Characteristics of Vaccine- and Infection-Induced Systemic IgA Anti-SARS-CoV-2 Spike Responses
Bibliographic record
Abstract
Mucosal IgA is widely accepted as providing protection against respiratory infections, but stimulation of mucosal immunity, collection of mucosal samples and measurement of mucosal IgA can be problematic. The relationship between mucosal and circulating IgA responses is unclear, however, whole blood is readily collected and circulating antigen-specific IgA easily measured. We measured circulating IgA against SARS-CoV-2 spike (S) to investigate vaccine- and infection-induced production and correlation with protection. Circulating IgA against ancestral (Wuhan-Hu-1) and Omicron (BA.1) S proteins was measured at different time points in a total of 143 subjects with varied backgrounds of vaccination and infection. Intramuscular vaccination induced circulating anti-SARS-CoV-2 S IgA. Subjects with higher levels of vaccine-induced IgA against SARS-CoV-2 S (p = 0.0333) or receptor binding domain (RBD) (p = 0.0266) were less likely to experience an Omicron breakthrough infection. The same associations did not hold for circulating IgG anti-SARS-CoV-2 S levels. Breakthrough infection following two vaccinations generated stronger IgA anti-SARS-CoV-2 S responses (p = 0.0002) than third vaccinations but did not selectively increase circulating IgA against Omicron over ancestral S, indicating immune imprinting of circulating IgA responses. Circulating IgA against SARS-CoV-2 S following breakthrough infection remained higher than vaccine-induced levels for over 150 days. In conclusion, intramuscular mRNA vaccination induces circulating IgA against SARS-CoV-2 S, and higher levels are associated with protection from breakthrough infection. Vaccination with ancestral S enacts imprinting within circulating IgA responses that become apparent after breakthrough infection with Omicron. Breakthrough infection generates stronger and more durable circulating IgA responses against SARS-CoV-2 S than vaccination alone.
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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".