Dynamic actin remodeling during epithelial–mesenchymal transition depends on increased moesin expression
Bibliographic record
Abstract
Remodeling of actin filaments is necessary for epithelial-mesenchymal transition (EMT); however, understanding of how this is regulated in real time is limited. We used an actin filament reporter and high-resolution live-cell imaging to analyze the regulated dynamics of actin filaments during transforming growth factor-β-induced EMT of mammary epithelial cells. Progressive changes in cell morphology were accompanied by reorganization of actin filaments from thin cortical bundles in epithelial cells to thick, parallel, contractile bundles that disassembled more slowly but remained dynamic in transdifferentiated cells. We show that efficient actin filament remodeling during EMT depends on increased expression of the ezrin/radixin/moesin (ERM) protein moesin. Cells suppressed for moesin expression by short hairpin RNA had fewer, thinner, and less stable actin bundles, incomplete morphological transition, and decreased invasive capacity. These cells also had less α-smooth muscle actin and phosphorylated myosin light chain in cortical patches, decreased abundance of the adhesion receptor CD44 at membrane protrusions, and attenuated autophosphorylation of focal adhesion kinase. Our findings suggest that increased moesin expression promotes EMT by regulating adhesion and contractile elements for changes in actin filament organization. We propose that the transciptional program driving EMT controls progressive remodeling of actin filament architectures.
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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".