PARP-1 Modulation of mTOR Signaling in Response to a DNA Alkylating Agent
Bibliographic record
Abstract
Poly(ADP-ribose) polymerase-1 (PARP-1) is widely involved in cell death responses. Depending on the degree of injury and on cell type, PARP activation may lead to autophagy, apoptosis or necrosis. In HEK293 cells exposed to the alkylating agent N-methyl-N'-nitro-N'-nitrosoguanine (MNNG), we show that PARP-1 activation triggers a necrotic cell death response. The massive poly(ADP-ribose) (PAR) synthesis following PARP-1 activation leads to the modulation of mTORC1 pathway. Shortly after MNNG exposure, NAD⁺ and ATP levels decrease, while AMP levels drastically increase. We characterized at the molecular level the consequences of these altered nucleotide levels. First, AMP-activated protein kinase (AMPK) is activated and the mTORC1 pathway is inhibited by the phosphorylation of Raptor, in an attempt to preserve cellular energy. Phosphorylation of the mTORC1 target S6 is decreased as well as the phosphorylation of the mTORC2 component Rictor on Thr1135. Finally, Akt phosphorylation on Ser473 is lost and then, cell death by necrosis occurs. Inhibition of PARP-1 with the potent PARP inhibitor AG14361 prevents all of these events. Moreover, the antioxidant N-acetyl-L-cysteine (NAC) can also abrogate all the signaling events caused by MNNG exposure suggesting that reactive oxygen species (ROS) production is involved in PARP-1 activation and modulation of mTOR signaling. In this study, we show that PARP-1 activation and PAR synthesis affect the energetic status of cells, inhibit the mTORC1 signaling pathway and possibly modulate the mTORC2 complex affecting cell fate. These results provide new evidence that cell death by necrosis is orchestrated by the balance between several signaling pathways, and that PARP-1 and PAR take part in these events.
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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) | 0.002 | 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".