Nuclear Factor-κB Modulates the p53 Response in Neurons Exposed to DNA Damage
Bibliographic record
Abstract
Previous studies have shown that DNA damage-evoked death of primary cortical neurons occurs in a p53 and cyclin-dependent kinase-dependent (CDK) manner. The manner by which these signals modulate death is unclear. Nuclear factor-kappaB (NF-kappaB) is a group of transcription factors that potentially interact with these pathways. Presently, we show that NF-kappaB is activated shortly after induction of DNA damage in a manner independent of the classic IkappaB kinase (IKK) activation pathway, CDKs, ATM, and p53. Acute inhibition of NF-kappaB via expression of a stable IkappaB mutant, downregulation of the p65 NF-kappaB subunit by RNA interference (RNAi), or pharmacological NF-kappaB inhibitors significantly protected against DNA damage-induced neuronal death. NF-kappaB inhibition also reduced p53 transcripts and p53 activity as measured by the p53-inducible messages, Puma and Noxa, implicating the p53 tumor suppressor in the mechanism of NF-kappaB-mediated neuronal death. Importantly, p53 expression still induces death in the presence of NF-kappaB inhibition, indicating that p53 acts downstream of NF-kappaB. Interestingly, neurons cultured from p65 or p50 NF-kappaB-deficient mice were not resistant to death and did not show diminished p53 activity, suggesting compensatory processes attributable to germline deficiencies, which allow p53 activation still to occur. In contrast to acute NF-kappaB inhibition, prolonged NF-kappaB inhibition caused neuronal death in the absence of DNA damage. These results uniquely define a signaling paradigm by which NF-kappaB serves both an acute p53-dependent pro-apoptotic function in the presence of DNA damage and an anti-apoptotic function in untreated normal neurons.
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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) | 0.001 | 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".