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Abstract

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

Objective

Susceptibility to inflammatory arthritis is determined by a complex set of environmental and genetic factors, but only a portion of the genetic effect can be explained. Conventional genome-wide screens of arthritis models using crosses between inbred mice have been hampered by the low resolution of results and by the restricted range of natural genetic variation sampled. The aim of this study was to address these limitations by performing a genome-wide screen for determinants of arthritis severity using a genetically heterogeneous cohort of mice.

Methods

Heterogeneous stock (HS) mice derive from 8 founder inbred strains by serial intercrossing (n > 60), resulting in fine-grained genetic variation. With a cohort of 570 HS mice, we performed a genome-wide screen for determinants of arthritis severity in the K/BxN serum–transfer model.

Results

We mapped regions on chromosomes 1, 2, 4, 6, 7, and 15 that contain quantitative trait loci influencing arthritis severity at a resolution of a few megabases. In several instances, these regions proved to contain 2 quantitative trait loci: the region on chromosome 2 included the C5 fraction of complement known to be required for K/BxN serum–transfer arthritis but also contained a second adjacent quantitative trait locus, for which an intriguing candidate is Ptgs1 (Cox1). Interesting candidates on chromosome 4 included the Padi family, encoding the peptidyl arginine deiminases responsible for citrulline protein modification; suggestively, Padi2 and Padi4 RNA expression was correlated with arthritis severity in HS mice.

Conclusion

These results provide a broad overview of the genetic variation that controls the severity of K/BxN serum–transfer arthritis and suggest intriguing candidate genes for further study.

The pathogenesis of rheumatoid arthritis (RA) remains poorly understood, and its complex genetic basis has been difficult to dissect. Genome-wide association studies (GWAS) have identified >30 genomic regions that contribute to the susceptibility to RA (1). Although the causal polymorphism has been formally identified in a few cases, most of the regions identified in GWAS remain ambiguous and not connected to a functionally relevant polymorphism. Similarly, genome-wide screens in mouse models of RA have implicated >50 loci in the pathogenesis of inflammatory arthritis, but only a few genes within these regions have been identified (2, 3).

The K/BxN serum–transfer model of arthritis is well-suited to genetic analysis. K/BxN mice develop a progressive inflammatory arthritis that shares the major histologic features of RA (4, 5). K/BxN mouse T cells and B cells recognize a ubiquitously expressed protein, glucose-6-phosphate isomerase (GPI), leading to very high production of anti-GPI antibodies. The transfer of serum from arthritic K/BxN mice or of purified anti-GPI antibodies into healthy mice, even lymphocyte-deficient mice, provokes arthritis within 1–4 days after injection; arthritis severity peaks at 10–14 days and resolves slowly over the next 2 weeks (6). There is considerable variation among inbred strains in the susceptibility to arthritis induced by the transfer of K/BxN mouse serum (7–9). The serum-transfer model isolates the inflammatory effector-phase cascade from the early immunologic initiation events, thereby reducing the complexity of the factors under examination. Indeed, we and other investigators have been able to pinpoint a few loci where natural genetic variation conditions the severity of arthritis induced by K/BxN mouse serum, including the Hc gene that encodes the C5 fraction of complement (10, 11) and the Il1b locus.

Quantitative trait locus (QTL) mapping experiments in crosses of inbred mouse strains are limited to studying the regions and polymorphisms that differ between a pair of strains. In addition, such intercross or backcross studies have poor resolving power, because the distances between informative recombinants are great. As a consequence, fine-resolution mapping for complex traits requires either impossibly large numbers of animals in a single cross or many generations of intercrossing to achieve sub-centimorgan resolution (12).

