Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations

Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition

Background

For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments.

Methods

We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs.

Results

Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs.

Conclusions

The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

GWAS for systemic sclerosis identifies six novel susceptibility loci including one in the Fcγ receptor region

Here we report the largest Asian genome-wide association study (GWAS) for systemic sclerosis performed to date, based on data from Japanese subjects and comprising of 1428 cases and 112,599 controls. The lead SNP is in the FCGR/FCRL region, which shows a penetrating association in the Asian population, while a complete linkage disequilibrium SNP, rs10917688, is found in a cis-regulatory element for IRF8. IRF8 is also a significant locus in European GWAS for systemic sclerosis, but rs10917688 only shows an association in the presence of the risk allele of IRF8 in the Japanese population. Further analysis shows that rs10917688 is marked with H3K4me1 in primary B cells. A meta-analysis with a European GWAS detects 30 additional significant loci. Polygenic risk scores constructed with the effect sizes of the meta-analysis suggest the potential portability of genetic associations beyond populations. Prioritizing the top 5% of SNPs of IRF8 binding sites in B cells improves the fitting of the polygenic risk scores, underscoring the roles of B cells and IRF8 in the development of systemic sclerosis. The results also suggest that systemic sclerosis shares a common genetic architecture across populations.

Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation and is mediated by multiple immune cell types. In this work, we aimed to determine the relevance of changes in cell proportions in peripheral blood mononuclear cells (PBMCs) during the development of disease and following treatment. Samples from healthy blood donors, newly diagnosed RA patients, and established RA patients that had an inadequate response to MTX and were about to start tumor necrosis factor inhibitors (TNFi) treatment were collected before and after 3 months of treatment. We used in parallel a computational deconvolution approach based on RNA expression and flow cytometry to determine the relative cell-type frequencies. Cell-type frequencies from deconvolution of gene expression indicate that monocytes (both classical and non-classical) and CD4⁺ cells (T_h1 and T_h2) were increased in RA patients compared to controls, while NK cells and B cells (naïve and mature) were significantly decreased in RA patients. Treatment with MTX caused a decrease in B cells (memory and plasma cell), and a decrease in CD4 T_h cells (T_h1 and T_h17), while treatment with TNFi resulted in a significant increase in the population of B cells. Characterization of the RNA expression patterns found that most of the differentially expressed genes in RA subjects after treatment can be explained by changes in cell frequencies (98% and 74% respectively for MTX and TNFi).

Genetic Polymorphism of PTPN22 in Autoimmune Diseases: A Comprehensive Review

It is known that the etiology and clinical outcomes of autoimmune diseases are associated with a combination of genetic and environmental factors. In the case of the genetic factor, the SNPs of the PTPN22 gene have shown strong associations with several diseases. The recent exploding numbers of genetic studies have made it possible to find these associations rapidly, and a variety of autoimmune diseases were found to be associated with PTPN22 polymorphisms. Proteins encoded by PTPN22 play a key role in the adaptative and immune systems by regulating both T and B cells. Gene variants, particularly SNPs, have been shown to significantly disrupt several immune functions. In this review, we summarize the mechanism of how PTPN22 and its genetic variants are involved in the pathophysiology of autoimmune diseases. In addition, we sum up the findings of studies reporting the genetic association of PTPN22 with different types of diseases, including type 1 diabetes mellitus, systemic lupus erythematosus, juvenile idiopathic arthritis, and several other diseases. By understanding these findings comprehensively, we can explain the complex etiology of autoimmunity and help to determine the criteria of disease diagnosis and prognosis, as well as medication developments.

Long Intergenic Noncoding RNA MIAT as a Regulator of Human Th17 Cell Differentiation

T helper 17 (Th17) cells protect against fungal and bacterial infections and are implicated in autoimmunity. Several long intergenic noncoding RNAs (lincRNA) are induced during Th17 differentiation, however, their contribution to Th17 differentiation is poorly understood. We aimed to characterize the function of the lincRNA Myocardial Infarction Associated Transcript (MIAT) during early human Th17 cell differentiation. We found MIAT to be upregulated early after induction of human Th17 cell differentiation along with an increase in the chromatin accessibility at the gene locus. STAT3, a key regulator of Th17 differentiation, directly bound to the MIAT promoter and induced its expression during the early stages of Th17 cell differentiation. MIAT resides in the nucleus and regulates the expression of several key Th17 genes, including IL17A, IL17F, CCR6 and CXCL13, possibly by altering the chromatin accessibility of key loci, including IL17A locus. Further, MIAT regulates the expression of protein kinase C alpha (PKCα), an upstream regulator of IL17A. A reanalysis of published single-cell RNA-seq data showed that MIAT was expressed in T cells from the synovium of RA patients. Our results demonstrate that MIAT contributes to human Th17 differentiation by upregulating several genes implicated in Th17 differentiation. High MIAT expression in T cells of RA patient synovia suggests a possible role of MIAT in Th17 mediated autoimmune pathologies.

RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.

Integrative epigenomics in Sjögren´s syndrome reveals novel pathways and a strong interaction between the HLA, autoantibodies and the interferon signature

Primary Sjögren's syndrome (SS) is a systemic autoimmune disease characterized by lymphocytic infiltration and damage of exocrine salivary and lacrimal glands. The etiology of SS is complex with environmental triggers and genetic factors involved. By conducting an integrated multi-omics study, we confirmed a vast coordinated hypomethylation and overexpression effects in IFN-related genes, what is known as the IFN signature. Stratified and conditional analyses suggest a strong interaction between SS-associated HLA genetic variation and the presence of Anti-Ro/SSA autoantibodies in driving the IFN epigenetic signature and determining SS. We report a novel epigenetic signature characterized by increased DNA methylation levels in a large number of genes enriched in pathways such as collagen metabolism and extracellular matrix organization. We identified potential new genetic variants associated with SS that might mediate their risk by altering DNA methylation or gene expression patterns, as well as disease-interacting genetic variants that exhibit regulatory function only in the SS population. Our study sheds new light on the interaction between genetics, autoantibody profiles, DNA methylation and gene expression in SS, and contributes to elucidate the genetic architecture of gene regulation in an autoimmune population.

Nutrition and Rheumatoid Arthritis in the 'Omics' Era

Modern high-throughput 'omics' science tools (including genomics, transcriptomics, proteomics, metabolomics and microbiomics) are currently being applied to nutritional sciences to unravel the fundamental processes of health effects ascribed to particular nutrients in humans and to contribute to more precise nutritional advice. Diet and food components are key environmental factors that interact with the genome, transcriptome, proteome, metabolome and the microbiota, and this life-long interplay defines health and diseases state of the individual. Rheumatoid arthritis (RA) is a chronic autoimmune disease featured by a systemic immune-inflammatory response, in genetically susceptible individuals exposed to environmental triggers, including diet. In recent years increasing evidences suggested that nutritional factors and gut microbiome have a central role in RA risk and progression. The aim of this review is to summarize the main and most recent applications of 'omics' technologies in human nutrition and in RA research, examining the possible influences of some nutrients and nutritional patterns on RA pathogenesis, following a nutrigenomics approach. The opportunities and challenges of novel 'omics technologies' in the exploration of new avenues in RA and nutritional research to prevent and manage RA will be also discussed.

Type 1 helper T cells generate CXCL9/10-producing T-bet+ effector B cells potentially involved in the pathogenesis of rheumatoid arthritis

Efficacy of B-cell depletion therapy highlights the antibody-independent effector functions of B cells in rheumatoid arthritis (RA). Given type 1 helper T (Th1) cells abundant in synovial fluid (SF) of RA, we have determined whether Th1 cells could generate novel effector B cells. Microarray and qPCR analysis identified CXCL9/10 transcripts as highly expressed genes upon BCR/CD40/IFN-γ stimulation. Activated Th1 cells promoted the generation of CXCL9/10-producing T-bet⁺ B cells. Expression of CXCL9/10 was most pronounced in CXCR3⁺ switched memory B cells. Compared with peripheral blood, SFRA enriched highly activated Th1 cells that coexisted with abundant CXCL9/10-producing T-bet⁺ B cells. Intriguingly, anti-IFN-γ antibody and JAK inhibitors significantly abrogated the generation of CXCL9/10-producing T-bet⁺ B cells. B cell derived CXCL9/10 significantly facilitated the migration of CD4⁺ T cells. These findings suggest that Th1 cells generate the novel CXCL9/10-producing T-bet⁺ effector B cells that could be an ideal pathogenic B cell target for RA therapy.

Distinct Single Cell Gene Expression in Peripheral Blood Monocytes Correlates With Tumor Necrosis Factor Inhibitor Treatment Response Groups Defined by Type I Interferon in Rheumatoid Arthritis

