Actin Cytoskeleton Remodeling Accompanied by Redistribution of Adhesion Proteins Drives Migration of Cells in Different EMT States
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) is a process during which epithelial cells lose epithelial characteristics and gain mesenchymal features. Here, we used several cell models to study migratory activity and redistribution of cell-cell adhesion proteins in cells in different EMT states: EGF-induced EMT of epithelial IAR-20 cells; IAR-6-1 cells with a hybrid epithelial-mesenchymal phenotype; and their more mesenchymal derivatives, IAR-6-1-DNE cells lacking adherens junctions. In migrating cells, the cell-cell adhesion protein α-catenin accumulated at the leading edges along with ArpC2/p34 and α-actinin. Suppression of α-catenin shifted cell morphology from fibroblast-like to discoid and attenuated cell migration. Expression of exogenous α-catenin in MDA-MB-468 cells devoid of α-catenin drastically increased their migratory capabilities. The Y654 phosphorylated form of β-catenin was detected at integrin adhesion complexes (IACs). Co-immunoprecipitation studies indicated that α-catenin and pY654-β-catenin were associated with IAC proteins: vinculin, zyxin, and α-actinin. Taken together, these data suggest that in cells undergoing EMT, catenins not participating in assembly of adherens junctions may affect cell migration.
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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".