Post-transcriptional Regulation of PCSK9 by miR-191, miR-222, and miR-224
Bibliographic record
Abstract
Since proprotein convertase subtilisin kexin 9 (PCSK9) discovery, a gene involved in LDL metabolism regulation and cardiovascular diseases (CVD), many therapeutic strategies have been introduced for direct targeting of PCSK9. The main goal of these strategies has been to reduce PCSK9 protein level either by application of antibodies or inhibition of its production. In this study, we have tried to discover microRNAs (miRNAs) which can target, and hence regulate, PCSK9 expression. Using bioinformatics tools, we selected three microRNAs with binding sites on 3'-UTR of PCSK9. The expression level of these miRNAs was examined in three different cell lines using real-time RT-PCR. We observed a reciprocal expression pattern between expression level of miR-191, miR-222, and miR-224 with that of PCSK9. Accordingly, the expression levels were highest in Huh7 cells which expressed the lowest level of PCSK9, compared to HepG2 and A549 cell lines. PCSK9 mRNA level also showed a significant decline in HepG2 cells transfected with the vectors overexpressing the aforementioned miRNAs. Furthermore, the miRNAs target sites were cloned in psiCHECK-2 vector, and a direct interaction of the miRNAs and the PCSK9 3'-UTR putative target sites was investigated by means of luciferase assay. Our findings revealed that miR-191, miR-222, and miR-224 can directly interact with PCSK9 3'-UTR and regulate its expression. In conclusion, our data introduces a role for miRNAs to regulate PCSK9 expression.
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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".