Dengue virus antibodies augment West Nile virus replication kinetics and immune mediator secretion in mast cells 4443
Bibliographic record
Abstract
Abstract Description Background Mast cells in the dermal milieu are among the first immune cells to encounter mosquito-borne viruses, such as West Nile virus (WNV). Previously generated dengue virus (DENV) antibodies have been shown to augment secondary DENV heterotypic infection, through Fcγ receptor internalization, and enhance infection of closely related viruses, including Zika. In this study, we investigated whether DENV antibodies could cross-react with WNV to augment viral replication and immune mediator secretion in mast cell line models (ROSA and KU812). Methodology and Results Plaque assay and qPCR analysis revealed that DENV monoclonal antibodies augmented WNV replication kinetics in KU812 cells 24 hours-post infection while ELISA results confirmed earlier release of chemokines (CCL3, CCL4, CCL5, CXCL8, and CXCL10). After characterization of surface expression of FcγRII by flow cytometry, blocking experiments with FcγRII inhibition prior to infection reduced enhanced replication kinetics of WNV and related mediator secretion. Conclusion We show for the first time that mast cells are permissive to WNV infection, DENV antibodies significantly augment WNV replication kinetics, and mediator secretion, and enhanced infection and chemokine secretion are mechanistically dependent on FcγRII. These results implicate mast cells as a potential source for WNV replication and suggest an antigen-specific antibody-mediated mechanism for augmented WNV replication coupled to chemokine secretion. Funding Sources Supported by the Natural Sciences and Engineering Research council of Canada (NSERC); Canada Foundation for innovation (CFI); Government of Ontario; and Brock University. Topic Categories Viral Immunology (VIR)
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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) | 0.003 | 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".