Bioluminescent imaging of a marking transgene and correction of Fabry mice by neonatal injection of recombinant lentiviral vectors
Bibliographic record
Abstract
Successful therapy for many inherited disorders could be improved if the intervention were initiated early. This is especially true for lysosomal storage disorders. Earlier intervention may allow metabolic correction to occur before lipid buildup has irreversible consequences and/or before the immune system mounts limiting responses. We have been developing gene therapy to treat lysosomal storage disorders, especially Fabry disease. We describe studies directed toward metabolic correction in neonatal animals mediated by recombinant lentiviral vectors. To develop this method, we first injected a marking lentiviral vector that engineers expression of luciferase into the temporal vein of recipient neonatal animals. The use of a cooled charged-coupled device camera allowed us to track transgene expression over time in live animals. We observed intense luciferase expression in many tissues, including the brain, that did not diminish over 24 weeks. Next, we injected neonatal Fabry mice a single time with a therapeutic lentiviral vector engineered to express human alpha-galactosidase A. The injection procedure was well tolerated. We observed increased plasma levels of alpha-galactosidase A activity starting at our first plasma collection point (4 weeks). Levels of alpha-galactosidase A activity were found to be significantly elevated in many tissues even after 28 weeks. No immune response was observed against the corrective transgene product. Increased levels of enzyme activity also led to significant reduction of globotriaosylceramide in the liver, spleen, and heart. This approach provides a method to treat lysosomal storage disorders and other disorders before destructive manifestations occur.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".