Differential and sequential immunomodulatory role of neutrophils and Ly6C <sup>hi</sup> inflammatory monocytes during antiviral antibody therapy
Bibliographic record
Abstract
Antiviral monoclonal antibodies (mAbs) can generate protective immunity through Fc-FcγRs interactions. We previously showed a role for immune complexes (ICs) in the enhancement of antiviral T-cell responses through FcγR-mediated activation of dendritic cells (DCs). Here we addressed how mAb therapy in retrovirus-infected mice affects the activation of neutrophils and inflammatory monocytes, two FcγR-expressing innate effector cells rapidly recruited to sites of infection. We found that both cell-types activated in vitro by viral ICs secreted chemokines able to recruit monocytes and neutrophils themselves. Moreover, inflammatory cytokines potentiated chemokines and cytokines release by IC-activated cells and induced FcγRIV upregulation. Similarly, infection and mAb-treatment upregulated FcγRIV on neutrophils and inflammatory monocytes and enhanced their cytokines/chemokines secretion. Notably, upon antibody therapy neutrophils and inflammatory monocytes displayed distinct functional activation states and sequentially modulated the antiviral immune response by secreting Th1-type polarizing cytokines and chemokines, which occurred in a FcγRIV-dependent manner. Consistently, FcγRIV- blocking in mAb-treated, infected mice led to reduced immune protection. Our work provides new findings on the immunomodulatory role of neutrophils and monocytes in the enhancement of immune responses upon antiviral mAb therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".