Identification of <i>Pappa</i> and <i>Sall3</i> as Gli3 direct target genes acting downstream of cilia signaling in corticogenesis
Bibliographic record
Abstract
The cerebral cortex is critical for advanced cognitive functions and relies on a vast network of neurons to carry out its highly intricate neural tasks. Generating cortical neurons in accurate numbers hinges on cell signaling orchestrated by primary cilia to coordinate the proliferation and differentiation of cortical stem cells. While recent research has shed light on multiple ciliary roles in corticogenesis, specific mechanisms downstream of cilia signaling remain largely unexplored. We previously showed that an excess of early-born cortical neurons in mice mutant for the ciliary gene Inpp5e was rescued by re-introducing Gli3 repressor. By comparing expression profiles between Inpp5e and Gli3 mutants, we here identified novel Gli3 target genes. This approach highlighted the transcription factor gene Sall3 and Pappalysin1 (Pappa), a metalloproteinase involved in IGF signaling, as upregulated genes in both mutants. Further examination revealed that Gli3 directly binds to Sall3 and Pappa enhancers and suppresses their activity in the dorsal telencephalon. Collectively, our analyses provide important mechanistic insights into how primary cilia govern the behavior of neural stem cells, ultimately ensuring the production of adequate numbers of neurons during 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".