Facial development and alterations in FGF signaling in a mouse model of Crouzon Syndrome
Bibliographic record
Abstract
Mutations in FgfR2 cause several craniosynostosis syndromes, including Crouzon Syndrome (CS), but knowledge of the specific mechanisms by which FGF effects morphogenesis is lacking. The gene CRISPLD2 is associated with non-syndromic cleft lip/palate. Preliminary data indicate it is a novel intracellular target of FGF signaling and alters facial morphology and fibroblast migration rates in vitro. Using a FgfR2W290R mouse model of CS, we investigated the development of the CS facial phenotype and how FGFR2 activity and CRISPLD2 dosage affect cell responses to extracellular FGF. Embryos at stages E9.5-E13.5 were genotyped, photographed, sectioned, and/or used for mouse embryonic fibroblast (MEF) cell lines. Facial shape was quantified using 2-D geometric morphometrics. Cell proliferation and apoptosis in facial primordia were assayed. MEF responses to extracellular FGF were assessed using pMEK expression and cell migration vectors. MEFs were infected with a CRISPLD2 virus (or control) and tests were repeated. At E10.5 (N=104), homozygous mutants (-/-) were rarer than expected, suggesting early embryonic lethality, and had significantly narrower faces and asymmetrical maxillary processes. This is surprising given the wider faces observed in CS patients and previously demonstrated in a chick embryo model. Facial shapes in +/- and +/+ embryos did not differ significantly after accounting for allometry from E9.5-E13.5. Preliminary results show increased apoptosis in the maxillary and mandibular processes of -/- embryos. We are currently analyzing cell proliferation results and performing in vitro analyses. This work will provide insight on how upstream and downstream modulators of FGF signaling regulate craniofacial form.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".