Cux-2 Controls the Proliferation of Neuronal Intermediate Precursors of the Cortical Subventricular Zone
Bibliographic record
Abstract
Whereas neurons of the lower layers (VI-V) of the cerebral cortex are first born from dividing precursors at the ventricular zone, upper layer neurons (II-IV) subsequently arise from divisions of intermediate neuronal precursors at the subventricular zone (SVZ). Little is known about mechanisms that control the proliferation of SVZ neuronal precursors. We herein report that the restricted expression of the homeodomain transcription factor Cux-2 in the SVZ regulates the proliferation of intermediate neuronal precursors and the number of upper layer neurons. In Cux-2-deficient mice (Cux-2-/-), there is excessive number of upper layer neurons and selective expansion of SVZ neuronal precursors. Double-labeling experiments demonstrate that Cux-2-/- upper layer precursors reenter the cell cycle in a higher frequency than wild-type precursors. Overexpression studies indicate that Cux-2 controls cell cycle exit in a cell-autonomous manner. Analysis of Cux-1-/-; Cux-2-/- double mutant revealed that Cux-2 controls SVZ proliferation independently of Cux-1, demonstrating that this is a unique function of Cux-2, not redundant with Cux-1 activities. Our results point to Cux-2 as a key element in the control of the proliferation rates of the SVZ precursors and the number of upper cortical neurons, without altering the number of deep cortical layers.
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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 | 0.000 |
| 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".