Developing AAV Gene Therapy Tools for Neurodevelopmental Epileptic Encephalopathies
Bibliographic record
Abstract
Mutations in voltage-gated sodium channel genes give rise to a number of neurodevelopmental and epileptic disorders, including Dravet syndrome and a range of diseases encompassed under the umbrella term developmental epileptic encephalopathy type 52 (DEE52). Pharmacological approaches to mitigate the epileptic phenotype of these disorders have met with limited success, and no small molecule drugs are available that address the development-associated behavioral and cognitive impairments. AAV gene therapy is a promising approach which could holistically mitigate the disease manifestations by correcting the underlying genetic defect. However, there are several drawbacks that currently restrict their usage. AAVs exhibit limited DNA packaging capacities, low innate abilities to selectivity transduce different subtypes of neurons, and may trigger an inflammatory response to the vector components and/or the transgene product. The work described in this thesis aimed to investigate these issues and also to test candidate AAV therapies for Dravet syndrome and DEE52. We show the first proof of concept of a gene replacement approach in DEE52 model using AAVs encoding Scn1b. We also characterize several novel GABAergic gene regulatory elements, which are operative in multiple brain regions in numerous GABA neuron types and represent substantial advancements over the current state-of-the-art GABA promoters. One of these promoters, composed of putative TFBSs from GABAergic genes, was used to test the effects of targeted transgenic expression of NaVβ1 alone or in tandem with the prokaryotic sodium channel NaChBac in GABA neurons in Dravet syndrome mice. Finally, we show that the immunological consequences of expressing Cas9, a prokaryotic protein used to edit genes, may be substantially mitigated when AAV-Cas9s are injected into the neonatal brain compared to the adult brain. Altogether, the findings gleaned from these studies have implications for the development and advancement of AAV gene therapies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".