Sequence determinants of human-cell entry identified in ACE2-independent bat sarbecoviruses: A combined laboratory and computational network science approach
- PMID: 35405384
- PMCID: PMC8989474
- DOI: 10.1016/j.ebiom.2022.103990
Sequence determinants of human-cell entry identified in ACE2-independent bat sarbecoviruses: A combined laboratory and computational network science approach
Abstract
Background: The sarbecovirus subgenus of betacoronaviruses is widely distributed throughout bats and other mammals globally and includes human pathogens, SARS-CoV and SARS-CoV-2. The most studied sarbecoviruses use the host protein, ACE2, to infect cells. Curiously, the majority of sarbecoviruses identified to date do not use ACE2 and cannot readily acquire ACE2 binding through point mutations. We previously screened a broad panel of sarbecovirus spikes for cell entry and observed bat-derived viruses that could infect human cells, independent of ACE2. Here we further investigate the sequence determinants of cell entry for ACE2-independent bat sarbecoviruses.
Methods: We employed a network science-based approach to visualize sequence and entry phenotype similarities across the diversity of sarbecovirus spike protein sequences. We then verified these computational results and mapped determinants of viral entry into human cells using recombinant chimeric spike proteins within an established viral pseudotype assay.
Findings: We show ACE2-independent viruses that can infect human and bat cells in culture have a similar putative receptor binding motif, which can impart human cell entry into other bat sarbecovirus spikes that cannot otherwise infect human cells. These sequence determinants of human cell entry map to a surface-exposed protrusion from the predicted bat sarbecovirus spike receptor binding domain structure.
Interpretation: Our findings provide further evidence of a group of bat-derived sarbecoviruses with zoonotic potential and demonstrate the utility in applying network science to phenotypic mapping and prediction.
Funding: This work was supported by Washington State University and the Paul G. Allen School for Global Health.
Keywords: Coronavirus; Entry assay; Mathematical model; Pseudotype; Sequence similarity network; Zoonosis.
Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of interests The authors have nothing to declare.
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