Abstract
Systemic sclerosis is an autoimmune disease characterized by generalized fibrosis in connective tissues and internal organs as consequences of microvascular dysfunction and immune dysfunctions, which leads to premature death in affected individuals. The etiology of systemic sclerosis is complex and poorly understood, but as with most autoimmune diseases, it is widely accepted that both environmental and genetic factors contribute to disease risk. During the last decade, the number of genetic markers convincingly associated with systemic sclerosis has exponentially increased. In this article, we briefly mention the genetic components of systemic sclerosis. Then, we review the classical and novel genetic associations with systemic sclerosis, analyzing the firmest and replicated signals within non–human leukocyte antigen genes, identified by both candidate gene approach and genome-wide association studies. We also provide an insight into the future perspectives that will shed more light into the complex genetic background of the disease. Despite the remarkable advance of systemic sclerosis genetics during the last decade, the use of the new genetic technologies such as next-generation sequencing, as well as the deep phenotyping of the study cohorts, to fully characterize the genetic component of this disease is imperative to identify causal variants, which leads to more targeted and effective treatment of systemic sclerosis.
Keywords: Systemic sclerosis, genetics, single-nucleotide polymorphism, genome-wide association study, candidate gene analysis, ImmunoChip analysis, genome-wide significance, non–human leukocyte antigen genes, human leukocyte antigen genes
Introduction
Systemic sclerosis (SSc) is an autoimmune disease (AID) characterized by generalized fibrosis in connective tissues and internal organs as consequences of microvascular dysfunction and immune dysfunctions, which leads to premature death in affected individuals.
As clinical phenotypes, SSc is divided into limited cutaneous systemic sclerosis (lcSSc) and diffuse cutaneous systemic sclerosis (dcSSc). 1 SSc is also classified according to supposedly mutually exclusive and disease-specific autoantibodies (anti-centromere antibodies (ACAs) and anti-topoisomerase antibodies (ATAs)), which are found in approximately 50%–70% of SSc cases. 2 Each of these autoantibodies is a marker for relatively distinct clinical subgroups of SSc; ACA is typically associated with lcSSc, uncommon pulmonary fibrosis (PF), late-onset pulmonary hypertension, and generally an overall good prognosis, while ATA is a marker for dcSSc and clinically significant PF with a resultant poorer prognosis than an ACA(+) subtype. 3
The etiology of SSc is complex and poorly understood, but as with most AIDs, such as rheumatoid arthritis (RA), it is widely accepted that gene–environment interactions trigger SSc onset. 4
The most frequent type of DNA variation is a single-nucleotide polymorphism (SNP). The resultant changes in the DNA sequence may or may not influence the protein function due to modification in the gene expression or alteration in the resulting protein. Two basic approaches often used for genetic association studies are the candidate gene approach (CGA) and the genome-wide association study (GWAS). Both modalities identify SNPs and determine the likelihood that the variant occurs more or less frequently in the cases than in the controls. For both approaches, association is first tested in a discovery cohort and then repeated in a non-overlapping group of cases and controls.
In CGA, candidate genes are selected based on either their association with other AIDs or their involvement in certain pathways hypothesized to be important for the disease pathogenesis. Thus, we can test for a particular SNP with known functional consequences,4,5 but CGA cannot identify new loci that have not been discovered yet due to the nature of its hypothesis-driven approach.
On the other hand, GWAS scans the entire genome for SNPs associated with a certain disease without any assumptions, and thus is an unbiased approach. In fact, GWAS has made possible the identification of new loci that would not have been discovered. 6 However, because most GWAS provide approximately 80% coverage for common polymorphisms in the human genome, they often miss unusual or rare variants. 4 Moreover, GWAS usually investigates SNPs that are in strong linkage disequilibrium (LD) with other SNPs and serve as proxies for them, and thus the identified SNPs by GWAS are regarded as just a tag for the yet-to-be-identified causal allele. Thus, the next step in genomic analyses will be whole genome sequencing, which covers all nucleotides in the genome.
