Genome-wide Fitness Profiles Reveal a Requirement for Autophagy During Yeast Fermentation
Bibliographic record
Abstract
The ability of cells to respond to environmental changes and adapt their metabolism enables cell survival under stressful conditions. The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is particularly well adapted to the harsh conditions of anaerobic wine fermentation. However, S. cerevisiae gene function has not been previously systematically interrogated under conditions of industrial fermentation. We performed a genome-wide study of essential and nonessential S. cerevisiae gene requirements during grape juice fermentation to identify deletion strains that are either depleted or enriched within the viable fermentative population. Genes that function in autophagy and ubiquitin-proteasome degradation are required for optimal survival during fermentation, whereas genes that function in ribosome assembly and peroxisome biogenesis impair fitness during fermentation. We also uncover fermentation phenotypes for 139 uncharacterized genes with no previously known cellular function. We demonstrate that autophagy is induced early in wine fermentation in a nitrogen-replete environment, suggesting that autophagy may be triggered by other forms of stress that arise during fermentation. These results provide insights into the complex fermentation process and suggest possible means for improvement of industrial fermentation strains.
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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 | 0.001 | 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 | 0.000 |
| 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 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".