Evaluation of Biomarker Discovery Approaches to Detect Protein

Evaluation of Biomarker Discovery Approaches to Detect Protein Biomarkers of Acute Renal Allograft Rejection† Hans Voshol,*,‡ Nathalie Brendlen,‡ Dieter Mu1 ller,‡ Bruno Inverardi,‡ Ange´ lique Augustin,‡ Charles Pally,§ Grazyna Wieczorek,§ Randall E. Morris,§ Friedrich Raulf,§ and Jan van Oostrum‡ Novartis Institutes for BioMedical Research, Genome and Proteome Sciences and Transplantation & Immunology Therapeutic Area, Basel, Switzerland Received March 14, 2005

Management of host responses to allografts by immunosuppressive therapy is the cornerstone of transplantation medicine, but it is still deficient in one important element: biomarkers that are readily accessible and predict the fate of the transplant early, specifically, and reliably. Using a Brown Norway (BN)-to-Lewis rat renal allograft model of kidney transplantation, this study aims at evaluating two proteomic approaches to discover biomarkers for acute rejection: SELDI-MS technology and 2D gel electrophoresis combined with mass spectrometry. Several novel potential serum biomarkers have been identified for follow up. Overall, the conclusion is that apparently at the serum protein level, dramatic changes only occur at a stage where kidney function is already severely affected. Multivariate analysis of serum profiles suggests that there is an ensemble of subtle changes, comprising a proteomic signature of acute rejection at an early stage, a more detailed evaluation of which might provide novel opportunities for the diagnosis of acute rejection. Profiling of the excreted proteins indicates that urine might even present the earliest signs of the rejection process. Keywords: proteomics • acute rejection • biomarkers • transplantation • animal model • 2D-electrophoresis • SELDIMS.

Introduction Suppression of allograft rejection is still the main focus of modern transplantation medicine. While cyclosporine has been the cornerstone of immunosuppressive therapy for the last twenty years, novel compounds and modes of action are starting to appear. However, a key element in successful management of host responses to allograft is still missing: biomarkers that are readily accessible and predict the fate of the transplant early, specifically, and reliably.1 Currently, serum creatinine (sCrea) is the gold standard biomarker for acute rejection (AR), but the rise of sCrea is a late event, occurring when kidney graft function is already severely and irreversibly impaired. Furthermore, sCrea lacks specificity, often requiring a subsequent biopsy for unequivocal confirmation. In short, novel noninvasive acute rejection biomarkers would contribute to the quality of life of transplantation patients in many aspects, e.g., by reducing the need for biopsies, by optimization of their drug regimen, by early detection of AR episodes, and finally by improving long-term graft survival, which is closely correlated with the number and severity of AR and subclinical AR episodes.2 †

Part of the Biomarkers special issue. * To whom correspondence should be addressed. Novartis Institutes for BioMedical Research, WSJ-88.8.05, CH-4002 Basel, Switzerland. E-mail: [email protected]. ‡ Novartis Institutes for BioMedical Research, Genome and Proteome Sciences. § Transplantation & Immunology Therapeutic Area.

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There are limited data available on expression profiling of kidney rejection, either at the protein or at the mRNA level. Moreover, most if not all data have been obtained on clinical samples, with the inherent problems of heterogeneity, but also the advantages of potential for rapid translation into a clinically valuable diagnostic. In the most extensive study using commercially available DNA microarrays, the investigators report the discovery of rejection ‘signatures’ both in transplant biopsies and in the peripheral blood lymphocytes, the two types of samples that are accessible to mRNA profiling.3 An earlier report describes a similar discrimination of different pathologies using gene expression patterns from allograft biopsies obtained with a custom DNA microarray.4 Up to this point, proteomics approaches to rejection biomarkers have focused on analyzing patient urine samples by surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry (SELDIMS).5-8 Some patterns of potential markers were reported, but without further validation and/or identification. Instead of clinical samples, the Brown Norway (BN) to Lewis rat orthotopic life-supporting kidney transplantation model, which consistently develops AR at day 7 after transplantation, was selected for profiling. This model showed a better timewindow for early marker detection than the DA-to-Lewis model (AR at day 5) and histology best resembled the clinical situation (Roth et al., submitted). The chosen model is based on the MHC class I differences between BN (RT1n) and Lewis (RT1l) rat strains leading to acute cellular rejection by infiltrating T lymphocytes and macrophages.9 Immunohistochemical analy10.1021/pr050060+ CCC: $30.25

