Hepatic PCSK9 Expression Is Regulated by Nutritional Status via Insulin and Sterol Regulatory Element-binding Protein 1c
Bibliographic record
Abstract
Familial autosomal dominant hypercholesterolemia is associated with high risk for cardiovascular accidents and is related to mutations in the low density lipoprotein receptor or its ligand apolipoprotein B (apoB). Mutations in a third gene, proprotein convertase subtilisin kexin 9 (PCSK9), were recently associated to this disease. PCSK9 acts as a natural inhibitor of the low density lipoprotein receptor pathway, and both genes are regulated by depletion of cholesterol cell content and statins, via sterol regulatory element-binding protein (SREBP). Here we investigated the regulation of PCSK9 gene expression during nutritional changes. We showed that PCSK9 mRNA quantity is decreased by 73% in mice after 24 h of fasting, leading to a 2-fold decrease in protein level. In contrast PCSK9 expression was restored upon high carbohydrate refeeding. PCSK9 mRNA increased by 4-5-fold in presence of insulin in rodent primary hepatocytes, whereas glucose had no effect. Moreover, insulin up-regulated hepatic PCSK9 expression in vivo during a hyperinsulinemic-euglycemic clamp in mice. Adenoviral mediated overexpression of a dominant or negative form of SREBP-1c confirmed the implication of this transcription factor in insulin-mediated stimulation of PCSK9 expression. Liver X receptor agonist T0901317 also regulated PCSK9 expression via this same pathway (a 2-fold increase in PCSK9 mRNA of primary hepatocytes cultured for 24 h in presence of 1 microm T0901317). As our last investigation, we isolated PCSK9 proximal promoter and verified the functionality of a SREBP-1c responsive element located from 335 bp to 355 bp upstream of the ATG. Together, these results show that PCSK9 expression is regulated by nutritional status and insulinemia.
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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 | 0.000 |
| 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".