Assessing mucosal antibody responses to SARS-CoV-2 in humans and mice.
Bibliographic record
Abstract
Abstract COVID-19 is a respiratory disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). We previously developed an enzyme-linked immunosorbent assay (ELISA) protocol to profile antibody responses to the SARS-CoV-2 Spike protein and its receptor-binding domain (RBD) in the saliva of patients with COVID-19. Anti-SARS-CoV-2 antibody responses were readily detected in saliva, peaking at 16–30 days post-symptom onset. While anti-SARS-CoV-2 IgM/IgA were found to decay, IgG responses were more long-lived, persisting up to 105 days. Now we wish to know (1) if anti-SARS-CoV-2 antibodies are generated locally in the oral cavity, (2) the relative kinetics of IgA versus IgG appearance in the oral cavity, and (3) whether salivary antibodies have the capacity to neutralize SARS-CoV-2. To answer these, I have adapted our ELISA to detect secretory component-associated SARS-CoV-2-specific antibodies. I found that 33.3% and 26.9% of COVID-19 patients were positive for secretory component-associated antibodies to Spike and RBD, respectively. Secretory component-associated antibodies correlated with antigen-specific IgA levels, particularly for the Spike antigen. Moreover, using saliva from contact-traced subjects, I found that anti-Spike/RBD IgM/IgA are detected in the saliva 7 days post-exposure, prior to IgG. Lastly, I detected Spike-specific antibody-secreting cells in lymphoid tissues draining the upper respiratory tract upon intranasal infection of K18-hACE2 mice with a non-lethal dose of SARS-CoV-2. In summary, I have provided evidence in humans and mice that a local antibody response that quickly class switches to IgA occurs in the oral cavity and draining lymph nodes upon infection with SARS-CoV-2.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".