A phenotype‐driven ENU mutagenesis screen identifies novel alleles with functional roles in early mouse craniofacial development
Bibliographic record
Abstract
Proper craniofacial development begins during gastrulation and requires the coordinated integration of each germ layer tissue (ectoderm, mesoderm, and endoderm) and its derivatives in concert with the precise regulation of cell proliferation, migration, and differentiation. Neural crest cells, which are derived from ectoderm, are a migratory progenitor cell population that generates most of the cartilage, bone, and connective tissue of the head and face. Neural crest cell development is regulated by a combination of intrinsic cell autonomous signals acquired during their formation, balanced with extrinsic signals from tissues with which the neural crest cells interact during their migration and differentiation. Although craniofacial anomalies are typically attributed to defects in neural crest cell development, the cause may be intrinsic or extrinsic. Therefore, we performed a phenotype-driven ENU mutagenesis screen in mice with the aim of identifying novel alleles in an unbiased manner, that are critically required for early craniofacial development. Here we describe 10 new mutant lines, which exhibit phenotypes affecting frontonasal and pharyngeal arch patterning, neural and vascular development as well as sensory organ morphogenesis. Interestingly, our data imply that neural crest cells and endothelial cells may employ similar developmental programs and be interdependent during early embryogenesis, which collectively is critical for normal craniofacial morphogenesis. Furthermore our novel mutants that model human conditions such as exencephaly, craniorachischisis, DiGeorge, and Velocardiofacial sydnromes could be very useful in furthering our understanding of the complexities of specific human diseases.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".