By de Seny, Dominique Fillet, Marianne; Ribbens, Clio; Maree, Raphael; Meuwis, Marie-Alice; Lutteri, Laurence; Chapelle, Jean-Paul; Wehenkel, Louis; Louis, Edouard; Merville, Marie-Paule; Malaise, Michel
BACKGROUND: SELDI-TOF mass spectrometry (MS) is a high- throughput proteomic approach with potential for identifying novel forms of serum biomarkers of arthritis. METHODS: We used SELDI-TOF MS to analyze serum samples from patients with various forms of inflammatory arthritis. Several protein profiles were collected on different Bio-Rad Laboratories ProteinChip arrays (CM10 and IMAC- Cu^sup 2+^) and were evaluated statistically to select potential biomarkers.
RESULTS: SELDI-TOF MS analyses identified several calgranulin proteins [S100A8 (calgranulin A), S100A9 (calgranulin B), S100A9*, and S100A12 (calgranulin C)], serum amyloid A (SAA), SAA des-Arg (SAA-R), and SAA des-Arg/des-Ser (SAA-RS) as biomarkers and confirmed the results with other techniques, such as western blotting, immunoprecipitation, and nano-LC-MS/MS. The S100 proteins were all able to significantly differentiate samples from patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) from those of patients with inflammatory bowel diseases used as an inflammatory control (IC) group, whereas the SAA, SAA-R, and SAA-RS proteins were not, with the exception of AS. The 4 S100 proteins were coproduced in all of the pathologies and were significantly correlated with the plasma calprotectin concentration; however, these S100 proteins were correlated with the SAA peak intensities only in the RA and IC patient groups. In RA, these S100 proteins (except for S100A12) were significantly correlated with the serum concentrations of C-reactive protein, matrix metalloproteinase 3, and anti-cyclic citrullinated peptide and with the Disease Activity Score (DAS^sub 28^).
CONCLUSIONS: The SELDI-TOF MS technology is a powerful approach for analyzing the status of monomeric, truncated, or posttranslationally modified forms of arthritis biomarkers, such as the S100A8, S100A9, S100A12, and SAA proteins. The fact that the SELDITOF MS data were correlated with results obtained with the classic calprotectin ELISA test supports the reliability of this new proteomic technique.
(c) 2008 American Association for Clinical Chemistry
Members of the S100 protein family are acidic proteins of low molecular mass characterized by cell type-specific production and the presence of 2 EF-hand calcium-binding domains (1). Of these proteins, S100A8 (also known as myeloid-related protein 8 and calgranulin A), S100A9 (myeloid-related protein 14, calgranulin B), and S100A12 (calgranulin C) have prompted particular interest among rheumatologists because they are valuable markers of phagocyte activation (2). The secretion of S100A8 and S100A9 by neutrophils and macrophages is stimulated during these cells’ interaction with activated endothelial cells (3, 4). A hallmark of the S100A8 and S100A9 proteins is to form dimers, with a heterodimer-homodimer ratio of about 10:1 (5,6). The S100A8/(S100A9)2 heterotrimer, better known as calprotectin, has a mass of 36.5 kDa, although trimer formation has never been confirmed by nuclear magnetic resonance analysis (5, 7). Nevertheless, the term calprotectin continues to be used to refer to the S100A8/S100A9 heterocomplex, and the ELISA technique has almost been used almost exclusively to quantify calprotectin status in disease states. Finally, tetramers (S100A8/ S100A9)^sub 2^ have also been described (8). In inflammatory conditions, S100A8 and S100A9 are produced independently; however, only S100A9 is found in acute inflammatory conditions such as gingivitis, with the production of S100A8 being restricted to chronic inflammation (9). Increased production of S100A8 and S100A9 has been observed in serum, synovial fluid, and synovium samples for many inflammatory rheumatic diseases, including rheumatoid arthritis (RA),6 psoriatic arthritis (PsA), and ankylosing spondylitis (AS) (10-13), and in the serum of individuals with nonrheumatic inflammatory diseases, such as inflammatory bowel diseases (IBDs) (14). The serum concentration of calprotectin has been correlated with many variables associated with disease activity in RA, PsA, and AS, including such clinical variables as the number of swollen joints (15), the Ritchie index (10), the Disease Activity Score (DAS^sub 28^) (16), and such biological variables as C-reactive protein (CRP) concentration and erythrocyte sedimentation rate (10, 15, 16).
