Omicron mutations enhance infectivity and reduce antibody neutralization of SARS-CoV-2 virus-like particles
Bibliographic record
Abstract
The Omicron SARS-CoV-2 virus contains extensive sequence changes relative to the earlier arising B.1, B.1.1 and Delta SARS-CoV-2 variants that have unknown effects on viral infectivity and response to existing vaccines. Using SARS-CoV-2 virus-like particles (SC2-VLPs), we examined mutations in all four structural proteins and found that Omicron showed increased infectivity relative to B.1, B.1.1 and similar to Delta, a property conferred by S and N protein mutations. Thirty-eight antisera samples from individuals vaccinated with tozinameran (Pfizer/BioNTech), elasomeran (Moderna), Johnson & Johnson vaccines and convalescent sera from unvaccinated COVID-19 survivors had moderately to dramatically reduced efficacy to prevent cell transduction by VLPs containing the Omicron mutations. The Pfizer/BioNTech and Moderna vaccine antisera showed strong neutralizing activity against VLPs possessing the ancestral spike protein (B.1, B.1.1), with 3-fold reduced efficacy against Delta and 15-fold lower neutralization against Omicron VLPs. Johnson & Johnson antisera showed minimal neutralization of any of the VLPs tested. Furthermore, the monoclonal antibody therapeutics Casirivimab and Imdevimab had robust neutralization activity against B.1, B.1.1 or Delta VLPs but no detectable neutralization of Omicron VLPs. Our results suggest that Omicron is at least as efficient at assembly and cell entry as Delta, and the antibody response triggered by existing vaccines or previous infection, at least prior to boost, will have limited ability to neutralize Omicron. In addition, some currently available monoclonal antibodies will not be useful in treating Omicron-infected patients.
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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".