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DOI 10.1002/prca.201400200

Proteomics Clin. Appl. 2015, 9, 574–585

RESEARCH ARTICLE

Urinary biomarkers of chronic allograft nephropathy Hilary Cassidy1 , Jennifer Slyne1 , Patrick O’Kelly2 , Carol Traynor3 , Peter J. Conlon2 , Olwyn Johnston4 , Craig Slattery1 , Michael P. Ryan1 and Tara McMorrow1 1

School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Belfield, Dublin, Ireland 2 Department of Nephrology & Transplantation, Beaumont Hospital, Beaumont, Dublin, Ireland 3 Nephrology Department, Mater Misercordiae University Hospital, Dublin, Ireland 4 Gordon & Leslie Diamond Centre, Division of Nephrology, University of British Columbia, Vancouver, Canada Purpose: Chronic allograft nephropathy (CAN) is widely accepted as the leading cause of renal allograft loss after the first year post transplantation. This study aimed to identify urinary biomarkers that could predict CAN in transplant patients. Experimental design: The study included 34 renal transplant patients with histologically proven CAN and 36 renal transplant patients with normal renal function. OrbiTrap MS was utilized to analysis a urinary fraction in order to identify other members of a previously identified biomarker tree [1]. This novel biomarker pattern offers the potential to distinguish between transplant recipients with CAN and those with normal renal function. Results: The primary node of the biomarker pattern was reconfirmed as ␤2 microglobulin. Three other members of this biomarker pattern were identified: neutrophil gelatinaseassociated lipocalin, clusterin, and kidney injury biomarker 1. Significantly higher urinary concentrations of these proteins were found in patients with CAN compared to those with normal kidney function. Conclusions and clinical relevance: While further validation in a larger more-diverse patient population is required to determine if this biomarker pattern provides a potential means of diagnosing CAN by noninvasive methods in a clinical setting, this study clearly demonstrates the biomarkers’ ability to stratify patients based on transplant function.

Received: December 15, 2014 Revised: April 23, 2015 Accepted: May 5, 2015

Keywords: Biomarkers / Chronic allograft nephropathy / Clusterin / NGAL / Renal transplantation

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Additional supporting information may be found in the online version of this article at the publisher’s web-site

Introduction

Since the first successful kidney transplant in 1954, kidney transplantation has become the routine management for patients presenting with end-stage renal disease (ESRD) [2, 3]. The two most common causes of long-term graft loss remain “death with a functioning graft,” usually from a marked Correspondence: Dr. Tara McMorrow, School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland E-mail: [email protected] Abbreviations: ␤2M, ␤2 microglobulin; CAN, chronic allograft nephropathy; DGF, delayed graft function; ESRD, end-stage renal disease; KIM-1, kidney injury biomarker 1; NGAL, neutrophil gelatinase-associated lipocalin; uCr, urinary creatinine; UMOD, uromodulin  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

excess of cardiovascular mortality in allograft recipients, and chronic allograft nephropathy (CAN) [4, 5], the term given to the development of fibrotic processes leading to progressive allograft dysfunction with variable proteinuria and hypertension [1, 6–8]. Despite the significant improvement in the rate of acute rejection over the last decade [9,10], CAN remains the principle cause of late graft loss after the first year post renal transplantation [11], accounting for 50–80% of graft losses after this time [1, 3, 4]. The reasons for this are multifactorial including immune and nonimmune factors—for example, acute rejection [12,13], delayed graft function (DGF) [14], and acute calcineurin-inhibitor toxicity [15]. CAN describes the development of fibrotic processes leading to progressive allograft dysfunction with variable proteinuria and hypertension, and encompasses the histological lesions that occur within a renal allograft including atherosclerosis, glomerulosclerosis, interstitial fibrosis, and www.clinical.proteomics-journal.com

