Gene–gene interaction and RNA splicing profiles of MAP2K4 gene in rheumatoid arthritis

Genetics of rheumatoid arthritis

Rheumatoid arthritis (RA) is an inflammatory autoimmune disease involving symmetric joints and is generally characterized by persistent pain, tenderness, and destruction of joints. The vast majority of RA patients produce autoantibodies, and immune cell involvement in disease development is well recognized, as is the contribution of other types of cells in synovial tissue, like fibroblasts. It is known that there are major genetic associations with the HLA locus, while multiple non-HLA genetic variants display relatively low risk of RA. Both HLA and non-HLA associations suggest that the profiles of genetic associations for autoantibody-positive vs. autoantibody-negative RA are different. Several alleles of HLA-DRB1 are associated with high risk for autoantibody-positive RA, with the strongest risk characterized by valine at position 11 of the protein sequence (HLA-DRB1*04 and *10 alleles). There is a strong protective effect for the risk of autoantibody-positive RA associated with HLA-DRB1*13 alleles. Although major genetic associations have been known for several years, understanding of the specific mechanisms in the development of increased risk of RA for these variations is work in progress. Current studies focus on the binding of immune receptors involved in recognition of putative peptides in activation of T cells, as well as investigation of cell signaling mechanisms. At least a part of RA risk could be explained by gene-gene and gene-environment interactions. There are currently more than 150 candidate loci with polymorphisms that associate with RA, mainly related to seropositive disease, and new discoveries are anticipated in the future from investigation of diverse human populations. This new research will help create a strong foundation for the continuing process of integrating genetic, epigenetic, transcriptomic, and proteomic data in studies of RA.

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with structural annotations of protein-protein interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.

Splicing machinery is impaired in rheumatoid arthritis, associated with disease activity and modulated by anti-TNF therapy

OBJECTIVES

To characterise splicing machinery (SM) alterations in leucocytes of patients with rheumatoid arthritis (RA), and to assess its influence on their clinical profile and therapeutic response.

METHODS

Leucocyte subtypes from 129 patients with RA and 29 healthy donors (HD) were purified, and 45 selected SM elements (SME) were evaluated by quantitative PCR-array based on microfluidic technology (Fluidigm). Modulation by anti-tumour necrosis factor (TNF) therapy and underlying regulatory mechanisms were assessed.

RESULTS

An altered expression of several SME was found in RA leucocytes. Eight elements (SNRNP70, SNRNP200, U2AF2, RNU4ATAC, RBM3, RBM17, KHDRBS1 and SRSF10) were equally altered in all leucocytes subtypes. Logistic regressions revealed that this signature might: discriminate RA and HD, and anti-citrullinated protein antibodies (ACPAs) positivity; classify high-disease activity (disease activity score-28 (DAS28) >5.1); recognise radiological involvement; and identify patients showing atheroma plaques. Furthermore, this signature was altered in RA synovial fluid and ankle joints of K/BxN-arthritic mice. An available RNA-seq data set enabled to validate data and identified distinctive splicing events and splicing variants among patients with RA expressing high and low SME levels. 3 and 6 months anti-TNF therapy reversed their expression in parallel to the reduction of the inflammatory profile. In vitro, ACPAs modulated SME, at least partially, by Fc Receptor (FcR)-dependent mechanisms. Key inflammatory cytokines further altered SME. Lastly, induced SNRNP70-overexpression and KHDRBS1-overexpression reversed inflammation in lymphocytes, NETosis in neutrophils and adhesion in RA monocytes and influenced activity of RA synovial fibroblasts.

CONCLUSIONS

Overall, we have characterised for the first time a signature comprising eight dysregulated SME in RA leucocytes from both peripheral blood and synovial fluid, linked to disease pathophysiology, modulated by ACPAs and reversed by anti-TNF therapy.

