Evolving Mistranslating tRNAs Through a Phenotypically Ambivalent Intermediate in<i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
Abstract It is increasingly apparent that the genetic code is not static and that organisms use variations in the genetic code for selective advantage. Berg... The genetic code converts information from nucleic acid into protein. The genetic code was thought to be immutable, yet many examples in nature indicate that variations to the code provide a selective advantage. We used a sensitive selection system involving suppression of a deleterious allele (tti2-L187P) in Saccharomyces cerevisiae to detect mistranslation and identify mechanisms that allow genetic code evolution. Though tRNASer containing a proline anticodon (UGG) is toxic, using our selection system we identified four tRNASer UGG variants, each with a single mutation, that mistranslate at a tolerable level. Mistranslating tRNALeu UGG variants were also obtained, demonstrating the generality of the approach. We characterized two of the tRNASer UGG variants. One contained a G26A mutation, which reduced cell growth to 70% of the wild-type rate, induced a heat shock response, and was lost in the absence of selection. The reduced toxicity of tRNASer UGG-G26A is likely through increased turnover of the tRNA, as lack of methylation at G26 leads to degradation via the rapid tRNA decay pathway. The second tRNASer UGG variant, with a G9A mutation, had minimal effect on cell growth, was relatively stable in cells, and gave rise to less of a heat shock response. In vitro, the G9A mutation decreases aminoacylation and affects folding of the tRNA. Notably, the G26A and G9A mutations were phenotypically neutral in the context of an otherwise wild-type tRNASer. These experiments reveal a model for genetic code evolution in which tRNA anticodon mutations and mistranslation evolve through phenotypically ambivalent intermediates that reduce tRNA function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".