Disruption of <i>CK2</i> β in Embryonic Neural Stem Cells Compromises Proliferation and Oligodendrogenesis in the Mouse Telencephalon
Bibliographic record
Abstract
Genetic programs that govern neural stem/progenitor cell (NSC) proliferation and differentiation are dependent on extracellular cues and a network of transcription factors, which can be regulated posttranslationally by phosphorylation. However, little is known about the kinase-dependent pathways regulating NSC maintenance and oligodendrocyte development. We used a conditional knockout approach to target the murine regulatory subunit (beta) of protein kinase casein kinase 2 (CK2beta) in embryonic neural progenitors. Loss of CK2beta leads to defects in proliferation and differentiation of embryonic NSCs. We establish CK2beta as a key positive regulator for the development of oligodendrocyte precursor cells (OPCs), both in vivo and in vitro. We show that CK2beta directly interacts with the basic helix-loop-helix (bHLH) transcription factor Olig2, a critical modulator of OPC development, and activates the CK2-dependent phosphorylation of its serine-threonine-rich (STR) domain. Finally, we reveal that the CK2-targeted STR domain is required for the oligodendroglial function of Olig2. These findings suggest that CK2 may control oligodendrogenesis, in part, by regulating the activity of the lineage-specific transcription factor Olig2. Thus, CK2beta appears to play an essential and uncompensated role in central nervous system development.
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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) | 0.001 | 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".