Differential Cellular Sensing of Fusion from within and Fusion from without during Virus Infection
Bibliographic record
Abstract
The physical entry of virus particles into cells triggers an innate immune response that is dependent on both calcium and nucleic acid sensors, with particles containing RNA or DNA genomes detected by RNA or DNA sensors, respectively. While membrane fusion in the absence of viral nucleic acid causes an innate immune response that is dependent on calcium, the involvement of nucleic acid sensors is poorly understood. Here, we used lipoplexes containing purified reovirus p14 fusion protein as a model of exogenous or fusion from without and a cell line expressing inducible p14 protein as a model of endogenous or fusion from within to examine cellular membrane fusion sensing events. We show that the cellular response to membrane fusion in both models is dependent on calcium, IRF3 and IFN. The method of sensing fusion, however, differs between fusion from without and fusion from within. Exogenous p14 lipoplexes are detected by RIG-I-like RNA sensors, whereas fusion by endogenous p14 requires both RIG-I and STING to trigger an IFN response. The source of nucleic acid that is sensed appears to be cellular in origin. Future studies will investigate the source of endogenous nucleic acids recognized following membrane fusion events.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".