IL-5 enhances human mast cell survival and interferon responses to viral infection
Bibliographic record
Abstract
BACKGROUND: Mast cells are important sentinel cells in defense against mucosal infection. Exacerbations of allergic asthma and asthma deaths have been associated with respiratory viral infections. Elevated levels of IL-5 have been associated with the pathogenesis of severe atopic diseases, many of which respond to IL-5 blockade. OBJECTIVE: We sought to examine the impact of IL-5 signaling on mast cells infected with respiratory viruses. METHODS: Cord blood-derived human mast cells were treated with IL-5 or left untreated and infected with human coronavirus OC43, respiratory syncytial virus (RSV), or oncolytic reovirus. Mast cell expression of interferons and of interferon-stimulated genes was evaluated. Total RNA sequencing was performed to determine the impact of IL-5 on the transcriptome of human mast cells, and related functional assays were performed. RESULTS: IL-5-treated mast cells produced significantly more type I and III interferons than did controls not treated with IL-5. Mechanistically, IL-5 treatment led to greater expression of the prosurvival factor B-cell lymphoma 2 (BCL2) and endothelial PAS domain protein 1 (EPAS1) and protected mast cells from apoptosis-induced stress. IL-5 blockade was associated with a decrease in EPAS1 expression in the peripheral blood of asthmatic patients, as shown by transcriptomic data from clinical trials of mepolizumab and benralizumab. CONCLUSIONS: IL-5 signaling selectively promotes interferon responses in mast cells and maintains mast cell populations during mucosal viral infection via a novel IL-5/EPAS1 axis.
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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 | 0.000 |
| 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".