A 588-gene microarray analysis of the peripheral blood mononuclear cells of spondyloarthropathy patients

J. Gu, E. Märker-Hermann3, D. Baeten5, W. C. Tsai6, D. Gladman7, M. Xiong1, H. Deister3, J. G. Kuipers4, F. Huang9, Y. W. Song10, W. Maksymowych8, J. Kalsi, M. Bannai11, N. Seta, M. Rihl, L. J. Crofford2, E. Veys5, F. De Keyser5 and D. T. Y. Yu

University of California at Los Angeles, CA,
1 University of Texas at Houston, Houston, TX and
2 University of Michigan, Ann Arbor, MI, USA,
3 University of Mainz, Mainz and
4 Hannover Medical School, Hannover, Germany,
5 University of Ghent, Ghent, Belgium,
6 Kaoshiung Medical College, Kaoshiung, Republic of China,
7 University of Toronto, Toronto and
8 University of Alberta, Edmonton, Canada,
9 PLA General Hospital, Beijing, China,
10 Seoul National University, Seoul, Korea and
11 Japanese Red Cross Tokyo Metropolitan Blood Center, Tokyo, Japan


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Objectives. To identify genes which are more highly expressed in the peripheral blood mononuclear cells (PBMC) of patients with spondyloarthropathy (SpA), rheumatoid arthritis (RA) and psoriatic arthritis (PsA), in comparison to normal subjects.

Methods. A 588-gene microarray was used as a screening tool to select a panel of such genes from PBMC of these subjects and of normal subjects. Results were then validated by reverse transcription–polymerase chain reaction (RT-PCR).

Results. The following genes were more highly expressed in arthritis patients than in normal subjects: macrophage differentiation marker MNDA (myeloid nuclear differentiation antigen), MRP8 and MRP14 (migratory inhibitory factor-related proteins); signalling molecules JAK3 (janus kinase 3) and MAP kinase p38 (mitogen-activated protein kinase); receptors TNFR2/p75, C-C-chemokine receptor type 1 (CCR1), C-X-C-chemokine receptor type 4 (CXCR4) and integrin ß1; and the cytokines/chemokines interleukin (IL) 1ß and IL-8. Expression of CXCR4 was unexpectedly high among all arthritis subjects. Using RT-PCR, ELISA and immunohistology, expression of stromal cell-derived factor 1 (SDF-1) was demonstrated in arthritis joints.

Conclusions. The CXCR4/SDF-1 is a potential pro-inflammatory axis for RA, PsA and SpA.

KEY WORDS: Microarray, Spondyloarthropathy, Rheumatoid arthritis, Spondyloarthropathy.


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Examining the pathogenesis of spondyloarthropathy (SpA) is a complex research problem because multiple genes and environmental factors are involved [1]. Several experimental approaches are open to investigators. One is hypothesis-driven, such as studies to identify bacterial antigens [2, 3]. The other approach is to use random screening for potentially disease-related factors. Screening can be at the levels of genome, transcripts or proteins. The recent success of microarray technology has led to the frequent use of transcript screening in multiple diseases [4].

In this study, we used a commercial microarray to study SpA and to compare the SpA results with those for subjects with rheumatoid arthritis (RA) and psoriatic arthritis (PsA) and normal subjects. This microarray contains 588 genes, mostly encoding cytokines, chemokines, signalling molecules, receptors and adhesion molecules. According to the manufacturer, these genes were selected because they have high citation rates. As a first step in a comprehensive microarray programme, we focused on the peripheral blood mononuclear cells (PBMC), which constitute the most accessible tissue. In addition, as it is known that certain genes are expressed at higher levels in the peripheral blood cells of patients with RA, we could use RA for comparison [5]. As the hope is that the overall results will provide some clues to previously unreported mediators of SpA, cells were not separated into monocytes, lymphocytes or their subsets.

