SARS-CoV-2 type I Interferon modulation by nonstructural proteins 1 and 2
Bibliographic record
Abstract
Abstract Since the beginning of the COVID-19 pandemic, enormous efforts were devoted to understanding how SARS-CoV-2 escapes the antiviral response. Yet, modulation of type I interferons (IFNs) by this virus is not completely understood. Using in vitro and in vivo approaches, we have characterized the type I IFN response during SARS-CoV-2 infection as well as immune evasion mechanisms. The transcriptional and translational expression of IFNs, cytokines and chemokines were measured in lung homogenates of Wuhan-like, Beta, and Delta SARS-CoV-2 K18-ACE2 transgenic mice. Using in vitro experiments, we measured SARS-CoV-2 and its non-structural proteins 1 and 2 (Nsp1-2) to modulate expression of IFNβ and interferon-stimulated genes (ISG). Our data show that infection of mice with Wuhan-like virus induces robust expression of Ifna and Ifnb1 mRNA and limited type I production. In contrast, Beta and Delta variant infected mice failed to activate and produce IFNα. Using in vitro systems, Ifnβ gene translation inhibition was observed using an Nsp1 expression vector. Conversely, SARS-CoV-2 and its variants induce robust expression of NF-κB-driven genes such as those encoding CCL2 ans CXCL10 chemokines. We also identified Nsp2 as an activator of NF-κB that partially counteracts the inhibitory actions of Nsp1. In summary, our work indicates that SARS-CoV-2 skews the antiviral response in favor of an NF-κB-driven inflammatory response, a hallmark of acute COVID-19, and that Nsp2 is partly responsible for this effect. Importance Several studies suggest that SARS-CoV-2 possess multiple mechanisms aimed shunting the type I interferon response. However, few studies have studied type I IFN modulation in the context of infection. Our work indicates that mice and human cells infected with SARS-CoV-2 produce sufficient type I IFN to activate an antiviral response, despite Nsp1 translational blockade of IFNΒ1 mRNA. In contrast to Wuhan-like virus, Beta and Delta variants failed to induce Ifna gene expression. Our work also showcases the importance of studying protein functions in the context of infection, as demonstrated by the partial antagonizing properties of the Nsp2 protein on the activities of Nsp1. Our studies also highlight that the innate immune response triggered by SARS-CoV-2 is chiefly driven by NF-κB responsive genes for which Nsp2 is partially responsible.
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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".