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Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Objective

To investigate the genetic regulation of rheumatoid factor (RF) in a rat model of rheumatoid arthritis, in order to gain understanding of the enigmatic role of RF in the disease.

Methods

IgM-RF and IgG-RF, as well as total levels of immunoglobulins of different subclasses, were measured in sera from rats with pristane-induced arthritis (PIA). The major gene regions were identified by linkage analysis of genetically segregating crosses.

Results

The production of RF was found to correlate with development of arthritis and to be higher in females than in males. Surprisingly, the relatively arthritis-resistant E3 strain had higher levels of RF than the arthritis-susceptible DA strain. In an (E3 × DA)F2 cohort a major locus controlling the levels of IgM-RF in serum was identified on chromosome 11 (Rf1) and another on chromosome 16 (Rf3), and these were not related to arthritis susceptibility. However, the Rf2 locus on chromosome 4 controlled IgG-RF levels, IgG2a levels, and chronic arthritis in males (Pia5). Some previously defined arthritis loci (Pia4, Pia6, Pia7, and Pia8) were found to also control immunoglobulin levels in serum.

Conclusion

RFs are produced in the rat PIA model and correlate with development of arthritis. Gene regions controlling RF and serum immunoglobulin levels were identified, of which some cosegregated with arthritis. This suggests a new focus of study to elucidate the role of RF in the pathogenesis of arthritis.

The discovery of high titers of rheumatoid factor (RF) in sera from patients with rheumatoid arthritis (RA) (1) was the beginning of a new era of molecular research on the disease (2, 3). This research not only represented a new scientific approach to RA, but also contributed to deeper understanding of B cell immunology, in particular, the role of autoantibodies in various diseases. Increased RF titer was incorporated as one of the criteria for the classification of RA, and RF was for a long time the only serum antibody that was helpful in the diagnosis. The sensitivity and specificity of RF for identification of patients with RA were, however, relatively low compared with more recently discovered autoantibodies, such as antibodies to citrullinated proteins (4). Nevertheless, it was anticipated that RF might play a pathogenic role and be a key factor in the regulation of the disease (5, 6).

It was observed that RF of both the IgG and the IgM isotypes, found in the joints of patients with active arthritis (7–9), was produced by plasma B cells within synovial germinal centers in inflamed tissue (10, 11). Importantly, it could be shown that RF in many cases was produced by B cells that had undergone an antigen-driven somatic mutation process, as demonstrated both in humans (12) and in sophisticated experimental models of the disease in animals (13). RF predominantly recognizes epitopes located in the Fc region of the IgG molecule, especially the CH2 and CH3 domains (14, 15), but also human β2-microglobulin (16, 17). However, molecular studies have not identified the T cell– and B cell–recognizing antigens that drive the process.

RF is produced also in healthy individuals and likely has important physiologic functional roles, such as clearance and transportation of immune complexes by crosslinking of IgG in complex with antigen (18–21) and enhancement of antigen capture for antigen-presenting cells. The possible pathogenic effects of overproduced RF might be related to such functions, but this has been difficult to demonstrate directly in RA or in animal models.

Several studies show, however, that RF is a predisposing factor in the development of RA (22–24). Increased levels of RF in symptom-free individuals greatly increase the risk of acquiring RA (25), and high titers predict continuing severity of radiographic damage in inflammatory polyarthritis (26). It has also been found that levels of RF were increased in relatives of RA patients (27), providing evidence of independent genetic control of RF production. Thus, identification of the genes controlling RF production could be helpful in understanding their role in the pathogenesis of RA. However, although this approach seems simple and reasonable, it has met with severe problems. RA is a complex disease affecting a large portion of the population and is influenced by both unknown environmental factors and many genes (28–30). The genetic influence, especially involving the class II major histocompatibility complex (MHC) DR4 haplotype (31, 32), is illustrated by studies demonstrating higher rates of disease concordance in monozygotic twins (33). Involvement of non-MHC regions has been suggested by the results of genome-wide scanning (34, 35), but studies are complicated by genetic heterogeneity, variable penetrance, and poorly defined disease phenotypes.

