Skip Navigation


Rheumatology Advance Access originally published online on April 25, 2006
Rheumatology 2006 45(12):1466-1476; doi:10.1093/rheumatology/kel095
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
45/12/1466    most recent
kel095v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (2)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Szodoray, P.
Right arrow Articles by Centola, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Szodoray, P.
Right arrow Articles by Centola, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A genome-scale assessment of peripheral blood B-cell molecular homeostasis in patients with rheumatoid arthritis

P. Szodoray1–3, P. Alex1, M. B. Frank1, M. Turner1, S. Turner1, N. Knowlton1, C. Cadwell1, I. Dozmorov1, Y. Tang1, P. C. Wilson2, R. Jonsson3 and M. Centola1

1Microarray Research Facility, Arthritis and Immunology Research Program and 2Molecular Immunogenetics Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA and 3Broegelmann Research Laboratory, The Gade Institute, University of Bergen, Bergen, Norway.

Correspondence to: Peter Szodoray MD, PhD, Broegelmann Research Laboratory, The Gade Institute, University of Bergen, Armauer Hansen Bldg, N-5021 Bergen, Norway. E-mail: peter.szodoray{at}gades.uib.no


   Abstract
Top
 Abstract
Introduction
 Patients and methods
 Results
 Discussion
 Acknowledgements
 References
 
Objective. While rheumatoid arthritis (RA) is considered a prototypical autoimmune disease, the specific roles of B-cells in RA pathogenesis is not fully delineated.

Methods. We performed microarray expression profiling of peripheral blood B-cells from RA patients and controls. Data were analysed using differential gene expression analysis and ‘gene networking’ analysis (characterizing clusters of functionally inter-relelated genes) to identify both regulatory genes and the pathways in which they participate. Results were confirmed by quantitative real-time polymerase chain reaction and by measuring the levels of 10 serum cytokines involved in the pathways identified.

Results. Genes regulating and effecting the cell-cycle, proliferation, apoptosis, autoimmunity, cytokine networks, angiogenesis and neuro-immune regulation were differentially expressed in RA B-cells. Moreover, the serum levels of several soluble factors that modulate these pathways, including IL-1ß, IL-5, IL-6, IL-10, IL-12p40, IL-17 and VEGF were significantly increased in this cohort of RA patients.

Conclusions. These results outline aspects of the multifaceted role B-cells play in RA pathogenesis in which immune dysregulation in RA modulates B-cell biology and thereby contributes to the induction and perpetuation of a pathogenic humoral immune response.

KEY WORDS: Rheumatoid arthritis, Peripheral blood B-cells, Microarray, Multiplex cytokine assay.


   Introduction
Top
 Abstract
 Introduction
Patients and methods
 Results
 Discussion
 Acknowledgements
 References
 
Rheumatoid arthritis (RA) is characterized by chronic inflammation involving connective tissues throughout the body, but particularly diarthrodial joints [1]. In a majority of patients with an active disease, circulating activated B-cells secrete rheumatoid factor (RF) in vitro [2, 3], the levels of which correlate with the degree of clinical inflammatory activity and the likelihood of a severe and erosive disease course [4]. Moreover, a significant proportion of patients also have raised levels of antibodies to cyclic citrullinated peptides (anti-CCP) [5] and to proteins such as the immunoglobulin heavy-chain binding protein (anti-BiP) [6], suggesting that the dysregulation of humoral immunity plays a significant role in RA.

Recently, the depletion of RA B-cells with targeted anti-B-cell therapy (rituximab) had profound disease-ameliorating effects, providing the first direct evidence that B-cells influence disease activity in RA [7]. Following B-lymphocyte depletion, a positive clinical response occurred in correlation with a significant drop in the levels of C-reactive protein (CRP) and auto-antibodies [IgA-, IgM- and IgG-class RFs, and anti-CCP]. These data suggest that B-cell dysregulation plays a significant role in RA pathogenesis that influences disease activity, prognosis and therapeutic response.

B-cells therefore mediate RA pathogenesis, which in turn affects B-cell biology, a complex interplay that can obscure our understanding of the relevance of a given B-cell process in RA. A small fraction of the total B-cell pool is found in the peripheral circulation and diminished numbers of circulating CD19+ B-cells are observed in RA patients—the likely result of increased trafficking and accumulation of activated and autoreactive B-cells in the synovial membrane of affected joints [8]. Since circulating B-lymphocytes are the source of synovial B-cells, it is informative to analyse the circulating counterparts of the locally accumulated autoreactive B-cells. Activated peripheral B-cells with autoreactive characteristics are typical in RA [8], and as such are a ready source for identification of disease-specific changes in B-cell gene-expression. Moreover, circulating B-cells have proven to be a useful source for identifying mechanistic anomalies in auto-immune diseases including RA [8–12].

Microarrays provide a powerful means of cataloguing genes that are affected by or are mediating a given disease. Increasingly informative softwares that aid in translating catalogues of genes from microarray experiments into an understanding of pathology are available. Biological systems are both redundant and highly networked. As a consequence, many functionally interrelated genes tend to be affected when consequences of pathology on a given pathway are significant. These pathways can then be characterized through secondary analysis of differentially regulated gene sets using clustering and networking algorithms, as well as visual analysis of gene function.

Herein, we utilized genome-scale microarrays with robust bioinformatics tools to create a catalogue of genes that are dysregulated in peripheral blood B-cells from RA patients relative to controls. To assess the relevance of these results, serum levels of 10 cytokines known to modulate a subset of the pathways identified were assessed. This combined-screening approach identified B-cell genes and B-cell growth, development and activation pathways affected in RA patients, and the potential disease modulators derived from these dysregulated cells.


   Patients and methods
Top
 Abstract
 Introduction
 Patients and methods
Results
 Discussion
 Acknowledgements
 References
 
Patients and controls
The study population consisted of 10 adult patients (four males and six females; mean age 42 yr, range 22–67; mean duration of disease 1.6 yr, range 0.5–7) with active RA who fulfilled the American College of Rheumatology criteria [13]. All patients were referred from the Arthritis Department of the McBride Clinic and from primary physicians within the Oklahoma City Metropolitan Area. All patients were disease-modifying anti-rheumatic drug (DMARD) naïve at enrolment, and stable doses of non-steroidal anti-inflammatory drugs (NSAIDs) and/or prednisolone (≤10 mg daily) were allowed. The Health Assessment Questionnaire (HAQ) and the Disease Activity Score (DAS28) [14] were assessed for all patients. Two patients from the original cohort were excluded from microarray analysis due to the low integrity of the separated RNA [cohort of patients for microarrays now being (n = 8) three males and five females (mean age 38.25 yr, range 22–67; mean duration of disease 1.7 yr, range 0.5–7)]. Clinical and laboratorial characteristics of the patients included in the study are summarized in Table 1. The cohort studied also included normal age- and sex-matched healthy controls (n = 10, of which two were excluded from microarray analysis to match with the microarray patient cohort). The institutional review board at each study site approved the protocol, and all patients gave written informed consent according to the declaration of Helsinki.