 2005 American Chemical Society

Protein Biomarkers for Acute Renal Allograft Rejection

sis of our model demonstrated similar levels of infiltrating cells as described,9 while B cells were nearly absent from the graft (Roth et al., submitted). The obvious advantage of the animal model is the large degree of control over experimental conditions and sampling, leading to a smaller heterogeneity than would ever be possible in clinical samples. Besides taking a step toward the discovery of predictive, clinically useful biomarkers of acute rejection, this investigation also aimed at further characterization of the animal model, which is used in development of novel immunosuppressive drugs. While in this study the focus is on protein biomarkers, in parallel experiments with the same samples, metabonomic and transcriptomic profiling have been carried out to obtain a comprehensive picture of the acute rejection phenomenon (manuscripts in preparation). Two different approaches to proteomic biomarker discovery in biofluids were evaluated: 2D gel electrophoresis combined with mass spectrometry and SELDI/ProteinChip technology.

Materials and Methods Transplantation Model and Sampling. The study was performed in male Brown-Norway (BN) (RT1n haplotype) and Lewis (RT1l) rats obtained from Harlan, Zeist, Netherlands, in accordance with the Swiss federal law for animal protection and approved by the Veterinary Office Basel (BS No. 1085 and 1152). BN-to-Lewis life-supporting orthotopic kidney allotransplantations were carried out with Lewis-to-Lewis syngeneic controls. The groups will be referred to as allo and syn groups, respectively. Warm ischemia times were kept to a minimum of 20.8 ( 0.4 min (n ) 72 transplants). Nephrectomy of the contralateral right host kidney was performed directly after transplantation. Parallel groups of 10 allo- and 10 syntransplanted rats each were sacrificed on day 3, 4, and 5 posttransplantation, at which points blood chemistry as well as histology and immunohistochemistry of grafts were performed. Details of these analyses will be described elsewhere (Roth et al., submitted). The study included two additional non lifesupporting control groups (syntransplanted) of six animals each, with unremoved contralateral kidneys, which were not subjected to in-depth proteomic analysis. Preparation of Serum and Urine Samples. Serum was prepared by collecting blood directly into the clotting activatortubes. (Sarstedt; REF: 41.1500.005). After a clotting time of 30 min at RT, samples were centrifuged for 10 min at 4000 rpm and the serum supernatant collected. Blood chemistry, including serum creatinine, blood urea nitrogen and total protein, was analyzed on a Beckman Synchron CX5 chemistry analyzer (Beckman Instruments, Palo Alto, CA). Urine was collected in metabolic cages with immediate cooling in refrigerated collection racks (B940, Techniplast, Italy) over 24 h periods, centrifuged at 10 000 × g and 4 °C for 30 min, and the cleared supernatant was subsequently stored at -80 °C. In case of pooled samples, equal amounts of urine or serum of each animal in a group were combined to yield the group sample. SELDI/Protein Chip Profiling. ProteinChip profiling was performed on CM10 and Q10 ProteinChip arrays (Ciphergen Biosystems, Fremont CA), following the procedures recommended by the manufacturer. All samples were run in quadruplicate on four different arrays. Arrays were processed in an automated fashion on a dedicated Biomek 2000 system as provided by Ciphergen. Briefly, 10 µL of serum was diluted with 90 µL of U9 buffer (Ciphergen Expression Difference Mapping KitsSerum Fractionation), incubated for 10 min at RT and then further diluted 1/10 with binding buffer, and applied to the

