Mistranslation, incorporating an amino acid not specified by the “standard” genetic code, has applications in research and synthetic biology. Since mistranslation is toxic, its level must be modulated. Using a serine tRNA with a proline anticodon, we identify...

To develop a system for conditional amino acid misincorporation, we engineered tRNAs in the yeast Saccharomyces cerevisiae to be substrates of the rapid tRNA decay (RTD) pathway, such that they accumulate when RTD is turned off. We used this system to test the effects on growth of a library of tRNASer variants with all possible anticodons, and show that many are lethal when RTD is inhibited and the tRNA accumulates. Using mass spectrometry, we measured serine misincorporation in yeast containing each of six tRNA variants, and for five of them identified hundreds of peptides with serine substitutions at the targeted amino acid sites. Unexpectedly, we found that there is not a simple correlation between toxicity and the level of serine misincorporation; in particular, high levels of serine misincorporation can occur at cysteine residues without obvious growth defects. We also showed that toxic tRNAs can be used as a tool to identify sequence variants that reduce tRNA function. Finally, we generalized this method to another tRNA species, and generated conditionally toxic tRNATyr variants in a similar manner. This method should facilitate the study of tRNA biology and provide a tool to probe the effects of amino acid misincorporation on cellular physiology.

Mistranslation, the mis-incorporation of an amino acid not specified by the “standard” genetic code, occurs in all organisms. tRNA variants that increase mistranslation arise spontaneously and engineered tRNAs can achieve mistranslation frequencies approaching 10% in yeast and bacteria. Interestingly, human genomes contain tRNA variants with the potential to mistranslate. Cells cope with increased mistranslation through multiple mechanisms, though high levels cause proteotoxic stress. The goal of this study was to compare the genetic interactions and the impact on transcriptome and cellular growth of two tRNA variants that mistranslate at a similar frequency but create different amino acid substitutions in Saccharomyces cerevisiae. One tRNA variant inserts alanine at proline codons whereas the other inserts serine for arginine. Both tRNAs decreased growth rate, with the effect being greater for arginine to serine than for proline to alanine. The tRNA that substituted serine for arginine resulted in a heat shock response. In contrast, heat shock response was minimal for proline to alanine substitution. Further demonstrating the significance of the amino acid substitution, transcriptome analysis identified unique up- and downregulated genes in response to each mistranslating tRNA. Number and extent of negative synthetic genetic interactions also differed depending upon type of mistranslation. Based on the unique responses observed for these mistranslating tRNAs, we predict that the potential of mistranslation to exacerbate diseases caused by proteotoxic stress depends on the tRNA variant. Furthermore, based on their unique transcriptomes and genetic interactions, different naturally occurring mistranslating tRNAs have the potential to negatively influence specific diseases.

Mistranslation, the mis-incorporation of an amino acid not specified by the standard genetic code, occurs in all organisms. tRNA variants that increase mistranslation arise spontaneously and engineered tRNAs can achieve mistranslation frequencies approaching 10% in yeast and bacteria. Interestingly, human genomes contain tRNA variants with the potential to mistranslate. Cells cope with increased mistranslation through multiple mechanisms, though high levels cause proteotoxic stress. The goal of this study was to compare the genetic interactions and the impact on transcriptome and cellular growth of two tRNA variants that mistranslate at a similar frequency but create different amino acid substitutions in Saccharomyces cerevisiae. One tRNA variant inserts alanine at proline codons whereas the other inserts serine for arginine. Both tRNAs decreased growth rate, with the effect being greater for arginine to serine than for proline to alanine. The tRNA that substituted serine for arginine resulted in a heat shock response. In contrast, heat shock response was minimal for proline to alanine substitution. Further demonstrating the significance of the amino acid substitution, transcriptome analysis identified unique up- and downregulated genes in response to each mistranslating tRNA. Number and extent of negative synthetic genetic interactions also differed depending upon type of mistranslation. Based on the unique responses observed for these mistranslating tRNAs, we predict that the potential of mistranslation to exacerbate diseases caused by proteotoxic stress depends on the tRNA variant. Furthermore, based on their unique transcriptomes and genetic interactions, different naturally occurring mistranslating tRNAs have the potential to negatively influence specific diseases.

Methods harnessing protein cross-linking and mass spectrometry (XL-MS) offer high-throughput means to identify protein-protein interactions (PPIs) and structural interfaces of protein complexes. Yet, specialized data dependent methods and search algorithms are often required to confidently assign peptide identifications to spectra. To improve the efficiency of matching high confidence spectra we developed a spectral library based approach to search cross-linked peptide data derived from Protein Interaction Reporter (PIR) methods using the spectral library search algorithm, SpectraST. Spectral library matching of cross-linked peptide data from query spectra increased the absolute number of confident peptide relationships matched to spectra, and thereby number of protein-protein interactions identified. By matching library spectra from bona fide, previously established PIR-cross-linked peptide relationships, spectral library searching reduces the need for continued, complex mass spectrometric methods to identify peptide relationships, increases coverage of relationship identifications and improves the accessibility of XL-MS technologies.