Differential topical susceptibility to TGFβ in intact and injured regions of the epithelium: key role in myofibroblast transition
Bibliographic record
Abstract
Induction of epithelial-myofibroblast transition (EMyT), a robust fibrogenic phenotype change hallmarked by α-smooth muscle actin (SMA) expression, requires transforming growth factor-β1 (TGFβ) and the absence/uncoupling of intracellular contacts. This suggests that an "injured" epithelium may be topically susceptible to TGFβ. To explore this concept, we use an epithelial wound model in which intact and contact-deprived regions of the same monolayer can be analyzed simultaneously. We show that TGFβ elicits dramatically different responses at these two loci. SMA expression and initially enhanced nuclear Smad3 accumulation followed by Smad3 mRNA and protein down-regulation occur exclusively at the wound. Mechanistically, three transcriptional coactivators whose localization is regulated by cell contact integrity are critical for these local effects. These are myocardin-related transcription factor (MRTF), the driver of the SMA promoter; β-catenin, which counteracts the known inhibitory effect of Smad3 on MRTF and maintains MRTF protein stability and mRNA expression in the wound; and TAZ, a Hippo effector and Smad3 retention factor. Remarkably, active TAZ stimulates the SMA and suppresses the Smad3 promoter, whereas TAZ silencing prevents wound-restricted expression of SMA and loss of Smad3. Such locus-specific reprogramming might play key roles in wound healing and the susceptibility of the injured epithelium to fibrogenesis.
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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) | 0.001 | 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".