Five-gene signature predicts acute kidney injury in early kidney transplant patients

Spotlight on plasticity-related genes: Current insights in health and disease

The development of the central nervous system is highly complex, involving numerous developmental processes that must take place with high spatial and temporal precision. This requires a series of complex and well-coordinated molecular processes that are tighly controlled and regulated by, for example, a variety of proteins and lipids. Deregulations in these processes, including genetic mutations, can lead to the most severe maldevelopments. The present review provides an overview of the protein family Plasticity-related genes (PRG1-5), including their role during neuronal differentiation, their molecular interactions, and their participation in various diseases. As these proteins can modulate the function of bioactive lipids, they are able to influence various cellular processes. Furthermore, they are dynamically regulated during development, thus playing an important role in the development and function of synapses. First studies, conducted not only in mouse experiments but also in humans, revealed that mutations or dysregulations of these proteins lead to changes in lipid metabolism, resulting in severe neurological deficits. In recent years, as more and more studies have shown their involvement in a broad range of diseases, the complexity and broad spectrum of known and as yet unknown interactions between PRGs, lipids, and proteins make them a promising and interesting group of potential novel therapeutic targets.

Chronic exposure to UVB induces nephritis and gut microbiota dysbiosis in mice based on the integration of renal transcriptome profiles and 16S rRNA sequencing data

Ultraviolet (UV) is a common and abundant environmental factor that affects daily life. Although the effects of UV radiation on the skin have been extensively reported, studies on the influence of UV radiation on internal organs are still limited. This study aimed to evaluate the influence of UVB exposure on the kidney of mice and to investigate the possible mechanism. In the present study, histopathology changes, oxidative stress, and inflammatory response were used to evaluate the kidney and colon injury induced by UVB exposure. The results showed that the 14-week chronic skin exposure to UVB triggers a kidney injury response characterized by macrophage infiltration, elevated oxidative stress as well as inflammatory and injury markers. The RNA sequencing demonstrated that chronic UVB exposure could alter the kidney transcriptomic profile distinguished by the regulation of genes involved in the Notch signaling pathway, JAK-STAT signaling pathway, and ECM-receptor interaction. Besides, chronic UVB exposure also resulted in gut dysbiosis, manifested as colon macrophage infiltration, stimulated inflammatory responses, impaired barrier integrity, and microbiota structural and functional disorders. The Spearman analysis results further revealed a strong correlation between gut microbiota and kidney injury. In conclusion, skin chronic exposure to UVB causes nephritis and gut microbiota dysbiosis in mice, and these findings provide new insight into the underlying risks of chronic UVB exposure to human wellness.

Characteristic Genes and Immune Infiltration Analysis for Acute Rejection after Kidney Transplantation

Background

Renal transplantation can significantly improve the survival rate and quality of life of patients with end-stage renal disease, but the probability of acute rejection (AR) in adult renal transplant recipients is still approximately 12.2%. Machine learning (ML) is superior to traditional statistical methods in various clinical scenarios. However, the current AR model is constructed only through simple difference analysis or a single queue, which cannot guarantee the accuracy of prediction. Therefore, this study identified and validated new gene sets that contribute to the early prediction of AR and the prognosis prediction of patients after renal transplantation by constructing a more accurate AR gene signature through ML technology.

Methods

Based on the Gene Expression Omnibus (GEO) database and multiple bioinformatic analyses, we identified differentially expressed genes (DEGs) and built a gene signature via LASSO regression and SVM analysis. Immune cell infiltration and immunocyte association analyses were also conducted. Furthermore, we investigated the relationship between AR genes and graft survival status.

Results

Twenty-four DEGs were identified. A 5 gene signature (CPA6, EFNA1, HBM, THEM5, and ZNF683) were obtained by LASSO analysis and SVM analysis, which had a satisfied ability to differentiate AR and NAR in the training cohort, internal validation cohort and external validation cohort. Additionally, ZNF683 was associated with graft survival.

Conclusion

A 5 gene signature, particularly ZNF683, provided insight into a precise therapeutic schedule and clinical applications for AR patients.

Machine learning algorithm-based identification and verification of characteristic genes in acute kidney injury

Background

Acute kidney injury is a common renal disease with high incidence and mortality. Early identification of high-risk acute renal injury patients following renal transplant could improve their prognosis, however, no biomarker exists for early detection.

Methods

The GSE139061 dataset was used to identify hub genes in 86 DEGs between acute kidney injury and control samples using three machine learning algorithms (LASSO, random forest, and support vector machine-recursive feature elimination). We used GSEA to identify the related signal pathways of six hub genes. Finally, we validated these potential biomarkers in an in vitro hypoxia/reoxygenation injury cell model using RT-qPCR.

Results

Six hub genes (MDFI, EHBP1L1, FBXW4, MDM4, RALYL, and ESM1) were identified as potentially predictive of an acute kidney injury. The expression of ESM1 and RALYL were markedly increased in control samples, while EHBP1L1, FBXW4, MDFI, and MDM4 were markedly increased in acute kidney injury samples.

Conclusion

We screened six hub genes related to acute kidney injury using three machine learning algorithms and identified genes with potential diagnostic utility. The hub genes identified in this study might play a significant role in the pathophysiology and progression of AKI. As such, they might be useful for the early diagnosis of AKI and provide the possibility of improving the prognosis of AKI patients.

Identification of Multiple Hub Genes in Acute Kidney Injury after Kidney Transplantation by Bioinformatics Analysis

Background and Objectives: The molecular mechanisms of the development of acute kidney injury (AKI) after kidney transplantation are not yet clear. The aim of this study was to confirm the genes and mechanisms related to AKI after transplantation. Materials and Methods: To investigate potential genetic targets for AKI, an analysis of the gene expression omnibus database was used to identify key genes and pathways. After identification of differentially expressed genes, Kyoto Encyclopedia of Genes and Genome pathway enrichment analyses were performed. We identified the hub genes and established the protein–protein interaction network. Results: Finally, we identified 137 differentially expressed genes (59 upregulated genes and 16 downregulated genes). AKAP12, AMOT, C3AR1, LY96, PIK3AP1, PLCD4, PLCG2, TENM2, TLR2, and TSPAN5 were filtrated by the hub genes related to the development of post-transplant AKI from the Protein–Protein Interaction (PPI) network. Conclusions: This may provide important evidence of the diagnostic and therapeutic biomarker of AKI.