Ligand-dependent interactions between SR-B1 and S1PR1 in macrophages and atherosclerotic plaques
Bibliographic record
Abstract
High-density lipoproteins (HDL) carry sphingosine-1-phosphate (S1P) and stimulate signaling pathways in different cells including macrophages and endothelial cells, involved in atherosclerotic plaque development. HDL signaling via S1P relies on the HDL receptor scavenger receptor class B, type I (SR-B1) and the sphingosine-1-phosphate receptor 1 (S1PR1), which interact when both are heterologously overexpressed in the HEK293 cell line. In this study, we set out to test if SR-B1 and S1PR1 interacted in primary murine macrophages in culture and atherosclerotic plaques. We used knock-in mice that endogenously expressed S1PR1 tagged with eGFP- ( S1pr1 eGFP/eGFP mice), combined with proximity ligation analysis to demonstrate that HDL stimulates the physical interaction between SR-B1 and S1PR1 in primary macrophages, that this is dependent on HDL-associated S1P and can be blocked by an inhibitor of SR-B1's lipid transfer activity or an antagonist of S1PR1. We also demonstrate that a synthetic S1PR1-selective agonist, SEW2871, stimulates the interaction between SR-B1 and S1PR1 and that this was also blocked by an inhibitor of SR-B1's lipid transport activity. Furthermore, we detected abundant SR-B1/S1PR1 complexes in atherosclerotic plaques of S1pr1 eGFP/eGFP mice that also lacked apolipoprotein E . Treatment of mice with the S1PR1 antagonist, Ex26, for 12 hours disrupted the SR-B1-S1PR1 interaction in atherosclerotic plaques. These findings demonstrate that SR-B1 and S1PR1 form ligand-dependent complexes both in cultured primary macrophages and within atherosclerotic plaques in mice and provide mechanistic insight into how SR-B1 and S1PR1 participate in mediating HDL signaling to activate atheroprotective responses in macrophages.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".