Induction of Immunological Tolerance to Adenoviral Vectors by Using a Novel Dendritic Cell-Based Strategy
Bibliographic record
Abstract
The success of helper-dependent adenoviral (HD-Ad) vector-mediated lung gene therapy is hampered by the host immune response, which limits pulmonary transgene expression following multiple rounds of vector readminstration. Here, we show that HD-Ad-mediated pulmonary gene expression is sustained even upon three rounds of readministration to immunodeficient mice, highlighting the need to suppress the adaptive immune response for sustained gene expression following vector readministration. Therefore, we devised a dendritic cell (DC)-based strategy for induction of immunological tolerance toward HD-Ad vectors. DCs derived in the presence of interleukin-10 (IL-10) are refractory to HD-Ad-induced maturation and instead facilitate generation of IL-10-producing Tr1 regulatory T cells which suppress HD-Ad-induced T cell proliferation. Delivery of HD-Ad-pulsed, IL-10-modified DCs to mice induces long-lasting immunological tolerance to HD-Ad vectors, whereby pulmonary DC maturation, the T cell response, and antibody response to HD-Ad vectors are suppressed even after three rounds of pulmonary HD-Ad readministration. Moreover, sustained transgene expression is also observed in the lungs of mice immunized with HD-Ad-pulsed, IL-10-modified DCs even after three rounds of pulmonary HD-Ad delivery. Taken together, these studies identify the use of DCs generated in the presence of IL-10 as a novel strategy to induce long-lasting immune tolerance to HD-Ad vectors.
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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) | 0.000 | 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".