Quantification of shape and cell polarity reveals a novel mechanism underlying malformations resulting from related FGF mutations during facial morphogenesis
Bibliographic record
Abstract
Fibroblast growth factor (FGF) signaling mutations are a frequent contributor to craniofacial malformations including midfacial anomalies and craniosynostosis. FGF signaling has been shown to control cellular mechanisms that contribute to facial morphogenesis and growth such as proliferation, survival, migration and differentiation. We hypothesized that FGF signaling not only controls the magnitude of growth during facial morphogenesis but also regulates the direction of growth via cell polarity. To test this idea, we infected migrating neural crest cells of chicken embryos with replication-competent avian sarcoma virus expressing either FgfR2(C278F), a receptor mutation found in Crouzon syndrome or the ligand Fgf8. Treated embryos exhibited craniofacial malformations resembling facial dysmorphologies in craniosynostosis syndrome. Consistent with our hypothesis, ectopic activation of FGF signaling resulted in decreased cell proliferation, increased expression of the Sprouty class of FGF signaling inhibitors, and repressed phosphorylation of ERK/MAPK. Furthermore, quantification of cell polarity in facial mesenchymal cells showed that while orientation of the Golgi body matches the direction of facial prominence outgrowth in normal cells, in FGF-treated embryos this direction is randomized, consistent with aberrant growth that we observed. Together, these data demonstrate that FGF signaling regulates cell proliferation and cell polarity and that these cell processes contribute to facial morphogenesis.
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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".