miR-1233-3p Inhibits Angiopoietin-1-Induced Endothelial Cell Survival, Migration, and Differentiation
Bibliographic record
Abstract
Angiopoietin-1 (Ang-1) and its receptor Tie-2 promote vascular integrity and angiogenesis. MicroRNAs (miRNAs) are involved in the regulation of many cellular functions, including endothelial cell (EC) survival, proliferation, and differentiation. Several reports indicate that these effects of miRNAs on EC functions are mediated through the modulation of angiogenesis factor signaling including that of vascular endothelial growth factor (VEGF). To date, very little is known about the roles played by miRNAs in the signaling and angiogenesis promoted by the Ang-1-Tie-2 receptor axis. Our high-throughput screening of miRNAs regulated by Ang-1 exposure in human umbilical vein endothelial cells (HUVECs) has identified miR-1233-3p as a mature miRNA whose cellular levels are significantly downregulated in response to Ang-1 exposure. The expression of miR-1233-3p in these cells is also downregulated by other angiogenesis factors including VEGF, fibroblast growth factor 2 (FGF-2), transforming growth factor β (TGFβ), and angiopoietin-2 (Ang-2). The overexpression of miR-1233-3p in HUVECs using specific mimics significantly attenuated cell survival, migration, and capillary-like tube formation, and promoted apoptosis. Moreover, miR-1233-3p overexpression resulted in reversal of the anti-apoptotic, pro-migration, and pro-differentiation effects of Ang-1. Biotinylated miRNA pull-down assays showed that p53 and DNA damage-regulated 1 (PDRG1) is a direct target of miR-1233-3p in HUVECs. The exposure of HUVECs to Ang-1, angiopoietin-2 (Ang-2), fibroblast growth factor 2 (FGF2), vascular endothelial growth factor (VEGF), or transforming growth factor β (TGFβ) triggers the regulation of PDRG1 expression. This study highlights that miR-1233-3p exerts inhibitory effects on Ang-1-induced survival, migration, and the differentiation of cultured ECs.
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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".