Nuclear prostaglandin signaling system: biogenesis and actions via heptahelical receptors
Bibliographic record
Abstract
Prostaglandins are ubiquitous lipid mediators that play pivotal roles in cardiovascular homeostasis, reproduction, and inflammation, as well as in many important cellular processes including gene expression and cell proliferation. The mechanism of action of these lipid messengers is thought to be primarily dependent on their interaction with specific cell surface receptors that belong to the heptahelical transmembrane spanning G protein-coupled receptor superfamily. Accumulating evidence suggests that these receptors may co-localize at the cell nucleus where they can modulate gene expression through a series of biochemical events. In this context, we have recently demonstrated that prostaglandin E2-EP3 receptors display an atypical nuclear compartmentalization in cerebral microvascular endothelial cells. Stimulation of these nuclear EP3 receptors leads to an increase of eNOS RNA in a cell-free isolated nuclear system. This review will emphasize these findings and describe how nuclear prostaglandin receptors, notably EP3 receptors, may affect gene expression, specifically of eNOS, by identifying putative transducing elements located within this organelle. The potential sources of lipid ligand activators for these intracellular sites will also be addressed. The expressional control of G-protein-coupled receptors located at the perinuclear envelope constitutes a novel and distinctive mode of gene regulation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".