Transcriptional regulation of nonfermentable carbon utilization in budding yeast
Bibliographic record
Abstract
Saccharomyces cerevisiae preferentially uses glucose as a carbon source, but following its depletion, it can utilize a wide variety of other carbons including nonfermentable compounds such as ethanol. A shift to a nonfermentable carbon source results in massive reprogramming of gene expression including genes involved in gluconeogenesis, the glyoxylate cycle, and the tricarboxylic acid cycle. This review is aimed at describing the recent progress made toward understanding the mechanism of transcriptional regulation of genes responsible for utilization of nonfermentable carbon sources. A central player for the use of nonfermentable carbons is the Snf1 kinase, which becomes activated under low glucose levels. Snf1 phosphorylates various targets including the transcriptional repressor Mig1, resulting in its inactivation allowing derepression of gene expression. For example, the expression of CAT8, encoding a member of the zinc cluster family of transcriptional regulators, is then no longer repressed by Mig1. Cat8 becomes activated through phosphorylation by Snf1, allowing upregulation of the zinc cluster gene SIP4. These regulators control the expression of various genes including those involved in gluconeogenesis. Recent data show that another zinc cluster protein, Rds2, plays a key role in regulating genes involved in gluconeogenesis and the glyoxylate pathway. Finally, the role of additional regulators such as Adr1, Ert1, Oaf1, and Pip2 is also discussed.
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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 | 0.001 | 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".