Original Article

Genes and Immunity (2010) 11, 279–293; doi:10.1038/gene.2009.111; published online 14 January 2010

RGMA and IL21R show association with experimental inflammation and multiple sclerosis

R Nohra1, A D Beyeen1, J P Guo2, M Khademi1, E Sundqvist1, M T Hedreul1, F Sellebjerg3, C Smestad4, A B Oturai3, H F Harbo4,5, E Wallström1, J Hillert6, L Alfredsson7, I Kockum1, M Jagodic1, J Lorentzen2 and T Olsson1

  1. 1Department of Clinical Neuroscience, Neuroimmunology Unit, Karolinska Institutet, Stockholm, Sweden
  2. 2Department of Biochemistry and Biophysics, Medical Inflammation Research, Karolinska Institutet, Stockholm, Sweden
  3. 3Danish Multiple Sclerosis Center Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
  4. 4Department of Neurology, Oslo University Hospital, Ullevål, Oslo, Norway
  5. 5Department of Neurology, Faculty Division Ullevål, Oslo University Hospital, University of Oslo, Oslo, Norway
  6. 6Department of Clinical Neuroscience, Division of Neurology, Karolinska Institutet, Stockholm, Sweden
  7. 7Department of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

Correspondence: Dr R Nohra, Department of Clinical Neuroscience, Neuroimmunology Unit, Neuroimmunology Unit, CMM, L8:04, Karolinska University Hospital, Stockholm SE-171 76, Sweden. E-mail: rita.nohra@ki.se

Received 9 June 2009; Revised 27 November 2009; Accepted 30 November 2009; Published online 14 January 2010.

Top

Abstract

Rat chromosome 1 harbors overlapping quantitative trait loci (QTL) for cytokine production and experimental models of inflammatory diseases. We fine-dissected this region that regulated cytokine production, myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalomyelitis (EAE), anti-MOG antibodies and pristane-induced arthritis (PIA) in advanced intercross lines (AILs). Analysis in the tenth and twelfth generation of AILs resolved the region in two narrow QTL, Eae30 and Eae31. Eae30 showed linkage to MOG-EAE, anti-MOG antibodies and levels of interleukin-6 (IL-6). Eae31 showed linkage to EAE, PIA, anti-MOG antibodies and levels of tumor necrosis factor (TNF) and IL-6. Confidence intervals defined a limited set of potential candidate genes, with the most interesting being RGMA, IL21R and IL4R. We tested the association with multiple sclerosis (MS) in a Nordic case–control material. A single nucleotide polymorphism in RGMA associated with MS in males (odds ratio (OR)=1.33). Polymorphisms of RGMA also correlated with changes in the expression of interferon-γ (IFN-γ) and TNF in cerebrospinal fluid of MS patients. In IL21R, there was one positively associated (OR=1.14) and two protective (OR=0.87 and 0.68) haplotypes. One of the protective haplotypes correlated to lower IFN-γ expression in peripheral blood mononuclear cells of MS patients. We conclude that RGMA and IL21R and their pathways are crucial in MS pathogenesis and warrant further studies as potential biomarkers and therapeutic targets.

Keywords:

multiple sclerosis; experimental autoimmune encephalomyelitis; autoimmunity; RGMA; IL21R

Top

Introduction

Common inflammatory autoimmune disorders, such as multiple sclerosis (MS), type 1 diabetes and rheumatoid arthritis (RA) are complex chronic diseases with poorly understood etiologies. We are particularly interested in MS that is a chronic inflammatory disease of the central nervous system. Both environmental and genetic factors contribute to its etiology.1 The human leukocyte antigen complex is a major genetic regulator of MS,2, 3, 4 whereas non-human leukocyte antigen genes are numerous and have low odds ratios (ORs).5, 6 Only recently, with analysis of very large cohorts, non-human leukocyte antigen MS genes are starting to be unambiguously identified.7, 8, 9, 10, 11, 12, 13, 14 Another important concept is the sharing of risk genes between inflammatory diseases,15 as now demonstrated for type 1 diabetes and MS genes.12 Therefore, cross-disciplinary genetics may be rewarding. Discovery of additional genes contributing to MS and their disease regulatory mechanisms may allow the development of more selective therapies and biomarkers in MS.

There are many obstacles in studying genetic regulation of autoimmune disorders in human cohorts, including limited possibility of functional studies and an uncontrolled contribution of environmental factors. Positioning of disease regulating loci can also be achieved using animal models in rodents mimicking the human diseases in which both genetic and environmental factors can be controlled. Numerous quantitative trait loci (QTL) have previously been mapped using crosses between inbred rodent strains with diverse susceptibilities to autoimmune inflammatory diseases.16 Recent progress suggests that this strategy is productive in revealing susceptibility genes and functional pathways shared between experimental models and complex human disorders.17

Experimental autoimmune encephalomyelitis (EAE), a model for MS, has defined pathogenic mechanisms underlying neuroinflammation, and has allowed development of treatments for MS.18 Experimental autoimmune encephalomyelitis induced with myelin oligodendrocyte glycopreotein (MOG) in rats closely mimics clinical and pathological features of human MS.19 Furthermore, the cytokine orchestration in MS and EAE correlate well.20, 21, 22 Similarly, various animal models for RA have been used, with pristane-induced arthritis (PIA) being the model of choice for studies on erosive RA and acute-phase responses in arthritis.23 It best fulfills the criteria for diagnosis of RA24 and is characterized by pronounced bone and cartilage erosions, presence of serum rheumatoid factors and T-cell infiltrations in joints.25

In this study, we investigate a quantitative trait locus on rat chromosome 1, originally identified in a (LEW.1AV1 × PVG.1AV1) F2 cross (Lewis × Piebald-Viral-Glaxo), which carries variants of gene(s) regulating levels of tumor necrosis factor (TNF), interleukin (IL)-6 and IL-1β.26 Interestingly, the QTL overlaps loci that regulate EAE and PIA.27, 28 Defining genes behind this region might therefore unravel genetically controlled pathways that regulate inflammation in general. Here we aimed first to fine-map candidate genes responsible for the regulation of EAE and PIA in vivo, as well as for in vitro cytokine production after stimulation with lipopolysaccharide (LPS), and secondly to determine whether any of the human homologous genes associate with MS or ex vivo cytokine production. We have refined this large80-Mb QTL into two narrow loci: Eae30 and Eae31/Pia32 using the tenth (G10) and twelfth (G12) generation of advanced intercross line (AIL) subjected to EAE and PIA, respectively. Subsequent investigation of candidate genes from Eae30 and Eae31 in a Nordic MS case–control cohort demonstrated association of RGMA and IL21R with MS.

Top

Results

A locus on rat chromosome 1 resolves into two independent QTL that regulate expression of TNF and IL-6

A region on rat chromosome 1 was previously linked to LPS-induced TNF responses in an F2 cross between the EAE-resistant PVG.1AV1 and EAE-susceptible LEW.1AV1 strains.26 To confirm and refine this region, peripheral blood from 465 rats of the twelfth generation (G12) of an AIL was stimulated with LPS and screened for TNF and IL-6 production. The linkage analysis defined two distinct loci regulating IL-6 production, whereas TNF production only linked to the distal locus (Figure 1a). Higher TNF and IL-6 levels were driven by the susceptible dark Agouti (DA) alleles at the distal QTL, whereas higher IL-6 levels were driven by the resistant PVG.1AV1 alleles at the proximal QTL. The G12 AIL thus confirmed and considerably refined previously reported linkage26 and provided evidence for at least two distinct genes that regulate levels of TNF and IL-6.

Figure 1.
Figure 1 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Log-likelihood (LOD: logarithm of the odds) plots for the quantitative trait loci (QTL) identified regulating myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis (MOG-EAE), pristane-induced arthritis (PIA), anti-MOG antibodies and cytokine levels in rats of advanced intercross line (AIL)-G10 and AIL-G12. All markers used for this linkage are not depicted in these plots. Full list of used markers can be found in the Materials and methods section. (a) Linkage to tumor necrosis factor (TNF; thick black line) and interleukin-6 (IL-6; thick gray line) production after WB-LPS stimulation in rats of AIL-G12 (data for IL-6 is from the female subset). Significance threshold for respective phenotype is depicted with the corresponding color and line type as the phenotype itself. (b) Linkage analysis on AIL-G10 rats with cumulative EAE score in MOG-EAE in G10 with sex as an interactive covariate (thick black line). Significance threshold is depicted as a dotted black line. (c) Linkage to total IgG (dashed black line), IgG1 (thick black line) and IgG2b (thick gray line) production on day 12 of MOG-EAE in G10 with sex as an interactive covariate. Significance threshold for respective phenotype is depicted with the corresponding color and line type as the phenotype itself. (d) Linkage analysis on cumulative score in PIA-G12 with sex as an interactive covariate (thick black line). Significance threshold is depicted as a dotted black line.

