Identifying genes related with rheumatoid arthritis via system biology analysis

Immunomodulatory Nanoparticles for Modulating Arthritis Flares

Disease-modifying drugs have improved the treatment for autoimmune joint disorders, such as rheumatoid arthritis, but inflammatory flares are a common experience. This work reports the development and application of flare-modulating poly(lactic-co-glycolic acid)-poly(ethylene glycol)-maleimide (PLGA-PEG-MAL)-based nanoparticles conjugated with joint-relevant peptide antigens, aggrecan70-84 and type 2 bovine collagen256-270. Peptide-conjugated PLGA-PEG-MAL nanoparticles encapsulated calcitriol, which acted as an immunoregulatory agent, and were termed calcitriol-loaded nanoparticles (CLNP). CLNP had a ∼200 nm hydrodynamic diameter with a low polydispersity index. In vitro, CLNP induced phenotypic changes in bone marrow derived dendritic cells (DC), reducing the expression of costimulatory and major histocompatibility complex class II molecules, and proinflammatory cytokines. Bulk RNA sequencing of DC showed that CLNP enhanced expression of Ctla4, a gene associated with downregulation of immune responses. In vivo, CLNP accumulated in the proximal lymph nodes after intramuscular injection. Administration of CLNP was not associated with changes in peripheral blood cell numbers or cytokine levels. In the collagen-induced arthritis and SKG mouse models of autoimmune joint disorders, CLNP reduced clinical scores, prevented bone erosion, and preserved cartilage proteoglycan, as assessed by high-resolution microcomputed tomography and histomorphometry analysis. The disease protective effects were associated with increased CTLA-4 expression in joint-localized DC and CD4+ T cells but without generalized suppression of T cell-dependent immune response. The results support the potential of CLNP as modulators of disease flares in autoimmune arthropathies.

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.

Genetic variants shape rheumatoid arthritis-specific transcriptomic features in CD4+ T cells through differential DNA methylation, explaining a substantial proportion of heritability

OBJECTIVE

CD4⁺ T cells have been suggested as the most disease-relevant cell type in rheumatoid arthritis (RA) in which RA-risk non-coding variants exhibit allele-specific effects on regulation of RA-driving genes. This study aimed to understand RA-specific signatures in CD4⁺ T cells using multi-omics data, interpreting inter-omics relationships in shaping the RA transcriptomic landscape.

METHODS

We profiled genome-wide variants, gene expression and DNA methylation in CD4⁺ T cells from 82 patients with RA and 40 healthy controls using high-throughput technologies. We investigated differentially expressed genes (DEGs) and differential methylated regions (DMRs) in RA and localised quantitative trait loci (QTLs) for expression and methylation. We then integrated these based on individual-level correlations to inspect DEG-regulating sources and investigated the potential regulatory roles of RA-risk variants by a partitioned-heritability enrichment analysis with RA genome-wide association summary statistics.

RESULTS

A large number of RA-specific DEGs were identified (n=2575), highlighting T cell differentiation and activation pathways. RA-specific DMRs, preferentially located in T cell regulatory regions, were correlated with the expression levels of 548 DEGs mostly in the same topologically associating domains. In addition, expressional variances in 771 and 83 DEGs were partially explained by expression QTLs for DEGs and methylation QTLs (meQTLs) for DEG-correlated DMRs, respectively. A large number of RA variants were moderately to strongly correlated with meQTLs. DEG-correlated DMRs, enriched with meQTLs, had strongly enriched heritability of RA.

CONCLUSION

Our findings revealed that the methylomic changes, driven by RA heritability-explaining variants, shape the differential expression of a substantial fraction of DEGs in CD4⁺ T cells in patients with RA, reinforcing the importance of a multidimensional approach in disease-relevant tissues.

