Lebectin, a <i>Macrovipera lebetina</i> venom‐derived C‐type lectin, inhibits angiogenesis both in vitro and in vivo
Bibliographic record
Abstract
Integrins play an essential role in endothelial cell motility processes during angiogenesis and thus present interesting targets for the development of new anti-angiogenic agents. Snake venoms naturally contain a variety of proteins that can affect integrin-ligand interactions. Recently, the C-type lectin proteins (CLPs) have been characterized as efficient modulators of integrin functions. In this study, we investigated the anti-angiogenic activity of lebectin, a newly discovered CLP from Macrovipera lebetina venom. Human brain microvascular endothelial cells (HBMEC), used as an in vitro model, express alphavbeta3, alphavbeta5, and alpha5beta1 integrins, as well as the alpha2, alpha3, alpha6, and beta4 subunits. Our data show that lebectin acts as a very potent inhibitor (IC(50) approximately 0.5 nM) of HBMEC adhesion and migration on fibronectin by blocking the adhesive functions of both the alpha5beta1 and alphaV integrins. In addition, lebectin strongly inhibits both HBMEC in vitro tubulogenesis on Matrigel trade mark (IC(50) = 0.4 nM) and proliferation. Finally, using both a chicken CAM assay and a Matrigel trade mark Plug assay in nude mice, our results show that lebectin displays potent anti-angiogenic activity in vivo. Lebectin thus represents a new C-type lectin with anti-angiogenic properties with great potential for the treatment of angiogenesis-related diseases.
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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".