PCSK9 reduces the protein levels of the LDL receptor in mouse brain during development and after ischemic stroke
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a major role in cholesterol homeostasis through enhanced degradation of the LDL receptor (LDLR) in liver. As novel inhibitors/silencers of PCSK9 are now being tested in clinical trials to treat hypercholesterolemia, it is crucial to define the physiological consequences of the lack of PCSK9 in various organs. LDLR regulation by PCSK9 has not been extensively described during mouse brain development and injury. Herein, we show that PCSK9 and LDLR are co-expressed in mouse brain during development and at adulthood. Although the protein levels of LDLR and apolipoprotein E (apoE) in the adult brain of Pcsk9(-/-) mice are similar to those of wild-type (WT) mice, LDLR levels increased and were accompanied by a reduction of apoE levels during development. This suggests that the upregulation of LDLR protein levels in Pcsk9(-/-) mice enhances apoE degradation. Upon ischemic stroke, PCSK9 was expressed in the dentate gyrus between 24 h and 72 h following brain reperfusion. Although mouse behavior and lesion volume were similar, LDLR protein levels dropped ∼2-fold less in the Pcsk9(-/-)-lesioned hippocampus, without affecting apoE levels and neurogenesis. Thus, PCSK9 downregulates LDLR levels during brain development and following transient ischemic stroke in adult mice.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".