Transcriptional profiling of wine yeast in fermenting grape juice: regulatory effect of diammonium phosphate
Bibliographic record
Abstract
The nitrogen composition of grape musts affects fermentation kinetics and production of aroma and spoilage compounds in wine. It is common practice in wineries to supplement grape musts with diammonium phosphate (DAP) to prevent nitrogen-related fermentation problems. Laboratory strains of Saccharomyces cerevisiae preferentially use rich nitrogen sources, such as ammonia, over poor nitrogen sources. We used global gene expression analysis to monitor the effect of DAP addition on gene expression patterns in wine yeast in fermenting Riesling grape must. The expression of 350 genes in the commercial wine yeast strain VIN13 was affected; 185 genes were down-regulated and 165 genes were up-regulated in response to DAP. Genes that were down-regulated encode small molecule transporters and nitrogen catabolic enzymes, including those linked to the production of urea, a precursor of ethyl carbamate in wine. Genes involved in amino acid metabolism, assimilation of sulfate, de novo purine biosynthesis, tetrahydrofolate one-carbon metabolism, and protein synthesis were up-regulated. The expression level of 86 orphan genes was also affected by DAP.
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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 | 0.000 | 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 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".