ImmunoChip array is another type of SNP genotyping with high-density mapping of 196,524 variants across 186 known AID-risk loci in European-Caucasian populations.7,8 It also includes several rare variants supposed to have significant functional effects that might have been previously overlooked. 9 This approach has recently shown promising results in various autoimmune- and immune-related diseases including SSc.7,8 These studies resulted in not only the identification of susceptibility genes but also the narrowing of the associations in previously reported risk loci for large cohorts at reduced costs. Moreover, these reports increased the number of shared genetic markers between the different disorders, further supporting the common genetic component of autoimmunity.
In this review article, we provide an overview of the known SSc-related genetic factors and address their possible functional implication in the pathogenic events. Specifically, we briefly mention the heritability of SSc first. Then, we present genes outside major histcompatibility complex (MHC) region, of which SNPs had association with SSc or its phenotypes at genome-wide significant (GWS) levels (p < 5.0 × 10−8). Recent advances in epigenetic studies have accumulated several evidences that epigenetic modifications of certain genes are also involved in SSc pathogenesis, 10 which have been precisely reviewed in several articles and thus is beyond the scope of this review.
Heritability
Feghali-Bostwick et al. 11 performed a classical twin study and found a similar SSc concordance between monozygotic and dizygotic twins, suggesting a low heritability of SSc. However, this study contained a quite limited number of twins and the subjects had a wide range of age.
On the other hand, Bossini-Castillo et al. 12 calculated SSc heritability with the use of a mixed model (GREML) 13 applying to two large-scale GWASs.7,14 While they found that SSc heritability (liability-scale) is lower than in other AIDs, such as inflammatory bowel disease (IBD) or type 1 diabetes,13,15 they also confirmed that the twin concordance study above 11 had underestimated heritability of SSc. 12
Arnett et al. 16 showed that the first-degree relatives of SSc patients had a 13 times higher SSc risk than the general population and 15 times higher in the case of SSc patients’ siblings, while the absolute risk of developing SSc is only 1.5%–1.7% in these relatives compared with 0.026% in the general population. These studies indicated that a positive family history of SSc is a strong risk of SSc, but the absolute risk of each family member remains still low (~1%), which was consistent with the estimated prevalence of SSc in first-degree family members by Englert et al. 17
Intriguingly, Arora-Singh et al. 18 showed an increased familial risk to develop other AIDs such as hypothyroidism, hyperthyroidism, systemic lupus erythematosus (SLE), or some of SSc manifestations including Raynaud’s phenomenon or interstitial lung disease, implicating genetic contribution to SSc development, and shared genetic components between SSc and other AIDs.
To sum up, it has been indicated that the heritability of SSc might be lower than other AIDs; 12 however, it is also evident that genetic component plays important roles for pathogenesis of SSc. Thus, estimating precise heritability and clarifying genetic roles in pathogenesis are of the future interests in the genetic study of SSc.
Not only human leukocyte antigen genes but also genes outside MHC region significantly associated with SSc
As seen in other AIDs, human leukocyte antigen (HLA) genes or haplotypes identified in SSc genetic studies have been shown to be associated with SSc susceptibility, autoantibody profiles, disease subtypes, or certain SSc clinical features. Several large-scale studies indicated that HLA-DQB1 and genes in LDs with HLA-DQB1 are strongly associated with SSc susceptibility and the effects are dependent on autoantibody status mainly in European-Caucasian descents3,7,14,19,20 More specifically, the associations of HLA-DPB1*13:01 with ATA, HLA-DQB1*05:01, and HLA-DQB1 alleles encoding a non-leucine residue at position 26 (DQB1*26 epi) with ACA, and HLA-DRB1*04:04, HLA-DRB1*11, and HLA-DQB1*03 with anti-RNA polymerase III antibody (ARA) were reported by Arnett et al. 21 Not surprisingly, these associations can be common in different ethnicities, while others are specific to certain populations. 22
Multiple non-HLA loci have been reported as SSc susceptibility genes with varying significance. However, most of them were based on the small-scale analyses and were not replicable. Recent advances, such as well-powered CGAs and replication studies together with the GWAS, have led to the establishment of multiple non-HLA loci as SSc susceptibility genes at the GWS levels. There have been 15 firmly established SSc-associated genes among non-HLA genes, which are involved in various functions, with the most robust associations including genes for B- and T-cell activation and innate immunity (Table 1). In the following sections, we focus more on some of these confirmed non-HLA genes.
