Type I IFN Induced by Adenovirus Serotypes 28 and 35 Has Multiple Effects on T Cell Immunogenicity
Bibliographic record
Abstract
Recombinant adenovirus (rAd) vectors are being investigated as vaccine delivery vehicles in preclinical and clinical studies. rAds constructed from different serotypes differ in receptor usage, tropism, and ability to activate cells, aspects of which likely contribute to their different immunogenicity profiles. In this study, we compared the infectivity and cell stimulatory capacity of recombinant adenovirus serotype 5 (rAd5), recombinant adenovirus serotype 28 (rAd28), and recombinant adenovirus serotype 35 (rAd35) in association with their respective immunogenicity profiles. We found that rAd28 and rAd35 infected and led to the in vitro maturation and activation of both human and mouse dendritic cells more efficiently compared with rAd5. In stark contrast to rAd5, rAd28 and rAd35 induced production of IFN-α and stimulated IFN-related intracellular pathways. However, the in vivo immunogenicity of rAd28 and rAd35 was significantly lower than that of rAd5. Deletion of IFN-α signaling during vaccination with rAd28 and rAd35 vectors increased the magnitude of the insert-specific T cell response to levels induced by vaccination with rAd5 vector. The negative impact of IFN-α signaling on the magnitude of the T cell response could be overcome by increasing the vaccine dose, which was also associated with greater polyfunctionality and a more favorable long-term memory phenotype of the CD8 T cell response in the presence of IFN-α signaling. Taken together, our results demonstrate that rAd-induced IFN-α production has multiple effects on T cell immunogenicity, the understanding of which should be considered in the design of rAd vaccine 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 | 0.000 |
| 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".