GSK3 Temporally Regulates Neurogenin 2 Proneural Activity in the Neocortex
Bibliographic record
Abstract
The neocortex is comprised of six neuronal layers that are generated in a defined temporal sequence. While extrinsic and intrinsic cues are known to regulate the sequential production of neocortical neurons, how these factors interact and function in a coordinated manner is poorly understood. The proneural gene Neurog2 is expressed in progenitors throughout corticogenesis, but is only required to specify early-born, deep-layer neuronal identities. Here, we examined how neuronal differentiation in general and Neurog2 function in particular are temporally controlled during murine neocortical development. We found that Neurog2 proneural activity declines in late corticogenesis, correlating with its phosphorylation by GSK3 kinase. Accordingly, GSK3 activity, which is negatively regulated by canonical Wnt signaling, increases over developmental time, while Wnt signaling correspondingly decreases. When ectopically activated, GSK3 inhibits Neurog2-mediated transcription in cultured cells and Neurog2 proneural activities in vivo. Conversely, a reduction in GSK3 activity promotes the precocious differentiation of later stage cortical progenitors without influencing laminar fate specification. Mechanistically, we show that GSK3 suppresses Neurog2 activity by influencing its choice of dimerization partner, promoting heterodimeric interactions with E47 (Tcfe2a), as opposed to Neurog2-Neurog2 homodimer formation, which occurs when GSK3 activity levels are low. At the functional level, Neurog2-E47 heterodimers have a reduced ability to transactivate neuronal differentiation genes compared with Neurog2-Neurog2 homodimers, both in vitro and in vivo. We thus conclude that the temporal regulation of Neurog2-E47 heterodimerization by GSK3 is a central component of the neuronal differentiation "clock" that coordinates the timing and tempo of neocortical neurogenesis in mouse.
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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".