Table 1.
Non-HLA genes outside HLA region significantly associated with SSc.
| Gene | Locus | SNP | Phenotypes | Samples | Study type | Genotyping | Ethnicity | First author | Publication | Ref. |
|---|---|---|---|---|---|---|---|---|---|---|
| IRF5 | 7q32.1 | rs2004640 | SSc, ATA+ dcSSc, PF | 427 cases/380 HCs (discovery) | CGA | TaqMan | Caucasian | Dieude | 2009 | 20 |
| 454 cases/380 HCs (replication) | ||||||||||
| SSc | Allanore et al. 19 and Ito et al. (Arthritis Rheum, 2009) | Meta-analysis | – | Caucasian, Asian | Xu | 2016 | 23 | |||
| rs10488631 | SSc | 2296 cases/5171 HCs (discovery) 2753 cases/4659 HCs (replication) |
GWAS | Illumina HumanCNV370K BeadChip | Caucasian | Arnett | 2010 | 14 | ||
| SSc | 564 cases/1776 HCs (discovery) 1682 cases/3926 HCs (replication) |
GWAS | Illumina Human610-Quad BeadChip | Caucasian | Allanore | 2011 | 19 | |||
| rs3757385 | “R” haplotype (C-T-A) for dcSSc “P” haplotype (A-G-G) for dcSSc and PF |
827 cases/989 HCs | CGA | Kaspar genotyping | Caucasian | Dieude | 2009 | 20 | ||
| rs10954213 | ||||||||||
| rs2004640 | ||||||||||
| IRF8 | 16q24.1 | rs11642873 | lcSSc | 2296 cases/5171 HCs (discovery) 3175 cases/4971 HCs (replication) |
GW AS | Immuno Chip | Caucasian | Gorlova | 2011 | 3 |
| SSc | 1280 cases/3593 HCs (discovery) 3156 cases/5148 HCs (replication) |
GWAS | a | Caucasian, Asian | Terao | 2017 | 22 | |||
| rs2280381 | SSc | 415 cases/16,891 HCs (discovery) 315 cases/21,054 HCs (replication) |
CGA | TaqMan | Asian | Terao | 2013 | 24 | ||
| rs11117432 | SSc | 1616 cases/3621 HCs | CGA | Kaspar genotyping | Caucasian | Arismendi | 2015 | – | ||
| STAT4 | 2q32.2-q32.3 | rs7574865 | lcSSc | 332 cases/1296 HCs (discovery) 985 cases/1817 HCs (replication) |
CGA | TaqMan | Caucasian | Rueda | 2009 | 25 |
| ATA+ SSc, PF, dcSSc | 453 cases/534 HCs | CGA | TaqMan | Asian | Yi | 2013 | 26 | |||
| SSc, lcSSc, dcSSc | Allanore et al., 19 Nguyen et al., 27 Abelson et al., 28 and Tsuchiya et al. (Ann Rheum Dis, 2009) | Meta-analysis | – | Caucasian, Asian | Xu | 2016 | 23 | |||
| SSc | 1280 cases/3593 HCs (discovery) 3156 cases/5148 HCs (replication) |
GWAS | a | Caucasian, Asian | Terao | 2017 | 22 | |||
| SSc | 440 cases/485 HCs (discovery) 445 cases/485 HCs (replication) |
CGA | Kaspar genotyping | Caucasian | Dieude | 2009 | 29 | |||
| rs3821236 | SSc | 2296 cases/5171 HCs (discovery) 2753 Cases/4659 HCs (replication) |
GWAS | TaqMan | Caucasian | Arnett | 2010 | 16 | ||
| SSc | 564 cases/1776 HCs (discovery) 1682 cases/3926 HCs (replication) |
GWAS | Illumina Human610-Quad BeadChip | Caucasian | Allanore | 2015 | 19 | |||
