No evidence for a role of the proximal IL-6 G/C -174 single nucleotide polymorphism in Italian patients with systemic sclerosis

Influence of the IL6 gene in susceptibility to systemic sclerosis

OBJECTIVE

Systemic sclerosis (SSc) is a genetically complex autoimmune disease; the genetic component has not been fully defined. Interleukin 6 (IL-6) plays a crucial role in immunity and fibrosis, both key aspects of SSc. We investigated the influence of IL6 gene in the susceptibility and phenotype expression of SSc.

METHODS

We performed a large metaanalysis including a total of 2749 cases and 3189 controls from 6 white populations (Germany, The Netherlands, Norway, Spain, Sweden, and United Kingdom). Three IL6 single-nucleotide polymorphisms (SNP; rs2069827, rs1800795, and rs2069840) were selected by SNP tagging and genotyped using TaqMan(®) allele discrimination technology.

RESULTS

Individual SNP metaanalysis showed no evidence of association of the 3 IL6 genetic variants with the global disease. Phenotype analyses revealed a significant association between the minor allele of rs2069840 and the limited cutaneous SSc clinical form (Bonferroni p = 0.036, OR 1.14, 95% CI 1.04-1.25). A trend of association between the minor allele of the rs1800795 and the diffuse cutaneous SSc clinical form was also evident (Bonferroni p = 0.072, OR 0.86, 95% CI 0.77-0.96). In the IL6 allelic combination analyses, the GGC allelic combination rs2069827-rs1800795-rs2069840 showed an association with overall SSc (Bonferroni p = 0.016, OR 1.13, 95% CI 1.04-1.23).

CONCLUSION

Our results suggest that the IL6 gene may influence the development of SSc and its progression.

A 3-factor epistatic model predicts digital ulcers in Italian scleroderma patients

BACKGROUND

The genetic background may predispose systemic sclerosis (SSc) patients to the development of digital ulcers (DUs).

METHODS

Twenty-two functional cytokine single nucleotide polymorphisms (SNPs) and 3 HLA class I and II antigens were typed at the genomic level by polymerase chain reaction in 200 Italian SSc patients. Associations with DUs were sought by parametric models and with the Multifactor Dimensionality Reduction (MDR) algorithm to depict the presence of epistasis. Biological models consistent with MDR results were built by means of Petri nets to describe the metabolic significance of our findings.

RESULTS

On the exploratory analysis, the diffuse cutaneous subset (dcSSc) was the only single factor statistically associated with DUs (p=0.045, ns after Bonferroni correction). Gene-gene analysis showed that a 3-factor model comprising the IL-6 C-174G, the IL-2 G-330T SNPs and the HLA-B*3501 allele was predictive for the occurrence of DUs in our population (testing accuracy=66.9%; p<0.0001, permutation testing).

CONCLUSION

Biological interpretation via Petri net showed that IL-6 is a key factor in determining DUs occurrence and that this cytokines may synergise with HLA-B*3501 to determine DUs onset. Owing to the limited number of patients included in the study, future research are needed to replicate our statistical findings as well as to better determine their functional meaning.

Ability of epistatic interactions of cytokine single-nucleotide polymorphisms to predict susceptibility to disease subsets in systemic sclerosis patients

OBJECTIVE

Gene-gene interaction, or epistasis, is considered a ubiquitous component of complex human diseases such as systemic sclerosis (SSc). Epistasis is difficult to model by traditional parametric approaches; therefore, nonparametric computational algorithms, such as multifactor dimensionality reduction (MDR), have been developed.

METHODS

A total of 242 consecutive unrelated Italian SSc patients and an equal number of well-matched healthy controls were genotyped for 22 cytokine single-nucleotide polymorphisms (SNPs; 13 cytokine genes). The distribution of the SNPs between controls and SSc patients, controls and limited cutaneous SSc (lcSSc) patients, and controls and diffuse cutaneous SSc (dcSSc) patients was tested by the MDR constructive induction algorithm and by focused interaction testing framework (FITF), a logistic regression-based approach.

RESULTS

None of the studied SNPs had main independent effects on SSc or disease subset susceptibility, therefore no epistatic interaction was detectable by FITF. The MDR analysis showed a significant epistatic interaction among the interleukin-2 (IL-2) G-330T, IL-6 C-174G, and interferon-gamma AUTR5644T SNPs and the IL-1 receptor Cpst1970T, IL-6 Ant565G, and IL-10 C-819T SNPs in lcSSc and dcSSc susceptibility, respectively. The relevance of the single multilocus attributes constructed by the MDR inductive algorithm was then confirmed by the parametric approach (P < 0.001 for both controls versus lcSSc patients and controls versus dcSSc patients).

CONCLUSION

We provide evidence for gene-gene interaction among cytokine SNPs in the context of SSc. The interaction among cytokine SNPs with a profibrotic or a regulatory function on profibrotic interleukins is relevant to the susceptibility to SSc subsets and it appears to be more important than the contribution of any single cytokine SNP.