Mutations in the DNA processivity factor <i>POL30</i> predispose the <i>FLO11</i> locus to epigenetic instability in <i>S. cerevisiae</i>
Bibliographic record
Abstract
The FLO genes in Saccharomyces cerevisiae are repressed by heterochromatin formation, involving histone deacetylases, transcription factors and non-coding RNAs. Here, we report that mutations in the processivity factor POL30 (PCNA) that show transient derepression at the subtelomeres and the mating-type loci do not derepress FLO loci. However, deletions of the replisome stability factors RRM3 and TOF1 along with pol30 mutations induced flocculation phenotypes. The phenotypes correlated with increased expression of reporter proteins driven by the FLO11 promoter, the frequency of silent to active conversions of FLO11, and reduced expression of the regulatory long non-coding RNAs ICR1 and PWR1. Alterations in the local replication landscape of FLO11 indicate a link between defects in the fork protection complex and the stability of gene silencing. Analyses of these mutants at the subtelomeres and the HMLα locus showed a similar derepression phenotype and suggest transient instability of both active and silent states of FLO11. We conclude that RRM3 and TOF1 interact differentially with the pol30 mutations to promote transient derepression or complete epigenetic conversions of FLO11. We suggest that the interaction between POL30, RRM3 and TOF1 is essential to maintain epigenetic stability at the studied loci.
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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".