An alternative approach that avoids many of the difficulties in human genetic studies is to use animal models, which provide better control of genetic heterogeneity and environmental influences. Experimental animal models such as collagen-induced arthritis (CIA) (36), proteoglycan-induced arthritis (37), and pristane-induced arthritis (PIA) (38) have been used to identify several disease loci, among which many are shared between strains and between disease models (39–43). The question of relevance to the human disease process is certainly an issue with animal studies, but their use can be justified if similar pathogenic pathways are used in the different species. RF production is possibly an indicator of some important pathways of RA, but unfortunately the production of similar types of RF has not been reported to occur in animal models. In the most commonly used model, CIA in mice, RFs are produced as self-associating IgG factors detectable only with diffusion in gel enzyme-linked immunosorbent assays (ELISAs) (44). We have now found that IgM- as well as IgG-RF production, measured by a direct ELISA method as used for clinical assays, is readily detectable in certain rat strains, e.g., the E3 and DA strains, and occurs during the development of PIA. This has enabled us to investigate the genetic control of RF production and to compare this with the genetic control of arthritis.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Rat experiments.

DA and E3 rats originating from the Zentralinstitute für Versuchstierzucht (Hannover, Germany) were kept and bred at the animal facilities of the University of Lund Section of Medical Inflammation Research, in a controlled environment with 12-hour light/dark cycles. The animals were housed in polystyrene cages containing wood shavings and fed standard rodent chow and water ad libitum. The rats were 8–9 weeks old when the experiments started.

Arthritis was induced by intradermal injection of 150 μl pristane (Aldrich, Milwaukee, WI) at the base of the tail. The disease course was monitored by grading of the macroscopic appearance of the 4 limbs, using a scoring system of 0–4 (38). Scores for the 4 limbs were summed, such that the highest attainable score was 16. The rats were evaluated 1–3 times per week for at least 110 days after pristane injection.

Phenotyping.

Blood samples were obtained from parental DA and E3 rats on day 0 and days 4, 16, 35, 49, and 100 after pristane injection, by cutting the tip of the tail. In the experiment with F2 animals, sera were obtained on days 6, 14, 35, 49, and 100. The samples were kept at room temperature for ∼3 hours and the sera were separated by centrifugation. Sera were stored at −70°C until assayed. The serum levels of RFs, total levels of IgG and IgM, and levels of IgG subclasses were quantified by ELISA. The RF assay was performed using ELISA plates (Costar, Cambridge, MA) coated overnight with 10 μg/μl rabbit IgG (Sigma, St. Louis, MO) in phosphate buffered saline (PBS; pH 7.4). Total IgG, total IgM, and isotype assays were performed using ELISA plates coated with 5 μg/ml unlabeled goat anti-rat IgG or goat anti-rat μ-chain–specific antibodies (Southern Biotechnology, Birmingham, AL). The conjugates showed no significant difference in binding to purified DA versus E3 immunoglobulin (data not shown). Prior to addition of the serum, all plates were preblocked for 1 hour at room temperature with 1% bovine serum albumin in PBS. All washings, except for preblocking, were performed using Tris buffered saline (pH 7.4) containing 0.1% Tween 20.

All ELISA reactions were performed in duplicate and using 4 steps of serial dilutions (1:10,000). The optimum starting dilutions were determined by titration experiments. For total IgG and IgM detection the sera were diluted 1:1,000 (starting dilution) in PBS–Tween 20 and detection was achieved using goat anti-rat IgG– or goat anti-rat IgM–specific conjugate coupled to horseradish peroxidase (1:5,000 dilution; Jackson ImmunoResearch, West Grove, PA). For isotype detection the sera were diluted 1:10,000 and detection was achieved using biotin-coupled isotype-specific (γ chain) conjugates (anti-IgG1, anti-IgG2a, and anti-IgG2b; Sigma) and streptavidin (1:4,000; Sigma). RF levels (starting dilution 1:100) were investigated using rabbit IgG. RF antibodies (IgG-RF and IgM-RF) were detected as described for total IgG and IgM. The relative concentrations of RF, total immunoglobulins, and isotypes were determined using high-titer reference sera, consisting of a 50% mixture of E3 and DA sera obtained on day 100. All ELISAs were developed using the ABTS system (Boehringer Mannheim, Mannheim, Germany), and absorbance levels at 405 nm were determined with an ELISA reader (Titertek, Huntsville, AL). HyperELISA 3.0 and MacELISA 3.0 software programs were used. Statistical analyses were performed using StatView 5.0 software (SAS Institute, Cary, NC).

Genotyping.

