved from the literature to finally obtain differential ferroptosis-related genes. An in vitro cell model of RIRI was constructed using mouse renal cortical proximal tubule epithelial cells (mRTEC cells) treated with hypoxia-reoxygenation (H/R). Bioinformatic analysis showed that NR4A1 may be involved in RIRI through the induction of ferroptosis; in addition, we predicted through online databases that the downstream target gene of NR4A1, MDM2, could be targeted and regulated by ChIP and dual luciferase assays, and that NR4A1 could prevent MDM2 by inhibiting it, and NR4A1 was able to promote ferroptosis by inhibiting the ubiquitinated degradation of P53. NR4A1 expression was significantly increased in mRTEC cells in the hypoxia/reoxygenation model, and the expression of ferroptosis-related genes was increased in vitro experiments. NR4A1 reduces the ubiquitinated degradation of P53 by targeting the inhibition of MDM2 expression, thereby inducing ferroptosis and ultimately exacerbating RIRI by affecting the oxidative respiration process in mitochondria and producing oxidized lipids. This study presents a novel therapeutic approach for the clinical treatment of renal ischemia-reperfusion injury by developing drugs that inhibit NR4A1 to alleviate kidney damage caused by renal ischemia-reperfusion.

Comprehensive Overview of Innovative Strategies in Preventing Renal Ischemia-reperfusion Injury: Insights from Bibliometric and In silico Analyses

BACKGROUND

Ischemia-reperfusion Injury (IRI) is a complex pathophysiological process with severe consequences, including irreversible loss of renal function. Various intraoperative prevention methods have been proposed to mitigate the harmful effects of warm ischemia and kidney reperfusion.

AIM

This comprehensive analysis provides an overview of pharmacological agents and intraoperative methods for preventing and treating renal IRI.

METHODS

Our analysis revealed that eplerenone exhibited the highest binding affinity to crucial targets, including Aldehyde Dehydrogenase (AD), Estrogen Receptor (ER), Klotho protein, Mineralocorticoid Receptor (MR), and Toll-like Receptor 4 (TLR4). This finding indicates eplerenone's potential as a potent preventive agent against IRI, surpassing other available therapeutics like Benzodioxole, Hydrocortisone, Indoles, Nicotinamide adenine dinucleotide, and Niacinamide. In preventing kidney IRI, our comprehensive analysis emphasizes the significance of eplerenone due to its strong binding affinity to key targets involved in the pathogenesis of IRI.

RESULTS

This finding positions eplerenone as a promising candidate for further clinical investigation and consideration for future clinical practice.

CONCLUSION

The insights provided in this analysis will assist clinicians and researchers in selecting effective preventive approaches for renal IRI in surgical settings, potentially improving patient outcomes.

Identification of signature genes for renal ischemia‒reperfusion injury based on machine learning and WGCNA

Background

As an inevitable event after kidney transplantation, ischemia‒reperfusion injury (IRI) can lead to a decrease in kidney transplant success. The search for signature genes of renal ischemia‒reperfusion injury (RIRI) is helpful in improving the diagnosis and guiding clinical treatment.

Methods

We first downloaded 3 datasets from the GEO database. Then, differentially expressed genes (DEGs) were identified and applied for functional enrichment analysis. After that, we performed three machine learning methods, including random forest (RF), Lasso regression analysis, and support vector machine recursive feature elimination (SVM-RFE), to further predict candidate genes. WGCNA was also executed to screen candidate genes from DEGs. Then, we took the intersection of candidate genes to obtain the signature genes of RIRI. Receiver operating characteristic (ROC) analysis was conducted to measure the predictive ability of the signature genes. Kaplan‒Meier analysis was used for association analysis between signature genes and graft survival. Verifying the expression of signature genes in the ischemia cell model.

Results

A total of 117 DEGs were screened out. Subsequently, RF, Lasso regression analysis, SVM-RFE and WGCNA identified 17, 25, 18 and 74 candidate genes, respectively. Finally, 3 signature genes (DUSP1, FOS, JUN) were screened out through the intersection of candidate genes. ROC analysis suggested that the 3 signature genes could well diagnose and predict RIRI. Kaplan‒Meier analysis indicated that patients with low FOS or JUN expression had a longer OS than those with high FOS or JUN expression. Finally, we validated using the ischemia cell model that compared to the control group, the expression level of JUN increased under hypoxic conditions.

Conclusions

Three signature genes (DUSP1, FOS, JUN) offer a good prediction for RIRI outcome and may serve as potential therapeutic targets for RIRI intervention, especially JUN. The prediction of graft survival by FOS and JUN may improve graft survival in patients with RIRI.

