Optimization of 5'UTR to evade SARS-CoV-2 Nonstructural protein 1-directed inhibition of protein synthesis in cells.

Maximizing the expression level of therapeutic proteins in cells is the general goal for DNA/mRNA therapies. It is particularly challenging to achieve efficient protein expression in the cellular contexts with inhibited translation machineries, such as in the presence of cellular Nonstructural protein 1 (Nsp1) of coronaviruses (CoVs) that has been reported to inhibit overall protein synthesis of host genes and exogenously delivered mRNAs/DNAs. In this study, we thoroughly examined the sequence and structure contexts of viral and non-viral 5'UTRs that determine the protein expression levels of exogenously delivered DNAs and mRNAs in cells expressing SARS-CoV-2 Nsp1. It was found that high 5'-proximal A/U content promotes an escape from Nsp1-directed inhibition of protein synthesis and results in selective protein expression. Furthermore, 5'-proximal Cs were found to significantly enhance the protein expression in an Nsp1-dependent manner, while Gs located at a specific window close to the 5'-end counteract such enhancement. The distinct protein expression levels resulted from different 5'UTRs were found correlated to Nsp1-induced mRNA degradations. These findings ultimately enabled rational designs for optimized 5'UTRs that lead to strong expression of exogenous proteins regardless of the translationally repressive Nsp1. On the other hand, we have also identified several 5'-proximal sequences derived from host genes that are capable of mediating the escapes. These results provided novel perspectives to the optimizations of 5'UTRs for DNA/mRNA therapies and/or vaccinations, as well as shedding light on the potential host escapees from Nsp1-directed translational shutoffs. KEY POINTS: • The 5'-proximal SL1 and 5a/b derived from SARS-CoV-2 genomic RNA promote exogenous protein synthesis in cells expressing Nsp1 comparing with non-specific 5'UTRs. • Specific 5'-proximal sequence contexts are the key determinants of the escapes from Nsp1-directed translational repression and thereby enhance protein expressions. • Systematic mutagenesis identified optimized 5'UTRs that strongly enhance protein expression and promote resistance to Nsp1-induced translational repression and RNA degradation.

Polyoxygenated Terpenoids and Polyketides from the Roots of Flueggea virosa and Their Inhibitory Effect against SARS-CoV-2-Induced Inflammation.

Six new polyoxygenated terpenoids, podovirosanes A-F (1-6), and two known polyketides (7 and 8) were isolated from the roots of F. virosa. Their structures, along with absolute configurations, were deduced using spectroscopic analysis as well as computational calculations, including TDDFT calculation of ECD spectra and GIAO NMR calculations combined with DP4+ probability analysis. Compounds 2, 3, 5, and 8 were found to reduce the phosphorylation levels of NF-κB p65 in SARS-CoV-2 pseudovirus-stimulated PMA-differentiated THP-1 cells.

Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries.

OBJECTIVES: The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. METHODS: This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. RESULTS: This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. CONCLUSIONS: This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.

Overcoming the impact of the COVID-19 pandemic on oncology early phase trials and drug development in Asia-Experiences and perspectives of the Asian Oncology Early Phase 1 Consortium.

AIM: The significance and prioritization of early phase oncology trial continuation during a global pandemic is unknown. This study reported the outcomes, multiple challenges, and broad recommendations associated with the impact of the novel coronavirus disease 2019 (COVID-19) on oncology early phase 1 trials-and on drug development in Asia-based on the experiences and perspectives of Asian oncology phase 1 centers. METHODS: Between March and April 2020 during the initial period of outbreak, the impact of COVID-19 across oncology phase 1 sites in five Asian countries-China (Hong Kong), Japan, South Korea, Taiwan, and Singapore-was retrospectively analyzed. RESULTS: There was no trial termination or treatment discontinuation in all five countries. Although the most common impact was new patient enrollment being placed on hold, which was based on pharmaceutical sponsors' decision-making, the situation varied per site. Most sites had no restrictions in place that would limit their ability to fully comply with the requirements of conducting the early phase studies. The number of protocol deviations during the pandemic was largely dependent on domestic transportation status during the outbreak rather than the ability of the clinical trial centers. CONCLUSION: Determining the risk to benefits ratio of patients with cancer who are enrolled in early phase 1 clinical trials under the unusual circumstances of a global pandemic is important. Specific guidance or guidelines on the conduct of early phase 1 clinical trials during public health emergencies that are based on the recent lessons learned is urgently required.