A liposome-displayed hemagglutinin vaccine platform protects mice and ferrets from heterologous influenza virus challenge
Bibliographic record
Abstract
Recombinant influenza virus vaccines based on hemagglutinin (HA) hold the potential to accelerate production timelines and improve efficacy relative to traditional egg-based platforms. Here, we assess a vaccine adjuvant system comprised of immunogenic liposomes that spontaneously convert soluble antigens into a particle format, displayed on the bilayer surface. When trimeric H3 HA was presented on liposomes, antigen delivery to macrophages was improved in vitro, and strong functional antibody responses were induced following intramuscular immunization of mice. Protection was conferred against challenge with a heterologous strain of H3N2 virus, and naive mice were also protected following passive serum transfer. When admixed with the particle-forming liposomes, immunization reduced viral infection severity at vaccine doses as low as 2 ng HA, highlighting dose-sparing potential. In ferrets, immunization induced neutralizing antibodies that reduced the upper respiratory viral load upon challenge with a more modern, heterologous H3N2 viral strain. To demonstrate the flexibility and modular nature of the liposome system, 10 recombinant surface antigens representing distinct influenza virus strains were bound simultaneously to generate a highly multivalent protein particle that with 5 ng individual antigen dosing induced antibodies in mice that specifically recognized the constituent immunogens and conferred protection against heterologous H5N1 influenza virus challenge. Taken together, these results show that stable presentation of recombinant HA on immunogenic liposome surfaces in an arrayed fashion enhances functional immune responses and warrants further attention for the development of broadly protective influenza virus vaccines.
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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 | 0.001 | 0.001 |
| 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".