GADD45A and GADD45B as Novel Biomarkers Associated with Chromatin Regulators in Renal Ischemia-Reperfusion Injury

Chromatin regulators (CRs) are essential upstream regulatory factors of epigenetic modification. The role of CRs in the pathogenesis of renal ischemia-reperfusion injury (IRI) remains unclear. We analyzed a bioinformatic analysis on the differentially expressed chromatin regulator genes in renal IRI patients using data from public domains. The hub CRs identified were used to develop a risk prediction model for renal IRI, and their expressions were also validated using Western blot, qRT-PCR, and immunohistochemistry in a murine renal IRI model. We also examined the relationships between hub CRs and infiltrating immune cells in renal IRI and used network analysis to explore drugs that target hub CRs and their relevant downstream microRNAs. The results of machine learning methods showed that five genes (DUSP1, GADD45A, GADD45B, GADD45G, HSPA1A) were upregulated in renal IRI, with key roles in the cell cycle, p38 MAPK signaling pathway, p53 signaling pathway, FoxO signaling pathway, and NF-κB signaling pathway. Two genes from the network, GADD45A and GADD45B (growth arrest and DNA damage-inducible protein 45 alpha and beta), were chosen for the renal IRI risk prediction model. They all showed good performance in the testing and validation cohorts. Mice with renal IRI showed significantly upregulated GADD45A and GADD45B expression within kidneys compared to sham-operated mice. GADD45A and GADD45B showed correlations with plasmacytoid dendritic cells (pDCs) in infiltrating immune cell analysis and enrichment in the MAPK pathway based on the weighted gene co-expression network analysis (WGCNA) method. Candidate drugs that target GADD45A and GADD45B include beta-escin, sertraline, primaquine, pimozide, and azacyclonol. The dysregulation of GADD45A and GADD45B is related to renal IRI and the infiltration of pDCs, and drugs that target GADD45A and GADD45B may have therapeutic potential for renal IRI.

GPX3 and GSTT1 as biomarkers related to oxidative stress during renal ischemia reperfusion injuries and their relationship with immune infiltration

Background

Renal ischemia reperfusion injuries (IRIs) are very common in clinical diagnoses and treatments, which are a common cause of impaired renal functions, worsening pathological damage, affecting disease progression and hindering recovery. Renal IRIs are an inflammatory disease mediated by the adaptive and innate immune system. There is a complex interaction between oxidative stress and immune cell infiltration. Therefore, we aimed to determine biomarkers associated with oxidative stress during renal IRIs and their relationship with immune cell infiltration.

Method

A differential gene expression analysis was made based on the GSE148420 dataset from the NCBI Gene Expression Comprehensive Database (GEO) combined with 92 oxidative-stress (OS)-related genes identified in the Molecular Signatures Database. Then we identified differentially-expressed genes (DEOSGs) associated with oxidative stress, which were used for gene ontology (GO) and a Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. At the same time, we used PPI protein interaction networks and Lasso regression analysis to identify key genes, which were verified by the validation sets GSE58438 and GSE71647, as well as Western Blot detection on rat renal IRI models. At the same time, PAS staining, HE staining and immunohistochemistry were used to detect tissue damage and expression of markers related to oxidative stress during renal ischemia-reperfusion. Single-gene enrichment analysis (GSEA) was used to further clarify the underlying biological functions of key genes. Cibersort was used to analyze the immune cell infiltration during renal IRI and the correlation of key genes with immune cells. At the same time, we constructed a network of transcription-factor (TF)-Hub genes and miRNA-Hub genes. DGIDB was used to predict drugs and molecular compounds that might interact with the Hub genes.

Results

Compared with the control group, a total of 5456 differential genes (DEGs) were measured in the renal IRI group, 2486 of which were upregulated and 2970 were down-regulated. Among them, we found 30 DEGs (DEOSGs) associated with oxidative stress. The results of GO and KEGG enrichment analysis showed that these DEOSGs were mainly enriched in glutathione metabolism, the response to oxidative stress stimulation, the regulation of T cell activation and apoptosis signaling pathways. Through a protein interaction network (PPI) and a LASSO regression analysis, a total of two Hub genes were identified, namely GPX3 and GSTT1, which were validated through external validation sets and animal experiments. Through pathological methods, we found that the pathological damage of renal tissue and the expression of oxidative stress markers increased after renal ischemia-reperfusion. The results of GSEA showed that the Hub genes were related to oxidative stress pathways, apoptosis signaling pathways and immune-response-related signaling pathways. An immunoinfiltration correlation analysis showed that genes GPX3 and GSTT1 were significantly positively correlated with plasma cells and macrophage M0, while were negatively correlated with monocytes and macrophages M1 and M2. Using the Strust, Starbase and DGIDB database, we predicted that 81 transcription factors, 49 miRNAs and 13 drug or molecular compounds might interact with the Hub genes.

Conclusion

Through a comprehensive analysis of gene expression, our findings may provide new potential biomarkers for the pathogenesis of renal IRIs and a reliable basis for its early diagnosis as well as treatment.