Background and Aims: Primary Non Function (PNF) after kidney transplantation is a devastating transplant outcome with profoundly long-term consequences for the recipient. On the same token, an anticipated increased risk for PNF leads to the discard of potentially viable organs. Because donor characteristics-based algorithms poorly predict incident PNF, we considered an exploration of the potential of a donor blood and/or urine-based molecular signature for predicting incident PNF relevant. In this study, we evaluated whether the Proteome and Metabolome profiling helps to identify a clinically relevant molecular signature for imminent PNF.
Study Design: For this study, blood (n=30) and urine (n=30) samples were selected from the UK Quality in Organ Donation (QUOD) biobank on basis of symmetrical transplant outcomes for the kidney donor pair. Matching control groups (delayed graft function (DGF) and immediate function (IF)) were created using propensity score matching. An equal quantity of protein and metabolites from each sample was subject to 1D liquid chromatography (LC), and analysis by high resolution mass spectrometry (MS) using a label-free quantitative strategy. All LC-MS data was processed against the Human proteome and metabolome databases. Significantly up and down regulated proteins and metabolites in each group were determined by Bioconductor software packages (proDA, LIMMA), in R Studio. The over-representation analyses of Gene Ontology terms were performed, to explore cellular components, biological processes, molecular function and cellular pathways between groups.
Results: A total of 4868 different gene products were identified in donor blood and urine; empirical bayes moderated t-statistics was used to define the molecular signatures of PNF, DGF and IF groups. We identified that the profile of PNF group was significantly (p value <0.05) enriched in gene products associated with Proteolysis, Apoptosis, Inflammatory response, Energy pathways, while depleted in cell growth and maintenance processes, compared to DGF and IF groups. Application of shotgun proteomics and metabolomics had revealed highly PNF specific-donor signatures that were hallmarked by (impaired) energy supply and activated apoptosis and proteolysis. Following these promising first findings, further validation in a separate cohort is currently performed.
Conclusions: These predictive genes can be helpful to give sufficient guidance to clinicians with regards to their decision whether to discriminate between accepting or declining a (marginal) kidney, thereby lowering the incidence of PNF and subsequently allowing for expansion of the donor pool.