Urine Proteomics as a Source of Biological Information and Outcome Predictor in Living Kidney Transplantation

Kidney transplantation (KTx) is the preferred treatment for kidney failure. However, post-transplant management is challenging due to the limited lifespan of transplanted organs. Current methods for monitoring post-transplant complications are invasive and have limitations. Therefore, there is an urgent need for novel non-invasive biomarkers. This study investigates the proteomic composition of urine to understand renal biology during the process of transplantation and to identify potential markers for outcome prediction. Urine samples were collected from donors before transplantation and from recipients 4 weeks and 1 year after transplantation. Proteomic analysis was performed using mass spectrometry and label-free quantification. Statistical analyses included principal component analysis (PCA) and enrichment analysis. The resulting key findings were confirmed in an independent validation cohort. In addition, correlative regression models to evaluate the relationship between protein abundance and clinical outcomes in the further course after transplantation were performed. 106 urine samples in the setting of 70 kidney transplantations were analyzed. PCA revealed distinct clustering of donor and recipient samples, indicating significant proteomic changes after transplantation. Hierarchical clustering and gene ontology analysis identified molecular changes as a response to transplantation and showed an over-representation of relevant pathways related to inflammation, cell immune response and coagulation in both the original and validation cohorts. Multivariate regression analysis, including linear and logistic regression, identified 11 potential protein biomarkers, including ORM2, IL1RAP, APP, and FABP4 as predictors of eGFR 12 months after transplantation and 1 HP as a predictor of infections within the first year after transplantation, respectively. This study underscores the potential of non-invasive urine proteomics for identifying biological processes involved in kidney transplantation and for enhancing post-transplant monitoring and outcome prediction. We identified 12 potential biomarkers with added value to standard clinical parameters linked to transplant outcomes, which will be promising candidates for future outcome monitoring after KTx.