Glycan reactive anti-HIV-1 antibodies bind the SARS-CoV-2 spike protein but do not block viral entry
Bibliographic record
Abstract
The SARS-CoV-2 spike glycoprotein is a focal point for vaccine immunogen and therapeutic antibody design, and also serves as a critical antigen in the evaluation of immune responses to COVID-19. A common feature amongst enveloped viruses such as SARS-CoV-2 and HIV-1 is the propensity for displaying host-derived glycans on entry spike proteins. Similarly displayed glycosylation motifs can serve as the basis for glyco-epitope mediated cross-reactivity by antibodies, which can have important implications on virus neutralization, antibody-dependent enhancement (ADE) of infection, and the interpretation of antibody titers in serological assays. From a panel of nine anti-HIV-1 gp120 reactive antibodies, we selected two (PGT126 and PGT128) that displayed high levels of cross-reactivity with the SARS-CoV-2 spike. We report that these antibodies are incapable of neutralizing pseudoviruses expressing SARS-CoV-2 spike proteins and are unlikely to mediate ADE via FcγRII receptor engagement. Nevertheless, ELISA and other immunoreactivity experiments demonstrate these antibodies are capable of binding the SARS-CoV-2 spike in a glycan-dependent manner. These results contribute to the growing literature surrounding SARS-CoV-2 S cross-reactivity, as we demonstrate the ability for cross-reactive antibodies to interfere in immunoassays.
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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) | 0.002 | 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".