Background Plasmacytoid dendritic cells have been implicated in the pathogenesis of systemic sclerosis through mechanisms beyond the previously suggested production of type I interferon. Methods We isolated plasmacytoid dendritic cells from healthy persons and from patients with systemic sclerosis who had distinct clinical phenotypes. We then performed proteome-wide analysis and validated these observations in five large cohorts of patients with systemic sclerosis. Next, we compared the results with those in patients with systemic lupus erythematosus, ankylosing spondylitis, and hepatic fibrosis. We correlated plasma levels of CXCL4 protein with features of systemic sclerosis and studied the direct effects of CXCL4 in vitro and in vivo. Results Proteome-wide analysis and validation showed that CXCL4 is the predominant protein secreted by plasmacytoid dendritic cells in systemic sclerosis, both in circulation and in skin. The mean (±SD) level of CXCL4 in patients with systemic sclerosis was 25,624±2652 pg per milliliter, which was significantly higher than the level in controls (92.5±77.9 pg per milliliter) and than the level in patients with systemic lupus erythematosus (1346±1011 pg per milliliter), ankylosing spondylitis (1368±1162 pg per milliliter), or liver fibrosis (1668±1263 pg per milliliter). CXCL4 levels correlated with skin and lung fibrosis and with pulmonary arterial hypertension. Among chemokines, only CXCL4 predicted the risk and progression of systemic sclerosis. In vitro, CXCL4 downregulated expression of transcription factor FLI1, induced markers of endothelial-cell activation, and potentiated responses of toll-like receptors. In vivo, CXCL4 induced the influx of inflammatory cells and skin transcriptome changes, as in systemic sclerosis. Conclusions Levels of CXCL4 were elevated in patients with systemic sclerosis and correlated with the presence and progression of complications, such as lung fibrosis and pulmonary arterial hypertension. (Funded by the Dutch Arthritis Association and others.)

BackgroundNearest neighbor (NN) imputation algorithms are efficient methods to fill in missing data where each missing value on some records is replaced by a value obtained from related cases in the whole set of records. Besides the capability to substitute the missing data with plausible values that are as close as possible to the true value, imputation algorithms should preserve the original data structure and avoid to distort the distribution of the imputed variable. Despite the efficiency of NN algorithms little is known about the effect of these methods on data structure.MethodsSimulation on synthetic datasets with different patterns and degrees of missingness were conducted to evaluate the performance of NN with one single neighbor (1NN) and with k neighbors without (kNN) or with weighting (wkNN) in the context of different learning frameworks: plain set, reduced set after ReliefF filtering, bagging, random choice of attributes, bagging combined with random choice of attributes (Random-Forest-like method).ResultsWhatever the framework, kNN usually outperformed 1NN in terms of precision of imputation and reduced errors in inferential statistics, 1NN was however the only method capable of preserving the data structure and data were distorted even when small values of k neighbors were considered; distortion was more severe for resampling schemas.ConclusionsThe use of three neighbors in conjunction with ReliefF seems to provide the best trade-off between imputation error and preservation of the data structure. The very same conclusions can be drawn when imputation experiments were conducted on the single proton emission computed tomography (SPECTF) heart dataset after introduction of missing data completely at random.

Systemic sclerosis (SSc) is an autoimmune disease characterized by fibrosis of the skin and internal organs that leads to profound disability and premature death. To identify novel SSc susceptibility loci we conducted the first genome wide association study (GWAS) in a population of Caucasian ancestry including a total of 2296 SSc patients and 5171 controls. Analysis of 279,621 autosomal single nucleotide polymorphisms (SNPs) followed by replication testing in an independent case-control set of European ancestry (2,753 SSc patients / 4,569 controls) identified a new susceptibility locus for systemic sclerosis at CD247 (1q22-23; rs2056626, P = 2.09 × 10−7 in the discovery samples, P = 3.39 × 10−9 in the combined analysis). Additionally, we confirm and firmly establish the role of MHC (2.31 × 10−18), IRF5 (P =1.86 × 10−13) and STAT4 (P =3.37 × 10−9) gene regions as SSc genetic risk factors.

