Surfactant Protein D Deficiency Aggravates Cigarette Smoke-Induced Lung Inflammation by Upregulation of Ceramide Synthesis
Bibliographic record
Abstract
Cigarette smoke (CS) is the main cause of chronic obstructive pulmonary disease. Surfactant protein D (SP-D) is an important anti-inflammatory protein that regulates host immune defence in the lungs. Here, we investigated the role of SP-D in a murine model of CS-induced inflammation. Pulmonary SP-D localization and abundance was compared between smoker and non-smoker individuals. For in vivo studies, wildtype and SP-D-deficient mice were exposed to CS for either 12 weeks or 3 days. Moreover, the effect of therapeutic administration of recombinant fragment of human SP-D on the acute CS-induced changes was evaluated. Pulmonary SP-D appeared with heterogenous expression in human smokers, while mouse lung SP-D was uniformly upregulated after CS exposure. We found that SP-D-deficient mice were more susceptible to CS-induced macrophage-rich airway inflammation. SP-D deficiency influenced local pro-inflammatory cytokine levels, with increased CCL3 and interleukin-6 but decreased CXCL1. Furthermore, CS exposure caused significant upregulation of pro-inflammatory ceramides and related ceramide synthase gene transcripts in SP-D-deficient mice compared to wildtype littermates. Administration of recombinant fragment of human SP-D (rfhSP-D) alleviated CS-induced macrophage infiltration and prevented induction of ceramide synthase gene expression. Finally, rfhSP-D treatment attenuated CS-induced human epithelial cell apoptosis in vitro. Our results indicate that SP-D deficiency aggravates CS-induced lung inflammation partly through regulation of ceramide synthesis and that local SP-D enrichment rescues CS-induced inflammation.
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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) | 0.002 | 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".