S100A12 is produced mainly by granulocytes upon inflammatory activation and acts independently of S100A8 and S100A9 (17). Its interaction with the receptor for advanced glycation end products (RAGE) induces proinflammatory signals in endothelium and in cells of the immune system (18). Increased S100A12 concentrations have been found in the serum, synovium, and synovial fluid of patients with RA (11, 19), PsA (19), and AS (19), as well as in the serum of IBD patients (20). Serum amyloid A (SAA), a key promoter of inflammatory events in RA and shown to be produced by inflamed synovial tissue, also induces RAGE activation (21).
The identification of the S100 and SAA groups of proteins as biomarkers is therefore challenging. In the absence of ELISAs specific for the monomeric forms of S100A8 and S100A9 and of a commercially available test for S100A12, we have hypothesized that mass spectrometry (MS) might be a possible approach for detecting these proteins (22). S100A8 has already been identified by 2- dimensional gel electrophoresis to be present in synovium (13), but not in serum (12). Two-dimensional liquid chromatography-coupled tandem MS has also been used successfully to identify several S100 proteins, including S100A8, in serum samples from a few RA patients (11); however, the labor-intensive nature of these 2 proteomics technologies allow the investigation of only small numbers of biological samples. Accordingly, we postulated SELDI-TOF MS technology to be a more appropriate proteomic approach for detecting low molecular weight proteins (
Patients and Methods
Blood samples were prospectively collected from 139 Caucasian patients after they had provided informed consent. The study protocol was approved by the ethics committee of our academic hospital (CHU de Liege). Beginning in 2002, we collected blood samples into 10-mL serum separator Vacutainer Tubes (BD Medical Systems). We allowed the blood to clot for 30 min at room temperature and centrifuged the samples at 700g for 10 min. All serum samples were aliquoted and immediately frozen at -80 [degrees]C until thawed for SELDITOF MS analysis. We defined 5 sets of patients: (a) a noninflammatory control (NIC) group of 36 individuals (16 healthy individuals and 20 osteoarthritis patients), (b) 34 RA patients, (c) 22 PsA patients, (d) 19 AS patients, and (e) an IC group of 28 patients with IBD (14 with Crohn disease and 14 with ulcerative colitis).
The epidemiologic characteristics of the patient groups are summarized in Table 1. RA patients fulfilling the 1987 American College of Rheumatology criteria (26) had a median DAS^sub 28^ of 6.3 (range, 3.5-8.8), with 86% of the scores >5.1 (high disease activity). PsA patients had active disease with at least 3 tender and swollen joints. The AS patients [modified New York criteria (27)] had active disease, as indicated by a median Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) of 6/10 (range, 4/10-10/ 10). Diagnosis of IBD patients was made according to validated criteria (28). Active Crohn disease was defined by a Harvey- Bradshaw index >/=7, and active ulcerative colitis was defined by both clinical and endoscopic signs of activity.
Table 1. Epidemiologic characteristics of the patients and controls.a
The preparation and reproducibility of Protein-Chip arrays (Vermillion/Ciphergen Biosystems) are described in the supplemental methods in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol54/ issue6.
ANALYSIS OF SELDi-TOF MS DATA
Peaks were detected with ProteinChip Biomarker Wizard software (version 3.0; Bio-Rad Laboratories). We analyzed the data by 2 approaches, nonparametric Mann-Whitney U tests and a machine- learning algorithm called random forests (29), after we had completed various preprocessing steps (24). Random forests is a decision-tree multivariate analysis that estimates the relevance or relative contribution of each peak to the classification of 2 groups (29). The latter approach allows m/z values to be ranked according to their relevance for differentiating the 2 groups based on quantitative estimates of the percentage of information (% of info) supplied. P values
SlOOAS, S100A9, S100A12, and SAA were assessed by Western blot (WB) analysis. In brief, 2 [mu]L of serum was run on 12% NuPAGE Bis- Tris polyacrylamide gels (Invitrogen), transferred, and incubated with antiS100A8 monoclonal antibody (20 [mu]L diluted in 10 mL; BMA Biomedicals), anti-S100A9 polyclonal antibody (10 [mu]L diluted in 10 mL; Santa Cruz Biotechnology), anti-S100A12 polyclonal antibody (20 [mu]L diluted in 10 mL: Santa Cruz Biotechnology), or anti-SAA monoclonal antibody (5 [mu]L diluted in 10 mL; Abeam). We then incubated with a mouse secondary antibody (2 [mu]L diluted in 10 mL; GE Healthcare/Amersham Biosciences) to specifically detect S100A8 and SAA, with a rabbit secondary antibody (2 [mu]L diluted in 10 mL; Dako) to detect S100A9, or with a goat secondary antibody (2 [mu]L diluted in 10 mL; Santa Cruz Biotechnology) to detect S100A12. Proteins were revealed with an enhanced chemiluminescence detection method according to the manufacturer’s instructions (GE Healthcare/ Amersham Biosciences).
