FGF signaling regulates development by processes beyond canonical pathways
Bibliographic record
Abstract
FGFs are key developmental regulators that engage a signal transduction cascade through receptor tyrosine kinases, prominently engaging ERK1/2 but also other pathways. However, it remains unknown whether all FGF activities depend on this canonical signal transduction cascade. To address this question, we generated allelic series of knock-in Fgfr1 and Fgfr2 mouse strains, carrying point mutations that disrupt binding of signaling effectors, and a kinase dead allele of Fgfr2 that broadly phenocopies the null mutant. When interrogated in cranial neural crest cells, we identified discrete functions for signaling pathways in specific craniofacial contexts, but point mutations, even when combined, failed to recapitulate the single or double null mutant phenotypes. Furthermore, the signaling mutations abrogated established FGF-induced signal transduction pathways, yet FGF functions such as cell–matrix and cell–cell adhesion remained unaffected, though these activities did require FGFR kinase activity. Our studies establish combinatorial roles of Fgfr1 and Fgfr2 in development and uncouple novel FGFR kinase-dependent cell adhesion properties from canonical intracellular signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".