PARP12-mediated mono-ADP-ribosylation as a checkpoint for necroptosis and apoptosis
Bibliographic record
Abstract
Necroptosis and apoptosis are two alternatively regulated cell death pathways. Activation of RIPK1 upon engagement of TNFR1 by TNFα may promote necroptosis by interacting with RIPK3 or apoptosis by activating caspases. RIPK1 is extensively regulated by a variety of dynamic posttranslational modifications which control its kinase activity and formation of downstream complexes to mediate necroptosis and apoptosis. Here, we investigate the functional significance and mechanism by which PARP12, a mono-ADP-ribosyltransferase, interacts with RIPK1 and RIPK3 in cells stimulated by IFNγ and TNFα. We show that PARP12 catalyzes the mono-ADP-ribosylation (MARylation) of RIPK1 in both the intermediate domain and the kinase domain, as well as the MARylation of RIPK3. PARP12 deficiency reduces necroptosis by inhibiting the activation of RIPK1 kinase and its interaction with RIPK3, as well as sensitizes to apoptosis by promoting the binding of RIPK1 with caspase-8. Thus, upon induction by IFNs, PARP12 may function as a cellular checkpoint that controls RIPK1 to promote necroptosis and inhibit apoptosis. Importantly, while PARP12 is a known interferon-stimulated gene (ISG), PARP12 deficiency promotes the expression of a subset of ISGs and confers protection against influenza A virus-induced mortality in mice. Our study demonstrates that PARP12 is an important modulator of cellular antiviral response.
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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) | 0.004 | 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".