Defining three dimensional chromatin structures of pediatric and adolescent B cells using primary B cell and EBV-immortalized B cell reference genomes.

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 four healthy individuals, ages 9-17. HiChIP was performed using a CTCF antibody, and CTCF peaks were called within each sample separately. Peaks observed in all four samples were identified. CTCF loops were called within the pediatric samples using three CTCF peak 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 ChIP-seq 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 fewer 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.

CONCLUSION

The 3D chromatin architecture in B cells is similar across pediatric, adult, and EBV-transformed cell lines. This conservation of 3D structure includes regions encompassing autoimmune risk haplotypes. 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.