Strong induction of PCSK9 gene expression through HNF1α and SREBP2: mechanism for the resistance to LDL-cholesterol lowering effect of statins in dyslipidemic hamsters
Bibliographic record
Abstract
We investigated the role of proprotein convertase subtilisin/kexin type 9 (PCSK9) in the resistance of dyslipidemic hamsters to statin-induced LDL-cholesterol (LDL-C) reduction and the molecular mechanism by which statins modulated PCSK9 gene expression in vivo. We utilized the fructose diet-induced dyslipidemic hamsters as an in vivo model and rosuvastatin to examine its effects on liver PCSK9 and LDL receptor (LDLR) expression and serum lipid levels. We showed that rosuvastatin induced PCSK9 mRNA to a greater extent than LDLR mRNA in the hamster liver. The net result was that hepatic LDLR protein level was reduced. This correlated closely with an increase in serum LDL-C with statin treatment. More importantly, we demonstrated that in addition to an increase in sterol response element binding protein 2 (SREBP2) expression, rosuvastatin treatment increased the liver expression of hepatocyte nuclear factor 1 alpha (HNF1alpha), the newly identified key transactivator for PCSK9 gene expression. Our study suggests that the inducing effect of rosuvastatin on HNF1alpha is likely a underlying mechanism accounting for the higher induction of PCSK9 than LDLR because of the utilization of two transactivators (HNF1alpha and SREBP2) in PCSK9 transcription versus one (SREBP2) in LDLR transcription. Thus, the net balance is in favor of PCSK9-induced degradation of LDLR in the hamster liver, abrogating the effect of rosuvastatin on LDL-C lowering.
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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".