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Biochem Biophys Res Commun ; 529(2): 251-256, 2020 08 20.
Article in English | MEDLINE | ID: covidwho-1220683


The nucleocapsid protein is significant in the formation of viral RNA of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), accounting for the largest proportion of viral structural proteins. Here, we report for the first time that the 11S proteasomal activator PA28γ regulates the intracellular abundance of the SARS-CoV-2 N protein (nCoV N). Furthermore, we have identified proteasome activator PA28γ as a nCoV N binding protein by co-immunoprecipitation assay. As a result of their interaction, nCoV N could be degraded by PA28γ-20S in vitro degradation assay. This was also demonstrated by blocking de novo protein synthesis with cycloheximide. The stability of nCoV N in PA28γ-knockout cells was greater than in PA28γ-wildtype cells. Notably, immunofluorescence staining revealed that knockout of the PA28γ gene in cells led to the transport of nCoV N from the nucleus to the cytoplasm. Overexpression of PA28γ enhanced proteolysis of nCoV N compared to that in PA28γ-N151Y cells containing a dominant-negative PA28γ mutation, which reduced this process. These results suggest that PA28γ binding is important in regulating 20S proteasome activity, which in turn regulates levels of the critical nCoV N nucleocapsid protein of SARS-CoV-2, furthering our understanding of the pathogenesis of COVID-19.

Autoantigens/metabolism , Betacoronavirus/metabolism , Coronavirus Infections/metabolism , Nucleocapsid Proteins/metabolism , Pneumonia, Viral/metabolism , Proteasome Endopeptidase Complex/metabolism , Proteolysis , COVID-19 , Coronavirus Infections/virology , Coronavirus Nucleocapsid Proteins , HEK293 Cells , Humans , In Vitro Techniques , Pandemics , Phosphoproteins , Pneumonia, Viral/virology , Protein Binding , Protein Stability , Protein Transport , SARS-CoV-2
BMJ Glob Health ; 5(11)2020 11.
Article in English | MEDLINE | ID: covidwho-917789


BACKGROUND: Respiratory viruses (RVs) is a common cause of illness in people of all ages, at present, different types of sampling methods are available for respiratory viral diagnosis. However, the diversity of available sampling methods and the limited direct comparisons in randomised controlled trials (RCTs) make decision-making difficult. We did a network meta-analysis, which accounted for both direct and indirect comparisons, to determine the detection rate of different sampling methods for RVs. METHODS: Relevant articles were retrieved comprehensively by searching the online databases of PubMed, Embase and Cochrane published before 25 March 2020. With the help of R V.3.6.3 software and 'GeMTC V.0.8.2' package, network meta-analysis was performed within a Bayesian framework. Node-splitting method and I 2 test combined leverage graphs and Gelman-Rubin-Brooks plots were conducted to evaluate the model's accuracy. The rank probabilities in direct and cumulative rank plots were also incorporated to rank the corresponding sampling methods for overall and specific virus. RESULTS: 16 sampling methods with 54 438 samples from 57 literatures were ultimately involved in this study. The model indicated good consistency and convergence but high heterogeneity, hence, random-effect analysis was applied. The top three sampling methods for RVs were nasopharyngeal wash (NPW), mid-turbinate swab (MTS) and nasopharyngeal swab (NPS). Despite certain differences, the results of virus-specific subanalysis were basically consistent with RVs: MTS, NPW and NPS for influenza; MTS, NPS and NPW for influenza-a and b; saliva, NPW and NPS for rhinovirus and parainfluenza; NPW, MTS and nasopharyngeal aspirate for respiratory syncytial virus; saliva, NPW and MTS for adenovirus and sputum; MTS and NPS for coronavirus. CONCLUSION: This network meta-analysis provides supporting evidences that NPW, MTS and NPS have higher diagnostic value regarding RVs infection, moreover, particular preferred methods should be considered in terms of specific virus pandemic. Of course, subsequent RCTs with larger samples are required to validate our findings.

Respiratory Tract Infections/virology , Specimen Handling/methods , Bayes Theorem , Humans , Network Meta-Analysis