Genome-scale quantification and prediction of pathogenic stop codon readthrough by small molecules

Publication date

2024-12-02T07:17:13Z

2024-12-02T07:17:13Z

2024

Abstract

Premature termination codons (PTCs) cause ~10-20% of inherited diseases and are a major mechanism of tumor suppressor gene inactivation in cancer. A general strategy to alleviate the effects of PTCs would be to promote translational readthrough. Nonsense suppression by small molecules has proven effective in diverse disease models, but translation into the clinic is hampered by ineffective readthrough of many PTCs. Here we directly tackle the challenge of defining drug efficacy by quantifying the readthrough of ~5,800 human pathogenic stop codons by eight drugs. We find that different drugs promote the readthrough of complementary subsets of PTCs defined by local sequence context. This allows us to build interpretable models that accurately predict drug-induced readthrough genome-wide, and we validate these models by quantifying endogenous stop codon readthrough. Accurate readthrough quantification and prediction will empower clinical trial design and the development of personalized nonsense suppression therapies.


Work in the Supek laboratory was supported by an ERC StG ‘HYPER-INSIGHT’ (757700) and ERC CoG ‘STRUCTOMATIC’ (101088342), Horizon2020 project ‘DECIDER’ (965193), Horizon Europe project ‘LUCIA’ (101096473), Spanish government project ‘REPAIRSCAPE’, CaixaResearch project ‘POTENT-IMMUNO’ (HR22-00402), an ICREA professorship to F.S., the SGR funding from the Catalan government and a Novo Nordisk Fonden starting package. Work in the Lehner laboratory was funded by European Research Council (ERC) Advanced (grant 883742) and Consolidator (grant 616434), the Spanish Ministry of Science and Innovation (BFU2017-89488-P, EMBL Partnership, Severo Ochoa Center of Excellence), the Bettencourt Schueller Foundation, the AXA Research Fund, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322) and the CERCA Program/Generalitat de Catalunya. The authors would like to thank the four members E. Ramírez, A. Bote, E. Julià and Ò. Fornàs of the Flow Cytometry CRG Core Unit for their support and time, together with G. Palou (IRB) for his help in retrieving the transcript sequences from the Ensembl database and all members of the Supek and Lehner laboratories for helpful discussions and suggestions.

Document Type

Article


Published version

Language

English

Publisher

Nature Research

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info:eu-repo/grantAgreement/EC/H2020/757700

info:eu-repo/grantAgreement/EC/HE/101088342

info:eu-repo/grantAgreement/EC/H2020/965193

info:eu-repo/grantAgreement/EC/HE/101096473

info:eu-repo/grantAgreement/EC/H2020/883742

info:eu-repo/grantAgreement/EC/FP7/616434

info:eu-repo/grantAgreement/ES/2PE/BFU2017-89488-P

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© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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