Broadening our understanding of the genetics of Juvenile Idiopathic Arthritis (JIA): Interrogation of three dimensional chromatin structures and genetic regulatory elements within JIA-associated risk loci

Genome Sequencing in an Individual Presenting with 22q11.2 Deletion Syndrome and Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis is a heterogeneous group of diseases characterized by arthritis with poorly known causes, including monogenic disorders and multifactorial etiology. 22q11.2 proximal deletion syndrome is a multisystemic disease with over 180 manifestations already described. In this report, the authors describe a patient presenting with a short stature, neurodevelopmental delay, and dysmorphisms, who had an episode of polyarticular arthritis at the age of three years and eight months, resulting in severe joint limitations, and was later diagnosed with 22q11.2 deletion syndrome. Investigation through Whole Genome Sequencing revealed that he had no pathogenic or likely-pathogenic variants in both alleles of the MIF gene or in genes associated with monogenic arthritis (LACC1, LPIN2, MAFB, NFIL3, NOD2, PRG4, PRF1, STX11, TNFAIP3, TRHR, UNC13DI). However, the patient presented 41 risk polymorphisms for juvenile idiopathic arthritis. Thus, in the present case, arthritis seems coincidental to 22q11.2 deletion syndrome, probably caused by a multifactorial etiology. The association of the MIF gene in individuals previously described with juvenile idiopathic arthritis and 22q11.2 deletion seems unlikely since it is located in the distal and less-frequently deleted region of 22q11.2 deletion syndrome.

Advancements and progress in juvenile idiopathic arthritis: A Review of pathophysiology and treatment

Juvenile idiopathic arthritis (JIA) is a chronic clinical condition characterized by arthritic features in children under the age of 16, with at least 6 weeks of active symptoms. The etiology of JIA remains unknown, and it is associated with prolonged synovial inflammation and structural joint damage influenced by environmental and genetic factors. This review aims to enhance the understanding of JIA by comprehensively analyzing relevant literature. The focus lies on current diagnostic and therapeutic approaches and investigations into the pathoaetiologies using diverse research modalities, including in vivo animal models and large-scale genome-wide studies. We aim to elucidate the multifactorial nature of JIA with a strong focus towards genetic predilection, while proposing potential strategies to improve therapeutic outcomes and enhance diagnostic risk stratification in light of recent advancements. This review underscores the need for further research due to the idiopathic nature of JIA, its heterogeneous phenotype, and the challenges associated with biomarkers and diagnostic criteria. Ultimately, this contribution seeks to advance the knowledge and promote effective management strategies in JIA.

Comparison of the three-dimensional chromatin structures of adolescent and adult peripheral blood B cells: implications for the study of pediatric autoimmune diseases

Background/Purpose

Knowledge of the 3D genome is essential to elucidate genetic mechanisms driving autoimmune diseases. The 3D genome is distinct for each cell type, and it is uncertain whether cell lines faithfully recapitulate the 3D architecture of primary human cells or whether developmental aspects of the pediatric immune system require use of pediatric samples. We undertook a systematic analysis of B cells and B cell lines to compare 3D genomic features encompassing risk loci for juvenile idiopathic arthritis (JIA), systemic lupus (SLE), and type 1 diabetes (T1D).

Methods

We isolated B cells from healthy individuals, ages 9-17. HiChIP was performed using CTCF antibody, and CTCF peaks were identified. CTCF loops within the pediatric were compared to three datasets: 1) self-called CTCF consensus peaks called within the pediatric samples, 2) ENCODE's publicly available GM12878 CTCF ChIP-seq peaks, and 3) ENCODE's primary B cell CTCF ChIPseq peaks from two adult females. Differential looping was assessed within the pediatric samples and each of the three peak datasets.

Results

The number of consensus peaks called in the pediatric samples was similar to that identified in ENCODE's GM12878 and primary B cell datasets. We observed <1% of loops that demonstrated significantly differential looping between peaks called within the pediatric samples themselves and when called using ENCODE GM12878 peaks . Significant looping differences were even less when comparing loops of the pediatric called peaks to those of the ENCODE primary B cell peaks. When querying loops found in juvenile idiopathic arthritis, type 1 diabetes, or systemic lupus erythematosus risk haplotypes, we observed significant differences in only 2.2%, 1.0%, and 1.3% loops, respectively, when comparing peaks called within the pediatric samples and ENCODE GM12878 dataset. The differences were even less apparent when comparing loops called with the pediatric vs ENCODE adult primary B cell peak datasets.The 3D chromatin architecture in B cells is similar across pediatric, adult, and EBVtransformed cell lines. This conservation of 3D structure includes regions encompassing autoimmune risk haplotypes.

