ICOS gene region among Norwegian multiple sclerosis patients

Journal of Neuroimmunology 166 (2005) 197 – 201 www.elsevier.com/locate/jneuroim

Short communication

Lack of association with the CD28/CTLA4/ICOS gene region among Norwegian multiple sclerosis patients ˚ slaug R. Lorentzen a,*, Elisabeth G. Celius b, Per O. Ekstrøm c, Kristine Wiencke d, A Benedicte A. Lie d, Kjell-Morten Myhr e, Vincent Ling f, Erik Thorsby d, Frode Vartdal d, Anne Spurkland g, Hanne F. Harbo a,b a

Institute of Immunology, University of Oslo and Rikshospitalet University Hospital, 00207 Oslo, Norway b Deparment of Neurology, Ulleva˚l University Hospital, Oslo, Norway c Department of Surgery Oncology, The Norwegian Radium Hospital, Oslo, Norway d Institute of Immunology, Rikshospitalet University Hospital and University of Oslo, Oslo, Norway e Deparment of Neurology, Haukeland University Hospital, Bergen, Norway f Molecular Biology and Genetics, Compound Therapeutics, Waltham, MA, USA g Institute of Basal Medical Science, University of Oslo, Oslo, Norway Received 10 March 2005; accepted 7 June 2005

Abstract Chromosome region 2q33 encodes several regulators of the immune system, among these the CD28, CTLA4 and ICOS molecules. Involvement of these genes in multiple sclerosis (MS) is not yet clear. We investigated six microsatellites and three SNPs in a relatively large and clinically well characterised Norwegian MS cohort. No associations were observed for any of the markers analysed in 575 MS patients and 551 controls. Associations were neither found when stratifying the material for the HLA-DRB1*1501, DQB1*0602 haplotype, gender, age at onset, disease course nor familial aggregation. In conclusion, this study could not confirm association with the CD28/CTLA4/ICOS gene region. D 2005 Elsevier B.V. All rights reserved. Keywords: CD28; CTLA4; ICOS; Association; Multiple sclerosis

1. Introduction Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. Both environmental and genetic factors contribute to the disease. The HLA class II region is the only region that is firmly established to be associated with MS (Olerup and Hillert, 1991; Spurkland et al., 1991), however, the genetic susceptibility probably involves many different genes (Dyment et al., 2004). Genes involved in T cell regulation have been investigated as some of the most interesting candidate genes in autoimmune diseases, among these are the cluster of differentiation molecule 28 (CD28) gene, the cytotoxic T lymphocyte antigen 4 (CTLA4) gene and the inducible co-stimulatory molecule * Corresponding author. Tel.: +47 23073500; fax: +47 23073510. ˚ .R. Lorentzen). E-mail address: [email protected] (A 0165-5728/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jneuroim.2005.06.002

(ICOS) gene located on chromosome 2q33. Especially, the CTLA4 gene has been shown to be linked to and associated with several autoimmune diseases (for review, see Kristiansen et al., 2000). Recently, the CT60 single nucleotide polymorphism (SNP), located in the CTLA4 region, was reported to be involved in Graves_ disease, autoimmune hypothyroidism and type 1 diabetes (Ueda et al., 2003). Since we published the first report on an association with the CTLA4+49 polymorphism in MS (Harbo et al., 1999), a series of genetic analyses of the CTLA4 gene and other closely located genes have been reported in MS (for overview; see Holopainen and Partanen, 2001; Kristiansen et al., 2000; Teutsch et al., 2004). The results obtained in these studies have, however, been inconsistent. In the present study, we therefore aimed at extending our previous analysis of CTLA4 polymorphisms, both by genotyping more markers in the CD28, CTLA4 and ICOS gene regions

