Dataset Information

Integrated Analysis of Prognostic Genes Associated With Ischemia-Reperfusion Injury in Renal Transplantation.

ABSTRACT:

Background

Ischemia-reperfusion injury (IRI) remains an inevitable and major challenge in renal transplantation. The current study aims to obtain deep insights into underlying mechanisms and seek prognostic genes as potential therapeutic targets for renal IRI (RIRI).

Methods

After systematically screening the Gene Expression Omnibus (GEO) database, we collected gene expression profiles of over 1,000 specimens from 11 independent cohorts. Differentially expressed genes (DEGs) were identified by comparing allograft kidney biopsies taken before and after reperfusion in the discovery cohort and further validated in another two independent transplant cohorts. Then, graft survival analysis and immune cell analysis of DEGs were performed in another independent renal transplant cohort with long-term follow-ups to further screen out prognostic genes. Cell type and time course analyses were performed for investigating the expression pattern of prognostic genes in more dimensions utilizing a mouse RIRI model. Finally, two novel genes firstly identified in RIRI were verified in the mouse model and comprehensively analyzed to investigate potential mechanisms.

Results

Twenty DEGs upregulated in the process of RIRI throughout different donor types (living donors, cardiac and brain death donors) were successfully identified and validated. Among them, upregulation of 10 genes was associated with poor long-term allograft outcomes and exhibited strong correlations with prognostic immune cells, like macrophages. Furthermore, certain genes were found to be only differentially expressed in specific cell types and remained with high expression levels even months after RIRI in the mouse model, which processed the potential to serve as therapeutic targets. Importantly, two newly identified genes in RIRI, Btg2 and Rhob, were successfully confirmed in the mouse model and found to have strong connections with NF-κB signaling.

Conclusions

We successfully identified and validated 10 IRI-associated prognostic genes in renal transplantation across different donor types, and two novel genes with crucial roles in RIRI were recognized for the first time. Our findings offered promising potential therapeutic targets for RIRI in renal transplantation.

SUBMITTER: Zhang D

PROVIDER: S-EPMC8452995 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundOxidative stress is the primary cause of ischemia-reperfusion injury (IRI) in kidney transplantation, leading to delayed graft function (DGF) and implications on patient health. Necroptosis is believed to play a role in renal IRI. This research presents a comprehensive analysis of necroptosis-related genes and their functional implications in the context of IRI in renal transplantation.MethodsThe necroptosis-related differentially expressed genes (NR-DEGs) were identified using gene expression data from pre- and post-reperfusion renal biopsies, and consensus clustering analysis was performed to distinguish necroptosis-related clusters. A predictive model for DGF was developed based on the NR-DEGs and patients were divided into high- and low-risk groups. We investigated the differences in functional enrichment and immune infiltration between different clusters and risk groups and further validated them in single-cell RNA-sequencing (scRNA-seq) data. Finally, we verified the expression changes of NR-DEGs in an IRI mouse model.ResultsFive NR-DEGs were identified and were involved in various biological processes. The renal samples were further stratified into two necroptosis-related clusters (C1 and C2) showing different occurrences of DGF. The predictive model had a reliable performance in identifying patients at higher risk of DGF with the area under the curve as 0.798. Additionally, immune infiltration analysis indicated more abundant proinflammatory cells in the high-risk group, which was also found in C2 cluster with more DGF patients. Validation of NR-DEG in scRNA-seq data further supported their involvement in immune cells. Lastly, the mouse model validated the up-regulation of NR-DEGs after IR and indicated the correlations with kidney function markers.ConclusionsOur research provides valuable insights into the identification and functional characterization of NR-DEGs in the context of renal transplantation and sheds light on their involvement in immune responses and the progression of IRI and DGF.

Project description:BackgroundAlthough AKI lacks effective therapeutic approaches, preventive strategies using preconditioning protocols, including caloric restriction and hypoxic preconditioning, have been shown to prevent injury in animal models. A better understanding of the molecular mechanisms that underlie the enhanced resistance to AKI conferred by such approaches is needed to facilitate clinical use. We hypothesized that these preconditioning strategies use similar pathways to augment cellular stress resistance.MethodsTo identify genes and pathways shared by caloric restriction and hypoxic preconditioning, we used RNA-sequencing transcriptome profiling to compare the transcriptional response with both modes of preconditioning in mice before and after renal ischemia-reperfusion injury.ResultsThe gene expression signatures induced by both preconditioning strategies involve distinct common genes and pathways that overlap significantly with the transcriptional changes observed after ischemia-reperfusion injury. These changes primarily affect oxidation-reduction processes and have a major effect on mitochondrial processes. We found that 16 of the genes differentially regulated by both modes of preconditioning were strongly correlated with clinical outcome; most of these genes had not previously been directly linked to AKI.ConclusionsThis comparative analysis of the gene expression signatures in preconditioning strategies shows overlapping patterns in caloric restriction and hypoxic preconditioning, pointing toward common molecular mechanisms. Our analysis identified a limited set of target genes not previously known to be associated with AKI; further study of their potential to provide the basis for novel preventive strategies is warranted. To allow for optimal interactive usability of the data by the kidney research community, we provide an online interface for user-defined interrogation of the gene expression datasets (http://shiny.cecad.uni-koeln.de:3838/IRaP/).

Project description: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.