Identification of a novel TIF-IA–NF-κB nucleolar stress response pathway
Bibliographic record
Abstract
p53 as an effector of nucleolar stress is well defined, but p53 independent mechanisms are largely unknown. Like p53, the NF-κB transcription factor plays a critical role in maintaining cellular homeostasis under stress. Many stresses that stimulate NF-κB also disrupt nucleoli. However, the link between nucleolar function and activation of the NF-κB pathway is as yet unknown. Here we demonstrate that artificial disruption of the PolI complex stimulates NF-κB signalling. Unlike p53 nucleolar stress response, this effect does not appear to be linked to inhibition of rDNA transcription. We show that specific stress stimuli of NF-κB induce degradation of a critical component of the PolI complex, TIF-IA. This degradation precedes activation of NF-κB and is associated with increased nucleolar size. It is mimicked by CDK4 inhibition and is dependent upon a novel pathway involving UBF/p14ARF and S44 of the protein. We show that blocking TIF-IA degradation blocks stress effects on nucleolar size and NF-κB signalling. Finally, using ex vivo culture, we show a strong correlation between degradation of TIF-IA and activation of NF-κB in freshly resected, human colorectal tumours exposed to the chemopreventative agent, aspirin. Together, our study provides compelling evidence for a new, TIF-IA-NF-κB nucleolar stress response pathway that has in vivo relevance and therapeutic implications.
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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 | 0.000 |
| 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".