Full figure and legend (179K)

Refined TNF and IL-6 QTL also regulate susceptibility to experimental encephalomyelitis and arthritis

We next sought to determine whether these loci controlling cytokine production also regulate experimental inflammatory diseases. We performed a linkage analysis in 794 (DA × PVG.1AV1) rats of an AIL-G10 subjected to MOG-induced EAE (MOG-EAE)29 and in 465 (DA × PVG.1AV1) rats of AIL-G12 subjected to PIA.

Linkage analysis in EAE confirmed two separate QTL overlapping with the loci controlling TNF and IL-6 production (Figure 1b). The first QTL, hereafter named Eae30, spans 6Mb between the markers D1Rat217 and D1Rat270, and showed significant linkage to all clinical phenotypes in addition to a linkage to the production of anti-MOG IgG2b (Supplementary Table S1; Figures 1b and c). Disease susceptibility and increased anti-MOG IgG2b levels were conferred by the EAE-susceptible DA alleles. The second QTL, Eae31, covering a region of ~10Mb between D1Rat193 and D1Rat68, was linked to all clinical phenotypes and to anti-MOG IgG1, IgG2b and total IgG titers (Supplementary Table S1; Figures 1b and c). The PVG allele drove more severe disease and higher levels of anti-MOG IgGs. For both QTL, there were effects of sex as an interactive covariate for all linked disease and immune sub-phenotypes in a complex manner. On analysis of female and male rats separately in Eae30, female rats displayed significant linkage to all clinical phenotypes, but not to anti-MOG IgGs, whereas male rats displayed significant linkage to incidence, day of onset and anti-MOG IgGs. For the Eae31 locus, female rats also displayed significant linkage to all clinical phenotypes, but not to the IgG response. In males, there was no linkage to clinical phenotypes, but instead to the anti-MOG IgGs (Supplementary Table S1).

In an analogous linkage study on PIA, we identified Pia32, spanning ~2.1Mb from D1Rat193 to D1Got334 and overlapping with Eae31 and the QTL of IL-6 and TNF. Pia32 linked to disease incidence, onset and disease severity (Supplementary Table S2; Figure 1d). Collectively, the Eae31/Pia32 defines a narrow locus controlling two different organ-specific inflammatory diseases.

Confirmation of linkage data in a congenic strain

On the basis of data from the F2 cross, we developed a congenic rat strain, PVG.LEW-D1Rat270-D1Rat68 (hereafter called PVG.LEW) by selectively breeding a fragment from the EAE-susceptible LEW.1AV1 into a genetic background of the major histocompatibility complex-identical, but EAE-resistant, PVG.1AV1. We used AIL to predict how the congenic strain should behave. Interestingly, in an interactive two-QTL model, we could identify additional influences in the region from an interactive QTL, intQTL, with Eae30 at marker D1Rat131 (Supplementary Table S3; Figure 2a). The allelic combination at different QTL present in the congenic strain should drive a more severe disease and higher levels of TNF and IL-6 (Supplementary Table S4; Figure 2b). Accordingly, in vitro experiments with LPS stimulation on peripheral blood mononuclear cells (PBMCs) from naive PVG.LEW congenics and PVG have demonstrated that PVG.LEW produced higher levels of TNF compared with the parental PVG.1AV1 strain (Table 1; Figure 3a). Furthermore, IL-6 production was elevated in PVG.LEW compared with PVG.1AV1 (Table 2; Figure 3b). Several experiments with different MOG batches were performed, in which the PVG.LEW congenic displayed a higher incidence and mortality and a more severe disease course displayed by a higher maximum and cumulative EAE score (Table 3; Figure 3c). Thus, the congenic strain confirmed the influence of the region on EAE, TNF and IL-6 in accordance with linkage analysis in AIL, associating disease susceptibility and severity with elevated levels of TNF and IL-6.

Figure 2.
Figure 2 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

(a) Interaction analysis using a two-dimensional scan for a two-quantitative trait loci (QTL) model between Eae30 and the epistatic intQTL. The matrix (genomic position × genomic position) depicts incidence in experimental autoimmune encephalomyelitis (EAE)-G10 with the lower triangle representing the interactive LOD (logarithm of the odds) scores and the upper one showing additive LOD values. The red areas indicate higher LOD values resulting from the interaction between D1Mit17 in the upper triangle and D1Rat131 in the lower triangle. The scale to the right of the matrix shows the additive LOD values (the right side of the scale ranging 1–10) and the epistatic LOD values (the left side of the scale ranging 1–5). (b) Effect plot illustrating the influence of the interaction between Eae30 and intQTL on incidence. On a scale between 0 to 1, where 0 means no disease incidence and 1 is for complete penetrance of disease with 100% incidence in a treated group. A PVG allele at D1Mit17 (Eae30) in combination with a dark Agouti (DA) allele at D1Rat131 (intQTL) leads to a higher EAE incidence (>60%).

Full figure and legend (203K)

Figure 3.
Figure 3 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

(a) Mean levels of tumor necrosis factor (TNF) as measured in supernatant of peripheral blood mononuclear cells (PBMCs) stimulated with lipopolysaccharide (LPS) for 18h. The figure represents data from experiment two in Table 1. The TNF protein levels are significantly higher in the PVG.LEW (Piebald-Viral-Glaxo.Lewis) strain compared with PVG. This is also observed in both females and males when analysis was segregated for the different sex subsets. (b) Mean levels of interleukin-6 (IL-6) as measured in supernatant of PBMCs stimulated with LPS for 18h. The figure represents data from experiment 1 as shown in 2. IL-6 levels are significantly higher in the PVG.LEW strain compared with PVG. When segregated for sex the effect was only observed in females with a weak tendency towards the same effect in males. (c) Clinical experimental autoimmune encephalomyelitis (EAE) course in PVG.LEW congenic strains compared with PVG rats. The LEW.1AV1 allele in the PVG.LEW congenic rats confers increased susceptibility and severity to myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis (MOG-EAE) compared with PVG rats. Data shown are from the second experiment as shown in Table 3. Threshold for significance *** corresponds to P<0.001.

Full figure and legend (85K)




Definition of candidate genes

We hypothesized that the linked phenotypes for each QTL were controlled by the same genetic variation in each of the QTL. On the basis of the combined data on AIL-G10 and AIL-G12, the shared regulatory region between Eae30, the QTL for IgG2b and IL-6, was reduced to less than 2.4Mb, spanning 127.9–130.3Mb on rat chromosome 1. This locus is gene sparse, harboring only a few genes and transcripts, including Apolipoprotein AI regulatory protein 1, the repulsive guidance molecule RGMA, chromodomain helicase DNA-binding protein 2 (Chd2), alpha-2,8-sialyltransferase 8B (ST8SiaII) and solute carrier organic anion transporter family member 3A1 (Slco3a1) (Figure 4).

Figure 4.
Figure 4 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

A summarizing figure showing rat chromosome 1 (RNO1) with all quantitative trait loci (QTL) depicted along the chromosome. Shadowed regions symbolize the shared areas between different QTL at respective position. Thick dashed line shows the region covered by the PVG.LEW (Piebald-Viral-Glaxo.Lewis) congenic. A list of the most important genes harbored within shared QTL is shown to the right (check appendix I for a complete list of genes and transcripts).

Full figure and legend (99K)

The 10-Mb region of Eae31 is more gene dense, with many genes having immunoregulatory functions. Implementing the same strategy, as used for Eae30, we focused on the common region shared among Eae31, Pia32 and the QTL for IL-6, TNF and the anti-MOG antibodies. We thus defined a combined confidence interval smaller than 2.1Mb. This region contains the gene for 60S ribosomal protein L13, JmjC domain-containing protein 5 (Jumonji domain-containing protein 5), gene of interleukin-4 receptor alpha (IL4R), interleukin-21 receptor (IL21R), general transcription factor 3C polypeptide 1 (Gtf3c1), GSG1-like, Xpo6 and serine/threonine protein kinase SBK1 (src homology 3 domain-binding kinase 1). A more exhaustive list of genes in these QTL can be retrieved from Ensembl Genome browser (Ensembl genome browser, release 50, July 2008; positions 127.9–131Mb and 183.3–185.3Mb)

Association of RGMA, IL-4 receptor alpha and IL-21 receptor with MS

Initially, we performed single nucleotide polymorphism (SNP) genotyping of tagSNPs in RGMA being candidate from Eae30, IL4R and IL21R being candidates from Eae31/Pia32, in a Swedish case–control study (SWE I) consisting of 1018 MS patients and 1215 controls. The association analysis identified some nominally significant associations in all genes (Supplementary Table S5). We also found certain haplotypes modestly associated (Supplementary Figure 1). We therefore pursued genotyping of the markers in these haplotypes in additional cases and controls from Sweden (SWE II), Norway (NOR) and Denmark (DEN), a total of 2353 additional cases and 1770 controls (see Supplementary Table S6).

