Improved influenza vaccine responses after expression of multiple viral glycoproteins from a single mRNA
Bibliographic record
Abstract
Influenza viruses cause substantial morbidity and mortality every year despite seasonal vaccination. mRNA-based vaccines have the potential to elicit more protective immune responses, but for maximal breadth and durability, it is desirable to deliver both the viral hemagglutinin and neuraminidase glycoproteins. Delivering multiple antigens individually, however, complicates manufacturing and increases cost, thus it would be beneficial to express both proteins from a single mRNA. Here, we develop an mRNA genetic configuration that allows the simultaneous expression of unmodified, full-length NA and HA proteins from a single open reading frame. We apply this approach to glycoproteins from contemporary influenza A and B viruses and, after vaccination, observe high levels of functional antibodies and protection from disease in female mouse and male ferret challenge models. This approach may further efforts to utilize mRNA technology to improve seasonal vaccine efficacy by efficiently delivering multiple viral antigens simultaneously and in their native state. Here, the authors report the development of a genetic platform for mRNA-LNP vaccines that encodes the two major influenza virus glycoprotein genes into a single mRNA molecule. They show that this approach is generalizable to diverse influenza virus strains and is immunogenic and protective in mouse and ferret models of influenza disease.
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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 | 0.001 |
| 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".