Article

Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease.

Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, USA.
Molecular &amp Cellular Proteomics (Impact Factor: 7.25). 11/2010; 9(11):2424-37. DOI: 10.1074/mcp.M110.001917
Source: PubMed

ABSTRACT Because of its availability, ease of collection, and correlation with physiology and pathology, urine is an attractive source for clinical proteomics/peptidomics. However, the lack of comparable data sets from large cohorts has greatly hindered the development of clinical proteomics. Here, we report the establishment of a reproducible, high resolution method for peptidome analysis of naturally occurring human urinary peptides and proteins, ranging from 800 to 17,000 Da, using samples from 3,600 individuals analyzed by capillary electrophoresis coupled to MS. All processed data were deposited in an Structured Query Language (SQL) database. This database currently contains 5,010 relevant unique urinary peptides that serve as a pool of potential classifiers for diagnosis and monitoring of various diseases. As an example, by using this source of information, we were able to define urinary peptide biomarkers for chronic kidney diseases, allowing diagnosis of these diseases with high accuracy. Application of the chronic kidney disease-specific biomarker set to an independent test cohort in the subsequent replication phase resulted in 85.5% sensitivity and 100% specificity. These results indicate the potential usefulness of capillary electrophoresis coupled to MS for clinical applications in the analysis of naturally occurring urinary peptides.

0 Bookmarks
 · 
229 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Biomarkers hold the promise of significantly improving health care by enabling prognosis and diagnosis with improved accuracy, and at earlier points in time. Previous results have indicated that single biomarkers are not suitable to describe complex diseases such as kidney disease. Here we provide an update on the progress of urinary proteomics-based studies and strategies to develop biomarker-based classifiers that tolerate instability and inconsistency of individual biomarkers. The examples focus on two major fields in nephrology: chronic kidney disease in the adult population and obstructive nephropathies in the pediatric population. When employed adequately, urinary proteomics demonstrates a clear value in kidney disease, indicating that the current status quo ruling for decades now could be changed by applying modern "omics" approaches. However, while research is able to deliver these useful tools for patient management, the issues associated with implementation are not yet solved. Active engagement of the relevant clinical professional societies, as well as patient's organizations, might help to implement these omics approaches that have shown a clear benefit for the patient.
    Pediatric Nephrology 03/2014; · 2.94 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The 'Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial' (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273). In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier. We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16-89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found. We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial.
    Nephrology Dialysis Transplantation 03/2014; · 3.37 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.
    Clinical and translational medicine. 03/2014; 3(1):7.

Full-text

View
99 Downloads
Available from
Jun 2, 2014