Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

No evidence that genetic predictors of susceptibility predict changes in core outcomes in JIA.

OBJECTIVES: The clinical progression of JIA is unpredictable. Knowing who will develop severe disease could facilitate rapid intensification of therapies. We use genetic variants conferring susceptibility to JIA to predict disease outcome measures. METHODS: A total of 713 JIA patients with genotype data and core outcome variables (COVs) at diagnosis (baseline) and 1 year follow-up were identified from the Childhood Arthritis Prospective Study (CAPS). A weighted genetic risk score (GRS) was generated, including all single nucleotide polymorphisms (SNPs) previously associated with JIA susceptibility (P-value < 5×10-08). We used multivariable linear regression to test the GRS for association with COVS (limited joint count, active joint count, physician global assessment, parent/patient general evaluation, childhood HAQ and ESR) at baseline and change in COVS from baseline to 1 year, adjusting for baseline COV and International League of Associations of Rheumatology (ILAR) category. The GRS was split into quintiles to identify high (quintile 5) and low (quintile 1) risk groups. RESULTS: Patients in the high-risk group for the GRS had a younger age at presentation (median low risk 7.79, median high risk 3.51). No association was observed between the GRS and any outcome measures at 1 year follow-up or baseline. CONCLUSION: For the first time we have used all known JIA genetic susceptibility loci (P=<5×10-08) in a GRS to predict changes in disease outcome measured over time. Genetic susceptibility variants are poor predictors of changes in core outcome measures, it is likely that genetic factors predicting disease outcome are independent to those predicting susceptibility. The next step will be to conduct a genome-wide association analysis of JIA outcome.

Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk.

Many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenomenon might be explained by shared genetic risk factors. We used data from large genome-wide association studies to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified five variants with potentially pleiotropic effects. While colocalization analyses were inconclusive, functional characterization of these variants provided the strongest evidence for a model in which genetic variation at rs1734907 modulates risk of schizophrenia and Crohn's disease via altered methylation and expression of EPHB4-a gene whose protein product guides the migration of neuronal axons in the brain and the migration of lymphocytes towards infected cells in the immune system. Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using cross-trait LD score regression. Of the 11 immune diseases with available genome-wide summary statistics, we observed genetic correlation between six immune diseases and schizophrenia: inflammatory bowel disease (rg = 0.12 ± 0.03, P = 2.49 × 10-4), Crohn's disease (rg = 0.097 ± 0.06, P = 3.27 × 10-3), ulcerative colitis (rg = 0.11 ± 0.04, P = 4.05 × 10-3), primary biliary cirrhosis (rg = 0.13 ± 0.05, P = 3.98 × 10-3), psoriasis (rg = 0.18 ± 0.07, P = 7.78 × 10-3) and systemic lupus erythematosus (rg = 0.13 ± 0.05, P = 3.76 × 10-3). With the exception of ulcerative colitis, the degree and direction of these genetic correlations were consistent with the expected phenotypic correlation based on epidemiological data. Our findings suggest shared genetic risk factors contribute to the epidemiological association of certain immune diseases and schizophrenia.

Combined genetic analysis of juvenile idiopathic arthritis clinical subtypes identifies novel risk loci, target genes and key regulatory mechanisms.

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is the most prevalent form of juvenile rheumatic disease. Our understanding of the genetic risk factors for this disease is limited due to low disease prevalence and extensive clinical heterogeneity. The objective of this research is to identify novel JIA susceptibility variants and link these variants to target genes, which is essential to facilitate the translation of genetic discoveries to clinical benefit. METHODS: We performed a genome-wide association study (GWAS) in 3305 patients and 9196 healthy controls, and used a Bayesian model selection approach to systematically investigate specificity and sharing of associated loci across JIA clinical subtypes. Suggestive signals were followed-up for meta-analysis with a previous GWAS (2751 cases/15 886 controls). We tested for enrichment of association signals in a broad range of functional annotations, and integrated statistical fine-mapping and experimental data to identify target genes. RESULTS: Our analysis provides evidence to support joint analysis of all JIA subtypes with the identification of five novel significant loci. Fine-mapping nominated causal single nucleotide polymorphisms with posterior inclusion probabilities ≥50% in five JIA loci. Enrichment analysis identified RELA and EBF1 as key transcription factors contributing to disease risk. Our integrative approach provided compelling evidence to prioritise target genes at six loci, highlighting mechanistic insights for the disease biology and IL6ST as a potential drug target. CONCLUSIONS: In a large JIA GWAS, we identify five novel risk loci and describe potential function of JIA association signals that will be informative for future experimental works and therapeutic strategies.