| rs10168266 | ACA+ SSc, PF | 453 cases/534 HCs | CGA | TaqMan | Asian | Yi | 2013 | 26 | ||
| IL12A | 3q25.33 | rs77583790 | SSc, ACA+ lcSSc | 1833 cases/3466 HCs (discovery) 4017 cases/5935 HCs (replication) |
ImmunoChip | ImmunoChip for discovery sets; TaqMan for replication sets | Caucasian | Mayes | 2014 | 7 |
| IL12RB1 | 19p13.11 | rs2305743 | SSc | 1871 case/3636 HCs (discovery) 3181 cases/5076 HCs (replication) |
CGA | ND | Caucasian | Lopez-Isac | 2014 | 30 |
| rs8109496 | ||||||||||
| rs436857 | ||||||||||
| rs11668601 | ||||||||||
| IL12RB2 | 1p31.3 | rs3790566 | SSc | 2296 cases/5171 HCs (discovery) 2753 cases/4659 HCs (replication) |
GWAS | TaqMan | Caucasian | Arnett | 2010 | 16 |
| rs924080 | ||||||||||
| rs3790567 | SSc | 2296 cases/5171 HCs (discovery) 2753 cases/4659 HCs (replication) |
GWAS | TaqMan | Caucasian | Arnett | 2010 | 16 | ||
| SSc | 3344 cases/3848 HCs (discovery) 597 cases/1139 (replication) |
CGA | TaqMan | Caucasian | Bossini-Castillo | 2012 | 31 | |||
| CD247 | 1q24.2 | rs2056626 | SSc | 2296 cases/5171 HCs (discovery) 2753 cases/4659 HCs (replication) |
GWAS | TaqMan | Caucasian | Arnett | 2010 | 16 |
| SSc | 1031 cases/1014 HCs | CGA | Kaspar genotyping | Caucasian | Dieude | 2011 | 32 | |||
| PRDM1 | 6q21 | rs4134466 | SSc | 1280 cases/3593 HCs (discovery) 3156 cases/5148 HCs (replication) |
GWAS | a | Caucasian, Asian | Terao | 2017 | 22 |
| ATG5 | 6q21 | rs9373839 | SSc | 1833 cases/3466 HCs (discovery) 4017 cases/5935 HCs (replication) |
ImmunoChip | ImmunoChip for discovery sets; TaqMan for replication sets | Caucasian | Mayes | 2014 | 7 |
| TNIP1 | 5q33.1 | rs2233287 | SSc | 564 cases/1776 HCs (discovery) 1682 cases/3926 HCs (replication) |
GWAS | Illumina Human610-Quad BeadChip | Caucasian | Allanore | 2015 | 19 |
| SSc | 4389 cases/7611 HCs | CGA | b | Caucasian | Bossini-Castillo | 2013 | 33 | |||
| rs4958881 | SSc | 564 cases/1776 HCs (discovery) 1682 cases/3926 HCs (replication) |
GWAS | Illumina Human610-Quad BeadChip | Caucasian | Allanore | 2015 | 19 | ||
| SSc | 4389 cases/7611 HCs | CGA | b | Caucasian | Bossini-Castillo | 2013 | 33 | |||
| rs3792783 | SSc | 564 cases/1776 HCs (discovery) 1682 cases/3926 HCs (replication) |
GWAS | Illumina Human610-Quad BeadChip | Caucasian | Allanore | 2015 | 19 | ||
| SSc | 4389 cases/7611 HCs | CGA | b | Caucasian | Bossini-Castillo | 2013 | 33 | |||
| TNFAIP3 | 6q23.3 | rs5029939 | SSc, dcSSc, ATA+, PF, PH | 1018 cases/1012 HCs (discovery) 649 cases/306 HCs (replication) |
CGA | Kaspar genotyping for a discovery set, TaqMan for a replication set | Caucasian | Dieude | 2010 | 34 |
| rs117480515 | SSc | 985 cases/1011 HCs (discovery) 662 cases/493 HCs (replication) |
