Quantification of Urinary Protein Biomarkers of Autosomal Dominant Polycystic Kidney Disease by Parallel Reaction Monitoring

Abstract

This study shows that sPRM is a promising approach to rapidly screen large numbers of proteins in urine in order to provide earlier diagnosis and potentially better understand the pathogenesis of ADPKD development and progression.

PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms

“…Study by Bakun et al 5 (ADPKD1) analyzed urine protein composition from 30 ADPKD patients and 30 healthy volunteers identifying 155 differentially abundant proteins (Supplementary Table S1 ). Study by Rauniyar et al 9 (ADPKD2) compared 14 urine samples from ADPKD patients to 18 normal controls and identified 69 significantly deregulated proteins (Supplementary Table S2 ). Lacroix et al 7 (UPJ1) explored the urinary proteome of newborns with (n = 8) or without (n = 10) UPJ and discovered 174 differentially abundant proteins (Supplementary Table S3 ).…”

“…While genomics is frequently used to unravel specific mutations in the genome that can increase the risk of developing certain diseases, disease activity is best captured by transcriptome or proteome analysis, as these traits are closer to the phenotype 4 . Moreover, whilst urine has been known for a very long time as a very informative and non-invasive source of potential candidates in the context of kidney disease 5 – 9 , the molecular changes observed in urine partially reflect the deregulated mechanisms within kidney tissue. Urinary proteins predominately originate (~ 70%) from kidney and urinary tract by mechanisms of secretion and cellular shedding 10 – 12 .…”

“…PRYNT is based on the integration of Search Tool for the Retrieval of Interacting (STRING, version 10.5) 29 PPI network and a combination of shortest-path and random walk, two closeness-based algorithms as it has been previously shown in the literature that this method outperformed other computational methods 14 , 26 – 28 . We used PRYNT in the context of two prototypic human kidney diseases: autosomal dominant polycystic kidney disease (ADPKD) 5 , 9 and ureteropelvic junction obstruction (UPJ) 6 , 7 . ADPKD is a well-characterized monogenic kidney disease induced by a mutation of the PKD1 or PKD2 gene.…”

“…All data analyzed during this study are included in Bakun et al 5 (ADPKD1) (Supplementary Table S1 ); Rauniyar et al 9 (ADPKD2) (Supplementary Table S2 ); Lacroix et al 7 (UPJ1) (Supplementary Table S3 ); Chen et al 6 (UPJ2) (Supplementary Table S4 ). Data are also available on GitHub at: https://github.com/Boizard/PRYNT/tree/master/datas .…”

Quantitative analysis of the renal pelvic and bladder urinary proteome

“…The differential proteins and knowledge of the associated signalling pathways could provide the basis for indepth validation. Urinary proteomic studies have proposed many candidate biomarkers for various urogenital diseases, including congenital and in ammatory anomalies of the urinary tract 1 2 , diabetic and cystic nephropathy 3 and importantly, urological malignancies of the kidney, bladder, and prostate 4 .…”