View this table:
[in this window]
[in a new window]

  		TABLE 1. Patient characteristics—clinical and laboratory data

 
RNA and sera preparation
Blood was collected in endotoxin-free silicone-coated tubes, both with and without additive. Sera were prepared as described [15]. B-cells were isolated by negative selection using a B-cell Negative Isolation Kit according to the manufacturer's instructions (Dynal Biotech Inc., Lake Success, NY, USA). To assess purity, B-cell preparations were stained with a monoclonal mouse, anti-human, anti-CD19-FITC-conjugated antibody (Sigma-Aldrich, St Louis, MO, USA), and the percent of CD19-positive cells was assessed by flow cytometry (FACSCalibur, Becton-Dickinson, San Jose, CA, USA). The purity of B-cells was over 95% for all samples. RNA was isolated from B-cells using a hybrid protocol that utilizes two common commercial RNA preparation reagents as previously described [16]. Separate pools of RNA from patients with RA or controls were formed by combining a total mass of 2 g total RNA within each group as previously described by Lawrance et al. [17].

Preparation of cDNA for microarray hybridization
An Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) was used to measure RNA concentration and integrity as specified by the manufacturer. Integrity of the samples was determined using 28:18 S rRNA ratios with threshold ratio of 1.1 to insure that the RNA integrity adequate for analysis. Samples with significantly lower ratios typically exhibit degradation that affects the gene-expression levels in a non-uniform manner. A representative electropherogram of total RNA from one patient and one control are shown in Fig. 1. Flourescently labelled cDNA was synthesized and purified as previously described [15].


Figure 1
View larger version (23K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 1. Quantitation of the purity of the RNA samples. Agilent 2100 Bioanalyzer was used to analyse the concentration and integrity of the RNA samples as specified by the manufacturer. The purity of the samples was determined by the 28S/18S rRNA ratio. (A) Simulated gel picture showing the reference RNA ladder and the specific RNA bands in a set of controls and patients samples. (B) Histogram, representing the purity of the RNA sample by showing the 28S/18S rRNA ratio in a control. (C) Histogram, representing the purity of the RNA sample by showing the 28S/18S rRNA ratio in an RA patient.

 
Microarrays
A commercially available genome-scale oligonucleotide library containing gene-specific 70 mer oligonucleotides representing 21 329 human genes was used for microarray production. The set includes 16 replicate spots of 12 random negative controls that have no significant homology to known human DNA sequences (Qiagen Inc., Valencia, CA, USA). Oligonucleotides were spotted onto Corning® UltraGAPSTM amino-silane coated slides, which were then rehydrated with water vapour, snap dried at 90°C. Oligonucleotide DNAs were covalently fixed to the surface of the glass using 300 mJ of ultraviolet radiation at a 254 nm wavelength. Unbound, free amines on the glass surface were blocked for 15 min with moderate agitation in a solution of 143 mM succinic anhydride dissolved in 1-methyl-2-pyrolidinone, 20 mM sodium borate, pH 8.0. Slides were rinsed for 2 min in distilled water, immersed for 1 min in 95% ethanol and dried with a stream of nitrogen gas.

Hybridization was performed in an automated liquid delivery, air-vortexed, hybridization station for 9 h at 58°C under an oil-based cover slip (Ventana Medical Systems Inc., Tucson, AZ, USA). Microarrays were washed at a final stringency of 0.1X SSC. Microarrays were scanned using a simultaneous dual-colour, 48-slide scanner (Agilent Technologies). Fluorescent intensity was quantified using ImageneTM software (BioDiscovery, Marina del Rey, CA, USA).

Quantitative real-time PCR (qRT-PCR)
Gene-specific PCR primers were identified for each gene using ‘Primer Express’ software (Applied Biosystems Inc., Foster City, CA, USA) and the primers commercially prepared (Qiagen Inc.). Reverse-transcription reactions were run using 2 mg of pooled total RNA from patients or from controls. An external standard was added to each sample prior to cDNA synthesis, and the resultant levels of the PCR product were used for normalization of gene-expression concentrations. This consisted of 0.1 ng of Arabidopsis thaliana (RCA) mRNA (Stratagene, La Jolla, CA, USA). Synthesis of cDNA was done using the reverse transcriptase Omniscript RTTM according to the manufacturer's instructions (Qiagen Inc.). Small molecules and enzymes were removed from the reaction and a buffer exchange was carried out using the Montage PCR 96-well size exclusion filter system (Millipore, Billerica, MA, USA). For qRT-PCR, 2 µl of this cDNA reaction was then added to a pre-made reaction mixture that contained the following components: 7.5 µl of a standard commercial premixed PCR reagent solution (Syber Green 2x PCR Master Mix, Applied Biosystems), 0.6 µl of a solution containing 30 pmoles of gene-specific forward and reverse primers, 30 pmoles of primers specific for the internal standard, and 4.9 ml of water. Reactions were denatured at 95°C for 10 min and then PCR was carried out on an ABI PRISM 7700 thermocycler (Applied Biosystems) using the following cycling conditions for 40 cycles: 95°C for 15 s and 60°C for 1 min. A control reaction that did not contain human RNA (‘no template control’) was also run for each primer set to determine the extent, if any, of non-specific amplification occurring in a given reaction. All reactions were run in triplicate and average values reported.

Simultaneous cytokine quantitation with a suspension array system
Serum levels of 10 serum proteins, including interleukin (IL)-1ß, IL-5, IL-6, IL-10, IL-12p40, IL-17, vascular endothelial growth factor (VEGF), Tumor Necrosis Factor (TNF-{alpha}) and Interferon (IFN-{gamma}), selected on the basis of the related genomics data were measured using a bead-based immunoflourescence assay (Luminex Inc., Austin, TX, USA) as previously described [18, 19].

Statistical analysis
Normalization was done using a robust two-step procedure as described [15, 20–22]. We employed a stringent cutoff, 10 S.D. above the mean of the random negative controls and a ratio in excess of three, to minimize the number of false positive results. From previous work [22], we demonstrated that microarray noise is well characterized, therefore Gaussian expectation can be used to estimate inter-array variability. Inter-patient variability tends to be higher than inter-array variability (3 vs 9% CV). Capitalizing on this knowledge allows us to run a pair of pooled chips and accurately estimate the number of false positive results. The probability of selecting a false positive in this experimental dataset using the stringent cutoffs described above is P<9.35 E-06. The calculated family-wise error rate (FWER) based on this probability is very low, there is only a 19.6% chance of selecting one false positive in this analysis [23, 24]. An independent verification by qRT-PCR was also performed to assess the validity of the microarray results (see aforesaid). For the biometric multiplex assay, statistical differences in measured values were analysed using a Mann–Whitney U-test (P<0.05).