Our strategy was as follows. Six to seven peripheral blood samples of each arthritis group were submitted for microarray examination. This step utilized the ability of microarrays to assay a large number of genes simultaneously. The weakness of microarrays is that they are susceptible to great experimental variability and the lack of a single unanimously accepted optimum method for statistical analysis. Hence, in our case, we used the microarrays as a convenient means of screening to generate a panel of candidate genes. These selected genes were then submitted to validation by a reverse transcription–polymerase chain reaction (RT-PCR) assay. Using these approaches, we identified several genes that were more highly expressed in the PBMC of arthritis patients. The unexpected discoveries were that the myeloid nuclear differentiation antigen (MNDA) was a discriminating gene and that CXCR4/ SDF-1 (stromal-derived factor 1) was a potential proinflammatory axis in SpA.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Characteristics of subjects providing PBMC
The characteristics of normal subjects and patients studied by microarray are shown in Table 1Go. Three of the normal subjects were HLA-B27-positive. Diagnosis and subclassification of arthritis were carried out according to classification criteria for ankylosing spondylitis (AS), SpA and RA [68]. Psoriatic arthritis was defined as having psoriasis and one of the five classical patterns of associated arthritis [9]. In the SpA (SpA) group, six were tested for HLA-B27. They were all positive. SpA patient 2 also had mild Crohn's disease, but the disease was in remission. All patients with SpA and RA showed active disease with an elevated erythrocyte sedimentation rate and at least inflammatory spinal pain or swollen joints. Patients with PsA showed mild disease in the spectrum of PsA. Patient 4 was in clinical remission. None of the patients providing peripheral blood for study were on corticosteroids or a disease-modifying anti-rheumatic drug. Additional patients were recruited for analysis of semiquantitative RT-PCR assays. The characteristics of these patients were similar to those in Table 1Go. The total numbers of patients were 9 for AS, 6 for undifferentiated SpA (USpA), 7 for reactive arthritis (ReA), 11 for RA and 7 for PsA.


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TABLE 1.  Characteristics of subjects providing samples for the microarray assay

 

Preparation of samples and microarray
PBMC were separated from heparinized blood samples by Histopaque (Sigma, MO, USA) centrifugation. Array filters were Atlas Human Array 7740–1 (Clontech, Palo Alto, CA, USA). Extraction and purification of total RNA and the microarray assays followed the protocols provided by the manufacturers. Five micrograms of total RNA was used in each microarray assay. RNA was labelled with [32P]ATP. Signals on hybridized filters were recorded by phospho-imager. The result of each gene in each subject was recorded as the intensity of hybridization signal. Using the Atlas Image software designed specifically for these microarray filters (Clontech), intensity signals in each array were normalized to the housekeeping (glyceraldehyde-3-phosphate dehydrogenase) G3PDH gene. Using the same software, we also created an arbitrary standard by generating an average result derived from seven normal individuals. To allow comparison among samples, using the same software, all microarray results were then normalized once more to this arbitrary standard.

Synovial fluids were obtained from five patients with AS, one with USpA, one with ReA, two with PsA, eight with RA and eight with osteoarthritis (OA). To reduce viscosity, synovial fluids were incubated at 37°C for 2 h with bovine testis hyaluronidase (200 U/ml; Sigma, Taufkirchen, Germany). Synovial tissue samples were obtained from 13 patients: five with RA and eight with SpA (which included three with associated PsA). All patients had active synovial effusion. Six samples were obtained from the knee of each patient by needle arthroscopy, as described previously [10]. Synovial fibroblast cultures were generated in two SpA and four RA patients using a method described previously [11]. Samples were assayed during the early generations.

Semi-quantitative RT-PCR, ELISA and histochemistry
Semi-quantitative RT-PCR tests were carried out using either an external standard for comparison as reported previously [12] or a commercial competitive RT-PCR kit following the protocol provided by the manufacturer (Maxim Biotech, San Francisco, CA, USA). The amount of SDF-1{alpha} in the synovial fluids was measured in duplicates using an enzyme-linked immunosorbent (ELISA) kit (R&D Systems, Wiesbaden, Germany) according to the manufacturer's instructions. Immunohistology of synovial biopsies followed procedures described previously [13] and used goat anti-human SDF-1 antibodies (reactive with SDF-1{alpha} and SDF-1ß; Research Diagnostics, Flanders, NJ, USA).

Statistical analysis
Discriminating genes in microarray data were identified by scalar feature selection and Fisher's linear discriminant analysis [14]. This discriminant analysis uses a portion of the samples in a ‘training set’ to determine the accuracy of the classifiers, and then all the samples together for validation. RT-PCR results were analysed with the Mann–Whitney test, the {chi}2 test and linear regression. For the first two tests, comparisons were regarded as statistically significant if P values were less than 0.05.


