Increased PARP1‐DNA binding due to autoPARylation inhibition of PARP1 on DNA rather than PARP1‐DNA trapping is correlated with PARP1 inhibitor's cytotoxicity
Bibliographic record
Abstract
PARP1 inhibitors (PARPis) are used clinically during cancer therapy and are thought to exert their cytotoxicity through PARP1 polymerase inhibition and PARP1-DNA trapping. Here, we showed no significant correlation between PARP1-DNA trapping and cytotoxicity induced by PARPis. We complemented PARP1-knockout sublines with wild-type PARP1 and 11 mutants with different point mutations that affect the polymerase activity. When examining the PARPi talazoparib, the induced cytotoxicity was highly significantly correlated with cellular PARP1 polymerase activity, but not with its PARP1-DNA trapping or polymerase inhibition. Similarly, talazoparib's PARP1-DNA trapping revealed significant correlation with the polymerase activity rather than its inhibition. Differently, however, when evaluating purified wild-type and mutated PARP1, we identified an almost linear relationship between PARPis' inhibiting PARP1 dissociation from DNA and their cytotoxicity in 17 cancer cell lines. In contrast, no significant correlation existed between PARP1 polymerase inhibition in the histone-based systems and the cytotoxicity. After careful comparisons on different methods and detection targets, we conclude that the PARPi-mediated increase in PARP1-DNA binding by inhibiting autoPARylation of PARP1 on DNA rather than in PARP1-DNA trapping is correlated with PARPi's cytotoxicity. Accordingly, we established a new PARPi screening model that more closely predicts cytotoxicity.
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 | 0.000 |
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".