Epitope specificity plays a critical role in regulating antibody-dependent cell-mediated cytotoxicity against influenza A virus
Bibliographic record
Abstract
The generation of strain-specific neutralizing antibodies against influenza A virus is known to confer potent protection against homologous infections. The majority of these antibodies bind to the hemagglutinin (HA) head domain and function by blocking the receptor binding site, preventing infection of host cells. Recently, elicitation of broadly neutralizing antibodies which target the conserved HA stalk domain has become a promising "universal" influenza virus vaccine strategy. The ability of these antibodies to elicit Fc-dependent effector functions has emerged as an important mechanism through which protection is achieved in vivo. However, the way in which Fc-dependent effector functions are regulated by polyclonal influenza virus-binding antibody mixtures in vivo has never been defined. Here, we demonstrate that interactions among viral glycoprotein-binding antibodies of varying specificities regulate the magnitude of antibody-dependent cell-mediated cytotoxicity induction. We show that the mechanism responsible for this phenotype relies upon competition for binding to HA on the surface of infected cells and virus particles. Nonneutralizing antibodies were poor inducers and did not inhibit antibody-dependent cell-mediated cytotoxicity. Interestingly, anti-neuraminidase antibodies weakly induced antibody-dependent cell-mediated cytotoxicity and enhanced induction in the presence of HA stalk-binding antibodies in an additive manner. Our data demonstrate that antibody specificity plays an important role in the regulation of ADCC, and that cross-talk among antibodies of varying specificities determines the magnitude of Fc receptor-mediated effector functions.
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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".