Wnt/β-Catenin Signaling Blockade Promotes Neuronal Induction and Dopaminergic Differentiation in Embryonic Stem Cells
Bibliographic record
Abstract
Embryonic stem cells (ESCs) represent not only a promising source of cells for cell replacement therapy, but also a tool to study the molecular mechanisms underlying cellular signaling and dopaminergic (DA) neuron development. One of the main regulators of DA neuron development is Wnt signaling. Here we used mouse ESCs (mESCs) lacking Wnt1 or the low-density lipoprotein receptor-related protein 6 (LRP6) to decipher the action of Wnt/beta-catenin signaling on DA neuron development in mESCs. We provide evidence that the absence of LRP6 abrogates responsiveness of mESCs to Wnt ligand stimulation. Using two differentiation protocols, we show that the loss of Wnt1 or LRP6 increases neuroectodermal differentiation and the number of mESC-derived DA neurons. These effects were similar to those observed following treatment of mESCs with the Wnt/beta-catenin pathway inhibitor Dickkopf1 (Dkk1). Combined, our results show that decreases in Wnt/beta-catenin signaling enhance neuronal and DA differentiation of mESCs. These findings suggest that: 1) Wnt1 or LRP6 are not strictly required for the DA differentiation of mESCs in vitro, 2) the levels of morphogens and their activity in ESC cultures need to be optimized to improve DA differentiation, and 3) by enhancing the differentiation and number of ESC-derived DA neurons with Dkk1, the application of ESCs for cell replacement therapy in Parkinson's disease may be improved.
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 | 0.001 |
| 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".