Gene therapy for guanidinoacetate methyltransferase deficiency restores cerebral and myocardial creatine while resolving behavioral abnormalities
Bibliographic record
Abstract
Creatine deficiency disorders are inborn errors of creatine metabolism, an energy homeostasis molecule. One of these, guanidinoacetate N -methyltransferase (GAMT) deficiency, has clinical characteristics that include features of autism, self-mutilation, intellectual disability, and seizures, with approximately 40% having a disorder of movement; failure to thrive can also be a component. Along with low creatine levels, guanidinoacetic acid (GAA) toxicity has been implicated in the pathophysiology of the disorder. Present-day therapy with oral creatine to control GAA lacks efficacy; seizures can persist. Dietary management and pharmacological ornithine treatment are challenging. Using an AAV-based gene therapy approach to express human codon-optimized GAMT in hepatocytes, in situ hybridization, and immunostaining, we demonstrated pan-hepatic GAMT expression. Serial collection of blood demonstrated a marked early and sustained reduction of GAA with normalization of plasma creatine; urinary GAA levels also markedly declined. The terminal time point demonstrated marked improvement in cerebral and myocardial creatine levels. In conjunction with the biochemical findings, treated mice gained weight to nearly match their wild-type littermates, while behavioral studies demonstrated resolution of abnormalities; PET-CT imaging demonstrated improvement in brain metabolism. In conclusion, a gene therapy approach can result in long-term normalization of GAA with increased creatine in guanidinoacetate N -methyltransferase deficiency and at the same time resolves the behavioral phenotype in a murine model of the disorder. These findings have important implications for the development of a new therapy for this abnormality of creatine metabolism.
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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) | 0.000 | 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".