Directed evolution as an approach to increase fructose utilization in synthetic grape juice by wine yeast AWRI 796
Bibliographic record
Abstract
In winemaking, slow or stuck alcoholic fermentation can impact processing efficiency and wine quality. Residual fructose in the later stages of fermentation can leave the wine 'out of specification' unless removed, which requires reinoculation or use of a more fructophilic yeast. As such, robust, fermentation efficient strains are still highly desirable to reduce this risk. We report on a combined EMS mutagenesis and Directed Evolution (DE) approach as a 'proof of concept' to improve fructose utilization and decrease fermentation duration. One evolved isolate, Tee 9, was evaluated against the parent, AWRI 796 in defined medium (CDGJM) and Semillon juice. Interestingly, Tee 9 exhibited improved fermentation in CDGJM at several nitrogen contents, but not in juice. Genomic comparison between AWRI 796 and Tee 9 identified 371 mutations, but no chromosomal copy number variation. A total of 95 noncoding and 276 coding mutations were identified in 297 genes (180 of which encode proteins with one or more substitutions). Whilst introduction of two of these, Gid7 (E726K) or Fba1 (G135S), into AWRI 796 did not lead to the fermentation improvement seen in Tee 9, similar allelic swaps with the other mutations are needed to understand Tee 9's adaption to CDGJM. Furthermore, the 378 isolates, potentially mutagenized but with the same genetic background, are likely a useful resource for future phenotyping and genome-wide association studies.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".