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.
Footnotes
↵d On behalf of the European Kidney and Urine Proteomics consortium.
↵m Supported by InGenious HyperCare Grant LSHM-CT-2006-037093 and British Heart Foundation Grant RG/02/012.
↵q On behalf of the European Uremic Toxin consortium.
↵x Supported by Federal Ministry of Education and Research Grant NGFN/01GR0807.
↵ee Supported by the Biotechnology and Biological Sciences Research Council and Engineering and Physical Sciences Research Council Interdisciplinary Research Collaboration in Proteomic Technology (IRColl) Grant Radical Solutions for Researching the Proteome (RASOR) and Wellcome Trust Joint Infrastructure Fund Proteomics Grant 29240.
↵oo Supported by INSERM, the “Direction Régional de la Recherche Clinique” (Centre Hospitalier Universitaire de Toulouse, France) under the Interface program and by the Fondation pour la Recherche Médicale.
↵qq Supported by European Union Prevention of Diabetic Complications (PREDICTIONS) Grant LSHM-CT-2005-018733.
↵vv Supported by European Union PREDICTIONS Grant LSHM-CT-2005-018733 and by InGenious HyperCare Grant LSHM-CT- 2006-037093.
↵ww Supported by European Union to Systems Biology towards Novel Chronic Kidney Disease Diagnosis and Treatment (sysKID) Grant HEALTH-F2-2009-241544.
↵* This work was supported, in whole or in part, by National Institutes of Health Grants DK067638 (to A. S. K.); Grants DK075868, DK078244, DK061525, DK080301, and DK082753 (to B. A. J. and J. N.); and Biotechnology Training Program Predoctoral Fellowship Grant 5T32GM08349 (to D. M. G.). This work was also supported by Eurotransbio Grant ETB-2006-016 to the Urosysteomics consortium (www.urosysteomics.com) (to E. S. and H. M.), Lower Saxony Ministry of Economy Grant 203.19-32329-5-461 (to A. G., E. M. W., and H. M.), and the Joslin Diabetes Center. H. Mischak is founder and co-owner of Mosaiques Diagnostics GmbH, which developed the CE-MS technology and the MosaiquesVisu software. P. Zürbig, M. Dakna, Igor Golovko, and Eric Schiffer are employees of Mosaiques Diagnostics GmbH.
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This article contains supplemental Tables I–III and figures showing mass spectra.
- Received June 11, 2010.
- Revision received June 30, 2010.
- © 2010 by The American Society for Biochemistry and Molecular Biology, Inc.
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