Development of a riboflavin-responsive model of riboflavin transporter deficiency in zebrafish
Bibliographic record
Abstract
Riboflavin transporter deficiency (RTD) is a rare and progressive neurodegenerative disease resulting from the disruption of RFVT2- and RFVT3- mediated riboflavin transport caused by biallelic mutations in SLC52A2 and SLC52A3, respectively. The resulting impaired mitochondrial metabolism leads to sensorimotor neurodegeneration and symptoms including muscle weakness, respiratory difficulty, and sensorineural deafness. Although over 70% of patients with RTD improve following high-dose riboflavin supplementation, remaining patients either stabilise or continue to deteriorate. This may be due to the rapid excretion of central nervous system (CNS) riboflavin by organic anion transporter 3 (OAT-3), highlighting the need for alternative or supplemental RTD treatments. Probenecid is a promising therapeutic candidate for RTD due to its known inhibitory effect on OAT-3. Therefore, this study aimed to generate morpholino-mediated knockdowns of human SLC52A3 ortholog slc52a3 in zebrafish larvae for use in therapeutic screening of riboflavin and probenecid. Knockdown of slc52a3 resulted in an RTD-like phenotype indicative of altered neurodevelopment, hearing loss, and reduced mobility. This RTD-like phenotype overlaps with the phenotype of CRISPR/Cas9-mediated knockout of slc52a3 in zebrafish, is maintained following slc52a3 morpholino + p53 morpholino co-injection, and is rescued following slc52a3 morpholino + human SLC52A3 mRNA co-injection, indicating specificity of the knockdown. Riboflavin treatment alone ameliorates locomotor activity and hearing ability in slc52a3 morphants. Riboflavin and probenecid co-treatment provides an additional small benefit to hearing but not to locomotion. Our findings demonstrate that this model recapitulates both the RTD phenotype and the riboflavin-responsiveness of RTD patients, and possible therapeutic benefit conferred by probenecid warrants further investigation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".