Previously, we demonstrated in test and validation cohorts that type I IFN (T1IFN) activity can predict non-response to tumor necrosis factor inhibitors (TNFi) in rheumatoid arthritis (RA). In this study, we examine the biology of non-classical and classical monocytes from RA patients defined by their pre-biologic treatment T1IFN activity. We compared single cell gene expression in purified classical (CL, n = 342) and non-classical (NC, n = 359) monocytes. In our previous work, RA patients who had either high IFNβ/α activity (>1.3) or undetectable T1IFN were likely to have EULAR non-response to TNFi. In this study comparisons were made among patients grouped according to their pre-biologic treatment T1IFN activity as clinically relevant: “T1IFN undetectable (T1IFN ND) or IFNβ/α >1.3” (n = 9) and “T1IFN detectable but IFNβ/α ≤ 1.3” (n = 6). In addition, comparisons were made among patients grouped according to their T1IFN activity itself: “T1IFN ND,” “T1IFN detected and IFNβ/α ≤ 1.3,” and “IFNβ/α >1.3.” Major differences in gene expression were apparent in principal component and unsupervised cluster analyses. CL monocytes from the T1IFN ND or IFNβ/α >1.3 group were unlikely to express JAK1 and IFI27 (p < 0.0001 and p 0.0005, respectively). In NC monocytes from the same group, expression of IFNAR1, IRF1, TNFA, TLR4 (p ≤ 0.0001 for each) and others was enriched. Interestingly, JAK1 expression was absent in CL and NC monocytes from nine patients. This pattern most strongly associated with the IFNβ/α>1.3 group. Differences in gene expression in monocytes among the groups suggest differential IFN pathway activation in RA patients who are either likely to respond or to have no response to TNFi. Additional transcripts enriched in NC cells of those in the T1IFN ND and IFNβ/α >1.3 groups included MYD88, CD86, IRF1, and IL8. This work could suggest key pathways active in biologically defined groups of patients, and potential therapeutic strategies for those patients unlikely to respond to TNFi.

Gut Microbiota Regulation of T Cells During Inflammation and Autoimmunity

The intestinal microbiota plays a crucial role in influencing the development of host immunity, and in turn the immune system also acts to regulate the microbiota through intestinal barrier maintenance and immune exclusion. Normally, these interactions are homeostatic, tightly controlled, and organized by both innate and adaptive immune responses. However, a combination of environmental exposures and genetic defects can result in a break in tolerance and intestinal homeostasis. The outcomes of these interactions at the mucosal interface have broad, systemic effects on host immunity and the development of chronic inflammatory or autoimmune disease. The underlying mechanisms and pathways the microbiota can utilize to regulate these diseases are just starting to emerge. Here, we discuss the recent evidence in this area describing the impact of microbiota-immune interactions during inflammation and autoimmunity, with a focus on barrier function and CD4⁺ T cell regulation.

Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls

This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions.

Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease

BACKGROUND

The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific.

OBJECTIVE

Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature.

METHODS

Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls.

RESULTS

The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4).

CONCLUSIONS

This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.

Individualized Drug Repositioning For Rheumatoid Arthritis Using Weighted Kolmogorov-Smirnov Algorithm

Background

Existing drugs are far from enough for investigators and patients to administrate the therapy of rheumatoid arthritis. Drug repositioning has drawn broad attention by reusing marketed drugs and clinical candidates for new uses.

Purpose

This study attempted to predict candidate drugs for rheumatoid arthritis treatment by mining the similarities of pathway aberrance induced by disease and various drugs, on a personalized or customized basis.

Methods

We firstly measured the individualized pathway aberrance induced by rheumatoid arthritis based on the microarray data and various drugs from CMap database, respectively. Then, the similarities of pathway aberrances between RA and various drugs were calculated using a Kolmogorov–Smirnov weighted enrichment score algorithm.

Results

Using this method, we identified 4 crucial pathways involved in rheumatoid arthritis development and predicted 9 underlying candidate drugs for rheumatoid arthritis treatment. Some candidates with current indications to treat other diseases might be repurposed to treat rheumatoid arthritis and complement the drug group for rheumatoid arthritis.

Conclusion

This study predicts candidate drugs for rheumatoid arthritis treatment through mining the similarities of pathway aberrance induced by disease and various drugs, on a personalized or customized basis. Our framework will provide novel insights in personalized drug discovery for rheumatoid arthritis and contribute to the future application of custom therapeutic decisions.

Monocyte subsets involved in the development of systemic lupus erythematosus and rheumatoid arthritis

AbstractMonocytes are evolutionally conserved innate immune cells that play essential roles for the protection of the host against pathogens and also produce several inflammatory cytokines. Thus, the aberrant functioning of monocytes may affect not only host defense but also the development of inflammatory diseases. Monocytes are a heterogeneous population with phenotypical and functional differences. Most recent studies have shown that monocytes are divided into three subsets, namely classical, intermediate and non-classical subsets, both in humans and mice. Accumulating evidence showed that monocyte activation is associated with the disease progression in autoimmune diseases, such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). However, it remains to be determined how monocytes contribute to the disease process and which subset is involved. In this review, we discuss the pathogenic role of monocyte subsets in SLE and RA on the basis of current studies by ourselves and others to shed light on the suitability of monocyte-targeted therapies in these diseases.

