Cellular health and growth requires protein synthesis to be both efficient to ensure sufficient production, and accurate to avoid producing defective or unstable proteins. The background of misreading error frequency by individual tRNAs is as low as 2 × 10−6 per codon but is codon-specific with some error frequencies above 10−3 per codon. Here we test the effect on error frequency of blocking post-transcriptional modifications of the anticodon loops of four tRNAs in Escherichia coli. We find two types of responses to removing modification. Blocking modification of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm tRNA}_{{\rm UUC}}^{{\rm Glu}}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm tRNA}^{\rm Asp}_{\rm QUC}$\end{document} increases errors, suggesting that the modifications act at least in part to maintain accuracy. Blocking even identical modifications of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm tRNA}^{\rm Lys}_{\rm UUU}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm tRNA}^{\rm Tyr}_{\rm QUA}$\end{document} has the opposite effect of decreasing errors. One explanation could be that the modifications play opposite roles in modulating misreading by the two classes of tRNAs. Given available evidence that modifications help preorder the anticodon to allow it to recognize the codons, however, the simpler explanation is that unmodified ‘weak’ tRNAs decode too inefficiently to compete against cognate tRNAs that normally decode target codons, which would reduce the frequency of misreading.
Protein synthesis requires both high speed and accuracy to ensure a healthy cellular environment. Estimates of errors during protein synthesis in Saccharomyces cerevisiae have varied from 10−3 to 10−4 errors per codon. Here, we show that errors made by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm{tRNA}}^{\rm Glu}_{\rm UUC}$\end{document} in yeast can vary 100-fold, from 10−6 to 10−4 errors per codon. The most frequent errors require a G•U mismatch at the second position for the near cognate codon GGA (Gly). We also show, contrary to our previous results, that yeast tRNAs can make errors involving mismatches at the wobble position but with low efficiency. We have also assessed the effect on misreading frequency of post-transcriptional modifications of tRNAs, which are known to regulate cognate codon decoding in yeast. We tested the roles of mcm5s2U34 and t6A37 and show that their effects depend on details of the codon anticodon interaction including the position of the modification with respect to the base mismatch and the nature of that mismatch. Both mcm5 and s2 modification of wobble uridine strongly stabilizes G2•U35 mismatches when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm{tRNA}}^{\rm Glu}_{\rm UUC}$\end{document} misreads the GGA Gly codon but has weaker effects on other mismatches. By contrast, t6A37 destabilizes U1•U36 mismatches when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\rm{tRNA}}^{\rm Lys}_{\rm UUU}$\end{document} misreads UAA or UAG but stabilizes mismatches at the second and wobble positions.
BackgroundA large number of Saccharomyces cerevisiae cellular factors modulate the movement of the retrovirus-like transposon Ty1. Surprisingly, a significant number of chromosomal genes required for Ty1 transposition encode components of the translational machinery, including ribosomal proteins, ribosomal biogenesis factors, protein trafficking proteins and protein or RNA modification enzymes.ResultsTo assess the mechanistic connection between Ty1 mobility and the translation machinery, we have determined the effect of these mutations on ribosome biogenesis and Ty1 transcriptional and post-transcriptional regulation. Lack of genes encoding ribosomal proteins or ribosome assembly factors causes reduced accumulation of the ribosomal subunit with which they are associated. In addition, these mutations cause decreased Ty1 + 1 programmed translational frameshifting, and reduced Gag protein accumulation despite at least normal levels of Ty1 mRNA. Several ribosome subunit mutations increase the level of both an internally initiated Ty1 transcript and its encoded truncated Gag-p22 protein, which inhibits transposition.ConclusionsTogether, our results suggest that this large class of cellular genes modulate Ty1 transposition through multiple pathways. The effects are largely post-transcriptional acting at a variety of levels that may include translation initiation, protein stability and subcellular protein localization.Electronic supplementary materialThe online version of this article (doi:10.1186/s13100-015-0053-5) contains supplementary material, which is available to authorized users.
Saccharomyces cerevisiae has been an important model for determining the frequency of translational misreading events, those in which a tRNA pairs incorrectly to the mRNA and inserts an amino acid not specified by the codon in the mRNA. Misreading errors have been quantified in vivo using reporter protein systems or mass spectrometry with both approaches converging on a simple model for most misreading. The available data show that misreading tRNAs must form stereotypical base mismatches that correspond to those that can mimic Watson–Crick base pairs when formed in the ribosomal A site. Errors involving other mismatches occur significantly less frequently. This work debunks the idea of an average misreading frequency of 5 × 10−4 per codon that extends across the genetic code. Instead, errors come in two distinct classes—high frequency and low frequency events—with most errors being of the low frequency type. A comparison of misreading errors in S. cerevisiae and Escherichia coli suggests the existence of a mechanism that reduces misreading frequency in yeast; this mechanism may operate in eukaryotes generally.
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