The responses of neural stem cells to the level of GSK-3 depend on the tissue of origin
Bibliographic record
Abstract
Neural stem cells (NSCs) can be obtained from a variety of sources, but not all NSCs exhibit the same characteristics. We have examined how the level of glycogen synthase kinase-3 activity regulates NSCs obtained from different sources: the mouse embryonic striatum, embryonic hippocampus, and mouse ES cells. Growth of striatal NSCs is enhanced by mild inhibition of GSK-3 but not by strong inhibition that is accompanied by Wnt/TCF transcriptional activation. In contrast, the growth of hippocampal NSCs is enhanced by both mild inhibition of GSK-3 as well as stronger inhibition. Active Wnt/TCF signaling, which occurs normally in the embryonic hippocampus, is required for growth of neural stem and progenitor cells. In the embryonic striatal germinal zone, however, TCF signaling is normally absent and its activation inhibits growth of NSCs from this region. Using a genetic model for progressive loss of GSK-3, we find that primitive ES cell-derived NSCs resemble striatal NSCs. That is, partial loss of GSK-3 alleles leads to an increase in NSCs while complete ablation of GSK-3, and activation of TCF-signaling, leads to their decline. Furthermore, expression of dominant negative TCF-4 in the GSK-3-null background was effective in blocking expression of Wnt-response genes and was also able to rescue neuronal gene expression. These results reveal that GSK-3 regulates NSCs by divergent pathways depending on the tissue of origin. The responses of these neural precursor cells may be contingent on baseline Wnt/TCF signaling occurring in a particular tissue.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".