Systemic and mucosal IgA responses are variably induced in response to SARS-CoV-2 mRNA vaccination and are associated with protection against subsequent infection
Bibliographic record
Abstract
Although SARS-CoV-2 infects the upper respiratory tract, we know little about the amount, type, and kinetics of antibodies (Ab) generated in the oral cavity in response to COVID-19 vaccination. We collected serum and saliva samples from participants receiving two doses of mRNA COVID-19 vaccines and measured the level of anti-SARS-CoV-2 Ab. We detected anti-Spike and anti-Receptor Binding Domain (RBD) IgG and IgA, as well as anti-Spike/RBD associated secretory component in the saliva of most participants after dose 1. Administration of a second dose of mRNA boosted the IgG but not the IgA response, with only 30% of participants remaining positive for IgA at this timepoint. At 6 months post-dose 2, these participants exhibited diminished anti-Spike/RBD IgG levels, although secretory component-associated anti-Spike Ab were more stable. Examining two prospective cohorts we found that participants who experienced breakthrough infections with SARS-CoV-2 variants had lower levels of vaccine-induced serum anti-Spike/RBD IgA at 2-4 weeks post-dose 2 compared to participants who did not experience an infection, whereas IgG levels were comparable between groups. These data suggest that COVID-19 vaccines that elicit a durable IgA response may have utility in preventing infection. Our study finds that a local secretory component-associated IgA response is induced by COVID-19 mRNA vaccination that persists in some, but not all participants. The serum and saliva IgA response modestly correlate at 2-4 weeks post-dose 2. Of note, levels of anti-Spike serum IgA (but not IgG) at this timepoint are lower in participants who subsequently become infected with SARS-CoV-2. As new surges of SARS-CoV-2 variants arise, developing COVID-19 booster shots that provoke high levels of IgA has the potential to reduce person-to-person transmission.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".