Force-induced changes of α-catenin conformation stabilize vascular junctions independently of vinculin
Bibliographic record
Abstract
Cadherin-mediated cell adhesion requires anchoring via the β-catenin-α-catenin complex to the actin cytoskeleton, yet, α-catenin only binds F-actin weakly. A covalent fusion of VE-cadherin to α-catenin enhances actin anchorage in endothelial cells and strongly stabilizes endothelial junctions in vivo, blocking inflammatory responses. Here, we have analyzed the underlying mechanism. We found that VE-cadherin-α-catenin constitutively recruits the actin adaptor vinculin. However, removal of the vinculin-binding region of α-catenin did not impair the ability of VE-cadherin-α-catenin to enhance junction integrity. Searching for an alternative explanation for the junction-stabilizing mechanism, we found that an antibody-defined epitope, normally buried in a short α1-helix of the actin-binding domain (ABD) of α-catenin, is openly displayed in junctional VE-cadherin-α-catenin chimera. We found that this epitope became exposed in normal α-catenin upon triggering thrombin-induced tension across the VE-cadherin complex. These results suggest that the VE-cadherin-α-catenin chimera stabilizes endothelial junctions due to conformational changes in the ABD of α-catenin that support constitutive strong binding to actin.
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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".