β‐Estradiol results in a proprotein convertase subtilisin/kexin type 9‐dependent increase in low‐density lipoprotein receptor levels in human hepatic HuH7 cells
Bibliographic record
Abstract
The lower risk of coronary artery disease in premenopausal women than in men and postmenopausal women implicates sex steroids in cardioprotective processes. β-Estradiol upregulates liver low-density lipoprotein receptor (LDLR), which, in turn, decreases circulating levels of low-density lipoprotein, which is a risk factor for coronary artery disease. Conversely, LDLR protein is negatively regulated by proprotein convertase subtilisin/kexin type 9 (PCSK9). Herein, we investigated PCSK9 regulation by β-estradiol and its impact on LDLR in human hepatocarcinoma HuH7 cells grown in the presence or absence of β-estradiol. Immunoblot analysis showed upregulation of LDLR at 3 μm β-estradiol (140%), and the upregulation reached 220% at 10 μm β-estradiol; only at the latter dose was an increase in LDLR mRNA detected by qPCR, suggesting post-translational regulation of LDLR. No changes in PCSK9 mRNA or secreted protein levels were detected by qPCR or ELISA, respectively. β-estradiol-conditioned medium devoid of PCSK9 failed to upregulate LDLR. Similarly, PCSK9 knockdown cells showed no upregulation of LDLR by β-estradiol. Together, these results indicate a requirement for PCSK9 in the β-estradiol-induced upregulation of LDLR. A radiolabeling assay showed a significant, dose-dependent decrease in the ratio of secreted phosphoPCSK9 to total secreted PCSK9 with increasing β-estradiol levels, suggesting a change in the functional state of PCSK9 in the presence of β-estradiol. Our results indicate that the protein upregulation of LDLR at subtranscriptionally effective doses of β-estradiol, and its supratranscriptional upregulation at 10 μm β-estradiol, occur through an extracellular PCSK9-dependent mechanism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".