ARC Epidemiology Unit, University of Manchester, Manchester and
1 Royal National Hospital for Rheumatic Diseases, Bath, UK
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Abstract |
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Methods. Markers mapping to TGFß1, TGFß2, TGFß3, PDGFB, TIMP1 and COL5A2 were genotyped and allele frequency distributions compared in 191 patients and 196 controls. As TIMP1 maps to the X chromosome, male and females were analysed separately. Markers associated with SSc were further investigated according to whether patients had limited (lcSSc) or diffuse (dcSSc) cutaneous fibrosis.
Results. Associations were found between SSc and markers for TGFß3 (2=17.3, df=8, P=0.02), TGFß2 (
2=25.2, df=13, P=0.02) and TIMP1 (with male SSc,
2=11.9, df=5, P=0.03), between lcSSc and the TGFß2 marker (
2=25.6, df=13, P=0.02), and between dcSSc and TGFß3 marker (
2=27.1, df=8, P=0.001). Between lcSSc and dcSSc patients, the allele frequency distribution differed only for the TGFß3 marker (
2=16.5, df=6, P=0.01).
Conclusion. These associations indicate a possible role for TGFß3, TGFß2 and TIMP1 in genetic susceptibility to SSc and for TGFß3 in determining the degree of cutaneous fibrosis.
KEY WORDS: Systemic sclerosis, TGFß, TIMP, Fibrosis, Microsatellite markers.
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Introduction |
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The aetiology of SSc is unknown. A genetic predisposition to SSc is indicated by familial data [2, 3], animal models [4, 5], associations between SSc and polymorphisms in a number of genes [68] and the associations between SSc autoantibodies and HLA alleles [9].
Whilst SSc is a clinically heterogeneous condition, fibrosis is a major hallmark. We hypothesize that polymorphisms of genes involved in fibrosis may determine susceptibility to SSc, and/or influence the development of lcSSc or dcSSc. We have selected for investigation genes for the following proteins that are involved in the fibrotic response: transforming growth factor beta, TGFß1, 2 and 3 [10], B chain of platelet-derived growth factor, PDGFB [11], tissue inhibitor of metalloproteinase-1, TIMP-1 [12] and the -2 subunit of collagen type V, COL5A2, which regulates the organization of type I collagen [13].
Functional polymorphisms have not yet been mapped for most of the candidate genes. We therefore adopted a strategy of using microsatellite markers that map either within the candidate gene or within a distance of 1 centimorgan (cM) of the gene. Given the close proximity of the marker and candidate genes, any association found between a marker and SSc is expected to occur as a result of linkage disequilibrium with a polymorphism within the candidate gene. Marker alleles were compared in a casecontrol study to investigate association with SSc.
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Patients and methods |
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The 196 controls (79 males, 117 females) consisted of ethnically matched healthy subjects. Mean age was 45.8 yr (range 2476 yr). Control subjects were obtained from the Norfolk Family Health Service Authority Register, which comprises individuals who had registered with a general practitioner, and from blood donors in the Oxford area.
DNA was extracted from EDTA-treated blood samples using a commercially available extraction kit (Bioline, London, UK). DNA concentrations were measured and diluted to 10 ng/µl using distilled, autoclaved water.
Genotyping of microsatellite markers for candidate genes
Suitable microsatellite markers were selected and distances determined using two Internet databases (Genome Database, Johns Hopkins University, Baltimore, Maryland, USA, Versions 5.6 and 6.0, http://gdbwww.gdb.org/; Genetic Location Database, University of Southampton, Southampton, UK, http://cedar.genetics.soton.as.uk/publichtml/). Markers mapping within 1 cM of candidate genes were selected (D19S400 for TGFß1, D1S419 for TGFß2, D14S277 for TGFß3, PDGFB and D22S284 for PDGFB, DXS426 for TIMP1 and D2S389 for COL5A2). The exact distances between markers and genes are shown in Table 1.
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Statistical analysis
For each microsatellite marker, associations with SSc were investigated by comparing the distribution of its allele frequencies between patients and controls using a single global Pearson's 2 test. Monte Carlo methods were used to calculate P values in SPSS for Windows, version 8.0. In this approach, contingency tables with the same marginal totals as the observed table are simulated, in proportion to their probabilities under the null hypothesis of no difference between cases and controls [14]. The empirical distribution of the usual Pearson
2 statistic is derived by calculating this statistic for each table in 50 000 simulations. The P-value is estimated by the proportion of the tables with a
2 value as extreme as that of the observed table. One test per marker (and not one test per allele) was carried out, which reduces multiple testing and the type-1 error rate. Alleles observed
5 times in both patients and controls were removed from this calculation, as inclusion of such rare alleles would reduce the power of the test. Examination of residuals indicated which alleles had a different frequency in patients and controls and are thus associated. Markers found to be associated with SSc were analysed further, stratifying according to the whether the patient had lcSSc or dcSSc. As DXS426 (marker for TIMP1) maps to the X chromosome, allele frequencies for males and females were analysed separately.
