Abstract
Background and Aims
Peritoneal dialysis (PD) is the worldwide preferred dialysis treatment for children affected by end stage kidney disease. However, due to the unphysiological composition of PD fluids, the peritoneal mesothelium of these patients may undergo structural and functional alterations, which may cause fibrosis. Several factors may accelerate this process and primary kidney disease may have a causative role. In particular, patients affected by corticosteroid resistant focal segmental glomerulosclerosis (FSGS), a rare glomerular disease leading to nephrotic syndrome, seems more prone to develop peritoneal fibrosis. The mechanism causing this predisposition is still unrecognized. To better define this condition, we carried out, for the first time, a new comprehensive comparative proteomic mass spectrometry analysis of mesothelial exosomes from peritoneal dialysis effluent (PDE).
Method
We enrolled 12 paediatric patients on PD. Half of them affected by FSGS and the others affected by other disorders (NO FSGS). Exosomes from mesothelial peritoneal cells were isolated by centrifugation and, using a biotinylated antibody and streptavidin magnetic beads. Exosome size was determined by dynamic light scattering, and antigen profile of exosomes was assayed by western blot. Mass spectrometry data were analyzed by unsupervised hierarchical clustering using multidimensional scaling, in order to identify outliers and dissimilarity between samples. The normalized data were used to construct a co-expression network using the weighted gene co-expression network analysis (WGCNA). A weighted adjacency matrix was constructed, and transformed into a topological overlap matrix, which measures the network connectivity of all proteins. To identify the relationship between each module and each clinical trait, we used module eigengenes (MEs) and calculated the correlation between MEs and the clinical traits. To identify the hub proteins of modules that maximize the discrimination between FSGS and NO FSGS samples, we applied T-test, and non-linear support vector machine (SVM) learning. Finally, gene set enrichment analysis was done to build a functional proteins network based on their Gene Ontology (GO) annotations extracted from the Gene Ontology Consortium.
Results
Our omic study demonstrated that SVM allowed a complete distinction of the whole proteomic exosome mesothelial content of FSGS versus NO FSGS (100% accuracy). Out of the 2490 identified proteins, 40% (995) were involved in endothelial to mesenchymal transition /fibrosis and in the TGF-B pathway. The WGCNA analysis highlighted a total of 40 proteins were that maximize the discrimination between FSGS and NO FSGS patients (Figure 1A). Their expression profile is reported in a heatmap diagram (Figure 1B).
Additionally, we performed GO enrichment analysis (Figure 2) and the algorithm identified that some of the discriminative proteins (TIMP1, CTHRC1, SPARC, CHMP4B, COL5A2, ANXA13, FNC2 and CENP-E) were also highly correlated to the peritoneal dialysis vintage, fibrosis, EMT and PM disease.
Conclusion
Our data demonstrated, for the first time, that mesothelial cells of FSGS are more prone to activate a pro-fibrotic machinery with exosomes having a primary role in this process. Moreover, they indicated that identified FSGS-associated element in mesothelial exosome protein could be employed as potential new biomarkers of mesothelial integrity. Finally, results highlighted that FSGS should be treated with more biocompatible dialysis solution, reduce the length of time on PD and personalize type and regimens of PD to minimize the risk of rapid loss of peritoneal membrane.
Proteomic profile of mesothelial exosomes isolated from peritoneal dialysis effluent of children with focal segmental glomerulosclerosis