Minimal Dataset for Characterization of a C9orf72 Knockout Danio rerio Model for ALS and Cross-Species Validation of Therapeutics in Caenorhabditis elegans
Bibliographic record
Abstract
This dataset is associated with the manuscript "Characterization of a C9orf72 Knockout Danio rerio Model for ALS and Cross-Species Validation of Potential Therapeutics Screened in Caenorhabditis elegans", currently under review at PLOS Genetics. It represents the minimal dataset required for reproducibility under PLOS Genetics data-sharing policies. Abstract: Intronic hexanucleotide repeat expansions in the C9orf72 gene are the most common genetic cause of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. This expansion reduces C9orf72 expression in affected patients, implicating loss of C9orf72 function (LOF) as a pathogenic mechanism. Various Danio rerio (zebrafish) models of C9orf72 depletion have been developed to investigate disease mechanisms and the effects of C9orf72 LOF. However, there are inconsistencies in reported phenotypes, and most have yet to be validated in stable germline ablation models. To address this, we generated a zebrafish C9orf72 knockout model using CRISPR/Cas9. The C9orf72 LOF model exhibits, in a generally dose-dependent manner, increased larval mortality, persistent growth reduction, and motor deficits. Additionally, homozygous C9orf72 LOF larvae displayed mild overbranching of spinal motoneurons. To identify potential therapeutic compounds, we conducted a screen in an established Caenorhabditis elegans (C. elegans) C9orf72 homologue (alfa-1) LOF model, identifying 12 compounds that improved the motility, neurodegeneration, and paralysis phenotypes. Prompted by the shared motor phenotype, 2 of those compounds were tested in our zebrafish C9orf72 LOF model. Pizotifen malate was found to significantly improve motor deficits in C9orf72 LOF zebrafish larvae. We present a novel zebrafish C9orf72 knockout model that exhibits phenotypic differences from depletion models, providing a valuable tool for in vivo C9orf72 research and ALS therapeutic validation. Furthermore, we identify pizotifen malate as a promising compound for further preclinical evaluation. Dataset Information: This dataset includes raw and processed numerical data from key experimental assays in zebrafish (Danio rerio) and Caenorhabditis elegans. In zebrafish, it contains data from qPCR, sequencing, western blots, survival assays, motor activity tracking, spinal motor neuron morphology analysis, neuromuscular junction integrity evaluation, and drug screening experiments. For C. elegans, it includes data on swimming activity, paralysis assays, neurodegeneration analysis, and drug screening experiments. This dataset represents the minimal dataset required for reproducibility in accordance with PLOS Genetics guidelines and provides all necessary numerical data to replicate the study’s findings. Where relevant, sample raw files are included to ensure data provenance and reproducibility.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.032 |
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".