research articles ProteinChip arrays. Binding buffers were 0.1 M sodium acetate pH 4 for CM10 and 0.1 M Tris-HCl pH 8 for Q10. The arrays were pre-equilibrated in binding buffer, incubated with sample for 30 min at RT, washed 3× with equilibration buffer and 2 times with HPLC quality water. Finally 2 × 1 µL matrix solution (sinapinic acid at 50% saturation in 50% acetonitrile, 0.1% TFA) was applied and allowed to dry. Arrays were read in the PBS IIc ProteinChip Reader (Ciphergen) with around 150 shots at optimized laser setting, generally in the intensity range of 150160 arbitrary units. The deflector was set at 1000 Da. For data analysis, spectra were exported into the Ciphergen Express Data Manager v 2.1 software. Spectra were normalized on total ion current and outlier spectra, with normalization coefficients outside the 95% confidence interval, removed. Subsequently, peaks were detected using a 3 times signal-to-noise ratio as cutoff and matched across the spectra. Generally, normalization and spot detection were performed within a 1.5-30 kD molecular weight range. Because of the relatively large differences between the different timepoints, the primary analysis was performed pairwise, comparing allo and syn groups of the same sampling timepoint using the Mann-Whitney test with Yates’ correction as provided in Ciphergen Express. Principal components analysis using a correlation matrix was carried out with the tools provided in the software. ProteinChip profiling of pooled urine samples was performed on CM10 ProteinChip arrays. Protein concentrations in the urine were determined with a Bradford assay (Bio-Rad). The urine samples were then diluted with CM10 binding/equilibration buffer (0.1M sodium acetate pH 4) to a protein concentration of 50µg/mL and profiled on the ProteinChip arrays as described above. For urine a MW range of 2-40 kD was used. 2-D Electrophoresis. Two-dimensional electrophoresis of pooled serum samples was carried out according to established procedures.10 For the first dimension, 10 µL of the pooled serum sample was diluted with 390 µL 7 M urea, 2M thiourea, 4% CHAPS, 1% DTT, and 2% Pharmalytes 3-10 (Amersham Biosciences) and loaded onto 18 cm pH 4-7 linear IPG strips (Amersham Biosciences) by reswelling the strips in sample solution.11 Iso-electric focusing was performed on the Multiphor II apparatus (Amersham Biosciences) for approximately 60 kVh at 20 °C, using the following voltage gradient: (i) 3 h 300 V, (ii) 5 h linear gradient from 300 to 3500 V and (iii) continue at 3500 V until target kVh. After focusing, IPG strips were equilibrated as described,10 with 2% DTT in the first step and 5% iodoacetamide in the second step. For the second dimension, IPG strips were applied to 20 × 25 cm SDS-PAGE gels (12% T, 2.6% C), which were run overnight at 20 mA/gel and 15 °C in an IsoDalt electrophoresis chamber (Amersham Biosciences). Gels were stained with Sypro Ruby10 or with colloidal Coomassie Blue G-250.12 For 2D gel electrophoresis of urine samples, 400 µL of each urine pool was concentrated to a final volume of about 200 µL in a centrifugal concentrator (Biomax, Millipore, 5 K cutoff). The concentrated sample was precipitated for 2D gel electrophoresis using the 2D Clean-Up Kit (Amersham Biosciences) according to the manufacturers’ instructions. For the first dimension, the obtained pellet was dissolved in 800 µL of 7 M urea, 2 M thiourea, 4% CHAPS, 1% DTT, and 2% Pharmalytes 3-10 (Amersham Biosciences) of which 400 µL were loaded per 18 cm pH 4-7 linear IPG strip followed by 2D gel electrophoresis as described above. Image Analysis. The primary analysis of the 2D gel data was performed on the day 3 and day 5 allo and syn samples, five Journal of Proteome Research • Vol. 4, No. 4, 2005 1193

