Novel Immune Response Evasion Strategy to Redose Adeno-associated Viral Vectors and Prolong Survival in Surfactant Protein-B–Deficient Mice
Bibliographic record
Abstract
Abstract Surfactant protein-B (SP-B) deficiency is a lethal neonatal respiratory disease with few therapeutic options. Gene therapy using adeno-associated viruses (AAVs) to deliver human SFTPB cDNA (AAV-hSPB) can improve survival in a mouse model of SP-B deficiency. However, the effect of this gene therapy wanes. Gene therapy efficacy could be prolonged if AAV vectors could be redosed, but readministering vectors is hindered by an immune response that includes TLR9 (Toll-like receptor 9) recognition of unmethylated CpG DNA motifs in the AAV genome. One strategy to mitigate TLR9 recognition of AAV is to incorporate decoy nucleotide sequences within the AAV genome. In this work we examined if an AAV containing these TLR9 inhibitory oligonucleotide sequences (AAV-hSPBTLR9i) could mitigate the immune response sufficiently to redose AAV in the lungs and prolong the survival of SP-B–deficient mice. Indeed, AAV-hSPBTLR9i could be redosed multiple times, which significantly improved survival in our mouse model. This was partially a result of long-term increased SFTPB RNA and SP-B protein expression. Conversely, redosing AAV-hSPB resulted in the rapid death of SP-B–deficient mice after the second AAV dose. TLR9 inhibition enabled readministration by avoiding the broad stimulation of genes belonging to multiple pathways in the host immune and inflammatory responses, including components of the IFN pathways. Thus, redosing of AAV vectors in the lungs using TLR9 inhibitory sequences is a promising strategy for prolonging gene therapy efficacy, with a proof of concept for AAV readministration in a clinically relevant mouse model of SP-B deficiency.
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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".