Sequence Matters: Primary COVID-19 Vaccination after Infection Elicits Similar Anti-spike Antibody Levels, but Stronger Antibody Dependent Cell-mediated Cytotoxicity than Breakthrough Infection
Bibliographic record
Abstract
Infection before primary vaccination (herein termed "hybrid immunity") engenders robust humoral immunity and broad Ab-dependent cell-mediated cytotoxicity (ADCC) across SARS-CoV-2 variants. We measured and compared plasma IgG and IgA against Wuhan-Hu-1 and Omicron (B.1.1.529) full-length spike (FLS) and receptor binding domain after three mRNA vaccines encoding Wuhan-Hu-1 spike (S) and after Omicron breakthrough infection. We also measured IgG binding to Wuhan-Hu-1 and Omicron S1, Wuhan-Hu-1 S2 and Wuhan-Hu-1 and Omicron cell-based S. We compared ADCC using human embryonic lung fibroblast (MRC-5) cells expressing Wuhan-Hu-1 or Omicron S. The effect of Omicron breakthrough infection on IgG anti-Wuhan-Hu-1 and Omicron FLS avidity was also considered. Despite Omicron breakthrough infection increasing IgG and IgA against FLS and receptor binding domain to levels similar to those seen with hybrid immunity, there was no boost to ADCC. Preferential recognition of Wuhan-Hu-1 persisted following Omicron breakthrough infection, which increased IgG avidity against Wuhan-Hu-1 FLS. Despite similar total anti-FLS IgG levels following breakthrough infection, 4-fold higher plasma concentrations were required to elicit ADCC comparable to that elicited by hybrid immunity. The greater capacity for hybrid immunity to elicit ADCC was associated with a differential IgG reactivity pattern against S1, S2, and linear determinants throughout FLS. Immunity against SARS-CoV-2 following Omicron breakthrough infection manifests significantly less ADCC capacity than hybrid immunity. Thus, the sequence of antigenic exposure by infection versus vaccination and other factors such as severity of infection affect antiviral functions of humoral immunity in the absence of overt quantitative differences in the humoral response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.061 |
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".