Genetics of rheumatoid arthritis: GWAS and beyond

MYLK*FLNB and DOCK1*LAMA2 gene-gene interactions associated with rheumatoid arthritis in the focal adhesion pathway

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.

Gene Interaction Network Analysis Reveals IFI44L as a Drug Target in Rheumatoid Arthritis and Periodontitis

Simple Summary

In spite of substantial investigation, the biological link between periodontitis and rheumatoid arthritis remains unexplained. This study intended to correlate periodontitis and rheumatoid arthritis gene expression patterns to find shared targets for both the disease. We identified the differentially expressed genes (DEGs) in periodontitis and rheumatoid arthritis. The network was built by integrating DEGs and ranking the genes using GeneMANIA. FINDSITEcomb2.0 was used to find a possible inhibitor for the top-ranked gene. Further, the binding effectiveness and protein-ligand complex stability were then determined by molecular docking and molecular dynamics. The network analysis showed IFI44L as a highly ranking gene implicated in most immunological pathways. A virtual screening of 6507 compounds revealed vemurafenib as the best candidate for the IFI44L target. Molecular docking and molecular dynamics modelling revealed the stability of the IFI44L-vemurafenib complex, which suggest IFI44L is potential target and vemurafenib could be the better candidate to treat both diseases.

Abstract

Objective: Despite extensive research on periodontitis and rheumatoid arthritis, the underlying molecular connectivity between these condition remains largely unknown. This research aimed to integrate periodontitis and rheumatoid arthritis gene expression profiles to identify interconnecting genes and focus to develop a common lead molecule against these inflammatory conditions. Materials and Methods: Differentially expressed genes (DEGs) of periodontitis and rheumatoid arthritis were identified from the datasets retrieved from the Gene Expression Omnibus database. The network was constructed by merging DEGs, and the interconnecting genes were identified and ranked using GeneMANIA. For the selected top ranked gene, the potential inhibitor was searched using FINDSITE^comb2.0. Subsequently, the molecular docking and molecular dynamics were performed to determine the binding efficiency and protein-ligand complex stability, respectively. Results: From the network analysis, IFN-induced protein 44-like (IFI44L) was identified as a top ranked gene involved in most of the immunological pathway. With further virtual screening of 6507 molecules, vemurafenib was identified to be the best fit against the IFI44L target. The binding energy and stability of IFI44L with vemurafenib were investigated using molecular docking and molecular dynamics simulation. Docking results show binding energy of −7.7 Kcal/mol, and the simulation results show stability till 100 ns. Conclusions: The identified IFI44L may represent a common drug target for periodontitis and rheumatoid arthritis. Vemurafenib could be a potent anti-inflammatory drug for both diseases.

Association of the DNASE1L3 rs35677470 polymorphism with systemic lupus erythematosus, rheumatoid arthritis and systemic sclerosis: Structural biological insights

Although genome-wide association studies (GWAS) have identified hundreds of autoimmune disease-associated loci, much of the genetics underlying these diseases remains unknown. In an attempt to identify potential causal variants, previous studies have determined that the rs35677470 missense variant of the Deoxyribonuclease I-like 3 (DNASE1L3) gene was associated with the development of systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and systemic sclerosis (SSc). DNase1L3 is a member of the human DNase I family, representing a nuclease that cleaves double-stranded DNA during apoptosis and serving a role in the development of autoimmune diseases. The present study aimed to determine the role of the rs35677470 variant at the DNASE1L3 gene leading to the R206C mutation in SLE, RA and SSc. The underlying mechanism potentially affecting protein structure loss of function was also assessed. DNASE1L3 evolution was investigated to define conservation elements in the protein sequence. Additionally, 3D homology modeling and in silico mutagenesis was performed to localize the polymorphism under investigation. Evolutionary analysis revealed heavily conserved sequence elements among species, indicating structural/functional importance. In silico mutagenesis and 3D protein structural analysis also demonstrated the potentially varied impact of the DNASE1L3 (rs35677470) single nucleotide polymorphism (SNP), providing an explanation for its effect on the R206C variant. Structural analysis demonstrated that the rs35677470 SNP encodes a non-conservative amino acid variation, R206C, which disrupted the conserved electrostatic network holding secondary protein structure elements in position. Specifically, the R206 to E170 interaction forming part of a salt bridge network stabilizing two α-helices was interrupted, thereby affecting the molecular architecture. Previous studies on the effect of this SNP in Caucasian populations demonstrated lower DNAse1L3 activity levels, which is consistent with the current results. The present study comprehensively evaluated the shared autoimmune locus of DNASE1L3 (rs35677470), which produced an inactive form of DNaseIL3. Furthermore, structural analysis explained the potential role of the produced mutation by modifying the placement of structural elements and consequently introducing disorder in protein folding, affecting biological function.

