Genome-wide expression analyses: Metabolic adaptation of to high sugar stress
Bibliographic record
Abstract
The transcriptional response of laboratory strains of Saccharomyces cerevisiae to salt or sorbitol stress has been well studied. These studies have yielded valuable data on how the yeast adapts to these stress conditions. However, S. cerevisiae is a saccharophilic fungus and in its natural environment this yeast encounters high concentrations of sugars. For the production of dessert wines, the sugar concentration may be as high as 50% (w/v). The metabolic pathways in S. cerevisiae under these fermentation conditions have not been studied and the transcriptional response of this yeast to sugar stress has not been investigated. High-density DNA microarrays showed that the transcription of 589 genes in an industrial strain of S. cerevisiae were affected more than two-fold in grape juice containing 40% (w/v) sugars (equimolar amounts of glucose and fructose). High sugar stress up-regulated the glycolytic and pentose phosphate pathway genes. The PDC6 gene, previously thought to encode a minor isozyme of pyruvate decarboxylase, was highly induced under these conditions. Gene expression profiles indicate that the oxidative and non-oxidative branches of the pentose phosphate pathway were up-regulated and might be used to shunt more glucose-6-phosphate and fructose-6-phosphate, respectively, from the glycolytic pathway into the pentose phosphate pathway. Structural genes involved in the formation of acetic acid from acetaldehyde, and succinic acid from glutamate, were also up-regulated. Genes involved in de novo biosynthesis of purines, pyrimidines, histidine and lysine were down-regulated by sugar stress.
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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) | 0.001 | 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".