The Hsk1(Cdc7) Replication Kinase Regulates Origin Efficiency
Bibliographic record
Abstract
Origins of DNA replication are generally inefficient, with most firing in fewer than half of cell cycles. However, neither the mechanism nor the importance of the regulation of origin efficiency is clear. In fission yeast, origin firing is stochastic, leading us to hypothesize that origin inefficiency and stochasticity are the result of a diffusible, rate-limiting activator. We show that the Hsk1-Dfp1 replication kinase (the fission yeast Cdc7-Dbf4 homologue) plays such a role. Increasing or decreasing Hsk1-Dfp1 levels correspondingly increases or decreases origin efficiency. Furthermore, tethering Hsk1-Dfp1 near an origin increases the efficiency of that origin, suggesting that the effective local concentration of Hsk1-Dfp1 regulates origin firing. Using photobleaching, we show that Hsk1-Dfp1 is freely diffusible in the nucleus. These results support a model in which the accessibility of replication origins to Hsk1-Dfp1 regulates origin efficiency and provides a potential mechanistic link between chromatin structure and replication timing. By manipulating Hsk1-Dfp1 levels, we show that increasing or decreasing origin firing rates leads to an increase in genomic instability, demonstrating the biological importance of appropriate origin efficiency.
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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 | 0.001 | 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".