Alternative Splicing: A New Cause and Potential Therapeutic Target in Autoimmune Disease

Alternative splicing (AS) is a complex coordinated transcriptional regulatory mechanism. It affects nearly 95% of all protein-coding genes and occurs in nearly all human organs. Aberrant alternative splicing can lead to various neurological diseases and cancers and is responsible for aging, infection, inflammation, immune and metabolic disorders, and so on. Though aberrant alternative splicing events and their regulatory mechanisms are widely recognized, the association between autoimmune disease and alternative splicing has not been extensively examined. Autoimmune diseases are characterized by the loss of tolerance of the immune system towards self-antigens and organ-specific or systemic inflammation and subsequent tissue damage. In the present review, we summarized the most recent reports on splicing events that occur in the immunopathogenesis of systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) and attempted to clarify the role that splicing events play in regulating autoimmune disease progression. We also identified the changes that occur in splicing factor expression. The foregoing information might improve our understanding of autoimmune diseases and help develop new diagnostic and therapeutic tools for them.

Inflammatory and immune response genes: A genetic analysis of inhibitor development in Iranian hemophilia A patients

A major problem of hemophilia A (HA) treatment is the development of factor VIII (FVIII) inhibitor, which usually occurs shortly after initiating replacement therapy. Several studies showed the correlation between inhibitor development and polymorphisms in inflammatory and immune response genes of HA patients; however, literature data are not available to prove this association in Iranian population. The aim of this study was to investigate a possible association between FVIII inhibitor formation and the polymorphisms of 16 inflammatory and immune response genes in Iranian severe HA patients (FVIII activity < 1%). This case-control study was performed on 55 patients with severe HA inhibitors and 45 samples without inhibitors from Iranian Comprehensive Hemophilia Care center. After extraction of whole genomic DNA from blood samples and design of primers for 16 genes, the genotyping was performed by Tetra primer ARMS PCR, and the validation of single nucleotide polymorphisms was determined by DNA sequencing. The data indicated that there was a significant association between inhibitor development, and F13A1 (TT), DOCK2 (CC& CT), and MAPK9 (TT) genotypes. Moreover, a considerably increased inhibitor risk carrying T, C, and T allele for F13A1, DOCK2, and MAPK9 genes was observed in patients with inhibitors, respectively. In contrast, there was no statistically significant difference between the genotypic and allelic frequencies for other genes in patients with inhibitors compared to patients without inhibitors. These results demonstrate that only polymorphisms in F13A1, DOCK2, and MAPK9 genes are associated with the risk of developing FVIII inhibitors in Iranian HA patients.

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.

T cells are influenced by a long non-coding RNA in the autoimmune associated PTPN2 locus

Non-coding SNPs in the protein tyrosine phosphatase non-receptor type 2 (PTPN2) locus have been linked with several autoimmune diseases, including rheumatoid arthritis, type I diabetes, and inflammatory bowel disease. However, the functional consequences of these SNPs are poorly characterized. Herein, we show in blood cells that SNPs in the PTPN2 locus are highly correlated with DNA methylation levels at four CpG sites downstream of PTPN2 and expression levels of the long non-coding RNA (lncRNA) LINC01882 downstream of these CpG sites. We observed that LINC01882 is mainly expressed in T cells and that anti-CD3/CD28 activated naïve CD4⁺ T cells downregulate the expression of LINC01882. RNA sequencing analysis of LINC01882 knockdown in Jurkat T cells, using a combination of antisense oligonucleotides and RNA interference, revealed the upregulation of the transcription factor ZEB1 and kinase MAP2K4, both involved in IL-2 regulation. Overall, our data suggests the involvement of LINC01882 in T cell activation and hints towards an auxiliary role of these non-coding SNPs in autoimmunity associated with the PTPN2 locus.

Fast and general tests of genetic interaction for genome-wide association studies

A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two ‘tag’ variants in the LPA locus (p = 2.42 ⋅ 10⁻⁰⁹) as well as replicate the interaction (p = 6.97 ⋅ 10⁻⁰⁷). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.