DNA, from samples obtained from the tips of the tail, was prepared according to a standard protocol (45). Microsatellite markers were purchased from Research Genetics (Huntsville, AL) and from the Wellcome Institute for Human Genetics (Oxford, UK). Polymerase chain reaction (PCR) was performed with 200 μM dNTP, 1.5 mM MgCl2, 20 mM Tris HCl (pH 9.0), 0.5 μM of each primer, 1.0 units Taq polymerase (Amersham Pharmacia Biotech, Uppsala, Sweden), and 20 ng genomic DNA, in a total volume of 10 μl. The forward primer was labeled with 32P-γATP (3,000 Ci/mmole, Dupont/NEN, Boston, MA) using T4 polynucleotide kinase (Amersham Pharmacia Biotech). The PCR was run using a thermal cycler (PTC 225; MJ Research, San Francisco, CA) for 25 cycles under the following conditions: 94°C for 3 minutes, 94°C for 15 seconds, 55°C for 2 minutes, 72°C for 3 minutes, and final extension at 72°C for 7 minutes. The PCR products were separated on 6% polyacrylamide gels (Bio-Rad, FMC, Richmond, CA) in 1× Tris–borate–EDTA. The gels were exposed on autoradiographic film (Amersham Hyperfilm MP) at −70°C for 12–36 hours.

Genetic linkage analysis was performed using Map Manager QTX software (46) (genetic map construction) and R/qtl (47) in R-Plus package base (CRAN-Project) (48). The statistics were executed using the imputation model (49). Sex was introduced as a covariate in the analysis and encoded (0 = female, 1 = male). Significance levels for each trait were determined by genome-wide permutation tests (n = 1,000). Significance levels for different traits on each marker were also tested by one-way analysis of variance (ANOVA) using QTL Cartographer (50, 51) and R-Plus.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Arthritis development.

Subcutaneous injection of parental DA rats (n = 10) with pristane led to an arthritis incidence of nearly 100%, while the E3 rats (n = 10) showed no signs of disease (Table 1). In an F2 intercross experiment with 153 rats ([E3 × DA]F2), 58% developed arthritis during the observation time of 100 days, with variation of clinical arthritis (onset, severity, duration, and chronicity) due to segregating genetic susceptibility (Figure 1A). Most commonly, arthritis appeared between day 14 and day 35 after pristane injection. The loci associated with arthritis in this cross have been identified previously (40).

Table 1. Susceptibility to pristane-induced arthritis
StrainArthritis incidence, %Day of onset, mean ± SEMMaximum arthritis score, mean ± SEM*
  • *

    Scale of 0–16.

DA9514 ± 212 ± 0
E30
(E3 × DA)F25832.3 ± 2.57.9 ± 0.55
thumbnail image

Figure 1. A, Arthritis development in male and female (E3 × DA)F2 rats over time, expressed as the mean ± SEM score. Arrowheads indicate the times at which serum samples were obtained. B, Regression plot showing score sum versus IgM rheumatoid factor (IgM-RF) levels on day 35 (only arthritic animals included in the analysis). C, IgG-RF production in parental DA and E3 and (E3 × DA)F2 rats. Sera were obtained on days 0, 4, 16, 35, 49, and 100 from parental animals and on days 6, 14, 35, 49, and 100 from F2 animals. D, RF and total immunoglobulin production on day 35 in healthy and arthritic (E3 × DA)F2 rats. E, IgM-RF production in parental DA and E3 and (E3 × DA)F2 rats. F, Total (TOT) immunoglobulin levels (IgG and IgM) in F2 animals. Antibody concentrations at all time points in C–F are expressed as mean and SEM arbitrary units, determined using high-titer reference sera as described in Materials and Methods. ∗ = P < 0.05 by two-way analysis of variance.

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RF production in E3 and DA rat strains.

Serum levels of IgG-RF and IgM-RF were analyzed in the parental E3 and DA rats with PIA on day 0 and on days 4, 16, 35, 49, and 100 after pristane injection (Figures 1C and E). Before pristane injection (day 0), the E3 rats already had higher levels of both IgM-RF and IgG-RF than the DA rats. In the E3 strain, in particular, IgM-RF titers increased over time, but increased titers of IgM-RF were also observed in the DA strain during the development of arthritis. In contrast, IgG-RF responses initially decreased after pristane injection, but they increased later with progression of the disease course, similar to findings for IgM-RF titers.

RF and immunoglobulin responses in F2 rats.

