Cigarette Smoke–Induced Egr-1 Upregulates Proinflammatory Cytokines in Pulmonary Epithelial Cells
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death worldwide and is a progressive and irreversible disorder. Cigarette smoking is associated with 80-90% of COPD cases; however, the genes involved in COPD-associated emphysema and chronic inflammation are poorly understood. It was recently demonstrated that early growth response gene 1 (Egr-1) is significantly upregulated in the lungs of smokers with COPD (Ning W and coworkers, Proc Natl Acad Sci 2004;101:14895-14900). We hypothesized that Egr-1 is activated in pulmonary epithelial cells during exposure to cigarette smoke extract (CSE). Using immunohistochemistry, we demonstrated that pulmonary adenocarcinoma cells (A-549) and primary epithelial cells lacking basal Egr-1 markedly induce Egr-1 expression after CSE exposure. To evaluate Egr-1-specific effects, we used antisense (alphaS) oligodeoxynucleotides (ODN) to knock down Egr-1 expression. Incorporation of Egr-1 alphaS ODN significantly decreased CSE-induced Egr-1 mRNA and protein, while sense ODN had no effect. Via Egr-1-mediated mechanisms, IL-1beta and TNF-alpha were significantly upregulated in pulmonary epithelial cells exposed to CSE or transfected with Egr-1. To investigate the relationship between Egr-1 induction by smoking and susceptibility to emphysema, we determined Egr-1 expression in strains of mice with different susceptibilities for the development of smoking-induced emphysema. Egr-1 was markedly increased in the lungs of emphysema-susceptible AKR/J mice chronically exposed to cigarette smoke, but only minimally increased in resistant NZWLac/J mice. In conclusion, Egr-1 is induced by cigarette smoke and functions in proinflammatory mechanisms that likely contribute to the development of COPD in the lungs of smokers.
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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".