The Northport heterogeneous stock (HS) was generated from 8 founder inbred strains by serial intercrossing for >50 generations (13). This breeding scheme accumulates recombinants and turns each HS chromosome into a fine-grained mosaic of the founder strain genomes. HS mice allow for QTL mapping at very high resolution, because the distance between recombinants is small (∼2 cM), sampling a diverse cross-section of the genetic variability in mice. The potential of genetic mapping in HS mice was exploited in a large-scale QTL mapping experiment, in which 843 QTLs for 97 traits were mapped to an average interval of 2.8 Mb (14). Here, we attempted to exploit these characteristics and performed a whole-genome scan in a cohort of HS mice tested for sensitivity to arthritis by transfer of arthritogenic K/BxN mouse serum. We identified a number of genomic regions that contribute to the effector phase of inflammatory arthritis

MATERIALS AND METHODS

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

Mice.

The HS/Npt mice tested were from the fifty-third generation of circular breeding from an initial combination of 8 inbred strains (A/J, AKR/J, BALB/cJ, C3H/HeJ, C57BL/6J, DBA/2J, CBA/J, and Lp/J) (13). Breedings were set up to avoid the production of homozygous C5-deficient mice, and 570 mice were challenged by the injection of K/BxN mouse serum (150 μl on days 0 and 2). The thickness of each ankle was measured, and a clinical score for each paw (range 0–3) was determined on days 0–10. Four phenotypes were calculated as follows: maximum ankle thickness achieved minus thickness on day 0 (MAT); maximum clinical index (MCI) (days 0–10); integrated ankle thickening (IAT) (days 0–10); and integrated clinical index (ICI) (days 0–10). On day 14, the mice were killed, their spleens were snap-frozen for RNA isolation, and serum samples were collected for determination of anti-GPI titers by enzyme-linked immunosorbent assay (7). Genotyping of tail DNA for 1,449 single-nucleotide polymorphisms (SNPs) was performed with the Illumina mouse medium-density linkage panel. Genotyping of additional SNPs for higher-density analysis was performed using the Sequenom iPLEX platform (15) or by allele-specific fluorogenic polymerase chain reaction (PCR) (16).

Genome-wide QTL mapping.

For linkage disequilibrium (LD) mapping of QTLs, the underlying haplotype structure of each mouse was inferred and then tested for association with MAT. Haplotype descent was inferred using the hidden Markov model implemented in HAPPY (17), using known founder genotypes and recombination distances to provide a probabilistic estimate of haplotype descent at each interval for each mouse. For mouse i at marker interval m, HAPPY computes a vector gi(m) containing the expected proportion of genetic material descended from each of the 36 possible distinct pairs of founder haplotypes. This vector is then used to characterize variation at the locus and for tests of phenotype association.

Genetics studies of outbred populations such as HS are prone to confounding from uneven genetic relatedness between individuals, requiring careful treatment to avoid false-positive associations (18) from familial structure (sibship), common environment (cage), and the day of testing (cohort). Using the method described by Solberg Woods et al (19), we modeled the effect of a putative QTL at locus m on the MAT of animal i as follows in equation 1:

  • equation image

where C is a set of fixed effects covariates that includes sex, xi(c) is the value of covariate c for individual i, εi ∼N(0, σ2) is the residual, and sibshipk[i], cohorth[i], and cagea[i] are normally distributed random effects. For (unconditional) mapping of single QTLs, we obtained the nominal significance of the QTLs at each locus m by comparing the fit of a null model, Q = 0, and an alternative (QTL) model, Q = βTgi(m), via a likelihood ratio test (LRT), using the procedures described by Solberg Woods et al (19). Variation at the Hc locus affects K/BxN serum–transfer arthritis (9, 10). To determine QTLs with variation uncorrelated with Hc and independent QTLs in the region around Hc, we controlled for the mean effect of the Hc heterozygote by including C5 genotype status among the covariates in C of equation 1.

Dissecting QTL regions.

Fine-mapping of imputed SNPs.

To maximize resolution, we imputed nongenotyped SNPs in regions of interest, based on strain distribution patterns (SDPs) available for HS founder strains (http://www.sanger.ac.uk/cgi-bin/modelorgs/mousegenomes/snps.pl), using a variant of the approach described by Yalcin et al (20), as follows: probabilities in gi(m) are combined with the SDP s to give the expected number of high alleles (additive dosage [ai(s)]) and the probability of being a heterozygote (dominance dosage [di(s)]). The association of a SNP with MAT, controlling for C5, was tested by comparing the fit of equation 1 with Q = ai(s) + di(s) versus Q = 0 via an LRT, as described above.

