The Influence of Tobacco Smoke/Nicotine on CYP2A Expression in Human and African Green Monkey Lungs
Bibliographic record
Abstract
CYP2A enzymes metabolically inactivate nicotine and activate tobacco-derived procarcinogens [e.g., 4-[methylnitrosamino]-1-(3-pyridyl)-1-butanone]. Smoking decreases nicotine clearance, and chronic nicotine reduces hepatic CYP2A activity. However, little is known about the impact of smoking or nicotine on the expression of CYP2A in the lung. We investigated 1) the levels of human lung CYP2A mRNA in smokers versus nonsmokers and 2) the impact of daily nicotine treatment on lung CYP2A protein levels in African green monkeys (AGMs). Lung CYP2A13, CYP2A6, and CYP2A7 (and CYP1A2) mRNA levels in smokers and nonsmokers were assessed in Gene Expression Omnibus data sets (GSE30063, GSE108134, and GSE11784). The impact of chronic, twice-daily, subcutaneous nicotine at two doses (0.3 and 0.5 mg/kg) versus vehicle on lung CYP2A protein levels was assessed. The impact of ethanol self-administration was also investigated, with and without nicotine treatment. Smokers versus nonsmokers (from GSE30063 and GSE108134) had lower (1.04- to 1.12-fold) levels of lung CYP2A13, CYP2A6, and CYP2A7 (and higher CYP1A2) mRNA. Both doses of nicotine tested decreased AGM lung CYP2A protein (3- to 7-fold). Ethanol self-administration had no effect on AGM lung CYP2A protein, and there was no interaction between ethanol and nicotine. Our results suggest that smoking was associated with a reduction in human lung CYP2A13, CYP2A6, and CYP2A7 mRNA, consistent with the role of nicotine treatment in reducing AGM lung CYP2A protein. This regulation by smoking/nicotine will increase interindividual variation in lung CYP2A levels, which may impact the localized metabolism of inhaled drugs and tobacco smoke procarcinogens. SIGNIFICANCE STATEMENT: CYP2A13 and CYP2A6 are expressed in the lung and may contribute to local procarcinogen activation. Smokers had lower lung CYP2A mRNA levels compared with nonsmokers. Lung CYP2A protein expression was decreased by systemic treatment with nicotine. Decreased lung CYP2A expression may alter smoking-related lung cancer risk and tissue damage from other inhaled toxins. This novel regulatory impact of nicotine, including nicotine found in smoking-cessation nicotine-replacement therapies, may have potential benefits on smoking-related lung cancer risk.
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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) | 0.002 | 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".