Genomic stratification by expression of HLA-DRB4 alleles identifies differential innate and adaptive immune transcriptional patterns - A strategy to detect predictors of methotrexate response in early rheumatoid arthritis

Identification of Key Genes Related to the Obesity Patients with Osteoarthritis Based on Weighted Gene Coexpression Network Analysis (WGCNA)

Background. Increasing evidence has suggested that obesity affects the occurrence and progression of osteoarthritis (OA). However, the underlying molecular mechanism that obesity affects the course of OA is not fully understood and remains to be studied. Methods. The gene expression profiles of the GSE117999 and GSE98460 datasets were derived from the Gene Expression Omnibus (GEO) database. Firstly, we explored the correlation between obesity and OA using chi-square test. Next, weighted gene coexpression network analysis (WGCNA) was executed to identify obesity patients with OA- (obesity OA-) related genes in the GSE117999 dataset by “WGCNA” package. Moreover, differential expression analysis was performed to select the hub genes by “limma” package. Furthermore, ingenuity pathway analysis (IPA) and functional enrichment analysis (“clusterProfiler” package) were conducted to investigate the functions of genes. Finally, the regulatory networks of hub genes and protein-protein interaction (PPI) network were created by the Cytoscape 3.5.1 software and STRING. Results. A total of 15 differentially expressed obesity OA-related genes, including 9 lncRNAs and 6 protein coding genes, were detected by overlapping 66 differentially expressed genes (DEGs) between normal BMI samples and obesity OA samples and 451 obesity OA-related genes. Moreover, CCR10, LENG8, QRFPR, UHRF1BP1, and HLA-DRB4 were identified as hub genes. IPA results indicated that the hub genes were noticeably enriched in antimicrobial response, inflammatory response, and humoral immune response. PPI network showed that CCR10 interacted more with other proteins. Gene set enrichment analysis (GSEA) indicated that the hub genes were related to protein translation, cancer, chromatin modification, antigen processing, and presentation. Conclusion. Our results further demonstrated the role of obesity in OA and might provide new targets for the treatment of obesity OA.

Toward Overcoming Treatment Failure in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation and bone erosion. The exact mechanism of RA is still unknown, but various immune cytokines, signaling pathways and effector cells are involved. Disease-modifying antirheumatic drugs (DMARDs) are commonly used in RA treatment and classified into different categories. Nevertheless, RA treatment is based on a "trial-and-error" approach, and a substantial proportion of patients show failed therapy for each DMARD. Over the past decades, great efforts have been made to overcome treatment failure, including identification of biomarkers, exploration of the reasons for loss of efficacy, development of sequential or combinational DMARDs strategies and approval of new DMARDs. Here, we summarize these efforts, which would provide valuable insights for accurate RA clinical medication. While gratifying, researchers realize that these efforts are still far from enough to recommend specific DMARDs for individual patients. Precision medicine is an emerging medical model that proposes a highly individualized and tailored approach for disease management. In this review, we also discuss the potential of precision medicine for overcoming RA treatment failure, with the introduction of various cutting-edge technologies and big data.

Peripheral blood mononuclear cells are hypomethylated in active rheumatoid arthritis and methylation correlates with disease activity

OBJECTIVE

Epigenetic modifications are dynamic and influence cellular disease activity. The aim of this study was to investigate global DNA methylation in peripheral blood mononuclear cells (PBMCs) of RA patients to clarify whether global DNA methylation pattern testing might be useful in monitoring disease activity as well as the response to therapeutics.

METHODS

Flow cytometric measurement of 5-methyl-cytosine (5'-mC) was established using the cell line U937. In the subsequent prospective study, 62 blood samples were investigated, including 17 healthy donors and 45 RA patients at baseline and after 3 months of treatment with methotrexate, the IL-6 receptor inhibitor sarilumab, and Janus kinase inhibitors. Methylation status was assessed with an anti-5'-mC antibody and analysed in PBMCs and CD4+, CD8+, CD14+ and CD19+ subsets. Signal intensities of 5'-mC were correlated with 28-joint DASs with ESR and CRP (DAS28-ESR and DAS28-CRP).

RESULTS

Compared with healthy individuals, PBMCs of RA patients showed a significant global DNA hypomethylation. Signal intensities of 5'-mC correlated with transcription levels of DNMT1, DNMT3B and MTR genes involved in methylation processes. Using flow cytometry, significant good correlations and linear regression values were achieved in RA patients between global methylation levels and DAS28-ESR values for PBMCs (r = -0.55, P = 0.002), lymphocytes (r = -0.57, P = 0.001), CD4+ (r = -0.57, P = 0.001), CD8+ (r = -0.54, P = 0.001), CD14+ (r = -0.49, P = 0.008) and CD19+ (r = -0.52, P = 0.004) cells.

