Lipid raft proteomics identify endothelial myosin-9 (MYH9) as a regulator of low-density lipoprotein transcytosis and atherosclerosis
Bibliographic record
Abstract
Background: In early atherosclerosis, circulating Low-Density Lipoprotein (LDL) crosses the endothelium by transcytosis. This involves caveolar uptake of LDL by scavenger receptor BI (SR-BI) and activin-like kinase 1 (ALK1) and requires the protein caveolin-1 (Cav-1). We identified mediators of LDL transcytosis by isolating membrane microdomains enriched in caveolin-1 from human coronary endothelial cells (HCAECs) treated with LDL and performing mass spectrometry. One of the proteins identified was myosin-9 (MYH9). Methods: Total internal reflection fluorescence microscopy was conducted to measure LDL transcytosis by HCAECs. We measured LDL transcytosis in vivo in mice lacking endothelial MYH9 (EC- Myh9 −/− ). Atherosclerosis studies were also performed in EC- Myh9 −/− deleted of hepatic LDLR via (adeno-associated virus, AAV)-CRISPR. Additionally, we performed analysis of human transcriptomic data. Results: Gene ontology analysis in human aortic endothelial cells suggested a role for MYH9 in exocytosis. Both knockdown and pharmacologic inhibition of MYH9 inhibited LDL transcytosis. MYH9 depletion caused an accumulation of LDL-containing vesicles at the base of the cell; overexpression caused an increase in LDL exocytosis. EC- Myh9 −/− mice accumulated less LDL in the aortic arch after acute injection with LDL. To investigate the role of MYH9 in atherosclerosis, we deleted hepatic LDL in EC- Myh9 −/− mice using AAV-CRISPR and fed them a high-fat diet. The aortic arch and root of AAV-CRISPR; EC- Myh9 −/− mice exhibited smaller plaques. Human transcriptomic data showed greater messenger RNA (mRNA) levels of aortic MYH9 in atherosclerotic aortas compared to healthy controls. Conclusions: Lipid raft proteomics identified MYH9 as a regulator of LDL transcytosis. MYH9 is required for endothelial LDL exocytosis and contributes to early 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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".