Smaddening Complexity: The Role of Smad3 in Epithelial-Myofibroblast Transition
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) has emerged as a major mechanism in the pathogenesis of organ fibrosis. The epithelium has been proposed to be a significant source of matrix-producing fibroblasts and of myofibroblasts (MFs), a motile and contractile cell type hallmarked by the expression of α-smooth muscle actin (SMA). Importantly, tissue accumulation of MFs shows strong correlation with the severity and progression of fibrotic diseases. The pleiotropic cytokine transforming growth factor-β(1) has been long known as the chief inducer of fibrosis, EMT and MF generation. Accordingly, receptor Smads (Smad2 and particularly Smad3), the direct targets of the activated transforming growth factor-β receptor have been implicated as critical mediators in fibrogenesis and EMT. However, evidence is accumulating that the role of Smad3 is complex and apparently controversial; in fact, Smad3 may differentially affect the various components of EMT, including the loss of epithelial markers (de-epithelialization), the production of extracellular matrix (fibrogenesis) and the expression of SMA (myogenic program). In this review, we revisit the role of Smad3 in epithelial-myofibroblast transition (EMyT). We first summarize the evidence supporting the thesis that Smad3 is a key mediator of EMT and MF generation; next, we present evidence supporting the antithesis that Smad3 is in fact a negative regulator of SMA expression and the activation of the myogenic program in the epithelium; finally, we propose a synthesis, which depicts Smad3 as a timekeeper and context-dependent modulator of EMyT. We suggest that EMyT is composed of an early, mesenchymal, Smad3-promoted phase and a late, myogenic, Smad3-inhibitable phase.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".