Comprehensive analysis of the major histocompatibility complex in systemic sclerosis identifies differential HLA associations by clinical and serological subtypes.

OBJECTIVE: The greatest genetic effect reported for systemic sclerosis (SSc) lies in the major histocompatibility complex (MHC) locus. Leveraging the largest SSc genome-wide association study, we aimed to fine-map this region to identify novel human leucocyte antigen (HLA) genetic variants associated with SSc susceptibility and its main clinical and serological subtypes. METHODS: 9095 patients with SSc and 17 584 controls genome-wide genotyped were used to impute and test single-nucleotide polymorphisms (SNPs) across the MHC, classical HLA alleles and their composite amino acid residues. Additionally, patients were stratified according to their clinical and serological status, namely, limited cutaneous systemic sclerosis (lcSSc), diffuse cutaneous systemic sclerosis (dcSSc), anticentromere (ACA), antitopoisomerase (ATA) and anti-RNApolIII autoantibodies (ARA). RESULTS: Sequential conditional analyses showed nine SNPs, nine classical alleles and seven amino acids that modelled the observed associations with SSc. This confirmed previously reported associations with HLA-DRB1*11:04 and HLA-DPB1*13:01, and revealed a novel association of HLA-B*08:01. Stratified analyses showed specific associations of HLA-DQA1*02:01 with lcSSc, and an exclusive association of HLA-DQA1*05:01 with dcSSc. Similarly, private associations were detected in HLA-DRB1*08:01 and confirmed the previously reported association of HLA-DRB1*07:01 with ACA-positive patients, as opposed to the HLA-DPA1*02:01 and HLA-DQB1*03:01 alleles associated with ATA presentation. CONCLUSIONS: This study confirms the contribution of HLA class II and reveals a novel association of HLA class I with SSc, suggesting novel pathways of disease pathogenesis. Furthermore, we describe specific HLA associations with SSc clinical and serological subtypes that could serve as biomarkers of disease severity and progression.

Genomic Risk Score impact on susceptibility to systemic sclerosis.

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.

Analysis of the genetic component of systemic sclerosis in Iranian and Turkish populations through a genome-wide association study.

Objectives: SSc is an autoimmune disease characterized by alteration of the immune response, vasculopathy and fibrosis. Most genetic studies on SSc have been performed in European-ancestry populations. The aim of this study was to analyse the genetic component of SSc in Middle Eastern patients from Iran and Turkey through a genome-wide association study. Methods: This study analysed data from a total of 834 patients diagnosed with SSc and 1455 healthy controls from Iran and Turkey. DNA was genotyped using high-throughput genotyping platforms. The data generated were imputed using the Michigan Imputation Server, and the Haplotype Reference Consortium as a reference panel. A meta-analysis combining both case-control sets was conducted by the inverse variance method. Results: The highest peak of association belonged to the HLA region in both the Iranian and Turkish populations. Strong and independent associations between the classical alleles HLA-DRB1*11: 04 [P = 2.10 × 10-24, odds ratio (OR) = 3.14] and DPB1*13: 01 (P = 5.37 × 10-14, OR = 5.75) and SSc were observed in the Iranian population. HLA-DRB1*11: 04 (P = 4.90 × 10-11, OR = 2.93) was the only independent signal associated in the Turkish cohort. An omnibus test yielded HLA-DRB1 58 and HLA-DPB1 76 as relevant amino acid positions for this disease. Concerning the meta-analysis, we also identified two associations close to the genome-wide significance level outside the HLA region, corresponding to IRF5-TNPO3 rs17424921-C (P = 1.34 × 10-7, OR = 1.68) and NFKB1 rs4648133-C (P = 3.11 × 10-7, OR = 1.47). Conclusion: We identified significant associations in the HLA region and suggestive associations in IRF5-TNPO3 and NFKB1 loci in Iranian and Turkish patients affected by SSc through a genome-wide association study and an extensive HLA analysis.

