IL4Rα Signaling Abrogates Hypoxic Neutrophil Survival and Limits Acute Lung Injury Responses <i>In Vivo</i>
Bibliographic record
Abstract
Abstract Rationale Acute respiratory distress syndrome is defined by the presence of systemic hypoxia and consequent on disordered neutrophilic inflammation. Local mechanisms limiting the duration and magnitude of this neutrophilic response remain poorly understood. Objectives To test the hypothesis that during acute lung inflammation tissue production of proresolution type 2 cytokines (IL-4 and IL-13) dampens the proinflammatory effects of hypoxia through suppression of HIF-1α (hypoxia-inducible factor-1α)-mediated neutrophil adaptation, resulting in resolution of lung injury. Methods Neutrophil activation of IL4Ra (IL-4 receptor α) signaling pathways was explored ex vivo in human acute respiratory distress syndrome patient samples, in vitro after the culture of human peripheral blood neutrophils with recombinant IL-4 under conditions of hypoxia, and in vivo through the study of IL4Ra-deficient neutrophils in competitive chimera models and wild-type mice treated with IL-4. Measurements and Main Results IL-4 was elevated in human BAL from patients with acute respiratory distress syndrome, and its receptor was identified on patient blood neutrophils. Treatment of human neutrophils with IL-4 suppressed HIF-1α–dependent hypoxic survival and limited proinflammatory transcriptional responses. Increased neutrophil apoptosis in hypoxia, also observed with IL-13, required active STAT signaling, and was dependent on expression of the oxygen-sensing prolyl hydroxylase PHD2. In vivo, IL-4Ra–deficient neutrophils had a survival advantage within a hypoxic inflamed niche; in contrast, inflamed lung treatment with IL-4 accelerated resolution through increased neutrophil apoptosis. Conclusions We describe an important interaction whereby IL4Rα-dependent type 2 cytokine signaling can directly inhibit hypoxic neutrophil survival in tissues and promote resolution of neutrophil-mediated acute lung injury.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".