EETs Attenuate Ox-LDL-Induced LTB4 Production and Activity by Inhibiting p38 MAPK Phosphorylation and 5-LO/BLT1 Receptor Expression in Rat Pulmonary Arterial Endothelial Cells
Bibliographic record
Abstract
Cytochrome P-450 epoxygenase (EPOX)-derived epoxyeicosatrienoic acids (EETs), 5-lipoxygenase (5-LO), and leukotriene B4 (LTB4), the product of 5-LO, all play a pivotal role in the vascular inflammatory process. We have previously shown that EETs can alleviate oxidized low-density lipoprotein (ox-LDL)-induced endothelial inflammation in primary rat pulmonary artery endothelial cells (RPAECs). Here, we investigated whether ox-LDL can promote LTB4 production through the 5-LO pathway. We further explored how exogenous EETs influence ox-LDL-induced LTB4 production and activity. We found that treatment with ox-LDL increased the production of LTB4 and further led to the expression and release of both monocyte chemoattractant protein-1 (MCP-1/CCL2) and intercellular adhesion molecule-1 (ICAM-1). All of the above ox-LDL-induced changes were attenuated by the presence of 11,12-EET and 14,15-EET, as these molecules inhibited the 5-LO pathway. Furthermore, the LTB4 receptor 1 (BLT1 receptor) antagonist U75302 attenuated ox-LDL-induced ICAM-1 and MCP-1/CCL2 expression and production, whereas LY255283, a LTB4 receptor 2 (BLT2 receptor) antagonist, produced no such effects. Moreover, in RPAECs, we demonstrated that the increased expression of 5-LO and BLT1 following ox-LDL treatment resulted from the activation of nuclear factor-κB (NF-κB) via the p38 mitogen-activated protein kinase (MAPK) pathway. Our results indicated that EETs suppress ox-LDL-induced LTB4 production and subsequent inflammatory responses by downregulating the 5-LO/BLT1 receptor pathway, in which p38 MAPK phosphorylation activates NF-κB. These results suggest that the metabolism of arachidonic acid via the 5-LO and EPOX pathways may present a mutual constraint on the physiological regulation of vascular endothelial cells.
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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 | 0.000 |
| 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 | 0.001 |
| 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".