IL-4 activates equine neutrophils and induces a mixed inflammatory cytokine expression profile with enhanced neutrophil chemotactic mediator release ex vivo
Bibliographic record
Abstract
Neutrophils are potent contributors to the lung pathophysiological changes occurring in allergic airway inflammation, which typically involve T helper type 2 (Th2) cytokine overexpression. We have previously reported that equine pulmonary endothelial cells are activated by the Th2 cytokine IL-4 and express chemotactic factors for neutrophils after stimulation. We have further explored the possible mechanisms linking Th2-driven inflammation and neutrophilia by studying the effects of recombinant equine IL-4, a prototypical Th2 cytokine, on peripheral blood neutrophils (PBN) isolated from normal animals and from horses with asthmatic airway inflammation (equine heaves). We found that IL-4 induced morphological changes in PBN, dose- and time-dependent expression of IL-8 mRNA, as well as the release of chemotactic factors for neutrophils in culture supernatants. Also, IL-4 induced a mixed inflammatory response in PBN from control and asthmatic-animals with increased expression of proinflammatory IL-8 and TNF-α but a marked inhibition of IL-1β. IL-4 type I receptor (IL-4Rα) and CD23 (FcεRII) expression were also upregulated by IL-4. Importantly, disease as well as chronic antigenic exposure modified gene expression by PBN. Finally, we found that activation of equine neutrophils with IL-4 involved STAT6 phosphorylation and p38 MAPK and phosphatidylinositol 3-kinase (PI3K); the pharmacological inhibitors, SB-203580 and LY-294002, respectively, significantly reversed IL-4-induced gene modulation in PBN. Overall, results from this study add to the link between Th2-driven inflammation and neutrophilia in the equine model and further extend the characterization of IL-4 effects on neutrophils.
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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) | 0.002 | 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".