Neuroimmune Guidance Cue Semaphorin 3E Is Expressed in Atherosclerotic Plaques and Regulates Macrophage Retention
Bibliographic record
Abstract
OBJECTIVE: The persistence of myeloid-derived cells in the artery wall is a characteristic of advanced atherosclerotic plaques. However, the mechanisms by which these cells are retained are poorly understood. Semaphorins, a class of neuronal guidance molecules, play a critical role in vascular patterning and development, and recent studies suggest that they may also have immunomodulatory functions. The present study evaluates the expression of Semaphorin 3E (Sema3E) in settings relevant to atherosclerosis and its contribution to macrophage accumulation in plaques. APPROACH AND RESULTS: Immunofluorescence staining of Sema3E, and its receptor PlexinD1, demonstrated their expression in macrophages of advanced atherosclerotic lesions of Apoe(-/-) mice. Notably, in 2 different mouse models of atherosclerosis regression, Sema3E mRNA was highly downregulated in plaque macrophages, coincident with a reduction in plaque macrophage content and an enrichment in markers of reparative M2 macrophages. In vitro, Sema3E mRNA was highly expressed in inflammatory M1 macrophages and in macrophages treated with physiological drivers of plaque progression and inflammation, such as oxidized low-density lipoprotein and hypoxia. To explore mechanistically how Sema3E affects macrophage behavior, we treated macrophages with recombinant protein in the presence/absence of chemokines, including CCL19, a chemokine implicated in the egress of macrophages from atherosclerotic plaques. Sema3E blocked actin polymerization and macrophage migration stimulated by the chemokines, suggesting that it may immobilize these cells in the plaque. CONCLUSIONS: Sema3E is upregulated in macrophages of advanced plaques, is dynamically regulated by multiple atherosclerosis-relevant factors, and acts as a negative regulator of macrophage migration, which may promote macrophage retention and chronic inflammation in vivo.
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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".