Interleukin-10 contributes to PGE2 signalling through upregulation of EP4 via SHIP1 and STAT3
Bibliographic record
Abstract
Macrophage cells form part of our first line defense against pathogens. Macrophages become activated by microbial products such as lipopolysaccharide (LPS) to produce inflammatory mediators, such as TNFα and other cytokines, which orchestrate the host defense against the pathogen. Once the pathogen has been eradicated, the activated macrophage must be appropriately deactivated or inflammatory diseases result. Interleukin-10 (IL10) is a key anti-inflammatory cytokine which deactivates the activated macrophage. The IL10 receptor (IL10R) signals through the Jak1/Tyk2 tyrosine kinases, STAT3 transcription factor and the SHIP1 inositol phosphatase. However, IL10 has also been described to induce the activation of the cyclic adenosine monophosphate (cAMP) regulated protein kinase A (PKA). We now report that IL10R signalling leads to STAT3/SHIP1 dependent expression of the EP4 receptor for prostaglandin E2 (PGE2). In macrophages, EP4 is a Gαs-protein coupled receptor that stimulates adenylate cyclase (AC) production of cAMP, leading to downstream activation of protein kinase A (PKA) and phosphorylation of the CREB transcription factor. IL10 induction of phospho-CREB and inhibition of LPS-induced phosphorylation of p85 PI3K and p70 S6 kinase required the presence of EP4. These data suggest that IL10R activation of STAT3/SHIP1 enhances EP4 expression, and that it is EP4 which activates cAMP-dependent signalling. The coordination between IL10R and EP4 signalling also provides an explanation for why cAMP elevating agents synergize with IL10 to elicit anti-inflammatory responses.
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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 | 0.001 |
| 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) | 0.004 | 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".