Novel Checkpoint Pathway Organization Promotes Genome Stability in Stationary-Phase Yeast Cells
Bibliographic record
Abstract
Most DNA alterations occur during DNA replication in the S phase of the cell cycle. However, the majority of eukaryotic cells exist in a nondividing, quiescent state. Little is known about the factors involved in preventing DNA instability within this stationary-phase cell population. Previously, we utilized a unique assay system to identify mutations that increased minisatellite alterations specifically in quiescent cells in Saccharomyces cerevisiae. Here we conducted a modified version of synthetic genetic array analysis to determine if checkpoint signaling components play a role in stabilizing minisatellites in stationary-phase yeast cells. Our results revealed that a subset of checkpoint components, specifically MRC1, CSM3, TOF1, DDC1, RAD17, MEC3, TEL1, MEC1, and RAD53, prevent stationary-phase minisatellite alterations within the quiescent cell subpopulation of stationary-phase cells. Pathway analysis revealed at least three pathways, with MRC1, CSM3, and TOF1 acting in a pathway independent of MEC1 and RAD53. Overall, our data indicate that some well-characterized checkpoint components maintain minisatellite stability in stationary-phase cells but are regulated differently in those cells than in actively growing cells. For the MRC1-dependent pathway, the checkpoint itself may not be the important element; rather, it may be loss of the checkpoint proteins' other functions that contributes to DNA instability.
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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".