Identifying Biomarkers from Transcriptomic Signatures in Renal Allograft Biopsies Using Deceased and Living Donors

Characterizing hub biomarkers for post-transplant renal fibrosis and unveiling their immunological functions through RNA sequencing and advanced machine learning techniques

BACKGROUND

Kidney transplantation stands out as the most effective renal replacement therapy for patients grappling with end-stage renal disease. However, post-transplant renal fibrosis is a prevalent and irreversible consequence, imposing a substantial clinical burden. Unfortunately, the clinical landscape remains devoid of reliable biological markers for diagnosing post-transplant renal interstitial fibrosis.

METHODS

We obtained transcriptome and single-cell sequencing datasets of patients with renal fibrosis from NCBI Gene Expression Omnibus (GEO). Subsequently, we employed Weighted Gene Co-Expression Network Analysis (WGCNA) to identify potential genes by integrating core modules and differential genes. Functional enrichment analysis was conducted to unveil the involvement of potential pathways. To identify key biomarkers for renal fibrosis, we utilized logistic analysis, a LASSO-based tenfold cross-validation approach, and gene topological analysis within Cytoscape. Furthermore, histological staining, Western blotting (WB), and quantitative PCR (qPCR) experiments were performed in a murine model of renal fibrosis to verify the identified hub genes. Moreover, molecular docking and molecular dynamics simulations were conducted to explore possible effective drugs.

RESULTS

Through WGCNA, the intersection of core modules and differential genes yielded a compendium of 92 potential genes. Logistic analysis, LASSO-based tenfold cross-validation, and gene topological analysis within Cytoscape identified four core genes (CD3G, CORO1A, FCGR2A, and GZMH) associated with renal fibrosis. The expression of these core genes was confirmed through single-cell data analysis and validated using various machine learning methods. Wet experiments also verified the upregulation of these core genes in the murine model of renal fibrosis. A positive correlation was observed between the core genes and immune cells, suggesting their potential role in bolstering immune system activity. Moreover, four potentially effective small molecules (ZINC000003830276-Tessalon, ZINC000003944422-Norvir, ZINC000008214629-Nonoxynol-9, and ZINC000085537014-Cobicistat) were identified through molecular docking and molecular dynamics simulations.

CONCLUSION

Four potential hub biomarkers most associated with post-transplant renal fibrosis, as well as four potentially effective small molecules, were identified, providing valuable insights for studying the molecular mechanisms underlying post-transplant renal fibrosis and exploring new targets.

Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis

Background

Chronic kidney disease (CKD) is the third-leading cause of premature mortality worldwide. It is characterized by rapid deterioration due to renal interstitial fibrosis (RIF) via excessive inflammatory infiltration. The aim of this study was to discover key immune-related genes (IRGs) to provide valuable insights and therapeutic targets for RIF in CKD.

Materials and methods

We screened differentially expressed genes (DEGs) between RIF samples from CKD patients and healthy controls from a public database. Least absolute shrinkage and selection operator regression analysis and receiver operating characteristic curve analysis were applied to identify significant key biomarkers. The single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to analyze the infiltration of immune cells between the RIF and control samples. The correlation between biomarkers and immune cell composition was assessed.

Results

A total of 928 DEGs between CKD and control samples from six microarray datasets were found, 17 overlapping immune-correlated DEGs were identified by integration with the ImmPort database, and six IRGs were finally identified in the model: apolipoprotein H (APOH), epidermal growth factor (EGF), lactotransferrin (LTF), lysozyme (LYZ), phospholipid transfer protein (PLTP), and secretory leukocyte peptidase inhibitor (SLPI). Two additional datasets and in vivo experiments indicated that the expression levels of APOH and EGF in the fibrosis group were significantly lower than those in the control group, while the expression levels of LTF, LYZ, PLTP, and SLPI were higher (all P < 0.05). These IRGs also showed a significant correlation with renal function impairment. Moreover, four upregulated IRGs were positively associated with various T cell populations, which were enriched in RIF tissues, whereas two downregulated IRGs had opposite results. Several signaling pathways, such as the "T cell receptor signaling pathway" and "positive regulation of NF-κB signaling pathway", were discovered to be associated not only with immune cell infiltration, but also with the expression levels of six IRGs.

Conclusion

In summary, six IRGs were identified as key biomarkers for RIF, and exhibited a strong correlation with various T cells and with the NF-κB signaling pathway. All these IRGs and their signaling pathways may evolve as valuable therapeutic targets for RIF in CKD.

The landscape of immune cell infiltration in the glomerulus of diabetic nephropathy: evidence based on bioinformatics

Background

Increasing evidence suggests that immune cell infiltration contributes to the pathogenesis and progression of diabetic nephropathy (DN). We aim to unveil the immune infiltration pattern in the glomerulus of DN and provide potential targets for immunotherapy.

Methods

Infiltrating percentage of 22 types of immune cell in the glomerulus tissues were estimated by the CIBERSORT algorithm based on three transcriptome datasets mined from the GEO database. Differentially expressed genes (DEGs) were identified by the “limma” package. Then immune-related DEGs were identified by intersecting DEGs with immune-related genes (downloaded from Immport database). The protein–protein interactions of Immune-related DEGs were explored using the STRING database and visualized by Cytoscape. The enrichment analyses for KEGG pathways and GO terms were carried out by the gene set enrichment analysis (GSEA) method.

Results

11 types of immune cell were revealed to be significantly altered in the glomerulus tissues of DN (Up: B cells memory, T cells gamma delta, NK cells activated, Macrophages.M1, Macrophages M2, Dendritic cells resting, Mast cells resting; Down: B cells naive, NK cells resting, Mast cells activated, Neutrophils). Several pathways related to immune, autophagy and metabolic process were significantly activated. Moreover, 6 hub genes with a medium to strong correlation with renal function (eGFR) were identified (SERPINA3, LTF, C3, PTGDS, EGF and ALB).

Conclusion

In the glomerulus of DN, the immune infiltration pattern changed significantly. A complicated and tightly regulated network of immune cells exists in the pathological of DN. The hub genes identified here will facilitate the development of immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-022-02906-4.

Cellular and molecular interrogation of kidney biopsy specimens

PURPOSE OF REVIEW

Traditional histopathology of the kidney biopsy specimen has been an essential and successful tool for the diagnosis and staging of kidney diseases. However, it is likely that the full potential of the kidney biopsy has not been tapped so far. Indeed, there is now a concerted worldwide effort to interrogate kidney biopsy samples at the cellular and molecular levels with unprecedented rigor and depth. This review examines these novel approaches to study kidney biopsy specimens and highlights their potential to refine our understanding of the pathophysiology of kidney disease and lead to precision-based diagnosis and therapy.

RECENT FINDINGS

Several consortia are now active at studying kidney biopsy samples from various patient cohorts with state-of-the art cellular and molecular techniques. These include advanced imaging approaches as well as deep molecular interrogation with tools such as epigenetics, transcriptomics, proteomics and metabolomics. The emphasis throughout is on rigor, reproducibility and quality control.

SUMMARY

Although these techniques to study kidney biopsies are complementary, each on its own can yield novel ways to define and classify kidney disease. Therefore, great efforts are needed in order to generate an integrated output that can propel the diagnosis and treatment of kidney disease into the realm of precision medicine.