Macrophage Metalloelastase Mediates Acute Cigarette Smoke–induced Inflammation via Tumor Necrosis Factor-α Release
Bibliographic record
Abstract
The cells and proteases that mediate cigarette smoke-induced emphysema are controversial, with evidence favoring either neutrophils and neutrophil-derived serine proteases or macrophages and macrophage-derived metalloproteases as the important effectors. We recently reported that both macrophage metalloelastase (MMP-12) and neutrophils are required for acute cigarette smoke-induced connective tissue breakdown, the precursor of emphysema. Here we show how these disparate observations can be linked. Both wild-type (MMP-12 +/+) mice and mice lacking MMP-12 (MMP-12 -/-) demonstrated rapid increases in whole-lung nuclear factor-kappaB activation and gene expression of proinflammatory cytokines after cigarette smoke exposure, indicating that a lack of MMP-12 does not produce a global failure to upregulate inflammatory mediators. However, only MMP-12 +/+ mice demonstrated increased whole-lung tumor necrosis factor-alpha (TNF-alpha) protein or release of TNF-alpha from cultured alveolar macrophages exposed to smoke in vitro. Levels of whole-lung E-selectin, an endothelial activation marker, were increased in only MMP-12 +/+ mice. These findings suggest that, acutely, MMP-12 mediates smoke-induced inflammation by releasing TNF-alpha from macrophages, with subsequent endothelial activation, neutrophil influx, and proteolytic matrix breakdown caused by neutrophil-derived proteases. TNF-alpha release may be a general mechanism whereby metalloproteases drive cigarette smoke-induced inflammation.
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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 | 0.001 |
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".