The Potential Role of MiRs-139-5p and -454-3p in Endoglin-Knockdown-Induced Angiogenic Dysfunction in HUVECs
Bibliographic record
Abstract
Hereditary hemorrhagic telangiectasia (HHT) is a rare genetic disease characterized by aberrant angiogenesis and vascular malformations. Mutations in the transforming growth factor beta co-receptor, endoglin (ENG), account for approximately half of known HHT cases and cause abnormal angiogenic activity in endothelial cells (ECs). To date, how ENG deficiency contributes to EC dysfunction remains to be fully understood. MicroRNAs (miRNAs) regulate virtually every cellular process. We hypothesized that ENG depletion results in miRNA dysregulation that plays an important role in mediating EC dysfunction. Our goal was to test the hypothesis by identifying dysregulated miRNAs in ENG-knockdown human umbilical vein endothelial cells (HUVECs) and characterizing their potential role in EC function. We identified 32 potentially downregulated miRNAs in ENG-knockdown HUVECs with a TaqMan miRNA microarray. MiRs-139-5p and -454-3p were found to be significantly downregulated after RT-qPCR validation. While the inhibition of miR-139-5p or miR-454-3p had no effect on HUVEC viability, proliferation or apoptosis, angiogenic capacity was significantly compromised as determined by a tube formation assay. Most notably, the overexpression of miRs-139-5p and -454-3p rescued impaired tube formation in HUVECs with ENG knockdown. To our knowledge, we are the first to demonstrate miRNA alterations after the knockdown of ENG in HUVECs. Our results indicate a potential role of miRs-139-5p and -454-3p in ENG-deficiency-induced angiogenic dysfunction in ECs. Further study to examine the involvement of miRs-139-5p and -454-3p in HHT pathogenesis is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".