Autophagy Activation in Asthma Airways Remodeling
Bibliographic record
Abstract
Abstract Current asthma therapies fail to target airway remodeling that correlates with asthma severity driving disease progression that ultimately leads to loss of lung function. Macroautophagy (hereinafter “autophagy”) is a fundamental cell-recycling mechanism in all eukaryotic cells; emerging evidence suggests that it is dysregulated in asthma. We investigated the interrelationship between autophagy and airway remodeling and assessed preclinical efficacy of a known autophagy inhibitor in murine models of asthma. Human asthmatic and nonasthmatic lung tissues were histologically evaluated and were immunostained for key autophagy markers. The percentage area of positive staining was quantified in the epithelium and airway smooth muscle bundles using ImageJ software. Furthermore, the autophagy inhibitor chloroquine was tested intranasally in prophylactic (3 wk) and treatment (5 wk) models of allergic asthma in mice. Human asthmatic tissues showed greater tissue inflammation and demonstrated hallmark features of airway remodeling, displaying thickened epithelium (P < 0.001) and reticular basement membrane (P < 0.0001), greater lamina propria depth (P < 0.005), and increased airway smooth muscle bundles (P < 0.001) with higher expression of Beclin-1 (P < 0.01) and ATG5 (autophagy-related gene 5) (P < 0.05) together with reduced p62 (P < 0.05) compared with nonasthmatic control tissues. Beclin-1 expression was significantly higher in asthmatic epithelium and ciliated cells (P < 0.05), suggesting a potential role of ciliophagy in asthma. Murine asthma models demonstrated effective preclinical efficacy (reduced key features of allergic asthma: airway inflammation, airway hyperresponsiveness, and airway remodeling) of the autophagy inhibitor chloroquine. Our data demonstrate cell context–dependent and selective activation of autophagy in structural cells in asthma. Furthermore, this pathway can be effectively targeted to ameliorate airway remodeling in asthma.
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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".