The neurorepellent, Slit2, prevents macrophage lipid loading by inhibiting CD36-dependent binding and internalization of oxidized low-density lipoprotein
Bibliographic record
Abstract
Atherosclerosis is characterized by retention of modified lipoproteins, especially oxidized low density lipoprotein (oxLDL) within the sub-endothelial space of affected blood vessels. Recruited monocyte-derived and tissue-resident macrophages subsequently ingest oxLDL by binding and internalizing oxLDL via scavenger receptors, particularly CD36. The secreted neurorepellent, Slit2, acting through its transmembrane receptor, Roundabout-1 (Robo-1), was previously shown to inhibit recruitment of monocytes into nascent atherosclerotic lesions. The effects of Slit2 on oxLDL uptake by macrophages have not been explored. We report here that Slit2 inhibits uptake of oxLDL by human and murine macrophages, and the resulting formation of foam cells, in a Rac1-dependent and CD36-dependent manner. Exposure of macrophages to Slit2 prevented binding of oxLDL to the surface of cells. Using super-resolution microscopy, we observed that exposure of macrophages to Slit2 induced profound cytoskeletal remodeling with formation of a thick ring of cortical actin within which clusters of CD36 could not aggregate, thereby attenuating binding of oxLDL to the surface of cells. By inhibiting recruitment of monocytes into early atherosclerotic lesions, and the subsequent binding and internalization of oxLDL by macrophages, Slit2 could represent a potent new tool to combat individual steps that collectively result in progression of atherosclerosis.
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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 | 0.000 |
| 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".