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The prevalence of precapillary PH in our multicenter study of SSc was 5%, and in the metaanalysis 9%. Our observations support use of RHC to confirm the presence of precapillary PH suspected by noninvasive testing. We also identified patients at high risk who should be carefully monitored.

In this study, 1,833 systemic sclerosis (SSc) cases and 3,466 controls were genotyped with the Immunochip array. Classical alleles, amino acid residues, and SNPs across the human leukocyte antigen (HLA) region were imputed and tested. These analyses resulted in a model composed of six polymorphic amino acid positions and seven SNPs that explained the observed significant associations in the region. In addition, a replication step comprising 4,017 SSc cases and 5,935 controls was carried out for several selected non-HLA variants, reaching a total of 5,850 cases and 9,401 controls of European ancestry. Following this strategy, we identified and validated three SSc risk loci, including DNASE1L3 at 3p14, the SCHIP1-IL12A locus at 3q25, and ATG5 at 6q21, as well as a suggested association of the TREH-DDX6 locus at 11q23. The associations of several previously reported SSc risk loci were validated and further refined, and the observed peak of association in PXK was related to DNASE1L3. Our study has increased the number of known genetic associations with SSc, provided further insight into the pleiotropic effects of shared autoimmune risk factors, and highlighted the power of dense mapping for detecting previously overlooked susceptibility loci.

The aim of this study was to determine, through a genome-wide association study (GWAS), the genetic components contributing to different clinical sub-phenotypes of systemic sclerosis (SSc). We considered limited (lcSSc) and diffuse (dcSSc) cutaneous involvement, and the relationships with presence of the SSc-specific auto-antibodies, anti-centromere (ACA), and anti-topoisomerase I (ATA). Four GWAS cohorts, comprising 2,296 SSc patients and 5,171 healthy controls, were meta-analyzed looking for associations in the selected subgroups. Eighteen polymorphisms were further tested in nine independent cohorts comprising an additional 3,175 SSc patients and 4,971 controls. Conditional analysis for associated SNPs in the HLA region was performed to explore their independent association in antibody subgroups. Overall analysis showed that non-HLA polymorphism rs11642873 in IRF8 gene to be associated at GWAS level with lcSSc (P = 2.32×10−12, OR = 0.75). Also, rs12540874 in GRB10 gene (P = 1.27 × 10−6, OR = 1.15) and rs11047102 in SOX5 gene (P = 1.39×10−7, OR = 1.36) showed a suggestive association with lcSSc and ACA subgroups respectively. In the HLA region, we observed highly associated allelic combinations in the HLA-DQB1 locus with ACA (P = 1.79×10−61, OR = 2.48), in the HLA-DPA1/B1 loci with ATA (P = 4.57×10−76, OR = 8.84), and in NOTCH4 with ACA P = 8.84×10−21, OR = 0.55) and ATA (P = 1.14×10−8, OR = 0.54). We have identified three new non-HLA genes (IRF8, GRB10, and SOX5) associated with SSc clinical and auto-antibody subgroups. Within the HLA region, HLA-DQB1, HLA-DPA1/B1, and NOTCH4 associations with SSc are likely confined to specific auto-antibodies. These data emphasize the differential genetic components of subphenotypes of SSc.

Systemic sclerosis (SSc) is an autoimmune disease that shows one of the highest mortality rates among rheumatic diseases. We perform a large genome-wide association study (GWAS), and meta-analysis with previous GWASs, in 26,679 individuals and identify 27 independent genome-wide associated signals, including 13 new risk loci. The novel associations nearly double the number of genome-wide hits reported for SSc thus far. We define 95% credible sets of less than 5 likely causal variants in 12 loci. Additionally, we identify specific SSc subtype-associated signals. Functional analysis of high-priority variants shows the potential function of SSc signals, with the identification of 43 robust target genes through HiChIP. Our results point towards molecular pathways potentially involved in vasculopathy and fibrosis, two main hallmarks in SSc, and highlight the spectrum of critical cell types for the disease. This work supports a better understanding of the genetic basis of SSc and provides directions for future functional experiments.