Engineering of papaya mosaic virus (PapMV) nanoparticles with a CTL epitope derived from influenza NP
Bibliographic record
Abstract
BACKGROUND: The ever-present threat of infectious disease, e.g. influenza pandemics, and the increasing need for new and effective treatments in immunotherapy are the driving forces that motivate research into new and innovative vaccine platforms. Ideally, such platforms should trigger an efficient CTL response, be safe, and easy to manufacture. We recently developed a novel nanoparticle adjuvant comprised of papaya mosaic virus (PapMV) coat protein (CP) assembled around an RNA. The PapMV nanoparticle is an efficient vaccine platform in which the peptide antigen is fused to the C-terminus of the PapMV CP, leading to nanoparticles presenting surface-exposed epitope. The fusion stabilizes the epitope and improves its immunogenicity. We found recently that C-terminal fusions are not always efficient, depending on the nature of the peptide fused to the platform. RESULTS: We chose a CTL epitope derived from the nucleocapsid (NP) of influenza virus (NP₁₄₇₋₁₅₅) for this proof-of-concept demonstration. Recombinant nanoparticles harbouring a fusion at the N-terminus were more efficient in triggering a CTL response. Efficacy appeared to be linked to the stability of the nanoparticles at 37°C. We also showed that discs--smaller than nanoparticles--made of 20 subunits of PapMV CP are less efficient for induction of a CTL response in mice, revealing that assembly of the recombinant PapMV CP into nanoparticles is crucial to triggering an efficient CTL response. CONCLUSION: The point of fusion on the PapMV vaccine platform is critical to triggering an efficient CTL response. Efficacy is linked to nanoparticle stability; nanoparticles must be stable at 37°C but remain susceptible to cellular proteases to ensure efficient processing of the CTL epitope by cells of the immune system. The results of this study improve our understanding of the PapMV vaccine platform, which will facilitate the design of efficient vaccines to various infectious threats.
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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) | 0.000 | 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".