Urine Proteomics Revealed a Significant Correlation Between Urine-Fibronectin Abundance and Estimated-GFR Decline in Patients with Bardet-Biedl Syndrome

Abstract

Background:/Aims: Renal disease is a common cause of morbidity in patients with Bardet-Biedl syndrome (BBS), however the severity of kidney dysfunction is highly variable. To date, there is little information on the pathogenesis, the risk and predictor factors for poor renal outcome in this setting. The present study aims to analyze the spectrum of urinary proteins in BBS patients, in order to potentially identify 1) disease-specific proteomic profiles that may differentiate the patients from normal subjects; 2) urinary markers of renal dysfunction. Methods: Fourteen individuals (7 males and 7 females) with a clinical diagnosis of BBS have been selected in this study. A pool of 10 aged-matched males and 10 aged-matched females have been used as controls for proteomic analysis. The glomerular filtration rate (eGFR) has been estimated using the CKD-EPI formula. Variability of eGFR has been retrospectively assessed calculating average annual eGFR decline (ΔeGFR) in a mean follow-up period of 4 years (3-7). Results: 42 proteins were significantly over- or under-represented in BBS patients compared with controls; the majority of these proteins are involved in fibrosis, cell adhesion and extracellular matrix organization. Statistic studies revealed a significant correlation between urine fibronectin (u-FN) (r²=0.28; p<0.05), CD44 antigen (r² =0.35; p<0.03) and lysosomal alfa glucosidase ( r²0.27; p<0.05) abundance with the eGFR. In addition, u-FN (r² =0.2389; p<0.05) was significantly correlated with ΔeGFR. Conclusion: The present study demonstrates that urine proteome of BBS patients differs from that of normal subjects; in addition, kidney dysfunction correlated with urine abundance of known markers of renal fibrosis.

Label-Free Quantitative Proteomics in a Methylmalonyl-CoA Mutase-Silenced Neuroblastoma Cell Line

“…The details of the protein identification are reported in Supplemental Table S1 . Label-free proteomic analysis was performed to estimate the relative abundance of each protein by two spectral counting parameters, R SC and Fold NSAF [ 20 ]. The spectral counting parameters, R SC and Fold NSAF , were correlated by the Pearson correlation coefficient r = 0.9788, R 2 = 0.9581, p < 0.0001 ( Supplemental Figure S2 and Table S2 ).…”

“…Cysteine Carboamidomethylation. Proteins identified by a minimum of two peptides were accepted [ 20 ].…”

“…R SC is the log ratio of abundance between samples 1 (Scramble) and 2 (siRNA_MUT); n1 and n2 are the SpCs for the given protein in sample groups 1 and 2, respectively; t1 and t2 are the total numbers of spectra over all of the proteins in the two sample groups; f is a correction factor set to 0.5 and used to eliminate discontinuity due to SpC = 0 [ 20 ]. The normalized spectral abundance factor (NSAF) for a given protein was calculated as the ratio of its spectral abundance factor SAF (SpC divided by protein length) and the sum of all SAFs for the proteins identified within that run.…”

Urinary Proteomics and Precision Medicine for Chronic Kidney Disease: Current Status and Future Perspectives

“…Using LC‐MS/MS analysis, Caterino and Zacchia et al . found 73 and 66 proteins of interest in female and male patients with Bardet–Biedl syndrome (BBS), respectively.…”

Proteomic Analysis of Mucopolysaccharidosis IIIB Mouse Brain

“…In order to measure the relative abundance for each identified protein and significantly represented into the two datasets, Fold NSAF was calculated as log2 (NSAF1/NSAF2), where NSAF1 was referred to the mean of NAGLU −/− samples NSAF, and NSAF2 to the mean of WT samples, respectively. Fold NSAF was reported as abundance index [38].…”