DACH1 Attenuates Airway Inflammation in Chronic Obstructive Pulmonary Disease by Activating NRF2 Signaling
Bibliographic record
Abstract
Abstract Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease of the airways characterized by impaired lung function induced by cigarette smoke (CS). Reduced DACH1 (dachshund homolog 1) expression has a detrimental role in numerous disorders, but its role in COPD remains understudied. This study aimed to elucidate the role and underlying mechanism of DACH1 in airway inflammation in COPD by measuring DACH1 expression in lung tissues of patients with COPD. Airway epithelium–specific DACH1-knockdown mice and adenoassociated virus–transfected DACH1-overexpressing mice were used to investigate the role of DACH1 and the potential for therapeutic targeting in experimental COPD caused by CS. Furthermore, we discovered a potential mechanism of DACH1 in inflammation induced by CS extract stimulation in vitro. Compared with nonsmokers and smokers without COPD, patients with COPD had reduced DACH1 expression, especially in the airway epithelium. Airway epithelium–specific DACH1 knockdown aggravated airway inflammation and lung function decline caused by CS in mice, whereas DACH1 overexpression protected mice from airway inflammation and lung function decline. DACH1 knockdown and overexpression promoted and inhibited IL-6 and IL-8 secretion, respectively, in 16HBE human bronchial epidermal cells after CS extract stimulation. NRF2 (nuclear factor erythroid 2–related factor 2) was discovered to be a novel downstream target of DACH1, which binds directly to its promoter. By activating NRF2 signaling, DACH1 induction reduced inflammation. DACH1 levels are lower in smokers and nonsmoking patients with COPD than in nonsmokers. DACH1 has protective effects against inflammation induced by CS by activating the NRF2 signaling pathway. Targeting DACH1 is a potentially viable therapeutic approach for COPD treatment.
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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".