BMP signaling regulates the fate of chondro‐osteoprogenitor cells in facial mesenchyme in a stage‐specific manner
Bibliographic record
Abstract
BACKGROUND: Lineage tracing has shown that most of the facial skeleton is derived from cranial neural crest cells. However, the local signals that influence postmigratory, neural crest-derived mesenchyme also play a major role in patterning the skeleton. Here, we study the role of BMP signaling in regulating the fate of chondro-osteoprogenitor cells in the face. RESULTS: A single Noggin-soaked bead inserted into stage 15 chicken embryos induced an ectopic cartilage resembling the interorbital septum within the palate and other midline structures. In contrast, the same treatment in stage 20 embryos caused a loss of bones. The molecular basis for the stage-specific response to Noggin lay in the simultaneous up-regulation of SOX9 and downregulation of RUNX2 in the maxillary mesenchyme, increased cell adhesiveness as shown by N-cadherin induction around the beads and increased RA pathway gene expression. None of these changes were observed in stage 20 embryos. CONCLUSIONS: These experiments demonstrate how slight changes in expression of growth factors such as BMPs could lead to gain or loss of cartilage in the upper jaw during vertebrate evolution. In addition, BMPs have at least two roles: one in patterning the skull and another in regulating the skeletogenic fates of neural crest-derived mesenchyme. Developmental Dynamics 245:947-962, 2016. © 2016 Wiley Periodicals, Inc.
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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) | 0.000 | 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) | 0.002 | 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".