PCSK9 Promotes Atherosclerotic Plaque Instability by Inducing VSMC Ferroptosis through the YAP1–NUPR1 Axis
Bibliographic record
Abstract
Atherosclerosis persists as a principal driver of global cardiovascular mortality and morbidity, and its sustained prevalence surge fuels the incidence of major adverse cardiovascular events (MACE). Plaque instability is a critical determinant of MACE, as fissure formation or rupture of vulnerable plaques can precipitate thromboembolic complications. In this study, we investigate a noncanonical role of proprotein convertase subtilisin/kexin type 9 (PCSK9) beyond its lipid regulatory function, focusing on its impact on vascular smooth muscle cells (VSMCs) in the context of plaque instability. Our results demonstrate that PCSK9 overactivity markedly promotes ferroptotic cell death in VSMCs, thereby exacerbating plaque vulnerability. Furthermore, we delineate the underlying mechanism: PCSK9 physically interacts with Yes-associated protein 1 and targets it for lysosomal degradation, which, in turn, suppresses the expression of nuclear protein 1. In conclusion, our findings unveil a novel role of PCSK9 in promoting plaque instability by driving ferroptosis in VSMCs, suggesting that targeting PCSK9 presents a potential avenue for plaque stabilization, thereby mitigating the incidence of major MACE.
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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) | 0.002 | 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".