A feline model of human LDLR-related atherosclerosis
Bibliographic record
Abstract
Background: Atherosclerosis, a chronic inflammatory vascular disease driven by the accumulation of LDL-derived cholesterol on arterial walls, is the leading cause of mortality worldwide but is rare in animals. We recently identified spontaneous atherosclerosis in the Korat cat breed, characterized by severe hypercholesterolemia and clinical signs of congestive heart failure, ultimately leading to death. Histopathological examination revealed lesions similar to those observed in human atherosclerosis. Given the close genetic relationship among affected cats, we hypothesized a genetic basis for the condition. Methods: We expanded our sample recruitment and employed whole genome sequencing to identify genetic variants associated with the condition. Results: We identified a homozygous XM_003981898.6:c.2406G>A variant specific to the cases in the LDLR gene. This variant is predicted to result in a premature stop codon, XP_003981947.3:p.Trp758*, leading to a truncated LDLR protein that lacks the last 108 amino acids, including the transmembrane and intracellular C-terminal domains. Genotyping this LDLR variant in an additional cohort of 309 Korat cats confirmed its segregation and revealed new affected cats for clinical follow-up. In silico analyses demonstrated that the identified variant appears optimal for gene-editing-based therapeutics. Conclusions: This is the first report of a spontaneous atherosclerosis animal model with an LDLR variant, the most common gene associated with familial hypercholesterolemia in humans. Given that PCSK9, another known hypercholesterolemia gene, has been lost in many mammalian genomes, including cats, our study provides an exciting double knockout model for human atherosclerosis. The affected Korats may also serve as a valuable model for DNA base editing therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".