Interplay between LPA2 and LPA3 in LPA-mediated phosphatidylserine cell surface exposure and extracellular vesicles release by erythrocytes
Bibliographic record
Abstract
Evidence is growing for the role of red blood cells (RBCs) in vascular homeostasis, including thrombogenic events and inflammation. Lysophosphatidic acid (LPA) is known to induce phosphatidylserine (PS) exposure and the release of RBC Extracellular Vesicles (REVs). Using high sensitivity flow cytometry, we examined the effects and the mechanisms by which the LPA species commonly found in human plasma could activate RBCs. We report that LPA 16:0, 18:0 and 18:1, but not LPA 20:4, induced PS exposure and the release of small PS− and large PS+ REVs through LPA3 receptor signalling in RBCs. The release of large PS+ REVs required higher concentrations of LPA. RBCs were not activated by LPA 20:4. Interestingly, blockade of LPA2 enhanced LPA-mediated PS− REV release in RBCs. Furthermore, LPA receptor agonists and antagonists highlighted that LPA 20:4 inhibited LPA3-dependent PS exposure and, through the LPA2 receptor, inhibited PS− REV production. Activation of RBCs with LPA 18:1 in normal plasma stimulated the release of PS− and PS+ REVs. REVs released in response to LPA were similar to those found in the plasma of systemic lupus erythematosus patients. Our results suggest that LPA species exhibit different biological activities in RBCs through targeting LPA2 and/or LPA3 receptors.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".