Functional enhancement of Sake yeast strains to minimize the production of ethyl carbamate in Sake wine
Bibliographic record
Abstract
AIMS: In fermented alcoholic beverages and particularly in Japanese Sake wine, the ubiquitous presence of the probable human carcinogen ethyl carbamate (EC) is a topic of significant concern. This study aims to develop novel methods for the reduction of EC in Sake wine. METHODS AND RESULTS: To reduce the high levels of EC in Sake wine, urea-degrading and urea-importing yeast strains were created by integrating linear cassettes containing either the respective DUR1,2 or DUR3 genes, under the control of the constitutively active Saccharomyces cerevisiae PGK1 promoter, into the Sake yeast strains K7 and K9. The self-cloned, urea-degrading Sake strains K7(DUR1,2) and K9(DUR1,2) produced Sake wine with 87 and 68% less EC, respectively, while the urea-importing Sake yeast strain K7(DUR3) reduced EC by 15%. All functionally enhanced yeast strains were shown to be substantially equivalent to their parental strains in terms of fermentation rate, ethanol production, phenotype and transcriptome. CONCLUSIONS: Under the conditions tested, urea-degrading yeast (constitutive DUR1,2 expression) are superior to urea-importing yeast (constitutive DUR3 expression) for EC reduction in Sake wine, and constitutive co-expression of DUR1,2 and DUR3 does not yield synergistic EC reduction. SIGNIFICANCE AND IMPACT OF THE STUDY: The self-cloned, substantially equivalent, urea-degrading Sake yeast strains K7(DUR1,2) and K9(DUR1,2), which contain the integrated DUR1,2 cassette, are capable of highly efficacious EC reduction during Sake brewing trials, are suitable for commercialization and are important tools for modern Sake makers in their efforts to reduce high EC levels in Sake wine.
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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 | 0.000 |
| 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".