Automated <i>in vivo</i> drug screen in zebrafish identifies synapse-stabilising drugs with relevance to spinal muscular atrophy
Bibliographic record
Abstract
Synapses are particularly vulnerable in many neurodegenerative diseases and often the first to degenerate, for example in the motor neuron disease spinal muscular atrophy (SMA). Compounds that can counteract synaptic destabilisation are rare. Here, we describe an automated screening paradigm in zebrafish for small-molecule compounds that stabilize the neuromuscular synapse in vivo. We make use of a mutant for the axonal C-type lectin chondrolectin (chodl), one of the main genes dysregulated in SMA. In chodl-/- mutants, neuromuscular synapses that are formed at the first synaptic site by growing axons are not fully mature, causing axons to stall, thereby impeding further axon growth beyond that synaptic site. This makes axon length a convenient read-out for synapse stability. We screened 982 small-molecule compounds in chodl chodl-/- mutants and found four that strongly rescued motor axon length. Aberrant presynaptic neuromuscular synapse morphology was also corrected. The most-effective compound, the adenosine uptake inhibitor drug dipyridamole, also rescued axon growth defects in the UBA1-dependent zebrafish model of SMA. Hence, we describe an automated screening pipeline that can detect compounds with relevance to SMA. This versatile platform can be used for drug and genetic screens, with wider relevance to synapse formation and stabilisation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".