Adaptive weighted sum tests via LASSO method in multi-locus family-based association analysis

Family based multi-locus tests integrate information from individual loci by weighted averaging of the marginal statistics, and have been proven to be more efficient and robust than the single-locus tests in genetic association studies. The power depends on how much information the weights can extract from data. The currently published weighted sum methods are only applicable to either common or rare variants and may suffer from substantial power loss especially for rare variants. In this paper, we propose a novel data-driven weight to improve the power under both common and rare variant circumstances. We use the l₁ regularization in Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the weight serving as a simultaneously adaptive marker selection process. Simulations for a dichotomous phenotype demonstrated that our LASSO-based approach outperformed the existing multi-locus methods in the sense of providing the highest statistical power while well controlled type I error rate under different scenarios. We also applied our methods to a real dataset for rheumatoid arthritis (GAW15 Problem 2). Two groups of alleles, in which individual SNPs had only modest and non-significant effects, were detected (P < 0.00001) using our proposed methods, whereas traditional multi-locus methods failed to identify them. In conclusion, the novel LASSO-based approach represents a superior weight-choosing strategy for multi-locus tests.

Genetic Polymorphisms Associated with Rheumatoid Arthritis Development and Antirheumatic Therapy Response

Rheumatoid arthritis (RA) is the most common inflammatory arthropathy worldwide. Possible manifestations of RA can be represented by a wide variability of symptoms, clinical forms, and course options. This multifactorial disease is triggered by a genetic predisposition and environmental factors. Both clinical and genealogical studies have demonstrated disease case accumulation in families. Revealing the impact of candidate gene missense variants on the disease course elucidates understanding of RA molecular pathogenesis. A multivariate genomewide association study (GWAS) based analysis identified the genes and signalling pathways involved in the pathogenesis of the disease. However, these identified RA candidate gene variants only explain 30% of familial disease cases. The genetic causes for a significant proportion of familial RA have not been determined until now. Therefore, it is important to identify RA risk groups in different populations, as well as the possible prognostic value of some genetic variants for disease development, progression, and treatment. Our review has two purposes. First, to summarise the data on RA candidate genes and the increased disease risk associated with these alleles in various populations. Second, to describe how the genetic variants can be used in the selection of drugs for the treatment of RA.

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.

Profiling of Stem/Progenitor Cell Regulatory Genes of the Synovial Joint by Genome-Wide RNA-Seq Analysis

Synovial joints suffer from arthritis and trauma that may be severely debilitative. Despite robust investigations in the roles of individual genes in synovial joint development and arthritis, little is known about global profiles of genes that regulate stem/progenitor cells of a synovial joint. The temporomandibular joint is a poorly understood synovial arthrosis with few clinical treatment options. Here, we isolated the articular and mature zones of the mandibular condyle by laser capture microdissection, performed genome-wide profiling, and analyzed molecular signaling pathways relevant to stem/progenitor cell functions. A total of 804 genes were differentially expressed between the articular and mature zones. Pathway analyses revealed 29 enriched signaling pathways, including the PI3K-Akt, Wnt, and Toll-like receptor signaling pathways that may regulate stem/progenitor cell homeostasis and differentiation into the chondrocyte lineage. Upstream regulator analyses further predicted potential upstream key regulators such as Xbp1, Nupr1, and Hif1a, and associated underlying mechanism networks were described. Among the multiple candidates of growth and transcriptional factors that may regulate stem/progenitor cells, we immunolocalized Sox9, Ihh, Frzb, Dkk1, Lgr5, and TGFβ3 in the articular and mature zones. These findings provide a comprehensive genetic mapping of growth and transcriptional genes in the articular and mature zones of a synovial joint condyle. Differentially expressed genes may play crucial roles in the regulation of stem/progenitor cells in development, homeostasis, and tissue regeneration.

The Role of Systems Biologic Approach in Cell Signaling and Drug Development Responses-A Mini Review

The immune system is an integral aspect of the human defense system and is primarily responsible for and involved in the communication between the immune cells. It also plays an important role in the protection of the organism from foreign invaders. Recent studies in the literature have described its role in the process of hematopoiesis, lymphocyte recruitment, T cell subset differentiation and inflammation. However, the specific molecular mechanisms underlying these observations remain elusive, impeding the elaborate manipulation of cytokine sequential delivery in tissue repair. Previously, the discovery of new drugs and systems biology went hand in hand; although Systems biology as a term has only originated in the last century. Various new chemicals were tested on the human body, and studied through observation. Animal models replaced humans for initial trials, but the interactions, response, dose and effect between animals and humans could not be directly correlated. Therefore, there is a need to form disease models outside of human subjects to check the effectiveness and response of the newer natural or synthetic chemicals. These emulate human disease conditions wherein the behavior of the chemicals would be similar in the disease model and humans.