Colocalization of Mec1 and Mrc1 is sufficient for Rad53 phosphorylation in vivo
Bibliographic record
Abstract
When DNA is damaged or DNA replication goes awry, cells activate checkpoints to allow time for damage to be repaired and replication to complete. In Saccharomyces cerevisiae, the DNA damage checkpoint, which responds to lesions such as double-strand breaks, is activated when the lesion promotes the association of the sensor kinase Mec1 and its targeting subunit Ddc2 with its activators Ddc1 (a member of the 9-1-1 complex) and Dpb11. It has been more difficult to determine what role these Mec1 activators play in the replication checkpoint, which recognizes stalled replication forks, since Dpb11 has a separate role in DNA replication itself. Therefore we constructed an in vivo replication-checkpoint mimic that recapitulates Mec1-dependent phosphorylation of the effector kinase Rad53, a crucial step in checkpoint activation. In the endogenous replication checkpoint, Mec1 phosphorylation of Rad53 requires Mrc1, a replisome component. The replication-checkpoint mimic requires colocalization of Mrc1-LacI and Ddc2-LacI and is independent of both Ddc1 and Dpb11. We show that these activators are also dispensable for Mec1 activity and cell survival in the endogenous replication checkpoint but that Ddc1 is absolutely required in the absence of Mrc1. We propose that colocalization of Mrc1 and Mec1 is the minimal signal required to activate the replication checkpoint.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".