Antibody idiotype plays a critical role in regulating antibody-dependent cell-mediated cytotoxicity (ADCC) against influenza A virus
Bibliographic record
Abstract
Abstract The generation of strain-specific neutralizing antibodies against influenza A virus is known to confer potent protection against homologous infections. Recently, elicitation of broadly-neutralizing antibodies which target the conserved hemagglutinin stalk domain have emerged as a promising “universal” influenza virus vaccine strategy. The ability of these antibodies to elicit Fc-dependent effector functions, such as antibody-dependent cell-mediated cytotoxicity, 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-binding antibody idiotypes in vivo has never been defined. Here, we demonstrate that complex interactions among viral glycoprotein-binding antibody idiotypes regulate the magnitude of antibody-dependent cell-mediated cytotoxicity induction. We propose that antibody idiotype serves as a critical determinant in the activation of ADCC. This is the first demonstration that cross-talk among antibody idiotypes is important for the regulation of Fc-dependent effector functions. This phenomenon will have major implications for not only the development of universal influenza virus vaccines, but also in any scenario wherein Fc-dependent effector functions are engaged in the context of polyclonal responses containing multiple antibody idiotypes.
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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".