Characterization and structural basis of a lethal mouse-adapted SARS-CoV-2 (preprint)

The ongoing SARS-CoV-2 pandemic has brought an urgent need for animal models to study the pathogenicity of the virus. Herein, we generated and characterized a novel mouse-adapted SARS-CoV-2 strain named MASCp36 that causes acute respiratory symptoms and mortality in standard laboratory mice. Particularly, this model exhibits age and gender related skewed distribution of mortality akin to severe COVID-19, and the 50% lethal dose (LD50) of MASCp36 was ~100 PFU in aged, male BALB/c mice. Deep sequencing identified three amino acid mutations, N501Y, Q493H, and K417N, subsequently emerged at the receptor binding domain (RBD) of MASCp36, which significantly enhanced the binding affinity to its endogenous receptor, mouse ACE2 (mACE2). Cryo-electron microscopy (cryo-EM) analysis of mACE2 in complex with the RBD of MASCp36 at 3.7-angstrom resolution elucidates molecular basis for the receptor-binding switch driven by amino acid substitutions. Our study not only provides a robust platform for studying the pathogenesis of severe COVID-19 and rapid evaluation of coutermeasures against SARS-CoV-2, but also unveils the molecular mechanism for the rapid adaption and evolution of SARS-CoV-2 in mice.

Network-based Virus-Host Interaction Prediction with Application to SARS-CoV-2 (preprint)

COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of under-utilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and their hosts' proteins. More in-depth and more comprehensive analyses of that knowledge and data can shed new insight into the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. A machine learning-based method, termed Infection Mechanism and Spectrum Prediction (IMSP), was developed to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2, which deserved public health attention, and eight highly possible interactions between SARS-CoV-2 proteins and human proteins. Given a new virus, IMSP can utilize existing knowledge and data about other highly relevant viruses to predict multi-scale interactions between the new virus and potential hosts.

SARS-CoV-2 infection causes transient olfactory dysfunction in mice (preprint)

Olfactory dysfunction caused by SARS-CoV-2 infection represents as one of the most predictive and common symptoms in COVID-19 patients. However, the causal link between SARS-CoV-2 infection and olfactory disorders remains lacking. Herein we demonstrate intranasal inoculation of SARS-CoV-2 induces robust viral replication in the olfactory epithelium (OE), resulting in transient olfactory dysfunction in humanized ACE2 mice. The sustentacular cells and Bowman's gland cells in OE were identified as the major targets of SARS-CoV-2 before the invasion into olfactory sensory neurons. Remarkably, SARS-CoV-2 infection triggers cell death and immune cell infiltration, and impairs the uniformity of OE structure. Combined transcriptomic and proteomic analyses reveal the induction of antiviral and inflammatory responses, as well as the downregulation of olfactory receptors in OE from the infected animals. Overall, our mouse model recapitulates the olfactory dysfunction in COVID-19 patients, and provides critical clues to understand the physiological basis for extrapulmonary manifestations of COVID-19.