MicroRNA miR‐378‐3p is an Essential Regulator of Autophagy and Proliferation in Endothelial Cells
Bibliographic record
Abstract
Background Macroautophagy is a highly conserved cellular process in which cytoplasmic materials are internalized into an autophagosome that later fuses with a lysosome for their degradation and recycling. MicroRNAs (miRNAs) are integral regulators in a variety of cellular processes and are known to regulate autophagy, and miRNA and autophagy both play roles in the regulation of endothelial function. Accordingly, we hypothesize that miRNA miR‐378‐3p is an essential regulator of endothelial autophagy and endothelial function. Methods and Results To test our hypothesis, we cultured human umbilical vein endothelial cells (HUVEC) and confirmed the basal expression of miR‐378‐3p. To determine the effect of autophagy on miR‐378a‐3p, autophagy was genetically (via silencing autophagy‐related gene ATG7) and pharmacologically (via chloroquine treatment) inhibited in HUVECs, which resulted in the upregulation of miRNA miR‐378a‐3p. We also measured miR‐378a‐3p expression following autophagy activation (via starvation) and observed a significant down‐regulation of miR‐378‐3p expression. Next, we over‐expressed miR‐378a‐3p (by mimic) in HUVECs and measured autophagy and endothelial function in the form of endothelial cell proliferation and migration. MiR‐378a‐3p over‐expression was associated with impaired autophagy indicated by reduced LC3‐II/LC‐3‐I ratio, reduced proliferation and migration in HUVECs. At the molecular level, miR‐378a‐3p over‐expression was associated with increased mTOR (mammalian target of rapamycin; an autophagy regulator) expression at the transcript and the protein levels in HUVECs. Conclusion Our preliminary findings indicate an essential role of miR‐378a‐3p in the regulation of endothelial autophagy and endothelial function. We demonstrate an inverse relationship between miR‐378a‐3p expression and endothelial autophagy and function, warranting future investigations.
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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 | 0.000 |
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".