research articles replicates of which were distributed across a batch of 20 gels for simultaneous processing. The day 4 samples were run in the same fashion, but for practical reasons using a different batch of gels. Sypro Ruby stained 2D gels were digitized as img files with the FLA-3000 fluorescence imager (Fuji, provided by Raytest, D-75339 Straubenhardt, Germany) using 473 nm excitation and a 520 nm high-pass emission filter. Where necessary, img files were converted to 16-bit TIFF files using the ‘Img2tiff’ program provided with the scanner (Raytest). Image analysis was carried out using the Progenesis Discovery package (Non-Linear Dynamics, Newcastle, UK). Spot intensity and matching data were exported to the Expressionist Pro package (GeneData, Basel, Switzerland) for quality control and statistical analysis, comparing the corresponding syn and allo groups using Student’s t-test. Significantly different spots (p < 0.05 and at least 2-fold difference) were subsequently excised with the GelPix (Genetix, New Milton, Hampshire UK) excision robot and in-gel digested. Since the digitized images sometimes contained artifacts, due to incomplete resolution of spots or background staining, differences were always checked and corrected, where necessary and feasible, by visual analysis of the original gels. In-Gel Trypsin Digestion. Excised spots were in-gel digested with modified porcine trypsin (Promega, Madison WI) as described,13 using a microtiter plate format (CB080, Proxeon, Odense, DK). Spots were finally eluted with 5% formic acid and tryptic hydrolysates collected in a second microtiter plate. For MALDI-MS and -MS/MS analysis, the tryptic peptides were purified on ZipTips (Millipore Corporation, Bedford, MA) using a Tecan Genesis ProTeam 150 system (Tecan, Maennedorf, Switzerland). After washing with 2 × 5 µL 80% ACN, 0.1% TFA the tips were equilibrated with 2 × 5 µL 0.1% TFA and the hydrolysate was applied; after washing with 4 × 5 µL 0.1% TFA, peptides were directly eluted onto ABI 4700 MALDI targets (100 well plates) with 2 µL of a solution of R-cyano-4-hydroxycinnamic acid matrix (5 mg/mL in 50% ACN, 0.1% TFA containing 2mM NH4H2PO4) applied to the backend of the ZipTips. MALDI-MS and -MS/MS. MALDI spots were analyzed using the Applied Biosystems 4700 Proteomics Analyzer (ABI, Framingham, MA) in automated, combined MS and MS/MS mode. Both MS and MS/MS data were acquired with a Nd: YAG laser with 200 Hz repetition rate; 2000 shots were accumulated for each spectrum in MS mode and 4000 shots for each of up to 5 precursor ions in MS/MS mode. For MS/MS, the 5 most intense precursor ions with a signal/noise ratio > 25 were selected after exclusion of common background signals. MS/MS mode was operated with 1 keV and products of metastable decomposition at elevated laser power were detected. MS data were acquired with close external calibration and MS/MS data using default instrument calibration. Database searches were performed using the Mascot search engine integrated in GPS Explorer 2.0 (part of the ABI 4700 Proteomics Analyzer). The UniProt/Swiss-Prot database was searched without taxonomy split in MS, MS/MS and combined mode with the option of methionine oxidation as variable posttranslational modification (Cys carbamidomethylated). Candidate hits were detected by different score restraints; final protein identification was based on manual verification of fragment assignment and manual check for correct fragment ion intensity distributions in relevant MS/MS spectra. 1194

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Figure 1. Bar graph depicts average serum creatinine levels (µmol/L) in the allotransplanted (filled bars) and syngraft control (open bars) groups. Error bars indicate standard deviation. n ) 10 for all groups except the day 3 control group where one outlier was removed.

Figure 2. Two-dimensional gels of pooled urine of the day 5 syngraft control (left) and allotransplanted group (right) showing upregulation of many serum proteins in the allo group

Results Classical Biomarkers of Rejection Correlate with Proteomic Patterns. Before starting the protein profiling, the rat model was first validated using a number of the parameters that are also used to diagnose acute rejection in the clinic. At day 5 serum creatinine levels were significantly elevated in allotransplanted animals (Figure 1) compared to controls. Histological analysis of graft tissue (data not shown) showed minute signs of ischemic damage in the syngeneic grafts at day 3 after transplantation but a return to normal morphology at days 4 and 5. In contrast, the histology of the allografts confirmed specific gradual infiltration of mononuclear cells into the allografts over the observation period. While the vast rise in serum creatinine is a clear indication that the transplanted kidney has lost most of its filtering functionality, the full extent of kidney dysfunction becomes manifest at the level of the urine proteome. The 2D gel pattern of a urine pool of the allograft group (Figure 2) shows drastic upregulation of many serum proteins, which makes the pattern resemble that of serum (see Figure 6) more than that of the control urine. This confirms what was already discussed above: a clinically useful marker has to be detectable before the creatinine levels start to rise significantly, in other words preferably earlier than day 4 in our rat model. SELDI Profiling on ProteinChips. The difficulty with a SELDI-based differential analysis is that inherently this type of mass spectrometric data is prone to quantitative variability, mainly due to the fact that ionization and thus detection of ions varies, depending on the composition of the sample (ion suppression). Therefore, a first quality control step was to