CGA | Kaspar genotyping for a discovery set, TaqMan for a replication set | Caucasian | Koumakis | 2012 | 35 | ||
| rs2230926 | SSc | 3237 cases/6097 HCs | CGA | Illumina GoldenGate array/Illumina iScan system | Caucasian | Martin | 2012 | 36 | ||
| SSc | 1280 cases/3593 HCs (discovery) 3156 cases/5148 HCs (replication) |
GWAS | a | Caucasian, Asian | Terao | 2017 | 22 | |||
| rs6932056 | SSc | 415 cases/16,891 HCs (discovery) 315 cases/21,054 HCs (replication) |
CGA | TaqMan | Asian | Terao | 2013 | 24 | ||
| rs892999 | SSc for Caucasians | 1833 cases/3466 HCs (Whites) 291 cases/260 HCs (Blacks) |
CGA | ImmunoChip | Caucasian, Black | Gorlova | 2018 | 37 | ||
| rs10223636 | SSc for African American | CGA | ImmunoChip | Caucasian, Black | Gorlova | 2018 | 37 | |||
| CSK | 15q24.1 | rs1378942 | SSc | 3237 cases/6097 HCs | CGA | Illumina GoldenGate array/Illumina iScan system | Caucasian | Martin | 2012 | 36 |
| DNASE1 L3 | 3p14.3 | rs35677470 | SSc | 1833 cases/3466 HCs (discovery) 4017 cases/5935 HCs (replication) |
ImmunoChip | ImmunoChip for discovery sets; TaqMan for replication sets | Caucasian | Mayes | 2014 | 7 |
| SSc | 1280 cases/3593 HCs (discovery) 3156 cases/5148 HCs (replication) |
GWAS | a | Caucasian, Asian | Terao | 2017 | 22 | |||
| TYK2 | 19p13.2 | rs2304256 | SSc | 2118 cases/4742 HCs (discovery) 4985 cases/7478 HCs (replication) |
CGA | TaqMan | Caucasian | Lopez-Isac | 2015 | – |
| SSc | 1280 cases/3593 HCs (discovery) 3156 cases/5148 HCs (replication) |
GWAS | a | Caucasian, Asian | Terao | 2017 | 22 | |||
| GSDMA | 17q21.1 | rs3894194 | SSc | 1280 cases/3593 HCs (discovery) 3156 cases/5148 HCs (replication) |
GWAS | a | Caucasian, Asian | Terao | 2017 | 22 |
| 57 patients/15 HCs | CGA | Illumina Infinium Omni2.5–8 1.3 platform | Caucasian, Black, Caribbean, Indian | Moreno-Moral | 2018 | 38 |
HLA: human leukocyte antigen; SSc: systemic sclerosis; dcSSc: diffuse cutaneous systemic sclerosis; lcSSc: limited cutaneous systemic sclerosis; ACA: anti-centromere antibody; ATA: anti-topoisomerase I antibody; PF: pulmonary fibrosis; PH: pulmonary hypertension; GWAS: genome-wide association study; CGA: candidate gene approach; SNP: single-nucleotide polymorphism; HCs: healthy controls; PCR: polymerase chain reaction; ND: not described.
Same as Allanore et al. 19 for French samples; genotyped with a competitive allele-specific PCR for European replication samples; genotyped in Kyoto University and University of Tsukuba for Japanese samples.
Illumina Human550K, Illumina Human CNV370K, Illumina Human CytoSNP-12, Illumina Human 1M-Duo, and TaqMan.
