The aryl hydrocarbon receptor promotes the resolution of pulmonary neutrophilia via regulation of macrophage efferocytosis
Bibliographic record
Abstract
Cigarette smoke is the primary cause of chronic obstructive pulmonary disease (COPD), an incurable condition characterized by irreversible airflow obstruction and alveolar destruction driven by chronic inflammation of the lungs and airways. The inflammatory response caused by cigarette smoke is typified by the recruitment of innate and adaptive immune cells to the lung. Paradoxically, many of these immune cells are functionally impaired by smoke. Notable among these are lung macrophages, which have reduced ability to clear apoptotic lung epithelial cells and neutrophils by efferocytosis when exposed to cigarette smoke. Lung macrophages may express the aryl hydrocarbon receptor (AhR), a receptor/transcription factor highly expressed in barrier organs including the lungs. The AhR protects against the damaging effects of cigarette smoke by attenuating pulmonary neutrophilia via an unknown mechanism. We used our preclinical cigarette smoke models, mutant AhR mice and techniques such as flow cytometry, Western blot and reverse transcription quantitative polymerase chain reaction (RT-qPCR) to show that the AhR promotes the resolution of cigarette smoke-induced inflammation in mice via enhanced efferocytosis. Moreover, the ability of macrophages to engulf apoptotic neutrophils in the lungs is due to a non-genomic AhR pathway that involves signaling through the IL-10/JAK/STAT pathway. Finally, we show that the non-toxic endogenous AhR ligand FICZ promotes macrophage uptake of neutrophils. Taken together, these results support the importance of AhR activity in mediating its anti-inflammatory functions in response to cigarette smoke. Further investigation of the precise mechanisms by which the AhR exerts its anti-inflammatory function may open the possibility for therapeutic agents to treat chronic inflammatory diseases.
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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) | 0.001 | 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".