Mutation of the GDP-Fucose Biosynthesis Gene gmds Increases Hair Cell Number and Neuromast Regenerative Capacity in Zebrafish
Bibliographic record
Abstract
Hearing loss affects millions and is often caused by irreversible damage to mechanosensory hair cells. Humans and other mammals lack the capacity to regenerate damaged hair cells; however zebrafish, Danio rerio, can regenerate hair cells that are present in the ear and mechanosensory neuromasts, making this animal an ideal model for understanding hair cell regenerative mechanisms. This study investigates the role of the GDP-fucose biosynthesis gene GDP-mannose 4,6-dehydratase (gmds) in regulating neuromast hair cell regeneration in zebrafish. Fucosylation is required for Notch signalling, a critical negative regulator of hair cell regeneration, and we therefore hypothesized that loss of gmds function would enhance hair cell regeneration. We demonstrate increased hair cell number in gmds mutants, and increased hair cell number following chemical ablation of hair cells with neomycin. Additionally, gmds mutants exhibited accelerated neuromast and hair cell regeneration, achieving complete restoration faster than wild-type siblings. Pharmacological inhibition of Notch signalling further enhanced hair cell regeneration in wild-type siblings but less so in gmds mutants, indicating that Notch signalling may partially regulate hair cell regeneration downstream of gmds. These findings highlight the importance of GDP-fucose biosynthesis in regulating hair cell number and regeneration, likely partially dependent on Notch 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".