About this item:

181 Views | 483 Downloads

Author Notes:

E-mail: andreas.kistler@usz.ch

Conceived and designed the experiments: ADK ALS HM ABC.

Performed the experiments: ADK JS WM.

Analyzed the data: JS ADK HM DP FK JEB WM.

Contributed reagents/materials/analysis tools: ADK ALS VET MM JJG KTB JEB RPW HM ABC.

Wrote the paper: ADK ALS HM ABC.

Competing Interests: The authors have read the journal's policy and have the following conflicts: HM is the founder and coowner of Mosaiques Diagnostics, who developed the CE-MS technology.

JS is an employee of Mosaiques Diagnostics. MosaiquesVisu software is a product of Mosaiques Diagnostics.

This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Furthermore, JEB is an employee of Booz Allen Hamilton, but his employement there started only after all his contributions to this manuscript, which he made as an employee of University of Pittsburgh.

This does not alter their adherence to all the PLOS ONE policies on sharing data and materials.

Subjects:

Research Funding:

Funding for this study was supported by the Association pour l'Information et la Recherche sur les maladies Rénales d'origine Génétique (AIRG), section Suisse Romande, the Binelli and Ehrsam Foundation, and the Swiss National Science Foundation (No 3100030_132597/1)

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Keywords:

  • Science & Technology
  • Multidisciplinary Sciences
  • Science & Technology - Other Topics
  • MULTIDISCIPLINARY SCIENCES
  • MONOCYTE CHEMOATTRACTANT PROTEIN-1
  • CORONARY-ARTERY-DISEASE
  • RENAL-DISEASE
  • VOLUME PROGRESSION
  • MASS-SPECTROMETRY
  • DISCOVERY
  • ADPKD
  • IDENTIFICATION
  • CANCER
  • GENES

Urinary Proteomic Biomarkers for Diagnosis and Risk Stratification of Autosomal Dominant Polycystic Kidney Disease: A Multicentric Study

Show all authors Show less authors

Tools:

Journal Title:

PLoS ONE

Volume:

Volume 8, Number 1

Publisher:

, Pages e53016-e53016

Type of Work:

Article | Final Publisher PDF

Abstract:

Treatment options for autosomal dominant polycystic kidney disease (ADPKD) will likely become available in the near future, hence reliable diagnostic and prognostic biomarkers for the disease are strongly needed. Here, we aimed to define urinary proteomic patterns in ADPKD patients, which aid diagnosis and risk stratification. By capillary electrophoresis online coupled to mass spectrometry (CE-MS), we compared the urinary peptidome of 41 ADPKD patients to 189 healthy controls and identified 657 peptides with significantly altered excretion, of which 209 could be sequenced using tandem mass spectrometry. A support-vector-machine based diagnostic biomarker model based on the 142 most consistent peptide markers achieved a diagnostic sensitivity of 84.5% and specificity of 94.2% in an independent validation cohort, consisting of 251 ADPKD patients from five different centers and 86 healthy controls. The proteomic alterations in ADPKD included, but were not limited to markers previously associated with acute kidney injury (AKI). The diagnostic biomarker model was highly specific for ADPKD when tested in a cohort consisting of 481 patients with a variety of renal and extrarenal diseases, including AKI. Similar to ultrasound, sensitivity and specificity of the diagnostic score depended on patient age and genotype. We were furthermore able to identify biomarkers for disease severity and progression. A proteomic severity score was developed to predict height adjusted total kidney volume (htTKV) based on proteomic analysis of 134 ADPKD patients and showed a correlation of r = 0.415 (p<0.0001) with htTKV in an independent validation cohort consisting of 158 ADPKD patients. In conclusion, the performance of peptidomic biomarker scores is superior to any other biochemical markers of ADPKD and the proteomic biomarker patterns are a promising tool for prognostic evaluation of ADPKD.

Copyright information:

© 2013 Kistler et al.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Export to EndNote