Scleraxis regulates Twist1 and Snai1 expression in the epithelial-to-mesenchymal transition
Bibliographic record
Abstract
Numerous physiological and pathological events, from organ development to cancer and fibrosis, are characterized by an epithelial-to-mesenchymal transition (EMT), whereby adherent epithelial cells convert to migratory mesenchymal cells. During cardiac development, proepicardial organ epithelial cells undergo EMT to generate fibroblasts. Subsequent stress or damage induces further phenotype conversion of fibroblasts to myofibroblasts, causing fibrosis via synthesis of an excessive extracellular matrix. We have previously shown that the transcription factor scleraxis is both sufficient and necessary for the conversion of cardiac fibroblasts to myofibroblasts and found that scleraxis knockout reduced cardiac fibroblast numbers by 50%, possibly via EMT attenuation. Scleraxis induced expression of the EMT transcriptional regulators Twist1 and Snai1 via an unknown mechanism. Here, we report that scleraxis binds to E-box consensus sequences within the Twist1 and Snai1 promoters to transactivate these genes directly. Scleraxis upregulates expression of both genes in A549 epithelial cells and in cardiac myofibroblasts. Transforming growth factor-β induces EMT, fibrosis, and scleraxis expression, and we found that transforming growth factor-β-mediated upregulation of Twist1 and Snai1 completely depends on the presence of scleraxis. Snai1 knockdown upregulated the epithelial marker E-cadherin; however, this effect was lost after scleraxis overexpression, suggesting that scleraxis may repress E-cadherin expression. Together, these results indicate that scleraxis can regulate EMT via direct transactivation of the Twist1 and Snai1 genes. Given the role of scleraxis in also driving the myofibroblast phenotype, scleraxis appears to be a critical controller of fibroblast genesis and fate in the myocardium and thus may play key roles in wound healing and fibrosis. NEW & NOTEWORTHY The molecular mechanism by which the transcription factor scleraxis mediates Twist1 and Snai1 gene expression was determined. These results reveal a novel means of transcriptional regulation of epithelial-to-mesenchymal transition and demonstrate that transforming growth factor-β-mediated epithelial-to-mesenchymal transition is dependent on scleraxis, providing a potential target for controlling this process.
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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) | 0.002 | 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".