For the RGMA gene, the results of single marker association studies in the different cohorts are shown in Table 4. In view of the gender influence observed in the rat experiments for Eae30, we also stratified the human material for gender. We then observed that the difference in allele frequency is mainly apparent for male and not for female patients. In fact, in the combined material (SWE I, SWE II and DEN), we observed a significant heterogeneity in the association between males and females for the rs34925346 marker with P<0.04. The C allele of this marker was more frequent in male patients (13%) than in controls (9.7%; OR=1.33 95% confidence interval (CI): 1.04–1.69; P<0.005), whereas no difference in the frequency of this allele was noticed in females (11%; Figure 5). Further, there is a significant interaction between the rs34925346 marker and sex, as judged by estimating relative excess risk due to interaction,30 which was 2.45 (95% CI=0.44–4.99) in an analysis of all cohorts except NOR. To evaluate the significance of our findings, we have estimated the false positive report probability (FPRP) for this reported association, which is a function of the prior probability of association and the power of the study (see Supplementary Information and see Supplementary Table S7). For prior odds ranging from 0.01 to 0.002, the FPRP is less than 0.06 (see Supplementary Table S8), which is less than the FPRP used for genome-wide significance in genome-wide association (GWA) studies.31

Figure 5.
Figure 5 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Association of rs34925346 in RGMA with multiple sclerosis (MS) among (a) males and (b) females. Fixed effect Mantel–Haenszel analysis and Woolf's test for heterogeneity were performed in R using the meta.MH command in the rmeta package, P-values were estimated in Unphased with study group as a covariate.62 The analysis was performed using the frequency of the C allele of rs34925346 in males and females separately. No significant heterogeneity between the study groups was observed. Odds ratio (OR)=1.33 among males (95% confidence interval (CI): 1.04–1.69; P<0.006), among females OR=1.04 (95% CI=0.89–1.21). The frequency of this haplotype was 9.7% among male controls. As sex was not available for the Norwegian (NOR) controls, this cohort was not included in the sex stratified analysis.

Full figure and legend (54K)


For IL4R, the rs2234897, rs1805011, rs1805015 and rs1801275 markers that were part of the associated IL4R haplotype in the SWE I material and rs12102586, which also showed nominal association in SWE I, were typed in the SWE II material. The rs1805011 marker replaced the rs2234900 marker that did not work in the TaqMan assay, these markers were in complete linkage disequilibrium with each other in the HapMap data, in our material; r2=0.86. These tested IL4R markers did not show any significant association in the SWE II material (data not shown). The rs1801275 marker that was part of the associated haplotype in the SWE I cohort, and has been analyzed in several published investigations,32, 33, 34 was genotyped in the NOR and SWE II materials and an overall association analysis was performed including reports in the literature. There was no association for this IL4R marker, nor was there significant heterogeneity between the materials (Figure 6). Further, given a previous report that suggested that the IL4R association is mainly observed among DR2-positive MS patients,32 we repeated the meta-analysis stratified for DR2 for the SWE I, SWEII and NOR materials, but no significant association for rs1801275 was observed (data not shown).

Figure 6.
Figure 6 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Association of rs1801275 in IL4R with multiple sclerosis (MS) in eight patient cohorts. Fixed effect Mantel–Haenszel analysis and Woolf's test for heterogeneity were performed in R using the meta.MH command in the rmeta package. The analysis was performed using frequency of the G allele for rs1801275 (also called Q576R and Q551R). The data have been collected from the studies by Suppaih et al.,33 Quirico-Santos et al.,34 and Hackstein et al.48 No significant heterogeneity between studies was detected. The overall odds ratio (OR) was 0.99 (95% confidence interval (CI): 0.91–1.07).

Full figure and legend (62K)

For the IL21R gene, the markers of the rs2107357–rs80603688–rs2214537–rs961914–rs12934152 haplotype were typed in SWE II, NOR and DEN materials (Table 5). The T allele of rs8060368 marker was less frequent among patients than controls in both SWE I and SWE II. The rs2107357–rs80603688–rs2214537–rs961914–rs12934152 GCCCT haplotype that was associated in SWE I was not associated in the other materials, whereas the GTCCC haplotype was associated in SWE II and GCGCT haplotype in the NOR material (Table 6). The rs8060368–rs2214537–rs961914–rs12934152 CGCT haplotype was positively associated (OR=1.14 95% CI=1.06–1.23; P<0.0009) with MS in a joint analysis including the SWE I, SWE II, NOR and DEN materials, whereas the TCCC and TGCT haplotypes with the same markers was negatively associated with MS (OR= 0.87 95% CI 0.80–0.96; P<0.004, and OR=0.68 95% CI 0.51–0.90; Figure 7). Allelic frequency for the CGCT haplotype was 55.1% in patients and 51.9% in controls, and for the TCCC haplotype 18.4% in patients and 20.4% in controls. For prior odds ranging from 0.01 to 0.002, the FPRP is less than 0.04 (see Supplementary Information and see Supplementary Table S9).

Figure 7.
Figure 7 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Association of rs8060368–rs2214537–rs961914–rs12934152 haplotypes in IL21R with multiple sclerosis (MS) in four Scandinavian case–control studies. Fixed effect Mantel–Haenszel analysis and Woolf's test for heterogeneity were performed in R using the meta.MH command in the rmeta package. P-values for overall association is from a joint association test in Unphased v3.0.13 using study cohort as a covariate.62 No significant heterogeneity between the study groups was observed. The haplotype frequencies among controls are given in the figure. (a) CGCT haplotype. P-value for association=0.0009. Odds ratio (OR): 1.14 (95% confidence interval (CI)=1.06–1.23). (b) TCCC haplotype. P-value for association=0.004. OR=0.87 (95% CI=0.80–0.96). (c) TGCT haplotype. P-value for association: 0.004. OR=0.68 (95% CI=0.51–0.90). Danish cohort was not included as the haplotype frequency is less than 1% in this cohort.

Full figure and legend (74K)



Correlation of RGMA and IL21R with cytokine expression

As expression of certain cytokines may represent important immune sub-phenotypes in MS, we determined whether there were any correlations between the disease-associated genotypes and expression levels of those studied in the rat, that is, TNF and IL6. In addition, the T helper 1-associated cytokine interferon-γ (IFN-γ) was included in view of its central roles in neuroinflammation. We thus tested for a correlation between the genotype of RGMA, IL4R and IL21R and expression levels of TNF, IL-6, IFN-γ in cerebrospinal fluid (CSF) and PBMCs of MS patients and in patients with other non-inflammatory neurological diseases.

For RGMA, we found no correlation between alleles of the disease-associated rs34925346 marker and IFN-γ expression in the CSF of MS patients (Supplementary Figure 2). However, two markers in the disease-associated region of RGMA correlated to higher IFN-γ expression: the G allele of rs6497019 and C allele of rs725458 (P<6 × 10−3 and P<2.0 × 10−2, respectively; data not shown). These expression correlations were acting in an additive manner and were only observed for female MS patients (Supplementary Figure 2).

In contrast to the positive impact on IFN-γ expression, lower IL-6 expression in CSF of MS patients was associated with the G allele of rs6497019 (P<0.05), again this correlation is only significant among female MS patients (P<0.04, data not shown).

No correlation was found between markers of IL4R and expression of any of the cytokines mentioned above (data not shown).

For IL21R, the rs8060368–rs2214537–rs961914–rs12934152 TGCT haplotype that was negatively associated with MS, displayed a reduced IFN-γ expression in PBMCs of MS patients (P<0.02; Supplementary Figure 3). Carriers of the CCTT haplotype showed increased expression of INF-γ and reduced expression of TNF (P<0.02 and P<5 × 10−5, respectively; Supplementary Figure 3). This haplotype was not significantly associated with MS.

