<i>Gys1</i> antisense therapy rescues neuropathological bases of murine Lafora disease
Bibliographic record
Abstract
Lafora disease is a fatal progressive myoclonus epilepsy. At root, it is due to constant acquisition of branches that are too long in a subgroup of glycogen molecules, leading them to precipitate and accumulate into Lafora bodies, which drive a neuroinflammatory response and neurodegeneration. As a potential therapy, we aimed to downregulate glycogen synthase, the enzyme responsible for glycogen branch elongation, in mouse models of the disease. We synthesized an antisense oligonucleotide (Gys1-ASO) that targets the mRNA of the brain-expressed glycogen synthase 1 gene (Gys1). We administered Gys1-ASO by intracerebroventricular injection and analysed the pathological hallmarks of Lafora disease, namely glycogen accumulation, Lafora body formation, and neuroinflammation. Gys1-ASO prevented Lafora body formation in young mice that had not yet formed them. In older mice that already exhibited Lafora bodies, Gys1-ASO inhibited further accumulation, markedly preventing large Lafora bodies characteristic of advanced disease. Inhibition of Lafora body formation was associated with prevention of astrogliosis and strong trends towards correction of dysregulated expression of disease immune and neuroinflammatory markers. Lafora disease manifests gradually in previously healthy teenagers. Our work provides proof of principle that an antisense oligonucleotide targeting the GYS1 mRNA could prevent, and halt progression of, this catastrophic epilepsy.
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) | 0.001 | 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 | 0.001 |
| 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".