Clinical PARP inhibitors do not abrogate PARP1 exchange at DNA damage sites in vivo
Bibliographic record
Abstract
DNA breaks recruit and activate PARP1/2, which deposit poly-ADP-ribose (PAR) to recruit XRCC1-Ligase3 and other repair factors to promote DNA repair. Clinical PARP inhibitors (PARPi) extend the lifetime of damage-induced PARP1/2 foci, referred to as 'trapping'. To understand the molecular nature of 'trapping' in cells, we employed quantitative live-cell imaging and fluorescence recovery after photo-bleaching. Unexpectedly, we found that PARP1 exchanges rapidly at DNA damage sites even in the presence of clinical PARPi, suggesting the persistent foci are not caused by physical stalling. Loss of Xrcc1, a major downstream effector of PAR, also caused persistent PARP1 foci without affecting PARP1 exchange. Thus, we propose that the persistent PARP1 foci are formed by different PARP1 molecules that are continuously recruited to and exchanging at DNA lesions due to attenuated XRCC1-LIG3 recruitment and delayed DNA repair. Moreover, mutation analyses of the NAD+ interacting residues of PARP1 showed that PARP1 can be physically trapped at DNA damage sites, and identified H862 as a potential regulator for PARP1 exchange. PARP1-H862D, but not PARylation-deficient PARP1-E988K, formed stable PARP1 foci upon activation. Together, these findings uncovered the nature of persistent PARP1 foci and identified NAD+ interacting residues involved in the PARP1 exchange.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".