Modeling joint action of SNPs by local multilocus analysis.

To identify plausible sets of independent signals within local regions, we performed a multilocus analysis using the resample model averaging approach described by Valdar et al (18). Starting with the mixed model in equation 1, we incorporated the effects of additional multiple SNPs into Q, choosing by stepwise selection SNPs that minimized the Bayesian information criterion (21). We applied this procedure to 200 random 63% subsamples, recording for each SNP the proportion of subsamples in which it was selected. This resample model inclusion probability (RMIP) provides a measure of how robust an association is to correlations among other candidate SNPs and to finite sampling of the individuals.

Due to LD, our set of imputed SNPs was highly redundant, such that ∼100,000 SNPs might reduce to ∼3,000 unique representatives. Our selection scheme chose between equally best-fitting SNPs at random. To identify regions in which SNPs are consistently included but where high correlation meant that no single SNP predominated, we used an RMIP sliding window based on the range probability described by Valdar and colleagues (18). For each SNP position, we report the average number of SNPs included in a 0.5-Mb radius.

Expression analysis.

Genome-wide expression profiles for whole spleens of each of the 8 HS mouse founder strains were determined using Affymetrix MuGene ST 1.0 microarrays. Transcripts from the Padi family were quantitated by TaqMan reverse transcription–PCR in splenic RNA from inbred mice and 278 of the experimental HS mice. Timed expression profiles from the synovial fluid and synovium of C57BL/6J mice after K/BxN serum transfer have been reported previously (22).

RESULTS

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

K/BxN serum–transfer arthritis in an HS mouse cohort.

The HS mice tested were from the fifty-third generation of the pedigree derived from a combination of 8 inbred strains (13), which include high as well as low arthritis responders (8). Three of the 8 strains harbor a spontaneous mutation in the Hc gene, which encodes C5 and was previously determined to have an important impact on arthritis in the K/BxN serum–transfer model (10). Therefore, the parents of the HS mice used in this study were genotyped for the Hc null allele, and breedings were set up to avoid the production of homozygous C5-deficient mice. Of the resulting progeny, 570 C5+/+ or C5+/I mice (4 weeks of age) were injected with K/BxN mouse serum. The experiment was performed over a 7-week period, and a strict operating protocol was adopted to minimize noise and drift, with a single operator and set times to minimize confounders from diurnal variation. A single lot of sera was used throughout, at a dose determined to yield robust disease in C57BL/6 mice.

A wide spectrum of arthritis severity was exhibited by the experimental mice, as shown for 16 representative mice in Figures 1A and B. Some mice exhibited strong disease, with scores as high as those in the strongest-responder inbred strains. Fifty mice showed no arthritis whatsoever, and this was unlikely to be attributable to misinjection, because residual anti-GPI titers in the sera of these nonresponders were not markedly different from those of responder mice (Figure 1C). Some mice with low residual anti-GPI titers exhibited robust arthritis, suggesting that residual titers might also be attributable to accelerated clearance or deposition in some cases. Therefore, all mice were included in the genetics analysis. The different metrics from measured ankle thickening (MAT, IAT) or from operator evaluation (MCI, ICI) were all highly correlated.

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Figure 1. Arthritis phenotype distribution in the heterogeneous stock (HS) mice. A, Change in ankle thickness (left) and the clinical index (right) over time in 16 representative mice. B, Correlations between integrated ankle thickness (IAT) and integrated clinical index (ICI) and between maximum ankle thickness (MAT) and maximum clinical index (MCI). C, Anti–glucose-6-phosphate isomerase (anti-GPI) antibody (Ab) titers in the sera of the experimental mice on day 10, expressed as a percentage of the original GPI titer of the injected sera, plotted against MCI. D, Genome-wide marker–trait association to the MAT phenotype, for the whole set of 570 HS mice tested. The y-axis shows the significance of association, and the x-axis shows the location in megabases. The broken line represents the threshold for genome-wide association (−log[P] = 4.67). E, Position of regions exceeding the genome-wide significance threshold for MAT. The start and end of the interval are defined as the first and last single-nucleotide polymorphisms (SNPs) to exceed the genome-wide significance threshold. The “peak position” is the location of the SNP with the highest −log(P) value within the interval for the full genetic model.

