The Neurorepellent Slit2 Inhibits Postadhesion Stabilization of Monocytes Tethered to Vascular Endothelial Cells
Bibliographic record
Abstract
The secreted neurorepellent Slit2, acting through its transmembrane receptor, Roundabout (Robo)-1, inhibits chemotaxis of varied cell types, including leukocytes, endothelial cells, and vascular smooth muscle cells, toward diverse attractants. The role of Slit2 in regulating the steps involved in recruitment of monocytes in vascular inflammation is not well understood. In this study, we showed that Slit2 inhibited adhesion of monocytic cells to activated human endothelial cells, as well as to immobilized ICAM-1 and VCAM-1. Microfluidic live cell imaging showed that Slit2 inhibited the ability of monocytes tethered to endothelial cells to stabilize their actin-associated anchors and to resist detachment in response to increasing shear forces. Transfection of constitutively active plasmids revealed that Slit2 inhibited postadhesion stabilization of monocytes on endothelial cells by preventing activation of Rac1. We further found that Slit2 inhibited chemotaxis of monocytes toward CXCL12 and CCL2. To determine whether Slit2 and Robo-1 modulate pathologic monocyte recruitment associated with vascular inflammation and cardiovascular disease, we tested PBMC from patients with coronary artery disease. PBMC from these patients had reduced surface levels of Robo-1 compared with healthy age- and sex-matched subjects, and Slit2 failed to inhibit chemotaxis of PBMC of affected patients, but not healthy control subjects, toward CCL2. Furthermore, administration of Slit2 to atherosclerosis-prone LDL receptor-deficient mice inhibited monocyte recruitment to nascent atherosclerotic lesions. These results demonstrate that Slit2 inhibits chemotaxis of monocytes, as well as their ability to stabilize adhesions and resist detachment forces. Slit2 may represent a powerful new tool to inhibit pathologic monocyte recruitment in vascular inflammation and atherosclerosis.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".