miR-31 Dysregulation in Cystic Fibrosis Airways Contributes to Increased Pulmonary Cathepsin S Production
Bibliographic record
Abstract
RATIONALE: Cathepsin S (CTSS) activity is increased in bronchoalveolar lavage (BAL) fluid from patients with cystic fibrosis (CF). This activity contributes to lung inflammation via degradation of antimicrobial proteins, such as lactoferrin and members of the β-defensin family. OBJECTIVES: In this study, we investigated the hypothesis that airway epithelial cells are a source of CTSS, and mechanisms underlying CTSS expression in the CF lung. METHODS: Protease activity was determined using fluorogenic activity assays. Protein and mRNA expression were analyzed by ELISA, Western blotting, and reverse-transcriptase polymerase chain reaction. MEASUREMENTS AND MAIN RESULTS: In contrast to neutrophil elastase, CTSS activity was detectable in 100% of CF BAL fluid samples from patients without Pseudomonas aeruginosa infection. In this study, we identified epithelial cells as a source of pulmonary CTSS activity with the demonstration that CF airway epithelial cells express and secrete significantly more CTSS than non-CF control cells in the absence of proinflammatory stimulation. Furthermore, levels of the transcription factor IRF-1 correlated with increased levels of its target gene CTSS. We discovered that miR-31, which is decreased in the CF airways, regulates IRF-1 in CF epithelial cells. Treating CF bronchial epithelial cells with a miR-31 mimic decreased IRF-1 protein levels with concomitant knockdown of CTSS expression and secretion. CONCLUSIONS: The miR-31/IRF-1/CTSS pathway may play a functional role in the pathogenesis of CF lung disease and may open up new avenues for exploration in the search for an effective therapeutic target.
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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 | 0.001 |
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".