A Genetic Screen for Mutations That Affect Cranial Nerve Development in the Mouse
Bibliographic record
Abstract
Cranial motor and sensory nerves arise stereotypically in the embryonic hindbrain, act as sensitive indicators of general and region-specific neuronal development, and are directly or indirectly affected in many human disorders, particularly craniofacial syndromes. The molecular genetic hierarchies that regulate cranial nerve development are mostly unknown. Here, we describe the first mouse genetic screen that has used direct immunohistochemical visualization methods to systematically identify genetic loci required for cranial nerve development. After screening 40 pedigrees, we recovered seven new neurodevelopmental mutations. Two mutations model human genetic syndromes. Mutation 7-1 causes facial nerve anomalies and a reduced lower jaw, and is located in a region of conserved synteny with an interval associated with the micrognathia and mental retardation of human cri-du-chat syndrome. Mutation 22-1 is in the Pax3 gene and, thus, models human Waardenburg syndrome. Three mutations cause global axon guidance deficits: one interferes with initial motor axon extension from the neural tube, another causes overall axon defasciculation, and the third affects general choice point selection. Another two mutations affect the oculomotor nerve specifically. Oculomotor nerve development, which is disrupted by six mutations, appears particularly sensitive to genetic perturbations. Phenotypic comparisons of these mutants identifies a "transition zone" that oculomotor axons enter after initial outgrowth and in which new factors govern additional progress. The number of interesting neurodevelopmental mutants revealed by this small-scale screen underscores the promise of similar focused genetic screens to contribute significantly to our understanding of cranial nerve development and human craniofacial syndromes.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".