The Immune Response in Mice to a scAAV9hGAMT Vector Used to Treat Guanidinoacetate Methyltransferase (GAMT) Deficiency
Bibliographic record
Abstract
Guanidinoacetate methyltransferase deficiency (GAMT-D) is a rare autosomal recessive disorder that disrupts creatine biosynthesis, leading to neurological impairments due to creatine deficiency and guanidinoacetate (GAA) accumulation. Current treatments, including oral creatine supplementation and dietary modifications, partially mitigate symptoms but do not address the root cause of the disorder or effectively restore brain creatine levels. Gene therapy using self-complementary adeno-associated virus 9 (scAAV9) vectors has emerged as a promising approach to directly restore GAMT function in the central nervous system (CNS). The scAAV9.hGAMT vector delivers a functional GAMT gene to GAMT-/- mice via intrathecal administration, aiming to restore creatine synthesis and reduce GAA toxicity. However, immune responses to AAV9 vectors, particularly the development of neutralizing antibodies (NAbs), pose a significant challenge to long-term therapeutic efficacy. This study optimizes an enzyme-linked immunosorbent assay (ELISA) for detecting anti-AAV9 antibodies in serum and utilizes it to assess the immune response in GAMT-/- mice treated with scAAV9.hGAMT. Mice were divided into cohorts receiving varying immunosuppression regimens with prednisone and rapamycin to mitigate immune responses. Serum samples collected at multiple time points were analyzed for NAbs, providing insights into the impact of immunosuppression on vector immunogenicity. The findings will contribute to refining immunomodulatory strategies for AAV-based gene therapies, facilitating their translation into clinical applications for GAMT-D and other neurological disorders.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".