Investigating Immune Responses to the scAAV9-HEXM Gene Therapy Treatment in Tay–Sachs Disease and Sandhoff Disease Mouse Models
Bibliographic record
Abstract
GM2 gangliosidosis disorders are a group of neurodegenerative diseases that result from a functional deficiency of the enzyme β-hexosaminidase A (HexA). HexA consists of an α- and β-subunit; a deficiency in either subunit results in Tay–Sachs Disease (TSD) or Sandhoff Disease (SD), respectively. Viral vector gene transfer is viewed as a potential method of treating these diseases. A recently constructed isoenzyme to HexA, called HexM, has the ability to effectively catabolize GM2 gangliosides in vivo. Previous gene transfer studies have revealed that the scAAV9-HEXM treatment can improve survival in the murine SD model. However, it is speculated that this treatment could elicit an immune response to the carrier capsid and “non-self”-expressed transgene. This study was designed to assess the immunocompetence of TSD and SD mice, and test the immune response to the scAAV9-HEXM gene transfer. HexM vector-treated mice developed a significant anti-HexM T cell response and antibody response. This study confirms that TSD and SD mouse models are immunocompetent, and that gene transfer expression can create an immune response in these mice. These mouse models could be utilized for investigating methods of mitigating immune responses to gene transfer-expressed “non-self” proteins, and potentially improve treatment efficacy.
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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 | 0.000 |
| 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 | 0.000 |
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".