Association tests with systemic lupus erythematosus (SLE) of IL10 markers indicate a direct involvement of a CA repeat in the 5′ regulatory region

Abstract

Many lines of evidence suggest that IL10 is a strong candidate gene for systemic lupus erythematosus (SLE) susceptibility. In our previously reported study an allele (IL10.G-140bp) of the microsatellite IL10.G located at position −1100 was significantly increased in Italian SLE patients in comparison with controls. Starting from this observation, we tested if sequence variations in the vicinity of IL10.G were more strongly associated with SLE. We performed a comprehensive association study including 26 SNPs (of which four were newly identified in the present study by DHPLC analysis) spanning 8.5 Kb of the 5′ flanking and the transcribed region of the IL10 gene. The association study was performed by the DNA pool method on an extended panel of Italian patients (205) and controls (631). Haplotypic associations were studied by individual typing of seven selected markers surrounding IL10.G. Gene, genotype and haplotype frequencies were not significantly different in patients and controls. Thus the IL10.G microsatellite remains to date the only IL10 marker associated with SLE in our population. A meta-analysis of all published results indicates a possible direct role of the IL10.G repeat number in SLE susceptibiliy.

Introduction

Many lines of evidence suggest that IL10 is a strong candidate gene for systemic lupus erythematosus (SLE) susceptibility. IL10 is an important immunoregulatory cytokine influencing many aspects of the immune response. It suppresses type 1 T helper lymphocytes by decreasing IL2 and interferon-γ production.1,2 It also inhibits certain functions of activated macrophages by down-regulating major histocompatibility complex (MHC) class II and B7 expression3 and by inhibiting production of proinflammatory cytokines such as TNFα, IL1, IL6, IL8 and IL12.1,4 Contrary to its T cell and macrophage inhibitory actions, IL10 has potent stimulatory effect on B lymphocytes, leading to their proliferation and differentiation.5

SLE is a multisystem disease characterised by impaired T cell responses and dysregulation of B cell activation leading to B cell hyperactivity and production of autoantibodies.6,7 Therefore, IL10 production may contribute to the disease by direct effect on B cell survival and on autoantibody production.8 Moreover, B cells and monocytes of SLE patients produce an increased amount of IL10 compared to non-affected individuals.9,10 Besides its functional relevance, IL10 is an attractive positional candidate gene since it maps in 1q32, in a region homologous to a murine SLE susceptibility region (reviewed in Ref. 11). In humans, different genome screens reported evidence of linkage with SLE in the 1q41–44 region spanning 30 cM and located 16 cM telomeric to IL10 (reviewed in Ref. 12). This distance does not exclude the IL10 gene since peaks of linkage in complex diseases only define a confidence interval for the location of a gene and, therefore, susceptibility genes may map near peaks of linkage rather than directly under them.13

Different polymorphisms were identified both in the 5′ flanking region14,15,16,17,18,19,20 and, more recently, in the transcribed region21 of the IL10 gene including two microsatellites at position −4000 (IL1015) and −1100 (IL1016). One of the two microsatellites, IL10.G, has been shown to be associated to SLE by three independent studies, including ours22,23,24 although this was not supported by a fourth work.25

In our study the IL10.G-140bp allele was significantly increased in Italian SLE patients.24 Starting from this observation, in the present analysis we tested sequence variations in the vicinity of IL10.G with the aim of finding some markers in linkage disequilibrium with IL10.G-140bp that could be more highly associated with the disease. We performed a comprehensive association study including 22 SNPs already available in the IL10 gene plus four additional SNPs that are newly des-cribed. Previously, only five of these SNPs had been tested.19,26,27,28 The markers covered the 5′ flanking and the transcribed region of the gene. Basically, the study was performed by the DNA pool method29,30,31,32 on an extended panel of patients and controls. Haplotypic associations were studied by individual typing of selected markers.

Results

Search of new SNPs

Sequence variations in the IL10 gene were searched by DHPLC scanning of the five exons and exon–intron junctions and of 4 kb of the 5′ flanking region in 23 SLE patients carrying the IL10.G-140bp allele pre-viously found to be associated with SLE in Italian patients.24 Sixteen different heteroduplex patterns in 10 PCR fragments were detected. Direct sequencing of the heteroduplex samples and one homoduplex as a reference for each fragment led to the identification of 16 sequence variations (Table 1). Most of them had been previously, identified in other studies 14,17,18,19,20,21 with the exception of four SNPs located at positions −1270, +3814, +4123 and +4230 (Table 1).

Table 1 List, detection and typing conditions of the analysed IL10 SNPs

Test of association by the DNA pool method

The above 16 SNPs and further 10 SNPs drawn from the literature were tested for association with SLE. The tested SNPs are located in the 5′ flanking region centromeric and telomeric to the IL10.G microsatellite, exon 1, intervening sequences and 3′UTR (Table 1).

