β-catenin Is Essential for Efficient In Vitro Premyogenic Mesoderm Formation but Can Be Partially Compensated by Retinoic Acid Signalling
Bibliographic record
Abstract
Previous studies have shown that P19 cells expressing a dominant negative β-catenin mutant (β-cat/EnR) cannot undergo myogenic differentiation in the presence or absence of muscle-inducing levels of retinoic acid (RA). While RA could upregulate premyogenic mesoderm expression, including Pax3/7 and Meox1, only Pax3/7 and Gli2 could be upregulated by RA in the presence of β-cat/EnR. However, the use of a dominant negative construct that cannot be compensated by other factors is limiting due to the possibility of negative chromatin remodelling overriding compensatory mechanisms. In this study, we set out to determine if β-catenin function is essential for myogenesis with and without RA, by creating P19 cells with reduced β-catenin transcriptional activity using an shRNA approach, termed P19[shβ-cat] cells. The loss of β-catenin resulted in a reduction of skeletal myogenesis in the absence of RA as early as premyogenic mesoderm, with the loss of Pax3/7, Eya2, Six1, Meox1, Gli2, Foxc1/2, and Sox7 transcript levels. Chromatin immunoprecipitation identified an association of β-catenin with the promoter region of the Sox7 gene. Differentiation of P19[shβ-cat] cells in the presence of RA resulted in the upregulation or lack of repression of all of the precursor genes, on day 5 and/or 9, with the exception of Foxc2. However, expression of Sox7, Gli2, the myogenic regulatory factors and terminal differentiation markers remained inhibited on day 9 and overall skeletal myogenesis was reduced. Thus, β-catenin is essential for in vitro formation of premyogenic mesoderm, leading to skeletal myogenesis. RA can at least partially compensate for the loss of β-catenin in the expression of many myogenic precursor genes, but not for myoblast gene expression or overall myogenesis.
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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) | 0.002 | 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".