Immunomodulatory innate defence regulator (IDR) peptide alleviates airway inflammation and hyper-responsiveness
Bibliographic record
Abstract
BACKGROUND: Exacerbation in asthma is associated with decreased expression of specific host defence peptides (HDPs) in the lungs. We examined the effects of a synthetic derivative of HDP, innate defence regulator (IDR) peptide IDR-1002, in house dust mite (HDM)-challenged murine model of asthma, in interleukin (IL)-33-challenged mice and in human primary bronchial epithelial cells (PBECs). METHODS: Vent small animal ventilator, cell differentials in bronchoalveolar lavage performed by modified Wright-Giemsa staining, and cytokines monitored by MesoScale Discovery assay and ELISA. PBECs stimulated with tumour necrosis factor alpha (TNF-α) and interferon gamma (IFN-γ), with or without IDR-1002, were analysed by western blots. RESULTS: IDR-1002 blunted HDM challenge-induced airway hyper-responsiveness (AHR), and lung leucocyte accumulation including that of eosinophils and neutrophils, in HDM-challenged mice. Concomitantly, IDR-1002 suppressed HDM-induced IL-33 in the lungs. IFN-γ/TNF-α-induced IL-33 production was abrogated by IDR-1002 in PBECs. Administration of IL-33 in HDM-challenged mice, or challenge with IL-33 alone, mitigated the ability of IDR-1002 to control leucocyte accumulation in the lungs, suggesting that the suppression of IL-33 is essential for the anti-inflammatory activity of IDR-1002. In contrast, the peptide significantly reduced either HDM, IL-33 or HDM+IL-33 co-challenge-induced AHR in vivo. CONCLUSION: This study demonstrates that an immunomodulatory IDR peptide controls the pathophysiology of asthma in a murine model. As IL-33 is implicated in steroid-refractory severe asthma, our findings on the effects of IDR-1002 may contribute to the development of novel therapies for steroid-refractory severe 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) | 0.000 | 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) | 0.001 | 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".