Anti-inflammatory Role of Microsomal Prostaglandin E Synthase-1 in a Model of Neuroinflammation
Bibliographic record
Abstract
A major immunological response during neuroinflammation is the activation of microglia, which subsequently release proinflammatory mediators such as prostaglandin E(2) (PGE(2)). Besides its proinflammatory properties, cyclooxygenase-2 (COX-2)-derived PGE(2) has been shown to exhibit anti-inflammatory effects on innate immune responses. Here, we investigated the role of microsomal PGE(2) synthase-1 (mPGES-1), which is functionally coupled to COX-2, in immune responses using a model of lipopolysaccharide (LPS)-induced spinal neuroinflammation. Interestingly, we found that activation of E-prostanoid (EP)2 and EP4 receptors, but not EP1, EP3, PGI(2) receptor (IP), thromboxane A(2) receptor (TP), PGD(2) receptor (DP), and PGF(2) receptor (FP), efficiently blocked LPS-induced tumor necrosis factor α (TNFα) synthesis and COX-2 and mPGES-1 induction as well as prostaglandin synthesis in spinal cultures. In vivo, spinal EP2 receptors were up-regulated in microglia in response to intrathecally injected LPS. Accordingly, LPS priming reduced spinal synthesis of TNFα, interleukin 1β (IL-1β), and prostaglandins in response to a second intrathecal LPS injection. Importantly, this reduction was only seen in wild-type but not in mPGES-1-deficient mice. Furthermore, intrathecal application of EP2 and EP4 agonists as well as genetic deletion of EP2 significantly reduced spinal TNFα and IL-1β synthesis in mPGES-1 knock-out mice after LPS priming. These data suggest that initial inflammation prepares the spinal cord for a negative feedback regulation by mPGES-1-derived PGE(2) followed by EP2 activation, which limits the synthesis of inflammatory mediators during chronic inflammation. Thus, our data suggest a role of mPGES-1-derived PGE(2) in resolution of neuroinflammation.
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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 | 0.001 | 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) | 0.001 | 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".