Virus-Like Particles Are a Superior Platform for Presenting M2e Epitopes to Prime Humoral and Cellular Immunity against Influenza Virus
Bibliographic record
Abstract
Influenza virus M2 protein has a highly conserved ectodomain (M2e) as a cross-protective antigenic target. We investigated the antigenic and immunogenic properties of tandem repeat M2e (5xM2e) proteins and virus-like particles (5xM2e VLP) to better understand how VLP and protein platform vaccines induce innate and protective adaptive immune responses. Despite the high antigenic properties of 5xM2e proteins, the 5xM2e VLP was superior to 5xM2e proteins in inducing IgG2a isotype antibodies, T cell responses, plasma cells and germinal center B cells as well as in conferring cross protection. Mice primed with 5xM2e VLP were found to be highly responsive to 5xM2e protein boost, overcoming the low immunogenicity and protective efficacy of 5xM2e proteins. Immunogenic differences between VLPs and proteins in priming immune responses might be due to an intrinsic ability of 5xM2e VLP to stimulate dendritic cells secreting T helper type 1 (Th1) cytokines. We also found that 5xM2e VLP was effective in inducing inflammatory cytokines and chemokines, and in recruiting macrophages, monocytes, neutrophils, and CD11b⁺ dendritic cells at the injection site. Therefore, this study provides evidence that 5xM2e VLP is an effective vaccine platform, inducing cross-protection by stimulating innate and adaptive immune responses.
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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".