LDL Receptor Pathway Regulation by miR-224 and miR-520d
Bibliographic record
Abstract
MicroRNAs (miRNA) have emerged as important post-transcriptional regulators of metabolic pathways that contribute to cellular and systemic lipoprotein homeostasis. Here we identify two conserved miRNAs, miR-224 and miR-520d, which target gene networks regulating hepatic expression of the low-density lipoprotein receptor (LDLR) and LDL clearance. In silico prediction of miR-224 and miR-520d target gene networks showed that they each repress multiple genes impacting the expression of the LDLR, including the chaperone molecules PCSK9 and IDOL that limit LDLR expression at the cell surface, and the rate-limiting enzyme for cholesterol synthesis HMGCR, which is the target of LDL-lowering statin drugs. Using gain- and loss-of-function studies, we tested the role of miR-224 and miR-520d in the regulation of those predicted targets and their impact on LDLR expression. We show that overexpression of miR-224 or miR-520d dose-dependently reduced the activity of PCSK9, IDOL and HMGCR 3’-UTR-luciferase reporter constructs, and this repression was abrogated by mutation of the putative miR-224 or miR-520d response elements in the PCSK9, IDOL and HMGCR 3’-UTRs. Compared to a control miRNA, overexpression of miR-224 or miR-520d in hepatocytes inhibited PCSK9, IDOL, and HMGCR mRNA and protein levels, and decreased PCSK9 secretion. Furthermore, miR-224 and miR-520d repression of PCSK9, IDOL, and HMGCR was associated with an increase in LDLR protein levels and cell surface expression, as well as enhanced LDL binding. Notably, the effects of miR-224 and miR-520d were additive to the effects of statins in upregulating LDLR expression. Finally, we show that overexpression of miR-224 in the livers of Ldlr+/- mice using lipid nanoparticle mediated delivery resulted in a 15% decrease in plasma levels of LDL cholesterol, compared to a control miRNA. Together, these findings identify roles for miR-224 and miR-520d in the post-transcriptional control of LDLR expression and function.
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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 | 0.001 |
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".