Genetic selection for mistranslation rescues a defective co-chaperone in yeast
Bibliographic record
Abstract
Despite the general requirement for translation fidelity, mistranslation can be an adaptive response. We selected spontaneous second site mutations that suppress the stress sensitivity caused by a Saccharomyces cerevisiae tti2 allele with a Leu to Pro mutation at residue 187, identifying a single nucleotide mutation at the same position (C70U) in four tRNAProUGG genes. Linkage analysis and suppression by SUF9G3:U70 expressed from a centromeric plasmid confirmed the causative nature of the suppressor mutation. Since the mutation incorporates the G3:U70 identity element for alanyl-tRNA synthetase into tRNAPro, we hypothesized that suppression results from mistranslation of Pro187 in Tti2L187P as Ala. A strain expressing Tti2L187A was not stress sensitive. In vitro, tRNAProUGG (C70U) was mis-aminoacylated with alanine by alanyl-tRNA synthetase, but was not a substrate for prolyl-tRNA synthetase. Mass spectrometry from protein expressed in vivo and a novel GFP reporter for mistranslation confirmed substitution of alanine for proline at a rate of ∼6%. Mistranslating cells expressing SUF9G3:U70 induce a partial heat shock response but grow nearly identically to wild-type. Introducing the same G3:U70 mutation in SUF2 (tRNAProAGG) suppressed a second tti2 allele (tti2L50P). We have thus identified a strategy that allows mistranslation to suppress deleterious missense Pro mutations in Tti2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".