Top

Discussion

We here demonstrate two narrow QTL: Eae30, linked to MOG-EAE, and Eae31/Pia32, linked to both MOG-EAE and PIA. This suggests central nervous system-specific mechanisms for Eae30 and disease-shared mechanisms for Eae31/Pia32. Furthermore, effects of Eae30 and Eae31/Pia32 on disease correlate with regulation of cytokine production, implicating underlying mechanisms that involve differential production of cytokines. We could confirm this in the PVG.LEW congenic strain that has a higher incidence, higher mortality rate and more severe disease course together with higher TNF and IL-6 production compared with the parental PVG.1AV1 strain. Despite the high resolution obtained by the AIL approach, formal proof for single genes controlling these important phenotypes is still lacking in the rat, and will require further experimentation. However, limited sets of genes allow association studies in the human population. Thus, findings from the experimental models allowed the formation of hypotheses on the underlying disease genes. We here provide evidence for an association of RGMA and IL21R with MS. The influence of these genes in human MS put them in focus in the rat system also. Breeding of minimal congenic strains are now focused on these genes, which will allow in vivo functional studies and testing of therapeutic strategies in rat models.

Eae30 is a newly defined QTL. Of the few genes within Eae30, our interest was focused on member A of the family of repulsive guidance molecule domains (RGMA), because of its role in the nervous system. The RGMA protein is a membrane-bound molecule originally defined as an axonal guidance molecule in the visual system.35 The RGMA protein and its receptor, neogenin, have thus mainly been implicated in nervous system development.36, 37, 38, 39 The role of RGMA in the nervous system and the fact that Eae30 showed linkage exclusively to EAE and not PIA, made it a particularly interesting candidate for studies of association with MS; however, as discussed below variants of the gene may well differentially regulate immune mechanisms. In addition, RGMA is not only expressed in neural cells, but also in macrophages and/or microglia and infiltrating leukocytes, as demonstrated in a spinal cord injury model.40 Owing to the observed gender influence in EAE, we also stratified our MS material for gender. A meta-analysis showed an association in males with a single marker within RGMA (OR=1.33) and an association with a CA haplotype (OR=1.31). Interestingly, SNPs in the associated region of RGMA are in potential transcription binding sites. Despite the extensive data on RGMA in neural cells, our data in EAE and MS thus suggest that variants of RGMA differentially affect immunoregulation, associated with proinflammatory cytokines and antibody responses. The linkage of the Eae30 region to anti-MOG IgG2b suggests that the same gene variant increasing disease susceptibility, also causes a T helper 1 bias of the immune response.41 It is interesting that SNPs in the disease-associated region of RGMA show association even with expression of INFγ and IL6 in CSF. However, at this point, the role of this observation is unclear given that disease association is with rs34925346 among male patients and the expression correlation is mainly for rs6497019 among female patients. Downstream events of RGMA may be considered mechanistically. The RGMA protein acts through the activation of a RhoA/Rho kinase-dependent pathway activation of myosin II.42, 43, 44 Inhibition of Rho family functions has been shown to ameliorate EAE in rats, associated with promotion of myelin repair, inhibition of leukocyte infiltration into the central nervous system and a reduced axonal damage.45, 46, 47 The strong expression of RhoA in active MS lesions and low expression in chronic MS lesions suggest that RhoA also has a role in MS.47 We can thus hypothesize that variants of RGMA through differential regulation of RhoA result in different immune activation and disease outcome.

In contrast to Eae30, Eae31/Pia32 showed linkage with two organ-specific diseases, EAE and PIA. The Eae31/Pia32 QTL also displayed linkage to total anti-MOG antibody, IgG1, IgG2b, TNF and IL-6 levels. This probably reflects an overall quantitative effect of the responsible gene(s) on the pathogenic autoimmune response. A remarkable overlap between QTL-affecting clinical phenotypes of both EAE and PIA, along with the production of pro-inflammatory cytokines and MOG antibodies, indicates the presence of shared genetic factors acting on each disease. In the 2.1-Mb region shared among Eae31, Pia32, IL-6, TNF and anti-MOG antibody QTL, we concentrated on two particular genes: the interleukin-4 receptor alpha (IL4R) and interleukin-21 receptor (IL21R). Both in rats and humans, these two receptors are positioned close together (Ensembl genome browser, release 50, July 2008).

Although an initial study of SNPs in IL4R suggested a disease association, additional screening in SWE II, NOR and DEN did not confirm any association of this gene, neither on a haplotype nor on a single marker level. Neither did meta-analysis of all Nordic materials and previous reports of IL4R associations with MS33, 34, 48 reveal any association. Nor did we find any correlation between expression levels of TNF, IL-6 and IFN-γ and the IL4R genotype. We therefore conclude that there is no contribution of IL4R variants to the susceptibility of MS.

Variants of IL21R displayed association with MS in a meta-analysis of all four Nordic cohorts with a susceptible CGCT haplotype (P-value <9 × 10−4; OR=1.14) and two protective haplotypes: TCCC (P-value <4 × 10−3; OR=0.87) and TGCT (P-value <4 × 10−3; OR=0.68). In addition, the disease-protective TGCT haplotype also showed an association with lower expression of IFN-γ in PBMCs of MS patients. As potential molecular mechanism for these effects, several of the SNPs in the associated region of IL21R can potentially affect the transcription factor-binding sites. Interestingly, after submission of this paper, and consistent with the disease gene-sharing theme, as observed in the rat experiments, an association of polymorphisms in IL21R with systemic lupus erythematosus was recently published.49 The reported association with systemic lupus erythematosus is not in the same region of the IL21R gene as the association with MS, however, only one marker has been tested in the region we report associated in this investigation. The downstream mechanisms for the genetic influence remain to be demonstrated. However, a differential effect on disease, as observed here, is plausible in view of the pleiotropic effects of the IL21–IL21R pathway on numerous immune mechanisms, including effects on both CD4+ and CD8+ T cells, T1/T2 bias, IL-17 production, and B cells and antibody production.50 Thus, our data on EAE/PIA and MS, and those on systemic lupus erythematosus49 strongly implicate IL21R variants in the regulation of inflammatory processes in general. Further functional studies on the congenic strain will help our understanding of how IL21R regulates chronic inflammation.

Neither RGMA, IL4R nor IL21R has been identified as the gene that is involved in autoimmune diseases in GWA scans.7, 11, 14, 31, 51, 52 The region that we report associated in the RGMA region has reasonable coverage of markers in these scans, although rs34925346 marker was not included in any of the scans, nor was stratification-based sex performed in all GWA scans. For IL21R, the poor marker coverage in GWA scans in the region we identified as associated with MS may explain why this gene has not associated with autoimmune diseases in GWA studies.

To the best of our knowledge, this is the first evidence of association of RGMA and IL21R with MS, supported by data from experimental models. Furthermore, our data suggest a new immunomodulatory role of RGMA in neuroinflammatory disease. The IL21R gene can be placed on the growing list of shared disease genes potentially relevant for the regulation of inflammation and inflammatory diseases in general. Both RGMA and IL21R need reproducibility studies in larger materials of MS patients, as is ongoing in large GWA studies. Characterization of RGMA- and IL21R-driven pathways both experimentally and in human material might help in the development of selective therapies and biomarkers.

Top

Materials and methods

Experimental animals

Dark Agouti and Lewis (LEW.1AV1) rats, originally obtained from the Zentralinstitut für Versuchstierzucht (Hannover, Germany), and Piebald-Viral-Glaxo (PVG.1AV1) rats from Harlan UK (Blackhon, UK) were bred in the animal facility at the Center for Molecular Medicine at Karolinska Hospital. Advanced intercross line breeding was established starting with two pairs of the major histocompatibility complex-identical DA and PVG.1AV1 female founders, respectively, producing F1 rats. Seven couples of F1 rats with both DA and PVG.1AV1 founders were then used to produce the F2 generation. The G3–G12 generations were created by randomly mating 50 pairs of rats from the previous generation avoiding brother–sister mating. Three litters with 794 AIL-G10 animals were included in MOG-EAE experiments and 463 AIL-G12 animals were collected from two litters and included in the PIA experiment.

