Self-assembling SARS-CoV-2 spike-HBsAg nanoparticles elicit potent and durable neutralizing antibody responses via genetic delivery
Bibliographic record
Abstract
While several COVID-19 vaccines have been in use, more effective and durable vaccines are needed to combat the ongoing COVID-19 pandemic. Here, we report highly immunogenic self-assembling SARS-CoV-2 spike-HBsAg nanoparticles displaying a six-proline-stabilized WA1 (wild type, WT) spike S6P on a HBsAg core. These S6P-HBsAgs bound diverse domain-specific SARS-CoV-2 monoclonal antibodies. In mice with and without a HBV pre-vaccination, DNA immunization with S6P-HBsAgs elicited significantly more potent and durable neutralizing antibody (nAb) responses against diverse SARS-CoV-2 strains than that of soluble S2P or S6P, or full-length S2P with its coding sequence matching mRNA-1273. The nAb responses elicited by S6P-HBsAgs persisted substantially longer than by soluble S2P or S6P and appeared to be enhanced by HBsAg pre-exposure. These data show that genetic delivery of SARS-CoV-2 S6P-HBsAg nanoparticles can elicit greater and more durable nAb responses than non-nanoparticle forms of stabilized spike. Our findings highlight the potential of S6P-HBsAgs as next generation genetic vaccine candidates against SARS-CoV-2.
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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) | 0.000 | 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".