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  3. A Global Collaborative Comparison of SARS-CoV-2 Antigenicity Across 15 Laboratories.
 

A Global Collaborative Comparison of SARS-CoV-2 Antigenicity Across 15 Laboratories.

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BORIS DOI
10.48620/84583
Publisher DOI
10.3390/v16121936
PubMed ID
39772242
Description
Setting up a global SARS-CoV-2 surveillance system requires an understanding of how virus isolation and propagation practices, use of animal or human sera, and different neutralisation assay platforms influence assessment of SARS-CoV-2 antigenicity. In this study, with the contribution of 15 independent laboratories across all WHO regions, we carried out a controlled analysis of neutralisation assay platforms using the first WHO International Standard for antibodies to SARS-CoV-2 variants of concern (source: NIBSC). Live virus isolates (source: WHO BioHub or individual labs) or spike plasmids (individual labs) for pseudovirus production were used to perform neutralisation assays using the same serum panels. When comparing fold drops, excellent data consistency was observed across the labs using common reagents, including between pseudovirus and live virus neutralisation assays (RMSD of data from mean fold drop was 0.59). Utilising a Bayesian model, geometric mean titres and assay titre magnitudes (offsets) can describe the data efficiently. Titre magnitudes were seen to vary largely even for labs within the same assay group. We have observed that overall, live Microneutralisation assays tend to have the lowest titres, whereas Pseudovirus Neutralisation have the highest (with a mean difference of 3.2 log2 units between the two). These findings are relevant for laboratory networks, such as the WHO Coronavirus Laboratory Network (CoViNet), that seek to support a global surveillance system for evolution and antigenic characterisation of variants to support monitoring of population immunity and vaccine composition policy.
Date of Publication
2024-12-18
Publication Type
Article
Subject(s)
600 - Technology::610 - Medicine & health
500 - Science::590 - Animals (Zoology)
600 - Technology::630 - Agriculture
Keyword(s)
Bayesian model
•
COVID-19
•
SARS-CoV-2
•
antigenicity
•
global surveillance
•
neutralisation
Language(s)
en
Contributor(s)
Brangel, Polina
Tureli, Sina
Mühlemann, Barbara
Liechti, Nicole
Zysset, Daniel
Engler, Olivier
Hunger-Glaser, Isabel
Ghiga, Ioana
Mattiuzzo, Giada
Eckerle, Isabella
Bekliz, Meriem
Rössler, Annika
Schmitt, Melanie M
Knabl, Ludwig
Kimpel, Janine
Tort, Luis Fernando Lopez
de Araujo, Mia Ferreira
de Oliveira, Any Caroline Alves
Caetano, Braulia Costa
Siqueira, Marilda Mendonça
Budt, Matthias
Gensch, Jean-Marc
Wolff, Thorsten
Hassan, Tarteel
Selvaraj, Francis Amirtharaj
Hermanus, Tandile
Kgagudi, Prudence
Crowther, Carol
Richardson, Simone I
Bhiman, Jinal N
Moore, Penny L
Cheng, Samuel M S
Li, John K C
Poon, Leo L M
Peiris, Malik
Corman, Victor M
Drosten, Christian
Lai, Lilin
Hunsawong, Taweewun
Rungrojcharoenkit, Kamonthip
Lohachanakul, Jindarat
Sigal, Alex
Khan, Khadija
Thiel, Volker
Institute of Virology and Immunology
Department of Infectious Diseases and Pathobiology (DIP)
Barut, G. Tubaorcid-logo
Institute of Virology and Immunology
Ebert, Nadine
Department of Infectious Diseases and Pathobiology (DIP)
Mykytyn, Anna Z
Owusu Donkor, Irene
Aboagye, James Odame
Nartey, Prince Adom
Van Kerkhove, Maria D
Cunningham, Jane
Haagmans, Bart L
Suthar, Mehul S
Smith, Derek
Subissi, Lorenzo
Additional Credits
Institute of Virology and Immunology
Department of Infectious Diseases and Pathobiology (DIP)
Series
Viruses
Publisher
MDPI
ISSN
1999-4915
Access(Rights)
open.access
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