Mistranslation of the genetic code by a new family of bacterial transfer RNAs
Bibliographic record
Abstract
The correct coupling of amino acids with transfer RNAs (tRNAs) is vital for translating genetic information into functional proteins. Errors during this process lead to mistranslation, where a codon is translated using the wrong amino acid. While unregulated and prolonged mistranslation is often toxic, growing evidence suggests that organisms, from bacteria to humans, can induce and use mistranslation as a mechanism to overcome unfavorable environmental conditions. Most known cases of mistranslation are caused by translation factors with poor substrate specificity or when substrate discrimination is sensitive to molecular changes such as mutations or posttranslational modifications. Here we report two novel families of tRNAs, encoded by bacteria from the Streptomyces and Kitasatospora genera, that adopted dual identities by integrating the anticodons AUU (for Asn) or AGU (for Thr) into the structure of a distinct proline tRNA. These tRNAs are typically encoded next to a full-length or truncated version of a distinct isoform of bacterial-type prolyl-tRNA synthetase. Using two protein reporters, we showed that these tRNAs translate asparagine and threonine codons with proline. Moreover, when expressed in Escherichia coli, the tRNAs cause varying growth defects due to global Asn-to-Pro and Thr-to-Pro mutations. Yet, proteome-wide substitutions of Asn with Pro induced by tRNA expression increased cell tolerance to the antibiotic carbenicillin, indicating that Pro mistranslation can be beneficial under certain conditions. Collectively, our results significantly expand the catalog of organisms known to possess dedicated mistranslation machinery and support the concept that mistranslation is a mechanism for cellular resiliency against environmental stress.
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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".