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Genetic loci associated with arthritis severity in the HS cohort.

We identified QTLs for arthritis severity using LD mapping of inferred haplotypes in combination with hierarchical modeling of family and covariate effects (19). This approach involved use of the HAPPY model (17) of genetic association, in which the phenotype is associated not with individual markers but instead with inferred descent between markers. We collected genotypes in 2 stages, first performing a mid-density screen to identify major regions of association, then genotyping at higher density around peaks identified in the first stage. For the first stage, we genotyped 1,449 SNPs (average spacing 1.5 Mb) including a SNP that identified the C5 loss-of-function mutation in the Hc gene. The proportion of mice successfully genotyped varied from 90% to 100% for the different SNPs.

Given the high degree of correlation among the different arthritis measures, we focused our QTL analysis on MAT. Potential QTLs identified in the mid-density scan as exceeding the genome-wide significance threshold were found on chromosomes 1, 2, 3, 4, 6, 7, and 15 and were subjected to higher-density genotyping at an average spacing of one marker every 200 kb, yielding the overall profile depicted in Figures 1D and E.

As expected, a highly significant peak on chromosome 2 near 34.9 Mb, the location of the Hc gene, was detected. This region proved much broader than others, suggesting more complexity. The 83.3–89.1-Mb region on chromosome 1 may coincide with that identified on distal chromosome 1 in a (C57BL/6 × NOD)F2 intercross (8) and in a selected backcross in the BALB/c × SJL combination (9). The other regions are novel with respect to K/BxN serum–transfer arthritis but may correspond with potential QTLs in the collagen- or pristane-induced arthritis models (Cia3 on chromosome 6, Cia41 and Cia7 on chromosome 7, Pgia9/Cia36 on chromosome 15 [3]).

To complement the genetics analysis, we searched previously reported gene expression profiles of joint tissue (synovium and synovial fluid) from C57BL/6J mice at different stages of K/BxN serum–transfer arthritis (22), under the assumption that causal variants would have a higher probability of corresponding to transcripts altered by arthritis (Figure 2). This search identified several genes whose expression changed robustly in the course of K/BxN serum–transfer arthritis. Some intriguing candidates mapping to the peak of the relevant intervals include the TNF-induced Tnfaip6 on chromosome 2 (a protease-inhibitor cofactor that participates in the protease network associated with inflammation), Cd52 on chromosome 4 (a cell surface activating protein, against which a depleting monoclonal antibody is currently in clinical trials for RA), or the GTPase oncogene Kras on chromosome 6.

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Figure 2. Expression of genes within associated loci. C57BL/6J mice were injected with K/BxN mouse serum. Synovium samples were collected on days 0, 1, 3, 7, 12, and 18, and synovial fluid was collected on days 3, 7, 12, and 18. RNA was isolated, and microarray analysis was performed. Each panel represents the genes encoded within one of the disease-associated quantitative trait loci. Star symbols represent the maximum (max) fold change in gene expression in synovium tissue compared with day 0. Circles represent the maximum fold change in synovial fluid compared with day 3.

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Focused analysis of chromosome 2.

The region of association on chromosome 2 included Hc, a known determinant of arthritis susceptibility in several mouse models, but the large interval suggested the possibility that it contained more than one QTL. In order to determine whether Hc was the only determinant of arthritis severity in this region, a scan conditioning on the genotype of the informative Hc SNP was performed for the region from 25.95104 Mb to 54.01145 Mb (Figure 3, middle panel). Higher-density SNP data were generated (97 markers spaced at 200-kb intervals on average), and an additional 54,891 informative SNPs in the region were imputed and tested for association with MAT, again controlling for C5 (Figure 3).

