CCAAT/Enhancer-Binding Protein Phosphorylation Biases Cortical Precursors to Generate Neurons Rather Than Astrocytes<i>In Vivo</i>
Bibliographic record
Abstract
The intracellular mechanisms that bias mammalian neural precursors to generate neurons versus glial cells are not well understood. We demonstrated previously that the growth factor-regulated mitogen-activated protein kinase kinase (MEK) and its downstream target, the CCAAT/enhancer-binding protein (C/EBP) family of transcription factors, are essential for neurogenesis in cultured cortical precursor cells (Ménard et al., 2002). Here, we examined a role for this pathway during cortical cell fate determination in vivo using in utero electroporation of the embryonic cortex. These studies demonstrate that inhibition of the activity of either MEK or the C/EBPs inhibits the genesis of neurons in vivo. Moreover, the MEK pathway mediates phosphorylation of C/EBPbeta in cortical precursors, and expression of a C/EBPbeta construct in which the MEK pathway phosphorylation sites are mutated inhibits neurogenesis. Conversely, expression of a C/EBPbeta construct, in which the same sites are mutated to glutamate and therefore are "constitutively" phosphorylated, enhances neurogenesis in the early embryonic cortex. A subpopulation of precursors in which C/EBP activity is inhibited are maintained as cycling precursors in the ventricular/subventricular zone of the cortex until early in postnatal life, when they have an enhanced propensity to generate astrocytes, presumably in response to gliogenic signals in the neonatal environment. Thus, activation of an MEK-C/EBP pathway in cortical precursors in vivo biases them to become neurons and against becoming astrocytes, thereby acting as a growth factor-regulated switch.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".