Breeding of the PVG.LEW-D1Rat270-D1Rat68 congenic rat strain (here named PVG.LEW congenic) was generated by selective transfer of an approximately 63-Mb fragment from LEW.1AV1 (donor strain) into a background of the major histocompatibility complex-identical PVG.1AV1 (recipient) rat strain. Initially, (PVG.1AV1 × LEW.1AV1) F1 rats were backcrossed to PVG.1AV1 females to produce the N2 generation. Rats from the N2 generation were genotyped with 11 markers on chromosome 1 spanning from D1Rat270 to D1Rat68. For each backcross, one male rat containing this fragment from LEW.1AV1 was selected and crossed to two PVG.1AV1 females. After the tenth generation, rats heterozygous for the whole region were intercrossed to produce homozygotes for this region. The congenic strain, PVG.LEW-D1Rat270-D1Rat68 (N10F1), was used in all MOG-EAE and in vitro experiments described.

Animals were kept in a pathogen-free and climate-controlled environment in polystyrene cages containing aspen wood shavings with free access to standard rodent chow and water with regulated 12-h light/dark cycles. Rats were continuously tested according to a health-monitoring program at the National Veterinary Institute (Statens Veterinärmedicinska Anstalt) in Uppsala, Sweden. All animal experiments were approved by the local ethics committee of Northern Stockholm.

Induction of EAE and clinical evaluation

Recombinant rat MOG (rMOG) corresponding to amino acids 1–125 of the N-terminus was expressed in Escherichia coli and purified to homogeneity by chelate chromatography.53 Rats were anesthetized with isoflurane (Forane; Abbott Laboratories, North Chicago, IL, USA) before immunization. In total, 794 AIL-G10 rats were immunized subcutaneously in the dorsal tail base with a 200-μl inoculum containing 20μg rMOG in saline, emulsified 1:1 with incomplete Freund's adjuvant (Sigma-Aldrich, St Louis, MO, USA). Congenic rats used in clinical MOG-EAE experiments were age-matched and were aged between 12 and 20 weeks. Congenic rats and control rats were immunized using the same induction protocol as for AIL rats and complemented with 20mgml−1 of Mycobacterium tuberculosis, strain 37 RA (Difco Laboratories, Detroit, MI, USA).

Different rMOG concentrations were used for congenic immunizations depending on the rMOG batch used for the particular experiment. Rats in experiments 1 and 2 (Table 3) were immunized with the same rMOG batch using recombinant rMOG in complete Freund's adjuvant, with the concentrations 150 and 200μg per rat, respectively. Rats used in experiment 4 were immunized with a second rMOG batch with 40μg rMOG in complete Freund's adjuvant per rat. In experiment 1, we used 60μg rMOG in complete Freund's adjuvant per rat of a third rMOG batch. A single immunization was performed in experiments 1 and 2 as described above. Owing to the variation of potency among different MOG batches, a boosting of immunization was required for disease induction in experiments 3 and 4. Rats were boosted on day 20 with the same rMOG dose as in the initial immunization but excluding the M. tuberculosis in the boosting experiment for an incomplete adjuvant. Animals were monitored daily for clinical EAE signs starting from day 7 after immunization to day 40 following a scoring scale as follows: 0, no clinical signs; 1, tail weakness or paralysis; 2, hind leg paraparesis or hemiparesis; 3, hind leg paralysis or hemiparalysis; 4, tetraplegy or moribund and 5, death. Rats were killed if severe balance disturbance, weight loss greater than 20% compared with the day of immunization and/or severe disease for more than 1 day were noticed.

Induction of pristane-induced arthritis

Pristane-induced arthritis experiments were carried out in G12 generation of AIL. Animals aged 120–150 days received one intradermal injection at the dorsal base of the tail of 200μl pristane (C19H40; Sigma). The animals were visually examined every second day, and arthritis was scored for each paw as follows: 0, no joints affected; 1, one type of joint affected (redness and/or swelling); 2, two types of joints affected; 3, three types of joints affected; and 4, entire paw affected. The types of joints examined were peritarsal, intratarsal and ankle joints. Scores were added, yielding a total score for all four limbs ranging from 0 to 16. Animals were scored until 72 days post-injection. Signs of chronic arthritis were evaluated from day 40 post-injection, and every 10 days by comparing paw maps depicturing inflamed joints at a given time point. Arthritis was classified as chronic when new sites of inflammation appeared over time, as previously described.54 Other macroscopic phenotypes analyzed were: incidence, the cumulated number of affected rats (scoresgreater than or equal to1) over time, divided by all tested rats; severity, the maximal score (1–16) attained by each affected rat; day of onset, the first recorded sign of joint inflammation; maximum arthritis, the maximal score (0–16) attained by each tested animal; and cumulative score of arthritis, the sum of score over time attained by each tested animal.

Genotyping of the AILs for MOG-EAE and PIA models

Genomic DNA was extracted from tail/ear tips. Primers for microsatellite markers (simple sequence length polymorphisms) spanning the 79-Mb region mapped in F226 were selected from two genomic databases: the Rat Genome Database and Ensembl Genome Browser. The following 28 simple sequence length polymorphisms were used for genotyping AIL both in G10 and G12: D1Rat217, D1Got101, D1Rat269, D1Rat200, D1Mit137, D1Rat321, D1Rat37, D1Rat270, D1Rat183, D1Rat41, D1Rat209, D1Rat273, D1Rat131, D1Rat158, D1Rat51, D1Rat139, D1Rat437, D1Rat155, D1Rat193, D1Rat66, D1Rat65, D1Got334, D1Arb19, D1Mit13, D1Rat287, D1Rat110, D1Got170 and D1Rat68. Forward primers fluorescently labeled with VIC, NED, PET (purchased from Applied Biosystems, Foster City, CA, USA) reverse primers and 6-FAM-labeled forward primers (Proligo, Paris, France) were amplified by PCR according to a standard protocol. The PCR products were separated using the capillary electrophoresis sequencer (ABI3730) and analyzed using the GeneMapper v3.7 software (Applied Biosystems).

Linkage analysis and statistical analysis of AIL and congenic experiments

Linkage analysis with interval mapping of rat chromosome one was performed on data obtained from the MOG-EAE experiment in AIL-G10 rats and PIA experiment in AIL-G12 rats, respectively, using R/qtl software version 2.5.1.55 Interval-mapping analysis was performed by implementation of the Haley–Knott regression model to identify QTL of main effect.55 Two-dimensional scans with a two-QTL model were used to identify epistatic QTL. Finally, a fit multiple-QTL model test was performed, providing information regarding the interaction of both an additive and epistatic character between two loci by making an association between all pairs of markers and inter-marker positions along the investigated region of a chromosome. We constructed a fit model to confirm the linkage to Eae30 and Eae31 and the observed interactive intQTL using the following formula of variance in R/qtl software: y ~Eae30+intQTL+Eae31+Eae30: intQTL. On dropping the effect of each QTL and the interaction at a time, an impact of the particular QTL is determined.

Permutation analysis could not be used for the determination of significance levels due to the structure of the AIL. Therefore, a residual threshold approach, in which each animal's value for a phenotype was subtracted from the mean value of this particular phenotype for the family of origin, was applied. Confidence intervals in linkage studies have previously been defined by a 1-LOD (logarithm of the odds) drop from the peak marker in a quantitative trait locus.56 In our case, we applied a more stringent confidence interval of 2-LOD drop from respective peak marker to insure a full coverage of potential candidate genes, as compared to the previously suggested 1.8-LOD drop for intercrosses.57

Data for ELISA, expression analysis and differences in cumulative and maximum EAE scores in the EAE experiments were analyzed using Kruskal–Wallis and Mann–Whitney ranking tests (JMP, version 7, SAS Institute, Cary, NC, USA). The binomial clinical EAE phenotypes, that is, EAE incidence and mortality, were analyzed using Fisher's exact test. The thresholds for statistical significance were set as follows: *P-valueless than or equal to0.05; **P-valueless than or equal to0.01; ***P-valueless than or equal to0.001.

Anti-MOG antibody measurement

Levels of MOG antibodies, IgG1, IgG2b and total IgG were determined by ELISA in blood sera from rats of AIL-G10 collected on day 12 post immunization with MOG. The 96-well ELISA plates (Nunc, Roskilde, Denmark) were coated with 10μgml−1 of rMOG (100μl per well) overnight at 4°C and then washed with phosphate buffered saline/0.05% Tween 20. A solution of 5% fat-free milk in phosphate buffered saline/0.05% Tween 20 was used to block free binding sites in the ELISA plates for 1h at room temperature. The diluted blood sera and control sera, previously determined from pilot studies as containing high concentration of respective isotype to be used for standard curves, were added after washing and incubated for 1h at room temperature. An additional wash was performed before incubation of rabbit anti-rat IgG (1:2000), IgG1 (1:1000) or IgG2b (1:2000) (Nordic, Tilburg, The Netherlands) for 1h at room temperature. Unbound antibodies were washed away and plates incubated with peroxidase-conjugated goat anti-rabbit antiserum (Nordic) (1:10000) for 30min at room temperature. A final wash was carried out before visualizing the bound antibodies with 3,3′,5,5′-tetramethylbenzidine (Sigma). Reactions were stopped by incubation with 1M HCl for 15min in darkness and optical density was read at 450nm.