The frequency of the 26 SNPs was estimated by primer extension followed by HPLC in a pool of 205 Italian SLE patients and in two pools of Italian controls consisting of 402 and 229 individuals respectively. Seventeen SNPs showed two detectable and measurable peaks in the pools. An example (namely −592 C/A) is reported in Figure 1. For none of these 17 SNPs, the gene frequencies were significantly different when comparing patients and controls (Table 2). The statistical power of the test for each SNP is also reported in Table 2.

Figure 1
figure1

HPLC chromatograms of primer extension products for IL10 –592C/A sequence variation. The products extended by the enzyme thermosequenase are resolved as two separated peaks. The two peaks are labelled as C and A according to the nucleotide variation present on the coding DNA strand. The peak labelled with P corresponds to an excess of unextended primer. The elution profiles of SLE patient pool, control pool and of a heterozygous individual are shown. For the heterozygous sample, the peak height ratio A/C (k) was ≠1. To calculate the A allele frequency in the pools the peak height ratio was corrected by k which accounts for the unequal representation of the two alleles in heterozygous individuals. Thus the frequency (f) of allele A in the SLE patient pool is f (A) = A/(A+kC) = 0.276 where A and C are the peak heights of the primers extended with ddC or with ddA in the patient pool.

Table 2 SNP allele frequencies estimated in the pools

For nine SNPs only one peak was detected in the pool, indicating that the frequency of the rarer allele was below the sensitivity limit of this method which lies between 0.01 and 0.05.30 These nine SNPs were investigated by individual genotyping of smaller panels of patients and controls. For none of them a significantly different allele distribution was detected (Table 3).

Table 3 SNPs analysed by individual typing

Test of haplotype association

In order to test a possible haplotype association, we analysed seven SNPs surrounding the IL10.G. microsatellite (namely −3533, −2739, −2013, −1349, −1082, −851 and −592) by individual typing of 99 SLE patients and 95 controls. Seventy-five of these patients were also included in the patient pool while none of the 95 controls was present in the control pools. A slight excess of the −592 C allele in patients vs controls (0.76 vs 0.65, P=0.028) was detected in this smaller sample. However, the significance of this result was not confirmed when individually typing a higher number of samples (0.75 in 257 patients vs 0.69 in 185 controls), in agreement with results obtained by testing the pools (Table 2). Genotype frequencies did not differ between patients and controls. High values of pairwise linkage disequilibria between alleles at the seven SNP loci were observed both in patients and controls (data not shown), confirming previously reported data.18 A maximum-likelihood haplotype frequency estimation yielded 26 likely haplotypic combinations of which 11 were also directly observed in individuals homozygous at all tested loci or at all loci but one (Table 4). Three haplotypic combinations were the most represented both in patients and controls. They correspond to the previously described most frequent haplotypes in the Dutch and Italian populations.17,18,19 The overall distribution of the haplotype frequencies was not significantly different in patients and controls (P=0.076). The frequency of the single haplotype TAGGAGA was higher in controls, but the difference was not significant (Frequencies overlapped when considering the ±2 standard deviation).

Table 4 Maximum-likelihood IL10 haplotype frequencies

Test of association of IL10.G alleles according to length of repeats

In each of the three previous reports showing a significant association of IL10.G microsatellite with SLE, alleles of different length were involved (ie 22, 23 and 25 repeats) while negative association with the 21 repeat allele was observed in all three studies (Table 5). Notably, the three alleles reported to be positively associated in the different studies were all longer repeats than the negatively associated allele. One possibility is that the association with the disease is not with single alleles but with the length of the repeated sequence. Therefore the four IL10.G–SLE association studies were reanalysed considering the IL10.G microsatellite as a biallelic marker consisting of a ‘long allele’ (L, including all the alleles containing >21 CA repeats) and a ‘short allele’ (S, including CA21 and alleles with <21 CA repeats). A significant association with the L allele was observed in three populations (Table 5). Moreover, the significance holds also combining the data of the four studies. These data suggest that the presence of a large stretch of CA repeats is associated with SLE susceptibility.

Table 5 Summary of association studies of IL10.G microsatellite with SLE

When considering also the L/S IL10.G alleles, 33 haplotypic combinations with the above seven SNPs were detected by maximum likelihood estimation. The overall distribution of the haplotype frequencies was significantly different in patients and controls (P=0.003), although no specific haplotype was significantly distorted. The L/S IL10.G alleles were not randomly distributed in the seven SNP haplotypes (Table 4) as reported in Table 6 for the three most frequent haplotypes. Interestingly, the TAGGAGC haplotype in combination with IL10.G-S was estimated to be present only in controls and not in patients.

Table 6 IL10.G long/short allele included in the three most frequent IL10 haplotypes

Discussion

A first aim of this study was to detect additional markers in the exons, exon–intron junctions and 4 kb upstreamthe transcription start site of the IL10 gene to be used in association tests. The approach we followed was to screen for sequence variations directly SLE patients carrying the IL10.G-140bp allele that we previously found to be associated with SLE.24 This approach increases the chance of detecting the causal sequence variation responsible for the previously reported association. A total of 16 sequence variations were detected, of which four are here described for the first time.