Landscape of stimulation-responsive chromatin across diverse human immune cells

A hallmark of the immune system is the interplay among specialized cell types transitioning between resting and stimulated states. The gene regulatory landscape of this dynamic system has not been fully characterized in human cells. Here, we collected ATAC-seq and RNA-seq data under resting and stimulated conditions for up to 32 immune cell populations. Stimulation caused widespread chromatin remodeling, including response elements shared between stimulated B and T cells. Furthermore, several autoimmune traits showed significant heritability in stimulation-responsive elements from distinct cell types, highlighting the importance of these cell states in autoimmunity. Use of allele-specific read-mapping identified variants that alter chromatin accessibility in particular conditions, allowing us to observe evidence of function for a candidate causal variant that is undetected by existing large-scale studies in resting cells. Our results provide a resource of chromatin dynamics and highlight the need for characterization of effects of genetic variation in stimulated cells.

Analysis of gene expression and open chromatin regions in up to 32 immune cell populations under resting and stimulated conditions identifies widespread chromatin remodeling and shared response elements between stimulated B and T cells.

Moving towards a molecular taxonomy of autoimmune rheumatic diseases

Autoimmune rheumatic diseases pose many problems that have, in general, already been solved in the field of cancer. The heterogeneity of each disease, the clinical similarities and differences between different autoimmune rheumatic diseases and the large number of patients that remain without a diagnosis underline the need to reclassify these diseases via new approaches. Knowledge about the molecular basis of systemic autoimmune diseases, along with the availability of bioinformatics tools capable of handling and integrating large volumes of various types of molecular data at once, offer the possibility of reclassifying these diseases. A new taxonomy could lead to the discovery of new biomarkers for patient stratification and prognosis. Most importantly, this taxonomy might enable important changes in clinical trial design to reach the expected outcomes or the design of molecularly targeted therapies. In this Review, we discuss the basis for a new molecular taxonomy for autoimmune rheumatic diseases. We highlight the evidence surrounding the idea that these diseases share molecular features related to their pathogenesis and development and discuss previous attempts to classify these diseases. We evaluate the tools available to analyse and combine different types of molecular data. Finally, we introduce PRECISESADS, a project aimed at reclassifying the systemic autoimmune diseases.

Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns

The era of next-generation sequencing has mounted the foundation of many gene expression studies. In rheumatoid arthritis research, this has led to the discovery of important candidate genes which offered novel insights into mechanisms and their possible roles in the cure of the disease. In the last years, data generation has outstripped data analysis and while many studies focused on specific aspects of the disease, a global picture of the disease is not yet accomplished. Here, we analyzed and compared a collection of gene expression information from healthy individuals and from patients suffering under different arthritis conditions from published studies containing the following clinical conditions: early and established rheumatoid arthritis, osteoarthritis and arthralgia. We show comprehensive overviews of this data collection and give new insights specifically on gene expression in the early stage, into sex-dependent gene expression, and we describe general differences in expression of different biotypes of genes. Many genes that are related to cytoskeleton changes (actin filament related genes) are differently expressed in early rheumatoid arthritis in comparison to healthy subjects; interestingly, eight of these genes reverse their expression ratio significantly between men and women compared early rheumatoid arthritis and healthy subjects. There are some slighter changes between men and woman between the conditions early and established rheumatoid arthritis. Another aspect are miRNAs and other gene biotypes which are not only promising candidates for diagnoses but also change their expression grossly in average at rheumatoid arthritis and arthralgia compared to the healthy condition. With a selection of intersecting genes, we were able to generate simple classification models to distinguish between healthy and rheumatoid arthritis as well as between early rheumatoid arthritis to other arthritides based on gene expression.

Biological characterization of expression quantitative trait loci (eQTLs) showing tissue-specific opposite directional effects

Interpreting the susceptible loci documented by genome-wide association studies (GWASs) is of utmost importance in the post-GWAS era. Since most complex traits are contributed by multiple tissues, analyzing tissue-specific effects of expression quantitative trait loci (eQTLs) is a promising approach. Here we describe “opposite eQTL effects”, i.e., gene expression effects of eQTLs that are in the opposite direction between different tissues, as the biologically meaningful annotations of genes and genetic variants for understanding the GWAS loci. The genes and single-nucleotide polymorphisms (SNPs) associated with the opposite eQTL effects (opp-multi-eQTL-Genes and opp-multi-eQTL-SNPs) were extracted from the largest eQTL database provided by the Genotype-Tissue Expression (GTEx) project (release version 7). The opposite eQTL effects were detected even between closely related tissues such as cerebellum and brain cortex, and a significant proportion of the genes having eQTLs were annotated as the opp-multi-eQTL-Genes (2,323 out of 31,212; 7.4%). The opp-multi-eQTL-SNPs showed locational enrichment at the transcription start site and also possible involvement of epigenetic regulation. The biological importance of the opposite eQTL effects was also assessed using the SNPs reported in GWASs (GWAS-SNPs), which demonstrated that a high proportion of the opp-multi-eQTL-SNPs are in linkage disequilibrium with the GWAS-SNPs (2,498 out of 9,290; 26.9%). Based on the results, the opposite eQTL effects can be a common phenomenon in the tissue-specific gene regulation with a possible contribution to the development of complex traits.

