Cigarette smoke upregulates pulmonary vascular matrix metalloproteinases via TNF-α signaling
Bibliographic record
Abstract
Cigarette smoke exposure causes vascular remodeling and pulmonary hypertension by poorly understood mechanisms. To ascertain whether cigarette smoke exposure affects production of matrix metalloproteinases (MMPs) in the pulmonary vessels, we exposed C57Bl/6 (C57) mice or mice lacking TNF-alpha receptors (TNFRKO) to smoke daily for 2 wk or 6 mo. Using laser capture microdissection and RT-PCR analysis, we examined gene expression of MMP-2, MMP-9, MMP-12, MMP-13, and tissue inhibitor of metalloproteinase (TIMP-1) and examined protein production by immunohistochemistry for MMP-2, MMP-9, and MMP-12 in small intrapulmonary arteries. At 2 wk, mRNA levels of TIMP-1 and all MMPs were increased in the C57, but not TNFRKO, mice, and immunoreactive protein for MMP-2, MMP-9, and MMP-12 was also increased in the C57 mice. Increased gelatinase activity was identified by in situ and bulk tissue zymography. At 6 mo, only MMP-12 mRNA levels remained increased in the C57 mice, but at a much lower level; however, MMP-2 mRNA levels increased in the TNFRKO mice. We conclude that smoke exposure increases MMP production in the small intrapulmonary arteries but that, with the exception of MMP-12, increased MMP production is transient. MMPs probably play a role in smoke-induced vascular remodeling, as they do in other forms of pulmonary hypertension, implying that MMP inhibitors might be beneficial. MMP production is largely TNF-alpha dependent, further supporting the importance of TNF-alpha in the pathogenesis of cigarette smoke-induced lung disease.
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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 | 0.000 |
| 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".