Stimulation of peripheral blood mononuclear cells with LPS

Blood samples from rat-tail tips were collected in lithium Heparin Microtainer tubes (Falcon; Becton-Dickinson, Franklin Lakes, NJ, USA). Stimulation with LPS was then performed as reported previously.26 Supernatants from whole blood stimulations were collected after 18h and stored at −70°C. Protein quantifications for TNF and IL-6 were performed using ELISA.

Protein quantification

We used commercially available ELISA kits for TNF (Eli-pair, Biosite, San Diego, CA, USA) and IL-6 (Biosource, Camarillo, CA, USA) protein quantifications. For TNF ELISA, 96-well flat-bottom ELISA plates (Nunc) were coated overnight at 4°C with a capture anti-TNF antibody and saturated with phosphate buffered saline/5% bovine serum albumin. Pre-coated and saturated plates were used to measure rat IL-6 protein levels. Assays were performed according to manufacturers' recommendation. Cytokine levels from supernatants of non-stimulated samples were below detectable levels in all ELISA assays.

MS patients and healthy controls for association studies

The first Swedish multiple sclerosis case–control study (SWE I) consisted of 1018 MS patients (84% females) and 1215 blood donor controls (63% females), all originating from Sweden or other Nordic countries. The patients fulfilled the McDonald criteria58 for definite multiple sclerosis and were recruited by neurologists at the Karolinska University Hospital Huddinge and Solna sites in Stockholm, Sweden. The patients were between 22 and 91 years of age (median: 53 years) and the controls were between 21 and 76 years (median: 47 years). For further details see the study by Roos et al.59

In a follow-up study, three new cohorts were used from Sweden, Norway and Denmark. The second Swedish case–controls study (SWE II) consisted of 705 newly diagnosed MS patients and 663 age- and sex-matched controls from Sweden, and additional 588 MS cases. The median age at onset for the SWE II cases was 31.5 years with a range of 11–64 years of age; median age at sampling was 37 years. About 67% of the SWE II cases and 77% of the controls were female.

The Norwegian cohort (NOR) of 548 MS patients (72% females) and 554 blood donor controls (54% females) had a median age of 54 years (range 22–90 years) and 46.6 years for the blood donor controls (range: 34–58 years).

The Danish cohort (DEN) consisted of 512 MS patients (56% females) between 20 and 80 years of age (median: 43 years), and 553 blood donor controls (39% females) between 20 and 78 years of age (median: 40 years).

Oral and/or written consent was given by all individuals involved in the study. All human experiments were approved by the local ethics committees of Stockholm, Denmark and Norway.

Haplotype tagging

Tagging SNPs (tSNPs) were identified using genotypes available at the HapMap database on Caucasian families for SNPs covering the investigated genes. We used TAGGER in Haploview v3.32 for identifying tSNPs with r2greater than or equal to0.8 and allowing aggressive two and three marker tagging. A total of 97 tSNPs were used for the mapping of RGMA (36 tSNPs), IL4R (25 tSNPs) and IL21R (36 tSNPs) in SWE I. In addition, nine markers were selected in regions in which there were large gaps between the markers in the HapMap data, and in which there did not seem to be strong linkage disequilibrium. Furthermore, two non-synonymous SNPs or markers in potential transcription binding sites, as judged by an analysis using the RAVEN program, were included. For a total of 14 markers our genotype assay did not work or was of poor quality (success rate <80% or did not follow Hardy–Weinberg equilibrium; P<0.01). Thus, 36 markers in RGMA, 24 markers in IL4R and 32 markers in IL21R were analyzed in the SWE I material (see Supplementary Table S1 and Supplementary Figure 1). The follow-up studies in SWE II, NOR and DAN were only genotyped with the associated tSNPs from SWE I screening, either through single marker association or haplotype associations. The RGMA region was genotyped with rs1881842, rs34925346, rs997941, rs6497019, rs10520720 and rs725458. The IL4R region was mapped with rs2234897, rs1805011, rs2234900, rs1805015, rs1801275 and rs12102586, whereas IL21R was mapped with rs2107357, 8060368, rs2214537, rs961914 and rs12934152.

Genotyping of the human material

Genotyping of the SWE I cohort took place at the Mutation Analysis Facility, Karolinska Institutet, on a Sequenom MassARRAY SNP Genotyping Platform that uses matrix-assisted laser desorption/ionization time-of-flight60 for detection of allelic variants. Extension assays were based on the IPLEX technique. Primer sequences are available upon request, for further details regarding the genotyping see the study by Roos et al.59 A total of 92 out of 106 initially considered SNPs passed quality control. The quality control requirements were Hardy–Weinberg P>0.01 and success rate >80%. The genotyping was further validated using a set of 14 trio families (42 individuals) with genotype data available through the HapMap consortium; concordance analysis with the HapMap data (97.7% concordance). The follow-up studies on SWE II, NOR and DEN were genotyped with TaqMan SNP genotyping assays using a 7900 HT Fast Real-time PCR system (Applied Biosystems), as previously described.61 The rs2234900 assay could not be designed for TaqMan and was thus replaced with rs1805011, a nearby SNP in complete linkage disequilibrium.

Association analysis and genotype-expression correlations

Differences in allele frequencies between MS patients and controls were tested through single marker association analysis using the model commands in PLINK v 1.04. Haplotype association analyses in blocks with high linkage disequilibrium were performed in Haploview 3.32. A joint meta-analysis test of association for all tested MS populations with the study cohort, as a covariate in Unphased62 was performed using the software Unphased v3.0.13. Evidence of association was also confirmed in Unphased and the software PLINK v1.04 (for the haplotype analysis) resulting in similar results.

Differences in expression levels between carriers of different alleles were assessed in Unphased program after transformation, if necessary, to achieve normal distribution of the expression levels. Association between haplotypes and expression levels was analyzed in the haplo.stats package in R using an additive model. Correlation between single marker alleles and expression levels was also confirmed in GraphPad Prism software Version 5.01 using a non-parametric Mann–Whitney test.

Preparation of PBMCs and CSF cells

Peripheral blood from patients with MS (n=370; mean age: 39 years; range: 16–77 years; 68.1% females and 31.9% males; 94% with and 6% without IgG oligoclonal bands in CSF) and individuals with other neurological diseases (n=90; mean age: 39 years; 72.2% females and 27.8% males; all without IgG oligoclonal bands in CSF) were used for our expression studies. Paired CSF cells from MS (n=322; mean age: 39 years; range: 16–77 years; 71.1% females and 28.9% males; 94% with and 6% without IgG oligoclonal bands in CSF) and other neurological diseases (n=74; mean age: 39 years; 68.9% females and 31.1% males; all without IgG oligoclonal bands in CSF) were also included. All MS patients fulfilled the McDonald criteria. The CSF was collected in siliconized glass tubes or polypropylene tubes, and immediately centrifuged to recover the pellet and stored at −80°C until use for RNA preparation. The peripheral blood was sampled in sodium citrate-containing cell preparation tubes (Vacutainer CPT, Becton-Dickinson). Peripheral blood mononuclear cells were separated by density gradient centrifugation. Cells from the interphase were collected and washed twice with phosphate buffered saline. More than 95% of the cells were viable. Finally, cells were pelleted, frozen on dry ice and stored at −80°C until use. All human experiments were approved by the local ethics committee of Northern Stockholm.

Relative quantification of mRNA by real-time quantitative PCR

Total RNA was extracted from cell pellets using PicoPure RNA isolation kit (Arcturus Bioscience, Mount View, CA, USA) according to the manufacturer's instructions. Samples were treated for 15min with DNase (Qiagen Rnase-free DNase set, Hilden, Germany) to eliminate contamination of genomic DNA. Preparation of complementary DNA was carried out with 10μl of total RNA, random hexamer primers (0.1μg; Gibco BRL, Life Technologies, Täby, Sweden) and Superscript Reverse Transcriptase (200U; Gibco BRL). Quantitative analysis of messenger RNA expression was performed with iQSYBR green Supermix (Bio-Rad Laboratories, Hercules, CA, USA) using the iCycler thermal cycler, iQ5 Real-Time PCR detection system. We used Beacon Designer 6.0 software (PREMIER Biosoft International, Palo Alto, CA, USA) to design the primers for glyceraldehyde-3-phosphate dehydrogenase, TNF, IFN-γ and IL-6 (primer sequences and PCR protocols can be provided on request). Sequencing of the different bands (Cybergene AB, Huddinge, Sweden) confirmed homology with the reported sequences for the human genes. Relative quantification of messenger RNA was calculated by the standard curve method using the Bio-Rad iQ5 Optical System Software Version 2.0 with endogenous glyceraldehyde-3-phosphate dehydrogenase as a background expression control. The standard curves were created using five serial dilutions (1:10, 1:102, 1:103, 1:104 and 1:105) of either tested amplicons for each target or complementary DNA from human blood cells stimulated with ConA.