A Novel Approach to Detecting Epistasis using Random Sampling Regularisation

Epistasis is a progressive approach that complements the 'common disease, common variant' hypothesis that highlights the potential for connected networks of genetic variants collaborating to produce a phenotypic expression. Epistasis is commonly performed as a pairwise or limitless-arity capacity that considers variant networks as either variant vs variant or as high order interactions. This type of analysis extends the number of tests that were previously performed in a standard approach such as Genome-Wide Association Study (GWAS), in which False Discovery Rate (FDR) is already an issue, therefore by multiplying the number of tests up to a factorial rate also increases the issue of FDR. Further to this, epistasis introduces its own limitations of computational complexity and intensity that are generated based on the analysis performed; to consider the most intense approach, a multivariate analysis introduces a time complexity of O(n!). Proposed in this paper is a novel methodology for the detection of epistasis using interpretable methods and best practice to outline interactions through filtering processes. Using a process of Random Sampling Regularisation which randomly splits and produces sample sets to conduct a voting system to regularise the significance and reliability of biological markers, SNPs. Preliminary results are promising, outlining a concise detection of interactions. Results for the detection of epistasis, in the classification of breast cancer patients, indicated eight outlined risk candidate interactions from five variants and a singular candidate variant with high protective association.

Update on the Pathomechanism, Diagnosis, and Treatment Options for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease that involves multiple joints bilaterally. It is characterized by an inflammation of the tendon (tenosynovitis) resulting in both cartilage destruction and bone erosion. While until the 1990s RA frequently resulted in disability, inability to work, and increased mortality, newer treatment options have made RA a manageable disease. Here, great progress has been made in the development of disease-modifying anti-rheumatic drugs (DMARDs) which target inflammation and thereby prevent further joint damage. The available DMARDs are subdivided into (1) conventional synthetic DMARDs (methotrexate, hydrochloroquine, and sulfadiazine), (2) targeted synthetic DMARDs (pan-JAK- and JAK1/2-inhibitors), and (3) biologic DMARDs (tumor necrosis factor (TNF)-α inhibitors, TNF-receptor (R) inhibitors, IL-6 inhibitors, IL-6R inhibitors, B cell depleting antibodies, and inhibitors of co-stimulatory molecules). While DMARDs have repeatedly demonstrated the potential to greatly improve disease symptoms and prevent disease progression in RA patients, they are associated with considerable side-effects and high financial costs. This review summarizes our current understanding of the underlying pathomechanism, diagnosis of RA, as well as the mode of action, clinical benefits, and side-effects of the currently available DMARDs.

Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.

Systematic approach demonstrates enrichment of multiple interactions between non-HLA risk variants and HLA-DRB1 risk alleles in rheumatoid arthritis

OBJECTIVE

In anti-citrullinated protein antibody positive rheumatoid arthritis (ACPA-positive RA), a particular subset of HLA-DRB1 alleles, called shared epitope (SE) alleles, is a highly influential genetic risk factor. Here, we investigated whether non-HLA single nucleotide polymorphisms (SNP), conferring low disease risk on their own, interact with SE alleles more frequently than expected by chance and if such genetic interactions influence the HLA-DRB1 SE effect concerning risk to ACPA-positive RA.

METHODS

We computed the attributable proportion (AP) due to additive interaction at genome-wide level for two independent ACPA-positive RA cohorts: the Swedish epidemiological investigation of rheumatoid arthritis (EIRA) and the North American rheumatoid arthritis consortium (NARAC). Then, we tested for differences in the AP p value distributions observed for two groups of SNPs, non-associated and associated with disease. We also evaluated whether the SNPs in interaction with HLA-DRB1 were cis-eQTLs in the SE alleles context in peripheral blood mononuclear cells from patients with ACPA-positive RA (SE-eQTLs).

RESULTS

We found a strong enrichment of significant interactions (AP p<0.05) between the HLA-DRB1 SE alleles and the group of SNPs associated with ACPA-positive RA in both cohorts (Kolmogorov-Smirnov test D=0.35 for EIRA and D=0.25 for NARAC, p<2.2e-16 for both). Interestingly, 564 out of 1492 SNPs in consistent interaction for both cohorts were significant SE-eQTLs. Finally, we observed that the effect size of HLA-DRB1 SE alleles for disease decreases from 5.2 to 2.5 after removal of the risk alleles of the two top interacting SNPs (rs2476601 and rs10739581).