As in the experiments with parental rats, the F2 rats developed increasing titers of RF after pristane injection (Table 2 and Figures 1C and E). In this experiment the total levels of IgG and IgM (Figure 1F) were also measured, and increased levels were observed over time. A sex difference in the F2 crosses regarding total IgG and IgM responses was noted: females exhibited higher antibody titers than males, especially at the later stages of disease (days 35–100). During the period that arthritis was most active (day 35), arthritic rats had relatively higher levels of IgM-RF, but not of IgG-RF or total IgG or IgM (Table 2 and Figure 1D). The levels of IgM-RF in arthritic animals on day 35 correlated with disease severity as analyzed using linear regression for score sum, i.e., the accumulated disease severity during the experiment (r = 0.297, P = 0.013) (Figure 1B). There was no synergistic sex effect influencing the IgM-RF response on day 35. The levels of IgG isotypes (IgG1, IgG2a, and IgG2b) on days 6, 49, and 100 were investigated (Table 3). Similar to the total IgG responses, increased levels over time were detected for all isotypes. Compared with healthy animals, rats with arthritis had higher levels of IgG1 and IgG2a.

Table 2. Rheumatoid factor (RF) and total immunoglobulin responses and the influence of sex and disease in (E3 × DA) F2 rats*
 Day 6Day 14Day 35Day 49Day 100
  • *

    One hundred fifty-three animals (83 male, 70 female) were analyzed. The number of healthy animals and the number of arthritic animals differed at each time point; values are based on the actual number of animals in each category at the given time point. Values are the mean ± SEM arbitrary units relative to a reference serum, determined by enzyme-linked immunosorbent assay as described in Materials and Methods. P values were determined by one-way analysis of variance (ANOVA), using normally distributed data.

  • ND = not determined (no disease was recorded on day 6).

  • A significant difference was also found in a two-way ANOVA for synergistic effects between sex and disease as interacting phenotypes.

  • §

    Significant due to sex effect.

IgG-RF     
 All animals50.5 ± 5.151.5 ± 7.9123.3 ± 7.8104.1 ± 6.0177.6 ± 11.1
 Male50.6 ± 7.547.5 ± 8.1110.6 ± 8.195.2 ± 4.9167.0 ± 13.9
 Female52.4 ± 7.458.8 ± 15.6136 ± 12.9112.6 ± 12.6194.4 ± 19.0
 P0.9460.2180.4970.8300.499
 HealthyND54.4 ± 8.9119.4 ± 8.499.2 ± 5.6169.5 ± 11.9
 SickND30.5 ± 3.2131.6 ± 16.9114.8 ± 14.9190.5 ± 21.8
 P0.5350.5940.2510.798
IgM-RF     
 All animals57.7 ± 3.383.0 ± 4.181.2 ± 2.789.4 ± 3.7106.7 ± 5.5
 Male56.7 ± 4.582.3 ± 7.274.6 ± 3.487.7 ± 5.2103.4 ± 7.8
 Female59.2 ± 5.384.4 ± 4.987.2 ± 4.589.8 ± 5.8106.9 ± 7.6
 P0.7840.8090.1370.7280.919
 HealthyND82.6 ± 4.676.2 ± 3.284.5 ± 4.398.8 ± 5.9
 SickND86.7 ± 9.191.9 ± 4.8100.0 ± 7.2119.2 ± 10.6
 P0.5610.0140.2330.460
Total IgG     
 All animals57.2 ± 4.6102.8 ± 10.8271.1 ± 22.8326.5 ± 30.9602.4 ± 37.8
 Male58.7 ± 7.887.3 ± 12.4236.0 ± 28.9252.1 ± 24.6525.3 ± 47.6
 Female54.1 ± 4.4117.4 ± 18.0328.4 ± 38.9415.5 ± 64.9703.5 ± 62.6
 P0.3220.0780.0190.0030.003
 HealthyND109.5 ± 12.1270.7 ± 30.2299.5 ± 38.8532.7 ± 44.0
 SickND54.1 ± 11.1271.9 ± 30.9384.5 ± 50.0712.9 ± 66.6
 P0.0500.3350.024§0.034§
Total IgM     
 All animals65.3 ± 1.978.0 ± 3.7108.9 ± 4.3130.0 ± 5.3172.9 ± 6.9
 Male61.9 ± 2.675.8 ± 5.698.4 ± 4.8109 ± 4.9150.7 ± 7.7
 Female71.1 ± 2.983.2 ± 5.6124.0 ± 6.9155.1 ± 9.8200.3 ± 11.3
 P0.0820.0390.010<0.001<0.001
 HealthyND76.6 ± 4.1100.5 ± 4.7126.8 ± 6.5166.9 ± 9.0
 SickND89.6 ± 8.4128.0 ± 8.8136.9 ± 9.2182.2 ± 10.5
 P0.2970.0820.1900.056
Table 3. Isotype responses and influence of sex and disease in (E3 × DA)F2 rats*
PhenotypeDay 6Day 49Day 100
  • *