Protein Biomarkers for Acute Renal Allograft Rejection

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Figure 3. Q10 ProteinChip profiling of serum, 5 days after transplantation. Depicted are normalized average peak intensities (with standard deviations) of the 10 most significant differences for the allotransplanted (filled bars) and syntransplanted (open bars) groups.

Figure 4. (A) Principal component analysis of Q10 ProteinChip serum profiles using a correlation matrix. Depicted are the first two principal components, comparing the allotransplanted (red) with the syngraft control (blue) group. (B) Peaks showing consistent significant differences increasing across the three timepoints. Depicted are the p-values comparing the allo and syn samples of day 3 (blue), day 4 (red) and day 5 (yellow) by Mann-Whitney test as described.

determine the variability in normalized peak intensity between replicates and within groups. Also at this stage, outlier spectra with aberrant numbers of peaks or normalization coefficients were removed from the analysis. Typically, the experimental variability, measured as coefficient of variability (CV) among four replicates was around 20%, calculated over some 70 peaks present in all the replicate spectra. The appearance at day 5 of the classical biomarkers for acute rejection is also reflected in the SELDI profile of serum on the Q10 chip (Figure 3), where

many upregulated proteins are immediately apparent. Statistical comparison of the day 5 serum samples yielded more than 20 peaks with a p-value < 0.005, five of which have an ROC (receiver-operator characteristic) value above 0.95, thus separating the groups with minimal or no overlap. The number of differences and especially their magnitude are clearly reduced at earlier time points, to the extent that in the day 3 samples none of the 9 remaining statistically significant differences (p < 0.005, Mann-Whitney test) exceeds the 1.3-fold level, with Journal of Proteome Research • Vol. 4, No. 4, 2005 1195

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In urine, like in serum, SELDI profiling shows a large number of differences at day 5 (not shown) and a gradual decrease of the changes and their magnitude 3 and 4 days after transplantation. Again, most differentials are upregulated in the allotransplanted sample. In contrast to serum however, even at 3 days some clear differences are visible (Figure 5), in particular a peak around 5.01 kDa which continues to show a >10-fold difference at day 5. This peak could be partially purified by adsorption onto a spin column with the CM HyperD matrix, which has comparable functional groups as the CM10 arrays, but the enrichment was not sufficient to allow for identification using direct MS/MS.14 Figure 5. CM10 ProteinChip profiling of pooled urine samples, 3 days after transplantation. Bars depict normalized average peak intensities of the allotransplanted (filled bars) and syntransplanted (open bars) groups for the significant differences between the groups (p < 0.05 and at least 2-fold difference). Error bars indicate standard deviation.

the best ROC value at 0.77 (not shown). Consequently, no individual peak can provide an acceptable separation of the allo and syn groups at that stage. An alternative to individual biomarkers would be to find a diagnostic pattern of a number of differentials, which in combination provide a robust group separation. A multivariate analysis of all peaks, e.g., using principal component analysis suggests that at the early timepoints the full data set is too noisy to obtain such a signature. From a complete separation between the groups at day 5, the groups merge to a large degree at day 3 (Figure 4a). On the other hand, there are several peaks, although not the most outstanding ones, which are significantly different throughout the whole sampling period, their p-values correlating with the increased severity of the rejection process (Figure 4b).