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Analysis of microarray results
A 588-gene microarray analysis was carried out with PBMC from seven normal individuals, seven patients with SpA, and six each with RA and PsA. Although a number of statistical tools are available for identifying candidate genes from microarrays, none has been unanimously accepted to be foolproof. We arbitrarily selected Fisher's discriminative test, which tests individual genes for their potential of being able to discriminate one group of samples from another. As reported previously, when applied to cancer arrays, this method has the practical advantage of being able to select only a very small number of genes as being potentially useful in tumour classification [15]. A total of 16 genes were identified. They were genes which encode differentiation markers, cytokines, cytokine/ chemokine receptors and signalling and adhesion molecules (Table 2Go).


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TABLE 2.  Discriminative genes

 

Analysis of RT-PCR results
Because of the known experimental variability of microarray assays and the lack of a unanimously accepted optimum statistical method for analysis, the 16 discriminating genes in Table 2Go could serve only as promising candidates. To the list, we added 14 others which share functional similarities with the discriminating genes. These 30 genes were then tested by RT-PCR using the same PBMC samples tested by microarray. Results are shown in Fig. 1aGo. In order to present the results diagrammatically instead of as raw values, we did the following. For each gene, we calculated the mean RT-PCR value of the seven normal subjects. Then, for each particular individual, the expression of that gene would be arbitrarily regarded as being ‘high’ if the RT-PCR value exceeded this mean normal value by a factor of 2. High values are represented by black squares in Fig. 1aGo in which each row represents a gene and each column an individual. Visual inspection indicates that the number of black squares was higher among patients. The percentages of highly expressed genes in normal subjects and subjects with SpA, RA and PsA were 8.6, 35.7, 48.3 and 49.4 respectively (P<0.0001 when comparing each arthritis group with normal subjects using the {chi}2 test; n=18/192, 75/135, 87/93 and 89/91 respectively). The differences between SpA with either RA or PsA were also statistically significant using the {chi}2 test (P=0.012 and 0.006 respectively). To test if this increase in the number of highly expressed genes might be specific for arthritis, two patients with active pulmonary tuberculosis were studied. As shown in the right-hand panel of Fig. 1aGo, the percentage of positive (i.e. highly expressed) genes expressed in these tuberculosis patients was also high, with a mean of 31.7 (19/40). However, the number of tuberculosis patients was too small for meaningful statistical comparisons.



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FIG. 1.  (a) RT-PCR results of 30 genes selected from microarray profiles. Each numbered column represents a subject of the group indicated at the top of the column. Each row represents a gene as labelled at the left. Each small square represents the result for one gene in one subject. A filled square indicates that the RT-PCR value is more than twice the mean of the normal subjects. (b) Results of statistical analysis of the RT-PCR results shown in (a). Each column represents an arthritis group, as shown at the top of the column. Each row represents a gene, as labelled at the left. The order of genes is exactly as in (a). There are three panels of data. On the left, arthritis groups are compared with normal subjects with respect to mean RT-PCR values. A filled square indicates that the mean RT-PCR value is more than 5 times the mean normal value for that particular gene in that specific patient group, and P<0.05. In the middle panel, the frequency of positive expression in an arthritis group is compared with that in normal subjects. Positive expression means that the RT-PCR value is more than twice that of normal subjects. A filled square indicates P<0.05. The right-hand panel is similar to the middle panel except that the comparison is with SpA. A filled square indicates that the mean RT-PCR value is more than 3-fold greater than the SpA RT-PCR value and P<0.05.

 
We then used two statistical tests to compare the expression of individual genes between arthritis patients and normal individuals (Fig. 1bGo, middle panel). To increase the stringency compared with Fig. 1aGo, genes were considered only if the mean RT-PCR values in patients were more than 5 times those of the normal subjects. In Fig. 1bGo, black squares denote patient groups in which the P value was <0.05. The first statistical test compared the mean RT-PCR values among each group using the Mann–Whitney test. Results are shown in the middle panel of Fig. 1bGo under the heading ‘Increase in RT-PCR values’. In all the panels of Fig. 1bGo, each row represents a particular gene, each column a particular type of patient. The second statistical test used was the {chi}2 test, to compare the frequencies of individuals with a high value. Results are shown in the middle panel of Fig. 1bGo under the heading ‘Increase in frequency’. Using both the Mann–Whitney and the {chi}2 test, the following genes were more frequently highly expressed in one or more arthritis groups: MNDA, migratory inhibitory factor-related protein 8 (MRP8), interleukin (IL) 1ß, IL-8, interferon {gamma} (IFN-{gamma}), IL-7 receptor (IL-7R), tumour necrosis factor receptor 2 (TNFR2/p75), C-C-chemokine receptor type 1 (CCR1), stromal cell-derived factor 1 receptor (CXCR4), mitogen-activated protein kinase p38 (MAP kinase p38), janus kinase 3 (JAK3) and integrin ß1.

