1. Academic Validation
  2. Translation velocity determines the efficacy of engineered suppressor tRNAs on pathogenic nonsense mutations

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

  • Nat Commun. 2024 Apr 5;15(1):2957. doi: 10.1038/s41467-024-47258-9.
Nikhil Bharti # 1 Leonardo Santos # 1 Marcos Davyt 1 Stine Behrmann 1 Marie Eichholtz 1 Alejandro Jimenez-Sanchez 1 Jeong S Hong 2 3 Andras Rab 2 3 Eric J Sorscher 2 3 Suki Albers 4 Zoya Ignatova 5
Affiliations

Affiliations

  • 1 Institute of Biochemistry and Molecular Biology, University of Hamburg, 20146, Hamburg, Germany.
  • 2 Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, 30322, USA.
  • 3 Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA.
  • 4 Institute of Biochemistry and Molecular Biology, University of Hamburg, 20146, Hamburg, Germany. suki.albers@uni-hamburg.de.
  • 5 Institute of Biochemistry and Molecular Biology, University of Hamburg, 20146, Hamburg, Germany. zoya.ignatova@uni-hamburg.de.
  • # Contributed equally.
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.

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