2D Gel Analysis. In qualitative terms the results of the 2D gel analysis of pooled serum samples correlate very well with the findings in the ProteinChip study. The same pattern of changes is evident: a large number of upregulated spots at day 5 after transplantation and fewer smaller changes at earlier timepoints. An overview of the differentially expressed spots comparing the day 5 samples is provided in Figure 6 and a list of the obtained identities in Table 1. The majority of the observed differences appears to reflect acute phase and/or inflammatory responses.14 Striking is the large number of different serum albumin fragments, consistent with the notion that levels of the intact albumin are reduced during acute phase.15 Three proteins, paraoxonase-1 (PON-1), rat urinary protein 1 (RUP-1) and vitamin D-binding protein do not relate directly to acute phase events, suggesting that these might be a more specific reflection of ongoing kidney dysfunction and hence potential biomarkers for the rejection process. Activity changes in PON-1, an esterase associated with lipoproteins in blood, have been observed previously in kidney transplanted patients,16,17 while vitamin D binding protein exerts its role in

Figure 6. Two-dimensional gel of a pooled serum sample of the allotransplanted day 5 group. Identified, differentially expressed spots are marked with the spotlabel (cf. Table 1) on a white background. Two additional control spots are marked in plain text: A ) albumin fragment, CRPdC-reactive protein (Swiss-Prot P48199). 1196

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Protein Biomarkers for Acute Renal Allograft Rejection

Table 1. Differentially Expressed Spots on 2D Gels of Serum of Day 5 Kidney Allotransplanted Rats Compared to the Syngeneic Controls (cf. Figure 6)a protein name

Immunoglobulin kappa, mu, J chains and fragments Serum Albumin (only fragments)

no. of spots

accession no.

sequence calcd. coverage (%) MW

MS/MS

obs. MW

spot labels

10

Acute phase proteins and antibodies NA NA

NA

NA

NA

18

P02770

15-66

66*

14-60 A

35, 47 16 8 18

34 84* 62 35

38 80 32 35

1532, 1540 786 1675 1577

54 28

36* 47

48 35

1401 1576

61

33*

60

1109

1439, 1455, 1465, 1609, 1960 1423, 1611, 1645, 1686 1251,1325,1848 1413 1104, 2118

Ig or J (J-chains)

MHC class Ib alpha chain Complement C4 alpha chain Complement component 9, fragment 35 kDa Inter-alpha-trypsin inhibitor heavy chain H4 Haptoglobin Contrapsin-like protease inhibitor 6

2 1 1 1

O62937 P08649 Q62930 NM_019369 (R)

1 1

P06866 P09006

Beta-2-glycoprotein I precursor (Apo-H) Alpha-1-antiproteinase (Alpha-1-antitrypsin)

1

P26644

2

P17475

44

44*

43

1464, 1472

1

Coagulation NW_047398 (R) 1634, 1673

9

243

40

1483

25

70

18

1947

1

P18292 1615 Proteins related to tissue damage P55159 1869

12

39

40

1493

12

39

45

PON

24 ND

50 42*

30 42

1743 1466

ND

9*

10

RUP

Coagulation factor V, N-terminal Fragment Prothrombin precursor, fragment

1

1204, 1308, 1845 and 1801 Mox 1035, 1531 (NG Hydrolysis), 1918, 2469

Serum paraoxonase upregulated isoform Serum paraoxonase downregulated isoforms Vitamin D-binding protein fragment Actin (beta- or gamma-)

3

P55159

1 1

Rat urinary protein 1

3

P04276 P60711 or P63259 Proteins of unknown function P81827 1297

1869

a All significant (p < 0.05, at least 2-fold difference) spots were upregulated, except the three down-regulated paraoxonase isoforms labeled PON. For all proteins the number of distinct differentially regulated spots is provided. Accession numbers refer to the Swiss-Prot database, unless otherwise noted (R ) RefSeq). Sequence coverage by MALDI-MS and sequence confirmed peptides by MS/MS are indicated. Calcd. MW based on unprocessed Db sequence, except (*) which indicates confirmed Db annotation of processing. Spot labels refer to Figure 6. NA, not applicable; ND, not determined.

