Zika virus NS3 and NS5 proteins determine strain-dependent differences in dsRNA accumulation in a host cell type-dependent manner
Bibliographic record
Abstract
For positive-sense RNA viruses, initiation of viral RNA replication represents a major target of antiviral responses to infection. Despite this, the interplay between viral replication and the innate antiviral response at early steps in the Zika virus (ZIKV) life cycle is not well understood. We have previously identified ZIKV isolates with differing levels of dsRNA accumulation, ZIKVPR (high dsRNA per infected cell) and ZIKVCDN (low dsRNA per infected cell), and we hypothesized that we could use reverse genetics to investigate how host and viral factors contribute to the establishment of viral RNA replication. We found that both the ZIKV NS3 and NS5 proteins as well as host factors were necessary to determine the dsRNA accumulation phenotype. Additionally, we show that dsRNA correlates with viral negative-strand RNA measured by strand-specific RT-qPCR, suggesting that dsRNA is an accurate readout of viral RNA replication. Interestingly, although we did not observe NS3- and NS5-dependent differences in cells with defects in interferon (IFN) production, differences in RNA accumulation precede induction of the IFN response, suggesting that RNA sensing pathways or intrinsic restriction factors may differentially restrict ZIKV in an NS3- and NS5-dependent manner. This work expands our understanding of the interplay of early steps of viral RNA replication and the induction of the innate antiviral response to ZIKV 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".