CONCLUSIONS

The degree of global DNA methylation was found to be associated with disease activity. Based on this novel approach, the degree of global methylation is a promising biomarker for therapy monitoring and the prediction of therapy outcome in inflammatory diseases.

Synovial tissue transcriptomes of long-standing rheumatoid arthritis are dominated by activated macrophages that reflect microbial stimulation

Advances in microbiome research suggest involvement in chronic inflammatory diseases such as rheumatoid arthritis (RA). Searching for initial trigger(s) in RA, we compared transcriptome profiles of highly inflamed RA synovial tissue (RA-ST) and osteoarthritis (OA)-ST with 182 selected reference transcriptomes of defined cell types and their activation by exogenous (microbial) and endogenous inflammatory stimuli. Screening for dominant changes in RA-ST demonstrated activation of monocytes/macrophages with gene-patterns induced by bacterial and fungal triggers. Gene-patterns of activated B- or T-cells in RA-ST reflected a response to activated monocytes/macrophages rather than inducing their activation. In contrast, OA-ST was dominated by gene-patterns of non-activated macrophages and fibroblasts. The difference between RA and OA was more prominent in transcripts of secreted proteins and was confirmed by protein quantification in synovial fluid (SF) and serum. In total, 24 proteins of activated cells were confirmed in RA-SF compared to OA-SF and some like CXCL13, CCL18, S100A8/A9, sCD14, LBP reflected this increase even in RA serum. Consequently, pathogen-like response patterns in RA suggest that direct microbial influences exist. This challenges the current concept of autoimmunity and immunosuppressive treatment and advocates new diagnostic and therapeutic strategies that consider microbial persistence as important trigger(s) in the etiopathogenesis of RA.

The etiology of rheumatoid arthritis

Rheumatoid arthritis is a heterogeneous disease, which can be, based on data combining genetic risk factors and autoantibodies, sub-classified into ACPA-positive and -negative RA. Presence of ACPA and RF as well as rising CRP-levels in some patients years before onset of clinical symptoms indicate that relevant immune responses for RA development are initiated very early. ACPA are highly specific for RA, whereas RF can also be found among healthy (elderly) individuals and patients with other autoimmune diseases or infection. The most important genetic risk factor for RA development, the shared epitope alleles, resides in the MHC class II region. Shared epitope alleles, however, only predispose to the development of ACPA-positive RA. Smoking is thus far the most important environmental risk factor associated with the development of RA. Studies on synovitis have shown the importance not only of adaptive but also of innate immune responses. In summary of the various results from immunological changes in blood and synovial tissue, the extension of the immune response from a diffuse myeloid to a lympho-myeloid inflammation appears to be associated with a more successful therapeutic response to biologics. With respect to advances in synovitis research, new targets for treatment against pathological subsets of immune cells or fibroblasts are already on the horizon. However, alternative strategies involving the microbiome may play an important role as well and research in this field is growing rapidly.

[Biomarkers and imaging for diagnosis and stratification of rheumatoid arthritis and spondylarthritis in the BMBF consortium ArthroMark]

Rheumatic diseases are among the most common chronic inflammatory disorders. Besides severe pain and progressive destruction of the joints, rheumatoid arthritis (RA), spondyloarthritides (SpA) and psoriatic arthritis (PsA) impair working ability, reduce quality of life and if treated insufficiently may enhance mortality. With the introduction of biologics to treat these diseases, the demand for biomarkers of early diagnosis and therapeutic stratification has been growing continuously. The main goal of the consortium ArthroMark is to identify new biomarkers and to apply modern imaging technologies for diagnosis, follow-up assessment and stratification of patients with RA, SpA and PsA. With the development of new biomarkers for these diseases, the ArthroMark project contributes to research in chronic diseases of the musculoskeletal system. The cooperation between different national centers will utilize site-specific resources, such as biobanks and clinical studies for sharing and gainful networking of individual core areas in biomarker analysis. Joint data management and harmonization of data assessment as well as best practice characterization of patients with new imaging technologies will optimize quality of marker validation.