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Figure 3. Fine-mapping of quantitative trait loci (QTLs) in the region of the C5 gene (Hc) on chromosome 2, using HAPPY and imputed single-nucleotide polymorphism (SNP) association. Top, Locations of the annotated genes. Middle, QTL mapping of HAPPY haplotype descent probabilities in 79 marker intervals (stepped lines) and 54,891 imputed SNPs (dots) (see Materials and Methods for details). Bottom, Multilocus mapping of the imputed SNPs. Black spikes show the proportion of times (resample model inclusion probability [RMIP]) each SNP was included in a multiple-SNP model predicting the phenotype. avg = average; Prob. = probability.

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With a density of SNPs so much higher than the expected density of recombinations in 53 generations, many of the imputed SNPs were highly or completely correlated, producing a large number of strongly associated but mostly confounded candidate variants. In order to prioritize individual SNPs or small SNP regions, we used multiple QTLs (multilocus). This provided, for each SNP, a reliability score (RMIP) corresponding to the probability it would be selected for inclusion in a parsimonious model of joint action. An imputed SNP with a high RMIP score represents an association that is robust and poorly explained by other SNPs or it represents a SNP that consistently is more associated with MAT than are its correlates. To identify general regions in which a SNP is often chosen but no one SNP stands out, we also calculated a region-based score (RMIP sliding window), which estimates for a given SNP location the number of SNPs that would be selected within 0.5 Mb.

Figure 3 (lower panel) shows the RMIP for individual SNPs and the RMIP sliding window. The RMIP sliding window strongly implicated the subregion centered at 36 Mb as containing a robust association (∼1 inclusion in an average subsample). This subregion includes 3 SNPs (1 in Mrrf, 2 nongenic) that are selected most often in the 30-Mb region, supporting a hypothesis of close linkage with a causal variant independent of Hc. Of note, within this subregion is the gene Ptgs1 (encoding cyclooxygenase 1 [COX-1]). K/BxN serum–transfer arthritis is dependent on COX-1, because mice deficient in this protein are completely resistant (23). Although a SNP within Ptgs1 was not selected, the selected SNPs may be in LD with a variant in Ptgs1, which remains an attractive candidate for an additional genetic determinant in the chromosome 2 locus.

The RMIP sliding window also highlighted the subregion centered on 51.45 Mb as being frequently included (0.571 inclusions on average), albeit with no single SNP distinguished by the RMIP due to high within-region correlation. This subregion, which was significantly associated with MAT in the unconditional single-marker interval scans (Figure 3, middle panel), may represent a weak but independent association. It contains the above-mentioned protease inhibitor Tnfaip6. In contrast, the associations around 30 Mb and 47 Mb in the single-marker interval scans were not well supported by the multilocus analysis, suggesting that their association is readily explained by LD with other loci within the family structure of the cohort.

Focused analysis of chromosome 4.

We also analyzed in more detail the 129–142-Mb interval on chromosome 4, which might also encompass several QTLs, with Padi4 as an attractive a priori candidate based on its association to RA in some human cohorts (24, 25); its product catalyzes arginine to citrulline conversion, and anticitrulline antibodies are arguably the most specific serum biomarker of RA (25). We therefore sought to refine the analysis and prioritize candidate SNPs within the chromosome 4 locus using the same fine-mapping procedure used for chromosome 2. Higher-density SNP genotypes were generated approximately every 200 kb; we imputed 102,809 SNPs from 131.506544 Mb to 141.019465 Mb and performed both the single-SNP and multi-SNP analyses described for chromosome 2 (not conditioning on C5). The Padi genes (Padi1, Padi2, Padi3, Padi4, and Padi6) are encoded from 140.283270 Mb to 140.507916 Mb, with Padi4 located from 140.301767 Mb to 140.330027 Mb.