The 16 identified sequence variations and 10 addi-tional SNPs selected from the literature, including SNPs located more distally in the 5′ flanking region, were tested for association with SLE. The analysed SNPs span the IL10 gene from about −8500 bp in the 5′ flanking region to exon 5, within a genomic region of about 13.2 kb, thus allowing to scan for association the whole gene.

Association with SLE was tested by comparing gene frequencies in patient and control DNA pools. This method, introduced by Barcellos et al,29 allows testing of a large number of individuals, thus increasing the chance of detecting susceptibility loci with small effect, a typical aspect of complex diseases as SLE. This counterbalances the disadvantages of losing information about genotype frequencies and haplotypes. The method we adopted for determining gene frequencies in a pool combines the genotyping specificity of allele-specific primer extension assay with the quantitative accuracy of high-performance liquid chromatography (HPLC). Previous validation experiments performed in our lab30 and by others31 demonstrated that this method is quantitative and highly reproducible and allows an accurate estimation of allele frequencies in pooled samples of DNA. The reported mean experimental error, ie the difference between the allele frequency calculated by individual genotyping and that estimated in the pool, was ±0.01330 and ±0.01431 respectively, which is a satisfactory level of accuracy. Previous quantitative analysis indicated that the lower detection threshold of this method lies between 0.01 and 0.05.30

For 17 of the 26 analysed IL10 SNPs, it was possible to estimate the allele frequencies in DNA pools including a large number of patients (N=205) and controls (N=631). For the remaining nine SNPs, the lower frequency allele was below detection in the pools and therefore they were analysed by individually typing smaller panels. Moreover, seven SNPs surrounding the IL10.G microsatellite in the 5' flanking region were also individually tested in a smaller panel of patients (N = 99) and controls (N = 95) in order to test the possible association with SLE of specific genotypic or haplotypic combinations. Gene, genotype and haplotype frequencies were not significantly different in patients and controls. Considering the total number of included samples and the frequency of the different sequence variations, the study was powered to detect an association with the single markers with an odds ratio (OR) ranging from 1.38 to 1.91 (Table 2). We thus exclude that 13 of the tested markers confer a risk higher than IL10.G-140bp (OR = 1.57; Table 5). Our results are in agreement with previous studies in different populations failing to detect an association with SLE susceptibility of five SNPs located at positions −3533, −2828, −1082, −819 and −59219,26,27,28,34 although a role in the clinical phenotype was suggested.26,27,35 Conversely, the SNP at position −2726 (named −2763 in Ref. 19) was found to be associated with SLE susceptibility in a group of African-American patients.19 Pooling our results with previous results reported in European populations,27,28,34 an association with −592 with an OR ≥1.25 and −1082 with an OR ≥1.22 can be excluded.

As a conclusion, no sequence variation in the IL10 gene had a detectable association with SLE in our population with the exception of the IL10.G microsatellite that remains to date the only IL10 marker significantly associated with SLE in Caucasoids.22,23,24 The association with the IL10 microsatellite could be due to linkage disequilibrium with some causal variation in another closely linked gene. Three genes belonging to the IL10 family (IL19, IL20 and IL24) have been recently mapped immediately telomeric to IL10 at a distance of 60 , 82 and 112 kb respectively from IL10G, and are possible candidates.36,37,38

However, could it be that the IL10.G microsatellite itself rather than some other variation in linkage disequilibrium with it is involved in SLE genetic susceptibility? At first sight this hypothesis seems unlikely since three different alleles were increased in three different IL10.G/SLE association studies. However, if the literature data are reanalysed considering the IL10.G microsatellite as a biallelic marker, ie consisting of a ‘long allele’ and a ‘short allele’, a significant association with the long allele is observed, suggesting that SLE susceptibility is associated with the presence of a longer stretch of CA repeats in the promoter.

There are several examples in the literature showing a direct correlation of the promoter transcriptional activity with the number of repeats of a microsatellite.39,40,41,42,43 The number of tandem repeats has been shown to influence the transcription rate either by a direct interaction with a transcription factor44,45 or by affecting spacing between flanking regions.46

Considering the data of the present paper it is tempting to speculate that a long IL10.G allele might be responsible for a high IL10 production, a typical aspect of SLE pathogenesis. This hypothesis is in agreement with previously reported data47 showing that cells of individuals carrying the longest analysed IL10.G allele (containing 26 CA repeats) produced the highest amount of IL10 upon induction with LPS, whereas the lowest production was observed for cells of individuals carrying the shortest analysed IL10.G allele (containing 19 CA repeats). Moreover, cells of individuals carrying IL10.G-S alleles produced a mean amount of IL10 lower than individuals with the IL10.G-L allele.47

Other functional data have been reported relating the IL10 production level to the SNP haplotypes. The −1082A allele either alone33 or in the haplotypic combination −1082A/−819T/−592A48 was shown to be associated with a low IL10 production. Other studies reported instead an association of −1082 A with a high IL10 production49 or were not able to detect any association.50 Moreover −1082A is part of the same haplotypic combination as –3533T/−2828G/−2726C shown to be associated with a high IL10 production.19,50,51 These discrepant results may find an explanation in the relationship between the SNP haplotypes and the IL10.G L/S alleles as shown in Table 6. Since the disequilibrium between the two is not complete, it is possible that the individuals analysed in the different studies carried in some cases an L allele and in other cases an S allele. Functional studies that take into account the complete IL10 haplotypes are needed to confirm the role of the stretch of CA repeats in the IL10 promoter region.