A˚.R. Lorentzen et al. / Journal of Neuroimmunology 166 (2005) 197 – 201

198

SARA 47

SARA 31

ICOS

SARA 1

CT60 CT61

CTLA4 + 49

CTLA4

SARA 43

CD28-B

CD28-A

CD28

kb 0

100

200

300

Fig. 1. Schematic drawing of chromosome 2q33. Location of microsatellite and single nucleotide polymorphism (SNP) markers in relation to the cluster of differentiation molecule 28 (CD28) gene, the cytotoxic T lymphocyte antigen 4 (CTLA4) gene and the inducible co-stimulatory molecule (ICOS) gene are marked in this figure. The CD28, CTLA4 and ICOS molecules are all located on the T-cell surface. Interaction between CD28 on the T-cell with the B7 molecule on the antigen-presenting cell (APC), results in proliferation of antigen specific T cells. Binding of CTLA4, results in down-regulation of the immune response (McCoy and Le Gros, 1999). ICOS contributes also to the immune response, for instance by up-regulating of interleukin-4 (IL-4) (Dong et al., 2001).

as well as typing an additional material of Norwegian MS patients and controls.

2. Materials and methods 2.1. MS patients and controls Two different sets of Norwegian unrelated MS cases and Norwegian controls were included in this study. Among the first set of 302 patients and 282 controls, most samples had previously been genotyped for the CTLA4+49 and CTLA4 318 polymorphisms with the polymerase chain reaction- restriction fragment length polymorphism (PCRRFLP) methodology (Harbo et al., 1999). These samples were in the current study genotyped for six microsatellite markers (CD28-A, CD28-B, SARA-43, SARA-1, SARA-31 and SARA-47) and three SNPs (CTLA4+49, CT60 and CT61) (Fig. 1). A second set of 273 patients and 269 controls were also genotyped for the CTLA4+49, CT60 and CT61 SNPs. All MS patients were diagnosed as clinically definite or laboratory supported definite MS according to the criteria of Poser (Poser et al., 1983). Among the 485 MS patients available for detailed clinical evaluation, the female: male ratio was 2 : 1, mean age at onset was 30.8 years (range 10– 62) and initial disease course was retrospectively classified either as relapsing –remitting MS (RRMS) (82%) or primaryprogressive MS (PPMS) (18%). All control samples were randomly collected among healthy blood donors recruited through the Norwegian Bone Marrow Registry. The regional Ethics Committee approved the protocol, and informed consent was obtained from each individual. 2.2. Genotyping Sequences of primers for the markers were either obtained through the Genome Database (www.gdb.org) or established in previous projects. The CT60 and CT61 SNPs were genotyped as one haplotype by amplifying a PCR

product including both SNPs. The other markers were amplified separately by PCR. (Conditions are available on request). The microsatellite alleles were identified by fragment length analysis on an ABI Prism\ 377XL DNA sequencer (Applied Biosystems, Foster city, California, USA). The SNPs were genotyped using denaturant capillary electrophoresis (DCE) (Ekstrom et al., 2002; Fischer and Lerman, 1983) on a MegaBASE i 1000 DNA Analysis System (Amersham Pharmacia Biotech, Oslo, Norway) or using TaqMan technology on an ABI Prism\ 7000 analyzer (Applied Biosytems, Foster city, California, USA). 2.3. Statistical analysis The case and control materials were compared by the Chi square test using the Public Domain Software for Epidemiology and Disease Surveillance EPI Info Version 5.01 b (Center of Disease Control, Epidemiology Program Office, Atlanta, GA, USA). Haplotype frequencies were estimated by the expectation-maximization (EM) algorithm using the COCAPHASE program (www.hgmp.mrc.ac.uk). Hardy– Weinberg equilibrium was calculated manually or using the Arlequin software (http://anthro.unige.ch/arlequin). Clinical correlations and logistic regression analysis were performed using NCSS 2004 (Number Cruncher Statistical Systems; www.ncss.com).

