<i>Foxc1</i> controls cell fate decisions during transforming growth factor β induced epithelial to mesenchymal transition through the regulation of fibroblast growth factor receptor 1 expression
Bibliographic record
Abstract
ABSTRACT Epithelial to mesenchymal transition (EMT) is an important physiological process that drives tissue formation during development but also contributes to disease pathogenesis including fibrosis and cancer metastasis. The forkhead box transcription factor gene FOXC1 is an important developmental regulator in the generation of mesenchymal cells necessary in the formation of the anterior segment of the eye, the craniofacial skeleton and the meninges. Recently elevated expression of FOXC1 has been detected in several metastatic cancers that have undergone EMT events. We sought to determine the role of FOXC1 in the initiation of EMT events using NMuMG cells treated with TFGβ1. We found that although Foxc1 expression was increased following TFGβ1 induced EMT, Foxc1 was not required for this induction. Instead we propose that Foxc1 is required for the specification of the mesenchyme cell type, promoting an activated fibroblast phenotype over a myofibroblast phenotype following the initiation of EMT. This cells type specification was achieved through the regulation of Fibroblast growth factor (Fgfr) 1 expression. Using an RNA sequencing approach, we determined that levels of Fgfr1 normally activated upon TFGβ1 treatment were reduced in Foxc1-knockdown cells. Through chromatin immunoprecipitation experiments we determined that FOXC1 could bind to an Fgfr1 upstream regulatory region. Furthermore, expression of the myofibroblast marker α-smooth muscle actin (αSMA) was elevated in Foxc1 knockdown cells. Finally we determined that FGF2 mediated three dimensional migratory ability was greatly impaired in Foxc1-knockdown cells. Together these results define a role for Foxc1 in specifying a mesenchymal cell type following TFGβ1 mediated EMT events.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".