Efficient Screening of the Cystinuria-Related C663T Slc3a1 Nonsense Mutation in Newfoundland Dogs by Denaturing High-Performance Liquid Chromatography
Bibliographic record
Abstract
Cystinuria in Newfoundland dogs is a metabolic disease associated with a nonsense mutation in the exon 2 of the Slc3a1 gene. Similar to type I human cystinuria, heterozygote carriers are not affected by the disease and do not reveal differences in urinary concentration of dibasic amino acids when compared with normal dogs. However, through a recessive mode of inheritance, these dogs are able to transmit the disease to their offspring. Early detection of mutation carriers through cost-effective reliable methods is therefore essential for the implementation of breeding methods aimed at the eradication of the disease. Denaturing high-performance liquid chromatography (DHPLC) is a recently developed technique for rapid and efficient screening of nucleotide polymorphisms in polymerase chain reaction-amplified products. This technique was used for the identification of the C663T Slc3a1 mutation in Portuguese Newfoundland dogs. Polymerase chain reaction products amplified from a region containing the C663T locus were subjected to DHPLC analysis, and results were double checked by DNA sequencing. Results showed the presence of the mutation in 6 of the 22 dogs tested. Urine biochemical parameters correlated well with the number of mutated Slc3a1 copies, and homozygotes for the C663T mutation were the only dogs diagnosed with cystinuria. Sequence analysis confirmed the DHPLC results, demonstrating that the technique could be a reliable alternative to sequencing for the rapid and cost-effective identification of mutations in canine breeds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".