A screen for downstream effectors of Neurogenin2 in the embryonic neocortex
Bibliographic record
Abstract
Neurogenin (Ngn) 1 and Ngn2 encode basic-helix-loop-helix transcription factors expressed in the developing neocortex. Like other proneural genes, Ngns participate in the specification of neural fates and neuronal identities, but downstream effectors remain poorly defined. We set out to identify Ngn2 effectors in the cortex using a subtractive hybridization screen and identified several regionally expressed genes that were misregulated in Ngn2 and Ngn1; Ngn2 mutants. Included were genes down-regulated in germinal zone progenitors (e.g., Nlgn1, Unc5H4, and Dcc) and in postmitotic neurons in the cortical plate (e.g., Bhlhb5 and NFIB) and subplate (e.g., Mef2c, srGAP3, and protocadherin 9). Further analysis revealed that Ngn2 mutant subplate neurons were misspecified and that thalamocortical afferents (TCAs) that normally target this layer instead inappropriately projected towards the germinal zone. Strikingly, EphA5 and Sema3c, which encode repulsive guidance cues, were down-regulated in the Ngn2 and Ngn1; Ngn2 mutant germinal zones, providing a possible molecular basis for axonal targeting defects. Thus, we identified several new components of the differentiation cascade(s) activated downstream of Ngn1 and Ngn2 and provided novel insights into a new developmental process controlled by these proneural genes. Further analysis of the genes isolated in our screen should provide a fertile basis for understanding the molecular mechanisms underlying corticogenesis.
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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".