Identification of fibroblast activation-related genes in two acute kidney injury models

Exploration of potential biomarkers and therapeutic targets for trauma-related acute kidney injury

PURPOSE

Acute kidney injury (AKI) is one of the most common functional injuries observed in trauma patients. However, certain trauma medications may exacerbate renal injury. Therefore, the early detection of trauma-related AKI holds paramount importance in improving trauma prognosis.

METHODS

Qualified datasets were selected from public databases, and common differentially expressed genes related to trauma-induced AKI and hub genes were identified through enrichment analysis and the establishment of protein-protein interaction (PPI) networks. Additionally, the specificity of these hub genes was investigated using the sepsis dataset and conducted a comprehensive literature review to assess their plausibility. The raw data from both datasets were downloaded using R software (version 4.2.1) and processed with the "affy" package19 for correction and normalization.

RESULTS

Our analysis revealed 585 upregulated and 629 downregulated differentially expressed genes in the AKI dataset, along with 586 upregulated and 948 downregulated differentially expressed genes in the trauma dataset. Concurrently, the establishment of the PPI network and subsequent topological analysis highlighted key hub genes, including CD44, CD163, TIMP metallopeptidase inhibitor 1, cytochrome b-245 beta chain, versican, membrane spanning 4-domains A4A, mitogen-activated protein kinase 14, and early growth response 1. Notably, their receiver operating characteristic curves displayed areas exceeding 75%, indicating good diagnostic performance. Moreover, our findings postulated a unique molecular mechanism underlying trauma-related AKI.

CONCLUSION

This study presents an alternative strategy for the early diagnosis and treatment of trauma-related AKI, based on the identification of potential biomarkers and therapeutic targets. Additionally, this study provides theoretical references for elucidating the mechanisms of trauma-related AKI.

Integration of transcriptomics and metabolomics reveals pathways involved in MDSC supernatant attenuation of TGF-β1-induced myofibroblastic differentiation of mesenchymal stem cells

Overexposure to transforming growth factor b1 (TGF-β1) induces myofibroblastic differentiation of mesenchymal stem cells (MSCs), which could be attenuated by myeloid-derived suppressor cell (MDSC) supernatant. However, the promyofibroblastic effects of TGF-β1 and the antimyofibroblastic effects of MDSC supernatant in MSCs have not been fully elucidated. To further clarify the latent mechanism and identify underlying therapeutic targets, we used an integrative strategy combining transcriptomics and metabolomics. Bone marrow MSCs were collected 24 h following TGF-β1 and MDSC supernatant treatment for RNA sequencing and untargeted metabolomic analysis. The integrated data were then analyzed to identify significant gene-metabolite correlations. Differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs) were assessed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses for exploring the mechanisms of myofibroblastic differentiation of MSCs. The integration of transcriptomic and metabolomic data highlighted significantly coordinated changes in glycolysis/gluconeogenesis and purine metabolism following TGF-β1 and MDSC supernatant treatment. By combining transcriptomic and metabolomic analyses, this study showed that glycolysis/gluconeogenesis and purine metabolism were essential for the myofibroblastic differentiation of MSCs and may serve as promising targets for mechanistic research and clinical practice in the treatment of fibrosis by MDSC supernatant.

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

Background

Renal ischemia-reperfusion injury (IRI) is a disease with high incidence rate in kidney related surgery. Micro RNA (miRNA) and transcription factors (TFs) are widely involved in the process of renal IRI through regulation of their target genes. However, the regulatory relationships and functional roles of TFs, miRNAs and mRNAs in the progression of renal IRI are insufficiently understood. The present study aimed to clarify the underlying mechanism of regulatory relationships in renal IRI.

Methods

Six gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Differently expressed genes (DEGs) and differently expressed miRNAs (DEMs) were identified through RRA integrated analysis of mRNA datasets (GSE39548, GSE87025, GSE52004, GSE71647, and GSE131288) and miRNA datasets (GSE29495). miRDB and TransmiR v2.0 database were applied to predict target genes of miRNA and TFs, respectively. DEGs were applied for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, followed with construction of protein-protein interaction (PPI) network. Then, the TF-miRNA-mRNA network was constructed. Correlation coefficient and ROC analysis were used to verify regulatory relationship between genes and their diagnostic value in GSE52004. Furthermore, in independent mouse RNA-seq datasets GSE98622, human RNA-seq GSE134386 and in vitro, the expression of hub genes and genes from the network were observed and correlation coefficient and ROC analysis were validated.

Results

A total of 21 DEMs and 187 DEGs were identified in renal IRI group compared to control group. The results of PPI analysis showed 15 hub genes. The TF-miRNA-mRNA regulatory network was constructed and several important pathways were identified and further verified, including Junb-miR-223-Ranbp3l, Cebpb-miR-223-Ranbp3l, Cebpb-miR-21-Ranbp3l and Cebpb-miR-181b-Bsnd. Four regulatory loops were identified, including Fosl2-miR-155, Fosl2-miR-146a, Cebpb-miR-155 and Mafk-miR-25. The hub genes and genes in the network showed good diagnostic value in mice and human.

Conclusions

In this study, we found 15 hub genes and several TF-miRNA-mRNA pathways, which are helpful for understanding the molecular and regulatory mechanisms in renal IRI. Junb-miR-223-Ranbp3l, Cebpb-miR-223-Ranbp3l, Cebpb-miR-21-Ranbp3l and Cebpb-miR-181b-Bsnd were the most important pathways, while Spp1, Fos, Timp1, Tnc, Fosl2 and Junb were the most important hub genes. Fosl2-miR-155, Fosl2-miR-146a, Cebpb-miR-155 and Mafk-miR-25 might be the negative feedback loops in renal IRI.

Fibroblast growth factor and kidney disease: Updates for emerging novel therapeutics

The discovery of fibroblast growth factors (FGFs) and fibroblast growth factor receptors (FGFRs) provided a profound new insight into physiological and metabolic functions. FGF has a large family by having divergent structural elements and enable functional divergence and specification. FGF and FGFRs are highly expressed during kidney development. Signals from the ureteric bud regulate morphogenesis, nephrogenesis, and nephron progenitor survival. Thus, FGF signaling plays an important role in kidney progenitor cell aggregation at the sites of new nephron formation. This review will summarize the current knowledge about functions of FGF signaling in kidney development and their ability to promote regeneration in injured kidneys and its use as a biomarker and therapeutic target in kidney diseases. Further studies are essential to determine the predictive significance of the various FGF/FGFR deviations and to integrate them into clinical algorithms.