Estimation of the false positive report probability

We have estimated the probability that the association between MS and Eae30 and Eae31 is false by estimating the FPRP as suggested by Wacholder et al.63 The FPRP depends on the prior probability of association, the power of the study and the significance threshold:

Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

The estimation of these measures is given in the Supplementary Information.

Top

Conflict of interest

The authors declare no conflict of interest.

Top

References

  1. Ebers GC. Environmental factors and multiple sclerosis. Lancet Neurol 2008; 7: 268–277. | Article | PubMed
  2. Chao MJ, Barnardo MC, Lincoln MR, Ramagopalan SV, Herrera BM, Dyment DA et al. HLA class I alleles tag HLA-DRB1*1501 haplotypes for differential risk in multiple sclerosis susceptibility. Proc Natl Acad Sci U S A 2008; 105: 13069–13074. | Article | PubMed | ChemPort |
  3. Lincoln MR, Montpetit A, Cader MZ, Saarela J, Dyment DA, Tiislar M et al. A predominant role for the HLA class II region in the association of the MHC region with multiple sclerosis. Nat Genet 2005; 37: 1108–1112. | Article | PubMed | ISI | ChemPort |
  4. Olerup O, Hillert J. HLA class II-associated genetic susceptibility in multiple sclerosis: a critical evaluation. Tissue Antigens 1991; 38: 1–15. | Article | PubMed | ISI | ChemPort |
  5. Olsson T, Hillert J. The genetics of multiple sclerosis and its experimental models. Curr Opin Neurol 2008; 21: 255–260. | Article | PubMed
  6. Sawcer S. The complex genetics of multiple sclerosis: pitfalls and prospects. Brain 2008; 131(Part 12): 3118–3131. | Article | PubMed
  7. Hafler DA, Compston A, Sawcer S, Lander ES, Daly MJ, De Jager PL et al. Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 2007; 357: 851–862. | Article | PubMed | ISI | ChemPort |
  8. Lundmark F, Duvefelt K, Iacobaeus E, Kockum I, Wallstrom E, Khademi M et al. Variation in interleukin 7 receptor alpha chain (IL7R) influences risk of multiple sclerosis. Nat Genet 2007; 39: 1108–1113. | Article | PubMed | ChemPort |
  9. Weber F, Fontaine B, Cournu-Rebeix I, Kroner A, Knop M, Lutz S et al. IL2RA and IL7RA genes confer susceptibility for multiple sclerosis in two independent European populations. Genes Immun 2008; 9: 259–263. | Article | PubMed | ChemPort |
  10. International Multiple Sclerosis Genetics Consortium (IMSGC). Refining genetic associations in multiple sclerosis. Lancet Neurol 2008; 7: 567–569. | Article | PubMed
  11. Australia New Zealand Multiple Sclerosis Genetics Consortium (ANZgene). Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet 2009; 41: 824–828. | Article | PubMed | ChemPort |
  12. International Multiple Sclerosis Genetics Consortium (IMSGC). The expanding genetic overlap between multiple sclerosis and type I diabetes. Genes Immun 2009; 10: 11–14. | Article
  13. Rubio JP, Stankovich J, Field J, Tubridy N, Marriott M, Chapman C et al. Replication of KIAA0350, IL2RA, RPL5 and CD58 as multiple sclerosis susceptibility genes in Australians. Genes Immun 2008; 9: 624–630. | Article | PubMed | ChemPort |
  14. De Jager PL, Jia X, Wang J, de Bakker PI, Ottoboni L, Aggarwal NT et al. Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 2009; 41: 776–782. | Article | PubMed | ChemPort |
  15. Vyse TJ, Todd JA. Genetic analysis of autoimmune disease. Cell 1996; 85: 311–318. | Article | PubMed | ISI | ChemPort |
  16. Jagodic M, Kornek B, Weissert R, Lassmann H, Olsson T, Dahlman I. Congenic mapping confirms a locus on rat chromosome 10 conferring strong protection against myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis. Immunogenetics 2001; 53: 410–415. | Article | PubMed | ChemPort |
  17. Aitman TJ, Critser JK, Cuppen E, Dominiczak A, Fernandez-Suarez XM, Flint J et al. Progress and prospects in rat genetics: a community view. Nat Genet 2008; 40: 516–522. | Article | PubMed | ChemPort |
  18. Steinman L. Assessment of animal models for MS and demyelinating disease in the design of rational therapy. Neuron 1999; 24: 511–514. | Article | PubMed | ISI | ChemPort |
  19. Weissert R, Wallstrom E, Storch MK, Stefferl A, Lorentzen J, Lassmann H et al. MHC haplotype-dependent regulation of MOG-induced EAE in rats. J Clin Invest 1998; 102: 1265–1273. | Article | PubMed | ISI | ChemPort |
  20. Issazadeh S, Lorentzen JC, Mustafa MI, Hojeberg B, Mussener A, Olsson T. Cytokines in relapsing experimental autoimmune encephalomyelitis in DA rats: persistent mRNA expression of proinflammatory cytokines and absent expression of interleukin-10 and transforming growth factor-beta. J Neuroimmunol 1996; 69: 103–115. | Article | PubMed | ISI | ChemPort |
  21. Olsson T, Zhi WW, Hojeberg B, Kostulas V, Jiang YP, Anderson G et al. Autoreactive T lymphocytes in multiple sclerosis determined by antigen-induced secretion of interferon-gamma. J Clin Invest 1990; 86: 981–985. | Article | PubMed | ISI | ChemPort |
  22. Olsson T. Cytokine-producing cells in experimental autoimmune encephalomyelitis and multiple sclerosis. Neurology 1995; 45: S11–S15. | PubMed | ChemPort |
  23. Olofsson P, Nordquist N, Vingsbo-Lundberg C, Larsson A, Falkenberg C, Pettersson U et al. Genetic links between the acute-phase response and arthritis development in rats. Arthritis Rheum 2002; 46: 259–268. | Article | PubMed | ISI
  24. Arnett FC, Edworthy SM, Bloch DA, McShane DJ, Fries JF, Cooper NS et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum 1988; 31: 315–324. | Article | PubMed | ISI | ChemPort |
  25. Vingsbo-Lundberg C, Nordquist N, Olofsson P, Sundvall M, Saxne T, Pettersson U et al. Genetic control of arthritis onset, severity and chronicity in a model for rheumatoid arthritis in rats. Nat Genet 1998; 20: 401–404. | Article | PubMed | ISI | ChemPort |
  26. Xu H, Wallstrom E, Becanovic K, Dahlman I, Lorentzen JC. Identification of rat quantitative trait loci that regulate LPS-induced pro-inflammatory cytokine responses. Scand J Immunol 2002; 56: 248–253. | Article | PubMed | ChemPort |
  27. Bergsteinsdottir K, Yang HT, Pettersson U, Holmdahl R. Evidence for common autoimmune disease genes controlling onset, severity, and chronicity based on experimental models for multiple sclerosis and rheumatoid arthritis. J Immunol 2000; 164: 1564–1568. | PubMed | ISI | ChemPort |
  28. Lu S, Nordquist N, Holmberg J, Olofsson P, Pettersson U, Holmdahl R. Both common and unique susceptibility genes in different rat strains with pristane-induced arthritis. Eur J Hum Genet 2002; 10: 475–483. | Article | PubMed | ISI | ChemPort |
  29. Jagodic M, Becanovic K, Sheng JR, Wu X, Backdahl L, Lorentzen JC et al. An advanced intercross line resolves Eae18 into two narrow quantitative trait loci syntenic to multiple sclerosis candidate loci. J Immunol 2004; 173: 1366–1373. | PubMed | ISI | ChemPort |
  30. Zou GY. On the estimation of additive interaction by use of the four-by-two table and beyond. Am J Epidemiol 2008; 168: 212–224. | Article | PubMed
  31. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007; 447: 661–678. | Article | PubMed | ISI | ChemPort |
  32. Mirel DB, Barcellos LF, Wang J, Hauser SL, Oksenberg JR, Erlich HA. Analysis of IL4R haplotypes in predisposition to multiple sclerosis. Genes Immun 2004; 5: 138–141. | Article | PubMed | ISI | ChemPort |