   Results
Top
 Abstract
 Introduction
 Patients and methods
 Results
Discussion
 Acknowledgements
 References
 
Microarray gene-expression profiles of peripheral B-cells from RA patients vs controls
RNA from B-cells of eight RA patients and eight unaffected healthy individuals was pooled, labelled and hybridized to genome-scale microarrays containing probes for 21 329 genes. Data were normalized, and differentially expressed genes were identified [15, 20–22]. In hybridization-based expression profiling, false positive selections can arise due to cross-hybridization, which results in misidentification of a given differentially expressed signal. The potential for cross-hybridization is inversely proportional to the signal level. Oligonucleotides with no significant homology to characterized human DNA sequences were included in the array to provide an internal standard for non-specific hybridization such that a selection threshold for minimizing cross-hybridization signals could be established. To insure the fidelity of selections, a stringent expression threshold was used to identify likely true positives. Only genes with signal intensities greater than 10 S.D. above the mean of the distribution of random negative controls, and induced or repressed 3-fold or greater in RA vs controls, were catalogued and used for further analysis. Using these selection criteria, 305 genes were identified as over-expressed in RA B-cells, and 231 genes were repressed, i.e. more highly expressed in control B-cells (Fig. 2). The online supplemental table summarizes expression levels for all differentially identified genes (http://microarray.omrf.org/szodoray/array.xls). The data discussed in this publication have been deposited in NCBIs Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/), and are accessible through GEO Series accession number GSE4255 [NCBI GEO] .


Figure 2
View larger version (63K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 2. Gene-expression profile of peripheral blood B-cells from patients with RA and healthy individuals. Scatterplot of log ratios of gene expression profiles from patients vs controls are plotted. Genes that are differentially expressed in patients are represented outside outlier lines with upregulated genes in red spots and repressed genes in green.

 
Functional clustering of differentially expressed genes
The most significant phenotypical changes will be the most likely to be represented by a group of differentially expressed and functionally related genes. As such, the inherent redundancies of biological systems facilitate the elucidation of pathways of likely pathological significance through the functional clustering of genes identified as differentially expressed.

Functionally associated networks of the differentially expressed genes were constructed using Pathway Assist software (Stratagene Inc.). This software uses the KEGG, DIP and BIND databases and natural language scans of Medline to identify functional associations among differentially expressed genes and represents these associations graphically as a network. A significant proportion of the genes with defined biological function could be associated with mechanisms of relevance to the normal and pathological function of B-cells defined as five functional classes that include: cell activation, proliferation and apoptosis (31 genes); autoimmunity (five genes); cytokines, cytokine-receptors and cytokine-mediated processes (eight genes); neuro-immune regulation (two genes); and angiogenesis (five genes) (Table 2, Fig. 3A–C).


View this table:
[in this window]
[in a new window]

  		TABLE 2. Microarray gene-expression profile of RA patients vs controls peripheral blood B-cell

 

Figure 3
View larger version (52K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 3. Functional associations of differentially expressed genes. Genes that are differentially expressed were analysed using Pathway Assist software. The graphical output delineating a functionally related network of genes is shown. Genes selected as being differentially expressed are represented as red ovals. Major biological processes related to these genes are represented as yellow squares. Pink circles represent genes that are functionally related to the genes used for analysis. Green ovals denote a hormonal process. Orange hexagons represent a small molecule regulated process. Functional interconnections between elements are represented by arrows containing small squares or circles, the legends of which are depicted in the figure. The plus or minus symbols within the squares represent positive and negative regulation, respectively. (A) Representation of genes involved in B-cell activation, cell-cycle progression, proliferation, apoptosis inhibition and genes of angiogenesis and angiogenic pathways. (B) Representation of genes regulating cytokines, cytokine-receptors, genes related to cytokine-mediated processes and genes related to autoimmunity. (C) Representation of genes related to the neuro-immune regulation of B-cells.

 
Confirmation of differential expression by qRT-PCR
The initial validation of the microarray results was done by re-evaluating the relative expression of 13 genes found to be up-regulated in RA B-cells with qRT-PCR. Genes from each of the five functional classes were randomly chosen for re-analysis including: CNNM4, BARD1, U5-116KD, TLR9, IL-5RA, IL-10, IL-12A, PTX3, CRLF1, CHRNB1, DRD2, MMP28 and VEGFC. Consistent with the microarray results, these genes were also found to be up-regulated in RA B-cells by RT–PCR, albeit minimally for one of the 13: IL-5RA (Fig. 4).


Figure 4
View larger version (17K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 4. Confirmation of the microarray data by qRT-PCR in B-cells from patients and controls. Total mRNA from B-cells of patients and controls were analysed by quantitative RT-PCR to test for differential expression of 13 genes, randomly selected from each subgroup. The y-axis shows the fold-increase in individual gene expression in patients over healthy controls. Genes are grouped based on functional subgroups as revealed by the microarray analysis.

 
Multiplex profiling of soluble inflammatory mediators in RA patients’ sera
Functional pathway analysis revealed that many of the genes modulated in RA B-cells are associated with cytokines and growth factors likely to influence B-cell biology (Fig. 3A–C). To assess if these soluble mediator-driven pathways may be of relevance to RA pathology and to further test the validity the microarray results, serum concentrations of nine B-cell-modulating cytokines and one growth factor identified by pathway analysis were measured in 10 RA patients and 10 unaffected individuals. Cytokines included in this analysis were chosen based on observed up-regulation of the gene encoding the cytokine or a subunit of the cytokine in RA patients. Also chosen were cytokines directly or indirectly associated, with disease-relevant pathways identified in the above analysis which suggested that a given cytokine-modulated pathway was disrupted in RA B-cell biology.

The average serum levels of seven cytokines and VEGF were significantly higher in patients including: IL-1ß (RA: mean 275±79.44 pg/ml vs control: mean 12±0.55 pg/ml, P = 0.023); IL-5 (RA: mean 146.6±62.34 pg/ml vs control: mean 5.95±2.84 pg/ml, P<0.0001); IL-6 (RA: mean 306±123.9 pg/ml vs control: mean 6.55±1.78 pg/ml, P = 0.0001); IL-10 (RA: mean 118.9±46.8 pg/ml vs control: mean 8.902±0.43 pg/ml, P<0.0001); IL-12p40 (RA: mean 564.9±117.3 pg/ml vs control: mean 165.4±43.93 pg/ml, P = 0.043); IL-17 (RA: mean 213.7± 91.81 pg/ml vs control: mean 6.52±0.57 pg/ml, P = 0.014) and VEGF (RA: mean 120.2±29.83 pg/ml vs control: mean 43.2±17.29 pg/ml, P = 0.028).

