SARS-CoV-2 Protein Nsp9 Is Involved in Viral Evasion through Interactions with Innate Immune Pathways
Bibliographic record
Abstract
The suppression of the host's innate antiviral immune response by SARS-CoV-2, a contributing factor to the severity of disease, has been considerably studied in recent years. Many of these studies have focused on the actions of the structural proteins of the virus because of their accessibility to host immunological components. However, less is known about SARS-CoV-2 nonstructural and accessory proteins in relation to viral evasion. Herein, we study SARS-CoV-2 nonstructural proteins Orf3a, Orf6, and Nsp9 in a mimicked virus-infected state using poly(I:C), a synthetic analog of viral dsRNA, that elicits the antiviral immune response. Through genome-wide expression profiling, we determined that Orf3a, Orf6, and Nsp9 all modulate the host antiviral signaling transcriptome to varying extents, uniquely suppressing aspects of innate immune signaling. Our data suggest that SARS-CoV-2 Nsp9 hinders viral detection through suppression of RIG-I expression and antagonizes the interferon antiviral cascade by downregulating NF-kB and TBK1. Our data point to unique molecular mechanisms through which the different SARS-CoV-2 proteins suppress immune signaling and promote viral evasion. Nsp9 in particular acts on major elements of the host antiviral pathways to impair the antiviral immune response.
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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".