American Journal of Transplantation 2005; 5: 2479–2488Blackwell Munksgaard

Copyright C© Blackwell Munksgaard 2005

doi: 10.1111/j.1600-6143.2005.01053.x

Detection of Acute Tubulointerstitial Rejection byProteomic Analysis of Urinary Samples in RenalTransplant Recipients

Stefan Wittkea, Marion Haubitzb, Michael

Waldena, Frank Rohdec, Anke Schwarzb,

Michael Mengeld, Harald Mischaka, Hermann

Hallerb and Wilfried Gwinnerb,∗

aMosaiques-diagnostics and therapeutics AG, Hannover,GermanybDepartment of Nephrology, Medical School Hannover,Hannover, GermanycHoffmann-La Roche AG, Grenzach-Wyhlen, GermanydPathology, Medical School Hannover, Hannover,Germany∗Corresponding author: Wilfried Gwinner, M.D.,Gwinner.Wilfried@mh-hannover.de

This study investigates proteomic analysis of urinarysamples as a non-invasive method to detect acute re-jection of renal allografts.

Capillary electrophoresis coupled to mass spectro-metry (CE-MS) was used to analyze urinary samples in19 patients with different grades of subclinical or clin-ical acute rejection (BANFF Ia to IIb), 10 patients withurinary tract infection and 29 patients without evi-dence of rejection or infection.

A distinct urinary polypeptide pattern identified 16 outof 17 cases of acute tubolointerstitial rejection, butwas absent in two cases of vascular rejection. Urinarytract infection resulted in a different polypeptide pat-tern that allowed to differentiate between infectionand acute rejection in all cases. Potentially confound-ing variables such as acute tubular lesions, tubular at-rophy, tubulointerstitial fibrosis, calcineurin inhibitortoxicity, proteinuria, hematuria, allograft function anddifferent immunosuppressive regimens did not inter-fere with test results. Blinded analysis of samples withand without rejection showed correct diagnosis byCE-MS in the majority of cases.

Detection of acute rejection by CE-MS offers a promis-ing non-invasive tool for the surveillance of renal allo-graft recipients. Further investigation is needed to es-tablish polypeptide patterns in vascular rejection andto explore whether changes in the urinary proteomeoccur before the onset of histologically discerniblerejection.

Key words: Acute rejection, CE-MS, kidney, pro-teomics, protocol biopsy, transplantation, urine

Received 5 January 2005; revised 1 May 2005 and ac-cepted for publication 17 June 2005

Introduction

Diagnosis of acute rejection after renal transplantation re-quires allograft biopsy and is usually performed in the set-ting of functional graft impairment or as part of a surveil-lance protocol. Limiting biopsies to patients with signs ofgraft impairment implies that earlier stages of graft injurymay remain undetected. Protocol biopsies may detect re-jection at an earlier subclinical stage and allow prompt initi-ation of treatment, which may translate into improved long-term graft survival (1). On the other hand, this also impliesthat patients with preserved graft function without rejec-tion undergo this procedure unnecessarily. Furthermore,renal biopsies are associated with the risk of proceduralcomplications and high costs.

Accordingly, research efforts have been aimed toward thedevelopment of non-invasive tests to diagnose acute rejec-tion. Levels of soluble adhesion molecules, cytokines andthe urokinase plasminogen activator receptor in blood sam-ples, and lymphocyte expression of cytokines, perforin,granzyme B and FAS ligand have been suggested as po-tential markers for rejection (2–7). In the urine, detection ofsoluble IL-2, adhesion molecules, complement C4d andurokinase plasminogen activator receptor (6–8) has beenstudied in acute rejection. Other investigators analyzedurinary cells for mRNA expression of granzyme B, per-forin, granulysin and CD103 (9–11), and studied variousT-cell markers by immune histochemistry (12,13). How-ever, none of these tests have yet gained wide clinicalapplication.

Based on the assumption that pathophysiological changesin the kidneys may result in a change of various proteinsin the urine, analysis of the proteome of urinary samplesby mass spectroscopy (MS) techniques has emerged asa novel diagnostic tool. Three studies reported the use-fulness of urine protein profiling using SELDI-MS for thedetection of acute rejection in renal allografts (14–16).

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Recently, Neuhoff et al. (17) investigated a SELDI-basedMS technique and capillary electrophoresis-MS (CE-MS)to identify potential biomarkers in patients with membra-nous glomerulonephritis. Both techniques allowed identifi-cation of specific biomarkers; however, CE-MS was foundto have a higher sample resolution and mass accuracy re-sulting in a larger number of biomarkers. Using CE-MS,Weissinger et al. (18) identified biomarkers that alloweddifferentiation between focal segmental glomerulosclero-sis, membranous and minimal change glomerulonephritis.Recently, Kaiser et al. identified biomarkers for the diagno-sis of graft versus host disease after stem cell transplan-tation by CE-MS (19). The CE-MS technology is currentlyreviewed by Kolch et al. (20,21) and Hernandez-Borges(22).

