Efficient Neuroprotective Rescue of Sacsin-Related Disease Phenotypes in Zebrafish
Bibliographic record
Abstract
Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a multisystem hereditary ataxia associated with mutations in SACS, which encodes sacsin, a protein of still only partially understood function. Although mouse models of ARSACS mimic largely the disease progression seen in humans, their use in the validation of effective therapies has not yet been proposed. Recently, the teleost Danio rerio has attracted increasing attention as a vertebrate model that allows rapid and economical screening, of candidate molecules, and thus combines the advantages of whole-organism phenotypic assays and in vitro high-throughput screening assays. Through CRISPR/Cas9-based mutagenesis, we generated and characterized a zebrafish sacs-null mutant line that replicates the main features of ARSACS. The sacs-null fish showed motor impairment, hindbrain atrophy, mitochondrial dysfunction, and reactive oxygen species accumulation. As proof of principle for using these mutant fish in high-throughput screening studies, we showed that both acetyl-DL-leucine and tauroursodeoxycholic acid improved locomotor and biochemical phenotypes in sacs−/− larvae treated with these neuroprotective agents, by mediating significant rescue of the molecular functions altered by sacsin loss. Taken together, the evidence here reported shows the zebrafish to be a valuable model organism for the identification of novel molecular mechanisms and for efficient and rapid in vivo optimization and screening of potential therapeutic compounds. These findings may pave the way for new interventions targeting the earliest phases of Purkinje cell degeneration in ARSACS.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".