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Clinical Relevance There is a significant clinical need to improve the diagnostic tests currently available for the identification of renal transplant patients at risk of developing chronic allograft nephropathy (CAN), due to the high percentage of transplant recipients, 50–80%, presenting with allograft rejection in the years after the first year post-transplantation. This study has provided

tubular atrophy [1, 16]. Diagnosis of this particular disease has been complicated as a result of its multifactorial etiology [17]. Over the past two decades, the terminology used to describe the disease has changed, with Banff 2005 renaming CAN as interstitial fibrosis and tubular atrophy (IF/TA), and more recently chronic allograft injury. However the term CAN persists today, with all three terms being used interchangeably in the literature. For the purposes of this study, the term CAN will be employed as the CAN classification system was in use at the time of sample collection. Accurate assessment and monitoring of graft function is essential in renal transplant recipients. The current “gold standard” tests for the assessment of the development and progression of kidney injury include blood urea nitrogen, serum creatinine, glomerular filtration rate (GFR) measurement, and albuminuria. Frequently, histological diagnosis with a renal transplant biopsy is required to confirm CAN. Unfortunately, these tests are insensitive to small changes in renal function, and do not adequately detect early graft damage. Biomarkers ideally should reflect minute changes in graft function allowing for timely intervention and be capable of predicting disease progression. Currently, there is a growing consensus that such complex and heterogeneous processes as those evident in CAN could best be fingerprinted using a pattern of collectively and individually informative biomarkers [18]. Using MS approaches to determine the urinary protein profiles associated with CAN, it could be possible to predict individuals susceptible to its development by noninvasive means compared to the very invasive current investigative tool of the renal biopsy [19]. Therefore, the overall purpose of this study was to employ MS to analyze urine from renal transplant patients with documented CAN and also normal renal function using proteomic techniques in order to identify potential novel biomarker members of a previously described CAN-specific biomarker pattern [1]. Identification of noninvasive biomarkers or a biomarker pattern of CAN would benefit the kidney allograft population by allowing frequent monitoring to optimize immunosuppressive therapy, thus preventing disease progression.

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a potential noninvasive protein biomarker pattern, including ␤2 microglobulin, neutrophil gelatinaseassociated lipocalin, kidney injury molecule 1, and clusterin, which could be used to detect and diagnose CAN at an early stage ensuring better healthcare provision for patients with CAN.

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Materials and methods

2.1 Study population and sample collection The study design was previously reported by Johnston et al. [1]. Briefly, approval was gained from the Beaumont hospital ethics in medical research committee for studies involving human subjects. Midstream urine samples were obtained from a cohort of renal transplant patients who were attending the renal transplant clinic at Beaumont Hospital, Dublin, between July 2004 and April 2005 and who complied with the inclusion and exclusion criteria (see Supporting Information Table 1) and provided informed consent. The “disease” group included 34 renal transplant patients with histologically proven CAN (according to Banff 1997 criteria, see Supporting Information Table 2) and the “control” group included 36 renal transplant patients with normal renal function (serum creatinine 50 mL/min) more than 1 year after transplantation. Additionally, patients diagnosed with CAN as a result of a renal transplant biopsy, between 1987 and 2005, (according to the Banff 1997 criteria) were invited to participate once the inclusion criteria were met [16]. Clinical and historical data were documented for each patient, including age, gender, and immunosuppression regimen and creatinine values (see Supporting Information Tables 3 and 4).

2.2 Purification of protein peak clusters Previous work was carried out by Johnston et al. whereby control and CAN samples were analyzed by SELDI-TOF-MS and anionic fractionation [1] (for further detailed information, see Supporting Information 5). Based on this study, the anionic fraction that contained the biomarker pattern of interest in most abundance was identified and underwent reverse-phase chromatography to allow further fractionation and enrichment of the markers [1]. Briefly, the control and CAN samples were pooled separately. These pooled samples underwent SELDI-assisted anionic fractionation using HyperQ resin (Biospera), followed by hydrophobic fractionation using PRC Poly-Bio beads (Biospera) to separate out the

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proteins with successively lower pH buffers (increasingly anionic proteins) or successively increasing concentrations of acetonitrile (ACN) buffers (increasingly hydrophobic proteins). The fraction with the peak of interest of the highest intensity was used for any subsequent analysis.

Proteomics Clin. Appl. 2015, 9, 574–585

filtered using an average local confidence of ࣙ65%, a total local confidence of ࣙ6, and peptide score (–10lgP) of ࣙ15.

