Nucleoside-modified mRNA-based influenza vaccines circumvent problems associated with H3N2 vaccine strain egg-adaptation
Bibliographic record
Abstract
ABSTRACT Most human influenza vaccine antigens are produced in fertilized chicken eggs. Recent H3N2 egg-based vaccine antigens have limited effectiveness, partially due to egg-adaptive substitutions that alter the antigenicity of the hemagglutinin (HA) protein. The nucleoside-modified messenger RNA encapsulated in lipid nanoparticle (mRNA-LNP) vaccine platform is a promising alternative for egg-based influenza vaccines because mRNA-LNP-derived antigens are not subject to adaptive pressures that arise during the production of antigens in chicken eggs. Here, we compared H3N2-specific antibody responses in mice vaccinated with either 3c.2A H3-encoded mRNA-LNP or a conventional egg-based Fluzone vaccine (which included an egg-adapted 3c.2A antigen) supplemented with an MF59-like adjuvant. We tested mRNA-LNP encoding wild-type and egg-adapted 3c.2A H3 antigens. We found that mRNA-LNP encoding wild-type 3c.2A H3 elicited antibodies that neutralized the wild-type 3c.2A H3N2 virus more effectively relative to antibodies elicited by mRNA-LNP encoding egg-adapted 3c2.A H3 or the egg-based Fluzone vaccine. mRNA-LNP expressing either wild-type or egg-adapted 3c2.A H3 protected mice against infection with the wild-type 3c2.A H3N2, whereas the egg-based Fluzone vaccine did not. We found that both mRNA-LNP vaccines elicited high levels of group 2 HA stalk-reactive antibodies that likely contributed to protection in vivo . Our studies indicate that nucleoside-modified mRNA-LNP-based vaccines can circumvent problems associated with egg-adaptations with recent 3c2.A H3N2 viruses. Summary This study shows that the nucleoside-modified messenger RNA encapsulated in lipid nanoparticle (mRNA-LNP) vaccine platform is a promising alternative for egg-based influenza vaccines. We show that mRNA-LNP expressing H3 antigens elicit high levels of antibodies in mice and protect against H3N2 influenza virus infection.
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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) | 0.001 | 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".