Bestrophin Gene Mutations Cause Canine Multifocal Retinopathy: A Novel Animal Model for Best Disease
Bibliographic record
Abstract
PURPOSE: Canine multifocal retinopathy (cmr) is an autosomal recessive disorder of multiple dog breeds. The disease shares a number of clinical and pathologic similarities with Best macular dystrophy (BMD), and cmr is proposed as a new large animal model for Best disease. METHODS: cmr was characterized by ophthalmoscopy and histopathology and compared with BMD-affected patients. BEST1 (alias VMD2), the bestrophin gene causally associated with BMD, was evaluated in the dog. Canine ortholog cDNA sequence was cloned and verified using RPE/choroid 5'- and 3'-RACE. Expression of the canine gene transcripts and protein was analyzed by Northern and Western blotting and immunocytochemistry. All exons and the flanking splice junctions were screened by direct sequencing. RESULTS: The clinical phenotype and pathology of cmr closely resemble lesions of BMD. Canine VMD2 spans 13.7 kb of genomic DNA on CFA18 and shows a high level of conservation among eukaryotes. The transcript is predominantly expressed in RPE/choroid and encodes bestrophin, a 580-amino acid protein of 66 kDa. Immunocytochemistry of normal canine retina demonstrated specific localization of protein to the RPE basolateral plasma membranes. Two disease-specific sequence alterations were identified in the canine VMD2 gene: a C(73)T stop mutation in cmr1 and a G(482)A missense mutation in cmr2. CONCLUSIONS: The authors propose these two spontaneous mutations in the canine VMD2 gene, which cause cmr, as the first naturally occurring animal model of BMD. Further development of the cmr models will permit elucidation of the complex molecular mechanism of these retinopathies and the development of potential therapies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".