Characterization of the first knock-out aldh7a1 zebrafish model for pyridoxine-dependent epilepsy using CRISPR-Cas9 technology
Bibliographic record
Abstract
Pyridoxine dependent epilepsy (PDE) is caused by likely pathogenic variants in ALDH7A1 (PDE-ALDH7A1) and inherited autosomal recessively. Neurotoxic alpha-amino adipic semialdehyde (alpha-AASA), piperideine 6-carboxylate and pipecolic acid accumulate in body fluids. Neonatal or infantile onset seizures refractory to anti-epileptic medications are clinical features. Treatment with pyridoxine, arginine and lysine-restricted diet does not normalize neurodevelopmental outcome or accumulation of neurotoxic metabolites. There is no animal model for high throughput drug screening. For this reason, we developed and characterized the first knock-out aldh7a1 zebrafish model using CRISPR-Cas9 technology. Zebrafish aldh7a1 mutants were generated by using a vector free method of CRISPR-Cas9 mutagenesis. Genotype analysis of aldh7a1 knock-out zebrafish was performed by high resolution melt analysis, direct sequencing and QIAxcel system. Electroencephalogram was performed. Alpha-AASA, piperideine 6-carboxylate and pipecolic acid, were measured by liquid chromatography-tandem mass spectrometry. Our knock-out aldh7a1 zebrafish has homozygous 5 base pair (bp) mutation in ALDH7A1. Knock-out aldh7a1 embryos have spontaneous rapid increase in locomotion and a rapid circling swim behavior earliest 8-day post fertilization (dpf). Electroencephalogram revealed large amplitude spike discharges compared to wild type. Knock-out aldh7a1 embryos have elevated alpha-AASA, piperideine 6-carboxylate and pipecolic acid compared to wild type embryos at 3 dpf. Knock-out aldh7a1 embryos showed no aldh7a1 protein by western blot compared to wild type. Our knock-out aldh7a1 zebrafish is a well characterized model for large-scale drug screening using behavioral and biochemical features and accurately recapitulates the human PDE-ALDH7A1 disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".