The single-locus analysis (Figure 4, middle panel) showed significant association in the region around the Padi genes that was dwarfed (at least visually) by a broad multimodal region of strong association from ∼132 Mb to ∼138 Mb. The multilocus analysis (Figure 4, bottom) suggested an alternative prioritization: it provided support for 3 independent sources of association, centered around 133.5 Mb, 137.5 Mb, and 140.5 Mb. The Padi genes were all within a region with a high probability of being associated with K/BxN serum–transfer arthritis in the multilocus model. The RMIP scores for individual SNPs in this region were generally low (maximum RMIP = 0.15, residing in Spata21), indicating that associations among the imputations in this region were highly confounded. The most significant association for a single imputed SNP and for the HAPPY model were within Padi2 (HAPPY log(P) = 5.76, SNP log(P) = 5.04) or immediately adjacent (SNP log[P] = 5.72 at 140.5088 Mb). Also, the region containing the Padi genes was more efficiently explained by the imputed SNPs than by a more flexible (but less precise) model allowing for multiallelic effects, as evidenced by the SNP log(P) values scoring higher than the HAPPY model for the interval.

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Figure 4. A, Fine-mapping of QTLs on chromosome 4 using HAPPY and imputed SNP association. Top, Locations of annotated genes. Middle, QTL mapping of HAPPY haplotype descent probabilities in 38 marker intervals (stepped lines) and 102,809 imputed SNPs (dots). The data are plotted as described in Figure 3. Bottom, Multilocus mapping of imputed SNPs, with inclusion probabilities for individual SNPs (black spikes) and an aggregate score (RMIP sliding window) for neighboring sets of SNPs. See Figure 3 for definitions.

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The other regions with high RMIP scores on chromosome 4 included, as intriguing candidates, Cd52 (the protease regulator noted above) and Lin28, an RNA-binding protein that regulates microRNA levels and forms, together with interleukin 6 and NF-κB, a strong positive-feedback loop connecting inflammation, cell growth, and tumorigenesis (26).

Analysis of Padi gene expression.

Given the association with the Padi gene region, we evaluated Padi genes as candidates, searching for variation in either protein-coding sequence or expression levels that could underpin the genetic association. Mining of the sequencing data (http://www.sanger.ac.uk/) revealed 11 nonsynonymous SNPs in the Padi genes. One of these (in Padi3) had been genotyped in our original screen. We selected an additional 4 coding SNPs in Padi2 and Padi4 for which the minor allele was present in >1 of the HS parental strains. However, none of these SNPs showed highly significant marker–trait association (data not shown), suggesting that none of these individual SNPs was uniquely responsible for the association.

We then sought to determine whether Padi messenger RNA expression varied in the HS founder mice by analyzing spleen RNA, which would not be confounded by variation in arthritis severity. Microarray gene expression profiles from whole spleen of all 8 founder strains were generated, and the expression of Padi1, Padi2, Padi3, Padi4, and Padi6 was extracted. A 2-fold difference in expression among the strains was detected in Padi2 and Padi4 but not in the other genes (data not shown); these differences were verified by quantitative PCR for these transcripts (Figure 5A). To test for a potential relationship between Padi gene expression and K/BxN serum–transfer arthritis, we assayed Padi2 and Padi4 expression by quantitative PCR in the spleen RNA of 278 of the experimental HS mice, stratifying by Hc genotype. High Padi2 or Padi4 expression in the spleen was associated with more severe arthritis in those mice that were heterozygous at the C5 locus, although the trend was not present in mice homozygous for the wild-type C5 allele (Figure 5B).

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Figure 5. A, Expression of Padi2 and Padi4 in spleen RNA of the 8 heterogeneous strain (HS) founder strains, as determined by real-time reverse transcription–polymerase chain reaction (RT-PCR). Bars show the mean ± SD results for 5 or 6 individual mice. B, Expression of Padi2 and Padi4 in the spleens of 278 of the experimental HS mice, as determined by real-time RT-PCR. The black lines represent the best fit, and the gray lines represent alternative slopes that are also compatible with the data. The P values represent the probability that the slope of the black line is due to chance, and that there is no association (a horizontal line). MAT = maximum ankle thickness; Het = heterozygous; WT = wild-type.