Towards a Better Classification and Novel Therapies Based on the Genetics of Systemic Sclerosis.

PURPOSE OF THE REVIEW: Nowadays, important advances have occurred in our understanding of the pathogenesis of systemic sclerosis (SSc), which is a rare immune-mediated inflammatory disease (IMID) characterized by vascular damage, immune imbalance, and fibrosis. Its etiology remains unknown; nevertheless, both environmental and genetic factors play a major role in the disease. This review will focus on the main advances made in the field of genetics of SSc. RECENT FINDINGS: The assessment of how interindividual genetic variability affects disease onset and progression has enhanced our knowledge of disease biology, and this will eventually translate in the development of new diagnostic and therapeutic tools, which is the final goal of personalized medicine. We will provide an overview of the most relevant achievements in the genetics of SSc, its shared genetics among IMIDs with special attention on drug repurposing, current challenges for the functional characterization of risk variants, and future directions.

Correction to: Towards a Better Classification and Novel Therapies Based on the Genetics of Systemic Sclerosis.

The original version of this article unfortunately contained a mistake. The legend of Fig. 1 was incorrect.

Immunochip analysis identifies multiple susceptibility loci for systemic sclerosis.

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.

Influence of TYK2 in systemic sclerosis susceptibility: a new locus in the IL-12 pathway.

OBJECTIVES: TYK2 is a common genetic risk factor for several autoimmune diseases. This gene encodes a protein kinase involved in interleukin 12 (IL-12) pathway, which is a well-known player in the pathogenesis of systemic sclerosis (SSc). Therefore, we aimed to assess the possible role of this locus in SSc. METHODS: This study comprised a total of 7103 patients with SSc and 12 220 healthy controls of European ancestry from Spain, USA, Germany, the Netherlands, Italy and the UK. Four TYK2 single-nucleotide polymorphisms (V362F (rs2304256), P1104A (rs34536443), I684S (rs12720356) and A928V (rs35018800)) were selected for follow-up based on the results of an Immunochip screening phase of the locus. Association and dependence analyses were performed by the means of logistic regression and conditional logistic regression. Meta-analyses were performed using the inverse variance method. RESULTS: Genome-wide significance level was reached for TYK2 V362F common variant in our pooled analysis (p=3.08×10(-13), OR=0.83), while the association of P1104A, A928V and I684S rare and low-frequency missense variants remained significant with nominal signals (p=2.28×10(-3), OR=0.80; p=1.27×10(-3), OR=0.59; p=2.63×10(-5), OR=0.83, respectively). Interestingly, dependence and allelic combination analyses showed that the strong association observed for V362F with SSc, corresponded to a synthetic association dependent on the effect of the three previously mentioned TYK2 missense variants. CONCLUSIONS: We report for the first time the association of TYK2 with SSc and reinforce the relevance of the IL-12 pathway in SSc pathophysiology.

Immunogenetics of systemic sclerosis: Defining heritability, functional variants and shared-autoimmunity pathways.

Systemic sclerosis (SSc) is a clinically heterogeneous connective tissue disorder of complex etiology. The development of large-scale genetic studies, such as genome-wide association studies (GWASs) or the Immunochip platform, has achieved remarkable progress in the knowledge of the genetic background of SSc. Herein, we provide an updated picture SSc genetic factors, offering an insight into their role in pathogenic mechanisms that characterize the disease. We review the most recent findings in the HLA region and the well-established non-HLA loci. Up to 18 non-HLA risk factors fulfilled the selected criteria and they were classified according to their role in the innate or adaptive immune response, in apoptosis, autophagy or fibrosis. Additionally, SSc heritability has remained as a controversial question since twin studies provided low SSc heritability estimates. However, we have recalculated the lower bond of narrow sense SSc heritability using GWAS data. Remarkably, our results suggest a greater influence of genetics on SSc than previously reported. Furthermore, we also offer a functional classification of SSc-associated SNPs and their proxies, based on annotated data, to provide clues for the identification of causal variants in these loci. Finally, we explore the genetic overlap between SSc and other autoimmune diseases (ADs). The vast majority of SSc risk loci are shared with at least one additional AD, being the overlap between SSc and systemic lupus erythematous the largest. Nevertheless, we found that an important portion of SSc risk factors are also common to rheumatoid arthritis or primary biliary cirrhosis. Considering all these evidences, we are confident that future research will be successful in understanding the relevant altered pathways in SSc and in identifying new biomarkers and therapeutic targets for the disease.