CONCLUSION

Our data demonstrate that there are massive genetic interactions between the HLA-DRB1 SE alleles and non-HLA genetic variants in ACPA-positive RA.

Genetics of rheumatoid arthritis susceptibility, severity, and treatment response

A decade after the first genome-wide association study in rheumatoid arthritis (RA), a plethora of genetic association studies have been published on RA and its clinical or serological subtypes. We review the major milestones in the study of the genetic architecture of RA susceptibility, severity, and response to treatment. We set the scientific context necessary for non-geneticists to understand the potential clinical applications of human genetics and its significance for a stratified approach to the management of RA in the future.

Association of the Smad3 and NFATc2 gene polymorphisms and their serum levels with susceptibility to rheumatoid arthritis in Polish cohorts

One among many factors involved in induction of rheumatoid arthritis (RA) are T cells, the differentiation of which depends upon a unique combination of stimulants and subsequent activation of diverse transcription factors. The aim of this study was to identify polymorphic variants in Smad3 and NFATc2 genes and their possible association with susceptibility to and severity of RA. A total of 272 RA patients, 321 for Smad3 and 304 for nuclear factor of activated T cells (NFAT)c2 healthy individuals, were examined for rs6494629 C/T and rs2289263 T/G Smad3 and rs880324 NFATc2 gene polymorphisms using the polymerase chain reaction-fragment length polymorphism (PCR-RFLP) method and TaqMan single nucleotide polymorphism (SNP) genotyping assay, respectively. Serum Smad3 and NFATc2 levels in RA patients and controls were measured by enzyme-linked immunosorbent assay (ELISA). The rs6494629 C/T Smad3 gene polymorphism under the recessive (TT versus CC+CT) and over-dominant (CC+TT versus CT) models were associated with RA (P=0.014 and P=0.008, respectively). Smad3 rs2289263 T/G revealed differences in the case-control distribution in co-dominant, recessive and over-dominant models (P=0.037, P=0.010, P=0.034). Overall, rs6494629 C/T and rs2289263 T/G Smad3 gene polymorphisms were in a weak linkage disequilibrium (LD) with D'=0.116 and r(2)=0.004. After Bonferroni correction, the genotype-phenotype analysis showed no significant correlation of the Smad3 rs6494629 C/T and rs2289263 T/G and NFATc2 rs2289263 TT polymorphisms with disease activity, joint damage and extra-articular manifestation in RA patients. Serum Smad3 and NFATc2 levels were significantly higher in RA patients than in control groups (both P=0 0000). The present findings indicated that Smad3 genetic polymorphisms may be associated with the susceptibility to RA in the Polish population.

Modulation of peripheral T-cell function by interleukin-7 in rheumatoid arthritis

Introduction

Interleukin-7 (IL-7) is a cytokine essential for T-cell lymphopoiesis, survival and polarization with an emerging role in autoimmunity. We previously demonstrated reduced levels of circulating IL-7 in rheumatoid arthritis (RA), although high amounts are expressed in joints, suggesting differences between systemic and synovial effects. We observed healthy levels of IL-7 in 48% of RA patients in clinical remission (CR) and aimed to investigate the consequences of IL-7 deficiency on T-cell responses.

Methods

We used RA patients with active disease and in CR presenting various levels of IL-7, to investigate its modulatory effects on T cells by analysing responses to phyto-haemagglutinin (PHA), expression of polarization or survival factors, or suppression by regulatory T cells (Tregs).

Results

IL-7 levels were normal (>10 pg/ml) in 48% of RA patients in CR. Amongst 63 CR patients followed up for 18 months, lack of IL-7 recovery was observed in 13 out of 15 (86%) patients experiencing relapse but only 11 out of 48 (23%) of those who did not (P = 0.0002). Binary regressions showed high significance for below normal IL-7 levels for self-reported maternal family history of arthritis (odds ratio (OR): 7.66, P = 0.006) and a trend for smoking (OR: 3.33, P = 0.068) with no further demographic or clinical associations. Serum IL-7 correlated with restored CD4⁺T-cell response to PHA (rho = 0.879); this was not related to an increase in T-cell proliferation capacity or expression of survival factors B-cell lymphoma 2 (BCL2) and BCL2-associated protein X (BAX). Expression of Th1 polarization factor (TBET) was also dependent on exposure to IL-7 in vivo (rho = 0.600). In contrast CD25^highTregs’ response to PHA was not affected by in vivo IL-7, but their suppression capabilities were related to circulating IL-7 (rho = 0.589). Co-stimulation with IL-7 (mimicking the joint environment) increased responsiveness of CD4⁺T-cells to PHA, lowering the ability of CD25^highTregs to suppress them.