The supplemental methods in the online Data Supplement describe the immunoprecipitation and 1-dimensional gel electrophoresis methods as well as LC-MS/MS identification of SAA.
Table 2. Discriminatory power of selected peaks as assessed with the Mann-Whitney U test and represented as P values.
We carried out ELISAs for anti-cyclic citrullinated peptide 2 (anti-CCP2) antibody (cutoff, 5 000 relative units/L; Euroimmun), calprotectin (range, 1.6-100 [mu]g/L; Hyeult biotechnology), and matrix metalloproteinase 3 (MMP-3) (range, 1.25-20 [mu]g/L; BioSource) (30) as recommended by the respective manufacturers. We used serum samples or, in the case of calprotectin assays, the corresponding plasma samples.
We used ranked data to calculate Spearman correlation coefficients and used the chi^sup 2^ test to compare qualitative data. P values
ANALYSIS OF SELDI-TOF MS DATA
We loaded 139 serum samples in duplicate on weak cation-exchange ProteinChip (CM10) arrays and collected 278 spectra. We first compared the spectra for the RA and NIC groups. The Bio-Rad software resolved 200 peaks. Four peaks detected at m/z values of 10 444, 10835, 12688, and 13 272 were differentiated with high statistical significance (P values
We also loaded the 139 serum samples in triplicate onto immobilized metal affinity capture ProteinChip (IMAC-Cu^sup 2+^) arrays and collected 417 spectra. Both statistical approaches detected 3 peaks as significantly increased in RA patients compared with the NIC group. These peaks had m/z values of 11 438 (P
IDENTIFICATION OF S100 FAMILY PROTEINS
We confirmed the identities of the S100A8, S100A9, and S100A12 proteins by comparing WB and SELDITOF MS results for the serum samples (Fig. 2). The WB analysis was performed with 8 serum samples (posilions 1-8) from each of the 5 sets of patients (NIC, RA, PsA, AS, and IC). The same RA sample was run in position 9 on each gel as a positive control. SELDI-TOF MS spectra for the same serum samples were monitored at m/z values of 10 835, 13 272, and 10 444 and compared with the corresponding WB results. We obtained similar profiles in the WB and SELDI analyses for each protein tested (Fig. 2, A-C). These data suggest that the peaks at 10 835,13 272, and 10 444 m/z values are the SlOOAS, S100A9, and S100A12 proteins, respectively. According to these 2 semiquantitative approaches, some RA, PsA1 AS> and, to a lesser extent, IC serum samples were positive for the 3 S100 proteins. We detected 2 S100A9 variants. The m/z value of 13 272 agreed well with the calculated mass for an oxidized form of S100A9 (13 242 Da plus 32 Da), and the 12 688 m/z value likely represents an S100A9 variant known as S100A9*, which has previously been characterized by ultraviolet MALDI MS (31 ). S100A9* results from translation beginning at amino acid residue 5 and acetylation of amino acid residue 6 (a Ser residue), yielding a calculated mass of 12 691 Da.
Fig. 1. Protein mass spectra collected on CM10 and IMAC-Cu^sup 2+^ ProteinChip arrays with serum samples from 5 patients with arthritis (2 RA, 2 AS, and 1 PsA) and 5 NIC individuals.
(A), S100A8 and S100A12 peaks are detected on CM10 arrays at m/z values of 10 835 and 10 444, respectively. S100A9 (m/z = 13 272) and the S100A9* variant (m/z = 12 688) are also present on these arrays. (B), Detection of SAA (m/z = 11 680), SAA-R (m/z = 11 528), and SAA- RS (m/z – 11 438) on IMAC-Cu^sup 2+^ ProteinChip arrays.
