Vaccine-mediated protection against merbecovirus and sarbecovirus challenge in mice
Bibliographic record
Abstract
SUMMARY The emergence of three distinct highly pathogenic human coronaviruses – SARS-CoV in 2003, MERS-CoV in 2012, and SARS-CoV-2 in 2019 – underlines the need to develop broadly active vaccines against the Merbecovirus and Sarbecovirus betacoronavirus subgenera. While SARS-CoV-2 vaccines are highly protective against severe COVID-19 disease, they do not protect against other sarbecoviruses or merbecoviruses. Here, we vaccinate mice with a trivalent sortase-conjugate nanoparticle (scNP) vaccine containing the SARS-CoV-2, RsSHC014, and MERS-CoV receptor binding domains (RBDs), which elicited live-virus neutralizing antibody responses and broad protection. Specifically, a monovalent SARS-CoV-2 RBD scNP vaccine only protected against sarbecovirus challenge, whereas the trivalent RBD scNP vaccine protected against both merbecovirus and sarbecovirus challenge in highly pathogenic and lethal mouse models. Moreover, the trivalent RBD scNP elicited serum neutralizing antibodies against SARS-CoV, MERS-CoV and SARS-CoV-2 BA.1 live viruses. Our findings show that a trivalent RBD nanoparticle vaccine displaying merbecovirus and sarbecovirus immunogens elicits immunity that broadly protects mice against disease. This study demonstrates proof-of-concept for a single pan-betacoronavirus vaccine to protect against three highly pathogenic human coronaviruses spanning two betacoronavirus subgenera.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".