Nonstabilized SARS-CoV-2 spike mRNA vaccination induces broadly neutralizing antibodies in nonhuman primates
Bibliographic record
Abstract
Immunization with messenger RNA (mRNA) or viral vectors encoding spike protein with diproline substitutions (S-2P) were shown to provide protective immunity, curbing the COVID-19 pandemic. However, in light of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) that can cause COVID-19, it is essential that we understand how immunization with spike protein elicits neutralizing antibodies (nAbs). Here, we compared immunization of macaques with mRNA vaccines expressing ancestral spike protein with or without diproline substitutions, showing that the diproline substitutions were not required for protection against SARS-CoV-2 challenge or induction of broadly neutralizing B cell lineages. One group of nAbs elicited by the ancestral spike protein lacking diproline substitutions targeted the outer face of the receptor binding domain (RBD), neutralized all tested SARS-CoV-2 VOC pseudotyped viruses including Omicron XBB.1.5 in vitro, but lacked cross-sarbecovirus neutralization. Structural analysis showed that the macaque nAbs that could broadly neutralize VOCs bound to the same epitope as a human nAb, DH1193. In contrast, vaccine-induced antibodies that targeted the RBD inner face neutralized multiple sarbecoviruses, protected mice from bat CoV RsSHC014 challenge, but lacked Omicron variant neutralization. Thus, ancestral SARS-CoV-2 spike mRNA vaccines lacking proline substitutions can induce B cell lineages binding to distinct RBD sites that either broadly neutralize animal and human sarbecoviruses or neutralize recent Omicron VOCs. Thus, the use of a nonstabilized spike protein design in some COVID-19 vaccines does not preclude the elicitation of broad sarbecovirus and broad VOC nAbs.
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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) | 0.002 | 0.001 |
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".