The relationship between BDNF and physical activity on depression.

BACKGROUND/OBJECTIVE: Major depressive disorder (MDD) is one of the leading causes of disease burden and disability worldwide. Brain-derived neurotrophic factor (BDNF) seems to have an important role in the molecular mechanisms underlying MDD aetiology, given its implication in regulating neuronal plasticity. There is evidence that physical activity (PA) improves depressive symptoms, with a key role of BDNF in this effect. We aim to perform a systematic review examining the relationship between the BDNF Val66Met polymorphism and the BDNF protein, PA and MDD. METHODS: Both observational and experimental design original articles or systematic reviews were selected, according to the PRISMA statement. RESULTS: Six studies evaluated the Val66Met polymorphism, suggesting a greater impact of physical activity on depression depending on the Val66Met genotype. More discordant findings were observed among the 13 studies assessing BDNF levels with acute or chronic exercise interventions, mainly due to the high heterogeneity found among intervention designs, limited sample size, and potential bias. CONCLUSIONS: Overall, there is cumulative evidence supporting the potential role of BDNF in the interaction between PA and MDD. However, this review highlights the need for further research with more homogeneous and standardised criteria, and pinpoints important confounding factors that must be considered in future studies to provide robust conclusions.

Interaction Effect between Physical Activity and the BDNF Val66Met Polymorphism on Depression in Women from the PISMA-ep Study.

The relationship between depression and the Val66Met polymorphism at the brain-derived neurotrophic factor gene (BDNF), has been largely studied. It has also been related to physical activity, although the results remain inconclusive. The aim of this study is to investigate the relationship between this polymorphism, depression and physical activity in a thoroughly characterised sample of community-based individuals from the PISMA-ep study. A total of 3123 participants from the PISMA-ep study were genotyped for the BDNF Val66Met polymorphism, of which 209 had depression. Our results are in line with previous studies reporting a protective effect of physical activity on depression, specifically in light intensity. Interestingly, we report a gene-environment interaction effect in which Met allele carriers of the BDNF Val66Met polymorphism who reported more hours of physical activity showed a decreased prevalence of depression. This effect was observed in the total sample (OR = 0.95, 95%CI = 0.90-0.99, p = 0.027) and was strengthened in women (OR = 0.93, 95%CI = 0.87-0.98, p = 0.019). These results highlight the potential role of physical activity as a promising therapeutic strategy for preventing and adjuvant treatment of depression and suggest molecular and genetic particularities of depression between sexes.

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals.

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.

The Effect of Body Fat Distribution on Systemic Sclerosis.

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.

Expression Quantitative Trait Locus Analysis in Systemic Sclerosis Identifies New Candidate Genes Associated With Multiple Aspects of Disease Pathology.

