Selective inhibition of the DNA-dependent protein kinase (DNA-PK) by the radiosensitizing agent caffeine
Bibliographic record
Abstract
Caffeine inhibits cell cycle checkpoints, sensitizes cells to ionizing radiation-induced cell killing and inhibits the protein kinase activity of two cell cycle checkpoint regulators, Ataxia-Telangiectasia mutated (ATM) and ATM- and Rad3-related (ATR). In contrast, caffeine has been reported to have little effect on the protein kinase activity of the DNA-dependent protein kinase (DNA-PK), which is essential for the repair of DNA double-strand breaks. Previously, we reported that DNA-PK phosphorylates Thr21 of the 32 kDa subunit of replication protein A (RPA32) in response to camptothecin. In this report we demonstrate that the camptothecin-induced phosphorylation of RPA32 on Thr21 is inhibited by 2 mM caffeine. In addition, we show that caffeine inhibits immunoprecipitated and purified DNA-PK, as well as DNA-PK in cell extracts, with an IC50 of 0.2-0.6 mM. Caffeine inhibited DNA-PK activity through a mixed non-competitive mechanism with respect to ATP. In contrast, 10-fold higher concentrations of caffeine were required to inhibit DNA-PK autophosphorylation in vitro and caffeine failed to inhibit DNA-PKcs dependent double-strand break repair in vivo. These data suggest that while DNA-PK does not appear to be the target of caffeine-induced radiosensitization, caffeine cannot be used to differentiate between ATM, ATR and DNA- PK-dependent substrate phosphorylation in vivo.
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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) | 0.001 | 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) | 0.001 | 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".