A genetic code change in progress: tRNA-Leu(CAG) is conserved in most <i>Saccharomycopsis</i> yeast species but is non-essential and does not compete with tRNA-Ser(CAG) in translation
Bibliographic record
Abstract
Abstract In the yeast genera Saccharomycopsis and Ascoidea , nuclear genes use a non-standard genetic code in which CUG codons are translated as serine instead of leucine, due to a tRNA-Ser with the unusual anticodon CAG. However, some species in this ‘CUG-Ser2’ clade also contain an ancestral tRNA-Leu gene with the same anticodon. One of these species, Ascoidea asiatica , has been shown to have a stochastic proteome in which proteins contain approximately 50% Ser and 50% Leu at CUG codon sites, whereas previously examined Saccharomycopsis species translate CUG only as Ser. Here, we investigated the presence, conservation, and possible functionality of the tRNA-Leu(CAG) gene in the genus Saccharomycopsis . We analyzed the genomes of 33 strains, including almost all known species of Saccharomycopsis , and found that most of them contain both tRNA-Ser(CAG) and tRNA-Leu(CAG) genes. The tRNA-Leu(CAG) gene is evolving faster than tRNA-Ser(CAG) and it has been lost in two species, S. microspora and S. synnaedendra . We deleted the single tRNA-Leu(CAG) gene in S. capsularis and found that it is not essential. Bioinformatic analysis suggested that some CUG codon sites in Saccharomycopsis species may be translated as Leu, specifically in genes with functions in meiosis or sporulation, but mass spectrometry of sporulating S. capsularis and S. fermentans cultures showed only CUG-Ser translation. Cloverleaf structures of tRNA-Leu(CAG) from all Saccharomycopsis species contain mutations that are likely to make them non-functional in translation, but the evolutionary conservation of the gene leads us to propose that it has been retained for an unknown non-translational role.
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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".