IRF5
Interferon regulatory factor 5 (IRF5) is a crucial regulator of type I interferon (IFN) signaling. IFN signatures, IFN-inducible genes, similar to those of SLE in the peripheral blood cells of SSc patients have also been determined. 39
Dieude et al. 20 found an association between the IRF5 intron 1 rs2004640T allele and SSc, especially the ATA(+) dcSSc with fibrosing alveolitis (FA) in French population, suggesting that the rs2004640 could influence the fibrotic phenotype via type I IFN activation. They further performed genotype–phenotype–haplotype correlation analyses of IRF5 with the SNPs rs3757385, rs2004640, and rs10954213, and found that the “R” haplotype (C-T-A) conferred susceptibility to SSc, especially dcSSc, and the “P” haplotype (A-G-G) had protective effect on both dcSSc and FA. 40 The “R” haplotype is known to infer the CGGGG insertion risk allele, which leads to increased binding of SP1 transcriptional factor and also known as the causative variant of SLE and Sjogren syndrome. 41 As all single markers failed to detect association signals, the study suggested the haplotype analyses may be more informative than single-marker analyses.
Association of IRF5 with SSc was also confirmed in the GWAS of SSc,14,19 and the meta-analysis conducted by Xu et al., 23 suggesting its association beyond populations. Collectively, these studies strongly indicated a key role of IRF5 in SSc pathogenesis.
STAT4
Signal transducer activator transcriptional factor 4 (STAT4) is a member of the STAT transcription factor family and is essential in mediating interleukin-12 (IL-12) signaling, as well as in helper T-cell differentiation. 42 STAT4 also links the innate and the adaptive immune responses through type I IFN-mediated IFN-γ production. 27
Rueda et al. 25 first reported the association of STAT4 in their Caucasian case–control study. The tag SNP rs7574865 (intron 3) T allele, which is correlated with STAT4 mRNA expression 28 and known for its association with other AIDs, 23 significantly associated with susceptibility to lcSSc as well as lacked the association with dcSSc. A dosage effect was also observed (rs7574865 TT genotype, p = 1.02 × 10−7). Subsequent two Caucasian GWASs14,19 also showed significant associations, although the study conducted by Allanore et al. might include populations overlapped with the first case–control study.
On the other hand, a Chinese case–control study conducted by Yi et al. reported different results. The T-allele carriers of rs7574865 as well as rs10168266 were associated with ATA (p = 0.0012 and p = 3.1 × 10−4, respectively) and PF (p = 1.2 × 10−4 and p = 7.7 × 10−4, respectively), as well as with dcSSc (p = 6.4 × 10−4 and p = 0.0015, respectively). The presence of ACA and lcSSc did not show significant association with any of the examined SNPs in this study. 26
According to a meta-analysis of six case–control studies including the studies above conducted by Xu et al., the rs7574865T allele showed a significant association with SSc susceptibility (p < 0.00001) in the overall population, and both lcSSc and dcSSc showed a significant association with each of the genetic models (lcSSc, p < 1.0 × 10−5; dcSSc, p < 5.0 × 10−4 to 1.0 × 10−5). 23
Furthermore, our transethnic meta-analysis of GWAS in the Japanese and European populations found a GWS association of rs7574865T allele with overall SSc. 22
The different effects of rs7574865T allele on SSc subtypes and autoantibody profiles among different ethnicities indicated that an association of the rs7574865T allele with ACA(+) SSc may be limited in European populations while that in Asian populations remained questionable partly because of small sample size of the studies. It might also be possible that different SNPs might be more important for non-European populations.
Notably, Dieude et al. observed that additive effects of STAT4 rs7574865 and IRF5 rs2004640 polymorphisms on susceptibility to both SSc and SSc-related FA. 29 The STAT4 rs7574865T allele and the IRF5 rs2004640T allele resulted in 1.28-fold higher risk of SSc and the odds ratio (OR) for SSc was 2.72 (1.86–3.99) for combinations of genotypes with ⩾3 risk alleles.
The functional role in SSc pathogenesis was also tested in Stat4-knockout mice, which presented decreased cytokine production and protection from skin fibrosis in bleomycin-induced model. 43 Despite several questions remaining to be answered, all the studies above further strongly supported functional consequences caused by STAT4 polymorphisms and their roles in SSc pathophysiology.