Conclusions

Our data demonstrate that IL-7 has a critical role in modulating T-cell function in vivo, possibly explaining opposing effects observed systemically and in the joint. Lack of IL-7 recovery in CR by maintaining a suppressed immune system may be a determinant factor in the occurrence of relapse.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0511-3) contains supplementary material, which is available to authorized users.

Targeted exon sequencing fails to identify rare coding variants with large effect in rheumatoid arthritis

Introduction

Although it has been suggested that rare coding variants could explain the substantial missing heritability, very few sequencing studies have been performed in rheumatoid arthritis (RA). We aimed to identify novel functional variants with rare to low frequency using targeted exon sequencing of RA in Korea.

Methods

We analyzed targeted exon sequencing data of 398 genes selected from a multifaceted approach in Korean RA patients (n = 1,217) and controls (n = 717). We conducted a single-marker association test and a gene-based analysis of rare variants. For meta-analysis or enrichment tests, we also used ethnically matched independent samples of Korean genome-wide association studies (GWAS) (n = 4,799) or immunochip data (n = 4,722).

Results

After stringent quality control, we analyzed 10,588 variants of 398 genes from 1,934 Korean RA case controls. We identified 13 nonsynonymous variants with nominal association in single-variant association tests. In a meta-analysis, we did not find any novel variant with genome-wide significance for RA risk. Using a gene-based approach, we identified 17 genes with nominal burden signals. Among them, VSTM1 showed the greatest association with RA (P = 7.80 × 10⁻⁴). In the enrichment test using Korean GWAS, although the significant signal appeared to be driven by total genic variants, we found no evidence for enriched association of coding variants only with RA.

Conclusions

We were unable to identify rare coding variants with large effect to explain the missing heritability for RA in the current targeted resequencing study. Our study raises skepticism about exon sequencing of targeted genes for complex diseases like RA.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0447-7) contains supplementary material, which is available to authorized users.

Gene expression analysis in RA: towards personalized medicine

Gene expression has recently been at the forefront of advance in personalized medicine, notably in the field of cancer and transplantation, providing a rational for a similar approach in rheumatoid arthritis (RA). RA is a prototypic inflammatory autoimmune disease with a poorly understood etiopathogenesis. Inflammation is the main feature of RA; however, many biological processes are involved at different stages of the disease. Gene expression signatures offer management tools to meet the current needs for personalization of RA patients' care. This review analyses currently available information with respect to RA diagnostic, prognostic and prediction of response to therapy with a view to highlight the abundance of data, whose comparison is often inconclusive due to the mixed use of material source, experimental methodologies and analysis tools, reinforcing the need for harmonization if gene expression signatures are to become a useful clinical tool in personalized medicine for RA patients.

PUMA: a unified framework for penalized multiple regression analysis of GWAS data

Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns's disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn's disease; and one novel association implicating a gene involved in apoptosis pathways in rheumatoid arthritis. We provide software for applying our PUMA analysis framework.

Genetics and epigenetics of rheumatoid arthritis

Investigators have made key advances in rheumatoid arthritis (RA) genetics in the past 10 years. Although genetic studies have had limited influence on clinical practice and drug discovery, they are currently generating testable hypotheses to explain disease pathogenesis. Firstly, we review here the major advances in identifying RA genetic susceptibility markers both within and outside of the MHC. Understanding how genetic variants translate into pathogenic mechanisms and ultimately into phenotypes remains a mystery for most of the polymorphisms that confer susceptibility to RA, but functional data are emerging. Interplay between environmental and genetic factors is poorly understood and in need of further investigation. Secondly, we review current knowledge of the role of epigenetics in RA susceptibility. Differences in the epigenome could represent one of the ways in which environmental exposures translate into phenotypic outcomes. The best understood epigenetic phenomena include post-translational histone modifications and DNA methylation events, both of which have critical roles in gene regulation. Epigenetic studies in RA represent a new area of research with the potential to answer unsolved questions.