Multiple orthoflaviviruses secrete sfRNA in mosquito saliva to promote transmission by inhibiting MDA5-mediated early interferon response
Bibliographic record
Abstract
Abstract Numerous orthoflaviviruses transmitted through the bites of different mosquito species infect more than 500 million people annually. Skin infection at the bite site represents a critical and conserved step in transmission and a deeper understanding of this process will promote the design of broad-spectrum interventions. Here, we identify and characterize a transmission-enhancing viral factor in mosquito saliva that is shared across orthoflaviviruses. Saliva from West Nile virus-infected Culex and Zika virus-infected Aedes contains a viral non-coding RNA, subgenomic flaviviral RNA (sfRNA), within lipid vesicles distinct from virions. Higher concentration of sfRNA in infectious saliva positively correlates with infection intensity in human cells and skin explants. Early sfRNA delivery into transmission-relevant skin cell types and human skin explant demonstrate that sfRNA is responsible for the infection enhancement. Co-inoculation of sfRNA in a mouse model of transmission enhanced skin infection and worsened disease severity, supporting the role of salivary sfRNA as a transmission-enhancer. Mechanistically, salivary sfRNA attenuates early interferon response in human skin cells and skin explants by disrupting MDA5 signaling. Our results, derived from two distinct orthoflaviviruses and supported by prior studies, establish salivary sfRNA as a pan-orthoflavivirus transmission-enhancing factor driven by a conserved viral non-coding RNA.
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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".