Identification of potential targets regulating neutrophil extracellular traps in acute rejection of kidney transplantation based on transcriptomics and animal experiments.

Background: Neutrophil extracellular traps (NETs) have been found to promote inflammatory responses and exacerbate tissue damage, as well as to be strongly associated with the development of acute rejection in kidney transplantation. Taking measures against NETs is important for the treatment of acute rejection in kidney transplantation.

Methods: We used the kidney ransplantation acute rejection dataset GSE50058 as a basis for identifying biomarkers associated with the regulation of NETs therein and constructing a diagnostic model using WGCNA and four machine learning algorithms. We also explored the infiltration levels of 64 immune cells and the correlation between NETs-related biomarkers and immune cells in acute rejection of kidney transplants using the xCell algorithm. Meanwhile, we established a rat kidney ransplantation acute rejection model and validated the expression of biomarkers in animal experiments. Finally, we also explored the role of one of the biomarkers in the regulation of NETs by injecting adeno-associated viruses into the tail vein of rats.

Results: In this study, we identified a total of four NETs-associated biomarkers in acute rejection of kidney transplantation: GPX3, B2M, CDK1 and MAP3K5. Among them, the expression of GPX3 was negatively correlated with acute rejection of kidney transplantation, while the remaining three markers were positively correlated with acute rejection. We constructed a diagnostic model based on the above four biomarkers, and both the ROC curve and the calibration curve proved the good diagnostic value of the model, and the DCA curve confirmed the clinical decision-making ability of the four biomarkers. The xCell algorithm identified 20 types of immune cells with significantly altered infiltration levels in acute rejection of kidney transplants, and the expression of four biomarkers was strongly associated with multiple immune cells. In animal experiments, the expression levels of the four biomarkers were consistent with the results analyzed in the dataset GSE50058. Finally, we also found through animal experiments that overexpression of GPX3 could inhibit the activation of NETs in renal tissues and reduce the secretion of inflammatory factors, thereby alleviating renal tissue injury caused by acute rejection of kidney transplantation.

Conclusion: GPX3, B2M, CDK1 and MAP3K5 as biomarkers associated with NETs in acute rejection of kidney transplantation. Among them, GPX3 can inhibit the activation of NETs and reduce the expression of inflammatory factors in the acute rejection of kidney transplantation, thus alleviating renal tissue injury.