Emerging patterns of genetic overlap across autoimmune disorders.

Most of the recently identified autoimmunity loci are shared among multiple autoimmune diseases. The pattern of genetic association with autoimmune phenotypes varies, suggesting that certain subgroups of autoimmune diseases are likely to share etiological similarities and underlying mechanisms of disease. In this review, we summarize the major findings from recent studies that have sought to refine genotype-phenotype associations in autoimmune disease by identifying both shared and distinct autoimmunity loci. More specifically, we focus on information from recent genome-wide association studies of rheumatoid arthritis, ankylosing spondylitis, celiac disease, multiple sclerosis, systemic lupus erythematosus, type 1 diabetes and inflammatory bowel disease. Additional work in this area is warranted given both the opportunity it provides to elucidate pathogenic mechanisms in autoimmunity and its potential to inform the development of improved diagnostic and therapeutic tools for this group on complex human disorders

Conditional analysis of the major histocompatibility complex in rheumatoid arthritis

We performed a whole-genome association study of rheumatoid arthritis susceptibility using Illumina 550k single-nucleotide polymorphism (SNP) genotypes of 868 cases and 1194 controls from the North American Rheumatoid Arthritis Consortium (NARAC). Structured association analysis with adjustment for potential population stratification yielded 200 SNPs with p < 1 × 10-8 for association with RA, all of which were on chromosome 6 in a 2.7-Mb region of the major histocompatibility complex (MHC). Given the extensive linkage equilibrium in the region and known risk of HLA-DRB1 alleles, we then applied conditional analyses to ascertain independent signals for RA susceptibility among these 200 candidate SNPs. Conditional analyses incorporating risk categories of the HLA-DRB1 "shared epitope" revealed three SNPs having independent associations with RA (conditional p < 0.001). This supports the presence of significant effects on RA susceptibility in the MHC in addition to the shared epitope

Hydroxychloroquine is associated with impaired interferon-alpha and tumor necrosis factor-alpha production by plasmacytoid dendritic cells in systemic lupus erythematosus

Abstract Introduction Plasmacytoid dendritic cells (pDCs) constitutively express two members of the Toll-like receptor (TLR) family, TLR-9 and TLR-7, through which they can be stimulated to produce high levels of interferon (IFN)-α, a key mediator of the pathogenesis of systemic lupus erythematosus (SLE). Given the known efficacy of hydroxychloroquine (HCQ) in the treatment of SLE, we examined its ability to inhibit such pDC function in vivo. Methods Peripheral blood mononuclear cells (PBMCs) from SLE subjects treated or not with HCQ and from healthy controls were stimulated with the TLR-9 agonist, CpG oligodeoxynucleotides (CpG-A ODN)-2216, and the TLR-7 agonist, imiquimod. The proportion of monocytes, B cells, myeloid dendritic cells, pDCs, and natural killer (NK) cells producing IFN-α and tumor necrosis factor alpha (TNF-α) was then analyzed by multiparameter flow cytometry. Results After TLR-9/7 stimulation in both SLE and healthy subjects, significant production of IFN-α and TNF-α was only observed in pDCs. TLR-7 and TLR-9 induced IFN-α and TNF-α production by pDCs from subjects with SLE was decreased relative to that found in controls (TLR-9/IFN-α, P &lt; 0.0001; TLR-9/TNF-α P &lt; 0.0001; TLR-7/TNF-α P = 0.01). TLR-9 and TLR-7 induced IFN-α and TNF-α production by pDCs was severely impaired in 36% (TLR-9) and 33% (TLR-7) of SLE subjects. In almost all cases, these subjects were being treated with HCQ (HCQ vs. no HCQ: impaired TLR-9/IFN-α, P = 0.0003; impaired TLR-7/IFN-α, P = 0.07; impaired TLR-9/TNF-α, P &lt; 0.009; impaired TLR-7/TNF-α, P &lt; 0.01). Conclusions Treatment with HCQ is associated with impaired ability of pDCs from subjects with SLE to produce IFN-α and TNF-α upon stimulation with TLR-9 and TLR-7 agonists

Health-related quality of life and depression among participants in the Sjögren's International Collaborative Clinical Alliance registry.

ObjectiveTo examine health-related quality of life (HRQoL) and depression among participants in an international Sjögren's syndrome (SS) registry, comparing those with and without SS.MethodsCross-sectional study of participants in the Sjögren's International Collaborative Clinical Alliance (SICCA) registry. The 2016 American College of Rheumatology/European League Against Rheumatism SS classification criteria were used to determine disease status. HRQoL was assessed using the Short Form 12, version 2 Health Survey to derive scores for physical component summary (PCS) and mental component summary (MCS). Depression was assessed using the 9-Item Patient Health Questionnaire. Multivariate linear and logistic regression analyses were performed to identify predictors of HRQoL and depression while controlling for potential confounders.ResultsAmong 2401 SICCA participants who had symptoms of dry eyes and dry mouth, 1051 had SS (44%) and 1350 did not (56%). After controlling for confounders, when compared with non-SS participants, those with SS had better PCS (p&lt;0.001, β=2.43, 95% CI 1.57 to 3.29), MCS (p=0.002, β=1.37, 95% CI 0.50 to 2.23) and lower adjusted odds of depression (p&lt;0.001, OR 0.67, 95% CI 0.55 to 0.81). Other significant predictors of HRQoL and depression included employment, country of residence and use of medication with anticholinergic effect or for management of SS-related signs and symptoms.ConclusionOur results suggest that among symptomatic patients, having a diagnosis of SS may be associated with better emotional and psychological well-being compared with patients without a diagnosis. Having a definitive diagnosis of SS may encourage patients to obtain a better understanding of their disease and have coping mechanisms in place to better manage their symptoms

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90

Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology.

High costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types

Hypomethylation of CYP2E1 and DUSP22 Promoters Associated With Disease Activity and Erosive Disease Among Rheumatoid Arthritis Patients.

OBJECTIVE:Epigenetic modifications have previously been associated with rheumatoid arthritis (RA). In this study, we aimed to determine whether differential DNA methylation in peripheral blood cell subpopulations is associated with any of 4 clinical outcomes among RA patients. METHODS:Peripheral blood samples were obtained from 63 patients in the University of California, San Francisco RA cohort (all satisfied the American College of Rheumatology classification criteria; 57 were seropositive for rheumatoid factor and/or anti-cyclic citrullinated protein). Fluorescence-activated cell sorting was used to separate the cells into 4 immune cell subpopulations (CD14+ monocytes, CD19+ B cells, CD4+ naive T cells, and CD4+ memory T cells) per individual, and 229 epigenome-wide DNA methylation profiles were generated using Illumina HumanMethylation450 BeadChips. Differentially methylated positions and regions associated with the Clinical Disease Activity Index score, erosive disease, RA Articular Damage score, Sharp score, medication at time of blood draw, smoking status, and disease duration were identified using robust regression models and empirical Bayes variance estimators. RESULTS:Differential methylation of CpG sites associated with clinical outcomes was observed in all 4 cell types. Hypomethylated regions in the CYP2E1 and DUSP22 gene promoters were associated with active and erosive disease, respectively. Pathway analyses suggested that the biologic mechanisms underlying each clinical outcome are cell type-specific. Evidence of independent effects on DNA methylation from smoking, medication use, and disease duration were also identified. CONCLUSION:Methylation signatures specific to RA clinical outcomes may have utility as biomarkers or predictors of exposure, disease progression, and disease severity