SVEP1 enables efficient binding of Angiopoietin-2 to the TIE1 receptor, allowing receptor phosphorylation and downstream signaling
Bibliographic record
Abstract
Abstract The molecular mechanisms that drive (lymph-)angiogenesis are crucial to understand diseases, such as lymphedema, that are caused due to malformations of the lymphatic vasculature. Recently, an interaction between the secreted protein Svep1, a key regulator in lymphangiogenesis, and the transmembrane receptor Tie1 was shown in zebrafish, human, and mice. Here, guided by in silico AlphaFold-multimer structure predictions of SVEP1 complexes, we assert with protein binding studies that the human CCP20 domain is the primary binding site for TIE1. We further demonstrate that SVEP1 mediates strong binding of ANG2 and TIE1, and that combined stimulation of hdLECs with SVEP1 and ANG2 leads to phosphorylation of TIE1. TIE1 activation by SVEP1 and ANG2 enables downstream signaling and, in turn, potentiates nuclear exclusion of FOXO1 and phosphorylation of AKT compared to SVEP1 or ANG2 alone. We present a model in which ANG1/2 dimers bind to both SVEP1 and TIE1, resulting in the recruitment of multiple TIE1 receptor molecules to a multimeric complex at the cell membrane, potentially amplifying its signaling capacity.
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) | 0.002 | 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".