The serum levels of TNF-{alpha} (RA: mean 55.81±34.08 pg/ml vs control: mean 29.02±15.82 pg/ml, P = 0.57) and IFN-{gamma} (RA: mean 17.72±11.6 pg/ml vs control: mean 4.89±0.85 pg/ml, P = 0.52) were non-significantly elevated in RA patients compared with healthy controls (Fig. 5).


Figure 5
View larger version (15K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		FIG. 5. Concentration of cytokines and VEGF in the sera of RA patients (n = 10) and controls (n = 10). Serum levels (in pg/ml) of 10 analytes were measured using a biometric sandwich immunoassay. Bars show the mean and SEM. *Significantly different from untreated values by Mann–Whitney U-test (P<0.05).

 

   Discussion
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
Acknowledgements
 References
 
In order to identify genes and pathways of significance to the pathological state of RA B-cells, gene expression profiles from genome-scale microarrays were compared between peripheral B-cells of RA patients and unaffected controls. A large set of genes with diverse functions were differentially expressed, suggesting that the dysregulation of B-cell biology in the RA patients within this cohort is multifaceted. Many clinically relevant regulatory processes in RA are highly coordinated. We utilized this principle to highlight likely regulators and effectors of disease pathology within the list of differentially regulated genes using software that identifies sets of genes that are members of established functional networks.

Our general interpretation of the data is that the gene-expression pattern of these B-cells both reflects a response to exogenous, pro-inflammatory mediators (e.g. cytokines), also endogenous gene-regulatory defects.

Functional clustering of genes from RA B-cells revealed complex networks of inflammatory mediators and pathways that likely influence disease pathology. These networks were categorized into five broad-based groups including: cell activation, proliferation and apoptosis; autoimmunity; neuro-immune regulation; angiogenesis; and cytokines, cytokine receptors and cytokine-mediated processes.

The largest functional group contains genes modulating B-cell activation, cell-cycle progression, proliferation and apoptosis, suggesting that RA B-cells are hyperproliferative and activated in response to disease pathology. In germinal centres, B-cells are rescued from cell death by antigen-binding; however, self-reactive B-cells undergo negative selection, which results in deletion by apoptosis or anergy. Herein, several apoptosis-inhibitory genes were overexpressed, suggesting that these RA B-cells bypass negative selection during auto-antigen encounter and escape from deletion. These results support the hypothesis that auto-reactive B-cells survive in RA due to disease-specific defects in pathways controlling proliferation, activation and apoptosis. This hypothesis is further supported by the fact that the genes identified are modulated by soluble factors known to support B-cell proliferation, survival and activation, including IL-1, IL-5, IL-6 and IL-10, and that these factors are themselves highly up-regulated in RA patients’ sera.

Genes, associated with autoimmunity comprise a second functional cluster. Particularly notable in this cluster is the Toll-like receptor 9 (TLR9), transcript variant A, which is overexpressed in RA B-cells. In humans, the expression of TLR9 appears to be relatively restricted to B-cells and CD123+ dendritic cells where it functions as a modulator of bacterial DNA recognition. Upon the detection of CpG motifs on bacterial DNA, B-cells are induced to proliferate, secrete immunoglobulins, up-regulate costimulatory molecules and have enhanced abilities to induce Th1 cell responses. Bacterial DNA costimulates murine B-cell activation through cell-membrane immunoglobulins, thereby promoting the development of antigen-specific responses [25]. Microbial pathogen-associated molecular patterns (PAMPs) engage and/or up-regulate TLR expression, and thus create a synergy with autoantibody–autoantigen immune complexes in mice [26, 27]. The importance of TLR9 activation in systemic lupus erythematosus pathophysiology was recently demonstrated in studies showing that DNA-containing immune complexes traffic to intracellular compartments, wherein they initiate TLR9 activation and a resulting production of proinflammatory cytokines [26].

TLR9-signalling thus provides an intriguing potential mechanistic link between the association of infection and disease flares in RA [27]. The fact that the TLR9 gene is up-regulated on average in RA B-cells suggests that it may also be of central importance in the initiation and perpetuation of autoimmunity and inflammation in RA in an infection-independent manner.

A third functional grouping was composed of neuro-immune modulators. This included the gene encoding the cholinergic receptor, nicotinic, ß-polypeptide 1 (CHRNB1). Acetylcholine (ACh) is well known as a neurotransmitter in both the central and peripheral nervous systems in mammalian species characteristically identified as a modulator of several classical immune reactions via the vagus nerve. Extensive parasympathetic and sympathetic inputs modulate immune activity through the ACh and adrenergic receptors. Both muscarinic and nicotinic ACh-receptors have been identified in murine lymphocytes and their stimulation by muscarinic and nicotinic agonists elicits a variety of functional and biochemical effects [28]. Recent findings support the idea that immunological stimulation leads to ACh-receptor gene expression in lymphocytes in animal models. Stimulation of nicotinic ACh-receptors with ACh or nicotine causes rapid and transient Ca2+ signalling and activation in B-cells [29]. On the basis of these findings, it has been postulated that the parasympathetic nervous system may play a role in immune-neurohumoral crosstalk.

A second neurotransmitter-receptor-gene overexpressed in RA B-cells was the dopamine receptor D2 (DRD2), transcript variant 1. Previously, it has been described that human B-cells have a consistent expression of D2 receptors [30]. Moreover, the serum levels of dopamine are higher in the RA subjects than in the control subjects, suggesting that neuro-immune regulation is altered in RA [31]. Our findings underscore the importance of communication between peripheral B-lymphocytes and the sympathetic and parasympathetic system, the imbalance of which may contribute to the activation of circulating B-lymphocytes and the development of humoral autoimmune processes in RA.

A fourth grouping is composed of genes related to angiogenesis, including the pro-angiogenic factors VEGFC, FGF1 and F2RF2. Angiogenesis is central to the development and perpetuation of rheumatoid synovitis. New blood vessel formation in the synovium occurs early in RA and likely fuels pannus development [32–34].