In the right-hand panel of Fig. 1bGo, the RT-PCR values of RA and PsA are compared with those of SpA using the Mann–Whitney test and the {chi}2 test. Filled squares represent patient groups for which the RT-PCR values exceeded those of SpA by at least 3-fold and the P value was <0.05. This 3-fold difference was less than the 5-fold difference used in comparisons with normal subjects. This is because the values in SpA patients were higher than those in normal subjects to begin with. The numbers of genes that were more highly expressed in the RA and PsA groups than in the SpA group were five and six respectively. All selection thresholds used here were arbitrary. However, regardless of the stringency, there was no gene that was more highly expressed in the SpA group than in the RA or PsA group.

Verifying the results for seven genes using a larger panel of subjects and by testing the validity of the RT-PCR method
Seven genes were selected for testing with PBMC from a larger panel of subjects: seven normal subjects, nine patients with AS, six with USpA, seven with ReA, 11 with RA and seven with PsA. The RT-PCR values and statistical interpretations using the Mann–Whitney test are shown in Table 3Go. The results were consistent with those observed with the smaller panel. The exceptions were IFN-{gamma} and IL-7R, the level of expression of which was less than 5-fold that in normal subjects.


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TABLE 3.  Level of expression in individual groups of subjects [RT-PCR value (S.D.)]

 
The RT-PCR data indicated increased expression of CXCR4 in all arthritis categories compared with normal subjects. When analysed with the Mann–Whitney test, the P values for the comparison of normal subjects with AS, SpA, ReA, RA and PsA patients were all significant (0.05, 0.015, 0.025, 0.0009 and 0.0017 respectively) (Fig. 2aGo).



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FIG. 2.  (a) RT-PCR values of CXCR4 in PBMC of SpA, RA and PsA patients. Each point represents one patient. Bars represent mean values. The numbers in each category are for seven normal individuals, nine patients with AS, six with USpA, seven with ReA, 11 with RA and seven with PsA. (b) Concentrations of SDF-1 in synovial fluids of patients with SpA, RA, PsA and DJD. Each bar represents the value for a single synovial fluid sample. (c) RT-PCR values for SDF-1 in PBMC and synovial fibroblast cultures. Each bar represents one sample.

 
To find out if repeating a semi-quantitative RT-PCR assay would generate the same trend of results, we repeated the CXCR4 RT-PCR measurements with the subjects shown in Fig. 1aGo four times. When the result of one of the experiments was compared consecutively with those of the subsequent three experiments, there was a high degree of correlation (correlation coefficient=0.78, 0.96 and 0.93 respectively). We also compared the results obtained by our semi-quantitative RT-PCR method with results obtained with the standard competitive RT-PCR method. Six CXCR4 samples were selected at random and RT-PCR using an external standard was compared with competitive RT-PCR. The correlation coefficient between the two sets of results was high (0.93). To further test the similarity of the external standard RT-PCR method with the competitive RT-PCR method, in an additional and separate experiment we compared the results of the two methods by assaying IL-1ß on 10 samples. The correlation co-efficient was high (0.87).

Testing for CXCR4 ligand
The ligand for CXCR4 is the chemokine SDF-1 [16]. We measured the SDF-1 protein levels in synovial fluid samples from seven SpA, eight RA, two PsA and eight DJD patients by ELISA. Nanogram quantities were detected in all samples. In two subjects (one each in the SpA and RA groups), the level exceeded 12 µg/ml (Fig. 2bGo). Because SDF-1 has been reported to be produced by synovial fibroblast cultures [17], we also tested two samples obtained from SpA patients (samples 11 and 12 in Fig. 2cGo) and four samples of from RA patients (samples 13–16 in Fig. 2cGo). In one of two SpA and two of four RA samples, RT-PCR values were much higher than the mean in PBMC (Fig. 2cGo). However, PBMC from the subjects providing the fibroblasts were not available for direct comparison. Immunohistochemical analysis of synovial tissue was also carried out with samples from 13 patients with inflammatory arthritis. The results revealed the presence of SDF-1 in samples from three of five SpA, two of five RA, and three of three PsA patients. In these eight positive samples, there was faint diffuse staining of the synovial lining layer and stronger intracellular staining of isolated mononuclear cells in the sublining layer (Fig. 3Go). There was no clear difference in staining intensity or number of positive cells between SpA, RA and PsA samples.



