Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection

Abstract

Noninvasive methods to diagnose rejection of renal allografts are unavailable. Mass spectrometry followed by multiple-reaction monitoring provides a unique approach to identify disease-specific urine peptide biomarkers. Here, we performed urine peptidomic analysis of 70 unique samples from 50 renal transplant patients and 20 controls (n = 20), identifying a specific panel of 40 peptides for acute rejection (AR). Peptide sequencing revealed suggestive mechanisms of graft injury with roles for proteolytic degradation of uromodulin (UMOD) and several collagens, including COL1A2 and COL3A1. The 40-peptide panel discriminated AR in training (n = 46) and test (n = 24) sets (area under ROC curve >0.96). Integrative analysis of transcriptional signals from paired renal transplant biopsies, matched with the urine samples, revealed coordinated transcriptional changes for the corresponding genes in addition to dysregulation of extracellular matrix proteins in AR (MMP-7, SERPING1, and TIMP1). Quantitative PCR on an independent set of 34 transplant biopsies with and without AR validated coordinated changes in expression for the corresponding genes in rejection tissue. A six-gene biomarker panel (COL1A2, COL3A1, UMOD, MMP-7, SERPING1, TIMP1) classified AR with high specificity and sensitivity (area under ROC curve = 0.98). These data suggest that changes in collagen remodeling characterize AR and that detection of the corresponding proteolytic degradation products in urine provides a noninvasive diagnostic approach.

Figures

Figure 1.

Peptidomics approach for biomarker discovery. (A) Schematics for peptidomic analysis of naturally occurring urinary peptides. (B) Study design for the urine peptide biomarker discovery.

Figure 2.

Statistical analyses of the peptide biomarker panel. (A) The discriminant of the peptide biomarker panel for the training (upper) and testing data (lower) probabilities for all transplant samples were calculated from the LDA. The maximum estimated probability for each of the wrongly classified samples is marked with a circle. Two of the 46 samples in the training set and 4 of the 24 samples in the test set were misclassified, giving a correct classification rate of 96% in the training set and 83% in the test set. (B) Left panel: Modified 2 × 2 contingency tables were used to calculate the percentage of classification that agreed with clinical diagnosis for the biomarker panel. P values were calculated with Fisher's exact test. Right panel: A prediction of AR from the non-AR phenotype (a so-called “two-class” prediction) was used to assess the performance of the biomarker panel in the classification of unknown samples. STA and BK were combined into one group as “NON-AR.” Fisher exact test was to compute the P value for the blind test. (C) Unsupervised clustering based on the peptide biomarker panel was used to construct a heat map in which the colors indicate the intensity of peptide concentration by LC-MALDI: red indicates high peptide abundance and green indicates low peptide abundance in the comparative analysis. It can be seen that by unsupervised analysis, the AR samples, save one, all co-cluster together and all of the non-AR samples cluster together. Modified 2 × 2 contingency tables were used to calculate the percentage of unsupervised clustering that agreed with clinical diagnosis for the biomarker panel. P values were calculated with Fisher's exact test.

Figure 3.

Discovery and verification of AR-specific peptides. (A) Discovery of the 40-peptide biomarker panel and their performance on the training set (top panel) and the test set (bottom panel) using ROC analysis. (B) MRM analyses of the two UMOD peptide biomarkers (top panels). The distribution of MRM signals were analyzed by box-whisker graphs according to the sample categories. The boxes are bound by 75th and 25th percentiles of the data, and the whiskers extend to the minimum and maximum values. ROC analysis (bottom panel) of the classification performance of the two UMOD peptide biomarkers. When ROC analysis was performed to test the diagnostic accuracy of the two UMOD peptide biomarkers for AR, the AUCs were computed as 0.83 for the UMOD 1679.98-Da peptide and 0.74 for the UMOD 1911.07-Da peptide.

Figure 4.

Mapping of collagen and UMOD peptides in the urine. Identified urine peptide biomarkers yielded clusters of overlapping (A) collagen and (B) UMOD peptides (mass/charge ratio, MH+). “P” in red indicates 4-hydroxyproline. Peptides in brackets derive from the same region of the same precursor proteins. Because the genes labeled in red were significantly regulated in microarray data, we tested them by Q-PCR. (C) Human UMOD precursor. Recent MS analyses proved that C-terminal cleavage of the precursor, which has 640 amino acids, occurred after phenylalanine residue 587. Because part of the C-terminal peptide cleaved from the UMOD precursor, the UMOD peptide biomarker cluster (colored in red) discovered in this study spans from serine residue 589, following arginine residue 588, and to lysine residue 607.

Figure 5.

A gene panel specific for AR. (A) The distribution of COL1A2, COL3A1, MMP-7, SERPING1, TIMP1, and UMOD genes' Q-PCR measurements in kidney biopsy were analyzed by box-whisker graphs. (B) ROC analysis was performed to evaluate the performance of the six-member RNA biomarker panel classifying AR from STA. The plotted ROC curve is the vertical average of the 500 bootstrapping runs, and the boxes and whiskers plot the vertical spread around the average.

Figure 6.

A proposed mechanism of fibrosis caused by AR as indicated by the observations of increased collagen gene transcription in the rejection biopsy and reduced collagen peptides in the urine during graft rejection.