A Role for Proneural Genes in the Maturation of Cortical Progenitor Cells
Bibliographic record
Abstract
We showed previously that the proneural genes Neurogenin1 (Ngn1) and Ngn2 are required to specify the phenotypes of early- and not late-born neurons in the neocortex, acting in part through repression of Mash1, a third cortically expressed proneural gene. The precise timing of Ngn1/2 specification activity was unexpected given these genes are expressed throughout cortical development, prompting us to search for a later function. Here we reveal that Ngn2 and Mash1 are expressed in a dynamic fashion, acquiring a cell cycle-biased, nonoverlapping distribution, with preferential expression in prospective basal progenitors, during mid corticogenesis. We also identified a new function for Ngn2 during this latter period, demonstrating that it is required to regulate the transit of cortical progenitors from the ventricular zone (VZ) to the subventricular zone. Notably, Ngn2 regulates progenitor maturation at least in part through repression of Mash1 as misexpression of Mash1 strongly enhanced progenitor cell exit from the VZ. Significantly, the ability of Mash1 to promote progenitor cell maturation occurred independently of its ability to respecify cortical cells and is thus a novel function for Mash1. Taken together, these data support a model whereby Ngn2 and Mash1 function together to regulate the zonal distribution of progenitors in the developing neocortex.
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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".