Role for TAK1 in cigarette smoke-induced proinflammatory signaling and IL-8 release by human airway smooth muscle cells
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is an inflammatory disease, characterized by a progressive decline in lung function. Airway smooth muscle (ASM) mass may be increased in COPD, contributing to airflow limitation and proinflammatory cytokine production. Cigarette smoke (CS), the major risk factor of COPD, causes ASM cell proliferation, as well as interleukin-8 (IL-8)-induced neutrophilia. In various cell types, transforming growth factor-β-activated kinase 1 (TAK1) plays a crucial role in MAP kinase and NF-κB activation, as well as IL-8 release induced by IL-1β, TNF-α, and lipopolysaccharide. The role of TAK1 in CS-induced IL-8 release is not known. The aim of this study was to investigate the role of TAK1 in CS-induced NF-κB and MAP kinase signaling and IL-8 release by human ASM cells. Stimulation of these cells with CS extract (CSE) increased IL-8 release and ERK-1/2 phosphorylation, as well as Iκ-Bα degradation and p65 NF-κB subunit phosphorylation. CSE-induced ERK-1/2 phosphorylation and Iκ-Bα degradation were both inhibited by pretreatment with the specific TAK1 inhibitor LL-Z-1640-2 (5Z-7-oxozeaenol; 100 nM). Similarly, expression of dominant-negative TAK1 inhibited CSE-induced ERK-1/2 phosphorylation. In addition, inhibitors of TAK1 and the NF-κB (SC-514; 50 μM) and ERK-1/2 (U-0126; 3 μM) signaling inhibited the CSE-induced IL-8 release by ASM cells. These data indicate that TAK1 plays a major role in CSE-induced ERK-1/2 and NF-κB signaling and in IL-8 release by human ASM cells. Furthermore, they identify TAK1 as a novel target for the inhibition of CS-induced inflammatory responses involved in the development and progression of COPD.
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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".