[Relevance of big data for molecular diagnostics]

Big data analysis raises the expectation that computerized algorithms may extract new knowledge from otherwise unmanageable vast data sets. What are the algorithms behind the big data discussion? In principle, high throughput technologies in molecular research already introduced big data and the development and application of analysis tools into the field of rheumatology some 15 years ago. This includes especially omics technologies, such as genomics, transcriptomics and cytomics. Some basic methods of data analysis are provided along with the technology, however, functional analysis and interpretation requires adaptation of existing or development of new software tools. For these steps, structuring and evaluating according to the biological context is extremely important and not only a mathematical problem. This aspect has to be considered much more for molecular big data than for those analyzed in health economy or epidemiology. Molecular data are structured in a first order determined by the applied technology and present quantitative characteristics that follow the principles of their biological nature. These biological dependencies have to be integrated into software solutions, which may require networks of molecular big data of the same or even different technologies in order to achieve cross-technology confirmation. More and more extensive recording of molecular processes also in individual patients are generating personal big data and require new strategies for management in order to develop data-driven individualized interpretation concepts. With this perspective in mind, translation of information derived from molecular big data will also require new specifications for education and professional competence.

The Potential Role of Genomic Medicine in the Therapeutic Management of Rheumatoid Arthritis

During the last decade, important advances have occurred regarding understanding of the pathogenesis and treatment of rheumatoid arthritis (RA). Nevertheless, response to treatment is not universal, and choosing among different therapies is currently based on a trial and error approach. The specific patient’s genetic background influences the response to therapy for many drugs: In this sense, genomic studies on RA have produced promising insights that could help us find an effective therapy for each patient. On the other hand, despite the great knowledge generated regarding the genetics of RA, most of the investigations performed to date have focused on identifying common variants associated with RA, which cannot explain the complete heritability of the disease. In this regard, rare variants could also contribute to this missing heritability as well as act as biomarkers that help in choosing the right therapy. In the present article, different aspects of genetics in the pathogenesis and treatment of RA are reviewed, from large-scale genomic studies to specific rare variant analyses. We also discuss the shared genetic architecture existing among autoimmune diseases and its implications for RA therapy, such as drug repositioning.

Is prediction of clinical response to methotrexate in individual rheumatoid arthritis patients possible? A systematic literature review

OBJECTIVES

To identify, by a systematic literature review, predictors of clinical response to methotrexate treatment in rheumatoid arthritis patients, which would facilitate personalised treatment.

METHODS

PubMed and Embase databases were searched for original articles. Additionally, congress abstracts of European League Against Rheumatism and American College of Rheumatology annual meetings of the past 2 years were screened. Articles describing predictors of clinical response to methotrexate after 3 to 6 months were included, since this reflects the time span used to determine treatment effectiveness and decide on treatment changes in treat-to-target recommendations.

RESULTS

Thirty articles were included, containing 100 different predictors and 11 predictive models. Nineteen predictors and 2 predictive models were studied in multiple cohorts. Female gender was found to be a predictor of non-response in two studies (odds ratios 0.55 and 0.54), but these findings could not be replicated in two other studies. In two studies, smoking predicted non-response (adjusted odds ratios 0.35 and 0.60), although this was inconsistent over all response criteria assessed. Rheumatoid factor positivity predicted non-response in two studies (adjusted hazard ratio 0.61, adjusted odds ratio 0.4), but this was not found in three other studies. Heterogeneity in studies prohibited further comparison of predictive values between studies. Additionally, a validated epigenetic model was found (area under the curve 0.90 and 0.91).

CONCLUSIONS

No predictors were identified reliably predicting clinical response to methotrexate after 3 to 6 months in the individual patient: clinical predictors were weak. However, a promising epigenetic model was found that needs further validation.

Hypogalactosylation of immunoglobulin G in rheumatoid arthritis: relationship to HLA-DRB1 shared epitope, anticitrullinated protein antibodies, rheumatoid factor, and correlation with inflammatory activity

Background

Galactosylation of immunoglobulin G (IgG) is reduced in rheumatoid arthritis (RA) and assumed to correlate with inflammation and altered humoral immunity. IgG hypogalactosylation also increases with age. To investigate dependencies in more detail, we compared IgG hypogalactosylation between patients with RA, patients with axial spondyloarthritis (axSpA), and healthy control subjects (HC), and we studied it in RA on the background of HLA-DRB1 shared epitope (SE), anticitrullinated protein antibodies (ACPA), and/or rheumatoid factor (RF) status.

Methods

Patients with RA (n = 178), patients with axSpA (n = 126), and HC (n = 119) were characterized clinically, and serum IgG galactosylation was determined by capillary electrophoresis. Markers of disease activity, genetic susceptibility, and serologic response included C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), DAS28, SE, HLA-B27, ACPA, and RF. Expression of glycosylation enzymes, including beta 1–4 galactosyltransferase (B4GALT3) activity, were estimated from transcriptome data for B-cell development (GSE19599) and differentiation to plasma cells (GSE12366).

