Metabolic regulation by prostaglandin <scp> E <sub>2</sub> </scp> impairs lung group 2 innate lymphoid cell responses
Bibliographic record
Abstract
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‐induced lung allergy model. 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 mouse lung ILC2 responses and eosinophilic inflammation in vivo. In contrast, exogenous agonism of EP4 and EP2 or inhibition of phosphodiesterase markedly restricts IL‐33‐induced lung ILC2 responses. 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 where endogenous PG synthesis or EP2 signaling is blocked but not in mice with intact PGE 2 ‐EP2 signaling. Conclusion We have defined a mechanism for optimal suppression of mouse 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 the ILC2‐initiated lung inflammation in asthma and NERD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".