OBJECTIVE: To identify the genetic variants that affect gene expression (expression quantitative trait loci [eQTLs]) in systemic sclerosis (SSc) and to investigate their role in the pathogenesis of the disease. METHODS: We performed an eQTL analysis using whole-blood sequencing data from 333 SSc patients and 524 controls and integrated them with SSc genome-wide association study (GWAS) data. We integrated our findings from expression modeling, differential expression analysis, and transcription factor binding site enrichment with key clinical features of SSc. RESULTS: We detected 49,123 validated cis-eQTLs from 4,539 SSc-associated single-nucleotide polymorphisms (SNPs) (PGWAS < 10-5 ). A total of 1,436 genes were within 1 Mb of the 4,539 SSc-associated SNPs. Of those 1,436 genes, 565 were detected as having ≥1 eQTL with an SSc-associated SNP. We developed a strategy to prioritize disease-associated genes based on their expression variance explained by SSc eQTLs (r2 > 0.05). As a result, 233 candidates were identified, 134 (58%) of them associated with hallmarks of SSc and 105 (45%) of them differentially expressed in the blood cells, skin, or lung tissue of SSc patients. Transcription factor binding site analysis revealed enriched motifs of 24 transcription factors (5%) among SSc eQTLs, 5 of which were found to be differentially regulated in the blood cells (ELF1 and MGA), skin (KLF4 and ID4), and lungs (TBX4) of SSc patients. Ten candidate genes (4%) can be targeted by approved medications for immune-mediated diseases, of which only 3 have been tested in clinical trials in patients with SSc. CONCLUSION: The findings of the present study indicate a new layer to the molecular complexity of SSc, contributing to a better understanding of the pathogenesis of the disease.

Epigenomics and transcriptomics of systemic sclerosis CD4+ T cells reveal long-range dysregulation of key inflammatory pathways mediated by disease-associated susceptibility loci.

BACKGROUND: Systemic sclerosis (SSc) is a genetically complex autoimmune disease mediated by the interplay between genetic and epigenetic factors in a multitude of immune cells, with CD4+ T lymphocytes as one of the principle drivers of pathogenesis. METHODS: DNA samples exacted from CD4+ T cells of 48 SSc patients and 16 healthy controls were hybridized on MethylationEPIC BeadChip array. In parallel, gene expression was interrogated by hybridizing total RNA on Clariom™ S array. Downstream bioinformatics analyses were performed to identify correlating differentially methylated CpG positions (DMPs) and differentially expressed genes (DEGs), which were then confirmed utilizing previously published promoter capture Hi-C (PCHi-C) data. RESULTS: We identified 9112 and 3929 DMPs and DEGs, respectively. These DMPs and DEGs are enriched in functional categories related to inflammation and T cell biology. Furthermore, correlation analysis identified 17,500 possible DMP-DEG interaction pairs within a window of 5 Mb, and utilizing PCHi-C data, we observed that 212 CD4+ T cell-specific pairs of DMP-DEG also formed part of three-dimensional promoter-enhancer networks, potentially involving CTCF. Finally, combining PCHi-C data with SSc GWAS data, we identified four important SSc-associated susceptibility loci, TNIP1 (rs3792783), GSDMB (rs9303277), IL12RB1 (rs2305743), and CSK (rs1378942), that could potentially interact with DMP-DEG pairs cg17239269-ANXA6, cg19458020-CCR7, cg10808810-JUND, and cg11062629-ULK3, respectively. CONCLUSION: Our study unveils a potential link between genetic, epigenetic, and transcriptional deregulation in CD4+ T cells of SSc patients, providing a novel integrated view of molecular components driving SSc pathogenesis.

A cross-disease meta-GWAS identifies four new susceptibility loci shared between systemic sclerosis and Crohn's disease.

Genome-wide association studies (GWASs) have identified a number of genetic risk loci associated with systemic sclerosis (SSc) and Crohn's disease (CD), some of which confer susceptibility to both diseases. In order to identify new risk loci shared between these two immune-mediated disorders, we performed a cross-disease meta-analysis including GWAS data from 5,734 SSc patients, 4,588 CD patients and 14,568 controls of European origin. We identified 4 new loci shared between SSc and CD, IL12RB2, IRF1/SLC22A5, STAT3 and an intergenic locus at 6p21.31. Pleiotropic variants within these loci showed opposite allelic effects in the two analysed diseases and all of them showed a significant effect on gene expression. In addition, an enrichment in the IL-12 family and type I interferon signaling pathways was observed among the set of SSc-CD common genetic risk loci. In conclusion, through the first cross-disease meta-analysis of SSc and CD, we identified genetic variants with pleiotropic effects on two clinically distinct immune-mediated disorders. The fact that all these pleiotropic SNPs have opposite allelic effects in SSc and CD reveals the complexity of the molecular mechanisms by which polymorphisms affect diseases.