Not surprisingly, cytokine and cytokine-receptor genes are also differentially expressed in RA B-cells. The receptor genes include IL-5 receptor-alpha (IL5RA) and an IL-5-specific subunit of the heterodimeric IL-5 receptor. IL-5 induces B-cell proliferation [35]. Within this cohort, serum IL-5 levels were increased in the RA patients 24-fold on average. The fact that the IL-5-receptor gene is up-regulated in RA B-cells and IL-5 is up-regulated in RA sera imply that this cytokine plays a role in the dysregulation of B-cell biology in RA. Interestingly, human B-cells express message for IL-5R but can respond to IL-5 only if appropriately stimulated to undergo terminal differentiation, suggesting that if IL-5 affects RA B-cells, it functions by modulating a B-cell pool that is in a higher state of activation on average than B-cells from healthy controls.

The receptor for IL-17 family member IL-17E [36], the IL-17 receptor homologue precursor (EVI27), was also up-regulated in RA B-cells. IL-17 is expressed in RA synovium where it contributes to inflammation, angiogenesis, tissue remodelling, and joint errosions directly and through induction of other proinflammatory cytokines [37–39]. Consistent with previous results, which suggest that IL-17 plays a significant role in RA, serum levels of IL-17 were found to be highly increased in this RA cohort.

The cytokine-gene transcripts encoding IL-10 and IL-12A (IL-12p35) were also up-regulated in RA B-cells. Similarly, serum levels of IL-10 and IL-12 are up-regulated in the RA patients within this cohort. IL-10 plays a complex and varied role in B-cell biology including modulating cell viability and differentiation, enhancing the expression of MHC class II antigens and immunoglobulins, and inhibition of apoptosis of germinal-centre B-cells. In activated T-cells, IL-12 augments antigen-dependent proliferation [40, 41], providing a potential positive feedback during cognate T–B interactions, which may contribute to the chronicity of immune dysregulation in RA.

Interestingly, a clear role for Th1 immunity has also been demonstrated, with recent evidence including characterization of disease-promoting Th1 CD4+ memory cells in RA synovium and dysregulation of Th2 and CD25+ regulatory T-cells in RA patients, which is thought to predispose patients to peripheral tolerance breakdown and subsequent pathological Th1-mediated immune responses, albeit in a Th2-dependent manner [42, 43].

The simultaneous secretion of IL-10 and IL-12 by B-cells (e.g. effector B-subsets, such as Be2-cells) has been previously described [44]. Our studies demonstrated that on average the RA B-cells derived from the patients in this cohort have higher IL-10 and IL-12p35 transcripts when compared with healthy controls, suggesting that B-cells are not strictly polarized in RA, but contribute to immunopathogenesis in a complex manner.

In conclusion, these studies suggest that the effects of RA on B-cells and the effects of B-cells on RA are pleotropic. Genes and cytokines regulating key aspects of B-cell immunology including growth, differentiation, apoptosis and activation are differentially expressed and likely dysregulated in RA B-cells. These results support the growing evidence most directly demonstrated in clinical trials of B-cell depletion therapy, that B-cells contribute to disease pathology in RA [45]. The results presented herein highlight multiple key pathophysiological processes of the disease likely affected by the B-cell-depletion agents and may aid in guiding future applications of this therapy as well as the development of novel therapies targeting the genes and pathways highlighted in this study.

Formula


   Acknowledgements
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 Acknowledgements
References
 
We thank Dr Vera Levy, Jennifer Pesina and Gemma L. Wallis for excellent technical help and for help in the recruitment of patients. We also thank Drs Eugene Arthur, Robert Hynd, Larry Willis and Don Flinn at the McBride Clinic (Bone and Joint Hospital) and OU Physicians for providing the patients’ samples and clinical data. This work was funded by Grants P20 RR16478-04, P20 RR020143, P20 RR017703, P20 RR15577 and NIH 01700172 from the National Institutes of Health.

The authors have declared no conflicts of interest.


   References
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 Acknowledgements
 References
 