In this study, we have used capillary electrophoresis onlinecoupled to ESI-TOF mass spectrometry to define specificurinary markers in patients with acute tubulointerstitial al-lograft rejection. These markers are absent in patients withurinary infection, and in control subjects without infectionor rejection.

Materials and Methods

Patients and sample collection

At our transplant centre, renal protocol biopsies are regularly performed at6 weeks, 3 and 6 months after kidney or combined kidney/pancreas trans-plantation. In addition, midstream spot-urine samples are collected immedi-ately before the biopsy and are subsequently frozen at −80◦C. Fresh urinesamples are routinely analyzed for protein concentration and screened forhematuria and leukocyturia by dipstick analysis and microscopic inspection.

Using the files from 428 patients who participated in the protocol biopsyprogram, available urine samples were divided into three groups: (a) patientswith biopsy-proven acute rejection, (b) patients with urinary tract infectionand (c) patients without acute rejection or urinary tract infection as controls.From these three groups, samples were randomly selected and subjectedto further analysis.

Interpretation of biopsies was performed according to the updated BANFF1997 classification (23). Urinary tract infection was defined by a positiveurine leukocyte dipstick result and bacterial urine culture with at least104 colonies/mL.

To establish the urinary patterns of acute rejection and infection, we ex-amined 29 samples from patients without acute rejection and urinary tractinfection, 19 samples with different grades of subclinical or clinical acuterejection (BANFF Ia to IIb) and 10 samples with urinary tract infection butwithout acute rejection at the time of biopsy. Twenty-four samples wereobtained at 6 weeks post transplant, 25 samples at 3 months and 9 sam-ples at 6 months. Demographic data of the three groups are summarizedin Table 1.

In additional validation experiments, 10 samples without acute rejection orurinary tract infection, 7 samples with urinary tract infection and 9 sampleswith acute tubulointerstitial rejection were analyzed in a blinded fashionby CE-MS. The corresponding biopsies to these urine samples were re-evaluated blindly by the same pathologist who had initially examined thebiopsies.

Table 1: Demographic data of the patients at the time of trans-plantation

Urinarytract Acute

Controls infection rejection

Number of patients 29 10 19Age at transplantation 55.6 ± 14.2 58.5 ± 7.4 48.6 ± 14.0

(years)Gender (male/female) 19/10 5/5 10/9Previous transplantation 1 1 0Combined 2 2 0

kidney/pancreas txHLA-mismatches 2.6±1.4 2.5±1.3 2.6±1.5Origin of the graft

Deceased donor 28 9 17Living donor 1 1 2Donor age (years) 57.6±14.5 46.0±13.5 47.3±20.7Donor gender 15/14 6/4 8/11

(male/female)

Furthermore, 66 urine samples from non-transplanted healthy volunteers(35 males, 31 females, mean age 35 ± 15 years) were included in theanalyses to establish the differences in urinary patterns between non-transplanted and transplanted individuals.

The study was performed with patients’ informed consent and approval ofthe internal ethics review committee.

Preparation of urine samples

After thawing the urine samples were centrifuged for 10 min at 2000 rpm.One milliliter of the supernatant was applied on a Pharmacia C2-column(Amersham Biosciences, Buckinghamshire, UK) to remove salts, urea, elec-trolytes and other interfering matrix components and to enrich the presentpolypeptides (PPs). The PPs were eluted with 50% (v/v) acetonitrile (Sigma-Aldrich, Taufkirchen, Germany) in HPLC-grade water (Roth, Karlsruhe, Ger-many) containing 0.5% (v/v) formic acid (Sigma-Aldrich). The eluate waslyophilized in a Christ Speed-Vac RVC 2–18/Alpha 1–2 (Christ, Osterodea.H., Germany) and resuspended in 50 lL HPLC-grade water shortly beforeanalysis by capillary electrophoresis mass spectrometry (for further detailssee Ref. [18]).

Capillary electrophoresis mass spectrometry (CE-MS)

Analyses were performed as described previously (18,19,24), using aCE-MS technique consisting of a P/ACE MDQ capillary electrophoresis(Beckmann-Coulter, Fullerton, CA, USA) online coupled to an ESI-TOF massspectrometer (Micro-TOF, Bruker-Daltonic, Bremen, Germany). The CE sys-tem was equipped with a 90 cm, 50 lm i.d., bare fused silica capillary(Beckmann-Coulter). The running buffer contained 20% (v/v) acetonitrileand 0.5 M formic acid (Sigma-Aldrich) in HPLC-grade water. The sampleswere injected for 99 s, with a positive pressure of 1 psi, resulting in a sampleplug of approximately 140 nL. The separation was performed unpressurizedfor 60 min, with +30 kV at the inlet of the capillary, resulting in a current ofapproximately 13 lA. During the entire run the capillary temperature wasmaintained at 35◦C.

