Sfp1 regulates the SAGA component Tra1 in response to proteotoxic stress in <i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
ABSTRACT Proteotoxic stress triggers transcriptional responses that allow cells to compensate for the accumulation of toxic misfolded proteins. Chromatin remodeling regulates gene expression in response to the accumulation of misfolded polyQ proteins associated with Huntington’s disease (HD). Tra1 is an essential component of both the SAGA/SLIK and NuA4 transcription co-activator complexes and is linked to multiple cellular processes associated with misfolded protein stress, including the heat shock response. Cells with compromised Tra1 activity display phenotypes distinct from deletions encoding components of the SAGA and NuA4 complexes, indicating a potentially unique regulatory role of Tra1 in the cellular response to protein misfolding. Here, we employed a yeast model of HD to define how the expression of toxic polyQ expansion proteins affects Tra1 expression and function. Expression of expanded polyQ proteins, mimics deletion of SAGA/NuA4 components and results in growth defects under stress conditions. Moreover, deleting genes encoding SAGA and, to a lesser extent, NuA4 components exacerbates polyQ toxicity. Also, cells carrying a mutant Tra1 allele displayed increased sensitivity to polyQ toxicity. Interestingly, expression of polyQ proteins also upregulated the expression of TRA1 and other genes encoding SAGA components, revealing a feedback mechanism aimed at maintaining Tra1and SAGA functional integrity. Moreover, deleting the TORC1 (Target of Rapamycin) effector SFP1 specifically abolished upregulation of TRA1 upon expression of polyQ proteins. While Sfp1 is known to adjust ribosome biogenesis and cell size in response to stress, we identified a new role for Sfp1 in the control of Tra1, linking TORC1 and cell growth regulation to functions of the SAGA acetyltransferase complex during misfolded protein 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 | 0.000 | 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 | 0.000 |
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".