Pre-existing IgG antibodies to HCoVs NL63 and OC43 Spike increased during the pandemic and after COVID-19 vaccination
Bibliographic record
Abstract
Preexisting immunity may be associated with increased protection against non-related pathogens such as, SARS-CoV-2. There is little information regarding endemic human coronaviruses (HCOVs) from Pakistan, which experienced a relatively low COVID-19 morbidity and mortality. We investigated antibodies to SARS-CoV-2 and HCoVs NL63 and OC43, comparing sera from prepandemic controls (PPC) period with responses in healthy controls from the pandemic (HC 2021). Further, we investigated the effect of inactivated and mRNA COVID-19 vaccinations on antibody responses to the pandemic and endemic coronaviruses. We measured IgG antibodies to Spike of SARS-CoV-2, HCoV-NL63 and HCoV-OC43 by ELISA. Serum neutralizing capacity was determined using a SARS-CoV-2 psuedotyped virus assay. Vaccinees were sampled prior to vaccination as well after 6, 12 and 24 weeks after COVID-19 inactivated (Sinovac), or mRNA (BNT162b2) vaccine administration. PPC sera showed seropositivity of 15 % to SARS-CoV-2, whilst it was 45 % in the HC 2021 group. Five percent of sera showed virus neutralizing activity in PPC whilst it was 50 % in HC 2021. IgG antibodies to Spike of NL63 and OC43 were also present in PPC; anti-NL63 was 2.9-fold, and anti-OC43 was 10.1-fold higher than to anti-SARS-CoV-2 levels. IgG antibodies to Spike SARS-CoV-2 were positively correlated with HCoV-NL63 in HC 2021, indicating recognition of shared conserved epitopes. IgG antibody levels increased during the pandemic; 2.7-fold to HCoV-NL63 and 1.9-fold to HCoV-OC43. SinoVac and BNT162b2 vaccine induced an increase in IgG antibodies to Spike SARS-CoV-2 as well as HCoV-NL63 and HCoV-OC43. Our data show that antibodies to spike protein of endemic coronaviruses were present in the prepandemic population. Antibodies to SARS-CoV-2, NL63 and OC43 were all raised during the pandemic and further enhanced after COVID-19 vaccinations. The increase in antibodies to spike of coronaviruses would contribute to protection against SARS-CoV-2.
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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 | 0.000 | 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 | 0.000 |
| 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".