The CE-MS was performed in a positive electrospray mode with an ESI-MSsprayer-kit (Agilent Technologies, Palo Alto, CA, USA). The ionspray inter-face potential was set to −3900 V and the sheath liquid—consisting of50% (v/v) of iso-propanole (Sigma-Aldrich) and 0.5% (v/v) formic acid inHPLC-grade water—was applied coaxial. The sheath flow rate was set

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Urinary Proteome in Renal Allograft Rejection

as low as possible and the nebulizer gas was turned off during themeasurement.

With these settings, signal/noise ratios were 20–50 000 for 25 fmol of a setof different standard polypeptides. Data acquisition and the MS run wereautomatically controlled by the control system of the CE by contact-closurerelays. Spectra were accumulated every 3 s over a mass range from 350 to3000 m/z.

Mass spectra data processing

To facilitate analysis of the vast amount of spectra (∼1200 per single CE-MSrun, range: 800–1500), a specific software tool, Mosaiquesvisu, was devel-oped to extract the information from the raw data file as described earlier(18,24). This program allows automated data processing for an entire CE-MSrun, using isotopic distribution and conjugated peaks for data deconvolution.This results in a polypeptide/protein list where each protein/polypeptide canbe identified by its molecular mass from 0.8 to 30 kDa and its normalizedmigration time (for more details see Refs. [18,24]). Polypeptides of differentsamples were presumed being identical if differences in mass were lessthan 0.05% and in migration time less than 5 min. The polypeptide listswere finally deposited in an Access database for further analysis.

Classification of polypeptide patterns

Mosacluster (18) was developed to discriminate different patient groups.This software tool allows the classification of samples in the high-dimensional parameter space by using support vector machines. Mosaclus-ter generates polypeptide models that rely on the presence of polypeptidesin certain diseases. Each of these polypeptides builds one dimension in then-dimensional parameter space.

Identification of polypeptides by MS-MS analysis

The MS-MS experiments were performed with an Ultraflex MALDI-TOF-TOF (Bruker Daltonik, Bremen, Germany). The entire CE run was initiallyspotted on a MALDI target plate using a Probot micro fraction collector(LC Packings, San Francisco, CA, USA) to maintain the information on mi-gration time. Matrix solution (2 mg/mL a-cyano-4-hydroxycinnamic acid in50% (v/v) acetonitrile and 0.1% (v/v) TFA) was added to the eluate at a flowrate of 4 lL/min and one spot was deposited every 15 s. To identify thespots containing the polypeptides of interest as defined by the precedingCE-MS runs, the plate was at first examined in MS mode. Subsequently,the polypeptides were fragmented in MS-MS mode and the resulting frag-ment spectra were submitted to Mascot for a search against the SwissprotProtein database.

Statistics

Demographic, clinical and laboratory data were analyzed with SPSS, ver-sion 12.0.1 (SPSS, Inc., Chicago, IL). Numerical data are given as mean ±standard deviation or median and range of values. Comparisons betweennumerical variables were made by one-way ANOVA and Welch’s post-hoctest or by the Kruskal–Wallis test. Categorical data were analyzed with thev 2 test. Statistical difference was defined for a p < 0.05.

Results

Patient characteristics

Demographics were similar across patient groups, al-though donor age tended to be higher in the control pa-tients without acute rejection or urinary tract infection (p =0.054; Table 1).

Table 2: Clinical and laboratory data related to the time of biopsy

Urinarytract Acute

Controls infection rejection(n = 29) (n = 10) (n = 19)

Diabetes mellitus 2 3 1Cigarette smoking 2 0 5Hydronephrosis 10 6 7

of the graft1

CMV viremiaConcurrent 1 0 1Recent 3 1 3Serum creatinine 141 (73–417) 144 (85–279) 146 (100–281)

(lmol/L)Hematuria 4 5a 2Leukocyturia 4 10b 4Urinary protein 120 240 135

concentration(mg/L) (0–510) (10–940) (20–440)

ImmunosuppressionCyclosporin A 24 6 16Tacrolimus 1 3c 3Mycophenolate 22 9 5d

mofetilRapamycin 1 1 1Prednisolone 28 9 19

Other drug treatmentsGanciclovir, 18e 3 6

valganciclovirCotrimoxazol 29 10 18

prophylaxisOther antibacterial 2 1 1

therapies1The majority of cases had only mild dilatation of the renal pelvis.ap < 0.05 vs. controls and patients with acute rejection.bp < 0.001 vs. controls and patients with acute rejection.cp < 0.05 vs. controls.dp < 0.002 vs. controls and patients with urinary tract infection.ep < 0.05 vs. patients with rejection.

