β-Catenin and Smad3 regulate the activity and stability of myocardin-related transcription factor during epithelial–myofibroblast transition
Bibliographic record
Abstract
Injury to the adherens junctions (AJs) synergizes with transforming growth factor-β1 (TGFβ) to activate a myogenic program (α-smooth muscle actin [SMA] expression) in the epithelium during epithelial-myofibroblast transition (EMyT). Although this synergy plays a key role in organ fibrosis, the underlying mechanisms have not been fully defined. Because we recently showed that Smad3 inhibits myocardin-related transcription factor (MRTF), the driver of the SMA promoter and many other CC(A/T)-rich GG element (CArG) box-dependent cytoskeletal genes, we asked whether AJ components might affect SMA expression through interfering with Smad3. We demonstrate that E-cadherin down-regulation potentiates, whereas β-catenin knockdown inhibits, SMA expression. Contact injury and TGFβ enhance the binding of β-catenin to Smad3, and this interaction facilitates MRTF signaling by two novel mechanisms. First, it inhibits the Smad3/MRTF association and thereby allows the binding of MRTF to its myogenic partner, serum response factor (SRF). Accordingly, β-catenin down-regulation disrupts the SRF/MRTF complex. Second, β-catenin maintains the stability of MRTF by suppressing the Smad3-mediated recruitment of glycogen synthase kinase-3β to MRTF, an event that otherwise leads to MRTF ubiquitination and degradation and the consequent loss of SRF/MRTF-dependent proteins. Thus β-catenin controls MRTF-dependent transcription and emerges as a critical regulator of an array of cytoskeletal genes, the "CArGome."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".