Protein Kinase C-ϵ Regulates Sphingosine 1-Phosphate-mediated Migration of Human Lung Endothelial Cells through Activation of Phospholipase D2, Protein Kinase C-ζ, and Rac1
Bibliographic record
Abstract
The signaling pathways by which sphingosine 1-phosphate (S1P) potently stimulates endothelial cell migration and angiogenesis are not yet fully defined. We, therefore, investigated the role of protein kinase C (PKC) isoforms, phospholipase D (PLD), and Rac in S1P-induced migration of human pulmonary artery endothelial cells (HPAECs). S1P-induced migration was sensitive to S1P(1) small interfering RNA (siRNA) and pertussis toxin, demonstrating coupling of S1P(1) to G(i). Overexpression of dominant negative (dn) PKC-epsilon or -zeta, but not PKC-alpha or -delta, blocked S1P-induced migration. Although S1P activated both PLD1 and PLD2, S1P-induced migration was attenuated by knocking down PLD2 or expressing dnPLD2 but not PLD1. Blocking PKC-epsilon, but not PKC-zeta, activity attenuated S1P-mediated PLD stimulation, demonstrating that PKC-epsilon, but not PKC-zeta, was upstream of PLD. Transfection of HPAECs with dnRac1 or Rac1 siRNA attenuated S1P-induced migration. Furthermore, transfection with PLD2 siRNA, infection of HPAECs with dnPKC-zeta, or treatment with myristoylated PKC-zeta peptide inhibitor abrogated S1P-induced Rac1 activation. These results establish that S1P signals through S1P(1) and G(i) to activate PKC-epsilon and, subsequently, a PLD2-PKC-zeta-Rac1 cascade. Activation of this pathway is necessary to stimulate the migration of lung endothelial cells, a key component of the angiogenic process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".