SARS-CoV-2 Nsp2 Contributes to Inflammation by Activating NF-κB
Bibliographic record
Abstract
COVID-19 is associated with robust inflammation and partially impaired antiviral responses. The modulation of inflammatory gene expression by SARS-CoV-2 is not completely understood. In this study, we characterized the inflammatory and antiviral responses mounted during SARS-CoV-2 infection. K18-hACE2 mice were infected with a Wuhan-like strain of SARS-CoV-2, and the transcriptional and translational expression interferons (IFNs), cytokines, and chemokines were analyzed in mouse lung homogenates. Our results show that the infection of mice with SARS-CoV-2 induces the expression of several pro-inflammatory CC and CXC chemokines activated through NF-κB but weakly IL1β and IL18 whose expression are more characteristic of inflammasome formation. We also observed the downregulation of several inflammasome effectors. The modulation of innate response, following expressions of non-structural protein 2 (Nsp2) and SARS-CoV-2 infection, was assessed by measuring IFNβ expression and NF-κB modulation in human pulmonary cells. A robust activation of the NF-κB p65 subunit was induced following the infection of human cells with the corresponding NF-κB-driven inflammatory signature. We identified that Nsp2 expression induced the activation of the IFNβ promoter through its NF-κB regulatory domain as well as activation of p65 subunit phosphorylation. The present studies suggest that SARS-CoV-2 skews the antiviral response in favor of an NF-κB-driven inflammatory response, a hallmark of acute COVID-19 and for which Nsp2 should be considered an important contributor.
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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) | 0.001 | 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 | 0.001 |
| 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".