Materials and methods

Subjects

A total of 205 SLE patients (7:1 female:male ratio) from the Italian population were included in this study. All patients fulfilled ≥4 of the American College of Rheumatology 1997 revised criteria for the classification of SLE.52 Enrolment followed their informed consent.

A total of 631 random Italian Caucasoid individuals (medical students, University and Hospital staff, blood donors) were used as control subjects (1:1 female:male ratio).

Pool preparation

Patient and control DNAs were purified by the different collecting centres utilising different procedures and were stored for variable lengths of time (months to years).

Prior to pooling, the DNA concentration of each sample was determined using the PicoGreen (Molecular Probes) fluorescent assay. Each DNA sample was quantified in duplicate and the mean value was considered. When the deviation between the two independently treated aliquots of the same sample was greater than 5%, quantification was repeated. If the deviation remained >5%, the samples were discarded. Each sample was then diluted to a final concentration of 20–100 ng/μl with a 10 mM TRIS, 1 mM EDTA solution. An equimolar aliquot of each sample was added to the pool.

DNA quantification and pooling procedures were automated utilising a specifically programmed Robotic Liquid Multihandling System (Multiprobe II, Packard).

A pool of SLE patients (205 individuals) and two pools of controls (402 and 229 individuals, respectively) were prepared.

Search for sequence variations in the IL10 gene

The IL10 gene was amplified from genomic DNA as 20 PCR fragments ranging from 201 to 588 bp and covering a total of 6435 bp including the five exons, the intron–exons boundaries and 4000 bp upstream of the transcription start site. PCR primers were designed on the genomic DNA Genbank sequence U16720.1 (version GI:1041812). All the fragments were amplified using the same touchdown protocol: an initial denaturation at 96°C for 10 min followed by 96°C for 30 s, 65°C to 55°C for 30 s with an annealing temperature decrement of 0.5°C per cycle for 20 cycles and 72°C for 30 s. The additional 30 cycles were at 94°C for 30 s, 55°C for 30 s and 72°C for 30 s. A final elongation step of 10 min at 72°C was added. The reactions were performed in a total volume of 50 μl containing 50 mM KCl, 10 mM Tris–HCl (pH 8.3), 1.5 mM MgCl 250 μM of each dNTP, 1 unit of Taq Gold polymerase (Perkin-Elmer), 20 pm of each primer and 100 ng of genomic DNA

Search for sequence variations was performed for all the fragments on a panel of 23 SLE patients by denaturing high-performance liquid chromatography (DHPLC) scanning on an automated HPLC instrument (Wave, Transgenomic Santa Clara, CA, USA). This method is based on the differential retention of homo- and heteroduplex DNA molecules under conditions of partial heat denaturation. To allow heteroduplex formation, PCR products were subjected to 3 min 95°C denaturation followed by a gradual reannealing from 95°C to 40°C in 30 min in the thermal cycler. The temperature required for successful resolution of heteroduplex molecules was determined using a specific program (website http://insertion.stanford.edu/melt.html). Samples were analysed at the predicted temperatures (RTm) and at RTm+2°C as recommended by the software authors53 and eluted from the column using a linear acetonitrile gradient at a constant flow rate of 0.9 ml/min. The gradient was created by mixing elution buffers A (0.1 M triethylamine acetate buffer, TEAA, pH 7) and B (25% acetonitrile in 0.1 M TEAA, pH 7). The start and end points of the gradient depended on the size of the PCR fragments. Primers used for amplification and DHPLC conditions for the 10 fragments containing a sequence variation are listed in Table 1.

Sequencing

The PCR products displaying a heteroduplex peak in at least one individual were sequenced in one heterozygous and one homozygous sample. Prior to sequencing, unincorporated dNTPs and primers were removed by 0.5 units shrimp alkaline phosphatase and 5 units of exonuclease I (both from Amersham) at 37°C for 30 min, after which the enzymes were deactivated by incubation at 80°C for 15 min. Samples were sequenced in both directions on an ABI 373 automated sequencer using the Big-dye terminator cycle sequencing reaction kit (Perkin-Elmer).

