Abstract 63: Post-transcriptional Control of the LDLR Pathway by Cholesterol-responsive microRNAs.
Bibliographic record
Abstract
Despite the success of statins to reduce levels of LDL-cholesterol (LDL-C), cardiovascular disease remains the leading cause of mortality in westernized countries. PCSK9 inhibition is emerging as an exciting new therapy to further lower LDL and holds tremendous promise to reduce the significant residual cardiovascular risk in many individuals. Recent advances from our group and others have shown that microRNAs (miRNAs) have emerged as critical components in the regulation of cholesterol metabolism. In addition to miR-33, which we discovered as a key regulator of metabolic programs including cholesterol and fatty acid homeostasis, we identified two new cholesterol responsive miRNAs, miR-520d-5p (miR-520d) and miR-224. Here we show that miR-520d and miR-224 target the 3’UTR of both PCSK9 and Idol, “accessory” proteins that regulate cell surface expression of the LDL receptor (LDLR). Moreover, miR-520d and miR-224 simultaneously target the 3’UTR of HMGCR, which codes for the limiting enzyme in cholesterol synthesis and is the target of statins. In mouse and human cells, the overexpression of miR-520d inhibits PCSK9, Idol and HMGCR mRNA levels by 30-50% compared to a control miRNA, while overexpression of miR-224 results in an even more potent 75% decrease in PCSK9, Idol and HMGCR mRNA compared to controls. In human hepatocytes (HepG2/Huh7) and mouse peritoneal macrophages, miR-520d and miR-224 overexpression caused a repression of PCSK9, Idol and HMGCR protein levels, and a decrease in PCSK9 protein secretion. Importantly, these miR-224 and miR-520d-induced changes in PCSK9, Idol and HMGCR expression were associated with a concomitant increase in LDLR protein expression, increased cell surface expression of an LDLR-GFP fusion protein and enhanced LDL binding to the cell surface. Notably, these increases in LDLR were above and beyond those achieved with statin treatment. Together, these findings suggest a role of miR-224 and miR-520d in contributing to the post-transcriptional regulation of LDLR expression, and represent the first evidence of miRNA control of the LDLR pathway. This highlights the therapeutic promise of miR-224/miR-520d for reducing LDL cholesterol beyond statin therapy alone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".