Pharmacological activation of liver X receptor during cigarette smoke exposure adversely affects alveolar macrophages and pulmonary surfactant homeostasis
Bibliographic record
Abstract
Smoking alters pulmonary reverse lipid transport and leads to intracellular lipid accumulation in alveolar macrophages. We investigated whether stimulating reverse lipid transport with an agonist of the liver X receptor (LXR) would help alveolar macrophages limit lipid accumulation and dampen lung inflammation in response to cigarette smoke. Mice were exposed to cigarette smoke and treated intraperitoneally with the LXR agonist T0901317. Expression of lipid capture and lipid export genes was assessed in lung tissue and alveolar macrophages. Pulmonary inflammation was assessed in the bronchoalveolar lavage (BAL). Finally, cholesterol efflux capacity and pulmonary surfactant levels were determined. In room air-exposed mice, T0901317 increased the expression of lipid export genes in macrophages and the whole lung and increased cholesterol efflux capacity without inducing inflammation or affecting the pulmonary surfactant. However, cigarette smoke-exposed mice treated with T0901317 showed a marked increase in BAL neutrophils, IL-1α, C-C motif chemokine ligand 2, and granulocyte-colony-stimulating factor levels. T0901317 treatment in cigarette smoke-exposed mice failed to increase the ability of alveolar macrophages to export cholesterol and markedly exacerbated IL-1α release. Finally, T0901317 led to pulmonary surfactant depletion only in cigarette smoke-exposed mice. This study shows that hyperactivation of LXR and the associated lipid capture/export mechanisms only have minor pulmonary effects on the normal lung. However, in the context of cigarette smoke exposure, where the pulmonary surfactant is constantly oxidized, hyperactivation of LXR has dramatic adverse effects, once again showing the central role of lipid homeostasis in the pulmonary response to cigarette smoke exposure.
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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".