Background/Purpose: 17 genetic loci have now been identified to confer susceptibility to JIA; several of these loci harbour genes involved in the IL2 pathway suggesting that this may be an important signalling cascade involved in JIA. It is hypothesised that the regions containing variants increasing the risk of disease may act as regulatory elements that modulate gene expression. Indeed capture Hi-C data, which identifies physical DNA interactions, has shown that a JIA susceptibility single nucleotide polymorphism (SNP), rs9979383, located in a gene enhancer region, makes strong contact with the promoter of RUNX1, a crucial transcription factor involved in the regulation of IL-2. Characterising the extent of these interaction and understanding the underlying mechanism is likely to provide important information as to how this variant increases risk of JIA. The aim of this study was to design a bioinformatics pipeline to prioritise the most likely functional candidate SNPs and to design and perform functional experiments to define the mechanisms by which these JIA associated variants contribute to disease pathogenesis.

Methods: In order to prioritise the most likely functional candidate SNPs for follow up a bioinformatics pipeline was designed, curating bioinformatics tools and data to create a step-wise assessment of each SNP. In-house generated Capture Hi-C data for the IL2 pathway regions were assessed for interactions between associated SNPs and nearby genes. Fragments near JIA associated SNPs showed looping at several points around the haematopoiesis master regulator gene; RUNX1, a key gene in the IL2pathway. Chromosome Conformation Capture (3C) experiments were implemented to validate interactions in the selected regions. To test for a genotype specific effect nine B-lymphocyte cell lines, three of each genotype, were selected for this experiment.

Results: The highest prioritised SNP in the RUNX1gene region was identified as rs9799383 based on transcription factors, Hi-C data and histone marks. Interactions in TT genotype cell lines occurs significantly more frequently in one tested interaction. Interestingly, a 1.7 fold increase in frequency is observed in the same interaction when data from all cell lines are grouped together, as well as a separate interaction showing a 2.7 fold increase in interaction frequency.

Conclusion: The bioinformatics approach to investigating potential functional variants proved to be highly informative and aided the design of 3C experiments. Cell lines grouped by genotype show mostly insignificant difference compared to controls, however one genotype specific interaction is observed in TT cell lines. This observed interaction appears to interact with the RUNX1 promoter region. The observed long range interactions are indicative of a distal regulatory effect that may influence gene expression of RUNX1. These findings inform further experiments and have suggested several potential transcription factors which may be driving JIA susceptibility in the RUNX1 region.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/identifying-distal-interactions-between-runx1-and-jia-associated-single-nucleotide-polymorphisms-by-chromosome-conformation-capture/