Endoplasmic Reticulum Stress and Ca2+ Depletion Differentially Modulate the Sterol Regulatory Protein PCSK9 to Control Lipid Metabolism
Bibliographic record
Abstract
Accumulating evidence implicates endoplasmic reticulum (ER) stress as a mediator of impaired lipid metabolism, thereby contributing to fatty liver disease and atherosclerosis. Previous studies demonstrated that ER stress can activate the sterol regulatory element-binding protein-2 (SREBP2), an ER-localized transcription factor that directly up-regulates sterol regulatory genes, including PCSK9 . Given that PCSK9 contributes to atherosclerosis by targeting low density lipoprotein (LDL) receptor (LDLR) degradation, this study investigates a novel mechanism by which ER stress plays a role in lipid metabolism by examining its ability to modulate PCSK9 expression. Herein, we demonstrate the existence of two independent effects of ER stress on PCSK9 expression and secretion. In cultured HuH7 and HepG2 cells, agents or conditions that cause ER Ca 2+ depletion, including thapsigargin, induced SREBP2-dependent up-regulation of PCSK9 expression. In contrast, a significant reduction in the secreted form of PCSK9 protein was observed in the media from both thapsigargin- and tunicamycin (TM)-treated HuH7 cells, mouse primary hepatocytes, and in the plasma of TM-treated C57BL/6 mice. Furthermore, TM significantly increased hepatic LDLR expression and reduced plasma LDL concentrations in mice. Based on these findings, we propose a model in which ER Ca 2+ depletion promotes the activation of SREBP2 and subsequent transcription of PCSK9. However, conditions that cause ER stress regardless of their ability to dysregulate ER Ca 2+ inhibit PCSK9 secretion, thereby reducing PCSK9-mediated LDLR degradation and promoting LDLR-dependent hepatic cholesterol uptake. Taken together, our studies provide evidence that the retention of PCSK9 in the ER may serve as a potential strategy for lowering LDL cholesterol levels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".