No abstract

Among 158 distinct variants, 80 (50.6%) were previously unreported, confirming broad allelic heterogeneity. Eleven patients showed more than one variant. Segregation analysis indicated biallelic disease in five patients, digenic in one, de novo variant with unknown phase in two. Furthermore, our NGS protocol allowed the identification of two patients with somatic mosaicism, which was undetectable with Sanger sequencing. Among patients without PKD1/PKD2 variants, we identified three with possible alternative diagnosis: a patient with biallelic mutations in PKHD1, confirming the overlap between recessive and dominant PKD, and two patients with variants in ALG8 and PRKCSH, respectively. Genotype-phenotype correlations showed that patients with PKD1 variants predicted to truncate (T) the protein experienced end-stage renal disease 9 years earlier than patients with PKD1 non-truncating (NT) mutations and >13 years earlier than patients with PKD2 mutations. ADPKD-PKD1 T cases showed a disease onset significantly earlier than ADPKD-PKD1 NT and ADPK-PKD2, as well as a significant earlier diagnosis. These data emphasize the need to combine clinical information with genetic data to achieve useful prognostic predictions.

Background A long-standing effort is dedicated toward identification of biomarkers allowing the prediction of graft outcome after kidney transplant. Extracellular vesicles (EVs) circulating in body fluids represent an attractive candidate, as their cargo mirrors the originating cell and its pathophysiological status. The aim of the study was to investigate EV surface antigens as potential predictors of renal outcome after kidney transplant. Methods We characterized 37 surface-antigens by flow-cytometry, in serum- and urine- EVs from 58 patients which were evaluated before, and at 10-14 days, 3 months, and 1 year after transplant, for a total of 426 analyzed samples. The outcome was defined according to estimated glomerular filtration rate (eGFR) at 1 year. Results Endothelial cells and platelets markers (CD31, CD41b, CD42a and CD62P) in serum-EVs were higher at baseline in patients with persistent kidney dysfunction at 1 year, and progressively decreased after kidney transplant. Conversely, mesenchymal progenitor cell marker (CD1c, CD105, CD133, SSEEA-4) in urine-EVs progressively increased after transplant in patients displaying renal recovery at follow-up. These markers correlated with eGFR, creatinine and proteinuria, associated to patient outcome at univariate analysis and were able to predict patient outcome at ROC curves analysis. A specific EV molecular signature obtained by supervised learning correctly classified patients according to 1-year renal outcome. Conclusions An EV-based signature, reflecting the cardiovascular profile of the recipient, and the repairing/regenerative features of the graft, could be introduced as a non-invasive tool for a tailored management of follow-up of patients undergoing kidney transplant.