Influenza Virus Non-Structural Protein 1 (NS1) Disrupts Interferon Signaling
Bibliographic record
Abstract
Type I interferons (IFNs) function as the first line of defense against viral infections by modulating cell growth, establishing an antiviral state and influencing the activation of various immune cells. Viruses such as influenza have developed mechanisms to evade this defense mechanism and during infection with influenza A viruses, the non-structural protein 1 (NS1) encoded by the virus genome suppresses induction of IFNs-α/β. Here we show that expression of avian H5N1 NS1 in HeLa cells leads to a block in IFN signaling. H5N1 NS1 reduces IFN-inducible tyrosine phosphorylation of STAT1, STAT2 and STAT3 and inhibits the nuclear translocation of phospho-STAT2 and the formation of IFN-inducible STAT1:1-, STAT1:3- and STAT3:3- DNA complexes. Inhibition of IFN-inducible STAT signaling by NS1 in HeLa cells is, in part, a consequence of NS1-mediated inhibition of expression of the IFN receptor subunit, IFNAR1. In support of this NS1-mediated inhibition, we observed a reduction in expression of ifnar1 in ex vivo human non-tumor lung tissues infected with H5N1 and H1N1 viruses. Moreover, H1N1 and H5N1 virus infection of human monocyte-derived macrophages led to inhibition of both ifnar1 and ifnar2 expression. In addition, NS1 expression induces up-regulation of the JAK/STAT inhibitors, SOCS1 and SOCS3. By contrast, treatment of ex vivo human lung tissues with IFN-α results in the up-regulation of a number of IFN-stimulated genes and inhibits both H5N1 and H1N1 virus replication. The data suggest that NS1 can directly interfere with IFN signaling to enhance viral replication, but that treatment with IFN can nevertheless override these inhibitory effects to block H5N1 and H1N1 virus infections.
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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".