mRNA delivery of mosaic-8 pan-sarbecovirus RBD vaccines elicits distinct antibody epitope signatures
Bibliographic record
Abstract
Abstract Effective pan-sarbecovirus vaccines could prevent future zoonotic spillovers of SARS-like betacoronaviruses. We previously developed protein-based mosaic-8 nanoparticles displaying eight diverse sarbecovirus RBDs, either individually (mosaic-8 RBD-NPs) or as two “quartets” of four tandemly-arranged RBDs (dual-quartet RBD-NPs), which elicited broadly cross-reactive antibodies but require multi-component manufacturing. Here, we address scalability challenges by extending the mosaic-8 concept to mRNA by encoding membrane-bound RBD quartets as dual-quartet RBD-mRNA and dual-quartet RBD-EABR-mRNA, the latter leveraging ESCRT- and ALIX-binding region (EABR) technology for immunogen display on cell surfaces and secreted virus-like particles. Compared with protein-based mosaic-8 immunogens, mRNA-encoded mosaic-8 vaccines induced equivalent or enhanced antibody breadth, neutralization potencies, T-cell responses, and targeting of conserved RBD epitopes. In addition, mRNA-encoded mosaic-8 vaccines elicited more balanced IgG subclass profiles and increased Fcγ receptor–binding IgGs, consistent with potentially superior Fc effector functions. These findings demonstrate successful translation of mosaic-8 RBD-NPs into mRNA/EABR-mRNA vaccines, enabling scalable manufacturing and improving protection against future sarbecovirus outbreaks. Finally, our newly developed technique, Systems Serology–Polyclonal Epitope Mapping (SySPEM), revealed distinct IgG-subclass-specific epitope signatures across mRNA, EABR-mRNA, and protein vaccines, demonstrating that the mode of antigen display can shape epitope recognition. Summary We translated a pan-sarbecovirus RBD vaccine from protein nanoparticles to scalable mRNA and EABR-mRNA platforms encoding RBD quartets. Compared with protein-based immunogens, mRNA-based vaccines matched or improved antibody breadth, T-cell responses, Fc functionality, and conserved epitope targeting. A newly-developed Systems Serology–Polyclonal Epitope Mapping (SySPEM) technique revealed that antigen presentation modality shapes IgG subclass–specific epitope recognition.
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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".