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Results |
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To determine whether these associations differed between lcSSc and dcSSc disease, the results for TGFß2, TGFß3 and TIMP1 were further analysed according to lcSSc and dcSSc status of SSc patients.
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Stratification according to lcSSc and dcSSc status |
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Discussion |
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Gene polymorphisms may affect both the function and the level of protein production, and polymorphisms in genes involved in fibrosis could result in individuals with a predisposition to excessive fibrosis. A mutation in the TGFß1 gene has been reported which is associated with high TGFß1 production, fibrotic lung disease and graft fibrosis after lung transplantation [15]. Whilst functional polymorphisms have not yet been described for TGFß2 and TGFß3 or TIMP1, similar mutations may exist.
All three TGFß isoforms can induce extracellular matrix (ECM) production [10]. Studies have suggested, however, that TGFß1 and TGFß2 may have a greater role in excess ECM production, causing fibrosis and scar formation, whilst TGFß3 may be more involved in the later downregulation of ECM production, possibly by downregulating TGFß1 production [16, 17]. The finding of high levels of TGFß1, TGFß2 and TGFß3 in progressively and highly involved SSc skin [18] may indicate that either insufficient TGFß3 is being produced or that TGFß3 is functionally not able to downregulate TGFß1 production/action. If gene polymorphisms exist which alter the levels or function of TGFß2 and TGFß3 leading to excess TGFß2 and reduced TGFß3 activity, this environment may make individuals susceptible to fibrosis.
The difference in the distribution of TGFß3 microsatellite marker alleles between patients with lcSSc and dcSSc disease indicates a possible role for TGFß3 in determining the degree of cutaneous fibrosis the patient develops, as this is the main criterion which determines lcSSc and dcSSc status [1]. Whilst the marker for TGFß2 had a significantly different allele frequency distribution in lcSSc patients compared with controls, the fact that no difference was found between lcSSc and dcSSc patients may indicate that the marker for TGFß2 is not associated with determining whether a patient develops lcSSc or dcSSc disease.
This study also indicates that TIMP1 may be a susceptibility gene for male individuals developing SSc. TIMP protects collagen and other matrix molecules from degradation by metalloproteinases. SSc fibroblasts produce elevated levels of TIMP-1 and secrete two major forms of TIMP-1 that differ in their glycosylation patterns, possibly indicating functional differences [19]. The lack of association between the TIMP1 marker and female SSc may indicate that predisposing genetic factors differ between male and female disease.
The fact that associations have not been detected between SSc and markers for TGFß1, PDGFB and COL5A2 does not exclude these genes from having a role in SSc susceptibility. The power to detect an association depends on sample size, disease allele frequency and the risk of disease conferred by the disease allele. Assuming a 5% significance level and a small genetic effect (equivalent to an odds ratio of 2), the maximum power that this sample set had was 88% to detect an association to a disease allele with a frequency of 1080% in controls (calculated using EPI Info, version 6; Centers for Disease Control and Prevention http://www.cdc.gov). In addition, it should be noted that, given the small numbers of dcSSc cases, this sample set may not have been sufficiently powerful to detect differences in subgroups. Given that the genetic susceptibility to SSc is most probably multigenic, larger numbers of samples may be required to detect small effects of genes.
A recognized problem of association studies is that false positive results may be generated due to population stratification and multiple testing. To ensure that the Caucasian controls obtained from two geographical locations did not differ, the allele frequency distributions for the seven microsatellite markers were compared. No difference in allele frequencies was found between controls from the two UK locations (data not shown), indicating that the two control groups were unlikely to be biased. In addition, an approach has been implemented which reduces the potential number of statistical tests, which will reduce the chances of obtaining a type-1 error, as the effect of disease subtype has only been investigated for markers with a statistically significant allele frequency distribution between patients and controls.
The proteins used in this study were selected on the basis of published information regarding their role in fibrosis. In the absence of known functional polymorphisms for the genes of these proteins, investigation was carried out using microsatellites as opposed to biallelic polymorphisms, as studies have shown that the more polymorphic microsatellite markers have more power to detect linkage disequilibrium than biallelic markers when the distance between the marker and disease locus is identical [20].
In summary, this study has identified associations between SSc and microsatellite markers for TGFß3, TGFß2 and between male SSc and a marker for TIMP1, indicating a possible role for these genes in genetic susceptibility to SSc. Further investigation of the genes for TGFß2, TGFß3 and TIMP-1 is required to determine whether the associations found in this study are due to the presence of functional gene polymorphisms.
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Acknowledgments |
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Notes |
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References |
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