3. Results 3.1. Evaluation of methods used for genotyping of the CTLA4+49 SNP Due to technical difficulties using the PCR-RFLP methodology, we performed an explorative comparison between the different SNP genotyping methods; PCR-RFLP, TaqMan\ analysis and DCE (Table 1). The genotyping error rates when comparing PCR-RFLP data with TaqMan\ data

Table 1 Genotype and allele frequencies of the CTLA4+49 polymorphism in 189 Norwegian controls achieved by different methodsa RFLPb

TaqMan\

DCEc

Genotype AA AG GG

0.32 0.46 0.22

0.33 0.49 0.18

0.33 0.49 0.18

Allele A G

0.55 0.45

0.57 0.43

0.57 0.43

a As a part of a method evaluation, some of the samples that previously had been genotyped for the CTLA4+49 marker using PCR-RFLP methodology were genotyped using both DCE method and TaqMan\ technology. b RFLP= restriction fragment length polymorphism method. c DCE = denaturant capillary electrophoresis method.

A˚.R. Lorentzen et al. / Journal of Neuroimmunology 166 (2005) 197 – 201 Table 2 Allele frequencies for analysed chromsome 2q33 markers in Norwegian MS patients and controls Allele

MS

Control

v 2globala

P global

CD28-A c 231 235 237 other

2n = 594 0.06 0.81 0.07 0.07

2n = 564 0.03 0.85 0.05 0.07

4.30

0.25

CD28-B c 198 200 202 204 other

2n = 590 0.33 0.18 0.12 0.35 0.01

2n = 560 0.39 0.18 0.13 0.30 0.01

5.57

0.23

SARA-43 c 218 220 222 224 226 other

2n = 592 0.14 0.09 0.49 0.07 0.16 0.05

2n = 498 0.11 0.06 0.55 0.08 0.13 0.06

10.24

0.07

CTLA4+49 d A G

2n = 1026 0.57 0.43

2n = 1018 0.58 0.42

0.37

0.54

CT60 d A G

2n = 956 0.41 0.59

2n = 1072 0.41 0.59

0.01

0.91

CT61 d A G

2n = 956 0.15 0.85

2n = 1072 0.16 0.84

0.51

0.48

SARA-1 c 260 264 268 272 other

2n = 594 0.05 0.29 0.28 0.36 0.02

2n = 498 0.06 0.33 0.27 0.32 0.02

3.27

0.35

SARA-31 c 211 217 other

2n = 602 0.50 0.49 0.00

2n = 498 0.51 0.49 0.00

1.69

SARA-47 c 149 151 153 other

2n = 604 0.37 0.34 0.24 0.05

2n = 498 0.33 0.34 0.27 0.06

2.86

nc

b

0.43

0.41

v 2global = global chi square value. p global nc = global p-value, not corrected for number of comparisons. c Microsatellite analysis of the first material of multiple sclerosis (MS) cases and randomly collected healthy controls using ABI Prism\ XL 377. d SNP analysis of both the first and second materials of MS cases and controls using denaturant capillary electrophoresis (DCE) method (MegaBacei 1000).

199

SNP typing, and only DCE data were included in the analyses presented in this paper. 3.2. No associations were found in MS with markers in the CD28/CTLA4/ICOS gene region Global analysis of allele frequencies of the nine markers in the CD28/CTLA4/ICOS gene region (Fig. 1) in the first set of MS patients and controls, showed no significant differences between cases and controls (data not shown). Since the CTLA4 gene previously has been reported to be associated with MS in some studies, the second set of Norwegian MS cases and controls was also genotyped for the CTLA4+49, CT60 and CT61 SNPs. No significant deviations in allele or genotype frequencies were found in this second case-control set, nor in the combined casecontrol material (Tables 2 and 3, first part). Moreover, CTLA4+49, CT60, CT61 haplotypes were compared between the MS cases and controls without showing significant associations (Table 3, second part). The cases and controls were found to be in Hardy Weinberg equilibrium. 3.3. Stratification of the material for HLA-DRB1*1501, DQB1*0602 or clinical subgroups of MS patients did not display significant differences Stratifying the part of the material that previously has been typed for the HLA-DRB1*1501, DQB1*0602 hapTable 3 CTLA4+49, CT60, CT61 genotype and haplotype frequencies in Norwegian MS patients and controls

