Molecular mechanisms regulating cortical precursor differentiation
Bibliographic record
Abstract
During development of the mammalian cerebral cortex, precursor cells in the ventricular zone sequentially produce neurons and glial cells. This process clearly involves a complex interplay between intrinsic cellular machinery and extrinsic cues. Remarkably, embryonic cortical precursor cells isolated at the onset of neurogenesis, and cultured under serum-free conditions, will mimic the temporal differentiation pattern observed in vivo, producing neurons first, and then glia. In the studies presented here, I have used this system to identify critical components of the intracellular machinery that co-ordinately control two essential steps in neuronal differentiation, exit from the cell cycle (via the retinoblastoma family) and induction of neuronal gene expression (via the basic helix-loop-helix transcription factors). I then demonstrate that endogenously produced neurotrophins are crucial autocrine/paracrine extracellular factors that regulate the precursor-to-neuron transition by promoting precursor survival (through the PI3-kinase-Akt signaling pathway) and neuronal differentiation (through the MEK-ERK pathway). Finally, I identify the nature of the "cortical timer mechanism" for sequential generation of neurons and glial cells: increasing levels of cytokines are produced by newly born neurons, which act to instruct the remaining precursors to produce glial cells. This neuron-based feedback mechanism was verified by using in utero electroporation to alter levels of cytokine signaling components within the developing cortex, and by analysing mice deficient for the cytokine cardiotrophin-1, which in both cases impaired the onset of gliogenesis. Taken together, our findings have led to a better molecular understanding of cortical precursor biology and cortex development, which could ultimately lead to the design of novel therapeutic strategies for neurodegenerative disorders.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".