Dissection of experimental asthma with DNA microarray analysis identifies arginase in asthma pathogenesis
Bibliographic record
Abstract
Asthma is on the rise despite intense, ongoing research underscoring the need for new scientific inquiry. In an effort to provide unbiased insight into disease pathogenesis, we took an approach involving expression profiling of lung tissue from mice with experimental asthma. Employing asthma models induced by different allergens and protocols, we identified 6.5% of the tested genome whose expression was altered in an asthmatic lung. Notably, two phenotypically similar models of experimental asthma were shown to have distinct transcript profiles. Genes related to metabolism of basic amino acids, specifically the cationic amino acid transporter 2, arginase I, and arginase II, were particularly prominent among the asthma signature genes. In situ hybridization demonstrated marked staining of arginase I, predominantly in submucosal inflammatory lesions. Arginase activity was increased in allergen-challenged lungs, as demonstrated by increased enzyme activity, and increased levels of putrescine, a downstream product. Lung arginase activity and mRNA expression were strongly induced by IL-4 and IL-13, and were differentially dependent on signal transducer and activator of transcription 6. Analysis of patients with asthma supported the importance of this pathway in human disease. Based on the ability of arginase to regulate generation of NO, polyamines, and collagen, these results provide a basis for pharmacologically targeting arginine metabolism in allergic disorders.
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 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 | 0.001 | 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".