Synaptogyrin-2 Promotes Replication and Immune Evasion of A Novel Tick-borne Bunyavirus through Interacting with Viral Nonstructural Protein NSs
Bibliographic record
Abstract
Abstract Synaptogyrin-2 is a non-neuronal member of the synaptogyrin family involved in synaptic vesicle biogenesis and trafficking. Little has been known about the function of synaptogyrin-2. Severe Fever with Thrombocytopenia Syndrome (SFTS) is an emerging infectious disease characterized by high fever, thrombocytopenia, and leukocytopenia with a high fatality and caused by a novel tick-borne phlebovirus in the family Bunyaviridae. Our previous studies have shown that viral nonstructural protein NSs formed inclusion bodies (IBs) which are involved in viral immune evasion as well as in viral RNA replication. In this report, we sought to elucidate the mechanism how NSs formed the IBs, a lipid droplet-based structure confirmed by NSs co-localization with perilipin A and ADRP. We identified synaptogyrin-2 to be highly upregulated in response to SFTS bunyavirus (SFTSV) infection through a high throughput screening and be a promoter of viral replication. We demonstrated that synaptogyrin-2 interacted with NSs and was translocated into the IBs, which were reconstructed from lipid droplets into large structures in infection. Viral RNA replication decreased and infectious virus titers were lowered significantly when synaptogyrin-2 was silenced in specific shRNA-expressing cells, which was in parallel to the reduced number of the large IBs restructured from regular lipid droplets and increased antiviral interferon induction. We hypothesize that synaptogyrin-2 is essential to promoting the formation of the IBs, to be virus factories for viral RNA replication and immune evasion through its interaction with NSs. The findings unveiled the function of synaptogyrin-2 as an enhancer in viral infection.
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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".