Preliminary Investigation of the Biomarkers of Acute Renal Transplant Rejection Using Integrated Proteomics Studies, Gene Expression Omnibus Datasets, and RNA Sequencing

Screening of novel biomarkers for acute kidney transplant rejection using DIA-MS based proteomics

BACKGROUND

Kidney transplantation is the preferred treatment for patients with end-stage renal disease. However, acute rejection poses a threat to the graft long-term survival. The aim of this study was to identify novel biomarkers to detect acute kidney transplant rejection.

METHODS

The serum proteomic profiling of kidney transplant patients with T cell-mediated acute rejection (TCMR) and stable allograft function (STA) was analyzed using data-independent acquisition mass spectrometry (DIA-MS). The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA).

RESULTS

A total of 131 DEPs were identified between STA and TCMR patients, 114 DEPs were identified between mild and severe TCMR patients. The verification results showed that remarkable higher concentrations of serum amyloid A protein 1 (SAA1) and insulin like growth factor binding protein 2 (IGFBP2), and lower fetuin-A (AHSG) concentration were found in TCMR patients when compared with STA patients. We also found higher SAA1 concentration in severe TCMR group when compared with mild TCMR group. The receiver operating characteristics (ROC) analysis further confirmed that combination of SAA1, AHSG, and IGFBP2 had excellent performance in the acute rejection diagnosis.

CONCLUSIONS

Our data demonstrated that serum SAA1, AHSG, and IGFBP2 could be effective biomarkers for diagnosing acute rejection after kidney transplantation. DIA-MS has great potential in biomarker screening of kidney transplantation.

Identification of Renal Transplantation Rejection Biomarkers in Blood Using the Systems Biology Approach

Background

Renal transplantation plays an essential role in the quality of life of patients with end-stage renal disease. At least 12% of the renal patients receiving transplantations show graft rejection. One of the methods used to diagnose renal transplantation rejection is renal allograft biopsy. This procedure is associated with some risks such as bleeding and arteriovenous fistula formation. In this study, we applied a bioinformatics approach to identify serum markers for graft rejection in patients receiving a renal transplantation.

Methods

Transcriptomic data were first retrieved from the blood of renal transplantation rejection patients using the GEO database. The data were then used to construct the protein-protein interaction and gene regulatory networks using Cytoscape software. Next, network analysis was performed to identify hub-bottlenecks, and key blood markers involved in renal graft rejection. Lastly, the gene ontology and functional pathways related to hub-bottlenecks were detected using PANTHER and DAVID servers.

Results

In PPIN and GRN, SYNCRIP, SQSTM1, GRAMD1A, FAM104A, ND2, TPGS2, ZNF652, RORA, and MALAT1 were the identified critical genes. In GRN, miR-155, miR17, miR146b, miR-200 family, and GATA2 were the factors that regulated critical genes. The MAPK, neurotrophin, and TNF signaling pathways, IL-17, and human cytomegalovirus infection, human papillomavirus infection, and shigellosis were identified as significant pathways involved in graft rejection.

Concusion

The above-mentioned genes can be used as diagnostic and therapeutic serum markers of transplantation rejection in renal patients. The newly predicted biomarkers and pathways require further studies.

Uncover diagnostic immunity/hypoxia/ferroptosis/epithelial mesenchymal transformation-related CCR5, CD86, CD8A, ITGAM, and PTPRC in kidney transplantation patients with allograft rejection

The aim of this study was to identify predictive immunity/hypoxia/ferroptosis/epithelial mesenchymal transformation (EMT)-related biomarkers, pathways and new drugs in allograft rejection in kidney transplant patients. First, gene expression data were downloaded followed by identification of differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) analysis. Second, diagnostic model was construction based on key genes, followed by correlation analysis between immune/hypoxia/ferroptosis/EMT and key diagnostic genes. Finally, drug prediction of diagnostic key genes was carried out. Five diagnostic genes were further identified, including CCR5, CD86, CD8A, ITGAM, and PTPRC, which were positively correlated with allograft rejection after the kidney transplant. Highly infiltrated immune cells, highly expression of hypoxia-related genes and activated status of EMT were significantly positively correlated with five diagnostic genes. Interestingly, suppressors of ferroptosis (SOFs) and drivers of ferroptosis (DOFs) showed a complex regulatory relationship between ferroptosis and five diagnostic genes. CD86, CCR5, and ITGAM were respectively drug target of ABATACEPT, MARAVIROC, and CLARITHROMYCIN. PTPRC was drug target of both PREDNISONE and EPOETIN BETA. In conclusion, the study could be useful in understanding changes in the microenvironment within transplantation, which may promote or sustain the development of allograft rejection after kidney transplantation.