A Novel Mistranslating tRNA Model in <i>Drosophila melanogaster</i> has Diverse, Sexually Dimorphic Effects
Bibliographic record
Abstract
ABSTRACT Transfer RNAs (tRNAs) are the adaptor molecules required for reading of the genetic code and the accurate production of proteins. tRNA variants can lead to genome-wide mistranslation, the misincorporation of amino acids not specified by the standard genetic code into nascent proteins. While genome sequencing has identified putative mistranslating tRNA variants in human populations, little is known regarding how mistranslation affects multicellular organisms. Here, we create a Drosophila melanogaster model for mistranslation by integrating a serine tRNA variant that mistranslates serine for proline (tRNA Ser UGG, G26A ) into the fly genome. Using mass spectrometry, we find that tRNA Ser UGG, G26A misincorporates serine for proline at a frequency of ∼ 0.6% per codon. We find that mistranslation extends development time and decreases the number of flies that reach adulthood. Adult flies containing tRNA Ser UGG, G26A present with more morphological deformities and worse climbing performance than flies expressing only wild type tRNA. Female flies with the serine tRNA variant have more deformities and experience a faster decline in climbing performance than males, suggesting sex-specific effects. This model will enable studies into the synergistic effects of mistranslating tRNA variants and disease-causing alleles.
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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) | 0.000 | 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) | 0.001 | 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 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".