Broadly-neutralizing anti-influenza virus antibodies: Enhancement of neutralizing potency in polyclonal mixtures and IgA backbones (VIR5P.1141)
Bibliographic record
Abstract
Abstract The recent discovery of broadly-neutralizing antibodies that target the hemagglutinin stalk domain has renewed hope that the development of “universal” influenza virus vaccines may be within reach. To this end, we have examined the functions of hemagglutinin stalk-binding antibodies in an endogenous setting - as polyclonal preparations isolated from human sera. Relative to monoclonal antibodies that bind to the HA head domain, the neutralization potency of monoclonal stalk-binding antibodies was vastly inferior in vitro, but was enhanced by several orders of magnitude in the polyclonal context. Furthermore, we demonstrated a surprising enhancement in IgA-mediated HA stalk neutralization relative to that achieved by antibodies of IgG isotypes. Mechanistically, this could be explained in two ways. Identical variable regions consistently neutralized virus more potently when in an IgA backbone compared to an IgG backbone. Additionally, HA-specific memory B cells isolated from human peripheral blood were more likely to be stalk-specific when secreting antibodies of IgA isotypes compared to those secreting IgG. Taken together, our data provide strong evidence that HA stalk-binding antibodies perform optimally when in a polyclonal context, and that the targeted elicitation of HA stalk-specific IgA should be an important consideration during “universal” influenza virus vaccine design.
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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) | 0.000 | 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 | 0.001 |
| 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".