Conclusion

Thus, even for pediatric autoimmune diseases, publicly available adult B cell and cell line datasets may be sufficient for assessing effects exerted in the 3D genomic space.

Analysis of chromatin data supports a role for CD14+ monocytes/macrophages in mediating genetic risk for juvenile idiopathic arthritis

Introduction

Genome wide association studies (GWAS) have identified multiple regions that confer genetic risk for the polyarticular/oligoarticular forms of juvenile idiopathic arthritis (JIA). However, genome-wide scans do not identify the cells impacted by genetic polymorphisms on the risk haplotypes or the genes impacted by those variants. We have shown that genetic variants driving JIA risk are likely to affect both innate and adaptive immune functions. We provide additional evidence that JIA risk variants impact innate immunity.

Materials and methods

We queried publicly available H3K4me1/H3K27ac ChIP-seq data in CD14+ monocytes to determine whether the linkage disequilibrium (LD) blocks incorporating the SNPs that tag JIA risk loci showed enrichment for these epigenetic marks. We also queried monocyte/macrophage GROseq data, a functional readout of active enhancers. We defined the topologically associated domains (TADs) encompassing enhancers on the risk haplotypes and identified genes within those TADs expressed in monocytes. We performed ontology analyses of these genes to identify cellular processes that may be impacted by these variants. We also used whole blood RNAseq data from the Genotype-Tissue Expression (GTEx) data base to determine whether SNPs lying within monocyte GROseq peaks influence plausible target genes within the TADs encompassing the JIA risk haplotypes.

Results

The LD blocks encompassing the JIA genetic risk regions were enriched for H3K4me1/H3K27ac ChIPseq peaks (p=0.00021 and p=0.022) when compared to genome background. Eleven and sixteen JIA were enriched for resting and activated macrophage GROseq peaks, respectively risk regions (p=0.04385 and p=0.00004). We identified 321 expressed genes within the TADs encompassing the JIA haplotypes in human monocytes. Ontological analysis of these genes showed enrichment for multiple immune functions. Finally, we found that SNPs lying within the GROseq peaks are strongly associated with expression levels of plausible target genes in human whole blood.

Conclusions

These findings support the idea that both innate and adaptive immunity are impacted by JIA genetic risk variants.

Interaction Of Immune Response Mediator Genes In A Predisposition To Juvenile Idiopathic Arthritis

Background/objective — The goal of our study was to investigate the role of interaction between the polymorphic loci of immune response mediator genes (TNFA rs1800629, LTA rs909253, IL1B rs16944, IL2-IL21 rs6822844, IL2RA rs2104286, IL6 rs1800795, IL10 rs1800872, MIF rs755622, CTLA4 rs3087243, NFKB1 rs28362491, PTPN22 rs2476601, and PADI4 rs2240336) in the formation of a genetic predisposition to juvenile idiopathic arthritis (JIA). Material and Methods — The study involved 330 JIA patients and 342 volunteers from the Republic of Bashkortostan. Genotyping was conducted via the real-time polymerase chain reaction. The gene-gene interactions were studied using the multifactor dimensionality reduction algorithm. Results — In general analysis, the best model of gene-gene interaction in JIA was a combination of IL1B rs16944 – IL10 rs1800872 – NFKB1 rs28362491 – PADI4 rs2240336 polymorphic loci. However, after gender-based stratification the best results were obtained when examining the combinations of IL6 rs1800795 – PADI4 rs2240336 loci in girls and of IL10 rs1800872 – IL6 rs1800795 – IL2RA rs2104286 loci in boys. Within all of these models, the genotype combinations associated with both augmented and reduced JIA risks were identified (taking into account gender-specific differences). Conclusion — The results of our study implied that an important role in the formation of a predisposition to JIA is played by gene-gene interactions of IL1B rs16944, IL2RA rs2104286, IL6 rs1800795, IL10 rs1800872, NFKB1 rs28362491, and PADI4 rs2240336 polymorphic loci (taking into account gender-specific differences).