An inducible glycogen synthase-1 knockout halts but does not reverse Lafora disease progression in mice
Bibliographic record
Abstract
Malstructured glycogen accumulates over time in Lafora disease (LD) and precipitates into Lafora bodies (LBs), leading to neurodegeneration and intractable fatal epilepsy. Constitutive reduction of glycogen synthase-1 (GYS1) activity prevents murine LD, but the effect of GYS1 reduction later in disease course is unknown. Our goal was to knock out Gys1 in laforin (Epm2a)-deficient LD mice after disease onset to determine whether LD can be halted in midcourse, or even reversed. We generated Epm2a-deficient LD mice with tamoxifen-inducible Cre-mediated Gys1 knockout. Tamoxifen was administered at 4 months and disease progression assessed at 12 months. We verified successful knockout at mRNA and protein levels using droplet digital PCR and Western blots. Glycogen determination and periodic acid-Schiff-diastase staining were used to analyze glycogen and LB accumulation. Immunohistochemistry using astrocytic (glial fibrillary acidic protein) and microglial (ionized calcium-binding adapter molecule 1) markers was performed to investigate neuroinflammation. In the disease-relevant organ, the brain, Gys1 mRNA levels were reduced by 85% and GYS1 protein depleted. Glycogen accumulation was halted at the 4-month level, while LB formation and neuroinflammation were significantly, though incompletely, prevented. Skeletal muscle analysis confirmed that Gys1 knockout inhibits glycogen and LB accumulation. However, tamoxifen-independent Cre recombination precluded determination of disease halting or reversal in this tissue. Our study shows that Gys1 knockdown is a powerful means to prevent LD progression, but this approach did not reduce brain glycogen or LBs to levels below those at the time of intervention. These data suggest that endogenous mechanisms to clear brain LBs are absent or, possibly, compromised in laforin-deficient murine LD.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".