Modulation of autoimmune arthritis by environmental 'hygiene' and commensal microbiota

Observations in patients with autoimmune diseases and studies in animal models of autoimmunity have revealed that external environmental factors including exposure to microbes and the state of the host gut microbiota can influence susceptibility to autoimmunity and subsequent disease development. Mechanisms underlying these outcomes continue to be elucidated. These include deviation of the cytokine response and imbalance between pathogenic versus regulatory T cell subsets. Furthermore, specific commensal organisms are associated with enhanced severity of arthritis in susceptible individuals, while exposure to certain microbes or helminths can afford protection against this disease. In addition, the role of metabolites (e.g., short-chain fatty acids, tryptophan catabolites), produced either by the microbes themselves or from their action on dietary products, in modulation of arthritis is increasingly being realized. In this context, re-setting of the microbial dysbiosis in RA using prebiotics, probiotics, or fecal microbial transplant is emerging as a promising approach for the prevention and treatment of arthritis. It is hoped that advances in defining the interplay between gut microbiota, dietary products, and bioactive metabolites would help in the development of therapeutic regimen customized for the needs of individual patients in the near future.

The Contribution of PTPN22 to Rheumatic Disease

One of the unresolved questions in modern medicine is why certain individuals develop a disorder such as rheumatoid arthritis (RA) or lupus, while others do not. Contemporary science indicates that genetics is partly responsible for disease development, while environmental and stochastic factors also play a role. Among the many genes that increase the risk of autoimmune conditions, the risk allele encoding the W620 variant of protein tyrosine phosphatase N22 (PTPN22) is shared between multiple rheumatic diseases, suggesting that it plays a fundamental role in the development of immune dysfunction. Herein, we discuss how the presence of the PTPN22 risk allele may shape the signs and symptoms of these diseases. Besides the emerging clarity regarding how PTPN22 tunes T and B cell antigen receptor signaling, we discuss recent discoveries of important functions of PTPN22 in myeloid cell lineages. Taken together, these new insights reveal important clues to the molecular mechanisms of prevalent diseases like RA and lupus and may open new avenues for the development of personalized therapies that spare the normal function of the immune system.

'RA and the microbiome: do host genetic factors provide the link?

Rheumatoid arthritis (RA) is a chronic autoimmune disease, characterised by painful synovium inflammation, bony erosions, immune activation and the circulation of autoantibodies. Despite recent advances in therapeutics enabling disease suppression, there is a considerable demand for alternative therapeutic strategies as well as optimising those available at present. The relatively low concordance rate between monozygotic twins, 20–30% contrasts with heritability estimates of ∼65%, indicating a substantive role of other risk factors in RA pathogenesis. There is established evidence that RA has an infective component to its aetiology. More recently, differences in the commensal microbiota in RA compared to controls have been identified. Studies have shown that the gut, oral and lung microbiota is different in new onset treatment naïve, and established RA patients, compared to controls. Key taxonomic associations are an increase in abundance of Porphyromonas gingivalis and Prevotella copri in RA patients, compared to healthy controls. Host genetics may provide the link between disease and the microbiome. Genetic influence may be mediated by the host immune system; a differential response to RA associated taxa is suggested. The gut microbiome contains elements which are as much as 30% heritable. A better understanding of the influence of host genetics will shed light onto the role of the microbiome in RA. Here we review the role of the microbiome in RA through the lens of host genetics, and consider future research areas addressing microbiome study design and bioinformatics approaches.

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Rheumatoid arthritis (RA) affects 1% of the population and is highly debilitating.

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RA is ~65% heritable, yet the concordance rate between monozygotic twins is just 20–30%, indicating a substantive role of other risk factors.

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Studies have shown that the gut, oral and lung microbiome is different in treatment naïve and established RA patients, compared to controls.

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Current findings suggest an important influence of host genetics on the microbiome, which may contribute to RA via the host immune system.

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Associations of the microbiome with RA described thus far are confounded by host genetics, and future studies need to take account of this.