Clinical and laboratory variables are shown in Table 2. Noneof the patients had serological evidence of active infec-tion with EBV or with hepatitis B or C. Urine tests for po-lioma virus were negative in all patients. CMV viremia atthe time of specimen collection was present in one pa-tient in the control group and in one patient with acuterejection. Recent CMV viremia, defined by CMVpp65 anti-genemia within 6 weeks before the biopsy, was similarin controls and patients with acute rejection. Serum cre-atinine and urinary protein concentrations at the time ofbiopsy varied widely in all three groups without significantdifferences in the median values. Patients with urinary tractinfection had more frequent hematuria and leukocyturia.Immunosuppression with tacrolimus was more prevalentin patients with urinary tract infection compared to the con-trol group. Compared to the patients with acute rejection,control patients and patients with urinary tract infection re-ceived more often mycophenolate mofetil. Treatment withganciclovir or valganciclovir, either prophylactically or as

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Table 3: Corresponding biopsy findings according to BANFF 1997

Urinarytract Acute

Controls infection rejection(n = 29) (n = 10) (n = 19)

aGradeIa – – 11Ib – – 6IIa – – 1IIb – – 1

cGradeMild 6 0 5Moderate 0 0 1

ATNFocal 12 4 9Diffuse 0 0 1

Calcineurin inhibitor toxicityIsometrical vacuolization 5 0 0

Tubulointerstitial calcification 2 1 3

treatment for CMV viremia, was more prevalent in the con-trol group.

Acute rejections were classified as subclinical (i.e. serumcreatinine increase at the time of biopsy by less than20% compared to previous determinations) in 13 patientsand clinical in 6 patients. The majority of rejections weregrade Ia according to the BANFF 1997 classification (Ta-ble 3). Additional biopsy findings included chronic changessuch as tubulointerstitial fibrosis and tubular atrophy, acutetubular necrosis, isometric vacuolization of tubular cellsand nephrocalcinosis. The incidence of these changeswas not statistically different among the three groups.None of the biopsies had signs of de novo or recurringglomerulonephritis.

Characteristics of the patients whose samples were ex-amined in the validation experiments were similar to thatdescribed in Tables 1–3 (data not shown).

Figure 1: Contour plots

of the urinary proteome

from healthy non-

transplanted subjects (A)

and from patients with a

renal allograft, regardless

of the presence of rejec-

tion or infection (B). Themigration time (min) is plot-ted on the x-axis and therelative protein/polypeptidemass (kDa) is plotted on they-axis. Up to 1500 differentproteins and polypeptidesin a mass range from 0.8 to20 kDa can be observed.

Urine polypeptide patterns in transplanted patients

and healthy control groups

Healthy control pattern: Examination of urine samplesfrom 66 non-transplanted healthy volunteers revealed sig-nificant sample to sample analogies. Fifty-three polypep-tides were present in more than 90%, additional 164polypeptides in more than 75% and further 582 polypep-tides in more than 50% of the samples. These 799 polypep-tides were used to establish the “normal” urine polypep-tide pattern which is shown in Figure 1.

Renal transplant pattern: To establish a common trans-plant urine pattern, samples from transplanted patientswere analysed without differentiation between rejection,infection and non-rejecting cases (Figure 1). This commontransplant pattern consisted of 92 polypeptides that werepresent in more than 90% of the samples, 191 polypep-tides present in more than 75% and further 289 polypep-tides detected in more than 50% of the urine samples.

Differentiation between transplanted and healthy non-

transplanted subjects: Comparison of the healthy con-trol pattern and the renal transplant pattern led to thedefinition of 17 polypeptides (Table 4), which allowed dis-crimination between these two groups in all cases.

Using MS/MS, one of the peptide fragments in the sam-ples from renal transplant recipients was identified asa fragment of the collagen a5(IV) protein (CA54 human;with the sequence: PPDQPGLPGLPGPP; rel. molecularmass: 1337.65; Figure 2A). This supports observations thatshowed that upregulation of a collagen a3(IV) protein islinked to chronic rejection and cyclosporine toxicity in re-nal transplant patients (25).

Detection of infection and acute rejection in renal

transplant patients

Renal transplant control pattern: Examination of 28samples (one sample was discarded because of poorsample quality) from patients without rejection and

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Urinary Proteome in Renal Allograft Rejection

Table 4: Discrimination between renal transplant patients and healthy non-transplanted subjects by 17urinary polypeptides

Frequency by group (%)Molecular Migration Discrimination

Protein-ID weight (Da) time (min) factor Transplant Nontransplant

11366 4309.0 31.4 0.71 73 211947 4449.5 36.4 0.69 83 1415129 5090.8 33.5 0.67 78 1110861 4139.5 29.7 0.64 75 1117245 5627.8 29.2 0.61 66 59293 3613.1 51.3 0.55 63 85459 2170.1 32.5 0.55 66 1112202 4519.7 30.2 0.55 66 112641 1337.6 38.8 0.47 74 279426 3669.2 32.5 0.54 93 394970 1981.0 33.3 −0.58 10 683963 1668.8 48.7 −0.59 15 744490 1817.8 40.4 −0.60 37 974029 1687.7 37.8 −0.63 5 685418 2154.1 41.7 −0.64 31 954376 1782.9 43.7 −0.65 8 734670 1882.8 39.6 −0.73 24 97

infection led to a characteristic polypeptide pattern, whichis shown in Figure 3A. Sixty-five polypeptides were presentin more than 90% of the examined samples. In addition,118 polypeptides were present in more than 75% and an-other 370 polypeptides in more than 50% of the samples.These 553 polypeptides were used to define the renaltransplant control pattern.