We also confirmed the identities of the S100A8 and S100A9 proteins by eluting the 2 proteins from their immunocomplexes on NP20 arrays after immunodepletion of a serum sample from an RA patient (see Fig. 1 in the online Data Supplement). We also detected a slight cross-reactivity with S100A12, S100A9*. and S100A9 on the NP20 spectra after eluting S100A8 (the sequence homologies of the S100A8, S100A9, and S100A12 proteins are around 40%) (32). We were unable to immunoprecipitate the S100A12 protein.
IDENTIFICATION OF SAA PROTEINS
To identity the proteins responsible for the 11 438, 11 528, and 11 680 m/z peak values in the RA spectra on IMAC-Cu^sup 2+^ arrays, we collected serum samples from a healthy control individual and an RA patient, depleted the samples of albumin and IgG, and ran them on a !-dimensional SDS-PAGE gel (see Fig. 2 in the online Data Supplement). After silver staining, we excised a band from the RA sample on the gel with an apparent molecular weight of 11 kDa and subjected it to LC-MS/MS analysis (see Fig. 2 in the onJine Data Supplement). We also excised another band from the gel at the same position for the serum sample from the healthy control individual. We digested these bands with trypsin and analyzed the resulting peptides by tandem MS (see Fig. 2 in the online Data Supplement). Sequencing analysis of 4 major tryptic fragments (sequence coverage, 51%; total score, 186) revealed the excised protein to be SAA.
Fig. 2. Analysis of S100A8, S100A9, S100A J 2, and SAA by WB and SELDI-TOF MS.
Comparison of 8 serum samples (1-8) for each of the 5 sets of serum samples (NIC, RA, PsA, AS, and IC) with both techniques. Serum sample 9 from an RA patient was used as a positive control in each WB analysis to normalize the experiment. SELDI-TOF MS results are presented in both electrophoresis gel and spectrum views. Shown are spectrum regions near m/z values of 10 835, 13 272, 10 444, and 11 680, which correspond to the S100A8, S100A9, S100A12, and SAA proteins, respectively.
We confirmed the identity of SAA by comparing the WB results with the SELDI-TOF MS results for the serum samples (Fig. 2D). Each set of patient samples (but not those of the NIC group) was positive for SAA in the WB analysis. This result confirmed the identity of the peak at m/z 11 680 to be the SAA protein (calculated mass, 11 682 Da). The peaks at m/z values of 11 438,11 528, and 11 680 were clustered on each spectrum. We therefore hypothesized that the 11 438 and 11 528 m/z peak values represented 2 variants of the original SAA protein. The 11 528 m/z value corresponds to the calculated mass for the SAA protein without its first N-terminal Arg residue ( – 156 Da); this peak represents the SAA-R protein (calculated mass, 11 526 Da). Similarly, the 11 438 m/z value corresponds to the calculated mass of the SAA protein truncated at the N-terminal end by 2 residues, Arg and Ser (-243 Da); this peak represents the SAA-RS protein (calculated mass, 11 439 Da). We also confirmed the identities of these proteins by eluting them from their immunocomplexes on NP20 after immunodepleting a serum sample from an RA patient (see Fig. 1 in the online Data Supplement).
THE DISTRIBUTION OF PEAK INTENSITIES AMONG PATIENT GROUPS
The discriminatory power of the peaks at m/z values of 10 835 (S100A8), 12 688 (S100A9*), 13 272 (S100A9), 10444 (S100A12), 11680 (SAA), 11528 (SAA-R), and 11 438 (SAA-RS) detected in the spectra of samples from each of the arthritis groups was assessed with the Mann- Whitney U test (Table 2). In brief, S100 proteins were found to be highly significantly effective (P
We next analyzed the percentages of serum samples that were positive for the 7 biomarkers. These percentages were calculated according to the peak intensities measured at tn/z values of 10444 (S100A12), 10 835 (S100A8), 13 272 (S100A9),and 12 688 (S100A9*) on CM10 arrays, and at m/z values of 11 680 (SAA), 11 528 (SAA-R), and 11 438 (SAA-RS) on IMAC-Cu^sup 2+^ arrays. Peak intensity values were averaged. The cutoff value was defined as the highest peak intensity of the corresponding m/z values in the spectra for the NIC serum samples. Similarly, the percentages of plasma samples positive for calprotectin were calculated with the cutoff defined as the highest calprotectin concentration observed in the NIC group. Increased S100 protein peak intensities and increased calprotectin concentrations were detected in 43%-89% and 33%-56% of arthritis serum samples, respectively, and in 15%34% and 33% of IC patients, respectively (Table 3). The positivity rates among the 4 S100 proteins were significantly linked [chi^sup 2^ (9) = 84; P
