Inhibition of Experimental Allergic Airways Disease by Local Application of a Cell-Penetrating Dominant-Negative STAT-6 Peptide
Bibliographic record
Abstract
Allergic airways disease is initiated and perpetuated by an aberrant Th2 inflammatory response regulated in part by the cytokines IL-4 and IL-13, each of which induces activation of the STAT-6 transcription factor. Data from murine models indicate that the clinical manifestations of acute asthma are STAT-6 dependent, and thus, STAT-6 is a target for drug development in allergic airways disease. We designed a novel chimeric peptide (STAT-6 inhibitory peptide (STAT-6-IP)) comprised of a sequence predicted to bind to and inhibit STAT-6, fused to a protein transduction domain, to facilitate cellular uptake of the STAT-6-binding peptide. Our data demonstrate that the STAT-6-IP inhibited OVA-induced production of Th2 cytokines IL-4 and IL-13 in vitro. In contrast, the STAT-6-IP did not affect production of IFN-gamma, demonstrating specificity for Th2 cytokine inhibition. Following intranasal administration, the STAT-6-IP was localized to epithelial cells in the airways. Finally, in in vivo murine models of allergic rhinitis and asthma, intranasal delivery of the STAT-6-IP inhibited OVA-induced lung inflammation and mucus production as well as accumulation of eosinophils and IL-13 in bronchoalveolar lavage fluid and OVA-dependent airway hyperresponsiveness. Together these data show that local application of cell-penetrating peptide inhibitors of STAT-6 has significant potential for the treatment of allergic rhinitis and asthma.
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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) | 0.001 | 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) | 0.001 | 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".