Renal transplant urinary tract infection pattern: Anal-ysis of the 10 urine samples from renal transplant patientswith urinary tract infection revealed a considerable increasein the number of polypeptides (Figure 3B). One hundredand four polypeptides were detected in more than 90% ofthe examined samples. Additional 98 polypeptides werefound in more than 75% and additional 717 polypeptideswere present in more than 50% of the samples. These919 polypeptides characterized the renal transplant urinarytract infection pattern.

Subsequently, the patterns of transplant controls werecompared with those of urinary tract infection (Table 5).Eight peptides were found to be highly prevalent in sam-ples with infection compared to controls. This combina-tion of peptides allowed correct identification of all patientswith infection. One patient in the control group was mis-classified as having infection.

Renal transplant rejection pattern: In samples from pa-tients with acute rejection, 34 polypeptides were presentin more than 90% of the cases. Additional 144 polypep-tides were found in more than 75% and 381 polypeptidesin more than 50% of the samples.

Comparison of the transplant control pattern to the rejec-tion pattern revealed five polypeptides that were highly

prevalent in samples from patients with rejection. Con-versely, 10 peptides were present in most of the controls,but only in a few samples with rejection (Table 6). Onepolypeptide was uniformly distributed in samples with re-jection and in the control samples but with significantlyhigher signal intensity in samples from rejecting patients.Using the combination of these peptide patterns, 16 of19 patients with acute rejection could be identified cor-rectly. Two of the misclassified samples belonged to pa-tients with vascular rejection and the other one with tubu-lointerstitial rejection appeared to be quite dilute (<800polypeptides/sample), which might have led to unreliableclassification. None of the control subjects were incorrectlyidentified as having rejection.

Finally, the rejection pattern was compared with the in-fection pattern, which led to the identification of 10 dis-criminating biomarkers (Table 7). Using these biomarkers,all samples with rejection and all samples with infectionwere correctly identified.

Using MS/MS, we attempted to identify the peptides thatwere upregulated in samples with rejection compared withthe controls. The resulting fragment spectrum from oneof the peptides (Protein-ID 4103; parent ion mass 1708.8)is exemplarically shown in Figure 2B. Subsequent searchwith Mascot against the Swissprot database could notidentify specific peptides (matching scores below 20), pre-sumably because of low signal intensities of the MS/MSspectra.

Blinded analysis of samples from renal transplant re-

cipients: Blind analysis of the 10 samples without rejec-tion or urinary tract infection correctly identified 8 samplesas controls (samples C3–C10) (Table 8). Chart review of the

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Figure 2: (A) Fragment spectrum of a peptide characteristic for transplant patients as obtained by MALDI-MS/MS. Comparisonwith the Swissprot database allowed the identification of a collagen a5(IV) protein fragment (CA54˙human), with a relative molecularmass of 1338.71 Da (MH+) and the sequence: PPDQPGLPGLPGPP. (B) Fragment spectrum by MALDI-MS/MS of the polypetide with theprotein-ID 4103 which was up-regulated in samples with rejection. Comparison with the Swissprot database did not allow the identificationof this potential biomarker for acute renal rejection.

two misidentified patients did not reveal infection or rejec-tion before or after the time of sampling. Also, blinded re-evaluation of the respective biopsies did not change the ini-tial assessment that rejection was absent in these patients.

Among the 7 samples with urinary tract infection, 5 sam-ples were identified as not having the control transplantpattern; however, only 3 samples (U3-U5) could definitivelybe classified as infection. Blinded biopsy re-evaluation

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Urinary Proteome in Renal Allograft Rejection

Figure 3: Contour plots

of urinary patterns from

renal allograft patients

without rejection and in-

fection (A), with urinary

tract infection (B) and

with acute rejection (C).

The migration time (min) isplotted on the x-axis and therelative protein/polypeptidemass (kDa) is plotted on they-axis.

Table 5: Discrimination between samples with urinary tract infection and samples without in-fection and rejection (control) in renal allograft recipients using eight polypeptides

Frequency by group (%)Molecular Migration Discrimination

Protein-ID weight (Da) time (min) factor Infection Control

13422 4779.9 36.8 0.62 70 814094 4899.7 38.8 0.58 70 122170 1251.6 42.5 0.56 80 244995 1989.0 41.9 0.56 60 414175 4911.9 39.3 0.56 60 414820 5033.3 37.8 0.56 60 46568 2589.3 35.4 0.54 90 3614233 4924.3 42.3 0.52 60 8

confirmed the absence of rejection in the two patients thatwere misclassified as having rejection. Seven of 9 sampleswith acute rejection showed urine patterns that were dif-ferent from the control pattern, but only 6 samples werespecifically identified as rejection. Note that blinded re-evaluation concluded “borderline rejection” in the biopsiesbelonging to the two patients (R8, R9) that were incorrectlyidentified as control or infection.

