A non-canonical role for the proneural gene<i>Neurog1</i>as a negative regulator of neocortical neurogenesis
Bibliographic record
Abstract
Neural progenitors undergo temporal identity transitions to sequentially generate the neuronal and glial cells that make up the mature brain. Proneural genes have well characterized roles in promoting neural cell differentiation and subtype specification, but they also regulate the timing of identity transitions through poorly understood mechanisms. Here we investigated how the highly-related proneural genes Neurog1 and Neurog2 interact to control the timing of neocortical neurogenesis. We found that Neurog1 acts in an atypical fashion as it is required to suppress rather than promote neuronal differentiation in early corticogenesis. In Neurog1−/− neocortices, early-born neurons differentiate in excess, while in vitro, Neurog1−/− progenitors have a decreased propensity to proliferate and form neurospheres. Instead, Neurog1−/− progenitors preferentially generate neurons, a phenotype restricted to the Neurog2+ progenitor pool. Mechanistically, Neurog1 and Neurog2 heterodimerize, and while Neurog1 and Neurog2 individually promote neurogenesis, misexpression together blocks this effect. Finally, Neurog1 is also required to induce the expression of neurogenic factors (Dll1, Hes5) and repress the expression of neuronal differentiation genes (Fezf2, Neurod6). Neurog1 thus employs different mechanisms to temper the pace of early neocortical neurogenesis.
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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 | 0.001 |
| 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".