Genetically Engineered, Biarsenically Labeled Influenza Virus Allows Visualization of Viral NS1 Protein in Living Cells
Bibliographic record
Abstract
Real-time fluorescence imaging of viral proteins in living cells provides a valuable means to study virus-host interactions. The challenge of generating replication-competent fluorescent influenza A virus is that the segmented genome does not allow fusion of a fluorescent protein gene to any viral gene. Here, we introduced the tetracysteine (TC) biarsenical labeling system into influenza virus in order to fluorescently label viral protein in the virus life cycle. We generated infectious influenza A viruses bearing a small TC tag (CCPGCC) in the loop/linker regions of the NS1 proteins. In the background of A/Puerto Rico/8/34 (H1N1) (PR8) virus, the TC tag can be inserted into NS1 after amino acid 52 (AA52) (PR8-410), AA79 (PR8-412), or AA102 (PR8-413) or the TC tag can be inserted and replace amino acids 79 to 84 (AA79-84) (PR8-411). Although PR8-410, PR8-411, and PR8-412 viruses are attenuated than the wild-type (WT) virus to some extent in multiple-cycle infection, their growth potential is similar to that of the WT virus during a single cycle of infection, and their NS1 subcellular localization and viral protein synthesis rate are quite similar to those of the WT virus. Furthermore, labeling with membrane-permeable biarsenical dye resulted in fluorescent NS1 protein in the context of virus infection. We could exploit this strategy on NS1 protein of A/Texas/36/91 (H1N1) (Tx91) by successfully rescuing a TC-tagged virus, Tx91-445, which carries the TC tag replacement of AA79-84. The infectivity of Tx91-445 virus was similar to that of WT Tx91 during multiple cycles of replication and a single cycle of replication. The NS1 protein derived from Tx91-445 can be fluorescently labeled in living cells. Finally, with biarsenical labeling, the engineered replication-competent virus allowed us to visualize NS1 protein nuclear import in virus-infected cells in real time.
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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) | 0.000 | 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".