Transcriptome of Angiopoietin 1–Activated Human Umbilical Vein Endothelial Cells
Bibliographic record
Abstract
Angiopoietin 1 (Ang-1) is the main ligand for endothelial cell-specific tyrosine kinase (Tie-2) receptors and it promotes migration and proliferation and inhibits apoptosis and vascular leakage. The exact mechanisms through which the Ang-1 exerts these effects remain unclear. The authors exposed human umbilical vein endothelial cells (HUVECs) to Ang-1 (300 ng/mL) for 4 h and conducted gene expression profiling using oligonucleotide microarrays. Real-time polymerase chain reaction (PCR) was also conducted to verify several of the genes that were regulated by Ang-1. Exposure to Ang-1 resulted in induction of 86 genes that are involved in endothelial cell (EC) proliferation, differentiation, migration, and survival. Thirty-six of these genes, including stanniocalcin, cyclin D1, vascular endothelial growth factor C, fms-related tyrosine kinase 1, interleukin 8, and CXCR4 have previously been shown to be induced by vascular endothelial growth factor (VEGF), suggesting significant similarities between VEGF and Ang-1 pathways. Ang-1 exposure also inhibited mRNA expressions of 49 genes, most of which are involved in cell cycle arrest, apoptosis, and suppression of transcription. These results indicate that Ang-1 triggers coordinated responses in endothelial cells designed to inhibit the expression of proapoptotic and antiproliferative genes and up-regulate proproliferative, proangiogenic, and antiapoptotic pathways. Moreover, we also found that the Erk1/2, phosphatidylinositol (PI) 3-kinase, and the mTOR pathways are involved in Ang-1-induced gene expression in HUVECs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".