Impact of Vaccine-Elicited Anti-Spike IgG4 Antibodies on Fc-Effector Functions Against SARS-CoV-2
Bibliographic record
Abstract
mRNA vaccines have demonstrated considerable efficacy and safety against SARS-CoV-2, limiting the pandemic burden worldwide. The emergence of new variants of concern and the decline in neutralizing activity observed several weeks post-vaccination reinforced the call for repeated mRNA vaccination. We and others have shown that vaccine efficacy does not exclusively rely on antibody neutralizing activites; Fc-effector functions play an important role as well. However, it is well known that long-term exposure and repeated antigen stimulation elicit the IgG4 subclass of antibodies, which are inefficient at mediating Fc-effector functions. In this regard, recent studies highlighted concerns about IgG4 induction by mRNA vaccines. Here, we explored the impact of repeated mRNA vaccination on IgG4 induction and its impact on Fc-effector functions. We observed anti-Spike IgG4 elicitation after three doses of mRNA vaccine; the antibody levels further increased with additional doses. Vaccine-elicited IgG4 preferentially bound the ancestral D614G Spike. We also observed that Breakthrough Infection (BTI) after several doses of vaccine strongly increased IgG1 levels but had no impact on IgG4 levels, thereby improving Fc-effector functions. Finally, we observed that elderly donors vaccinated with Moderna mRNA vaccines elicited higher IgG4 levels and presented lower Fc-effector functions than donors vaccinated with the Pfizer mRNA vaccine. Altogether, our results highlight the importance of monitoring the IgG subclasses elicited by vaccination.
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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".