FGF-receptor signalling controls neural cell diversity in the zebrafish hindbrain by regulating <i>olig2</i> and <i>sox9</i>
Bibliographic record
Abstract
The mechanisms underlying the generation of neural cell diversity are the subject of intense investigation, which has highlighted the involvement of different signalling molecules including Shh, BMP and Wnt. By contrast, relatively little is known about FGF in this process. In this report we identify an FGF-receptor-dependent pathway in zebrafish hindbrain neural progenitors that give rise to somatic motoneurons, oligodendrocyte progenitors and differentiating astroglia. Using a combination of chemical and genetic approaches to conditionally inactivate FGF-receptor signalling, we investigate the role of this pathway. We show that FGF-receptor signalling is not essential for the survival or maintenance of hindbrain neural progenitors but controls their fate by coordinately regulating key transcription factors. First, by cooperating with Shh, FGF-receptor signalling controls the expression of olig2, a patterning gene essential for the specification of somatic motoneurons and oligodendrocytes. Second, FGF-receptor signalling controls the development of both oligodendrocyte progenitors and astroglia through the regulation of sox9, a gliogenic transcription factor the function of which we show to be conserved in the zebrafish hindbrain. Overall, for the first time in vivo, our results reveal a mechanism of FGF in the control of neural cell diversity.
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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) | 0.001 | 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".