Suppressor of fused controls cerebellum granule cell proliferation by suppressing Fgf8 and spatially regulating Gli proteins
Bibliographic record
Abstract
Cerebellar granule cell (GC) development relies on precise regulation of sonic hedgehog (Shh)-Gli signalling activity, failure of which is associated with motor disorders and medulloblastoma. Mutations in the pathway regulator suppressor of fused (Sufu), which modulates Gli activators and repressors, are linked to cerebellar dysfunction and tumourigenesis. The mechanism by which Sufu calibrates Shh signalling in GCs is unknown. Math1-Cre-mediated deletion of Sufu in mouse GC progenitors (GCPs) demonstrated that Sufu restricts GCP proliferation and promotes cell cycle exit, by promoting expression of Gli3R and suppressing Gli2 levels. Sufu is also required to promote a high threshold of pathway activity in GCPs. Remarkably, central cerebellar lobules are more deleteriously impacted by Sufu deletion, but are less sensitive to downstream genetic manipulations to reduce Gli2 expression or overexpress a Gli3R mimic, compared with anterior lobules. Transcriptome sequencing uncovered new Sufu targets, especially Fgf8, which is upregulated in Sufu-mutant GCPs. We demonstrate that Fgf8 is necessary and sufficient to drive Sufu-mutant GCP proliferation. This study reveals new insights into the spatial and temporal regulation of cerebellar Shh-Gli signalling, while uncovering new targets, such as Fgf8.
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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".