Genetic variants differentially associated with rheumatoid arthritis and systemic lupus erythematosus reveal the disease-specific biology

Two rheumatic autoimmune diseases, rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), have distinct clinical features despite their genetic similarities. We hypothesized that disease-specific variants exclusively associated with only one disease could contribute to disease-specific phenotypes. We calculated the strength of disease specificity for each variant in each disease against the other disease using summary association statistics reported in the largest genome-wide association studies of RA and SLE. Most of highly disease-specific associations were explained by non-coding variants that were significantly enriched within regulatory regions (enhancers or H3K4me3 histone modification marks) in specific cell or organ types. (e.g., In RA, regulatory T primary cells, CD4+ memory T primary cells, thymus and lung; In SLE, CD19+ B primary cells, mobilized CD34+ primary cells, regulatory T primary cells and monocytes). Consistently, genes in the disease-specific loci were significantly involved in T cell- and B cell-related gene sets in RA and SLE. In summary, this study identified disease-specific variants between RA and SLE, and provided statistical evidence for disease-specific cell types, organ and gene sets that may drive the disease-specific phenotypes.

Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial

BACKGROUND

Cytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms.

RESULTS

In a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus, we measure interferon (IFN) status, anti-IL-6 drug exposure, and whole blood genome-wide gene expression at three time points. We show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point. We observe a statistically significant enrichment of in vivo eQTL interactions with IFN status and anti-IL-6 drug exposure and find many novel interactions that have not been previously described. Finally, we find transcription factor binding motifs interrupted by eQTL interaction SNPs, which point to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs.

CONCLUSIONS

This study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.

An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome

Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."

The Tumor-Like Phenotype of Rheumatoid Synovium: Molecular Profiling and Prospects for Precision Medicine

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by destructive hyperplasia of the synovium. Fibroblast-like synoviocytes (FLS) are a major component of synovial pannus and actively participate in the pathologic progression of RA. How rheumatoid FLS acquire and sustain such a uniquely aggressive phenotype remains poorly understood. We describe the current state of knowledge of the molecular alterations in rheumatoid FLS at the genomic, epigenomic, transcriptomic, proteomic, and metabolomic levels, which offers a means to reconstruct the pathways leading to rheumatoid pannus. Such data provide new pathologic insight and suggest means to more sensitively assess disease activity and response to therapy, as well as support new avenues for therapeutic development.

CD4+ and B Lymphocyte Expression Quantitative Traits at Rheumatoid Arthritis Risk Loci in Patients With Untreated Early Arthritis: Implications for Causal Gene Identification

OBJECTIVE

Rheumatoid arthritis (RA) is a genetically complex disease of immune dysregulation. This study sought to gain further insight into the genetic risk mechanisms of RA by conducting an expression quantitative trait locus (eQTL) analysis of confirmed genetic risk loci in CD4+ T cells and B cells from carefully phenotyped patients with early arthritis who were naive to therapeutic immunomodulation.

METHODS

RNA and DNA were isolated from purified B and/or CD4+ T cells obtained from the peripheral blood of 344 patients with early arthritis. Genotyping and global gene expression measurements were carried out using Illumina BeadChip microarrays. Variants in linkage disequilibrium (LD) with non-HLA RA single-nucleotide polymorphisms (defined as r2 ≥ 0.8) were analyzed, seeking evidence of cis- or trans-eQTLs according to whether the associated probes were or were not within 4 Mb of these LD blocks.

RESULTS

Genes subject to cis-eQTL effects that were common to both CD4+ and B lymphocytes at RA risk loci were FADS1, FADS2, BLK, FCRL3, ORMDL3, PPIL3, and GSDMB. In contrast, those acting on METTL21B, JAZF1, IKZF3, and PADI4 were unique to CD4+ lymphocytes, with the latter candidate risk gene being identified for the first time in this cell subset. B lymphocyte-specific eQTLs for SYNGR1 and CD83 were also found. At the 8p23 BLK-FAM167A locus, adjacent genes were subject to eQTLs whose activity differed markedly between cell types; in particular, the FAM167A effect displayed striking B lymphocyte specificity. No trans-eQTLs approached experiment-wide significance, and linear modeling did not identify a significant influence of biologic covariates on cis-eQTL effect sizes.

CONCLUSION

These findings further refine the understanding of candidate causal genes in RA pathogenesis, thus providing an important platform from which downstream functional studies, directed toward particular cell types, may be prioritized.

Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis

Background

There is a pressing need in rheumatoid arthritis (RA) to identify patients who will not respond to first-line disease-modifying anti-rheumatic drugs (DMARD). We explored whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from early RA patients before methotrexate (MTX) treatment could assist in identifying non-response to DMARD and, whether there is an association between such a signature and RA specific expression quantitative trait loci (eQTL).