  1. Jasin HE. Mechanisms of tissue damage in rheumatoid arthritis. In Koopman WJ (Ed.). Arthritis and allied conditions: a textbook of rheumatology 14 edn (Lippincott Williams & Wilkins, Philadelphia) 2001: pp. 1128–52.
  2. Olsen N, Ziff M, Jasin HE. (1982) In vitro synthesis of immunoglobulins and IgM-rheumatoid factor by blood mononuclear cells of patients with rheumatoid arthritis. Rheumatol Int 2:59–66.[CrossRef][ISI][Medline]
  3. Olsen NJ and Jasin HE. (1985) Synthesis of rheumatoid factor in vitro: implications for the pathogenesis of rheumatoid arthritis. Semin Arthritis Rheum 15:146–56.[CrossRef][ISI][Medline]
  4. Boling EP, Ohishi T, Wahl SM, Misiti J, Wistar R Jr, Wilder RL. (1987) Humoral immune function in severe, active rheumatoid arthritis. Clin Immunol Immunopathol 43:185–94.[CrossRef][ISI][Medline]
  5. Reparon-Schuijt CC, van Esch WJ, van Kooten C, et al. (2001) Secretion of anti-citrulline-containing peptide antibody by B lymphocytes in rheumatoid arthritis. Arthritis Rheum 44:41–7.[CrossRef][ISI][Medline]
  6. Corrigall VM, Bodman-Smith MD, Fife MS, et al. (2001) The human endoplasmic reticulum molecular chaperone BiP is an autoantigen for rheumatoid arthritis and prevents the induction of experimental arthritis. J Immunol 166:1492–8.[Abstract/Free Full Text]
  7. Dorner T and Burmester GR. (2003) The role of B-cells in rheumatoid arthritis: mechanisms and therapeutic targets. Curr Opin Rheumatol 15:246–52.[CrossRef][ISI][Medline]
  8. Wagner U, Kaltenhauser S, Pierer M, Wilke B, Arnold S, Hantzschel H. (2002) B lymphocytopenia in rheumatoid arthritis is associated with the DRB1 shared epitope and increased acute phase response. Arthritis Res 4:R1.[CrossRef][Medline]
  9. Hansen A, Gosemann M, Pruss A, et al. (2004) Abnormalities in peripheral B cell memory of patients with primary Sjögren's syndrome. Arthritis Rheum 50:1897–908.[CrossRef][ISI][Medline]
  10. Odendahl M, Keitzer R, Wahn U, et al. (2003) Perturbations of peripheral B lymphocyte homoeostasis in children with systemic lupus erythematosus. Ann Rheum Dis 62:851–58.[Abstract/Free Full Text]
  11. Manda G, Neagu M, Livescu A, Constantin C, Codreanu C, Radulescu A. (2003) Imbalance of peripheral B lymphocytes and NK cells in rheumatoid arthritis. J Cell Mol Med 7:79–88.[ISI][Medline]
  12. Itoh K, Patki V, Furie RA, et al. (2000) Clonal expansion is a characteristic feature of the B-cell repetoire of patients with rheumatoid arthritis. Arthritis Res 2:50–8.[CrossRef][ISI][Medline]
  13. Arnett FC, Edworthy SM, Bloch DA, et al. (1988) The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum 31:315–24.[ISI][Medline]
  14. van der Heijde DM, van't Hof M, van Riel PL, van de Putte LB. (1993) Development of a disease activity score based on judgment in clinical practice by rheumatologists. J Rheumatol 20:579–81.[ISI][Medline]
  15. Jarvis JN, Dozmorov I, Jiang K, et al. (2004) Novel approaches to gene expression analysis of active polyarticular juvenile rheumatoid arthritis. Arthritis Res Ther 6:R15–32.[CrossRef][ISI][Medline]
  16. Khan J, Bittner ML, Saal LH, et al. (1999) cDNA microarrays detect activation of a myogenic transcription program by the PAX3-FKHR fusion oncogene. Proc Natl Acad Sci USA 96:13264–9.[Abstract/Free Full Text]
  17. Lawrance IC, Fiocchi C, Chakravarti S. (2001) Ulcerative colitis and Crohn's disease: distinctive gene expression profiles and novel susceptibility candidate genes. Hum Mol Genet 10:445–56.[Abstract/Free Full Text]
  18. Szodoray P, Jellestad S, Alex P, et al. (2004) Programmed cell death of peripheral blood B cells determined by laser scanning cytometry in Sjögren's syndrome with a special emphasis on BAFF. J Clin Immunol 24:600–11.[CrossRef][ISI][Medline]
  19. Szodoray P, Alex P, Brun JG, Centola M, Jonsson R. (2004) Circulating cytokines in primary Sjögren's syndrome determined by a multiplex cytokine array system. Scand J Immunol 59:592–9.[CrossRef][ISI][Medline]
  20. Dozmorov I and Centola M. (2003) An associative analysis of gene expression array data. Bioinformatics 19:204–11.[Abstract/Free Full Text]
  21. Dozmorov IM, Knowlton NS, Tang Y, Centola M. (2004) Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays. BMC Bioinformatics 5:53.[CrossRef][Medline]
  22. Knowlton N, Dozmorov IM, Centola M. (2004) Microarray data analysis toolbox (MDAT): for normalization, adjustment and analysis of gene expression data. Bioinformatics.
  23. Benjamini Y and Hochberg Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J ROY STAT SOC B MET 57:289–300.
  24. Xiao Y, Segal MR, Rabert D, et al. (2002) Assessment of differential gene expression in human peripheral nerve injury. BMC Genomics 3:28.[Medline]
  25. Krieg AM, Yi AK, Matson S, et al. (1995) CpG motifs in bacterial DNA trigger direct B-cell activation. Nature 374:546–9.[CrossRef][Medline]
  26. Means TK, Latz E, Hayashi F, Murali MR, Golenbock DT, Luster AD. (2005) Human lupus autoantibody-DNA complexes activate DCs through cooperation of CD32 and TLR9. J Clin Invest 115:407–17.[CrossRef][ISI][Medline]
  27. He B, Qiao X, Cerutti A. (2004) CpG DNA induces IgG class switch DNA recombination by activating human B cells through an innate pathway that requires TLR9 and cooperates with IL-10. J Immunol 173:4479–91.[Abstract/Free Full Text]
  28. Kawashima K and Fujii T. (2003) The lymphocytic cholinergic system and its biological function. Life Sci 72:2101–9.[CrossRef][ISI][Medline]
  29. Kawashima K and Fujii T. (2003) The lymphocytic cholinergic system and its contribution to the regulation of immune activity. Life Sci 74:675–96.[CrossRef][ISI][Medline]
  30. McKenna F, McLaughlin PJ, Lewis BJ, et al. (2002) Dopamine receptor expression on human T- and B-lymphocytes, monocytes, neutrophils, eosinophils and NK cells: a flow cytometric study. J Neuroimmunol 132:34–40.[CrossRef][ISI][Medline]
  31. Mukai E, Nagashima M, Hirano D, Yoshino S. (2000) Comparative study of symptoms and neuroendocrine-immune network mediator levels between rheumatoid arthritis patients and healthy subjects. Clin Exp Rheumatol 18:585–90.[ISI][Medline]
  32. Firestein GS. (1999) Starving the synovium: angiogenesis and inflammation in rheumatoid arthritis. J Clin Invest 103:3–4.[ISI][Medline]
  33. Koch AE. (1998) Angiogenesis: implications for rheumatoid arthritis. Arthritis Rheum 41:951–62.[CrossRef][ISI][Medline]
