OX40 boosts and sustains humoral and cellular immune responses to SARS-CoV2 spike protein and RNA vaccinations
Bibliographic record
Abstract
Abstract To prevent SARS-CoV-2 infections and generate long-lasting immunity, vaccines need to generate both, a strong humoral immune response as well as viral-specific CD4 and CD8 T cells. Previous results from our lab have shown that immunization in the presence of an agonist antibody targeting OX40 led to higher antibody titers and increased numbers of antigen-specific CD4 and CD8 T cells. Using the same strategy, we explored the effect of OX40 co-stimulation in the prime and boost together with the SARS-CoV-2 spike protein + adjuvant in C57Bl/6 mice. Our results show that OX40 engagement led to a significant increase in long-lived antibody responses when compared to mice that did not receive additional co-stimulation. In addition, spike protein and peptide-specific proliferation were greatly increased for both CD4 and CD8 T cells, with augmented secretion of IFN-γ, TNF-α and IL-2. Booster immunizations (7 months post prime) did not lead to anergy, but instead further increased the antibody and T cell responses. In initial experiments, the self-amplifying RNA (saRNA) vaccination generated lower antibody titers, independent of OX40 co-stimulation. However, the saRNA vaccine did induce a robust expansion of antigen-specific CD8 T cells, that expressed high levels of GrzmB, which was further increased by OX40 administration. The strong immune responses and the differential effects of the protein and saRNA vaccines suggest that heterologous prime-boost approaches, as currently approved in adults, might be beneficial in boosting different arms of the anti-viral immune response against SARS-CoV2, with OX40 agonists enhancing both approaches. Further studies in animals will aid defining the effects and benefits of this approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".