IL-1β stimulates a novel, IKKα -dependent, NIK -independent activation of non-canonical NFκB signalling
Bibliographic record
Abstract
In this study, we examined the activation of non-canonical nuclear factor Kappa B (NFκB) signalling in U2OS cells, a cellular metastatic bone cancer model. Whilst Lymphotoxin α1β2 (LTα1β2) stimulated the expected slow, delayed, sustained activation of serine 866/870 p100 phosphorylation and increased cellular expression of p52 NFκB, we found that canonical agonists, Interleukin-1β (IL-1β) and also Tumour necrosis factor-α (TNFα) generated a rapid transient increase in pp100, which was maximal by 15–30 min. This rapid phosphorylation was also observed in other cells types, such as DU145 and HCAECs suggesting the phenomenon is universal. IKKα deletion using CRISPR/Cas9 revealed an IKKα-dependent mechanism for serine 866/870 and additionally serine 872 p100 phosphorylation for both IL-1β and LTα1β2. In contrast, knockdown of IKKβ using siRNA or pharmacological inhibition of IKKβ activity was without effect on p100 phosphorylation. Pre-incubation of cells with the NFκB inducing-kinase (NIK) inhibitor, CW15337, had no effect on IL-1β induced phosphorylation of p100 however, the response to LTα1β2 was virtually abolished. Surprisingly IL-1β also stimulated p52 nuclear translocation as early as 60 min, this response and the concomitant p65 translocation was partially reduced by IKKα deletion. Furthermore, p52 nuclear translocation was unaffected by CW15337. In contrast, the response to LTα1β2 was essentially abolished by both IKKα deletion and CW15337. Taken together, these finding reveal novel forms of NFκB non-canonical signalling stimulated by ligands that activate the canonical NFκB pathway strongly such as IL-1β.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".