Proteomic signatures in plasma during early acute renal allograft rejection

Abstract

Acute graft rejection is an important clinical problem in renal transplantation and an adverse predictor for long term graft survival. Plasma biomarkers may offer an important option for post-transplant monitoring and permit timely and effective therapeutic intervention to minimize graft damage. This case-control discovery study (n = 32) used isobaric tagging for relative and absolute protein quantification (iTRAQ) technology to quantitate plasma protein relative concentrations in precise cohorts of patients with and without biopsy-confirmed acute rejection (BCAR). Plasma samples were depleted of the 14 most abundant plasma proteins to enhance detection sensitivity. A total of 18 plasma proteins that encompassed processes related to inflammation, complement activation, blood coagulation, and wound repair exhibited significantly different relative concentrations between patient cohorts with and without BCAR (p value <0.05). Twelve proteins with a fold-change >or=1.15 were selected for diagnostic purposes: seven were increased (titin, lipopolysaccharide-binding protein, peptidase inhibitor 16, complement factor D, mannose-binding lectin, protein Z-dependent protease and beta(2)-microglobulin) and five were decreased (kininogen-1, afamin, serine protease inhibitor, phosphatidylcholine-sterol acyltransferase, and sex hormone-binding globulin) in patients with BCAR. The first three principal components of these proteins showed clear separation of cohorts with and without BCAR. Performance improved with the inclusion of sequential proteins, reaching a primary asymptote after the first three (titin, kininogen-1, and lipopolysaccharide-binding protein). Longitudinal monitoring over the first 3 months post-transplant based on ratios of these three proteins showed clear discrimination between the two patient cohorts at time of rejection. The score then declined to baseline following treatment and resolution of the rejection episode and remained comparable between cases and controls throughout the period of quiescent follow-up. Results were validated using ELISA where possible, and initial cross-validation estimated a sensitivity of 80% and specificity of 90% for classification of BCAR based on a four-protein ELISA classifier. This study provides evidence that protein concentrations in plasma may provide a relevant measure for the occurrence of BCAR and offers a potential tool for immunologic monitoring.

Figures

Fig. 1.

Differential concentration of PGCs between subjects with and without BCAR detected by iTRAQ. A, points in gray indicate the 144 PGCs identified in at least two-thirds of the samples from patients with and without BCAR, whereas those in black indicate the 18 PGCs that differed significantly (p value <0.05) between subjects with or without BCAR. Circles indicate the 12 PGCs with fold-changes ≥1.15. The x axis shows the logarithm (base 10) of the ratio between median relative levels in patients with and without BCAR. The y axis shows the −log10 p values. B, gray and white bars represent the averages of the weighted-logged peptide ratios (base 10) in samples with and without BCAR, respectively. Groups of trypsin-cleaved and miscleaved trypsin peptides and the number of samples in which each group was detected are summarized in the adjacent table. Similarly, the averages of the logged PGC ratios (base 10) for the samples with and without BCAR are represented with gray and white bars, respectively, in a separate plot. Vertical lines in all plots represent S.E.

Fig. 2.

Gene ontologies. The most significantly enriched biological processes (A) and biological categories (B) based on the 18 protein group codes differentially expressed in BCAR are shown. The x axis shows −log10 (p values).

Fig. 3.

Performance of multivariate panel. A, three-dimensional plot of the first three principal components based on 12 PGCs in the panel. Black and gray spheres represent samples with and without BCAR, respectively. B, incremental classification accuracy demonstrating stepwise inclusion of 12 PGCs in the panel. At each step, a classifier score is built using all PGCs to the left on the x axis, and its corresponding classification accuracy is indicated on the y axis. C, longitudinal change in average classifier score using the top three PGCs for subjects with (solid line) and without (dashed line) BCAR and for pre- (▴) and post-transplant samples with (●) and without (○) BCAR. “BL” represents the time before transplant (baseline), and other time points correspond to weeks (W) after transplant. Vertical lines represent S.E.

Fig. 4.

Technical validation. A, comparison of ELISA and iTRAQ analyses using 10 samples with BCAR and 19 without BCAR processed in both platforms. B, ELISA protein concentrations for four validated proteins from groups with BCAR (AR; filled circles) and without BCAR (NR; open circles). Horizontal lines represent the median within each group. C, classifier performance based on four proteins measured by ELISA: sensitivity (●), specificity (▴), and accuracy (□). pval, p value.