Statistically significant correlations were found petween the various S100 proteins, SAA, calprotectin, and other evaluated variables in the RA, PsA, AS, and IC patient groups (Table 4). In brief, values for the 4 SlOO proteins were both highly intercorrelated and correlated with plasma calprotectin concentration in all of the patient groups. S100 protein peaks were also correlated with SAA peaks, but only in the RA and IC groups. In the RA group, S100 protein peaks and the calprotectin concentration were correlated with CRP, log MMP-3, and anti-CCP2 antibody (except for S100A12) serum concentrations and with the DAS^sub 28^, but not with the number of tender or swollen joints (data not shown). RA patients who produced S100 proteins or calprotectin had longer disease durations than SlOO-negative and calprotectin-negative patients (132 months vs 17 months and 117 months vs 16 months, respectively; P
Table 4. Correlation coefficients for S100 proteins, SAA, and clinical variables for serum samples collected from RA, PsA, AS, and IC patients.a
SAA was correlated with the serum concentration of CRP in all patient groups and with DAS28 and logarithm MMP-3 serum concentration in the RA patient group. The plasma calprotectin concentration was correlated with log MMP-3 serum concentration in the RA, PsA, and IC patient groups and with the SAA protein in the RA, AS, and IC patient groups.
In this study, we used the SELDI-TOF MS approach to detect S100A8, S100A9, an S100A9 variant (S100A9*), S100A12, SAA, SAA-R, and SAA-RS, and we identified^ these proteins by WB, immunodepletion, and nanoLC-MS/MS. In a previous study, we had already demonstrated by WB analysis that the peak at m/z 10 835 in one RA serum sample was S100A8 (24). We confirmed the identity of this peak in the present study, not only in serum samples from RA patients but also in samples from patients with other inflammatory diseases. The mass of the protein for the peak identified as S100A9* was in good agreement with that of a truncated form of S100A9, a variant that has already been detected by ultraviolet MALDI MS in human buffy coats (31), in head and neck sauamous cell carcinoma (33), and in neutrophils from pediatric cystic fibrosis patients (34). The SAA variants had previously been identified in renal cancer, but not in arthritis or in patients with IBD. SELDI-TOF MS is therefore a very efficient technology for detecting such arthritis biomarkers as S100 and SAA proteins in their monomeric, truncated, or posttranslationally modified forms.
The highly significant linear correlations between the peak intensities for the S100 proteins and the calprotectin plasma concentration as measured by ELISA confirm the reliability of this new proteomic approach for investigating these inflammation-related proteins. We conclude that these proteins are up-regulated in each of the diseases we studied because the S100 proteins and calprotectin are correlated both qualitatively, as shown by the strong concordance between WB positivity and SELDI-TOF MS positivity, and quantitatively, as shown by the significant linear correlations in peak intensities. Furthermore, the peak intensities of S100A8, SIOOA9, S100A9*, and S100A12, as well as the calprotectin concentration, are correlated with variables that reflect the biological and clinical activity of RA, such as the DAS^sub 28^ and serum concentrations of CRP, MMP-3, and anti-CCP2 antibody. These results provide strong support for the clinical relevance of proteomic detection of calgranulins. The anti-CCP2 antibody may also be related to radiologically observed structural damage in RA because antiCCP antibodies have been demonstrated to be independent predictors of joint damage (35 ), as is the serum concentration of calprotectin (16). We could not address this question directly because we conducted no x-ray-imaging studies; however, we did find that patients with increased SlOO protein or calprotectin concentrations had significantly longer disease durations, which are expected to be associated with greater radiographically detectable damage.
We observed a discrepancy between RA patients and PsA and AS patients in that the 4 S100 proteins were significantly correlated with CRP and MMP-3 concentrations in RA patients, but not in the other 2 groups of patients. We attribute this finding to the fact that AS and PsA patients exhibit abnormal CRP and MMP-3 concentrations less frequently than RA patients (30). The strong correlation between the 4 S100 proteins and the serum concentration of MMP-3 in the IC group agrees with findings of abundant production of both types of proteins in inflamed intestinal tissue (36).