Genotype CTLA4+49 AA AG GG CT60 AA AG GG CT61 AA AG GG

MSalla

Controlalla v 2global

n = 513 0.32 0.49 0.19 n = 478 0.18 0.47 0.35 n = 478 0.02 0.27 0.71

n = 509 0.33 0.49 0.18 n = 536 0.17 0.49 0.34 n = 536 0.03 0.27 0.70

b all

p global

0.37

0.83

0.59

0.75

0.48

0.48

0.04 0.51 0.38

0.85 0.47 0.54

c nc all

a

b

or the DCE data were both 13%. Comparing TaqMan\ and the DCE data showed a genotyping error rate of 1%. Based on these results, the DCE method was selected for further

Haplotype CTLA4+49, CT60, CT61 2n = 878 A, A, G 0.40 A, G, A 0.14 G, G, G 0.44 other 0.02

2n = 974 0.40 0.16 0.42 0.02

a Total population of multiple sclerosis (MS) patients and randomly collected healthy controls. b 2 v global all = global chi square value for the combined case-control material. c p global nc all = global p-value for the combined case-control material, not corrected for number of comparisons.

200

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lotype (Harbo et al., 2004; Spurkland et al., 1991) (n = 274 MS cases and n = 300 controls), did not reveal additional information (data not shown). Among the 485 Norwegian MS patients available for clinical evaluation, the frequencies of genotypes and haplotypes showed no correlations neither to gender, disease course, age at onset nor presence or absence of familial cases of MS (data not shown). Logistic regression analysis, which included these clinical variables and genotyping data, did not reveal additional information.

4. Discussion In this study we extended our previous analysis of the CTLA4 gene region in MS by applying six microsatellite and three SNP markers covering the CD28, CTLA4 and ICOS gene regions. We could not confirm association with this region in our relatively large Norwegian MS cohort. T cell regulatory genes, like those coding for CD28, CTLA4 and ICOS, have been investigated as some of the most promising candidate genes in autoimmune diseases. We were, however, unable to confirm the previously reported association with the CTLA4+49 polymorphism, and did neither find evidence for association with other polymorphisms in the CD28/CTLA4/ICOS gene region, CT60 and CT61 included. The negative result of the present study is in accordance with a recently published metaanalysis of nine Caucasian datasets of MS patients typed for the CTLA4+49 polymorphism (Teutsch et al., 2004), including our previously published Norwegian study (Harbo et al., 1999). In order to select the most accurate method for SNP typing, parts of the material were genotyped for the CTLA4+49 marker using three different techniques. These analyses showed that both the DCE and the TaqMan\ methods were superior to the PCR-RFLP method for accurate genotyping of the selected SNPs. The previously obtained PCR-RFLP data (Harbo et al., 1999), were only included in methodology studies, and only DCE SNP data were included in the statistical analyses of the present paper. This study did not perform extensive fine-mapping of the region, thus other polymorphisms in this region could potentially influence MS susceptibility. Our data indicate, however, that the haplotype found to be associated with other autoimmune diseases (Amundsen et al., 2004; Blomhoff et al., 2004; Ueda et al., 2003) does not give a major contribution to MS susceptibility. Due to limited power of our study, we can still not exclude a minor contribution of this gene region to MS susceptibility. In conclusion, this study could not confirm association with the CD28/CTLA4/ICOS gene region in this relatively large and well characterized Norwegian MS cohort. Our data underline the importance of genotyping large materials in genetic studies of complex disorders.

Acknowledgements This study has been supported by the Medical Research Curriculum, the University of Oslo (project number 131409/ 000075/410993), the Norwegian Research Council (project number 154888/V40), Helse
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