the sequestering of vitamin D at the megalin receptors on the kidney tubules.19 Little is known about the biological role of RUP-1, other than that it contains a UPAR/Ly-6 domain, found in members of the CD59 membrane protein family.18 At the 3 and 4 days timepoints, where macroscopic damage to the kidney is not yet apparent, only a 15 kD fragment of albumin is consistently upregulated in the allotransplanted animals, while paraoxonase and vitamin D binding protein are not significantly different. At those stages, RUP-1 is not detectable on 2D gels of either the syn or the allo group. Unfortunately, antibodies to RUP-1 were not available to probe the earlier timepoints for differential regulation at a higher sensitivity. Still, already at the early stages a latent signature of the rejection process seems to be present, in view of the fact that a multivariate method, in this case heuristic clustering, provides a suggestive separation of the 3- and 5-day allo and syn groups (Figure 7), which is unlikely to be an experimental artifact because of the complete mixing of the samples across a single batch of gels.

Discussion There is increasing interest in the concept that the human plasma proteome is likely to contain most, if not all, human proteins, and that almost any disease state causes some specific protein expression changes in the blood.20 To a lesser degree this is true as well for other body fluids such as urine or cerebrospinal fluid. This has stimulated a great deal of research

Figure 7. Hierarchical clustering of two-dimensional gel datasets of the day 3 and day 5 samples. Five replicates of each sample (numbered 1-5) were distributed across a batch of 20 gels and processed simultaneously. Intensity and matching data of the 633 spots that were matched across all 20 gels were clustered using the hierarchical clustering algorithm in the Expressionist Pro package. Settings: normalized Euclidean distance, complete linkage. Gels ‘allo 3d-2’ and ‘syn 5d-3’ were already identified as outliers by visual inspection.

into proteomic profiling methods for and protein catalogues of human body fluids.20-22 While it will almost always be preferable to search for disease markers in the affected tissue Journal of Proteome Research • Vol. 4, No. 4, 2005 1197

research articles first, it is equally clear that in a clinical situation a marker in the periphery is highly valuable. From an analytical point of view, cell-free biofluids have the further advantage of being fairly homogeneous, while the analysis of tissue almost inevitably implies dealing with a mixture of tissue types to obtain enough material for a proteomic analysis. Nevertheless, the blood proteome is arguably the most complex, because it potentially absorbs proteins from every tissue in the body. Consequently, the full diagnostic potential can only be exploited using highly resolving fractionation methods, generally applying a combination of electrophoretic or chromatographic separation methods with prefractionation and/or depletion of abundant proteins. At the same time, a balance must be found between analysis depth, speed, throughput, and sample requirements. While the most extensive catalogues of plasma have been obtained using peptide-based methods (for an overview see ref 20), for this study we have focused on two protein-based profiling methods. It is wellknown that a significant part of the complexity of the plasma proteome is based on protein isoforms, differing in posttranslational modifications, or protein fragments. In this study, the serum of the day 5 allotransplanted group contained more than 15 different fragments of serum albumin alone. Using peptide-based ‘shotgun’ approaches,23 few if any of these fragments would have been detected, since their tryptic peptides are the same as those of the parent protein. At the same time, it is crucial to capture the full heterogeneity at the protein level, since protein fragments leaking from dying cells into the blood are potentially among the earliest signs of damage to the allograft. Therefore, the best possible analytical resolution at the protein level is a prerequisite to capture the full spectrum of alterations in the serum proteome. In terms of proteome coverage, the two methods, SELDI/ ProteinChip and 2D electrophoresis are not comparable but rather complementary. 2D gels provide the most comprehensive tool for profiling at the protein level, in a wide molecular weight range at the cost of somewhat elevated sample requirements. ProteinChips are a rapid and sensitive tool for the analysis of peptides and small proteins below 20 kDa, with the drawback that only a subset of around 100 species is actually monitored, when the starting material is unfractionated serum. A significant problem for both methods is the presence of abundant proteins in biofluids, which limits the number of peptides/proteins that can be analyzed. Efficient prefractionation strategies at the protein level are still lacking and the existing tool for depletion of the 7 most abundant proteins from human serum24 does not (yet) allow depletion of rodent serum or of many samples in a parallel fashion. We have tested the human multi-affinity depletion system on the six serum pools, but found that depletion was incomplete and variable. Meanwhile an affinity removal serum that depletes albumin, transferrin, and IgG from mouse serum is available from the same supplier (Agilent), which is claimed to work on rat serum as well. In this study, 5 days after transplantation, the ProteinChip data reveal a large number of changes at the protein level in serum of allotransplanted rats compared to the controls. Some of these changes are already significant at earlier stages, before a rise in serum creatinine is observed. However, at those early timepoints quantitative differences are small, not enough for an individual protein species to have practical value as a biomarker. It is still conceivable though, that a specific signature for acute rejection can be derived at that stage. 1198

