Differential Roles for Checkpoint Kinases in DNA Damage-dependent Degradation of the Cdc25A Protein Phosphatase
Bibliographic record
Abstract
In response to DNA damage, cells activate a signaling pathway that promotes cell cycle arrest and degradation of the cell cycle regulator Cdc25A. Cdc25A degradation occurs via the SCFbeta-TRCP pathway and phosphorylation of Ser-76. Previous work indicates that the checkpoint kinase Checkpoint kinase 1 (Chk1) is capable of phosphorylating Ser-76 in Cdc25A, thereby promoting its degradation. In contrast, other experiments involving overexpression of dominant Chk2 mutant proteins point to a role for Chk2 in Cdc25A degradation. However, loss-of-function studies that implicate Chk2 in Cdc25A turnover are lacking, and there is no evidence that Chk2 is capable of phosphorylating Ser-76 in Cdc25A despite the finding that Chk1 and Chk2 sometimes share overlapping primary specificity. We find that although Chk2 can phosphorylate many of the same sites in Cdc25A that Chk1 phosphorylates, albeit with reduced efficiency, Chk2 is unable to efficiently phosphorylate Ser-76. Consistent with this, Chk2, unlike Chk1, is unable to support SCFbeta-TRCP-mediated ubiquitination of Cdc25A in vitro. In CHK2(-/-) HCT116 cells, the kinetics of Cdc25A degradation in response to ionizing radiation is comparable with that seen in HCT116 cells containing Chk2, indicating that Chk2 is not generally required for timely DNA damage-dependent Cdc25A turnover. In contrast, depletion of Chk1 by RNA interference in CHK2(-/-) cells leads to Cdc25A stabilization in response to ionizing radiation. These data support the idea that Chk1 is the primary signal transducer linking activation of the ATM/ATR kinases to Cdc25A destruction in response to ionizing radiation.
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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) | 0.002 | 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".