2.5 Protein detection in urine samples by Western blot 2.3 Protein isolation After purification, the best hydrophobic fraction was concentrated in a vacuum centrifuge at 30⬚C. Sample was resuspended in 1× sample buffer (Invitrogen) and run on 1D SDS-PAGE gels (Invitrogen) alongside the 10 ␮L of SeeBlue Plus2 prestained standard (Invitrogen) using NuPAGE MES SDS running buffer (Invitrogen) and the Sure Lock mini cell system (Invitrogen). Gels underwent colloidal blue staining using the Novex Colloidal Blue Staining Kit (Invitrogen). The protein bands were then excised and in-gel trypsinization was carried out using 0.1 ␮g sequence-grade modified trypsin (Promega). Following agitation in a thermomixer at 300 rpm at 27⬚C overnight, the samples were centrifuged and the supernatants were transferred to fresh 0.5 mL tubes. The supernatants were dried down in a vacuum centrifuge at 60⬚C to a final volume of 1–2 ␮L. The dried down peptides were reconstituted in 20 ␮L of 1% acetic acid (Sigma–Aldrich) and the peptide solution was transferred into MS vials.

2.4 Dionex Orbitrap MS MS was conducted at the proteomics core facility in the Conway Institute UCD, using a Thermo-Scientific LTQ ORBITRAP XL mass spectrometer connected to a Dionex Ultimate 3000 (SLCnano) chromatography system. Tryptic peptides were resuspended in 0.1% formic acid (Sigma–Aldrich). Each sample was loaded onto Biobasic Picotip Emitter (120 mm length, 75 ␮m id) packed with Reprocil Pur C18 (1.9 ␮m) reverse-phase media column and was separated by an increasing ACN gradient over 30 minutes. The mass spectrometer was operated in positive ion mode with a capillary temperature of 200⬚C, a capillary voltage of 46 V, a tube lens voltage of 140 V, and with a potential of 1900 V applied to the frit. All data were acquired with the mass spectrometer operating in automatic data-dependent switching mode. A high-resolution MS scan (300–2000 Da) was performed using the Orbitrap to select the seven most intense ions prior to MS/MS analysis using the ion trap. The raw data were de novo sequenced and searched against the Homo sapiens subset of the Uniprot Swiss-Prot database using the search engine PEAKS Studio 6 database for peptides cleaved with trypsin. Each peptide used for protein identification met specific Peaks parameters—that is, only peptide scores that corresponded to a false discovery rate of ࣘ1% were accepted from the Peaks database search. The Peaks de novo results were  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

If the appropriate antibody was available, the presence of the identified proteins in the urine of control and CAN patients was confirmed using Western blot. Equal volumes of urinary protein were analyzed by SDS-PAGE as described by Laemmli [20]. Immunoblotting was performed with antibodies specific for apoliporotein A1 (1:1000, Acris Antibodies), ␤2 microglobulin (␤2M, 1:500; Abcam), clusterin (1:200, Biovendor), neutrophil gelatinase-associated lipocalin (NGAL, 1:100; Acris Antibodies), kidney injury molecule 1 (also known as hepatitis A virus cellular receptor 1 or HAVCR1; KIM-1, 1:100; (R&D Systems), and uromodulin (also known as Tamm-Horsfall protein; (UMOD, 1:500; Santa Cruz). Results shown are representative of at least three experiments with similar results.

2.6 Protein quantification Protein quantification was performed using enzyme immunoassay test kit (ELISA). NGAL levels were quantified using the NGAL rapid ELISA kit (Bioporto). UMOD and ␤2M were quantified using Raybiotech ELISA kits (Raybiotech). Alpha-2-macroglobulin, alpha-1-antitrypsin, and alpha-1-acid glycoprotein ELISAs were sourced from ICL labs (Immunology Consultants Laboratory). Apolipoprotein A1 was measured using the ApoA1 Elisa (Mabtech). Albumin, KIM-1, osteopontin, cystatin C, clusterin, renin, and trefoil factor 3 were quantified using the Milliplex MAP kit (Millipore). All ELISAs were performed as per the manufacturer’s instructions. The protein concentrations were then calculated from the absorbance value by GraphPad Prism 4 by constructing a standard curve (the mean absorbance obtained from each reference standard was plotted against its concentration).

2.7 Statistical analysis All ELISA data were normalized to urinary creatinine (uCr) prior to performing statistical analyses using the program GraphPad Prism 4.0. Data were analyzed by a Student t-test. To compare the difference in protein concentration between control and CAN patient urine, unpaired nonparametric Mann–Whitney test (rank-sum test) that did not assume a Gaussian distribution was conducted. Two-tailed p-values were calculated with 95% confidence intervals. Results were expressed as the mean ± SEM. The following scheme was used throughout the study: * p

Urinary biomarkers of chronic allograft nephropathy.

Chronic allograft nephropathy (CAN) is widely accepted as the leading cause of renal allograft loss after the first year post transplantation. This st...
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