OX40 agonist stimulation increases and sustains humoral and cell-mediated responses to SARS-CoV-2 protein and saRNA vaccines
Bibliographic record
Abstract
To prevent SARS-CoV-2 infections and generate long-lasting immunity, vaccines need to generate strong viral-specific B and T cell responses. Previous results from our lab and others have shown that immunizations in the presence of an OX40 agonist antibody lead to higher antibody titers and increased numbers of long-lived antigen-specific CD4 and CD8 T cells. Using a similar strategy, we explored the effect of OX40 co-stimulation in a prime and boost vaccination scheme using an adjuvanted SARS-CoV-2 spike protein vaccine in C57BL/6 mice. Our results show that OX40 engagement during vaccination significantly increases long-lived antibody responses to the spike protein. In addition, after immunization spike protein-specific proliferation was greatly increased for both CD4 and CD8 T cells, with enhanced, spike-specific secretion of IFN-γ and IL-2. Booster (3rd injection) immunizations combined with an OX40 agonist (7 months post-prime) further increased vaccine-specific antibody and T cell responses. Initial experiments assessing a self-amplifying mRNA (saRNA) vaccine encoding the spike protein antigen show a robust antigen-specific CD8 T cell response. The saRNA spike-specific CD8 T cells express high levels of GrzmB, IFN-γ and TNF-α which was not observed with protein immunization and this response was further increased by the OX40 agonist. Similar to protein immunizations the OX40 agonist also increased vaccine-specific CD4 T cell responses. In summary, this study compares and contrasts the effects and benefits of both protein and saRNA vaccination and the extent to which an OX40 agonist enhances and sustains the immune response against the SARS-CoV-2 spike protein.
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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) | 0.002 | 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".