Increased flexibility of the SARS-CoV-2 RNA-binding site causes resistance to remdesivir.

Mutations continue to accumulate within the SARS-CoV-2 genome, and the ongoing epidemic has shown no signs of ending. It is critical to predict problematic mutations that may arise in clinical environments and assess their properties in advance to quickly implement countermeasures against future variant infections. In this study, we identified mutations resistant to remdesivir, which is widely administered to SARS-CoV-2-infected patients, and discuss the cause of resistance. First, we simultaneously constructed eight recombinant viruses carrying the mutations detected in in vitro serial passages of SARS-CoV-2 in the presence of remdesivir. We confirmed that all the mutant viruses didn't gain the virus production efficiency without remdesivir treatment. Time course analyses of cellular virus infections showed significantly higher infectious titers and infection rates in mutant viruses than wild type virus under treatment with remdesivir. Next, we developed a mathematical model in consideration of the changing dynamic of cells infected with mutant viruses with distinct propagation properties and defined that mutations detected in in vitro passages canceled the antiviral activities of remdesivir without raising virus production capacity. Finally, molecular dynamics simulations of the NSP12 protein of SARS-CoV-2 revealed that the molecular vibration around the RNA-binding site was increased by the introduction of mutations on NSP12. Taken together, we identified multiple mutations that affected the flexibility of the RNA binding site and decreased the antiviral activity of remdesivir. Our new insights will contribute to developing further antiviral measures against SARS-CoV-2 infection.

Smoking enhances the expression of angiotensin-converting enzyme 2 involved in the efficiency of severe acute respiratory syndrome coronavirus 2 infection.

Smoking is one of the risk factors most closely related to the severity of coronavirus disease 2019 (COVID-19). However, the relationship between smoking history and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is unknown. In this study, we evaluated the ACE2 expression level in the lungs of current smokers, ex-smokers, and nonsmokers. The ACE2 expression level of ex-smokers who smoked cigarettes until recently (cessation period shorter than 6 months) was higher than that of nonsmokers and ex-smokers with a long history of nonsmoking (cessation period longer than 6 months). We also showed that the efficiency of SARS-CoV-2 infection was enhanced in a manner dependent on the angiotensin-converting enzyme 2 (ACE2) expression level. Using RNA-seq analysis on the lungs of smokers, we identified that the expression of inflammatory signaling genes was correlated with ACE2 expression. Notably, with increasing duration of smoking cessation among ex-smokers, not only ACE2 expression level but also the expression levels of inflammatory signaling genes decreased. These results indicated that smoking enhances the expression levels of ACE2 and inflammatory signaling genes. Our data suggest that the efficiency of SARS-CoV-2 infection is enhanced by smoking-mediated upregulation of ACE2 expression level.

Detecting time-evolving phenotypic components of adverse reactions against BNT162b2 SARS-CoV-2 vaccine via non-negative tensor factorization.

Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based platform. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization.

Detecting time-evolving phenotypic components of adverse reactions against BNT162b2 mRNA SARS-CoV-2 vaccine via non-negative tensor factorization

Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based platform. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization. Graphical