A SACS deletion variant in Great Pyrenees dogs causes autosomal recessive neuronal degeneration
Bibliographic record
Abstract
ARSACS (autosomal recessive spastic ataxia of Charlevoix-Saguenay) is a human neurological disorder characterized by progressive cerebellar ataxia and peripheral neuropathy. A recently recognized disorder in Great Pyrenees dogs is similarly characterized by widespread central nervous system degeneration leading to progressive cerebellar ataxia and spasticity, combined with peripheral neuropathy. Onset of clinical signs occurred in puppies as young as 4 months of age, with slow progression over several years. A multi-generation pedigree suggested an autosomal recessive mode of inheritance. Histopathology revealed consistent cerebellar Purkinje cell degeneration, neuronal degeneration in brainstem nuclei, widespread spinal cord white matter degeneration, ganglion cell degeneration, inappropriately thin myelin sheaths or fully demyelinated peripheral nerve fibers, and normal or only mild patterns of denervation atrophy in skeletal muscles. Genome-wide single nucleotide polymorphism (SNP) genotype data was collected from 6 cases and 26 controls, where homozygosity mapping identified a 3.3 Mb region on CFA25 in which all cases were homozygous and all controls were either heterozygous or homozygous for alternate haplotypes. This region tagged the SACS gene where variants are known to cause ARSACS. Sanger sequencing of SACS in affected dogs identified a 4 bp deletion that causes a frame shift and truncates 343 amino acids from the C terminus of the encoded sacsin protein (p.Val4244AlafsTer32). Our clinical and histopathological descriptions of this canine disorder contribute to the description of human ARSACS and represents the first naturally occurring large animal model of this disorder.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".