Fc-dependent effector functions of neutrophils induced by hemagglutinin stalk-specific antibodies
Bibliographic record
Abstract
Abstract Broadly neutralizing antibodies that recognize the conserved hemagglutinin (HA) stalk have emerged as exciting new biotherapeutic tools to combat both seasonal and pandemic influenza viruses. Our general understanding of the mechanisms by which stalk-specific antibodies achieve protection is rapidly evolving. It has recently been demonstrated that broadly neutralizing HA stalk-specific IgG antibodies require Fc-FcR interactions for optimal protection in vivo. Here we explore the relationship between such stalk-specific antibodies and neutrophils. Neutrophils represent a critical innate effector cell population and play an important role in orchestrating downstream adaptive responses to influenza virus infection. Yet, at present the interplay of HA stalk-specific IgG, Fc-FcR engagement, and neutrophils has remained largely uncharacterized. Using an in vitro assay to detect the production of reactive oxygen-species (ROS), we show that both human and mouse monoclonal HA stalk-specific IgG antibodies are able to induce the production of ROS in neutrophils, while antibodies specific to the HA head domain do not. Furthermore, our results indicate that the production of ROS is dependent on Fc receptor engagement and phagocytosis. We went on to assess the ability of monoclonal HA stalk-specific IgA antibodies to induce ROS. We found that monoclonal IgA lead to a greater induction of ROS as compared to monoclonal IgG. Our data demonstrate this activity is dependent on the engagement of FcαR1. Taken together our findings describe a novel FcR-dependent effector function induced by HA stalk-specific IgG and IgA antibodies.
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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".