Recurrent background mutations in WHI2 impair proteostasis and degradation of misfolded cytosolic proteins in Saccharomyces cerevisiae
Bibliographic record
Abstract
Proteostasis promotes viability at both the cellular and organism levels by maintaining a functional proteome. This requires an intricate protein quality control (PQC) network that mediates protein folding by molecular chaperones and removes terminally misfolded proteins via the ubiquitin proteasome system and autophagy. How changes within the PQC network can perturb proteostasis and shift the balance between protein folding and proteolysis remain poorly understood. However, given that proteostasis is altered in a number of conditions such as cancer and ageing, it is critical that we identify the factors that mediate PQC and understand the interplay between members of the proteostatic network. In this study, we investigated the degradation of a thermally unstable cytosolic model substrate and identified a surprisingly high number of strains in the yeast knockout collection that displayed impaired turnover of the misfolded substrate. We found that this phenotype was caused by frequent background mutations in the general stress response gene WHI2. We linked this proteostatic defect to the lack of activity of the stress response transcription factor Msn2, potentially under conditions where the TOR pathway is active. Our results underscore how changes to the elaborate PQC network can perturb proteostasis and impair degradation of misfolded cytosolic proteins.
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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) | 0.001 | 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 | 0.001 |
| 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".