β-Catenin Directs Nuclear Factor-κB p65 Output via CREB-Binding Protein/p300 in Human Airway Smooth Muscle
Bibliographic record
Abstract
interaction with nuclear factor-κB (NF-κB). However, there is still controversy as to whether β-catenin facilitates or represses NF-κB output. Insights into the molecular mechanisms underlying the interaction between β-catenin and NF-κB have highlighted the cofactors CREB-binding protein (CBP) and p300 as important candidates. Here, we hypothesized that the interaction of β-catenin with CBP/p300 directs NF-κB output. Using human airway smooth muscle (ASM) cells, we found that β-catenin is essential in interleukin -1β (IL-1β)-mediated expression of interleukin-6 (IL-6) by promoting nuclear translocation of the p65 subunit of NF-κB. These effects were independent from WNT pathway activation or other factors that promote β-catenin signaling. In the nucleus, inhibition of either the CBP- or p300-β-catenin interaction could regulate NF-κB output, by enhancing (CBP inhibition) or inhibiting (p300 inhibition) IL-1β-induced expression of IL-6, respectively. Acetylation of p65 by p300 likely underlies these events, as inhibition of the p300-β-catenin interaction diminished levels of acetylated p65 at lysine 310, thereby reducing p65 transcriptional activity. In conclusion, β-catenin is a critical component of NF-κB-mediated inflammation in human ASM, affecting transcriptional output by interacting with the nuclear cofactors CBP and p300. Targeting β-catenin may be an alternative strategy to treat airway inflammation in patients with airway disease, such as asthma.
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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) | 0.002 | 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".