Differential and sequential immunomodulatory role of neutrophils and Ly6C <sup>hi</sup> inflammatory monocytes during antiviral antibody therapy
Bibliographic record
Abstract
Abstract Antiviral monoclonal antibodies (mAbs) can generate protective immunity through Fc-Fcγ Rs interactions. Using a mouse model of retroviral infection, we previously showed a crucial role for immune complexes (ICs) in the enhancement of T-cell responses through FcγR-mediated activation of dendritic cells (DCs). However, IC-FcγR interactions involve different cells of the immune system other than DCs such as neutrophils and monocytes. These two myeloid cell-types are innate effector cells rapidly recruited to sites of infection. In addition to being key cells to fight against invading pathogens, they are also endowed with immunomodulatory properties. While the role of DCs in enhancing antiviral immune responses upon mAb treatment has been addressed in several studies, the role of neutrophils and monocytes has been much less studied. Here we addressed how mAb therapy affects the functional activation of neutrophils and inflammatory monocytes in retrovirus-infected mice. We found that both cell-types activated in vitro by viral ICs secreted high levels of 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 expression on neutrophils and inflammatory monocytes and enhanced their cytokines and chemokines secretion. Notably, upon antibody therapy neutrophils and inflammatory monocytes displayed distinct functional activation states and sequentially modulated the antiviral immune response through the secretion of Th1-type polarizing cytokines and chemokines. Our work provides novel 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 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".