Annexin A2 Is a Natural Extrahepatic Inhibitor of the PCSK9-Induced LDL Receptor Degradation
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin-9 (PCSK9) enhances the degradation of hepatic low-density lipoprotein receptor (LDLR). Deletion of PCSK9, and loss-of-function mutants in humans result in lower levels of circulating LDL-cholesterol and a strong protection against coronary heart disease. Accordingly, the quest for PCSK9 inhibitors has major clinical implications. We have previously identified annexin A2 (AnxA2) as an endogenous binding partner and functional inhibitor of PCSK9. Herein, we studied the relevance of AnxA2 in PCSK9 inhibition and lipid metabolism in vivo. Plasma analyses of AnxA2(-/-) mice revealed: i) a ∼1.4-fold increase in LDL-cholesterol without significant changes in VLDLs or HDLs, and ii) a ∼2-fold increase in circulating PCSK9 levels. Western blotting and immunohistochemistry of AnxA2(-/-) tissues revealed that the LDLR was decreased by ∼50% in extrahepatic tissues, such as adrenals and colon. We also show that AnxA2-derived synthetic peptides block the PCSK9≡LDLR interaction in vitro, and adenoviral overexpression of AnxA2 in mouse liver increases LDLR protein levels in vivo. These results suggest that AnxA2 acts as an endogenous regulator of LDLR degradation, mostly in extrahepatic tissues. Finally, we identified an AnxA2 coding polymorphism, V98L, that correlates with lower circulating levels of PCSK9 thereby extending our results on the physiological role of AnxA2 in humans.
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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".