Combination of poly I:C and Pam3CSK4 enhances activation of B cells in vitro and boosts antibody responses to protein vaccines in vivo
Bibliographic record
Abstract
Vaccines that can rapidly induce strong and robust antibody-mediated immunity could improve protection from certain infectious diseases for which current vaccine formulations are inefficient. For indications such as anthrax and influenza, antibody production in vivo is a correlate of efficacy. Toll-like receptor (TLR) agonists are frequently studied for their role as vaccine adjuvants, largely because of their ability to enhance initiation of immune responses to antigens by activating dendritic cells. However, TLRs are also expressed on B cells and may contribute to effective B cell activation and promote differentiation into antigen-specific antibody producing plasma cells in vivo. We sought to discover an adjuvant system that could be used to augment antibody responses to influenza and anthrax vaccines. We first characterized an adjuvant system in vitro which consisted of two TLR ligands, poly I:C (TLR3) and Pam3CSK4 (TLR2), by evaluating its effects on B cell activation. Each agonist enhanced B cell activation through increased expression of surface receptors, cytokine secretion and proliferation. However, when B cells were stimulated with poly I:C and Pam3CSK4 in combination, further enhancement to cell activation was observed. Using B cells isolated from knockout mice we confirmed that poly I:C and Pam3CSK4 were signaling through TLR3 and TLR2, respectively. B cells activated with Poly I:C and Pam3CSK4 displayed enhanced capacity to stimulate allogeneic CD4+ T cell activation and differentiate into antibody-producing plasma cells in vitro. Mice vaccinated with influenza or anthrax antigens formulated with poly I:C and Pam3CSK4 in DepoVax™ vaccine platform developed a rapid and strong antigen-specific serum antibody titer that persisted for at least 12 weeks after a single immunization. These results demonstrate that combinations of TLR adjuvants promote more effective B cell activation in vitro and can be used to augment antibody responses to vaccines in vivo.
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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) | 0.001 | 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 | 0.001 |
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".