A Focused and Efficient Genetic Screening Strategy in the Mouse: Identification of Mutations That Disrupt Cortical Development
Bibliographic record
Abstract
Although the mechanisms that regulate development of the cerebral cortex have begun to emerge, in large part through the analysis of mutant mice (Boncinelli et al. 2000; Molnar and Hannan 2000; Walsh and Goffinet 2000), many questions remain unanswered. To provide resources for further dissecting cortical development, we have carried out a focused screen for recessive mutations that disrupt cortical development. One aim of the screen was to identify mutants that disrupt the tangential migration of interneurons into the cortex. At the same time, we also screened for mutations that altered the growth or morphology of the cerebral cortex. We report here the identification of thirteen mutants with defects in aspects of cortical development ranging from the establishment of epithelial polarity to the invasion of thalamocortical axons. Among the collection are three novel alleles of genes for which mutant alleles had already been used to explore forebrain development, and four mutants with defects in interneuron migration. The mutants that we describe here will aid in deciphering the molecules and mechanisms that regulate cortical development. Our results also highlight the utility of focused screens in the mouse, in addition to the large-scale and broadly targeted screens that are being carried out at mutagenesis centers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".