Evaluation of Gene Modification Strategies for the Development of Low-Alcohol-Wine Yeasts
Why this work is in the frame
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Bibliographic record
Abstract
Saccharomyces cerevisiae has evolved a highly efficient strategy for energy generation which maximizes ATP energy production from sugar. This adaptation enables efficient energy generation under anaerobic conditions and limits competition from other microorganisms by producing toxic metabolites, such as ethanol and CO(2). Yeast fermentative and flavor capacity forms the biotechnological basis of a wide range of alcohol-containing beverages. Largely as a result of consumer demand for improved flavor, the alcohol content of some beverages like wine has increased. However, a global trend has recently emerged toward lowering the ethanol content of alcoholic beverages. One option for decreasing ethanol concentration is to use yeast strains able to divert some carbon away from ethanol production. In the case of wine, we have generated and evaluated a large number of gene modifications that were predicted, or known, to impact ethanol formation. Using the same yeast genetic background, 41 modifications were assessed. Enhancing glycerol production by increasing expression of the glyceraldehyde-3-phosphate dehydrogenase gene, GPD1, was the most efficient strategy to lower ethanol concentration. However, additional modifications were needed to avoid negatively affecting wine quality. Two strains carrying several stable, chromosomally integrated modifications showed significantly lower ethanol production in fermenting grape juice. Strain AWRI2531 was able to decrease ethanol concentrations from 15.6% (vol/vol) to 13.2% (vol/vol), whereas AWRI2532 lowered ethanol content from 15.6% (vol/vol) to 12% (vol/vol) in both Chardonnay and Cabernet Sauvignon juices. Both strains, however, produced high concentrations of acetaldehyde and acetoin, which negatively affect wine flavor. Further modifications of these strains allowed reduction of these metabolites.
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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 it