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Interestingly, the ProteinChip data mirror quite accurately, what can be detected at a higher analysis depth on the 2D gels. Hence, one could envisage a two-tier approach where serum samples would first be screened on ProteinChips, to assess the variability and exclude outliers, possibly even to divide a heterogeneous population into relatively homogeneous subgroups, followed by a more in-depth analysis of individual or pooled samples of relevant subsets. Profiling of a larger sample set with more variability, such as a collection of human serum samples, could confirm whether this is an effective strategy. Another attractive aspect of the combination of the two approaches is that in a sense they unite profiling at the protein and the peptide level, because of their complementary coverage of the molecular weight spectrum. This could help to further investigate the specificity and relevance of the patterns of (serum) protein fragments that were observed not only in this study, but also characterized in depth in clinical urine samples in a recent paper by Schaub et al.25 These authors detected specific cleavage patterns of beta2-microglobulin in patient urine, associated with acute tubulointerstitial renal allograft rejection and suggest that one or more of these fragments might ultimately be a biomarker for noninvasive monitoring of renal allografts. While the SELDI-based method is ideally suited to profile the end products of these proteolytic cascades, the 2D gels could help to elucidate the initial steps at the protein level. The current study has provided novel information on the early stages of the rejection process. Arguably the most promising data were obtained from urine, where by ProteinChip analysis a number of significant differences could be detected. Identification and validation of these molecules might bring the early detection of acute rejection a step closer. However, here the inherent problem in the SELDI approach to efficiently identify proteins or peptides of interest is a major handicap for the interpretation of the data. Without the identity, there is no way of ascertaining that these changes are particular to the rejection process. On the other hand, the importance of marker specificity, a chief point of discussion in biomarker research,26 has to be put in perspective of its purpose. A marker for acute rejection will never be used for screening the general population; hence the requirements for specificity are quite different than for a breast cancer marker. Overall, the conclusion is that apparently at the serum protein level, dramatic changes only occur at the stage where kidney function is already severely affected. Nevertheless, a small number of potential serum biomarkers has been identified for follow up with more sensitive detection methods. Consistent with literature data on human studies,3-8 multivariate analysis results suggest that there is an ensemble of subtle changes that distinguishes the groups at an early stage. Turning such a proteomic signature into a tool for drug development in the rat model or into a useful clinical diagnostic is as much an opportunity as a challenge. One caveat is that here we have analyzed allograft rejection in the absence of immunosuppressive treatment. While one could hypothesize that successful management of acute rejection should result in reversal of the biomarkers of the most severe untreated rejection stages, it is equally possible that other, more meaningful markers of immunosuppression appear with drug treatment. With the current study a baseline in the rat model has been set, which should now be extended by expanding the analysis to transplanted rats treated with known and novel immunosuppressives. Moreover, extensive validation will be required, since the

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Protein Biomarkers for Acute Renal Allograft Rejection

clinical transplant patient population is expected to show a much higher variability due to the increased individualization of immunotherapy and various co-morbidities. Notwithstanding the technical and practical difficulties associated with such studies, it is vital that the quest for noninvasive early diagnostic markers of acute rejection continues, because of their potential to improve the quality of life of thousands of patients. Abbreviations: allo, experimental group of transplanted allografts; AR, acute rejection; BN, Brown-Norway; IPG, immobilized pH gradient; MALDI-MS, matrix-assisted laser desorption/ionization mass spectrometry; MS/MS, tandem mass spectrometry; syn, syngeneic control group; sCrea, serum creatinine; SELDI-MS, surface-enhanced laser desorption ionization/time-of-flight mass spectrometry

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