Estimation of the gene frequency in the pool

The 17 fragments containing the 16 SNPs detected by DHPLC plus 10 SNPs reported in the literature were amplified from each pool. Primers used for amplification are reported in Table 1. The frequency of the two alleles of each SNP was estimated using primer extension followed by HPLC analysis on the WAVE (Transgenomic) instrument.31 For each SNP, a primer ending at the nucleotide preceding the variation was annealed to the amplified products previously purified by membrane filtration using the Montage PCR Clean up system (Millipore) to remove unincorporated PCR dNTPs and primers and extended by one or more nucleotides to obtain maximum resolution between the two alleles.

Primer extension reactions were carried out in 20 μl containing about 40 ng of the purified fragment, 50 μM of the appropriate ddNTPs and/or dNTPs, 15 pmol primer and 0.5 U ThermoSequenase (Amersham), in the buffer provided by the manufacturer. The reactions were carried out in a thermal cycler with an initial denaturation step of 1 min at 96°C followed by 50 cycles of 96°C for 10 s, 43°C for 15 s and 60°C for 1 min. At the end of the thermal cycling, the reaction was heated to 96°C for 30 s and immediately placed on ice. A 15–24 bp extension primer annealing upstream or downstream the sequence variation was used (Table 1). The primer was extended by one or more nucleotides as reported in Table 1.

The extended products were then analysed by the WAVE HPLC column at a column temperature of 70°C and with an 18–30% gradient of buffer B.

The frequency was estimated from the height of the peak corresponding to each extended primer in the HPLC elution profile as previously reported 30,31,32 and is summarised in Figure 1. For each sequence variation, each pool in duplicate (two PCR reactions) and at least three heterozygotes were analysed in the same experiment (including PCR, primer extension and HPLC analysis). Reported frequency for each pool is the mean of the frequency estimated in the two duplicates. When the difference between the two duplicates was greater than 0.02, the result was discarded and the PCR and primer extension were repeated for all pools.

Individual genotypin

Genotyping of individual samples was performed either by primer extension analysis as described for the pools or according to previously reported standard methods (Table 3).

Pool validatio

The quality of the three pools was verified using five unlinked SNP markers and comparing their frequency estimated on the pool with the real frequency (ie calculated by genotyping each individual included in the pool). The difference between the two determinations gave a measure of the experimental error in the pool preparation and frequency estimation. The following experimental errors were calculated (tested SNPs are indicated in brackets): SLE patients = 0.02 (IL10 –592C/A), control pool 1 (402 individuals) = 0.018 (GLAST-1 IVS8 C22T) and 0.001 (WI12996 A/G), control pool 2 (229 individuals) = 0.006 (IL12 A1188C) and 0.035 (MOG val142leu). The mean experimental error from all the above comparisons was 0.016 (±0.0133).

Statistical analysis

Significance was evaluated from 2×2 contingency tables by χ2 test (Yates correction). When required by the small number of expected cases, the two-tailed Fisher's exact test was used. All comparisons and OR estimations were performed considering allele frequencies. For analysis performed on DNA pools, 2×2 contingency tables were obtained by calculating the absolute frequencies from the relative frequencies estimated in the pools on the basis of the total number of alleles at each locus included in each pool.

Maximum-likelihood estimation of haplotype frequencies and standard deviations were calculated from data with unknown gametic phase using an expectation-maximisation (EM) algorithm by the Arlequin software ver 1.1.54

The overall difference of haplotype frequencies between patients and controls was evaluated by estimating the following value: L= (L1+L2) −L3 where L1, L2 and L3 correspond to −2 logarithm of the sample maximum-likelihood estimated by Arlequin respectively in the patients (L1), the controls (L2) and the patients+controls (L3). The L value has a χ2 similar distribution. The degrees of freedom correspond to the total number of estimated haplotypes −1. The significance of the differences of single haplotype frequencies between patients and controls was evaluated by analysing the standard deviation. A P<0.05 corresponds to patient/control haplotype frequencies not overlapping when considering frequencies±2 standard deviations.

Power calculation was perfomed according to the binomial distribution test utilising the program provided by the website http://ebook.stat.ucla.edu/calculators/powercalc/binomial/case-control/b-case-control-power.html.

References

  1. 1

    Fiorentino DF, Bond MW, Mosmann TR . Two types of mouse T helper cell. IV.Th2 clones secrete a factor that inhibits cytokine production by Th1 clones J Exp Med 1989 170: 2081–2095

    CAS  Article  Google Scholar 

  2. 2

    Taga K, Tosato G . IL-10 inhibits human T cell proliferation and IL-2 production J Immunol 1992 148: 1143–1148

    CAS  PubMed  Google Scholar 

  3. 3

    Ding L, Linsley PS, Huang LY, Germain RN, Shevach EM . IL-10 inhibits macrophage costimulatory activity by selectively inhibiting the up-regulation of B7 expression J Immunol 1993 151: 1224–1234

    CAS  PubMed  Google Scholar 

  4. 4

    de Waal Malefyt R, Abrams J, Bennett B, Figdor CG, de Vries JE . Interleukin 10 (IL-10) inhibits cytokine synthesis by human monocytes: an autoregulatory role of IL-10 produced by monocytes J Exp Med 1991 174: 1209–1220

