Objectives Genomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those individuals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time. Methods Allelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model. Results The best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren’s syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC<0.5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693. Conclusions GRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.

Objective: Systemic sclerosis (SSc) is a complex autoimmune disease with a strong genetic component. However, most of the genes associated with the disease are still unknown because associated variants affect mostly noncoding intergenic elements of the genome. We used functional genomics to translate the genetic findings into a better understanding of the disease.Methods: Promoter capture Hi‐C and RNA‐sequencing experiments were performed in CD4+ T cells and CD14+ monocytes from 10 SSc patients and 5 healthy controls to link SSc‐associated variants with their target genes, followed by differential expression and differential interaction analyses between cell types.Results: We linked SSc‐associated loci to 39 new potential target genes and confirmed 7 previously known SSc‐associated genes. We highlight novel causal genes, such as CXCR5, as the most probable candidate gene for the DDX6 locus. Some previously known SSc‐associated genes, such as IRF8, STAT4, and CD247, showed cell type–specific interactions. We also identified 15 potential drug targets already in use in other similar immune‐mediated diseases that could be repurposed for SSc treatment. Furthermore, we observed that interactions were directly correlated with the expression of important genes implicated in cell type–specific pathways and found evidence that chromatin conformation is associated with genotype.Conclusion: Our study revealed potential causal genes for SSc‐associated loci, some of them acting in a cell type–specific manner, suggesting novel biologic mechanisms that might mediate SSc pathogenesis.

Immune-mediated diseases (IMDs) are complex pathologies that are strongly influenced by environmental and genetic factors. Associations between genetic loci and susceptibility to these diseases have been widely studied, and hundreds of risk variants have emerged during the last two decades, with researchers observing a shared genetic pattern among them. Nevertheless, the pathological mechanism behind these associations remains a challenge that has just started to be understood thanks to functional genomic approaches. Transcriptomics, regulatory elements, chromatin interactome, as well as the experimental characterization of genomic findings, constitute key elements in the emerging understandings of how genetics affects the etiopathogenesis of IMDs. In this review, we will focus on the latest advances in the field of functional genomics, centering our attention on systemic rheumatic IMDs.

Although previous studies have suggested a relationship between telomere shortening and systemic sclerosis (SSc), the association between these two traits remains poorly understood. The objective of this study was to assess the causal relationship between telomere length in leukocytes (LTL) and SSc using the two-sample Mendelian randomization approach, with the genome-wide association study data for both LTL and SSc. The results of inverse-variance weighted regression (OR = 0.716 [95% CI 0.528–0.970], p = 0.031) and the Mendelian randomization pleiotropy residual sum and outlier method (OR = 0.716 [95% CI 0.563–0.911], p = 0.035) indicate an association between telomere length and SSc. Specifically, longer genetically predicted LTL is associated with a reduced risk of SSc. Sensitivity tests highlight the significant roles of the variants rs10936599 and rs2736100 annotated to the TERC and TERT genes, respectively. Our findings suggest an influence of telomere length in leukocytes on the development of SSc.

Obesity contributes to a chronic proinflammatory state, which is a known risk factor to develop immune-mediated diseases. However, its role in systemic sclerosis (SSc) remains to be elucidated. Therefore, we conducted a two-sample mendelian randomization (2SMR) study to analyze the effect of three body fat distribution parameters in SSc. As instrumental variables, we used the allele effects described for single nucleotide polymorphisms (SNPs) in different genome-wide association studies (GWAS) for SSc, body mass index (BMI), waist-to-hip ratio (WHR) and WHR adjusted for BMI (WHRadjBMI). We performed local (pHESS) and genome-wide (LDSC) genetic correlation analyses between each of the traits and SSc and we applied several Mendelian randomization (MR) methods (i.e., random effects inverse-variance weight, MR-Egger regression, MR pleiotropy residual sum and outlier method and a multivariable model). Our results show no genetic correlation or causal relationship between any of these traits and SSc. Nevertheless, we observed a negative causal association between WHRadjBMI and SSc, which might be due to the effect of gastrointestinal complications suffered by the majority of SSc patients. In conclusion, reverse causality might be an especially difficult confounding factor to define the effect of obesity in the onset of SSc.

Objective: This resource deeply characterizes the CD4+ T cell subpopulations populations in SSc patients, with a novel single-cell RNA sequencing (scRNA-seq) approach, offering unprecedented insights into the cellular and molecular underpinnings of the disease. Methods: We performed scRNA-seq to analyze over 80,000 CD4+ T cells from 8 SSc patients and 8 healthy controls, integrating abundance, transcriptional, and TCR repertoire analysis, to define CD4+ T cell subtype-specific signatures associated with SSc. Results: SSc CD4+ T cells displayed a global interferon-driven activation signature and significantly reduced TNFα signaling. Key transcriptomic alterations included the downregulation of SOX4 and CD83 , alongside an expansion of Th2 and proinflammatory, steroid-resistant Th17 cells. Furthermore, Tregs exhibited a destabilized state characterized by high FCRL3 and reduced FOXP3 expression. Finally, TCR repertoire analysis identified increased clonal expansion specifically within the central memory (Tcm) compartment. Conclusion: Together, this data revealed SSc-associated cell population states in CD4+ T cells with unique transcriptomic signatures and provided a publicly accessible interactive platform to facilitate exploration of this dataset by the research community.