A Common Polymorphism in the <i>FADS1</i> Locus Links miR1908 to Low-Density Lipoprotein Cholesterol Through BMP1
Bibliographic record
Abstract
OBJECTIVE: Leveraging microRNA-Seq data and the 1000 Genomes imputed genotypes, we identified rs174561 as a strong microRNA quantitative trait loci for circulating microRNA-1908-5p with higher miR-1908-5p and reduced LDL (lowdensity lipoprotein)-cholesterol, fasting glucose and A1c concentrations in carriers of the rs-174561-C allele. Here, we have investigated the molecular mechanism(s) linking miR-1908-5p to LDL-C concentrations. APPROACH AND RESULTS: Transfection experiments demonstrate that the presence of the C allele significantly increases miR- 1908-5p abundance relative to the T allele. LDLR mRNA and low-density lipoprotein receptor (LDLR) total protein were unchanged in response to differential miR-1908-5p expression. However, the ratio of the cleaved to full-length form of LDLR decreased with miR-1908-5p mimic and increased with miR-1908-5p inhibitor treatment. BMP1 (bone morphogenetic protein 1) is a protease responsible for LDLR cleavage, and we show that miR-1908-5p mimic reduces BMP1 mRNA. Using a reporter array, we identified the TGF-β (transforming growth factor-beta) signaling pathway activity to be reduced by miR- 1908-5p mimic treatment, and this was associated with reduced TGFB1 expression. TGF-β signaling increases BMP1, and we further demonstrate that the effect of miR-1908-5p on LDLR cleavage is abolished by exogenous TGF-β treatment. CONCLUSIONS: These findings uncover a mechanism whereby miR-1908-5p reduces TGFB1 abundance resulting in lower expression of BMP1, ultimately leading to reduced LDLR cleavage. Cleavage of the mature LDLR is known to reduce cell surface affinity for LDL, thereby linking miR-1908-5p to lower circulating 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".