    CAS  Article  Google Scholar 

  5. 5

    Rousset F, Garcia E, Defrance T et al. Interleukin 10 is a potent growth and differentiation factor for activated human B lymphocytes Proc Natl Acad Sci USA 1992 89: 1890–1893

    CAS  Article  Google Scholar 

  6. 6

    Horwitz DA, Stohl W, Gray JD . T lymphocytes, natural killer cells, cytokines, and immune regulation In Wallace DJ, Hahn BH (eds) Dubois' Lupus Erythematosus 5th ed Williams and Wilkins: Baltimore 1997 pp 155–194

    Google Scholar 

  7. 7

    Shlomchik MJ, Craft JE, Mamula MJ . From T to B and back again: positive feedback in systemic autoimmune disease Nat Rev Immunol 2001 1: 147–153

    CAS  Article  Google Scholar 

  8. 8

    Llorente L, Zou W, Levy Y et al. Role of interleukin 10 in the B lymphocyte hyperactivity and autoantibody production of human systemic lupus erythematosus J Exp Med 1995 181: 839–844

    CAS  Article  Google Scholar 

  9. 9

    Llorente L, Richaud-Patin Y, Fior R et al. In vivo production of interleukin-10 by non-T cells in rheumatoid arthritis, Sjogren's syndrome, and systemic lupus erythematosus: a potential mechanism of B lymphocyte hyperactivity and autoimmunity Arthritis Rheum 1994 37: 1647–1655

    CAS  Article  Google Scholar 

  10. 10

    Hagiwara E, Gourley MF, Lee S, Klinman DM . Disease severity in patients with systemic lupus erythematosus correlates with an increased ratio of interleukin-10:interferon-γ-secreting cells in the peripheral blood Arthritis Rheum 1996 39: 379–385

    CAS  Article  Google Scholar 

  11. 11

    Tsao BP, Cantor RM, Kalunian KC et al. Evidence for linkage of a candidate chromosome 1 region to human systemic lupus erythematosus J Clin Invest 1997 99: 725–731

    CAS  Article  Google Scholar 

  12. 12

    Shirai T, Nishimura H, Jiang Y, Hirose S . Genome screening for susceptibility loci in Systemic Lupus Erythematosus Am J Pharmaco Genomics 2002 2: 1–12

    CAS  Article  Google Scholar 

  13. 13

    Roberts SB, MacLean CJ, Neale MC . Replication of linkage studies of complex traits: an examination of variation in location estimates Am J Hum Genet 1999 65: 876–884

    CAS  Article  Google Scholar 

  14. 14

    Bidwell J, Keen L, Gallagher G et al. Cytokine gene polymorphism in human disease: on-line databases, supplement 1 Genes Immun 2001 2: 61–70

    CAS  Article  Google Scholar 

  15. 15

    Eskdale J, Kube D, Gallagher G . A second polymorphic dinucleotide repeat in the 5' flanking region of the human IL-10 gene Immunogenetics 1996 45: 82–83

    CAS  Article  Google Scholar 

  16. 16

    Eskdale J, Gallagher G . A polymorphic dinucleotide repeat in the human IL-10 promoter Immunogenetics 1995 42: 444–445

    CAS  Article  Google Scholar 

  17. 17

    Eskdale J, Keijsers V, Huizinga T, Gallagher G . Microsatellite alleles and single nucleotide polymorphisms (SNP) combine to form four major haplotype families at the human interleukin-10 (IL-10) locus Genes Immun 1999 1: 151–155

    CAS  Article  Google Scholar 

  18. 18

    D'Alfonso S, Rampi M, Rolando V, Giordano M, Momigliano-Richiardi P . New polymorphisms in the IL-10 promoter region Genes Immun 2000 1: 231–233

    CAS  Article  Google Scholar 

  19. 19

    Gibson AW, Edberg JC, Wu J, Westendorp RG, Huizinga TW, Kimberly RP . Novel single nucleotide polymorphisms in the distal IL-10 promoter affect IL-10 production and enhance the risk of systemic lupus erythematosus J Immunol 2001 166: 3915–3922

    CAS  Article  Google Scholar 

  20. 20

    Kube D, Rieth H, Eskdale J, Kremsner PG, Gallagher G . Structural characterisation of the distal 5' flanking region of the human interleukin-10 gene Genes Immun 2001 2: 181–190

    CAS  Article  Google Scholar 

  21. 21

    Donger C, Georges JL, Nicaud V et al. New polymorphisms in the interleukin 10 gene—relationships to myocardial infarction Eur J Clin Invest 2001 31: 9–14

    CAS  Article  Google Scholar 

  22. 22

    Eskdale J, Wordsworth P, Bowman S, Field M, Gallagher G . Association between polymorphisms at the human IL-10 locus and systemic lupus erythematosus Tissue Antigens 1997 49: 635–639

