Sphingomyelins in mosquito saliva modify the host lipidome to enhance transmission of flaviviruses by promoting viral protein levels
Bibliographic record
Abstract
Abstract Mosquito saliva plays a determining role in flavivirus transmission. Here, we discover and elucidate how salivary lipids enhance transmission. Building upon our discovery of salivary extracellular vesicles (EV), we determined that lipids within mosquito EVs, and neither within human EVs nor virions, enhance infection for flaviviruses in primary cell types relevant for transmission. Mechanistically, mosquito EV-lipids specifically promote viral protein levels by reducing ER-associated degradation. Infection enhancement is caused by sphingomyelins within mosquito salivary EVs that elevate sphingomyelin concentration within host cells. Transmission assays showed that mosquito EV-lipids exacerbate disease severity. Our study reveals that EV-associated sphingomyelins within mosquito saliva enhance transmission for multiple flaviviruses by reconfiguring the host lipidome to promote viral protein levels and the resulting skin infection. Our findings open a new dimension centered on lipids in the interplay between hosts, mosquitoes and flaviviruses that determine transmission, unveiling lipids as a new pan-flavivirus target. Highlights Lipids within mosquito extracellular vesicles (EVs) enhance infection in primary skin and immune cells for multiple flaviviruses. Mosquito EV-lipids increase flaviviral protein levels by dampening ER-associated degradation. Sphingomyelins within salivary EVs are responsible for the infection enhancement by altering host lipidome. Co-injection of mosquito EV-lipids exacerbate disease severity.
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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".