IL12, IL12RB1, and IL12RB2
The protein encoded by IL12, the p35 subunit, promotes type 1 helper T-cell differentiation and in combination with IFN-γ antagonizes type 2 helper T-cell differentiation. 44
IL12A was one of the non-HLA loci identified by ImmunoChip study conducted by Mayes et al. The missense SNP rs77583790 in an intergenic region between IL12A and SCHIP1 encoding schwannomin interacting protein 1 was significantly associated with overall SSc, and the association was also significant in the lcSSc and ACA(+) subgroup. 7
IL12RB1 and IL12RB2 encode both chains of the IL-12 receptor, and both of genes showed suggestive associations with SSc in Caucasian GWAS. 14 Bossini-Castillo et al. 31 subsequently conducted a follow-up study using an independent cohort and observed GWS association of an intronic SNP rs3790567 of IL12RB2 with SSc. The SNP may be a tag of certain functional variant or may have a yet unknown functional implication.
According to the suggestive association signals of IL12RB1 locus in immunoChip study, 7 Lopez-Isac et al. 30 conducted a follow-up study, in which four SNPs showed GWS associations with SSc susceptibility (rs8109496, rs2305743, rs436857, and rs11668601). They also suggested that SNP rs436857 in IL12RB1 promoter region was the most likely causal variant. The minor allele of this SNP showed a protective effect and was located in an IL12RB1 cis-eQTL that decreased IL12RB1 expression, 45 which would lead to a reduced IL-12 response consistent with a lower SSc susceptibility. 12 Furthermore, they also reported an association of a common variant (V362F) of TYK2, which encodes tyrosine kinase 2 involved in IL-12 pathway, with SSc susceptibility. 46
Collectively, these data in conjunction with those of STAT4, IL-12/STAT4 axis are further implicated as parts of the central roles in the pathogenesis of SSc.
CD247
CD247 encodes CD3ζ chain, 47 and altered low expression of CD3ζ chain results in impaired immune response. 48 Genetic variants in the 3′ untranslated region of this gene have shown functional implications leading to a reduced expression of the CD3ζ chain that could be manifested in systemic autoimmunity. 49
Radstake et al. 14 identified CD247 (rs2056626) as a susceptibility locus for SSc by European ancestry GWAS. An independent French study confirmed the association and indicated that the minor allele rs2056656G was associated with a protective effect. 32 In sharp contrast, there was no association between the same variant and SSc or any SSc subtypes in a Han Chinese cohort, 50 and our transethnic meta-GWAS analysis also failed to replicate the association observed in the European studies above. 22 Further studies to better explain the exact role of CD247 in SSc in different ethnic populations will be of great interest.
PRDM1
PRDM1, B-lymphocyte-induced maturation protein 1, also known as BLIMP1, is a transcription factor influencing a broad range of genes involved with cell proliferation and immune systems. PRDM1 is also critical for epithelial and B-cell differentiation and associated with immune functions and other AIDs. 51
Our meta-GWAS analysis in the Japanese and European populations identified a significant association of PRDM1 rs4134466 with SSc susceptibility. 22 The rs4134466 was not dependent on rs9373839 in ATG5, a previously reported susceptibility gene to SSc. 7 Intriguingly, the rs4134466 provided the highest score of H3K4me3 in naïve CD4+ T cell among the SSc susceptibility variants, suggesting a functional consequence of this variant.
Tumor necrosis factor-α-related genes
TNFAIP3 and TNIP1 encode for tumor necrosis factor (TNF)-α-induced protein (A20) 52 and TNFAIP3-interacting protein, respectively. Both A20 and TNIP1 are involved in negatively regulating the TNF-induced nuclear factor (NF)-κB signaling pathway. 53
A European GWAS conducted by Allanore et al. found a significant association of TNIP1 rs4958881 with SSc. 19 They also observed a markedly reduced expression of the TNIP1 gene and its protein product both in skin lesions and in cultured dermal fibroblasts from SSc patients. Furthermore, TNIP1 showed in vitro inhibitory effects on inflammatory cytokine-induced collagen production. On the other hand, Bossini-Castillo et al. 33 conducted a replication study in independent Caucasian populations and confirmed the association of rs4958881 (p = 3.26 × 10−5) as well as other variants (rs2233287, p = 1.94 × 10−4; rs3792783, p = 2.16 × 10−4) with SSc and its subgroups.
