Deep indel mutagenesis reveals the regulatory and modulatory architecture of alternative exon splicing

Abstract

Altered splicing is a frequent mechanism by which genetic variants cause disease and antisense oligonucleotides (AONs) that target pre-mRNA splicing have been approved as therapeutics for multiple pathologies including patient-customized treatments for rare diseases. However, the regulatory architecture of human exons remains poorly understood and AON discovery is currently slow and expensive, limiting the wider adoption of the approach. Here we show that that systematic deletion scans -which can be made experimentally at very low cost - provide an efficient strategy to chart the regulatory landscape of human exons and to rapidly identify effective splicing-modulating oligonucleotides in a fully quantitative manner. Our results suggest a mechanism for the evolutionary origins of unusually short microexons and the repression of transmembrane domain-encoding exons, and reveal a checkerboard architecture of sequential enhancers and silencers in a model alternative exon. Accurate prediction of the effects of deletions using deep learning provides a resource, DANGO, that maps the splicing regulatory landscape of all human exons and predicts effective splicing-altering antisense oligonucleotides genome-wide.