IKK-i/IKKϵ Controls Constitutive, Cancer Cell-associated NF-κB Activity via Regulation of Ser-536 p65/RelA Phosphorylation
Bibliographic record
Abstract
Nuclear factor kappaB (NF-kappaB) has been studied extensively as an inducible transcriptional regulator of the immune and inflammatory response. NF-kappaB activation downstream of lipopolysaccharide or cytokine stimulation is controlled by the IkappaB kinase complex, which contains IKKalpha and IKKbeta. Significantly, the constitutive activity of NF-kappaB has been implicated as an important aspect of many cancer cells, but mechanisms associated with this activity are poorly understood. An inducible kinase, IKK-i/IKKepsilon, related to the catalytic forms of the IkappaB kinase, has been studied as an anti-viral, innate immune regulator through its ability to control the activity of the transcription factors IRF-3 and IRF-7. Here, we demonstrate that IKK-i/IKKepsilon is expressed in a number of cancer cells and is involved in regulating NF-kappaB activity through its ability to control basal/constitutive, but not cytokine-induced, p65/RelA phosphorylation at Ser-536, a modification proposed to contribute to the transactivation function of NF-kappaB. Knockdown of IKK-i/IKKepsilon or expression of a S536A mutant form of p65 suppresses HeLa cell proliferation. The data indicate a role for IKK-i/IKKepsilon in controlling proliferation of certain cancer cells through regulation of constitutive NF-kappaB activity.
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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".