Discussion

This study demonstrates that acute tubulointerstitial re-jection of renal allografts causes significant changes inthe urine proteome. These changes occur even in caseswith subclinical and histologically mild rejection. Specificpolypeptide patterns were identified for patients withtubulointerstitial rejection, infection and control transplant

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Wittke et al.

Table 6: Discrimination between samples with acute renal allograft rejection and samples with-out (control) in renal allograft recipients by 16 polypeptides

Frequency by group (%)Molecular Migration Discrimination

Protein-ID weight (Da) time (min) factor Rejection Control

5227 2078.9 36.2 0.47 59 121804 1168.6 40.8 0.43 71 285342 2121.1 38.0 0.43 59 168614 3359.6 48.1 0.41 65 244103 1707.8 37.7 0.39 47 84481 1813.8 44.1 0.02 82 807150 2815.9 36.0 −0.4 12 529023 3516.1 30.0 −0.41 47 884062 1697.9 34.7 −0.43 41 847203 2838.9 37.8 −0.43 41 848935 3482.1 31.4 −0.43 41 8418848 8052.3 27.8 −0.43 29 721079 1027.6 35.8 −0.44 24 681102 1031.6 37.5 −0.44 12 568223 3240.2 34.0 −0.44 24 684475 1811.0 42.3 −0.55 29 84

Table 7: Discrimination between samples with acute renal allograft rejection and samples withurinary tract infection in renal allograft recipients using 10 polypeptides

Frequency by group (%)Molecular Migration Discrimination

Protein-ID weight (Da) time (min) factor Rejection Infection

2024 1221.6 42.8 0.62 82 206455 2559.1 32.7 0.62 82 201586 1128.5 43 0.51 71 201972 1207.6 42 −0.51 29 805873 2327.2 36.9 −0.51 29 8015005 5065 36.6 −0.52 18 708946 3485.1 37.5 −0.54 6 6019658 16356.9 32.9 −0.54 6 6013666 4825 37.4 −0.58 12 706647 2623.2 34.9 −0.6 0 60

recipients without these conditions. In addition, urinarypolypeptide patterns of transplanted patients are differentfrom healthy subjects without transplant.

Recently, urine proteome analysis with SELDI-MS wasevaluated in renal transplant recipients, and findings in re-gards to polypeptide patterns in subjects with and withoutrenal transplant were variable. Schaub et al. (14) reportedno differences in the urinary patterns in transplanted andnon-transplanted individuals. O’Riordan et al. (16), how-ever, described a 78.5 kDa protein, which allowed to dif-ferentiate the two groups with 100% sensitivity and speci-ficity. Similarly, our group identified a 1.34 kDa fragmentwith sequence homology to human collagen a5(IV) in re-nal transplant patients.

Compared to the present study, previous reports usingSELDI-MS found lower numbers of polypeptides distin-guishing patients with rejection from those without rejec-tion (14,16). In addition, the polypeptides found to be spe-

cific for rejection were different among these studies. Inpart this could be related to differences in patient popula-tions. In addition, the detection of polypeptides by SELDI-MS is highly dependent on binding properties of the matrixbeing used (26,27). Such limitations do not exist for CE-MS,which could be advantageous when a more complete pat-tern of polypeptides is sought to identify novel mediatorsof rejection. In addition, the larger number of spectra maybe helpful to identify different disease processes that aresimultaneously present (e.g. acute tubular necrosis, cal-cineurin inhibitor toxicity).

In contrast to the work reported by Schaub et al. (14), wehave decided to include specimens of patients that hadconcomitant pathological findings on renal biopsies, suchas tubulointerstitial fibrosis, isometrical vacuolization andcalcification. These findings are highly prevalent in allograftbiopsies (28). Our goal was to define specific polypeptidepatterns for rejection and infection even when co-existingand potentially confounding conditions were present. Our

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Urinary Proteome in Renal Allograft Rejection

Table 8: Validation experiments with blinded analysis of urine samples from patients with rejection,urinary tract infection, and without these conditions

Sample aGrade initial aGrade Classification byID diagnosis re-evaluation proteome analysis

Control (n = 10) C1 No AR No AR RejectionC2 No AR No AR InfectionC3 No AR Borderline rejection ControlC4-C10 No AR No AR Control

Urinary tract infection (n = 7) U1 No AR No AR RejectionU2 No AR No AR RejectionU3–U5 No AR No AR InfectionU6 No AR No AR ControlU7 No AR No AR Control

Rejection (n = 9) R1 Ia Ia RejectionR2 Ib Ia RejectionR3 Ib Ia RejectionR4 Ia Ib RejectionR5 Ia Ia RejectionR6 Ib Ia ControlR7 Ia Ia RejectionR8 Ia Borderline rejection InfectionR9 Ia Borderline rejection Control

In addition, the corresponding biopsies were re-evaluated blindly by the same pathologist who hadinitially examined the biopsies (no AR: no acute rejection).

results were apparently not affected by other factors likediabetes mellitus, smoking, concurrent or recent CMVviremia, hematuria or different immunosuppressive regi-mens and other drug therapies. Allograft function and urineprotein concentration varied widely in all three groups andalso did not interfere with test results.

