Chk1 Activation Protects Rad9A from Degradation as Part of a Positive Feedback Loop during Checkpoint Signalling
Bibliographic record
Abstract
Phosphorylation of Rad9A at S387 is critical for establishing a physical interaction with TopBP1, and to downstream activation of Chk1 for checkpoint activation. We have previously demonstrated a phosphorylation of Rad9A that occurs at late time points in cells exposed to genotoxic agents, which is eliminated by either Rad9A overexpression, or conversion of S387 to a non-phosphorylatable analogue. Based on this, we hypothesized that this late Rad9A phosphorylation is part of a feedback loop regulating the checkpoint. Here, we show that Rad9A is hyperphosphorylated and accumulates in cells exposed to bleomycin. Following the removal of bleomycin, Rad9A is polyubiquitinated, and Rad9A protein levels drop, indicating an active degradation process for Rad9A. Chk1 inhibition by UCN-01 or siRNA reduces Rad9A levels in cells synchronized in S-phase or exposed to DNA damage, indicating that Chk1 activation is required for Rad9A stabilization in S-phase and during checkpoint activation. Together, these results demonstrate a positive feedback loop involving Rad9A-dependend activation of Chk1, coupled with Chk1-dependent stabilization of Rad9A that is critical for checkpoint regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".