Sumoylation controls the timing of Tup1-mediated transcriptional deactivation
Bibliographic record
Abstract
The small ubiquitin-like modifier (SUMO) is implicated in various cellular activities, including transcriptional regulation. We previously showed that the yeast activator Gcn4 becomes sumoylated during activation, facilitating its eventual promoter eviction and transcriptional shut off. Here we show that the corepressor Tup1 is sumoylated, at two specific lysines, under various stress conditions. Mutation of these sites has no effect on Tup1 recruitment or RNAP II promoter occupancy immediately following induction. However, Tup1 levels subsequently decrease, while RNAP II and transcription increase in Tup1 mutant cells. Consistent with this, a Tup1 mutant displaying increased sumoylation led to reduced transcription. We also show that coordinated sumoylation of Gcn4 and Tup1 enhances Gcn4 promoter eviction and that multiple Tup1-interacting proteins become sumoylated after stress. Together, our studies provide evidence that coordinated sumoylation of Gcn4, Tup1 and likely other factors dampens activated transcription by stabilizing Tup1 binding and stimulating Gcn4 and RNAP II removal. Tup1 was identified as a transcriptional repressor; however the protein remains associated with promoters of target genes after activation. Ng et al. show that Tup1 is SUMOylated under stress conditions, and that SUMOylation is required for persistence of Tup1 at active promoters.
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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) | 0.002 | 0.001 |
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".