Methods

We looked for the presence of a CCS in blood from early RA patients commencing MTX using chromosome conformation capture by EpiSwitch™. Using blood samples from MTX responders, non-responders and healthy controls, a custom designed biomarker discovery array was refined to a 5-marker CCS that could discriminate between responders and non-responders to MTX. We cross-validated the predictive power of the CCS by generating 150 randomized groups of 59 early RA patients (30 responders and 29 non-responders) before MTX treatment. The CCS was validated using a blinded, independent cohort of 19 early RA patients (9 responders and 10 non-responders). Last, the loci of the CCS markers were mapped to RA-specific eQTL.

Results

We identified a 5-marker CCS that could identify, at baseline, responders and non-responders to MTX. The CCS consisted of binary chromosome conformations in the genomic regions of IFNAR1, IL-21R, IL-23, CXCL13 and IL-17A. When tested on a cohort of 59 RA patients, the CCS provided a negative predictive value of 90.0% for MTX response. When tested on a blinded independent validation cohort of 19 early RA patients, the signature demonstrated a true negative response rate of 86 and a 90% sensitivity for detection of non-responders to MTX. Only conformations in responders mapped to RA-specific eQTL.

Conclusions

Here we demonstrate that detection of a CCS in blood in early RA is able to predict inadequate response to MTX with a high degree of accuracy. Our results provide a proof of principle that a priori stratification of response to MTX is possible, offering a mechanism to provide alternative treatments for non-responders to MTX earlier in the course of the disease.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1387-9) contains supplementary material, which is available to authorized users.

Interleukin-34 Promotes Fibrocyte Proliferation

Rheumatoid arthritis (RA) is a systemic autoimmune disease affecting multiple joints. It remains unclear which factors in the circulation are associated with the systemic spread of the disease. Fibrocytes are pluripotent mesenchymal stem cells present in the circulation of RA patients. Our earlier findings implicated activated fibrocytes in the etiology of onset and pathogenesis of RA. Elevated levels of interleukin-34 (IL-34) in the serum and synovial fluid of RA patients are associated with rheumatoid factor and anticyclic citrullinated peptide antibodies, indicators of RA. Moreover, IL-34 levels are independent predictors of radiographic progression in RA patients. We provide evidence of simultaneous elevated levels of IL-34 and increased numbers of activated fibrocytes in the circulation of mice induced to develop arthritis. In vitro, IL-34 treatment induced the proliferation of fibrocytes, mediated by activation of cognate CSF-R1s on fibrocytes. Taken together, we infer that IL-34 has a role in stimulating fibrocyte proliferation and activation during arthritis, thereby contributing to both onset of RA and systemic spread of disease.

Protein tyrosine phosphatase non-receptor 22 and C-Src tyrosine kinase genes are down-regulated in patients with rheumatoid arthritis

Several protein tyrosine phosphatase non-receptor 22 (PTPN22) single-nucleotide polymorphisms (SNPs) have been significantly related with rheumatoid arthritis (RA) susceptibility. Nevertheless, its potential influence on PTPN22 expression in RA has not been completely elucidated. Furthermore, PTPN22 binds to C-Src tyrosine kinase (CSK) forming a key complex in autoimmunity. However, the information of CSK gene in RA is scarce. In this study, we analyzed the relative PTPN22 and CSK expression in peripheral blood from 89 RA patients and 43 controls to determine if the most relevant PTPN22 (rs2488457, rs2476601 and rs33996649) and CSK (rs34933034 and rs1378942) polymorphisms may influence on PTPN22 and CSK expression in RA. The association between PTPN22 and CSK expression in RA patients and their clinical characteristics was also evaluated. Our study shows for the first time a marked down-regulation of PTPN22 expression in RA patients carrying the risk alleles of PTPN22 rs2488457 and rs2476601 compared to controls (p = 0.004 and p = 0.007, respectively). Furthermore, CSK expression was significantly lower in RA patients than in controls (p < 0.0001). Interestingly, a reduced PTPN22 expression was disclosed in RA patients with ischemic heart disease (p = 0.009). The transcriptional suppression of this PTPN22/CSK complex may have a noteworthy clinical relevance in RA patients.

