Determinants of the Ubiquitin-mediated Degradation of the Met4 Transcription Factor
Bibliographic record
Abstract
In yeast, the Met4 transcription factor and its cofactors Cbf1, Met28, Met31, and Met32 control the expression of sulfur metabolism and oxidative stress response genes. Met4 activity is tuned to nutrient and oxidative stress conditions by the SCF(Met30) ubiquitin ligase. The mechanism whereby SCF(Met30)-dependent ubiquitylation of Met4 controls Met4 activity remains contentious. Here, we have demonstrated that intracellular cysteine levels dictate the degradation of Met4 in vivo, as shown by the ability of cysteine, but not methionine or S-adenosylmethionine (AdoMet), to trigger Met4 degradation in an str4Delta strain, which lacks the ability to produce cysteine from methionine or AdoMet. Met4 degradation requires its nuclear localization and activity of the 26 S proteasome. Analysis of the regulated degradation of a fully functional Met4-Cbf1 chimera, in which Met4 is fused to the DNA binding domain of Cbf1, demonstrates that elimination of Met4 in vivo can be triggered independently of both its normal protein interactions. Strains that harbor the Met4-Cbf1 fusion as the only source of Cbf1 activity needed for proper kinetochore function exhibit high rates of methionine-dependent chromosomal instability. We suggest that SCF(Met30) activity or Met4 utilization as a substrate may be directly regulated by intracellular cysteine concentrations.
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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) | 0.001 | 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".