Understanding, predicting and preventing the impact of nonsense mutations on gene expression by deep mutational scanning
[eng] Premature termination codons (PTCs) are responsible for ~10–20% of inherited diseases and represent a major mechanism of tumor suppressor gene inactivation in cancer. Traditionally, PTCs are considered to induce transcript degradation via nonsense-mediated mRNA decay (NMD) and lead to the prod...
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/219877 |
| Acesso em linha: | https://hdl.handle.net/2445/219877 http://hdl.handle.net/10803/694048 |
| Access Level: | acceso abierto |
| Palavra-chave: | Mutació (Biologia) RNA Transcripció genètica Mutation (Biology) Genetic transcription |
| Resumo: | [eng] Premature termination codons (PTCs) are responsible for ~10–20% of inherited diseases and represent a major mechanism of tumor suppressor gene inactivation in cancer. Traditionally, PTCs are considered to induce transcript degradation via nonsense-mediated mRNA decay (NMD) and lead to the production of truncated non-functional proteins. Nonsense suppression therapies aim to promote translational readthrough over PTCs, enabling the synthesis of full-length proteins. Both NMD and readthrough modulate the severity of disease phenotypes by regulating the abundance of the mRNA and the full-length protein; respectively. However, their efficiencies vary across PTCs. In this thesis, we employed deep mutagenesis methods to systematically quantify how sequence context and other genetic factors influence the mRNA levels and the full-length protein abundance of PTC-containing transcripts. First, we developed a methodological improvement for deep mutagenesis libraries generation. Second, a comprehensive assessment of drug-induced readthrough was performed, encompassing over 140,000 measurements and generating readthrough scores for 6,000 PTCs that cause genetic diseases and cancer. This massive dataset was subsequently leveraged to elucidate the effect of sequence context on readthrough and to train accurate predictive models to estimate drug-specific PTC readthrough genome-wide. We envisage these datasets will become a valuable resource to improve clinical trial design and the development of personalized nonsense suppression therapies. Third, we combined different libraries to test and extend hypotheses for how PTC position, exon length, sequence context and translation reinitiation interplay to determine NMD efficiency. Overall, this thesis provides a comprehensive view of how the sequence landscape influences the fate of PTC-containing transcripts. More broadly, it demonstrates the effectiveness of deep mutagenesis in uncovering sequence-to- activity relationships, highlighting the potential of this approach for investigating other mRNA-related processes. |
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