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DISCUSSION

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

In the current study, we exploited the unique genetic characteristics of HS mice in order to delineate the range of QTLs that contribute to the severity of K/BxN serum–transfer arthritis. The 2 key advantages of HS mice over traditional F2 intercrosses between inbred strain pairs proved beneficial. First, the broad representation of genetic variants inherited by HS mice from their 8 founder strains allowed us to define novel regions of association not previously observed in K/BxN mouse screens based on inbred pairs. Second, the associated intervals defined in this study, particularly after the use of multiple QTL modeling to reduce confounding signals from SNPs in tight LD, were only a few megabases in length, considerably smaller than the 15–40-Mb intervals that are the norm in analyses of F2 mice. When longer intervals were initially observed (e.g., on chromosome 2 and chromosome 4), further analysis showed these to represent the combined effects of several likely QTLs.

Table 1 shows a comparison between the regions associated with arthritis in GWAS meta-analyses (1) and in the syntenic regions of HS mice. The concordance was limited, although 2 corresponding regions were observed around TRAF1/C5 and around PADI4. Genome-wide association studies address only the effect of SNPs frequently represented in the population, and it may be of interest to analyze more directly, in the next-generation resequencing programs, the impact of candidate loci identified here.

Table 1. Comparison between the regions associated with arthritis in GWAS meta-analyses and in the syntenic regions of HS mice*
Human locationLikeliest candidate geneMouse genome locationAssociation in HS mice
  • *

    GWAS = genome-wide association study; HS = heterogeneous strain; NA = not applicable.

  • Only associated in certain populations.

2q11AFF3Chr 1, 38No
2q32STAT4Chr 1, 52No
2q33CD28Chr 1, 61No
2q33CTLA4Chr 1, 61No
5q21C5orf30Chr 1, 100No
1q31PTPRCChr 1, 140No
10p15PRKCQChr 2, 11No
10p15IL2RAChr 2, 12No
9q33TRAF1, C5Chr 2, 35Yes
1p13CD2, CD58Chr 2, 101; no CD58No
11p12TRAF6Chr 2, 102No
20q13CD40Chr 2, 165No
4q27IL2, IL21Chr 3, 37No
1p13PTPN22Chr 3, 104No
9p13CCL21Chr 4, 42No
1p36PADI4Chr 4, 140Yes
1p36TNFRSF14Chr 4, 157No
4p15RBPJChr 5, 54No
7q32IRF5Chr 6, 29No
12q13KIF5A, PIP4K2CChr 10, 126No
6q23TNFAIP3Chr 10, 19No
6q21PRDM1Chr 10, 44No
2p14SPRED2Chr 11, 19No
2p16RELChr 11, 24No
5q11ANKRD55, IL6STChr 13, 113No
8p23BLKChr 14, 64No
3p14PXKChr 14, 90No
22q12IL2RBChr 15, 78No
6p21HLA---DRB1 (*0401)Chr 17, 34No
6q25TAGAPChr 17, 81No
6q27CCR6Chr 17, 84No
1q23FCGR2ANo Fcgr2a in miceNA

HS mouse populations pose significant analytic challenges compared with F2 crosses or designs that are similarly outbred but with fewer founders, such as advanced intercross lines (18). We built upon the analysis methods developed for previous HS mouse studies, utilizing haplotype reconstruction (17), imputation of SNPs (20), mixed models to control for uneven relatedness (19), and simultaneous consideration of multiple loci (14, 18). In particular, employing a novel analytic algorithm, we explored the pattern of association within QTL regions and identified independent associations by combining imputation of SNPs with modeling of the combined effect of multiple SNPs. This multilocus imputation mapping, based on cross-validation (18), prioritizes imputed associations that are conditionally independent and robust to resampling, similar in spirit to Bayesian methods that have proven useful in human fine-mapping studies (27). Importantly, it controls for statistical dependencies between associated SNPs that may not be reflected by local LD. Nonetheless, such mapping relies on and is sensitive to several rather simplistic assumptions about the joint action of SNP effects and is applied to incomplete data subject to imputation uncertainty. Thus, although we consider the multilocus analysis a useful way of characterizing a complex pattern of association, we use it as prelude to, rather than a substitute for, more focused subsequent experimental investigation.

