Serological evaluation of coronavirus IgA and IgG antibodies in a repeated cross-sectional cohort of unvaccinated and vaccinated pregnant individuals over three months following SARS-CoV-2 infection
Bibliographic record
Abstract
ABSTRACT Antibody surveillance provided valuable public health insights during the Coronavirus Disease 2019 (COVID-19) pandemic. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, vaccination, and cross-reactive responses from endemic coronaviruses (human coronaviruses [HCoVs]) can influence SARS-CoV-2 antibody responses, impacting reliability and interpretation of serological findings. Here, we investigated population-level SARS-CoV-2 and HCoV IgA and IgG responses following SARS-CoV-2 infection among unvaccinated and vaccinated pregnant individuals over 3 months. Using residual sera from routine antenatal screening of pregnant individuals in British Columbia between March 2020 to May 2022, we designed a retrospective repeated cross-sectional cohort of infected individuals either unvaccinated (UV + COV, N = 171) or two- to three-dose vaccinated (V + COV, N = 137). A total of 30 pre-pandemic sera served as negative controls. Sera were collected within 3 months of respiratory PCR-positivity in half-month intervals and tested for IgA and IgG against Spike (S) of alpha-HCoV (HCoV-229E, HCoV-NL63) and beta-HCoV (HCoV-HKU1, HCoV-OC43) and S, receptor binding domain, and nucleocapsid (N) of SARS-CoV-2 using a multiplex immunoassay. Following SARS-CoV-2 infection, V + COV had lower anti-N IgG levels ( P = 0.004) and seropositivity rates than UV + COV. One month post-infection, V + COV (38%) had lower anti-N IgA seropositivity than UV + COV (73%). Both groups had significantly higher anti-S IgA and IgG levels against beta-HCoV vs controls, with signals correlating positively with SARS-CoV-2 anti-S levels for each isotype. These results suggest that neither IgA nor IgG can reliably identify recent infections in vaccinated populations, emphasizing the importance of considering complex interplay of antibody responses when interpreting serological data and recognizing the potential and limitations of serological testing for diagnostics and surveillance. IMPORTANCE This study provides key insights into how Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) vaccination and infection shape the magnitude and longevity of IgA and IgG antibody responses following infection in pregnant individuals. It also highlights the interplay of serological responses to related viruses, such as the human coronaviruses. By leveraging population-level antenatal sera, our findings highlight important considerations for the design and interpretation of future seroprevalence studies, antibody-based surveillance, and diagnostic strategies. As SARS-CoV-2 transitions into endemic circulation, understanding the complexity of SARS-CoV-2 antibody responses provides additional insights into the strengths and limitations of serological data interpretation in a real-world setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".