Lipid Incorporation Inhibits Src-Dependent Assembly of Fibronectin and Type I Collagen by Vascular Smooth Muscle Cells
Bibliographic record
Abstract
A vital role of vascular smooth muscle cells (SMCs) is to stabilize the artery wall by elaborating fibrils of type I collagen. This is especially important in atherosclerotic lesions. However, SMCs in these lesions can be laden with lipids and the impact of this modification on collagen fibril formation is unknown. To address this, we converted human vascular SMCs to a foam cell state by incubating them with either LDL or VLDL. Biochemical markers of a SMC phenotype were preserved. However, microscopic tracking revealed a profound perturbation in the ability of the cells to assemble collagen fibrils, reducing assembly by up to 79%. This dysfunction was mirrored by an inability of smooth muscle foam cells to assemble fibronectin. Lipid-loaded SMCs did not display a generalized defect in the actin cytoskeleton and the formation of vinculin-containing focal adhesion complexes was preserved. However, lipid-loaded SMCs were unable to assemble fibrillar adhesion complexes and clustering of tensin and alpha5beta1 integrin was disordered. Moreover, phosphorylation of tensin, required for fibrillar adhesion complex formation, was suppressed by up to 57%, with a concomitant decrease in activation of Src and FAK and restriction of activated Src to the cell edges. Forced activation of Src-FAK signaling in lipid-engorged SMCs rescued both fibrillar adhesion formation and fibrillogenesis. We conclude that lipid accumulation by SMCs disables the machinery for collagen and fibronectin assembly. This previously unknown relationship between atherogenic lipids and integrin-based signaling could underlie plaque vulnerability.
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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 | 0.001 |
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".