Temporospatial sonic hedgehog signalling is essential for neural crest-dependent patterning of the intrinsic tongue musculature
Bibliographic record
Abstract
ABSTRACT The tongue is a highly specialised muscular organ with a complex anatomy required for normal function. We have utilised multiple genetic approaches to investigate local temporospatial requirements for sonic hedgehog (SHH) signalling during tongue development. Mice lacking a Shh cis-enhancer, MFCS4 (ShhMFCS4/−), with reduced SHH in dorsal tongue epithelium have perturbed lingual septum tendon formation and disrupted intrinsic muscle patterning, with these defects reproduced following global Shh deletion from E10.5 in pCag-CreERTM; Shhflox/flox embryos. SHH responsiveness was diminished in local cranial neural crest cell (CNCC) populations in both mutants, with SHH targeting these cells through the primary cilium. CNCC-specific deletion of orofaciodigital syndrome 1 (Ofd1), which encodes a ciliary protein, in Wnt1-Cre; Ofdfl/Y mice led to a complete loss of normal myotube arrangement and hypoglossia. In contrast, mesoderm-specific deletion of Ofd1 in Mesp1-Cre; Ofdfl/Y embryos resulted in normal intrinsic muscle arrangement. Collectively, these findings suggest key temporospatial requirements for local SHH signalling in tongue development (specifically, lingual tendon differentiation and intrinsic muscle patterning through signalling to CNCCs) and provide further mechanistic insight into the tongue anomalies seen in patients with disrupted hedgehog signalling.
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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".