  33. Suppiah V, Goris A, Alloza I, Heggarty S, Dubois B, Carton H et al. Polymorphisms in the interleukin-4 and IL-4 receptor genes and multiple sclerosis: a study in Spanish-Basque, Northern Irish and Belgian populations. Int J Immunogenet 2005; 32: 383–388. | Article | PubMed | ISI | ChemPort |
  34. Quirico-Santos T, Suppiah V, Heggarty S, Caetano R, Alves-Leon S, Vandenbroeck K. Study of polymorphisms in the interleukin-4 and IL-4 receptor genes in a population of Brazilian patients with multiple sclerosis. Arq Neuropsiquiatr 2007; 65: 15–19. | PubMed |
  35. Monnier PP, Sierra A, Macchi P, Deitinghoff L, Andersen JS, Mann M et al. RGM is a repulsive guidance molecule for retinal axons. Nature 2002; 419: 392–395. | Article | PubMed | ISI | ChemPort |
  36. Matsunaga E, Chedotal A. Repulsive guidance molecule/neogenin: a novel ligand-receptor system playing multiple roles in neural development. Dev Growth Differ 2004; 46: 481–486. | Article | PubMed | ChemPort |
  37. Matsunaga E, Nakamura H, Chedotal A. Repulsive guidance molecule plays multiple roles in neuronal differentiation and axon guidance. J Neurosci 2006; 26: 6082–6088. | Article | PubMed | ChemPort |
  38. Yamashita T, Mueller BK, Hata K. Neogenin and repulsive guidance molecule signaling in the central nervous system. Curr Opin Neurobiol 2007; 17: 29–34. | Article | PubMed | ChemPort |
  39. De Vries M, Cooper HM. Emerging roles for neogenin and its ligands in CNS development. J Neurochem 2008; 106: 1483–1492. | Article | PubMed | ChemPort |
  40. Schwab JM, Monnier PP, Schluesener HJ, Conrad S, Beschorner R, Chen L et al. Central nervous system injury-induced repulsive guidance molecule expression in the adult human brain. Arch Neurol 2005; 62: 1561–1568. | Article | PubMed
  41. Gracie JA, Bradley JA. Interleukin-12 induces interferon-gamma-dependent switching of IgG alloantibody subclass. Eur J Immunol 1996; 26: 1217–1221. | Article | PubMed | ChemPort |
  42. Hata K, Fujitani M, Yasuda Y, Doya H, Saito T, Yamagishi S et al. RGMa inhibition promotes axonal growth and recovery after spinal cord injury. J Cell Biol 2006; 173: 47–58. | Article | PubMed | ChemPort |
  43. Conrad S, Genth H, Hofmann F, Just I, Skutella T. Neogenin-RGMa signaling at the growth cone is bone morphogenetic protein-independent and involves RhoA, ROCK, and PKC. J Biol Chem 2007; 282: 16423–16433. | Article | PubMed | ChemPort |
  44. Kubo T, Endo M, Hata K, Taniguchi J, Kitajo K, Tomura S et al. Myosin IIA is required for neurite outgrowth inhibition produced by repulsive guidance molecule. J Neurochem 2008; 105: 113–126. | Article | PubMed | ChemPort |
  45. Paintlia AS, Paintlia MK, Singh AK, Singh I. Inhibition of rho family functions by lovastatin promotes myelin repair in ameliorating experimental autoimmune encephalomyelitis. Mol Pharmacol 2008; 73: 1381–1393. | Article | PubMed | ChemPort |
  46. Walters CE, Pryce G, Hankey DJ, Sebti SM, Hamilton AD, Baker D et al. Inhibition of Rho GTPases with protein prenyltransferase inhibitors prevents leukocyte recruitment to the central nervous system and attenuates clinical signs of disease in an animal model of multiple sclerosis. J Immunol 2002; 168: 4087–4094. | PubMed | ISI | ChemPort |
  47. Zhang Z, Schittenhelm J, Meyermann R, Schluesener HJ. Lesional accumulation of RhoA(+) cells in brains of experimental autoimmune encephalomyelitis and multiple sclerosis. Neuropathol Appl Neurobiol 2008; 34: 231–240. | Article | PubMed | ChemPort |
  48. Hackstein H, Bitsch A, Bohnert A, Hofmann H, Weber F, Ohly A et al. Analysis of interleukin-4 receptor alpha chain variants in multiple sclerosis. J Neuroimmunol 2001; 113: 240–248. | Article | PubMed | ChemPort |
  49. Webb R, Merrill JT, Kelly JA, Sestak A, Kaufman KM, Langefeld CD et al. A polymorphism within IL21R confers risk for systemic lupus erythematosus. Arthritis Rheum 2009; 60: 2402–2407. | Article | PubMed | ChemPort |
  50. Spolski R, Leonard WJ. Interleukin-21: Basic biology and implications for cancer and autoimmunity. Annual Review of Immunology 2008; 26: 57–79. | Article | PubMed | ChemPort |
  51. Plenge RM, Seielstad M, Padyukov L, Lee AT, Remmers EF, Ding B et al. TRAF1-C5 as a risk locus for rheumatoid arthritis—a genomewide study. N Engl J Med 2007; 357: 1199–1209. | Article | PubMed | ChemPort |
  52. Baranzini SE, Wang J, Gibson RA, Galwey N, Naegelin Y, Barkhof F et al. Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet 2009; 18: 767–778. | Article | PubMed | ChemPort |
  53. Amor S, Groome N, Linington C, Morris MM, Dornmair K, Gardinier MV et al. Identification of epitopes of myelin oligodendrocyte glycoprotein for the induction of experimental allergic encephalomyelitis in SJL and Biozzi AB/H mice. J Immunol 1994; 153: 4349–4356. | PubMed | ISI | ChemPort |
  54. Carlson BC, Jansson AM, Larsson A, Bucht A, Lorentzen JC. The endogenous adjuvant squalene can induce a chronic T-cell-mediated arthritis in rats. Am J Pathol 2000; 156: 2057–2065. | PubMed | ChemPort |
  55. Broman KW, Wu H, Sen S, Churchill GA. R/qtl: QTL mapping in experimental crosses. Bioinformatics 2003; 19: 889–890. | Article | PubMed | ISI | ChemPort |
  56. Lander ES, Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 1989; 121: 185–199. | PubMed | ISI | ChemPort |
  57. Manichaikul A, Dupuis J, Sen S, Broman KW. Poor performance of bootstrap confidence intervals for the location of a quantitative trait locus. Genetics 2006; 174: 481–489. | Article | PubMed
  58. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001; 50: 121–127. | Article | PubMed | ISI | ChemPort |
  59. Roos IM, Kockum I, Hillert J. The interleukin 23 receptor gene in multiple sclerosis: a case-control study. J Neuroimmunol 2008; 194: 173–180. | Article | PubMed | ChemPort |
  60. Isler JA, Vesterqvist OE, Burczynski ME. Analytical validation of genotyping assays in the biomarker laboratory. Pharmacogenomics 2007; 8: 353–368. | Article | PubMed | ChemPort |
  61. Ekelund E, Saaf A, Tengvall-Linder M, Melen E, Link J, Barker J et al. Elevated expression and genetic association links the SOCS3 gene to atopic dermatitis. Am J Hum Genet 2006; 78: 1060–1065. | Article | PubMed | ChemPort |
  62. Dudbridge F. Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data. Hum Hered 2008; 66: 87–98. | Article | PubMed
  63. Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 2004; 96: 434–442. | Article | PubMed
Top

Acknowledgements

This study was supported by grants from the Swedish Research council; the Swedish foundation for neurologically handicapped (NHR), Bibbi and Nils Jensens Foundation, Montel Williams Foundation, Söderbergs Foundation, the fp 6 EU programs Neuropromise (LSHM-CT-2005-018637 and Euratools (LSHG-CT-2005-019015). The Norwegian part of the study was supported by Norwegian Foundation for Health and Rehabilitation (2004/2/0125) and Odd Fellow MS society, Norway. The Norwegian Bone Marrow Donor Registry is thanked for its collaboration in the establishment of the Norwegian control material.

Supplementary Information accompanies the paper on Genes and Immunity website