Plasma arginine metabolites reflect airway dysfunction in a murine model of allergic airway inflammation
Bibliographic record
Abstract
L-arginine metabolism is important in the maintenance of airway tone. Shift of metabolism from the nitric oxide synthase to arginase pathways contributes to the increased airway responsiveness in asthma. We tested the hypothesis that systemic levels of L-arginine metabolites are biomarkers reflective of airway dysfunction. We used a mouse model of acute allergic airway inflammation to OVA that manifests with significant airway hyperresponsiveness to methacholine. To determine tissue arginase activity in vivo, the isotopic enrichment of an infused L-arginine stable isotope and its product amino acid L-ornithine were measured in lung and airway homogenates using liquid chromatography-tandem mass spectrometry. Tissue and plasma concentrations of other L-arginine metabolites, including L-citrulline and symmetric and asymmetric dimethylarginine, were measured and correlated with lung arginase activity and methacholine responsiveness of the airways. The effectiveness of intratracheal instillation of an arginase inhibitor (boronoethylcysteine) on pulmonary arginase activity and circulating concentrations of L-arginine metabolites was also studied. We demonstrate that 1) plasma indexes of L-arginine bioavailability and impairment of nitric oxide synthase function correlate with airway responsiveness to methacholine; 2) plasma levels of L-ornithine predict in vivo pulmonary arginase activity and airway function; and 3) acute arginase inhibition reduces in vivo pulmonary arginase activity to control levels and normalizes plasma L-ornithine, but not L-arginine, bioavailability in this model. We conclude that plasma L-ornithine may be useful as a systemic biomarker to predict responses to therapeutic interventions targeting airway arginase in asthma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".