Critical importance of dietary methionine and choline in the maintenance of lung homeostasis during normal and cigarette smoke exposure conditions
Bibliographic record
Abstract
Genetic predispositions and environmental exposures are regarded as the main predictors of respiratory disease development. Although the impact of dietary essential nutrient deficiencies on cardiovascular disease, obesity, and type II diabetes has been widely studied, it remains poorly explored in chronic respiratory diseases. Dietary choline and methionine deficiencies are common in the population, and their impact on pulmonary homeostasis is currently unknown. Mice were fed choline- and/or methionine-deficient diets while being exposed to room-air or cigarette smoke for up to 4 wk. Lung functions were assessed using the FlexiVent. Pulmonary transcriptional activity was assessed using gene expression microarrays and quantitative PCR. Immune cells, cytokines, and phosphatidylcholine were quantified in the bronchoalveolar lavage. In this study, we found that short-term dietary choline and/or methionine deficiencies significantly affect lung function in mice in a reversible manner. It also reduced transcriptional levels of collagens and elastin as well as pulmonary surfactant phosphatidylcholine levels. We also found that dietary choline and/or methionine deficiencies markedly interfered with the pulmonary response to cigarette smoke exposure, modulating lung function and dampening inflammation. These findings clearly show that dietary choline and/or methionine deficiencies can have dramatic pathophysiological effects on the lungs and can also affect the pathobiology of cigarette smoke-induced pulmonary alterations. Expanding our knowledge in the field of "nutri-respiratory research" may reveal a crucial role for essential nutrients in pulmonary health and disease, which may prove to be as relevant as genetic predispositions and environmental exposures.
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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 | 0.001 | 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".