Interaction between organic molecules forms the basis of all biological systems. The availability of high-throughput genotyping and sequencing platforms enables us to cost-effectively genotype a large number of individuals. For sufficiently large datasets it is possible to reconstruct the genetic dependencies that underlie complex traits and diseases. However, there is a need for efficient statistical methodologies that can tackle the large sample size and computational resources required to study interaction. In this work we provide theory that reduces the required computational resources, and enable multiple research groups to effectively combine their results.

KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies

Background

Finding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved issue. Few previous studies could handle genome-wide data due to the intractable difficulties met in searching a combinatorial explosive search space and statistically evaluating epistatic interactions given a limited number of samples. Our work is a contribution to this field. We propose a novel approach combining K-Nearest Neighbors (KNN) and Multi Dimensional Reduction (MDR) methods for detecting gene-gene interactions as a possible alternative to existing algorithms, e especially in situations where the number of involved determinants is high. After describing the approach, a comparison of our method (KNN-MDR) to a set of the other most performing methods (i.e., MDR, BOOST, BHIT, MegaSNPHunter and AntEpiSeeker) is carried on to detect interactions using simulated data as well as real genome-wide data.

Results

Experimental results on both simulated data and real genome-wide data show that KNN-MDR has interesting properties in terms of accuracy and power, and that, in many cases, it significantly outperforms its recent competitors.

Conclusions

The presented methodology (KNN-MDR) is valuable in the context of loci and interactions mapping and can be seen as an interesting addition to the arsenal used in complex traits analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1599-7) contains supplementary material, which is available to authorized users.

Association of two synonymous splicing-associated CpG single nucleotide polymorphisms in calpain 10 and solute carrier family 2 member 2 with type 2 diabetes

Coding synonymous single nucleotide polymorphisms (SNPs) have attracted little attention until recently. However, such SNPs located in epigenetic, CpG sites modifying exonic splicing enhancers (ESEs) can be informative with regards to the recently verified association of intragenic methylation and splicing. The present study describes the association of type 2 diabetes (T2D) with the exonic, synonymous, epigenetic SNPs, rs3749166 in calpain 10 (CAPN10) glucose transporter (GLUT4) translocator and rs5404 in solute carrier family 2, member 2 (SLC2A2), also termed GLUT2, which, according to prior bioinformatic analysis, strongly modify the splicing potential of glucose transport-associated genes. Previous association studies reveal that only rs5404 exhibits a strong negative T2D association, while data on the CAPN10 polymorphism are contradictory. In the present study DNA from blood samples of 99 Greek non-diabetic control subjects and 71 T2D patients was analyzed. In addition, relevant publicly available cases (40) resulting from examination of 110 Personal Genome Project data files were analyzed. The frequency of the rs3749166 A allele, was similar in the patients and non-diabetic control subjects. However, AG heterozygotes were more frequent among patients (73.24% for Greek patients and 54.55% for corresponding non-diabetic control subjects; P=0.0262; total cases, 52.99 and 75.00%, respectively; P=0.0039). The rs5404 T allele was only observed in CT heterozygotes (Greek non-diabetic control subjects, 39.39% and Greek patients, 22.54%; P=0.0205; total cases, 34.69 and 21.28%, respectively; P=0.0258). Notably, only one genotype, heterozygous AG/CC, was T2D-associated (Greek non-diabetic control subjects, 29.29% and Greek patients, 56.33%; P=0.004; total cases, 32.84 and 56.58%, respectively; P=0.0008). Furthermore, AG/CC was strongly associated with very high (≥8.5%) glycosylated plasma hemoglobin levels among patients (P=0.0002 for all cases). These results reveal the complex heterozygotic SNP association with T2D, and indicate possible synergies of these epigenetic, splicing-regulatory, synonymous SNPs, which modify the splicing potential of two alternative glucose transport-associated genes.