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

Abstract

While altered pre-mRNA splicing is a frequent mechanism by which genetic variants cause disease, the regulatory architecture of human exons remains poorly understood. Antisense oligonucleotides (AONs) that target pre-mRNA splicing have been approved as therapeutics for various pathologies including patient-customised treatments for rare diseases, but AON discovery is currently slow and expensive, limiting the wider adoption of the approach. Here we show that deep indel mutagenesis (DIM) -which can be made experimentally at very low cost - provides an efficient strategy to chart the regulatory landscape of human exons and rapidly identify candidate splicing-modulating oligonucleotides. DIM reveals autonomous effects of insertions, while systematic deletion scans delineate the checkerboard architecture of sequential enhancers and silencers in a model alternative exon. The results also suggest a mechanism for repression of transmembrane domain-encoding exons and for the generation of microexons. Leveraging deep learning tools, we provide a resource, DANGO, that predicts the splicing regulatory landscape of all human exons and can help to identify effective splicing-modulating antisense oligonucleotides.

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreements 670146 and 883742) and from the European Union's Horizon Europe under the grant agreement No 101071936. We also acknowledge support of the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI/10.13039/501100011033), and the Generalitat de Catalunya through the CERCA programme. We are grateful to the CRG Core Technologies Programme for their support and assistance in this work. We received funding from the Spanish State Research Agency (PID2020-114630GB-I00/AEI/10.13039/501100011033), LCF/PR/HR21/52410004, EMBL Partnership, the Bettencourt Schueller Foundation, the AXA Research Fund, and Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322). GQ was supported by PRE2022-102744, financed by MCIN/AEI/10.13039/501100011033 and FSE + . J.C. is funded by the BBSRC DTP (Bio-technology and Biological Sciences Research Council, Biosciences Doctoral Training Programme, Cambridge, UK. The Genotype-Tissue Expression (GTEx) data used for the analyses described in this manu-script were obtained from the GTEx Portal on May 8, 2018 and dbGaP accession number phs000424.v7.p2 on May 8, 2018. The GTEx Project was supported by the Common Fund of the Office of the Director of the NIH and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.