ATR kinase is required for global genomic nucleotide excision repair exclusively during S phase in human cells
Bibliographic record
Abstract
Global-genomic nucleotide excision repair (GG-NER) is the only pathway available to humans for removal, from the genome overall, of highly genotoxic helix-distorting DNA adducts generated by many environmental mutagens and certain chemotherapeutic agents, e.g., UV-induced 6-4 photoproducts (6-4PPs) and cyclobutane pyrimidine dimers (CPDs). The ataxia telangiectasia and rad-3-related kinase (ATR) is rapidly activated in response to UV-induced replication stress and proceeds to phosphorylate a plethora of downstream effectors that modulate primarily cell cycle checkpoints but also apoptosis and DNA repair. To investigate whether this critical kinase might participate in the regulation of GG-NER, we developed a novel flow cytometry-based DNA repair assay that allows precise evaluation of GG-NER kinetics as a function of cell cycle. Remarkably, inhibition of ATR signaling in primary human lung fibroblasts by treatment with caffeine, or with siRNA specifically targeting ATR, resulted in total inhibition of 6-4PP removal during S phase, whereas cells repaired normally during either G(0)/G(1) or G(2)/M. Similarly striking S-phase-specific defects in GG-NER of both 6-4PPs and CPDs were documented in ATR-deficient Seckel syndrome skin fibroblasts. Finally, among six diverse model human tumor strains investigated, three manifested complete abrogation of 6-4PP repair exclusively in S-phase populations. Our data reveal a highly novel role for ATR in the regulation of GG-NER uniquely during S phase of the cell cycle, and indicate that many human cancers may be characterized by a defect in this regulation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".