  34. Gibaldi M. (1998) Regulating angiogenesis: a new therapeutic strategy. J Clin Pharm 38:898–903.
  35. Huston MM, Moore JP, Mettes HJ, Tavana G, Huston DP. (1996) Human B-cells express IL-5 receptor messenger ribonucleic acid and respond to IL-5 with enhanced IgM production after mitogenic stimulation with Moraxella catarrhalis. J Immunol 156:1392–401.[Abstract]
  36. Lee J, Ho WH, Maruoka M, et al. (2001) IL-17E, a novel proinflammatory ligand for the IL-17 receptor homolog IL-17Rh1. J Biol Chem 276:1660–4.[Abstract/Free Full Text]
  37. Kehlen A, Thiele K, Riemann D, Langner J. (2002) Expression, modulation and signalling of IL-17 receptor in fibroblast-like synoviocytes of patients with rheumatoid arthritis. Clin Exp Immunol 127:539–46.[CrossRef][ISI][Medline]
  38. Lubberts E. (2003) The role of IL-17 and family members in the pathogenesis of arthritis. Curr Opin Investig Drugs 4:572–7.[Medline]
  39. Romas E, Gillespie MT, Martin TJ. (2002) Involvement of receptor activator of NFkappaB ligand and tumor necrosis factor-alpha in bone destruction in rheumatoid arthritis. Bone 30:340–6.[Medline]
  40. Bertagnolli MM, Lin BY, Young D, Herrmann SH. (1992) IL-12 augments antigen-dependent proliferation of activated T lymphocytes. J Immunol 149:3778–83.[Abstract]
  41. Zhang Z and Bridges SL Jr. (2001) Pathogenesis of rheumatoid arthritis: role of B lymphocytes. Rheum Dis Clin North Am 27:335–53.[CrossRef][ISI][Medline]
  42. Nanki T and Lipsky PE. (2000) Cytokine, activation marker, and chemokine receptor expression by individual CD4(+) memory T cells in rheumatoid arthritis synovium. Arthritis Res 2:415–23.[CrossRef][ISI][Medline]
  43. Skapenko A, Leipe J, Lipsky PE, Schulze-Koops H. (2005) The role of the T cell in autoimmune inflammation. Arthritis Res Ther 7:(Suppl 2), S4–14.[Medline]
  44. Mosmann T. (2000) Complexity or coherence? Cytokine secretion by B cells. Nat Immunol 1:465–6.[CrossRef][ISI][Medline]
  45. In De Vita S, Zaja F, Sacco S, De Candia A, Fanin R, Ferraccioli G (Eds.). Efficacy of selective B cell blockade in the treatment of rheumatoid arthritis: evidence for a pathogenetic role of B cells. Arthritis Rheum (2002) 46:2029–33.[CrossRef][ISI][Medline]
  46. Arumugam T, Simeone DM, Schmidt AM, Logsdon CD. (2004) S100P stimulates cell proliferation and survival via receptor for activated glycation end products (RAGE). J Biol Chem 279:5059–65.[Abstract/Free Full Text]
  47. Schafer BW and Heizmann CW. (1996) The S100 family of EF-hand calcium-binding proteins: functions and pathology. Trends Biochem Sci 21:134–40.[CrossRef][ISI][Medline]
  48. Shapiro MA, Fitzsimmons SP, Clark KJ. (1999) Characterization of a B-cell surface antigen with homology to the S100 protein MRP8. Biochem Biophys Res Commun 263:17–22.[CrossRef][ISI][Medline]
  49. Egawa T, Albrecht B, Favier B, et al. (2003) Requirement for CARMA1 in antigen receptor-induced NF-kappa B activation and lymphocyte proliferation. Curr Biol 13:1252–8.[CrossRef][ISI][Medline]
  50. Newton K and Dixit VM. (2003) Mice lacking the CARD of CARMA1 exhibit defective B lymphocyte development and impaired proliferation of their B- and T-lymphocytes. Curr Biol 13:1247–51.[CrossRef][ISI][Medline]
  51. Hoshino S, Imai M, Mizutani M, et al. (1998) Molecular cloning of a novel member of the eukaryotic polypeptide chain-releasing factors (eRF). Its identification as eRF3 interacting with eRF1. J Biol Chem 273:22254–9.[Abstract/Free Full Text]
  52. Wang CY, Shi JD, Yang P, et al. (2003) Molecular cloning and characterization of a novel gene family of four ancient conserved domain proteins (ACDP). Gene 306:37–44.[CrossRef][ISI][Medline]
  53. Girling R, Partridge JF, Bandara LR, et al. (1993) A new component of the transcription factor DRTF1/E2F. Nature 365:468.[Medline]
  54. Chan JA, Olvera M, Lai R, Naing W, Rezk SA, Brynes RK. (2002) Immunohistochemical expression of the transcription factor DP-1 and its heterodimeric partner E2F-1 in non-Hodgkin lymphoma. Appl Immunohistochem Mol Morphol 10:322–6.[CrossRef][ISI][Medline]
  55. Fry CJ, Slansky JE, Farnham PJ. (1997) Position-dependent transcriptional regulation of the murine dihydrofolate reductase promoter by the E2F transactivation domain. Mol Cell Biol 17:1966–76.[Abstract]
  56. Nal B, Mohr E, Silva MI, et al. (2002) Wdr12, a mouse gene encoding a novel WD-Repeat Protein with a notchless-like amino-terminal domain. Genomics 79:77–86.[CrossRef][ISI][Medline]
  57. Roussel MF, Ashmun RA, Sherr CJ, Eisenman RN, Ayer DE. (1996) Inhibition of cell proliferation by the Mad1 transcriptional repressor. Mol Cell Biol 16:2796–801.[Abstract]
  58. Loijens JC, Boronenkov IV, Parker GJ, Anderson RA. (1996) The phosphatidylinositol 4-phosphate 5-kinase family. Adv Enzyme Regul 36:115–40.[CrossRef][ISI][Medline]
  59. Wu LC, Wang ZW, Tsan JT, et al. (1996) Identification of a RING protein that can interact in vivo with the BRCA1 gene product. Nat Genet 14:430–40.[CrossRef][ISI][Medline]
  60. Fujii J and Ikeda Y. (2002) Advances in our understanding of peroxiredoxin, a multifunctional, mammalian redox protein. Redox Rep 7:123–30.[CrossRef][ISI][Medline]
  61. Li XL, Blackford JA, Judge CS, et al. (2000) RNase-L-dependent destabilization of interferon-induced mRNAs. A role for the 2–5A system in attenuation of the interferon response. J Biol Chem 275:8880–8.[Abstract/Free Full Text]
  62. Sugiura T and Yamashita U. (2000) B-cell stimulating activity of metallothionein in vitro. Int J Immunopharmacol 22:113–22.[CrossRef][ISI][Medline]
  63. Phipps RP, Stein SH, Roper RL. (1991) A new view of prostaglandin E regulation of the immune response. Immunol Today 12:349–52.[CrossRef][ISI][Medline]
  64. Szala S, Kasai Y, Steplewski Z, Rodeck U, Koprowski H, Linnenbach AJ. (1990) Molecular cloning of cDNA for the human tumor-associated antigen CO-029 and identification of related transmembrane antigens. Proc Natl Acad Sci USA 87:6833–7.[Abstract/Free Full Text]
  65. Turner M. (2002) B-cell development and antigen receptor signalling. Biochem Soc Trans 30:812–5.[CrossRef][ISI][Medline]
  66. Olayioye MA, Neve RM, Lane HA, Hynes NE. (2000) The ErbB signaling network: receptor heterodimerization in development and cancer. EMBO J 19:3159–67.[CrossRef][ISI][Medline]
  67. Yarden Y and Sliwkowski MX. (2001) Untangling the ErbB signalling network. Nat Rev Mol Cell Biol 2:127–37.[CrossRef][ISI][Medline]