Two patients in the group with acute rejection had re-nal biopsies consistent with vascular rejection. Thesepatients could not be identified correctly using the polypep-tide pattern that was present in patients with tubulointer-stitial rejection. This may indicate that this particular typeof rejection is a separate entity not only pathomorpholog-ically but also with regards to the urinary proteome. Fur-ther studies in larger numbers of patients with vascularrejection are needed to confirm this theory and to iden-tify a urinary polypeptide pattern characteristic of vascularrejection.

Blinded analysis of urinary samples by CE-MS yielded ac-curate results in 8 out of 10 patients without acute re-jection and infection. Differentiation between urinary tractinfection and either acute rejection or normal was diffi-cult. Nonetheless, correct identification of acute rejectionsucceeded in 6 out of 9 patients, and abnormal polypep-tide patterns were found in 7 out of 9 patients. In twocases, blinded re-evaluation of renal biopsies from mis-classified patients identified only borderline rejection. Theproteome pattern in borderline rejection may be different,which would explain why urinary samples in these patientswere misclassified. Alternatively, it is possible that the pro-cess of rejection has to reach a certain extent to result inthe characteristic proteome pattern.

Larger numbers of samples will have to be analyzed to de-termine sensitivity and specificity of the CE-MS methodfor the diagnosis of acute rejection. The technique needsto be optimized to improve diagnostic accuracy in differ-entiating acute rejection from infection. Technical issues insample preparation may be also important as some sam-ples were found to be inadequate for reliable analysis be-cause of low numbers of detected polypeptides. We wereunable to determine whether this was related to decreasedexcretion of polypeptides or sample dilution from polyuria.Possible ways to avoid such difficulties in the future wouldbe simply increasing the volume of the analyte or moreaccurately, calculating the required sample volume accord-ing to the creatinine concentration in the sample as donein the study by Clarke et al. (15). This may be of particularvalue when quantitative rather than qualitative measures(such as in the present study) are used to analyze urine pro-teomes. Quantitative analysis of polypeptide peaks withmass spectroscopy could potentially enhance the accuracyof the CE-MS method in differentiating rejection, infectionand normal samples.

Several issues will have to be addressed in further studies.First, it will have to be determined by serial urine analyseswhether changes in urinary proteome in rejection occur be-fore tissue damage can be detected microscopically. Sec-ond, it would be interesting to investigate whether singlepolypeptides can be identified to accurately detect rejec-tion, as this may eventually allow the design of a simplifieddiagnostic tool, e.g. ELISA.

In summary, we have established urinary polypeptidepatterns in transplant patients that allow to differentiate

American Journal of Transplantation 2005; 5: 2479–2488 2487

Wittke et al.

between patients with acute tubulointerstitial rejection, uri-nary tract infection and control patients without rejection orinfection. This method provides an interesting non-invasivetool to monitor patients after renal transplantation and mayaid in identifying patients with possible rejection who needfurther diagnostic workup with allograft biopsy. Addition-ally, this method may help to identify novel mediators ofrejection and allograft injury.

Acknowledgment

We thankfully acknowledge Professor Just, Department of Toxicology, Med-ical School Hannover, for providing the MS/MS facilities and his expert opin-ion for our sequencing experiments.

References

1. Rush DN, Nickerson P, Jeffery JR, McKenna RM, Grimm PC,Gough J. Protocol biopsies in renal transplantation: Research toolor clinically useful? Curr Opin Nephrol Hypertens 1998; 6: 691–694.

2. Dugre FJ, Gaudreau S, Belles-Isles M, Houde I, Roy R. Cytokineand cytotoxic molecule gene expression determined peripheralblood mononuclear cells in diagnosis of acute rejection. Trans-plantation 2004; 70: 1074–1080.

3. Kutukculer N, Shenton BK, Clark K et al. Renal allograft rejection:The temporal relationship and predictive value of plasma TNF (al-pha and beta), IFN-gamma and soluble ICAM-1. Transpl Int 1995;8: 45–50.

4. Lebranchu Y, Kapahi P, al Najjar A et al. Soluble e-selectin, ICAM-1, and VCAM-1 levels in renal allograft recipients. Transplant Proc1994; 26: 1873–1874.

5. Vasconcellos LM, Schachter AD, Zheng XX et al. Cytotoxic lym-phocyte gene expression in peripheral blood leukocytes corre-lates with rejecting renal allografts. Transplantation 1198; 66:562–566.