Multiple studies with different ethnicities have repeatedly reported the associations of TNFAIP3 SNP variants with susceptibility of SSc and its subtypes.22,24,34 –37 However, only one of them reached GWS in its association with dcSSc. 34 Considering the close relationship between TNIP1 and TNFAIP3 as described above, it may be important to consider the effects of both of these gene polymorphisms simultaneously.
CSK
C-Src kinase, CSK, is a tyrosine-protein kinase that negatively regulates lymphocyte activation through phosphorylation of the Src-family kinases. 54 It has been reported that Src kinases play a central role in the activation of fibroblasts and in the development of experimental fibrosis and Src kinase inhibition reduced the transcriptional activity of COL1A1, COL1A2, and FN1 (which encode collagen type 1 α1, α2 chains, and fibronectin 1) and prevented experimental dermal fibrosis. 55
In the GWAS follow-up study conducted by Martin et al., 36 CSK was newly identified as a gene associated with SSc with genome-wide significance (rs1378942). Although the functional relevance of the CSK associated SNP is unknown, genetic variants affecting its inhibitory effect may contribute to the uncontrolled fibroblast activation in SSc pathogenesis. 12
GSDMA
Our transethnic meta-GWAS analysis identified a missense variant rs3894194 in GSDMA, which encodes gasdermin A, was associated with overall SSc as well as lcSSc. 22 GSDMA also showed a significant association with other AIDs including RA 56 and IBD.57,58 Importantly, the rs3894194 showed enhancer activity and enrichment of histone marks, and strongly associated with expression of neighboring genes, GSDMB, and ORMDL3, encoding gasdermin B and ORMDL sphingolipid biosynthesis regulator 3, respectively, suggesting this variant as a potential causative SNP. On the other hand, Moreno-Moral et al. 38 recently reported that the rs3894194 conferred changes of transcriptome signatures in SSc macrophages. Although lack of GWS or replications, TLR (toll-like receptor) 59 and NLRP (NLR family pyrin domain) 60 were also reported to be associated with SSc and encoding proteins as well as gasdermin A are essential components for inflammasome formation and pyroptosis induction, implicating their roles in the innate immune/inflammasome pathway underlying SSc pathogenesis.
Concluding remarks
Recent advances of knowledge and technology of the genetic study have enabled us to discover numbers of novel and significant genes associated with susceptibility of SSc and its subtypes and autoantibody profiles in SSc patients (Figure 1). Of note, several SNPs of HLA and non-HLA genes, which have not reached GWS but have been replicated in independent studies, are not described in this review. Despite the recent advances, only a small proportion of the heritability of SSc has been explained yet due to the limitations of available technologies, such as what we described with regard to GWAS in the “Introduction” section. As potentially powerful tools in near future, ongoing studies include fine mapping and sequencing studies together with CRISPR-Cas technology to pinpoint causal variants, whereas other studies focus on functional consequences of these variants to identify the link between these genetic variants and disease susceptibility. Such knowledge should lead to more targeted and effective treatment of SSc.
Figure 1.
Different kinds of cells, including antigen presenting cells (APC), T cells, B cells, fibroblasts, and endothelial cells, play roles for SSc pathogenesis. Gene polymorphisms associated with SSc susceptibility may affect functions of these cells, leading to endothelial dysfunction, excessive deposition of extracellular matrix, and ultimately organ dysfunction.
Footnotes
Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iDs: Yuki Ishikawa
https://orcid.org/0000-0002-6514-8239
Chikashi Terao
https://orcid.org/0000-0002-6452-4095
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