Molecular Analyses of Clinical Isolates and Recombinant SARS-CoV-2 Carrying B.1 and B.1.617.2 Spike Mutations Suggest a Potential Role of Non-Spike Mutations in Infection Kinetics

Kinetics of SARS-CoV-2 infection biomarkers in a household transmission study

SARS-CoV-2 is the causative agent of COVID-19. Timely and accurate diagnostic testing is vital to contain the spread of infection, reduce delays in treatment and care, and inform patient management. Optimal specimen type (e.g. nasal swabs or saliva), timing of sampling, viral marker assayed (RNA or antigen), and correlation with viral infectivity and COVID-19 symptoms severity remain incompletely defined. We conducted a field study to evaluate SARS-CoV-2 viral marker kinetics starting from very early times after infection. We measured RNA and antigen levels in nasal swabs and saliva, virus outgrowth in cell culture from nasal swabs, and antibody levels in blood in a cohort of 30 households. Nine household contacts (HHC) became infected with SARS-CoV-2 during the study. Viral RNA was detected in saliva specimens approximately 1-2 days before nasal swabs in six HHC. Detection of RNA was more sensitive than of antigen, but antigen detection was better correlated with culture positivity, a proxy for contagiousness. Anti-nucleocapsid antibodies peaked one to three weeks post-infection. Viral RNA and antigen levels were higher in specimens yielding replication competent virus in cell culture. This study provides important data that can inform how to optimally interpret SARS-CoV-2 diagnostic test results.

Bacterial Artificial Chromosome Reverse Genetics Approaches for SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new member of the Coronaviridae family responsible for the coronavirus disease 19 (COVID-19) pandemic. To date, SARS-CoV-2 has been accountable for over 624 million infection cases and more than 6.5 million human deaths. The development and implementation of SARS-CoV-2 reverse genetics approaches have allowed researchers to genetically engineer infectious recombinant (r)SARS-CoV-2 to answer important questions in the biology of SARS-CoV-2 infection. Reverse genetics techniques have also facilitated the generation of rSARS-CoV-2 expressing reporter genes to expedite the identification of compounds with antiviral activity in vivo and in vitro. Likewise, reverse genetics has been used to generate attenuated forms of the virus for their potential implementation as live-attenuated vaccines (LAV) for the prevention of SARS-CoV-2 infection. Here we describe the experimental procedures for the generation of rSARS-CoV-2 using a well-established and robust bacterial artificial chromosome (BAC)-based reverse genetics system. The protocol allows to produce wild-type and mutant rSARS-CoV-2 that can be used to understand the contribution of viral proteins and/or amino acid residues in viral replication and transcription, pathogenesis and transmission, and interaction with cellular host factors.

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves under selection pressure which already led to the emergence of several drug resistant strains. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning, based on experimental data from several knockout screens and a drug screen. We trained classifiers using genes essential for virus life cycle obtained from the knockout screens. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance suggesting patterns of intrinsic data consistency. The predicted HDF were enriched in sets of genes particularly encoding development, morphogenesis, and neural processes. Focusing on development and morphogenesis-associated gene sets, we found β-catenin to be central and selected PRI-724, a canonical β-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in cytopathic effects, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept supports focusing and accelerating the discovery of host dependency factors and identification of potential host-directed antivirals.