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FIG. 3.  Immunohistology of SDF-1 in a synovial biopsy of an AS patient at x320 magnification. Brownish cells are positive for SDF-1.

 


    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Our results identified several functional groups of highly expressed genes in the three types of arthritis. One group consists of the signalling molecules JAK3 and MAP kinase p38. Expression of JAK3 is known to be associated with activated T lymphocytes [18]. MAP kinase p38 is a central intermediate in a cascade that can activate the transcription of inflammatory cytokines [19]. Another group consists of MRP8 and MRP14, which, like MNDA, are markers of monocyte/macrophages in chronic inflammation [20, 21]. MRP8 and MRP14 encode proteins that form complexes with each other, thought to be capable of activating endothelial cells. Another group consists of TNF receptor, CCR1, CXCR4 and integrin ß1. The first three are receptors for cytokines/chemokines and the last is a receptor for matrix proteins. The final group, consisting of IL-1ß and IL-8, are well-known inflammatory mediators [22]. MAP kinase p38, CCR1, TNF receptor and IL-1ß are also targets for therapeutic intervention in arthritis.

Some of these genes have already been reported to be highly expressed in RA. The new finding here was that expression of these genes was higher in RA and PsA than in SpA. The reason for the smaller repertoire in SpA is not clear. It does highlight the question whether the same narrowness of gene expression repertoire will also be observed in the SpA disease areas, i.e. synovia and entheses. Another new finding is that the gene which discriminated SpA from normal individuals was MNDA. Unlike most of the other genes reported in patients with arthritis, MNDA is not a cytokine/chemokine or one of their receptors. MNDA is a transcription modulator. It is also a marker of monocyte/macrophages in chronic inflammation. Thus, it is significant that we also observed an increase in MRP8, which is also a marker of monocyte/ macrophages differentiation in chronic inflammation. Taken together, our results indicate that a feature which distinguishes PBMC from SpA patients from those of normal subjects is that SpA PBMC contain circulatory monocytes of proinflammatory differentiation. However, this is not specific for SpA, as it can also be observed in other arthritides and pulmonary tuberculosis.

The most unexpected microarray finding was increased expression of CXCR4 in all three arthritis groups. Among 11 different chemokine receptors expressed in RA synovial CD4+ T cells, CXCR4 is the most frequent [17]. Unlike most chemokine receptors, CXCR4 has only one ligand, SDF-1 [16]. SDF-1 was first recognized as being essential in both haematopoiesis and cardiogenesis, and directs the homing of haematopoietic progenitor cells. It is expressed constitutively in bone marrow-derived stromal cell lines and in many adult organs. More importantly for arthritis, SDF-1 is a chemokine for lymphocytes and perhaps monocytes. Because the level of SDF-1 in synovial fluid has not been reported in SpA, we measured SDF-1 levels in seven subjects and subjects with DJD and RA. We found that levels in SpA patients were comparable to those in RA patients. In RA, SDF-1 has been reported to be generated by cultured fibroblasts. Using RT-PCR, we observed high levels of SDF-1 transcripts in one of two SpA fibroblast cultures. By immunohistology, we also noticed that cells generating SDF-1 were sparse and were situated mostly in the lining layers. As CXCR4 was first recognized as a co-receptor for HIV, antagonists are now being developed for the clinical treatment of AIDS. With the availability of specific antagonists, it is now important to understand more about the role of the CXCR4/SDF-1 axis in SpA as well as in RA.


    Acknowledgments
 
Funding for this study was provided by the Deutsche Forschungsgemeinschaft grant KU1182 (JGK), Seoul National University Hospital Clinical Research Institute (YWS), Ghent University Hospital (DB, EMV, FdeK), the Alberta Heritage Foundation for Medical Research (WPM), Deutsche Forschungsgemeinschaft grants SFB548 and TPB4 (EMH), the Nora Eccles Treadwell Foundation and the Southern California Chapter of Arthritis Foundation (DTYY).


    Notes
 
Correspondence to: D. Yu, Rheumatology Division, 35–40 Rehabilitation Center, UCLA, 1000 Veteran Avenue, Los Angeles, CA 90095–167022, USA. Back


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Submitted 21 February 2001; Accepted 12 February 2002