Results

IgG hypogalactosylation was restricted to RA and associated with increasing CRP levels (p < 0.0001). In axSpA, IgG hypogalactosylation was comparable to HC and only marginally increased upon elevated CRP. Restriction to RA was maintained after correction for CRP and age. Treatment with sulfasalazine resulted in significantly reduced IgG hypogalactosylation (p = 0.003) even after adjusting for age, sex, and CRP (p = 0.009). SE-negative/ACPA-negative RA exhibited significantly less IgG hypogalactosylation than all other strata (vs SE-negative/ACPA-positive, p = 0.009; vs SE-positive/ACPA-negative, p = 0.04; vs SE-positive/ACPA-positive, p < 0.02); however, this indicated a trend only after Bonferroni correction for multiple testing. In SE-positive/ACPA-negative RA IgG hypogalactosylation was comparable to ACPA-positive subsets. The relationship between IgG hypogalactosylation and disease activity was significantly different between strata defined by SE (CRP, p = 0.0003, p_Bonferroni = 0.0036) and RF (CRP, p < 0.0001, p_Bonferroni < 0.0012), whereas ACPA strata revealed only a nonsignificant trend (p = 0.15). Gene expression data indicated that the key enzyme for galactosylation of immunoglobulins, B4GALT3, is expressed at lower levels in B cells than in plasma cells.

Conclusions

Increased IgG hypogalactosylation in RA but not in axSpA points to humoral immune response as a precondition. Reduced B4GALT3 expression in B cells compared with plasma cells supports relatedness to early B-cell triggering. The differential influence of RA treatment on IgG hypogalactosylation renders it a potential diagnostic target for further studies.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1540-0) contains supplementary material, which is available to authorized users.

Theory of signs and statistical approach to big data in assessing the relevance of clinical biomarkers of inflammation and oxidative stress

Biomarkers are widely used not only as prognostic or diagnostic indicators, or as surrogate markers of disease in clinical trials, but also to formulate theories of pathogenesis. We identify two problems in the use of biomarkers in mechanistic studies. The first problem arises in the case of multifactorial diseases, where different combinations of multiple causes result in patient heterogeneity. The second problem arises when a pathogenic mediator is difficult to measure. This is the case of the oxidative stress (OS) theory of disease, where the causal components are reactive oxygen species (ROS) that have very short half-lives. In this case, it is usual to measure the traces left by the reaction of ROS with biological molecules, rather than the ROS themselves. Borrowing from the philosophical theories of signs, we look at the different facets of biomarkers and discuss their different value and meaning in multifactorial diseases and system medicine to inform their use in patient stratification in personalized medicine.

Monocyte alterations in rheumatoid arthritis are dominated by preterm release from bone marrow and prominent triggering in the joint

OBJECTIVE

Rheumatoid arthritis (RA) accompanies infiltration and activation of monocytes in inflamed joints. We investigated dominant alterations of RA monocytes in bone marrow (BM), blood and inflamed joints.

METHODS

CD14⁺ cells from BM and peripheral blood (PB) of patients with RA and osteoarthritis (OA) were profiled with GeneChip microarrays. Detailed functional analysis was performed with reference transcriptomes of BM precursors, monocyte blood subsets, monocyte activation and mobilisation. Cytometric profiling determined monocyte subsets of CD14⁺⁺CD16^-, CD14⁺⁺CD16⁺ and CD14⁺CD16⁺ cells in BM, PB and synovial fluid (SF) and ELISAs quantified the release of activation markers into SF and serum.

RESULTS

Investigation of genes differentially expressed between RA and OA monocytes with reference transcriptomes revealed gene patterns of early myeloid precursors in RA-BM and late myeloid precursors along with reduced terminal differentiation to CD14⁺CD16⁺monocytes in RA-PB. Patterns associated with tumor necrosis factor/lipopolysaccharide (TNF/LPS) stimulation were weak and more pronounced in RA-PB than RA-BM. Cytometric phenotyping of cells in BM, blood and SF disclosed differences related to monocyte subsets and confirmed the reduced frequency of terminally differentiated CD14⁺CD16⁺monocytes in RA-PB. Monocyte activation in SF was characterised by the predominance of CD14⁺⁺CD16⁺⁺CD163⁺HLA-DR⁺ cells and elevated concentrations of sCD14, sCD163 and S100P.

CONCLUSION

Patterns of less mature and less differentiated RA-BM and RA-PB monocytes suggest increased turnover with accelerated monocytopoiesis, BM egress and migration into inflamed joints. Predominant activation in the joint indicates the action of local and primary stimuli, which may also promote adaptive immune triggering through monocytes, potentially leading to new diagnostic and therapeutic strategies.