Senataxin and RNase H2 act redundantly to suppress genome instability during class switch recombination

Class switch recombination generates antibody distinct isotypes critical to a robust adaptive immune system and defects are associated with auto-immune disorders and lymphomagenesis. Transcription is required during class switch recombination for the formation of DNA double-strand breaks by AID, and strongly induces the formation of R loops within the immunoglobulin heavy chain locus. However the impact of R loops on double-strand break formation and repair during class switch recombination remains unclear. Here we report that cells lacking two enzymes involved in R loop removal--Senataxin and RNase H2--exhibit increased R loop formation and genome instability at the immunoglobulin heavy chain locus without impacting class switch recombination efficiency or AID recruitment. We propose that Senataxin acts redundantly with RNase H2 to mediate timely R loop removal, promoting efficient repair and suppressing AID-dependent genome instability.

A novel framework for characterizing genomic haplotype diversity in the human immunoglobulin heavy chain locus

An incomplete ascertainment of genetic variation within the highly polymorphic immunoglobulin heavy chain locus (IGH) has hindered our ability to define genetic factors that influence antibody and B cell mediated processes. To date, methods for locus-wide genotyping of all IGH variant types do not exist. Here, we combine targeted long-read sequencing with a novel bioinformatics tool, IGenotyper, to fully characterize genetic variation within IGH in a haplotype-specific manner. We apply this approach to eight human samples, including a haploid cell line and two mother-father-child trios, and demonstrate the ability to generate high-quality assemblies (>98% complete and >99% accurate), genotypes, and gene annotations, including 2 novel structural variants and 16 novel gene alleles. We show that multiplexing allows for scaling of the approach without impacting data quality, and that our genotype call sets are more accurate than short-read (>35% increase in true positives and >97% decrease in false-positives) and array/imputation-based datasets. This framework establishes a foundation for leveraging IG genomic data to study population-level variation in the antibody response.