Lung arginase expression and activity is increased in cystic fibrosis mouse models
Bibliographic record
Abstract
The activity of arginase is increased in airway secretions of patients with cystic fibrosis (CF). Downstream products of arginase activity may contribute to CF lung disease. We hypothesized that pulmonary arginase expression and activity would be increased in mouse models of CF and disproportionally increased in CF mice with Pseudomonas aeruginosa pneumonia. Expression of arginase isoforms in lung tissue was quantified with reverse transcriptase-PCR in naive cystic fibrosis transmembrane conductance regulator (Cftr)-deficient mice and β-epithelial sodium channel-overexpressing [β-ENaC-transgenic (Tg)] mice. An isolated lung stable isotope perfusion model was used to measure arginase activity in Cftr-deficient mice before and after intratracheal instillation of Pseudomonas aeruginosa. The expression of arginase-2 in lung was increased in adult Cftr-deficient animals and in newborn β-ENaC-Tg. Arginase-1 lung expression was normal in Cftr-deficient and in newborn β-ENaC-Tg mice, but was increased in β-ENaC-Tg mice at age 1, 3, and 6 wk. Arginase activity was significantly higher in lung (5.0 ± 0.7 vs. 3.2 ± 0.3 nmol·(-1)·h(-1), P = 0.016) and airways (204.6 ± 49.8 vs. 79.3 ± 17.2 nmol·(-1)·h(-1), P = 0.045) of naive Cftr-deficient mice compared with sex-matched wild-type littermate controls. Infection with Pseudomonas aeruginosa resulted in a far greater increase in lung arginase activity in Cftr-deficient mice (10-fold) than in wild-type controls (6-fold) (P = 0.01). This is the first ex vivo characterization of arginase expression and activity in CF mouse lung and airways. Our data show that pulmonary arginase expression and activity is increased in CF mice, especially with Pseudomonas aeruginosa infections.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".