    Values are the mean ± SEM arbitrary units relative to a reference serum, determined by enzyme-linked immunosorbent assay as described in Materials and Methods. See Table 2 for details.

  • ND = not determined (no disease was recorded on day 6).

IgG1   
 All animals85.9 ± 7.5291.9 ± 14.4321.2 ± 22.9
 Male90.3 ± 11.8256.3 ± 16.8294.3 ± 28.8
 Female83.4 ± 10.1335.5 ± 25.9353.6 ± 40.5
 P0.3310.0060.487
 HealthyND262.5 ± 13.9320.2 ± 28.9
 SickND355.1 ± 32.5322.7 ± 38.1
 P0.0010.981
IgG2a   
 All animals89.2 ± 6.9392.8 ± 23.3496.6 ± 20.1
 Male86.8 ± 9.0373.3 ± 34.1422.1 ± 19.6
 Female83.9 ± 7.5397.0 ± 27.7584.9 ± 36.8
 P0.8350.2060.0002
 HealthyND363.9 ± 26.2468.6 ± 23.8
 SickND456.4 ± 46.6540.4 ± 35.2
 P0.0350.058
IgG2b   
 All animals101.7 ± 3.4177.9 ± 45.3480.4 ± 34.2
 Male103.0 ± 4.8174.1 ± 5.3485.9 ± 46.8
 Female100.2 ± 5.1186.4 ± 8.1484.2 ± 51.6
 P0.5930.3540.924
 HealthyND181.6 ± 5.1470.6 ± 39.1
 SickND169.8 ± 9.1495.9 ± 63.6
 P0.0990.568

Genetic control of RF and immunoglobulin responses in F2 rats.

To investigate whether the increased RF levels could be explained by the general increase in serum immunoglobulin, and whether they were controlled by the same gene regions as those earlier shown to be associated with arthritis (40), we performed a linkage analysis of the (E3 × DA)F2 rats using RF, serum immunoglobulin, and arthritis as traits. The locations of the markers were determined by linear regression, using a P value of 0.001 (46). Analyses of the quantitative trait locus (QTL) locations were performed with the R/qtl package (47) using the imputation model (49). Determinations of significance levels by permutations and influence of covariates, such as sex, were also obtained. The statistics on each marker were tested by one-way ANOVA and found to be significant for all reported loci. Thirteen different QTLs associated with antibody phenotypes could be identified (Table 4 and Figure 2). Seven of the QTLs colocalized with previously characterized arthritis loci (39, 40, 52, 53).

Table 4. Quantitative trait loci for rheumatoid factor (RF) and immunoglobulin responses in pristane-induced arthritis
PhenotypeChromosomeMarkerPeak and flanking marker positions cMLOD scoreThreshold of significance (LOD score) as determined by permutationInheritance patternLocus§Reference
No covariate*CovariateNo covariateCovariate
  • *

    Logarithm of odds (LOD) scores determined according to the imputation model (47). Only significant linkages are reported.

  • LOD scores determined using sex as covariate, imputation model. The contributing phenotype is indicated (M = male; F = female). Only significant linkages are reported.

  • Determined by permutations (n = 1,000), imputation model. P values less than or equal to 0.05 were considered significant.

  • §

    Locus reported in present study, and colocalization to previously published locus.

  • Significant LOD scores were obtained at all time points; highest scores are reported.