    CAS  Article  Google Scholar 

  23. 23

    Mehrian R, Quisimorio Jr FP, Strassmann G et al. Synergistic effect between IL-10 and bcl-2 genotypes in determining susceptibility to systemic lupus erythematosus Arthritis Rheum 1998 41: 596–602

    CAS  Article  Google Scholar 

  24. 24

    D'Alfonso S, Rampi M, Bocchio D, Colombo G, Scorza-Smeraldi R, Momigliano-Richiardi P . Systemic lupus erythematosus candidate genes in the Italian population; evidence for a significant association with interleukin-10 Arthritis Rheum 2000 43: 120–128

    CAS  Article  Google Scholar 

  25. 25

    Alarcon-Riquelme ME, Lindqvist AK, Jonasson I et al. Genetic analysis of the contribution of IL10 to systemic lupus erythematosus J Rheumatol 1999 26: 2148–2152

    CAS  PubMed  Google Scholar 

  26. 26

    Mok CC, Lanchbury JS, Chan DW, Lau CS . Interleukin-10 promoter polymorphisms in Southern Chinese patients with systemic lupus erythematosus Arthritis Rheum 1998 41: 1090–1095

    CAS  Article  Google Scholar 

  27. 27

    Lazarus M, Hajeer AH, Turner D et al. Genetic variant in the interleukin-10 gene promoter and systemic lupus erythematosus J Rheumatol 1997 24: 2314–2317

    CAS  PubMed  Google Scholar 

  28. 28

    Crawley E, Woo P, Isenberg D . Single nucleotide polymorphic haplotypes of the interleukin-10 flanking region are not associated with renal disease or serology in Caucasian patients with systemic lupus erythematosus Arthritis Rheum 1999 42: 2017–2018

    CAS  Article  Google Scholar 

  29. 29

    Barcellos LF, Klitz W, Field LL et al. Association mapping of disease loci, by use of a pooled DNA genomic screen Am J Hum Genet 997 61: 734–747

    Article  Google Scholar 

  30. 30

    Giordano M, Mellai M, Hoogendoorn B, Momigliano-Richiardi P . Determination of SNP allele frequencies in pooled DNAs by primer extension genotyping and denaturing high-performance liquid chromatography J Biochem Biophys Methods 2001 47: 101–110

    CAS  Article  Google Scholar 

  31. 31

    Hoogendoorn B, Norton N, Kirov G et al. Cheap, accurate and rapid allele frequency estimation of single nucleotide polymorphisms by primer extension and DHPLC in DNA pools Hum Genet 2000 107: 488–493

    CAS  Article  Google Scholar 

  32. 32

    D'Alfonso S, Mellai M, Giordano M et al. Identification of single nucleotide variations in the coding and regulatory regions of the myelin-associated glycoprotein gene (MAG) and study of their association with multiple sclerosis J Neuroimmunol 2002 126: 196–204

    CAS  Article  Google Scholar 

  33. 33

    Turner DM, Williams DM, Sankaran D, Lazarus M, Sinnott PJ, Hutchinson IV . An investigation of polymorphism in the interleukin-10 gene promoter Eur J Immunogenet 1997 24: 1–8

    CAS  Article  Google Scholar 

  34. 34

    Dijstelbloem HM, Hepkema BG, Kallenberg CG et al. The R-H polymorphism of FCgamma receptor IIa as a risk factor for systemic lupus erythematosus is independent of single-nucleotide polymorphisms in the interleukin-10 gene promoter Arthritis Rheum 2002 46: 1125–1126

    CAS  Article  Google Scholar 

  35. 35

    Rood MJ, Keijsers V, van der Linden MW et al. Neuro-psychiatric systemic lupus erythematosus is associated with imbalance in interleukin 10 promoter haplotypes Ann Rheum Dis 1999 58: 85–89

    CAS  Article  Google Scholar 

  36. 36

    Gallagher G, Dickensheets H, Eskdale J et al. Cloning, expression and initial characterization of interleukin-19 (IL-19), a novel homologue of human interleukin-10 (IL-10) Genes Immun 2000 1: 442–450

    CAS  Article  Google Scholar 

  37. 37

    Blumberg H, Conklin D, Xu WF et al. Interleukin 20: discovery, receptor identification, and role in epidermal function Cell 2001 104: 9–19

    CAS  Article  Google Scholar 

  38. 38

    Caudell EG, Mumm JB, Poindexter N et al. The protein product of the tumor suppressor gene, melanoma differentiation-associated gene 7, exhibits immunostimulatory activity and is designated IL-24 J Immunol 2002 168: 6041–6046

    CAS  Article  Google Scholar 

  39. 39

    Okladnova O, Syagailo YV, Mossner R, Riederer P, Lesch KP . Regulation of PAX-6 gene transcription: alternate promoter usage in human brain Brain Res Mol Brain Res 1998 60: 177–192

