ARF6 controls VSMC cell phenotypic switching upon lipid stimulation to promote inflammatory signaling contributing to the progression of atherosclerosis
Bibliographic record
Abstract
Abstract Vascular smooth muscle cells (VSMCs) play a pivotal role in the development and progression of atherosclerosis. Traditionally viewed as contractile cells that maintain vascular tone and structure, VSMCs undergo phenotypic switching in response to atherogenic stimuli, such as high circulating levels of LDL, thus adopting synthetic, osteogenic, or macrophage-like phenotypes. This plasticity contributes to plaque formation, extracellular matrix remodeling, and inflammatory signaling. We have previously shown that ADP-ribosylation factor 1 (ARF1), a small GTP-binding protein, regulates the expression and function of actin, which is important for maintaining the contractile phenotype of VSMCs. However, the role of ARF6 in phenotypic switching remains to be elucidated. Here, we demonstrate that ARF6 knockdown in human aortic smooth muscle cells (HASMCs) reduced lipid uptake through alterations in the expression of scavenger receptors (LOX-1, MSR1), cytokine production (IL-6) as well as modulation of inflammatory markers and pathways (adhesion molecules, PI3K, NFkB, p38). To confirm our findings in an in vivo setting, we engineered a novel conditional smooth muscle cell-specific ARF6 KO mouse in an atheroprone background ( Acta2-Cre-ERT2 +/− /ApoE −/− /Arf6 f/f mice). Mice were fed a high-fat diet to accelerate plaque formation. ARF6 knockout resulted in a significant reduction of atherosclerotic lesions in the aortic arch, which was associated with a reduction of collagen and foam cells. Furthermore, we observed that ARF6 regulates the expression of inflammatory markers. These findings highlight the importance of ARF6 expression in VSMCs and its role in the pathogenesis 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".