Nuclear Actin Polymerization Regulates Cell Epithelial‐Mesenchymal Transition
Bibliographic record
Abstract
Current studies on actin function primarily rely on cytoplasmic actin due to the absence of cellular models specifically expressing nuclear actin. Here, cell models capable of expressing varying levels of nuclear F/G-actin are generated and a significant role of nuclear actin in the regulation of epithelial-mesenchymal transition (EMT) is uncovered. Through immunoprecipitation and mass spectrometry analyses, distinct binding partners for nuclear F-actin (β-catenin, SMAD2, and SMAD3) and nuclear G-actin (MYBBP1A, NKRF, and MYPOP) are investigated, which respectively modulate EMT-promoting and EMT-repressing transcriptional events. While nuclear F-actin promotes EMT with enhanced cell migration, survival, and elongated mesenchymal morphology, nuclear G-actin represses EMT and related cell activities. Mechanistically, nuclear F-actin enhances β-catenin, SMAD2, and SMAD3 expression and stability in the nuclei, while nuclear G-actin increases MYBBP1A, NKRF, and MYPOP expression and stability in the nuclei. The association between nuclear F/G-actin and N-cadherin/E-cadherin in the cell lines (in vitro), and increased nuclear actin polymerization in the wound healing cells (in vivo) affirm a significant role of nuclear actin in EMT regulation. With evidence of nuclear actin polymerization and EMT during development, and irregularities in disease states such as cancer and fibrosis, targeting nuclear actin dynamics to trigger dysregulated EMT warrants ongoing study.
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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".