A Recent SARS-CoV-2 Infection Enhances Antibody-Dependent Cellular Cytotoxicity against Several Omicron Subvariants following a Fourth mRNA Vaccine Dose
Bibliographic record
Abstract
Since the beginning of the SARS-CoV-2 pandemic, several variants of concern (VOCs), such as the Alpha, Beta, Gamma, Delta and Omicron variants, have arisen and spread worldwide. Today, the predominant circulating subvariants are sublineages of the Omicron variant, which have more than 30 mutations in their Spike glycoprotein compared to the ancestral strain. The Omicron subvariants were significantly less recognized and neutralized by antibodies from vaccinated individuals. This resulted in a surge in the number of infections, and booster shots were recommended to improve responses against these variants. While most studies mainly measured the neutralizing activity against variants, we and others previously reported that Fc-effector functions, including antibody-dependent cellular cytotoxicity (ADCC), play an important role in humoral responses against SARS-CoV-2. In this study, we analyzed Spike recognition and ADCC activity against several Omicron subvariants by generating cell lines expressing different Omicron subvariant Spikes. We tested these responses in a cohort of donors, who were recently infected or not, before and after a fourth dose of mRNA vaccine. We showed that ADCC activity is less affected than neutralization by the antigenic shift of the tested Omicron subvariant Spikes. Moreover, we found that individuals with a history of recent infection have higher antibody binding and ADCC activity against all Omicron subvariants than people who were not recently infected. With an increase in the number of reinfections, this study helps better understand Fc-effector responses in the context of hybrid immunity.
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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".