    CAS  Article  Google Scholar 

  40. 40

    Meloni R, Albanese V, Ravassard P, Treilhou F, Mallet J . A tetranucleotide polymorphic microsatellite, located in the first intron of the tyrosine hydroxylase gene, acts as a transcription regulatory element in vitro Hum Mol Genet 1998 7: 423–428

    CAS  Article  Google Scholar 

  41. 41

    Akai J, Kimura A, Hata RI . Transcriptional regulation of the human type I collagen alpha2 (COL1A2) gene by the com-bination of two dinucleotide repeats Gene 1999 239: 65–73

    CAS  Article  Google Scholar 

  42. 42

    Shimajiri S, Arima N, Tanimoto A et al. Shortened microsatellite d(CA)21 sequence down-regulates promoter activity of matrix metalloproteinase 9 gene FEBS Lett 1999 455: 70–77

    CAS  Article  Google Scholar 

  43. 43

    Gebhardt F, Zanker KS, Brandt B . Modulation of epidermal growth factor receptor gene transcription by a polymorphic dinucleotide repeat in intron 1 J Biol Chem 1999 274: 13 176–13 180

    Article  Google Scholar 

  44. 44

    Contente A, Dittmer A, Koch MC, Roth J, Dobbelstein M . A polymorphic microsatellite that mediates induction of PIG3 by p53 Nat Genet 2002 30: 315–320

    Article  Google Scholar 

  45. 45

    Albanese V, Biguet NF, Kiefer H, Bayard E, Mallet J, Meloni R . Quantitative effects on gene silencing by allelic variation at a tetranucleotide microsatellite Hum Mol Genet 2001 10: 1785–1792

    CAS  Article  Google Scholar 

  46. 46

    Liu L, Panangala VS, Dybvig K . Trinucleotide GAA repeats dictate pMGA gene expression in Mycoplasma gallisepticum by affecting spacing between flanking regions J Bacteriol 2002 184: 1335–1339

    CAS  Article  Google Scholar 

  47. 47

    Eskdale J, Gallagher G, Verweij CL, Keijsers V, Westendorp RG, Huizinga TW . Interleukin 10 secretion in relation to human IL-10 locus haplotypes Proc Natl Acad Sci USA 1998 95: 9465–9470

    CAS  Article  Google Scholar 

  48. 48

    Crawley E, Kay R, Sillibourne J, Patel P, Hutchinson I, Woo P . Polymorphic haplotypes in the interleukin-10 flanking region determine variable interleukin-10 transcription and are associated with particular phenotypes of juvenile rheumatoid arthritis Arthritis Rheum 1999 42: 1101–1108

    CAS  Article  Google Scholar 

  49. 49

    Huizinga TW, Keijsers V, Yanni G et al. Are differences in interleukin 10 production associated with joint damage? Rheumatology 2000 39: 1180–1188

    CAS  Article  Google Scholar 

  50. 50

    de Jong BA, Westendorp RGJ, Eskdale J, Uitdehaag BMJ, Huizinga TWJ . Frequency of functional interleukin-10 promoter polymorphism is different between Relapse-Onset and Primary Progressive Multiple Sclerosis Hum Immunol 2002 63: 281–285

    CAS  Article  Google Scholar 

  51. 51

    Westendorp RG, van Dunne FM, Kirkwood TB, Helmerhorst FM, Huizinga TW . Optimizing human fertility and survival Nat Med 2001 7: 873–873

    CAS  Article  Google Scholar 

  52. 52

    Hochberg MC . Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus Arthritis Rheum 1997 40: 1725

    CAS  Article  Google Scholar 

  53. 53

    Jones AC, Austin J, Hansen N et al. Optimal temperature selection for mutation detection by denaturing HPLC and comparison to single-stranded conformation poly-morphism and heteroduplex analysis Clin Chem 1999 45: 1133–1140

    CAS  PubMed  Google Scholar 

  54. 54

    Schneider S, Kueffer JM, Roessli D, Excoffier L . Arlequin ver.1.1: a software for population genetic data analysis Genetics and Biometry Laboratory, University of Geneva, Switzerland

Download references

Acknowledgements

This work was supported by Telethon grant E1221 and by Eastern Piedmont University (fondi ex 60%). Marta Mellai is a PhD student in Molecular Medicine. The authors are grateful to Dr Roberto Tosi for critically reading the manuscript, and to Dr Dieter Kube for sending reference DNA samples to set typing conditions for SNPs of the distal IL10 5′ flanking region.

Author information

Affiliations

Authors

Corresponding author

Correspondence to S D' Alfonso.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

D' Alfonso, S., Giordano, M., Mellai, M. et al. Association tests with systemic lupus erythematosus (SLE) of IL10 markers indicate a direct involvement of a CA repeat in the 5′ regulatory region. Genes Immun 3, 454–463 (2002). https://doi.org/10.1038/sj.gene.6363928

Download citation

Keywords

  • IL10
  • SLE
  • SNPs
  • DNA pools

Further reading

Search