Tissue tension permits β-catenin phosphorylation to drive mesoderm specification in human embryonic stem cells
Bibliographic record
Abstract
The role of morphogenetic forces in cell fate specification is an area of intense interest. Using an ectopically expressed nonphosphorylatable mutant of β-catenin (Y654F) in human embryonic stem cell colonies, we provide evidence that impeding tension-dependent Src-mediated β-catenin phosphorylation compromises BMP4-driven Brachyury (T) expression. This impediment also disrupts the epithelial-to-mesenchymal transition essential for mesoderm specification. Mechanistically, the Y654F mutation prevents the translocation of the junctional and cytoskeletal pool of β-catenin to the nucleus. However, saturation of Wnt signaling with exogenous Wnt3a or the inhibition of GSK3β rescues mesoderm expression. These findings suggest that BMP4 initiates a force-dependent junctional β-catenin translocation upstream of both Wnt secretion and cytosolic Wnt/β-catenin stabilization to drive mesoderm specification. Ultimately, our work highlights the importance of force-dependent Wnt/β-catenin signaling in the self-organization of tissues during developmental processes, such as gastrulation, and emphasizes a role for fine-tuned molecular regulation of the Wnt signaling pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".