We also observed that the 4 S100 proteins distinguished arthritis conditions from nonarthritis conditions (NIC and IC groups), whereas SAA patterns distinguished inflammatory diseases (RA, PsA, and IC groups) from noninflammatory conditions (the NIC group). It is also interesting that within the arthritis groups the peak intensities for the 4 S100 proteins were correlated with the SAA peak intensities in the RA group but not in the AS and PsA groups. These results mimic what we had already observed with the CRP and MMP-3 variables. We therefore conclude that the arthritis process is correlated with the inflammatory process in RA, in which the acute- phase response is well developed, but not in AS or PsA, in which it is weaker. This conclusion thus suggests that the regulatory pathways at the sites of inflammation and the patterns of local production of S100 and SAA proteins in RA patients are different from those in AS and PsA patients (37).
SELDI-TOF MS is a unique method for distinguishing SlOO monomers from multimeric forms and for detecting SAA variants, which are not possible with current ELISAs. The identification of the various SAA forms may be important because they may have different pathophysiological roles. The recent identification via SELDI-TOF analysis of several truncated forms of S100A8 and S100A12 in cystic fibrosis patients suggests that C-terminal truncations affect protein function (34). The presence of S100A8 and S100A12, but not calprotectin, have been found to be characteristic of intra- amniotic inflammation (22), and SAA variants with different properties, such as differential susceptibility to matrix metalloproteinase digestion, have been described (38). These findings demonstrate the relevance of developing new proteomic approaches sufficiently powerful for investigating proteins in their modified or monomeric forms.
In conclusion, we have used SELDI-TOF MS technology to identify several relevant arthritis biomarkers that are correlated with several biological or clinical variables associated with disease activity. We could not address the functional role of these biomarkers in the pathophysiology of arthritis, because we selected serum samples from individuals with the respective dis ease characteristics and not from individuals at different stages of each disease. Studies that take advantage of the SELDI-TOF MS technology to evaluate the effects of disease stage on these biomarkers may shed light on this question. Grant/Funding Support: This research was supported by the National Fund for Scientific Research (FNRS, Belgium) and the Fonds d’Investissement pour la Recherche Scientifique (FIRS), CHU de Liege, Belgium.
Financial Disclosures: None declared.
Acknowledgments: The authors thank Aline Desoroux and Gael Cobraiville for their expert technical assistance. M.F. is a Research Associate, and E.L. and M.P.M. are Senior Research Associates at FNRS (National Fund for Scientific Research).
6 Nonstandard abbreviations: RA, rheumatoid arthritis; PsA, psoriatic arthritis; AS, ankylosing spondylitis; IBD, inflammatory bowel disease; DAS28, Disease Activity Score; CRP, C-reactive protein; RAGE, receptor for advanced gtycation end products; SAA, serum amyloid A; MS, mass spectrometry; IC, inflammatory control; NIC, noninflammatory control; BASDAI, Bath Ankylosing Spondylitis Disease Activity Index; WB, western blot anti-CCP2, anti-cyclic citrullinated peptkie 2; MMP-3, matrix metalloproteinase 3; CMIO, weak cation-exchange ProteinChip; % of info, percentage of information; IMAC-Cu^sup 2+^. immobilized metal affinity capture ProteinChip; SAA-R, SAA des-Arg; SAA-RS, SAA des-Arg/ des-Ser.
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Dominique de Seny,1* Marianne Fillet,2 Clio Ribbens,1 Raphael Maree,3 Marie-Alice Meuwis,2 Laurence Lutteri,2 Jean-Paul Chapelle,2 Louis Wehenkel,4 Edouard Louis,5 Marie-Paule Merville,2 and Michel Malaise1 1 Laboratory of Rheumatology, GIGA Research, CHU, University of Liege;2 Laboratory of Clinical Chemistry, GIGA Research, University of Liege; 3 GIGA Bioinformatics Platform, University of Liege; 4 Bioinfomiatics and Modeling Unit Department of Electrical Engineering & Computer Science, GIGA Research, University of Liege;5 Laboratory of Hepato-Gastroenterology, CHU, University of Liege, Liege, Belgium.
* Address correspondence to this author at: Laboratory of Rheumatology, Tour GIGA +2, CHU. 4000 Liege, Belgium. Fax (32) 4 366 45 34; e-mail [email protected] chu.ulg.ac.be.
Received November 6, 2007; accepted March 28, 2008.
Previously published online at DOI: 10.1373/clindwm.2007.099549
Copyright American Association for Clinical Chemistry Jun 2008
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