PARP activation regulates the RNA-binding protein NONO in the DNA damage response to DNA double-strand breaks
Bibliographic record
Abstract
After the generation of DNA double-strand breaks (DSBs), poly(ADP-ribose) polymerase-1 (PARP-1) is one of the first proteins to be recruited and activated through its binding to the free DNA ends. Upon activation, PARP-1 uses NAD+ to generate large amounts of poly(ADP-ribose) (PAR), which facilitates the recruitment of DNA repair factors. Here, we identify the RNA-binding protein NONO, a partner protein of SFPQ, as a novel PAR-binding protein. The protein motif being primarily responsible for PAR-binding is the RNA recognition motif 1 (RRM1), which is also crucial for RNA-binding, highlighting a competition between RNA and PAR as they share the same binding site. Strikingly, the in vivo recruitment of NONO to DNA damage sites completely depends on PAR, generated by activated PARP-1. Furthermore, we show that upon PAR-dependent recruitment, NONO stimulates nonhomologous end joining (NHEJ) and represses homologous recombination (HR) in vivo. Our results therefore place NONO after PARP activation in the context of DNA DSB repair pathway decision. Understanding the mechanism of action of proteins that act in the same pathway as PARP-1 is crucial to shed more light onto the effect of interference on PAR-mediated pathways with PARP inhibitors, which have already reached phase III clinical trials but are until date poorly understood.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".