Effects of Type 1 Diabetes Risk Alleles on Immune Cell Gene Expression

Genetic studies have identified 61 variants associated with the risk of developing Type 1 Diabetes (T1D). The functions of most of the non-HLA (Human Leukocyte Antigen) genetic variants remain unknown. We found that only 16 of these risk variants could potentially be linked to a protein-coding change. Therefore, we investigated whether these variants affected susceptibility by regulating changes in gene expression. To do so, we examined whole transcriptome profiles of 600 samples from the Type 1 Diabetes Genetics Consortium (T1DGC). These comprised four different immune cell types (Epstein-Barr virus (EBV)-transformed B cells, either basal or after stimulation; and cluster of differentiation (CD)4+ and CD8+ T cells). Many of the T1D-associated risk variants regulated expression of either neighboring (cis-) or distant (trans-) genes. In brief, 24 of the non-HLA T1D variants affected the expression of 31 nearby genes (cis) while 25 affected 38 distant genes (trans). The effects were highly significant (False Discovery Rate p < 0.001). In addition, we searched in public databases for expression effects of T1D single nucleotide polymorphisms (SNPs) in other immune cell types such as CD14+ monocytes, lipopolysaccharide (LPS) stimulated monocytes, and CD19+ B cells. In this paper, we review the (expression quantitative trait loci (eQTLs) associated with each of the 60 T1D variants and provide a summary of the genes impacted by T1D risk alleles in various immune cells. We then review the methodological steps involved in analyzing the function of genome wide association studies (GWAS)-identified variants, with emphasis on those affecting gene expression. We also discuss recent advancements in the methodologies and their advantages. We conclude by suggesting future study designs that will aid in the study of T1D risk variants.

Genetic insights into juvenile idiopathic arthritis derived from deep whole genome sequencing

Deep whole genome sequencing (WGS) allows for the comprehensive study of genetic landscapes at finer resolution than array based methods. We conducted deep WGS on children with the polyarticular form of juvenile idiopathic arthritis (JIA), using 2 independent cohorts to ascertain the sequencing fidelity. Genome wide SNP density analysis identified 18 SNP hotspots with comparison to the 1000 Genome Projects (1KGP) data. A subset of the genes adjacent to SNP hotspots showed statistically significant enrichment in immunological processes. Genes adjacent to indel hotspots were functionally related to G-protein coupled signaling pathways. Further analyses elucidated significantly more JIA SNPs with regulatory potential compared to 1KGP data. Furthermore, SNPs located within linkage disequibilium (LD) blocks containing previously identified JIA-associated SNPs demonstrated higher regulation potential compared to SNPs outside LD blocks. We also demonstrated enrichment of novel JIA variants in histone modification peaks and DNase hypersensitivity sites in B cells. This study greatly expands the number of genetic variants that may contribute to JIA and give us some clues into what may trigger this disease. To date, this study is the first deep WGS effort on children with JIA and provides useful genetic resources for research communities particularly in understanding JIA etiology.

Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics

Transcriptomics, the high-throughput characterization of RNAs, has been instrumental in defining pathogenic signatures in human autoimmunity and autoinflammation. It enabled the identification of new therapeutic targets in IFN-, IL-1- and IL-17-mediated diseases. Applied to immunomonitoring, transcriptomics is starting to unravel diagnostic and prognostic signatures that stratify patients, track molecular changes associated with disease activity, define personalized treatment strategies, and generally inform clinical practice. Herein, we review the use of transcriptomics to define mechanistic, diagnostic, and predictive signatures in human autoimmunity and autoinflammation. We discuss some of the analytical approaches applied to extract biological knowledge from high-dimensional data sets. Finally, we touch upon emerging applications of transcriptomics to study eQTLs, B and T cell repertoire diversity, and isoform usage.

Interplay between genetic and epigenetic mechanisms in rheumatoid arthritis

Genetic and environmental factors contribute to the risk for rheumatoid arthritis (RA), with epigenetics serving as a possible interface through which risk factors contribute to RA. High-throughput technologies for interrogating genome and epigenome, and the availability of genetic and epigenetic datasets across a diversity of cell types, enable the identification of candidate causal genetic variants for RA to study their function in core RA processes. To date, RA risk variants were studied in the immune cells but not joint resident cells, for example, synovial fibroblasts. Synovial fibroblasts from different joints are distinct, anatomically specialized cells, defined by joint-specific transcriptomes, epigenomes and phenotypes. Cell type-specific analysis of epigenetic changes, together with genetic fine mapping and interrogation of chromatin 3D interactions may identify new disease relevant pathways, potential therapeutic targets and biomarkers for RA progression or therapy response.

cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant’s regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test.

Personalized medicine in rheumatology

In the era of the 21st century, rheumatoid arthritis (RA) is still poorly characterized. Rheumatoid arthritis is a common but heterogeneous disease, not only in the course and clinical symptoms, but also in the clinical response to treatment. Now it is known that early, correct diagnosis and starting treatment with disease-modifying drugs (DMARDs), of which methotrexate (MTX) remains the gold standard in the treatment of RA, is crucial in order to prevent joint destruction, functional disability and an unfavourable disease outcome. Early diagnosis of rheumatoid arthritis is significant in so much as the primary treatment can be started better. Pharmacogenetic and pharmacogenomic studies, which help determine the genetic profile of individual patients, may bring us closer to personalized medicine. Further studies on RA should allow for the identification of disease-specific genes at the stage when their tolerance by the organism is still preserved (before auto-aggression develops).