Association analysis of juvenile idiopathic arthritis genetic susceptibility factors in Estonian patients

Gene Signature of Regulatory T Cells Isolated from Children with Selective IgA Deficiency and Common Variable Immunodeficiency

Selective IgA deficiency (SIgAD) is the most common form and common variable immunodeficiency (CVID) is the most symptomatic form of predominant antibody deficiency. Despite differences in the clinical picture, a similar genetic background is suggested. A common feature of both disorders is the occurrence of autoimmune conditions. Regulatory T cells (Tregs) are the major immune cell type that maintains autoimmune tolerance. As the different types of abnormalities of Treg cells have been associated with autoimmune disorders in primary immunodeficiency (PID) patients, in our study we aimed to analyze the gene expression profiles of Treg cells in CVID and SIgAD patients compared to age-matched healthy controls. The transcriptome-wide gene profiling was performed by microarray technology. As a result, we analyzed and visualized gene expression patterns of isolated population of Treg cells. We showed the differences at the gene level between patients with and without autoimmunizations. Our findings suggest that the gene signatures of Treg cells isolated from SIgAD and CVID patients differ from age-matched healthy controls and from each other, presenting transcriptional profiles enriched in innate immune or Th response, respectively. The occurrence of autoimmunity in both types of PID is associated with down-regulation of class I IFNs signaling pathways. In summary, our findings improve our understanding of Treg dysfunctions in patients with common PIDs and associated autoimmunity.

Juvenile idiopathic arthritis-associated genetic loci exhibit spatially constrained gene regulatory effects across multiple tissues and immune cell types

Juvenile idiopathic arthritis (JIA) is an autoimmune, inflammatory joint disease with complex genetic etiology. Previous GWAS have found many genetic loci associated with JIA. However, the biological mechanism behind JIA remains unknown mainly because most risk loci are located in non-coding genetic regions. Interestingly, increasing evidence has found that regulatory elements in the non-coding regions can regulate the expression of distant target genes through spatial (physical) interactions. Here, we used information on the 3D genome organization (Hi-C data) to identify target genes that physically interact with SNPs within JIA risk loci. Subsequent analysis of these SNP-gene pairs using data from tissue and immune cell type-specific expression quantitative trait loci (eQTL) databases allowed the identification of risk loci that regulate the expression of their target genes. In total, we identified 59 JIA-risk loci that regulate the expression of 210 target genes across diverse tissues and immune cell types. Functional annotation of spatial eQTLs within JIA risk loci identified significant overlap with gene regulatory elements (i.e., enhancers and transcription factor binding sites). We found target genes involved in immune-related pathways such as antigen processing and presentation (e.g., ERAP2, HLA class I and II), the release of pro-inflammatory cytokines (e.g., LTBR, TYK2), proliferation and differentiation of specific immune cell types (e.g., AURKA in Th17 cells), and genes involved in physiological mechanisms related to pathological joint inflammation (e.g., LRG1 in arteries). Notably, many of the tissues where JIA-risk loci act as spatial eQTLs are not classically considered central to JIA pathology. Overall, our findings highlight the potential tissue and immune cell type-specific regulatory changes contributing to JIA pathogenesis. Future integration of our data with clinical studies can contribute to the development of improved JIA therapy.

Causal association of juvenile idiopathic arthritis-associated uveitis with depression and anxiety: a bidirectional Mendelian randomization study

PURPOSE

The objective of this article was to examine the potential effect of juvenile idiopathic arthritis-associated uveitis (JIAU) on the risk of major depressive and anxiety disorders through Mendelian randomization (MR) study.

METHODS

Genetic instrumental variables from the largest available genome-wide association study for JIAU, major depressive disorder, and anxiety disorder were applied. A set of complementary MR approaches including inverse-variance weighted (IVW) were carried out to verify the estimate association and assess horizontal pleiotropy.

RESULTS

Our results indicated that genetically driven JIAU did not causally produce changes in major depressive or anxiety disorders (IVW: OR = 1.001, 95% CI = 0.997-1.006, P = 0.581; IVW: OR = 1.006, 95% CI = 0.980-1.033, P = 0.649, respectively). In addition, the risk of JIAU could not be influenced by genetically predicted major depressive or anxiety disorders (IVW: OR = 1.132, 95% CI = 0.914-1.404, P = 0.256; IVW: OR = 1.019, 95% CI = 0.548-1.896, P = 0.953, respectively). Besides, several sensitivity analyses indicated that our MR results were robust and no horizontal pleiotropy was observed (P > 0.05).

CONCLUSIONS

Our MR study does not reveal sufficient evidence to support the causal association of JIAU with the development of major depressive or anxiety disorders in both directions. Further large studies are warranted to validate the undetermined relationship between JIAU and the risk of major depressive or anxiety disorders.

Genetic Background and Molecular Mechanisms of Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in the paediatric population. JIA comprises a heterogeneous group of disorders with different onset patterns and clinical presentations with the only element in common being chronic joint inflammation. This review sought to evaluate the most relevant and up-to-date evidence on current knowledge regarding the pathogenesis of JIA subtypes to provide a better understanding of these disorders. Despite significant improvements over the past decade, the aetiology and molecular mechanisms of JIA remain unclear. It has been suggested that the immunopathogenesis is characterised by complex interactions between genetic background and environmental factors that may differ between JIA subtypes. Human leukocyte antigen (HLA) haplotypes and non-HLA genes play a crucial role in the abnormal activation of both innate and adaptive immune cells that cooperate in causing the inflammatory process. This results in the involvement of proinflammatory cytokines, including tumour necrosis factor (TNF)α, interleukin (IL)-1, IL-6, IL-10, IL-17, IL-21, IL-23, and others. These mediators, interacting with the surrounding tissue, cause cartilage stress and bone damage, including irreversible erosions. The purpose of this review is to provide a comprehensive overview of the genetic background and molecular mechanisms of JIA.

Identification of candidate genes and pathways associated with juvenile idiopathic arthritis by integrative transcriptome-wide association studies and mRNA expression profiles

AIM

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease of childhood, with genetic susceptibility and pathological processes such as autoimmunity and autoinflammation, but its pathogenesis is unclear. We conducted a transcriptome-wide association study (TWAS) using expression interpolation from a large-scale genome-wide association study (GWAS) dataset to identify genes, biological pathways, and environmental chemicals associated with JIA.

METHODS

We obtained published GWAS data on JIA for TWAS and used mRNA expression profiling to validate the genes identified by TWAS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. A protein-protein interaction (PPI) network was generated, and central genes were obtained using Molecular Complex Detection (MCODE). Finally, chemical gene expression datasets were obtained from the Comparative Toxicogenomics database for chemical genome enrichment analysis.

RESULTS

TWAS identified 1481 genes associated with JIA, and 154 differentially expressed genes were identified based on mRNA expression profiles. After comparing the results of TWAS and mRNA expression profiles, we obtained eight overlapping genes. GO and KEGG enrichment analyses of the genes identified by TWAS yielded 163 pathways, and PPI network analysis as well as MCODE resolution identified a total of eight clusters. Through chemical gene set enrichment analysis, 287 environmental chemicals associated with JIA were identified.

CONCLUSION

By integrating TWAS and mRNA expression profiles, genes, biological pathways, and environmental chemicals associated with JIA were identified. Our findings provide new insights into the pathogenesis of JIA, including candidate genetic and environmental factors contributing to its onset and progression.

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study

BACKGROUND

Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models.

METHODS

RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted.

RESULTS

Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups.

CONCLUSION

Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.