Cigarette Smoke Exposure Attenuates Cytokine Production by Mouse Alveolar Macrophages
Bibliographic record
Abstract
Alveolar macrophages (aMs) play a central role in respiratory host defense by sensing microbial antigens and initiating immune-inflammatory responses early in the course of an infection. The purpose of this study was to investigate the effect of cigarette smoke exposure on aMs after stimulation of innate pattern recognition receptors (PRRs) in a murine model. To accomplish this, C57BL/6 mice were exposed for 8 weeks using two models of cigarette smoke exposure, nose-only or whole-body exposure, and aMs isolated from the bronchoalveolar lavage. After stimulation of aMs with pI:C, a mimic of viral replication, and bacterial cell-wall constituent LPS, aMs from cigarette smoke-exposed mice produced significantly attenuated levels of the inflammatory cytokines TNF-alpha and IL-6, and the chemokine RANTES. This attenuation was specific to the aM compartment, and not related to changes in aM viability or expression of Toll-like receptor (TLR)3 or TLR4 between groups. Furthermore, aMs from smoke-exposed mice had decreased cytokine RNA as compared with aMs from sham-exposed mice. Mechanistically, this was associated with decreased nuclear translocation of the proinflammatory transcription factor NF-kappaB, and increased activator protein-1 nuclear translocation, in aMs from smoke-exposed mice. Attenuated cytokine production was reversible after smoking cessation. Cigarette smoke exposure also attenuated TNF-alpha production after stimulation with nucleotide-oligomerization domain-like receptor agonists, showing that the effect applies more broadly to other PRR pathways. Our data demonstrate that cigarette smoke exposure attenuates aM responses after innate stimulation, including pathways typically associated with bacterial and viral infections.
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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 | 0.000 |
| 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".