Rheumatoid factors          
 IgM-RF (day 49)11D11Wox52.4, 2.7, 17.318.63.5E3 dominantRf1Present study
 IgG-RF (day 6)4D4Rat4186.0, 96.5, 97.55.3 (M)4.6E3 additiveRf2, Pia540
 Igm-RF (day 14)16D16Mit364.7, 66.7, 72.13.63.5E3 recessiveRf3Present study
Total immunoglobulin          
 Total IgM (day 35)3D3Wox1468.5, 69.8, 70.43.53.4aa>bb>abIgm1Present study
 Total IgM (day 14)1D1Rat1229.5, 38.6, 58.05.53.4DA additiveIgm2, Pia852
 Total IgG (day 100)4D4Mit16114.2, 121.6, 130.45.13.4E3 recessiveIgg1, Pia752
 Total IgG (day 6)4D4Wox2097.5, 102.3, 112.75.1 (M)4.8E3 additivePia540
 Total IgG (day 6)5D5Mgh20.0, 15.55.0 (M)4.7E3 dominantIgg2Present study
Isotypes          
 IgG1 (day 100)19D19Mit13.4, 6.6, 16.75.8 (F)4.7DA additiveIgg3, Eae854
 IgG1 (day 49)1D1Wox25149.1, 173.2, 192.13.83.4Da additiveIgg4, Eae754
 IgG1 (day 100)1D1Wox1163.9, 68.4, 70.53.83.4ab>aa>bbIgg5, Cia239
 IgG1 (day 6)12D12Mgh745.3, 48.2, 49.56.13.5E3 additiveIgg6, Apr153
 IgG1 (day 49)12D12Wox1424.7, 27.8, 28.23.53.4aa=bb>abIgg7, Pia4, Cia1240,55
 IgG2a (day 6)14D14Rat3657.2, 65.6, 65.94.9 (F)4.7DA recessiveIgg8, Pia640
 IgG2a (day 49)4D4Rat4186.0, 96.5, 97.55.26.1 (M)3.64.6E3 additivePia540
thumbnail image

Figure 2. A–O, Logarithm of odds (LOD) likelihood plots for chromosomes with identified loci. Traits with the highest LOD scores are shown (see Table 4). The genetic map and markers are available online at http://net.inflam.lu.se. Solid lines represent LOD score curves generated from linkage analysis (without covariate). Broken lines represent LOD scores obtained using sex as a covariate. Horizontal lines represent experiment-wide significance levels (LOD scores obtaining significance) at P = 0.05 according to permutation tests (n = 1,000). Sex-linked associations are indicated as male or female. Analysis was performed using the imputation model (47) in R/qtl (49). RF = rheumatoid factor.

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Loci controlling RF levels in serum.

The enhanced RF production in E3 rats could largely be explained by a QTL (Rf1) on chromosome 11 (D11Wox6–D11Wox5) (Figure 2A). The highest logarithm of odds (LOD) score was obtained for levels of IgM-RF on day 49 (LOD score 18.6, free genetic model), but highly significant LOD scores were obtained at all time points measured. The inheritance pattern was E3 dominant (Figure 3). Since no linkage to Rf1 was found for total levels of IgG or IgM, we concluded that the enhanced RF titers in E3 rats are independent of increased immunoglobulin levels. The identification of the Rf1 locus did not, however, exclude the possibility of other loci controlling RF production and could not explain the arthritis association.

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Figure 3. Influence of genotype at marker D11Wox5 on rheumatoid factor (RF) production over time. Values are the mean ± SEM levels of IgM-RF, in arbitrary units. ∗ = P < 0.0001 by Kruskal Wallis test; ‡ = P < 0.05 by Mann-Whitney U test, E3 homozygous versus E3/DA heterozygous and DA homozygous.

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Another locus (Rf2), on chromosome 4, was associated with IgG-RF production (E3 additive) and had, in addition, previously been identified as a major disease locus, Pia5 (40) (arthritis chronic inflammation score, males, DA additive) (Figure 2B). As early as 6 days after pristane injection, there was a highly significant association, with both IgG-RF and total IgG levels colocalizing to Pia5. As with the originally noted disease linkage to Pia5, linkages could only be detected in males.

Increased IgM-RF levels early after pristane injection (day 14) were significantly linked to chromosome 16 (Rf3) (E3 recessive) (Figure 2C). However, as with Rf1, no association with either disease or immunoglobulin levels could be identified in this cross, indicating that this is another locus harboring genes specifically associated with IgM-RF production, but with an uncertain role in the arthritis process.

Loci controlling immunoglobulin levels in serum.

Several loci were identified as being associated with immunoglobulin levels but not with RF production. Some of these colocalized with disease loci.

