Alveolar macrophages are critical for broadly-reactive antibody-mediated protection against influenza A virus in mice
Bibliographic record
Abstract
The aim of candidate universal influenza vaccines is to provide broad protection against influenza A and B viruses. Studies have demonstrated that broadly reactive antibodies require Fc-Fc gamma receptor interactions for optimal protection; however, the innate effector cells responsible for mediating this protection remain largely unknown. Here, we examine the roles of alveolar macrophages, natural killer cells, and neutrophils in antibody-mediated protection. We demonstrate that alveolar macrophages play a dominant role in conferring protection provided by both broadly neutralizing and non-neutralizing antibodies in mice. Our data also reveal the potential mechanisms by which alveolar macrophages mediate protection in vivo, namely antibody-induced inflammation and antibody-dependent cellular phagocytosis. This study highlights the importance of innate effector cells in establishing a broad-spectrum antiviral state, as well as providing a better understanding of how multiple arms of the immune system cooperate to achieve an optimal antiviral response following influenza virus infection or immunization.Broadly reactive antibodies that recognize influenza A virus HA can be protective, but the mechanism is not completely understood. Here, He et al. show that the inflammatory response and phagocytosis mediated by the interaction between protective antibodies and macrophages are essential for protection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".