Smad4 and β-Catenin Co-activators Functionally Interact with Lymphoid-enhancing Factor to Regulate Graded Expression of Msx2
Bibliographic record
Abstract
Recent in vivo evidence suggests that Wnt signaling plays a central role in determining the fate of stem cells in the ectoderm and in the neural crest by modulating bone morphogenetic protein (BMP) levels, which, in turn, influence Msx gene expression. However, the molecular mechanism regulating the expression of the Msx genes as key regulators of cell fate has not been elucidated. Here we show in murine embryonic stem cells that BMP-dependent activation of Msx2 is mediated via the cooperative binding of Smad4 at two Smad binding elements and of lymphoid enhancing factor (Lef1) at two Lef1/TCF binding sites. Lef1 can synergize with Smad4 and Smad1 to activate Msx2 promoter, and this transcriptional complex is assembled on the endogenous promoter in response to BMP2. The Wnt/beta-catenin signaling pathway can activate Msx2 via the binding of Lef1 to its promoter and synergizes with BMP2 to activate Msx2 expression, possibly via enhanced recruitment of the p300/cAMP-response element-binding protein-binding protein co-factor. Interestingly, the Wnt/beta-catenin-dependent activation of Msx2 was defective in Smad4-deficient embryonic stem cells or when Smad binding elements were mutated but persisted in the presence of various BMP antagonists, indicating that Smad4 was involved in transducing the Wnt/beta-catenin signals in the absence of a BMP autocrine loop. A chromatin immunoprecipitation analysis revealed that endogenous Smad4, but not Smad1, was part of the Lef1 transcriptional complex in response to beta-catenin activation, dismissing any implication of BMP signaling in this response. We propose that Wnt signaling pathway could dictate cell fate not only by modulating BMP levels but also by directly regulating cooperatively BMP-target genes.
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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) | 0.001 | 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 | 0.001 |
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".