Ceramide activation of RhoA/Rho kinase impairs actin polymerization during aggregated LDL catabolism
Bibliographic record
Abstract
Macrophages use an extracellular, hydrolytic compartment formed by local actin polymerization to digest aggregated LDL (agLDL). Catabolism of agLDL promotes foam cell formation and creates an environment rich in LDL catabolites, including cholesterol and ceramide. Increased ceramide levels are present in lesional LDL, but the effect of ceramide on macrophage proatherogenic processes remains unknown. Here, we show that macrophages accumulate ceramide in atherosclerotic lesions. Using macrophages from sphingosine kinase 2 KO (SK2KO) mice to mimic ceramide-rich conditions of atherosclerotic lesions, we show that SK2KO macrophages display impaired actin polymerization and foam cell formation in response to contact with agLDL. C16-ceramide treatment impaired wild-type but not SK2KO macrophage actin polymerization, confirming that this effect is due to increased ceramide levels. We demonstrate that knockdown of RhoA or inhibition of Rho kinase restores agLDL-induced actin polymerization in SK2KO macrophages. Activation of RhoA in macrophages was sufficient to impair actin polymerization and foam cell formation in response to agLDL. Finally, we establish that during catabolism, macrophages take up ceramide from agLDL, and inhibition of ceramide generation modulates actin polymerization. These findings highlight a critical regulatory pathway by which ceramide impairs actin polymerization through increased RhoA/Rho kinase signaling and regulates foam cell formation.
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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) | 0.001 | 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".