The power of our study to detect QTLs may have been limited by the number of HS mice tested; prior studies used ∼2,000 mice (14), but a screen of this magnitude for arthritis would pose serious logistical challenges. In most cases, the intervals defined still included too many candidate genes to study functional approaches. Thus, further definition of the QTLs on chromosomes 1, 6, 7, and 15 will require additional analysis by independent screening in HS mice or outbred mice that would increase resolving power and/or provide independent overlapping intervals, or by taking advantage of future resources such as “Collaborative Cross” mice (28). Nonetheless, 2 intriguing candidates emerged from the chromosome 2 and chromosome 4 intervals.

In-depth analysis of the chromosome 2 region suggested that a gene or genes in addition to Hc are contributing to this peak. One striking candidate is Ptgs1 (Cox1), which showed a distinct change in expression over the course of arthritis (Figure 2). Cyclooxygenases are the key synthetic enzymes in the prostaglandin/leukotriene pathway and are primary targets of nonsteroidal antiinflammatory drugs. Chen at al (23) showed that both cyclooxygenase isoforms are found in the inflamed joints of K/BxN mice, but Cox1-deficient mice were fully resistant, while Cox2-deficient mice were susceptible. The Ptgs1 candidate thus comes “prevalidated,” but our data support the notion that allelic polymorphism in Ptgs1/Cox1 is a modifier of inflammatory arthritis, warranting further exploration in humans.

The Padi genes are encoded within the chromosome 4 QTLs. They produce enzymes that posttranslationally deiminate arginine to citrulline. A subset of patients with RA form antibodies to citrullinated peptides, a reactivity that is quite specific for RA. An RA-associated Padi4 haplotype correlating with increased transcript stability and higher anticitrulline antibody levels has been described, with the association being most significant in Asian populations (24, 25). Mechanistically, citrullination might contribute to the pathogenesis of RA by improving the affinity of peptides for disease-associated major histocompatibility complex molecules (29) or by generating neoepitopes that are not encountered during T cell maturation in the thymus and thus are more likely to evade tolerance pathways. Another possibility is that citrullinated proteins of the joint extracellular matrix may provide additional targets for pathogenic autoantibodies.

Arthritis in the K/BxN serum–transfer model or other serum-transfer models is due to immune complexes depositing on joint surfaces and thus is limited to events downstream from autoantigen recognition by T cells and B cells during autoimmune activation. If a member of the Padi family is indeed the QTL on chromosome 4, this may suggest that preformed anticitrulline antibodies in K/BxN mouse serum are binding citrullinated targets, and that the variation in the degree of citrullination might impact arthritis severity. Alternative mechanisms are also possible, because hypercitrullination by Padi4 mediates chromatin decondensation (30), and citrullination of various other proteins can affect their function, as was demonstrated for CXCL8 (31). Interestingly, the correlation between Padi2/4 expression and arthritis severity was detectable only in C5 heterozygous mice within the HS cohort, suggesting a possible interaction between complement activation and citrullination.

In conclusion, these results provide a broad overview of the genetic variation that controls the severity of K/BxN serum–transfer arthritis in mice and are likely to be relevant to other antibody-dependent models such as collagen-induced arthritis and to human pathology as well. The confirmation of candidate genes will require assessing susceptibility to K/BxN serum–transfer arthritis in existing knockout or customized RNA interference–knockdown mice. Genes shown to be important in controlling K/BxN serum–transfer arthritis may suggest novel molecules and pathways in the pathophysiology of human RA.

AUTHOR CONTRIBUTIONS

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

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Benoist had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Johnsen, Valdar, Golden, Ortiz-Lopez, Flint, Mathis, Benoist.

Acquisition of data. Johnsen, Golden, Ortiz-Lopez.

Analysis and interpretation of data. Johnsen, Valdar, Hitzemann, Flint, Mathis, Benoist.

Acknowledgements

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

We would like to thank Christopher Campbell and Angela Wilcox for their help with genotyping and Catherine LaPlace, who helped with the figures.

REFERENCES

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