6. Gupta RK, Jain M, Sharma RK. Serum & urinary interleukin-2 lev-els as predictors in acute renal allograft rejection. Indian J MedRes 2004; 119: 24–27.

7. Roelofs JJ, Rowshani AT, van den Berg JG et al. Expression ofurokinase plasminogen activator and its receptor during acute re-nal allograft rejection. Kidney Int 2003; 64: 1843–1853.

8. Lederer SR, Friedrich N, Regenbogen C, Getto R, Toepfer M,Sitter T. Non-invasive monitoring of renal transplant recipients:Urinary excretion of soluble adhesion molecules and of thecomplement-split product C4d. Nephron Clin Pract 2003; 94: 19–26.

9. Li B, Hartono C, Ding R et al. Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for per-forin and granzyme B in urine. N Engl J Med 2001; 344: 947–954.

10. Kotsch K, Mashreghi MF, Bold G et al. Enhanced granulysinmRNA expression in urinary sediment in early and delayed acuterenal allograft rejection. Transplantation 2004; 77: 1866–1875.

11. Ding J, Vouros P. Capillary electrochromatography and capillary

electrochromatography–mass spectrometry for the analysis ofDNA adduct mixtures. Anal Chem 1997; 69: 379–384.

12. Pefaur J, Trivino R, Navarrete C et al. Clinical graft evolution oflymphocytes, polymorphonuclear cells, and antigen expressionin tubular renal cells in the urine sediment of 20 renal allograftrecipients. Transplant Proc 2003; 35: 2500–2505.

13. Kyo M, Toki K, Nishimura K et al. Differential diagnosis of kid-ney transplant rejection and cyclosporin/tacrolimus nephropathyusing urine cytology. Clin Transplant 2002; 16: 40–44.

14. Schaub S, Rush D, Wilkins J et al. Proteomic-based detection ofurine proteins associated with acute renal allograft rejection. JAm Soc Nephrol 2004; 15: 219–227.

15. Clarke W, Silverman BC, Zhang Z, Chan DW, Klein AS, MolmentiEP. Characterization of renal allograft rejection by urinary pro-teomic analysis. Ann Surg 2003; 237: 660–664.

16. O’Riordan E, Orlova TN, Mei JJ et al. Bioinformatic analysis ofthe urine proteome of acute allograft rejection. J Am Soc Nephrol2004; 15: 3240–3248.

17. Neuhoff N, Kaiser T, Wittke S et al. Mass spectrometry for thedetection of differentially expressed proteins: A comparison ofsurface-enhanced laser desorption/ionization and capillary elec-trophoresis/mass spectrometry. Rapid Commun Mass Spectrom2004; 18: 149–156.

18. Weissinger EM, Wittke S, Kaiser T et al. Proteomic patterns es-tablished with capillary electrophoresis and mass spectrometryfor diagnostic purposes. Kidney Int 2004; 65: 2426–2434.

19. Kaiser T, Kamal H, Rank A et al. Proteomics applied to the clini-cal follow-up of patients after allogeneic hematopoietic stem celltransplantation. Blood 2004; 104: 340–349.

20. Kolch W, Mischak H, Chalmers MJ, Pitt A, Marshall AG. Clini-cal proteomics: A question of technology. Rapid Commun MassSpectrom 2004; 18: 2365.

21. Kolch W, Neususs C, Pelzing M, Mischak H. Capillaryelectrophoresis—mass spectrometry as a powerful tool in clinicaldiagnosis and biomarker discovery. Mass Spectrom Rev 2005; inpress. DOI 10.1002/mas.2005/

22. Hernandez-Borges J, Neususs C, Cifuentes A, Pelzing M. On-linecapillary electrophoresis–mass spectrometry for the analysis ofbiomolecules. Electrophoresis 2004; 25: 2257–2281.

23. Racusen LC, Halloran PF, Solez K. Banff 2003 meeting report:New diagnostic insights and standards. Am J Transplant 2004; 4:1562–1566.

24. Wittke S, Fliser D, Haubitz M et al. Determination of peptidesand proteins in human urine with capillary electrophoresis–massspectrometry, a suitable tool for the establishment of new diag-nostic markers. J Chromatogr A 2003; 1013: 173–181.

25. Koop K, Bakker RC, Eikmans M et al. Differentiation betweenchronic rejection and chronic cyclosporine toxicity by analysis ofrenal cortical mRNA. Kidney Int 2004; 66: 2036–2048.

26. Issaq HJ, Veenstra TD, Conrads TP, Felschow D. The SELDI-TOFMS approach to proteomics: Protein profiling and biomarker iden-tification. Biochem Biophys Res Commun 2002; 292: 587–592.

27. Tang N, Tornatore P, Weinberger SR. Current developments inSELDI affinity technology. Mass Spectrom Rev 2004; 23: 34–44.

28. Schwarz A, Mengel M, Gwinner W et al. Protocol biopsy programafter renal transplantation:structure and first results. TransplantProc 2004; 34: 2238–2239.

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