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

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...

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Detalles Bibliográficos
Autores: Baeza Centurión, Pablo, 1989-, Miñana Gómez, Belén, Faure, Andre J., Thompson, Mike, Bonnal, Sophie, Quarantani, Gioia, Clarke, Joseph, Lehner, Ben, 1978-, Valcárcel, J. (Juan)
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/71517
Acceso en línea:http://hdl.handle.net/10230/71517
http://dx.doi.org/10.1038/s41467-025-62957-7
Access Level:acceso abierto
Palabra clave:Genetics
RNA splicing
Descripción
Sumario: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.