Ascl1/Mash1 Is a Novel Target of Gli2 during Gli2-Induced Neurogenesis in P19 EC Cells
Bibliographic record
Abstract
The Sonic Hedgehog (Shh) signaling pathway is important for neurogenesis in vivo. Gli transcription factors, effector proteins of the Shh signaling pathway, have neurogenic properties in vivo, which are still poorly understood. To study the molecular basis of neurogenic properties of Gli2, we used a well-established embryonic stem cell model, the P19 embryonal carcinoma (EC) cell line, which can be induced to differentiate into neurons in the presence of retinoic acid (RA). We found that, in the absence of RA, overexpression of Gli2 induced P19 EC cells to differentiate into neurons, but not astrocytes during the first ten days of differentiation. To our knowledge, this is the first indication that the expression of Gli factors can convert EC cells into neurons. Furthermore, Gli2 upregulated expression of the neurogenic basic helix-loop-helix (bHLH) factors, such as NeuroD, Neurog1 and Ascl1/Mash1 in P19 EC cells. Using chromatin immunoprecipitation assays, we showed that Gli2 bound to multiple regulatory regions in the Ascl1 gene, including promoter and enhancer regions during Gli2-induced neurogenesis. In addition, Gli2 activated the Ascl1/Mash1 promoter in vitro. Using the expression of a dominant-negative form of Gli2, fused to the Engrailed repression domain, we observed a reduction in gliogenesis and a significant downregulation of the bHLH factors Ascl1/Mash1, Neurog1 and NeuroD, leading to delayed neurogenesis in P19 EC cells, further supporting the hypothesis that Ascl1/Mash1 is a direct target of Gli2. In summary, Gli2 is sufficient to induce neurogenesis in P19 stem cells at least in part by directly upregulating Ascl1/Mash1. Our results provide mechanistic insight into the neurogenic properties of Gli2 in vitro, and offer novel plausible explanations for its in vivo neurogenic properties.
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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) | 0.001 | 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".