  68. Lee JR, Ha YJ, Kim HJ. (2003) Cutting edge: induced expression of a RhoA-specific guanine nucleotide exchange factor, p190RhoGEF, following CD40 stimulation and WEHI 231 B-cell activation. J Immunol 170:19–23.[Abstract/Free Full Text]
  69. Takuwa Y, Takuwa N, Sugimoto N. (2002) The Edg family G protein-coupled receptors for lysophospholipids: their signaling properties and biological activities. J Biochem 131:767–71.[Abstract/Free Full Text]
  70. Neel BG. (1997) Role of phosphatases in lymphocyte activation. Curr Opin Immunol 9:405–20.[Medline]
  71. Lombardi G, Dianzani C, Miglio G, Canonico PL, Fantozzi R. (2001) Characterization of ionotropic glutamate receptors in human lymphocytes. Br J Pharmacol 133:936–44.[CrossRef][ISI][Medline]
  72. Foley J, Ley CA, Parysek LM. (1994) The structure of the human peripherin gene (PRPH) and identification of potential regulatory elements. Genomics 22:456–61.[CrossRef][ISI][Medline]
  73. Unger A, Panayi GS, Lessof MH. (1975) Carcinoembryonic antigen in rheumatic diseases. Rheumatol Rehabil 14:19–24.
  74. Aasheim HC, Munthe E, Funderud S, Smeland EB, Beiske K, Logtenberg T. (2000) A splice variant of human ephrin-A4 encodes a soluble molecule that is secreted by activated human B lymphocytes. Blood 95:221–30.[Abstract/Free Full Text]
  75. Ishimi Y. (1997) A DNA helicase activity is associated with an MCM4, -6, and -7 protein complex. J Biol Chem 272:24508–13.[Abstract/Free Full Text]
  76. Ohi N, Tokunaga A, Tsunoda H, et al. (1999) A novel adenovirus E1B19K-binding protein B5 inhibits apoptosis induced by Nip3 by forming a heterodimer through the C-terminal hydrophobic region. Cell Death Differ 6:314–25.[CrossRef][ISI][Medline]
  77. Zhang YW, Ding LS, Lai MD. (2003) Reg gene family and human diseases. World J Gastroenterol 9:2635–41.[ISI][Medline]
  78. Liston P, Roy N, Tamai K, et al. (1996) Suppression of apoptosis in mammalian cells by NAIP and a related family of IAP genes. Nature 379:349–53.[CrossRef][Medline]
  79. Brunet A, Bonni A, Zigmond MJ, et al. (1999) Akt promotes cell survival by phosphorylating and inhibiting a Forkhead transcription factor. Cell 96:857–68.[CrossRef][ISI][Medline]
  80. Liu C, Xu Z, Gupta D, Dziarski R. (2001) Peptidoglycan recognition proteins: a novel family of four human innate immunity pattern recognition molecules. J Biol Chem 276:34686–94.[Abstract/Free Full Text]
  81. Leadbetter EA, Rifkin IR, Hohlbaum AM, Beaudette BC, Shlomchik MJ. (2002) Marshak-Rothstein A. Chromatin-IgG complexes activate B-cells by dual engagement of IgM and Toll-like receptors. Nature 416:603–7.[CrossRef][Medline]
  82. Breviario F, d'Aniello EM, Golay J, et al. (1992) Interleukin-1-inducible genes in endothelial cells. Cloning of a new gene related to C-reactive protein and serum amyloid P component. J Biol Chem 267:22190–7.[Abstract/Free Full Text]
  83. Franceschini F, Cavazzana I, Malacarne F, et al. (2003) Anti-Ro/SSA antibodies in rheumatoid arthritis (RA). Arthritis Res Ther Suppl 5:8.[ISI][Medline]
  84. Kubo M, Ihn H, Kuwana M, et al. (2002) Anti-U5 snRNP antibody as a possible serological marker for scleroderma-polymyositis overlap. Rheumatology 41:531–4.[Abstract/Free Full Text]
  85. Okano Y, Targoff IN, Oddis CV, et al. (1996) Anti-U5 small nuclear ribonucleoprotein (snRNP) antibodies: a rare anti-U snRNP specificity. Clin Immunol Immunopathol 81:41–7.[CrossRef][ISI][Medline]
  86. Lorain S, Quivy JP, Monier-Gavelle F, et al. (1998) Core histones and HIRIP3, a novel histone-binding protein, directly interact with WD repeat protein HIRA. Mol Cell Biol 18:5546–56.[Abstract/Free Full Text]
  87. Giri JG, Kincade PW, Mizel SB. (1984) Interleukin 1-mediated induction of kappa-light chain synthesis and surface immunoglobulin expression on pre-B cells. J Immunol 132:223–8.[Abstract]
  88. Huston MM, Moore JP, Mettes HJ, Tavana G, Huston DP. (1996) Human B cells express IL-5 receptor messenger ribonucleic acid and respond to IL-5 with enhanced IgM production after mitogenic stimulation with Moraxella catarrhalis. J Immunol 156:1392–401.[Abstract]
  89. Itoh K and Hirohata S. (1995) The role of IL-10 in human B-cell activation, proliferation, and differentiation. J Immunol 154:4341–50.[Abstract]
  90. Elenkov IJ, Wilder RL, Chrousos GP, Vizi ES. (2000) The sympathetic nerve – an integrative interface between two supersystems: the brain and the immune system. Pharmacol Rev 52:595–638.[Abstract/Free Full Text]
  91. Platzer C, Fritsch E, Elsner T, Lehmann MH, Volk HD, Prosch S. (1999) Cyclic adenosine monophosphate-responsive elements are involved in the transcriptional activation of the human IL-10 gene in monocytic cells. Eur J Immunol 29:3098–104.[CrossRef][ISI][Medline]
  92. Elson GC, Graber P, Losberger C, et al. (1998) Cytokine-like factor-1, a novel soluble protein, shares homology with members of the cytokine type I receptor family. J Immunol 161:1371–9.[Abstract/Free Full Text]
  93. Wolfsberg TG, Primakoff P, Myles DG, White JM. (1995) ADAM, a novel family of membrane proteins containing A Disintegrin And Metalloprotease domain: multipotential functions in cell–cell and cell–matrix interactions. J Cell Biol 131:275–8.[Free Full Text]
  94. Trochon V, Li H, Vasse M, et al. (1998) Endothelial metalloprotease-disintegrin protein (ADAM) is implicated in angiogenesis in vitro. Angiogenesis 2:277–85.[CrossRef][Medline]
  95. Skjerpen CS, Nilsen T, Wesche J, Olsnes S. (2002) Binding of FGF-1 variants to protein kinase CK2 correlates with mitogenicity. EMBO J 21:4058–69.[CrossRef][ISI][Medline]
  96. Folkman J and Shing Y. (1992) Angiogenesis. J Biol Chem 267:10931–4.[Free Full Text]
  97. Tsopanoglou NE and Maragoudakis ME. (2004) Role of thrombin in angiogenesis and tumor progression. Semin Thromb Hemost 30:63–9.[CrossRef][ISI][Medline]
  98. Kevorkian L, Young DA, Darrah C, et al. (2004) Expression profiling of metalloproteinases and their inhibitors in cartilage. Arthritis Rheum 50:131–41.[CrossRef][ISI][Medline]
  99. Clavel G, Bessis N, Boissier MC. (2003) Recent data on the role for angiogenesis in rheumatoid arthritis. Joint Bone Spine 70:321–6.[CrossRef][ISI][Medline]
  100. Chen H, Treweeke AT, West DC, et al. (2000) In vitro and in vivo production of vascular endothelial growth factor by chronic lymphocytic leukemia cells. Blood 96:3181–7.[Abstract/Free Full Text]
revised version accepted 21 February 2006.