SARS-CoV-2 conjugate vaccine elicits robust immune responses that can protect against evolving variants.
Bibliographic record
Abstract
The SARS-CoV-2 pandemic necessitated effective vaccines that can endure antigenic mutations. Here we demonstrate highly immunogenic conjugate vaccines that elicit broad cross-neutralization to variants of concern (VOC) in animal studies. By utilizing protein-protein conjugation and Toll-Like Receptor (TLR) agonist adjuvants we achieve enhanced immunogenicity compared to unconjugated equivalents. These vaccine candidates induced broad cross-protection against several VOC, a characteristic lacking in early COVID-19 vaccines. Murine neutralizing antibody (nAb) titers from animals vaccinated with Beta-only conjugates were equivalent between Beta, Delta, Omicron BA.1, BA.2, and BA.4/BA.5 variants, which were circulating up to three years after the antigenic Beta strain. Additionally, Beta-Delta bivalent conjugate vaccines readily prevented disease in hamster challenge. Together this demonstrates a vaccine with remarkably broad cross-protection and potential to protect for extended periods despite mutations, without requiring modified boosters or antigen adaption. These techniques can be applied to more recent SARS-CoV-2 strains, and other viruses, highlighting the benefits of protein-protein conjugation coupled with TLR agonist secondary adjuvants. • SARS-CoV-2 spike protein conjugated to CRM-197 with TLR agonist adjuvants leads to high IgG and nAb. • Beta-Only conjugates with TLR agonists induced broadly cross-reactive nAb in mice. • Beta-only conjugate led to neutralization of variants that emerged 3 years after antigenic strain. • Bivalent conjugate vaccines effectively protect hamsters from viral challenge. • Protein conjugate platform applicable to any disease requiring enhanced immunogenicity.
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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".