Activation of Tie2 by angiopoietin-1 and angiopoietin-2 results in their release and receptor internalization
Bibliographic record
Abstract
The receptor tyrosine kinase Tie2 is highly expressed in endothelial cells and is crucial for angiogenesis and vascular maintenance. The ligands for Tie2 are the angiopoietins, of which angiopoietin-1 and angiopoietin-2 have been the most studied. Angiopoietin-1 has been characterized as the primary activating ligand for Tie2 whereas the role of angiopoietin-2 remains controversial; activating Tie2 in some studies and inhibiting Tie2 in others. Our studies were aimed at understanding the regulation of Tie2 in endothelial cells by angiopoietin-1 and angiopoietin-2 and revealed that both ligands activated Tie2 in a concentration-dependent manner. Angiopoietin-2 was considerably weaker at activating Tie2 compared with angiopoietin-1 suggesting that angiopoietin-2 may be a partial agonist. Activation of Tie2 by these ligands resulted in differential turnover of the receptor where binding of angiopoietin-1, and to a lesser extent angiopoietin-2, induced rapid internalization and degradation of Tie2. Furthermore, our binding studies demonstrate that both ligands are differentially released from the endothelial cell surface after receptor activation and accumulate in the surrounding medium. Altogether, these data begin our understanding of the regulation of Tie2 and the activity of the angiopoietins after engaging the endothelial cell surface.
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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".