Total IgM levels were controlled by loci on chromosomes 1 and 3 (Figures 2D and E). The linkage to chromosome 3 (Igm1) was established for total IgM (day 35). The other locus for total IgM (day 14) on chromosome 1 (Igm2) colocalized with an arthritis locus, Pia8 (maximum clinical score) (40), as detected in another cross involving only DA and E3 genes, the (DA × DXEC)F2 intercross (52).

Total IgG levels were associated with loci on chromosomes 4 and 5 (Figures 2F–H), and in addition, loci associated with various IgG isotypes were found on chromosomes 1, 4, 12, 14, and 19 (Figures 2I–O). Total IgG (day 100) was associated with Pia7 on chromosome 4 (Igg1). Total IgG (day 6) had a sex-dependent linkage (male-dependent) to chromosomes 4 (Pia5) and 5 (Igg2). Another sex-influenced (female-dependent) linkage was seen for the late IgG1 response (day 100) to chromosome 19 (Igg3). IgG1 isotype levels (day 49 and day 100) also showed significant linkages to chromosome 1 Day-49 IgG1 levels (Igg4) colocalized with the Eae7 locus (54) associated with experimental allergic encephalomyelitis (EAE), a model for multiple sclerosis, and day-100 levels (Igg5) colocalized with the Cia2 locus (39). In addition, IgG1 levels were significantly associated with 2 different and non-overlapping loci on chromosome 12. The level of IgG1 on day 6 (Igg6) also colocalized with the Apr1 locus, a locus associated with the acute-phase response (53). The IgG1 level on day 49 (Igg7) colocalized with the Pia4 locus, known be strongly associated with arthritis severity (40, 55). IgG2a associated with 2 different arthritis loci, on day 6 (Igg8) with a chronic arthritis locus (Pia6) on chromosome 14 and on day 49 with chromosome 4 (Pia5). The Igg8 linkage was highly sex dependent and was detected only in the covariate analysis, but its identification could be valuable since it precedes the chronic arthritis linkage in time.

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

In this study we investigated the roles of antibody production in pristane-induced arthritis. Blood samples were obtained at 5 different time points and the sera were analyzed, focusing on rheumatoid factor production and total immunoglobulin levels. A cohort of 153 F2 animals of either the pristane-susceptible DA strain or the E3 strain, which is completely resistant to PIA, was investigated using genome-wide linkage techniques with 288 informative microsatellites. We identified 13 different regions controlling antibody responses, of which 5 loci colocalized with previously defined arthritis loci (PIA loci). The E3 × DA cross has been studied thoroughly, and several disease linkages have been described (Pia1Pia9) (40, 52). Shared PIA loci for antibody responses were found for Pia4, Pia5, Pia6, Pia7, and Pia8. Linkages to previously defined nonarthritis loci were also detected, including the EAE loci Eae7 and Eae8 (54) and the Apr1 locus, an acute-phase protein response locus in the E3 × DA cross (53). Associations with disease loci were established for total immunoglobulin as well as for RF and isotype responses. Total IgG and IgM shared chromosome regions with Pia5, Pia7, and Pia8. RF associated with chromosomes 4 (Pia5), 11, and 16. Isotype responses colocalized with Pia4, Pia5, Pia6, Cia2, Apr1, Eae7, and Eae8.

Several investigations implicate RF as a predisposing factor in arthritis development and severity. Although this has been debated, recent studies clearly show a high correlation between early findings of high-titer RF and later RA disease severity (22–24). The mechanism of persistent production of RF is, however, not known. Elevated RF titers can also be detected in other rheumatic disorders, after infections, and in elderly healthy individuals (56, 57). The RF production following inflammation, which depends on the continuous presence of infectious antigen, can be abolished by antibiotic treatment (58, 59). The pathologic RF involved in RA has been shown to differ from normal anti-IgG in specificity and mutation rates (60, 61). The V-gene utilization in RF is diverse, and the number of somatic mutations indicates an antigen-driven response (7, 62). However, nonmutated RF has also been described, suggesting the possibility that low-affinity RF could be produced directly from the germline repertoire (63). Complement proteins can be produced locally in the synovial tissue (64), and activation of the complement system can cause inflammation by cytokine release following ligation of Fcγ receptors on macrophages (65). Complement has, in addition, been shown to have a role in the induction of the humoral immune response against T cell–dependent antigens (66, 67).

