Long-acting β<sub>2</sub>-agonists promote glucocorticoid-mediated repression of NF-κB by enhancing expression of the feedback regulator TNFAIP3
Bibliographic record
Abstract
-adrenoceptor agonists (LABAs) enhance the efficacy of inhaled corticosteroids (ICSs) more than increasing the ICS dose. In human bronchial epithelial, BEAS-2B, cells, expression of TNFα-induced protein-3 (TNFAIP3), or A20, a dual-ubiquitin ligase that provides feedback inhibition of NF-κB, was induced by budesonide, an ICS, and formoterol, a LABA, and was further enhanced by budesonide-formoterol combination. The proinflammatory cytokine TNF induced TNFAIP3 and TNF expression. Whereas subsequent budesonide treatment enhanced TNF-induced TNFAIP3 and reduced TNF expression, formoterol amplified these differential effects. In primary human airway smooth muscle cells, TNFAIP3 expression was induced by TNF. This was largely unaffected by budesonide but was acutely enhanced by budesonide-formoterol combination. In BEAS-2B cells, TNF recruited RELA, the main NF-κB transactivating subunit, to a 3' region of the TNF gene. RELA binding was reduced by budesonide, was further reduced by formoterol cotreatment, and was associated with reduced RNA polymerase II recruitment to the TNF gene. This is consistent with reduced TNF expression. TNFAIP3 knockdown enhanced TNF expression in the presence of TNF, TNF plus budesonide, and TNF plus budesonide-formoterol combination and confirms feedback inhibition. A luciferase reporter containing the TNF 3' RELA binding region recapitulated TNF inducibility and was inhibited by an IκB kinase inhibitor and TNFAIP3 overexpression. Repression of reporter activity by budesonide was increased by formoterol and involved TNFAIP3. Thus LABAs may improve the anti-inflammatory properties of ICSs by augmenting TNFAIP3 expression to negatively regulate NF-κB.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".