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

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.

Fig. 1.

Peptide patterns distinguishing patients with CKD from HC. The compiled data sets of urine samples from 230 patients with CKD (left panel) and 379 healthy control subjects (right panel) included in the training set are shown. Normalized molecular mass (y axis) is plotted against normalized CE migration time (x axis). The mean signal intensity is represented in three-dimensional depiction.

Fig. 2.

ROC curves for classification of patient cohorts with “CKD pattern.” ROC analysis for CKD diagnosis of the training set and the test set after unblinding is shown. AUC, area under the curve.

Fig. 3.

Analytical variability of CE-MS-based CKD platform. A, statistical spread of temperature stability, freeze/thaw stability, and postpreparation stability are shown. Reproducibility and intermediate precision (B) and time course (C) are plotted against their statistical spread. p values are depicted in boxes.