High Density Lipoprotein Stimulated Migration of Macrophages Depends on the Scavenger Receptor Class B, Type I, PDZK1 and Akt1 and Is Blocked by Sphingosine 1 Phosphate Receptor Antagonists
Bibliographic record
Abstract
HDL carries biologically active lipids such as sphingosine-1-phosphate (S1P) and stimulates a variety of cell signaling pathways in diverse cell types, which may contribute to its ability to protect against atherosclerosis. HDL and sphingosine-1-phosphate receptor agonists, FTY720 and SEW2871 triggered macrophage migration. HDL-, but not FTY720-stimulated migration was inhibited by an antibody against the HDL receptor, SR-BI, and an inhibitor of SR-BI mediated lipid transfer. HDL and FTY720-stimulated migration was also inhibited in macrophages lacking either SR-BI or PDZK1, an adaptor protein that binds to SR-BI's C-terminal cytoplasmic tail. Migration in response to HDL and S1P receptor agonists was inhibited by treatment of macrophages with sphingosine-1-phosphate receptor type 1 (S1PR1) antagonists and by pertussis toxin. S1PR1 activates signaling pathways including PI3K-Akt, PKC, p38 MAPK, ERK1/2 and Rho kinases. Using selective inhibitors or macrophages from gene targeted mice, we demonstrated the involvement of each of these pathways in HDL-dependent macrophage migration. These data suggest that HDL stimulates the migration of macrophages in a manner that requires the activities of the HDL receptor SR-BI as well as S1PR1 activity.
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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) | 0.003 | 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".