PCSK9 deficiency reduces atherosclerosis, apolipoprotein B secretion, and endothelial dysfunction
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) interacts directly with cytoplasmic apoB and prevents its degradation via the autophagosome/lysosome pathway. This process affects VLDL and LDL production and influences atherogenesis. Here, we investigated the molecular machinery by which PCSK9 modulates autophagy and affects atherogenesis. We backcrossed Pcsk9 −/− mice with atherosclerosis-prone Ldlr −/− Apobec1 −/− (LDb) mice to generate Ldlr −/− Apobec1 −/− Pcsk9 −/− (LTp) mice. Deletion of PCSK9 resulted in decreased hepatic apoB secretion, increased autophagic flux, and decreased plasma levels of IDL and LDL particles. The LDLs from LTp mice (LTp-LDLs) were less atherogenic and contained less cholesteryl ester and phospholipids than LDb-LDLs. Moreover LTp-LDLs induced lower endothelial expression of the genes encoding TLR2 , Lox-1 , ICAM-1 , CCL2 , CCL7 , IL-6 , IL-1β , Beclin-1 , p62 , and TRAF6 . Collectively, these effects were associated with substantially less atherosclerosis development (>4-fold) in LTp mice. The absence of PCSK9 in LDb mice results in decreased lipid and apoB levels, fewer atherogenic LDLs, and marked reduction of atherosclerosis. The effect on atherogenesis may be mediated in part by the effects of modified LDLs on endothelial cell receptors and proinflammatory and autophagy molecules. These findings suggest that there may be clinical benefits of PCSK9 inhibition due to mechanisms unrelated to increased LDL receptor activity.
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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 | 0.001 | 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".