Translation velocity determines the efficacy of engineered suppressor tRNAs on pathogenic nonsense mutations

Nikhil Bharti, Leonardo Santos, Marcos Davyt, Stine Behrmann, Marie Eichholtz, Alejandro Jimenez-Sanchez, Jeong S. Hong, Andras Rab, Eric J. Sorscher, Suki Albers () and Zoya Ignatova ()
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Nikhil Bharti: University of Hamburg
Leonardo Santos: University of Hamburg
Marcos Davyt: University of Hamburg
Stine Behrmann: University of Hamburg
Marie Eichholtz: University of Hamburg
Alejandro Jimenez-Sanchez: University of Hamburg
Jeong S. Hong: Emory University
Andras Rab: Emory University
Eric J. Sorscher: Emory University
Suki Albers: University of Hamburg
Zoya Ignatova: University of Hamburg

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Nonsense mutations – the underlying cause of approximately 11% of all genetic diseases – prematurely terminate protein synthesis by mutating a sense codon to a premature stop or termination codon (PTC). An emerging therapeutic strategy to suppress nonsense defects is to engineer sense-codon decoding tRNAs to readthrough and restore translation at PTCs. However, the readthrough efficiency of the engineered suppressor tRNAs (sup-tRNAs) largely varies in a tissue- and sequence context-dependent manner and has not yet yielded optimal clinical efficacy for many nonsense mutations. Here, we systematically analyze the suppression efficacy at various pathogenic nonsense mutations. We discover that the translation velocity of the sequence upstream of PTCs modulates the sup-tRNA readthrough efficacy. The PTCs most refractory to suppression are embedded in a sequence context translated with an abrupt reversal of the translation speed leading to ribosomal collisions. Moreover, modeling translation velocity using Ribo-seq data can accurately predict the suppression efficacy at PTCs. These results reveal previously unknown molecular signatures contributing to genotype-phenotype relationships and treatment-response heterogeneity, and provide the framework for the development of personalized tRNA-based gene therapies.

Date: 2024
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DOI: 10.1038/s41467-024-47258-9

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