Omicron mutations enhance infectivity and reduce antibody neutralization of SARS-CoV-2 virus-like particles
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant 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 (VLPs), we examined mutations in all four structural proteins and found that Omicron and Delta showed 4.6-fold higher luciferase delivery overall relative to the ancestral B.1 lineage, a property conferred mostly by enhancements in the S and N proteins, while mutations in M and E were mostly detrimental to assembly. Thirty-eight antisera samples from individuals vaccinated with Pfizer/BioNTech, Moderna, or Johnson & Johnson vaccines and convalescent sera from unvaccinated COVID-19 survivors had 15-fold lower efficacy to prevent cell transduction by VLPs containing the Omicron mutations relative to the ancestral B.1 spike protein. A third dose of Pfizer vaccine elicited substantially higher neutralization titers against Omicron, resulting in detectable neutralizing antibodies in eight out of eight subjects compared to one out of eight preboosting. Furthermore, the monoclonal antibody therapeutics casirivimab and imdevimab had robust neutralization activity against B.1 and Delta VLPs but no detectable neutralization of Omicron VLPs, while newly authorized bebtelovimab maintained robust neutralization across variants. Our results suggest that Omicron has similar assembly efficiency and cell entry compared to Delta and that its rapid spread is due mostly to reduced neutralization in sera from previously vaccinated subjects. In addition, most currently available monoclonal antibodies will not be useful in treating Omicron-infected patients with the exception of bebtelovimab.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".