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Antigenic characterization of SARS-CoV-2 Omicron subvariant BA.4.6

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causing agent for COVID-19, has led to over 7 million deaths since it was first detected in Wuhan, China in December 2019. Antibodies induced by both vaccination and infection are crucial for the immune protection against SARS-CoV-2, with some monoclonal antibodies (mAbs) therapeutically in use. Throughout the pandemic, we dedicated ourselves to isolate thousands of human monoclonal antibodies recognising the spike protein (S) from human peripheral blood mononuclear cells (PBMCs) of convalescent patients recovered from primary or breakthrough infections and characterised their neutralising abilities against eight SARS-CoV-2 live variants and more than 30 pseudo-viruses expressing variant S proteins. We also devised a competition data-driven method to map RBD binding sites of RBD-specific antibodies. To assess the immune escape abilities of variants of concern (VoC), we measured their neutralisation by a panel of plasma obtained from vaccinees or convalescent individuals infected by different variants. Using these data, we built a model to analyse the antigenic distance between variants to illustrate the emerging antigenic landscape of SARS-CoV-2. This provides important new insights for immunisation policy and sheds light on the construction of future variant vaccines
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41421-022-00493-0

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-4850-9709
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Institution:
University of Oxford
Role:
Author
More by this author
Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-9251-4292
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-7188-5243
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-4502-2209


Publisher:
Springer Nature [academic journals on nature.com]
Journal:
Cell Discovery More from this journal
Volume:
8
Issue:
1
Pages:
127-127
Article number:
127
Publication date:
2022-11-29
DOI:
EISSN:
2056-5968
ISSN:
2056-5968


Language:
English
Keywords:
Pubs id:
1310838
Local pid:
pubs:1310838
Source identifiers:
W4310191251
Deposit date:
2026-04-30
ARK identifier:
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