Disruption of pulmonary lipid homeostasis drives cigarette smoke-induced lung inflammation in mice
Bibliographic record
Abstract
Overwhelming evidence links inflammation to the pathogenesis of smoking-related pulmonary diseases, especially chronic obstructive pulmonary disease (COPD). Despite an increased understanding of the disease pathogenesis, mechanisms initiating smoking-induced inflammatory processes remain incompletely understood. To investigate the mechanisms that initiate and propagate smoke-induced inflammation, we used a well-characterised mouse model of cigarette smoke exposure, mice deficient for interleukin (IL)-1α, IL-1β and Toll-like receptor 4, and antibodies blocking granulocyte-macrophage colony-stimulating factor (GM-CSF). Studies were also pursued using intranasal delivery of human oxidised low-density lipoprotein (hOxLDL), a source of oxidised lipids, to investigate the inflammatory processes associated with impaired lipid homeostasis. We found that cigarette smoke exposure rapidly led to lipid accumulation in pulmonary macrophages, a defining feature of foam cells, which in turn released high levels of IL-1α. In smoke-exposed IL-1α-deficient mice, phospholipids accumulated in the bronchoalveolar lavage, a phenomenon also observed when blocking GM-CSF. Intranasal administration of hOxLDL led to lipid accumulation in macrophages and initiated an inflammatory process that mirrored the characteristics of cigarette smoke-induced inflammation. These findings identify a link between lipid accumulation in macrophages, inflammation and damaged surfactant, suggesting that the response to damaged pulmonary surfactant is a central mechanism that drives cigarette smoke-induced inflammation. Further investigations are required to explore the role of distorted lipid homeostasis in the pathogenesis of COPD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".