The Aryl Hydrocarbon Receptor Attenuates Acute Cigarette Smoke-Induced Airway Neutrophilia Independent of the Dioxin Response Element
Bibliographic record
Abstract
Cigarette smoke is a prevalent respiratory toxicant that remains a leading cause of death worldwide. Cigarette smoke induces inflammation in the lungs and airways that contributes to the development of diseases such as lung cancer and chronic obstructive pulmonary disease (COPD). Due to the presence of aryl hydrocarbon receptor (AhR) ligands in cigarette smoke, activation of the AhR has been implicated in driving this inflammatory response. However, we have previously shown that the AhR suppresses cigarette smoke-induced pulmonary inflammation, but the mechanism by which the AhR achieves its anti-inflammatory function is unknown. In this study, we use the AhR antagonist CH-223191 to inhibit AhR activity in mice. After an acute (3-day) cigarette smoke exposure, AhR inhibition was associated with significantly enhanced neutrophilia in the airways in response to cigarette smoke, mimicking the phenotype of AhR-deficient mice. We then used genetically-modified mouse strains which express an AhR that can bind ligand but either cannot translocate to the nucleus or bind its cognate response element, to show that these features of the AhR pathway are not required for the AhR to suppress pulmonary neutrophilia. Finally, using the non-toxic endogenous AhR ligand FICZ, we provide proof-of-concept that activation of pulmonary AhR attenuates smoke-induced inflammation. Collectively, these results support the importance of AhR activity in mediating its anti-inflammatory function in response to cigarette smoke. Further investigation of the precise mechanisms by which the AhR exerts is protective functions may lead to the development of therapeutic agents to treat people with chronic lung diseases that have an inflammatory etiology, but for which few therapeutic options exist.
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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) | 0.001 | 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) | 0.001 | 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".