A Regulatory Network Involving β-Catenin, e-Cadherin, PI3k/Akt, and Slug Balances Self-Renewal and Differentiation of Human Pluripotent Stem Cells In Response to Wnt Signaling
Bibliographic record
Abstract
The mechanisms underlying disparate roles of the canonical Wnt signaling pathway in maintaining self-renewal or inducing differentiation and lineage specification in embryonic stem cells (ESCs) are not clear. In this study, we provide the first demonstration that self-renewal versus differentiation of human ESCs (hESCs) in response to Wnt signaling is predominantly determined by a two-layer regulatory circuit involving β-catenin, E-cadherin, PI3K/Akt, and Slug in a time-dependent manner. Short-term upregulation of β-catenin does not lead to the activation of T-cell factor (TCF)-eGFP Wnt reporter in hESCs. Instead, it enhances E-cadherin expression on the cell membrane, thereby enhancing hESC self-renewal through E-cadherin-associated PI3K/Akt signaling. Conversely, long-term Wnt activation or loss of E-cadherin intracellular β-catenin binding domain induces TCF-eGFP activity and promotes hESC differentiation through β-catenin-induced upregulation of Slug. Enhanced expression of Slug leads to a further reduction of E-cadherin that serves as a β-catenin "sink" sequestering free cytoplasmic β-catenin. The formation of such a framework reinforces hESCs to switch from a state of temporal self-renewal associated with short-term Wnt/β-catenin activation to definitive differentiation. Stem Cells 2015;33:1419-1433.
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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 | 0.000 |
| 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 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".