Identification of RCN1 and RSA3 as ethanol-tolerant genes in Saccharomyces cerevisiae using a high copy barcoded library
Bibliographic record
Abstract
Saccharomyces cerevisiae (S. cerevisiae) encounters a multitude of stresses during industrial processes such as wine fermentation including ethanol toxicity. High levels of ethanol reduce the viability of yeast and may prevent completion of fermentation. The identification of ethanol-tolerant genes is important for creating stress-resistant industrial yeast, and S. cerevisiae genomic resources have been utilized for this purpose. We have employed a molecular barcoded yeast open reading frame (MoBY-ORF) high copy plasmid library to identify ethanol-tolerant genes in both the S. cerevisiae S288C laboratory and M2 wine strains. We find that increased dosage of either RCN1 or RSA3 improves tolerance of S288C and M2 to toxic levels of ethanol. RCN1 is a regulator of calcineurin, whereas RSA3 has a role in ribosome maturation. Additional fitness advantages conferred upon overproduction of RCN1 and RSA3 include increased resistance to cell wall degradation, heat, osmotic and oxidative stress. We find that the M2 wine yeast strain is generally more tolerant of stress than S288C with the exception of translation inhibition, which affects M2 growth more severely than S288C. We conclude that regulation of ribosome biogenesis and ultimately translation is a critical factor for S. cerevisiae survival during industrial-related environmental stress.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".