New Insights Into the Intricacies of Proneural Gene Regulation in the Embryonic and Adult Cerebral Cortex
Bibliographic record
Abstract
Historically, the mammalian brain was thought to lack stem cells as no new neurons were found to be made in adulthood. That dogma changed ∼25 years ago with the identification of neural stem cells (NSCs) in the adult rodent forebrain. However, unlike rapidly self-renewing mature tissues (e.g., blood, intestinal crypts, skin), the majority of adult NSCs are quiescent, and those that become 'activated' are restricted to a few neurogenic zones that repopulate specific brain regions. Conversely, embryonic NSCs are actively proliferating and neurogenic. Investigations into the molecular control of the quiescence-to-proliferation-to-differentiation continuum in the embryonic and adult brain have identified proneural genes encoding basic-helix-loop-helix (bHLH) transcription factors (TFs) as critical regulators. These bHLH TFs initiate genetic programs that remove NSCs from quiescence and drive daughter neural progenitor cells (NPCs) to differentiate into specific neural cell subtypes, thereby contributing to the enormous cellular diversity of the adult brain. However, new insights have revealed that proneural gene activities are context-dependent and tightly regulated. Here we review how proneural bHLH TFs are regulated, with a focus on the murine cerebral cortex, drawing parallels where appropriate to other organisms and neural tissues. We discuss upstream regulatory events, post-translational modifications (phosphorylation, ubiquitinylation), protein-protein interactions, epigenetic and metabolic mechanisms that govern bHLH TF expression, stability, localization, and consequent transactivation of downstream target genes. These tight regulatory controls help to explain paradoxical findings of changes to bHLH activity in different cellular contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".