A Novel Checkpoint and RPA Inhibitory Pathway Regulated by Rif1
Bibliographic record
Abstract
Cells accumulate single-stranded DNA (ssDNA) when telomere capping, DNA replication, or DNA repair is impeded. This accumulation leads to cell cycle arrest through activating the DNA-damage checkpoints involved in cancer protection. Hence, ssDNA accumulation could be an anti-cancer mechanism. However, ssDNA has to accumulate above a certain threshold to activate checkpoints. What determines this checkpoint-activation threshold is an important, yet unanswered question. Here we identify Rif1 (Rap1-Interacting Factor 1) as a threshold-setter. Following telomere uncapping, we show that budding yeast Rif1 has unprecedented effects for a protein, inhibiting the recruitment of checkpoint proteins and RPA (Replication Protein A) to damaged chromosome regions, without significantly affecting the accumulation of ssDNA at those regions. Using chromatin immuno-precipitation, we provide evidence that Rif1 acts as a molecular "band-aid" for ssDNA lesions, associating with DNA damage independently of Rap1. In consequence, small or incipient lesions are protected from RPA and checkpoint proteins. When longer stretches of ssDNA are generated, they extend beyond the junction-proximal Rif1-protected regions. In consequence, the damage is detected and checkpoint signals are fired, resulting in cell cycle arrest. However, increased Rif1 expression raises the checkpoint-activation threshold to the point it simulates a checkpoint knockout and can also terminate a checkpoint arrest, despite persistent telomere deficiency. Our work has important implications for understanding the checkpoint and RPA-dependent DNA-damage responses in eukaryotic cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".