Metabolic regulation by prostaglandin E 2 impairs lung group 2 innate lymphoid cell responses
Bibliographic record
Abstract
Background: Group 2 innate lymphoid cells (ILC2s) play a critical role in asthma pathogenesis. Non-steroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (NERD) is associated with reduced signaling via EP2, a receptor for prostaglandin E 2 (PGE 2 ). However, the respective roles for the PGE 2 receptors EP2 and EP4 (both share same downstream signaling) in the regulation of lung ILC2 responses has yet been deciphered. Methods: The roles of PGE 2 receptors EP2 and EP4 on ILC2-mediated lung inflammation were investigated using genetically modified mouse lines and pharmacological approaches in IL-33- and Alternaria alternata (A.A.)-induced lung allergy models. The effects of PGE 2 receptors and downstream signals on ILC2 metabolic activation and effector function were examined using in vitro cell cultures. Results: Deficiency of EP2 rather than EP4 augments IL-33-induced lung ILC2 responses and eosinophilic inflammation in vivo . In contrast, exogenous agonism of EP4 but not EP2 markedly restricts IL-33- and Alternaria alternata -induced lung ILC2 responses and eosinophilic inflammation. Mechanistically, PGE 2 directly suppresses IL-33-dependent ILC2 activation through the EP2/EP4-cAMP pathway, which downregulates STAT5 and MYC pathway gene expression and ILC2 energy metabolism. Blocking glycolysis diminishes IL-33-dependent ILC2 responses in mice lacking endogenous PG synthesis but not in PG-competent mice. Conclusion: We have defined a mechanism for optimal suppression of lung ILC2 responses by endogenous PGE 2 -EP2 signaling which underpins the clinical findings of defective EP2 signaling in patients with NERD. Our findings also indicate that exogenously targeting the PGE 2 -EP4-cAMP and energy metabolic pathways may provide novel opportunities for treating ILC2-initiated lung inflammation in asthma and NERD.
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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) | 0.002 | 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".