Modulation of Pro-inflammatory Gene Expression by Nuclear Lysophosphatidic Acid Receptor Type-1
Bibliographic record
Abstract
Lysophosphatidic acid (LPA) is a bioactive molecule involved in inflammation, immunity, wound healing, and neoplasia. Its pleiotropic actions arise presumably by interaction with their cell surface G protein-coupled receptors. Herein, the presence of the specific nuclear lysophosphatidic acid receptor-1 (LPA1R) was revealed in unstimulated porcine cerebral microvascular endothelial cells (pCMVECs), LPA1R stably transfected HTC4 rat hepatoma cells, and rat liver tissue using complementary approaches, including radioligand binding experiments, electron- and cryomicroscopy, cell fractionation, and immunoblotting with three distinct antibodies. Coimmunoprecipitation studies in enriched plasmalemmal fractions of unstimulated pCMVEC showed that LPA1Rs are dually sequestrated in caveolin-1 and clathrin subcompartments, whereas in nuclear fractions LPA1R appeared primarily in caveolae. Immunofluorescent assays using a cell-free isolated nuclear system confirmed LPA1R and caveolin-1 co-localization. In pCMVEC, LPA-stimulated increases in cyclooxygenase-2 and inducible nitric-oxide synthase RNA and protein expression were insensitive to caveolea-disrupting agents but sensitive to LPA-generating phospholipase A2 enzyme and tyrosine kinase inhibitors. Moreover, LPA-induced increases in Ca2+ transients and/or iNOS expression in highly purified rat liver nuclei were prevented by pertussis toxin, phosphoinositide 3-kinase/Akt inhibitor wortmannin and Ca2+ chelator and channel blockers EGTA and SK&F96365, respectively. This study describes for the first time the nucleus as a potential organelle for LPA intracrine signaling in the regulation of pro-inflammatory gene expression.
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 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 | 0.000 |
| 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 | 0.000 |
| 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".