Human PCSK9 promotes hepatic lipogenesis and atherosclerosis development via apoE- and LDLR-mediated mechanisms
Bibliographic record
Abstract
AIMS: Proprotein convertase subtilisin/kexin type 9 (PCSK9) promotes the degradation of hepatic low-density lipoprotein (LDL) receptors (LDLR), thereby, decreasing hepatocyte LDL-cholesterol (LDL-C) uptake. However, it is unknown whether PCSK9 has effects on atherogenesis that are independent of lipid changes. The present study investigated the effect of human (h) PCSK9 on plasma lipids, hepatic lipogenesis, and atherosclerotic lesion size and composition in transgenic mice expressing hPCSK9 (hPCSK9tg) on wild-type (WT), LDLR⁻/⁻, or apoE⁻/⁻ background. METHODS AND RESULTS: hPCSK9 expression significantly increased plasma cholesterol (+91%), triglycerides (+18%), and apoB (+57%) levels only in WT mice. The increase in plasma lipids was a consequence of both decreased hepatic LDLR and increased hepatic lipid production, mediated transcriptionally and post-transcriptionally by PCSK9 and dependent on both LDLR and apoE. Despite the lack of changes in plasma lipids in mice expressing hPCSK9 and lacking LDLR (the main target for PCSK9) or apoE (a canonical ligand for the LDLR), hPCSK9 expression increased aortic lesion size in the absence of apoE (268 655 ± 97 972 µm² in hPCSK9tg/apoE⁻/⁻ vs. 189 423 ± 65 700 µm(2) in apoE⁻/⁻) but not in the absence of LDLR. Additionally, hPCSK9 accumulated in the atheroma and increased lesion Ly6C(hi) monocytes (by 21%) in apoE⁻/⁻ mice, but not in LDLR⁻/⁻ mice. CONCLUSIONS: PCSK9 increases hepatic lipid and lipoprotein production via apoE- and LDLR-dependent mechanisms. However, hPCSK9 also accumulate in the artery wall and directly affects atherosclerosis lesion size and composition independently of such plasma lipid and lipoprotein changes. These effects of hPCSK9 are dependent on LDLR but are independent of apoE.
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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 | 0.001 |
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".