Antigen modifications improve nucleoside-modified mRNA-based influenza virus vaccines in mice
Bibliographic record
Abstract
Nucleoside-modified, lipid nanoparticle-encapsulated mRNAs have recently emerged as suitable vaccines for influenza viruses and other pathogens in part because the platform allows delivery of multiple antigens in a single immunization. mRNA vaccines allow for easy antigen modification, enabling rapid iterative design. We studied protein modifications such as mutating functional sites, changing secretion potential, and altering protein conformation, which could improve the safety and/or potency of mRNA-based influenza virus vaccines. Mice were vaccinated intradermally with wild-type or mutant constructs of influenza virus hemagglutinin (HA), neuraminidase (NA), matrix protein 2 (M2), nucleoprotein (NP), or matrix protein 1 (M1). Membrane-bound HA constructs elicited more potent and protective antibody responses than secreted forms. Altering the catalytic site of NA to reduce enzymatic activity decreased reactogenicity while protective immunity was maintained. Disruption of M2 ion channel activity improved immunogenicity and protective efficacy. A comparison of internal proteins NP and M1 revealed the superiority of NP in conferring protection from influenza virus challenge. These findings support the use of the nucleoside-modified mRNA platform for guided antigen design for influenza virus with extension to other pathogens.
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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) | 0.001 | 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".