In spite of the identification of several antibody-associated linkages to disease loci, only IgM-RF (day 35) could be shown to correlate with disease severity, expressed as maximum score sum (r = 0.297 by regression analysis). The time of maximum disease severity was found to be close to day 35 (day 32). We could not detect any correlations with RF titers early in the disease course. Investigation of the levels of antibody titers in the parental E3 and DA rats showed that the disease-resistant E3 animals developed, both initially and constantly, higher titers of RF antibodies than the DA rats. By genetically mapping the IgM-RF phenotypes, we demonstrated an E3 allele–dependent linkage to chromosome 11 (Rf1), which was highly significant and unique for the IgM-RF isotype. The facts that E3 rats are resistant to PIA and that the linkage to Rf1 is E3 dependent raise questions about the role of RF in arthritis. It is likely that the major genetic influence was not detected in this study; these loci could have remained undetected due to lower penetrance or genetic interactions. It is also possible that the genetic associations with arthritis by the detected RF loci have not been fully identified. A possible means to clarify this would be to establish congenic strains and to identify the underlying genes explaining the RF loci, and subsequently to directly investigate their role in arthritis.

It is interesting to note that the Igλ locus is located within the Rf1 QTL, and a possible explanation could be that the E3 rat expresses a unique Vλ gene used for RF production. Indeed, the RFs seem to be predominantly of the lambda type, but conclusive evidence for this hypothesis will require positional cloning of the gene. This will take some time since the region needs to be isolated in a minimal, congenic fragment as has been shown to be essential for the positional cloning of the Ncf1 gene in the Pia4 locus (55). There are indeed other genes and mechanisms that could also explain the increased RF production by the Rf1 locus and in the E3 strain. Several studies have shown the importance of RF in complement binding and clearance of immune complexes (18–21, 25, 26). One possible explanation would be a defect in complement binding or activation in the E3 rat. This would, if complement activation contributes to disease development, explain the fact that E3 rats do not develop arthritis. Furthermore, the consistently higher levels of IgM-RF and IgG-RF in these rats could indicate unregulated production secondary to the inflammation process.

Chromosome 4 displayed several interesting linkages. Associations with the Pia5 locus, identified for inflammation score, were found for RF as well as for total IgG and IgG2a isotypes. Comparison of the allelic influences on disease and antibody responses revealed different inheritance origins. Apparently, E3 genes control the immunoglobulin responses, whereas disease is influenced by DA genes. Other studies have shown linkages to chromosome 4 in autoimmune thyroiditis (68) and adjuvant arthritis (69). Interestingly, it has been shown that the homologous mouse region, on chromosome 6, harbors genes controlling antibody responses (70). Another linkage to rat chromosome 4, total IgG levels (day 100), colocalized to the Pia7 locus, originally defined from a 2-locus interaction model for clinical score (day 19) in E3 × DA and DA × DXEC crosses (52). The region also harbors major loci controlling oil-induced arthritis in the DA × LEW.1A cross (71) and CIA in DA × BN rats (72). Taken together, these findings indicate that chromosome 4 contains important genes that regulate several forms of autoimmune disease.

Another difference in inheritance pattern was seen for the Pia8 locus, previously established for maximum clinical score in the DA × DXEC cross (E3 dominant, females) (52). In the present study, total IgM (day 14) linked in a DA-additive mode. The Pia4 locus, originally described for arthritis severity in E3 × DA animals and now identified as the Ncf1 gene (55), and Pia6 (chronic arthritis) were shared for isotype responses (IgG1 and IgG2a). An early phenotype linkage for interleukin-6 (IL-6) (day14) to Pia6 has also been reported (53), indicating a possible role of IL-6 in the B cell response. The IgG1 linkage to Pia4 could indicate involvement of the complement system; in mice, small RF-like immune complexes induce an IgG1-RF response (67).

In summary, our findings indicate that antibody responses in pristane-induced arthritis are under genetic control and contribute to disease development. The cosegregation of several of the antibody-controlling genes with arthritis loci indicates a pathogenic connection. However, although E3 rats are genetically resistant to arthritis, they have higher RF levels, and some E3 genes dominantly control the RF production. Interestingly, other loci operate in the opposite direction. The identification of the responsible genes, and their interactions, for both arthritis association and RF production will be of critical importance, and the present study identifies a platform for this work. The genetic control of arthritis and RF production is likely equally as complex in humans, and there is a clear need to identify the basic biologic pathways operating, in order to explain these phenomena.

Acknowledgements

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

We would like to thank Lennart Lindström and Carlos Palestro for taking care of the animals.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES
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