Dataset Information

Characterizing SARS-CoV-2 neutralization profiles after bivalent boosting using antigenic cartography.

ABSTRACT: Since emergence of the initial SARS-CoV-2 BA.1, BA.2 and BA.5 variants, Omicron has diversified substantially. Antigenic characterization of these new variants is important to analyze their potential immune escape from population immunity and implications for future vaccine composition. Here, we describe an antigenic map based on human single-exposure sera and live-virus isolates that includes a broad selection of recently emerged Omicron variants such as BA.2.75, BF.7, BQ, XBB and XBF variants. Recent Omicron variants clustered around BA.1 and BA.5 with some variants further extending the antigenic space. Based on this antigenic map we constructed antibody landscapes to describe neutralization profiles after booster immunization with bivalent mRNA vaccines based on ancestral virus and either BA.1 or BA.4/5. Immune escape of BA.2.75, BQ, XBB and XBF variants was also evident in bivalently boosted individuals, however, cross-neutralization was improved for those with hybrid immunity. Our results indicate that future vaccine updates are needed to induce cross-neutralizing antibodies against currently circulating variants.

SUBMITTER: Rossler A

PROVIDER: S-EPMC10460376 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterizing SARS-CoV-2 neutralization profiles after bivalent boosting using antigenic cartography.

Rössler Annika A Netzl Antonia A Knabl Ludwig L Bante David D Wilks Samuel H SH Borena Wegene W von Laer Dorothee D Smith Derek J DJ Kimpel Janine J

Nature communications 20230826 1

Since emergence of the initial SARS-CoV-2 BA.1, BA.2 and BA.5 variants, Omicron has diversified substantially. Antigenic characterization of these new variants is important to analyze their potential immune escape from population immunity and implications for future vaccine composition. Here, we describe an antigenic map based on human single-exposure sera and live-virus isolates that includes a broad selection of recently emerged Omicron variants such as BA.2.75, BF.7, BQ, XBB and XBF variants. ...[more]

PMID: 37633965

Similar Datasets

Project description:BackgroundThe ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to researchers. Antigenic mismatch between circulating strains and vaccine strains results in significantly decreased vaccine effectiveness. Furthermore, antigenic relatedness between the vaccine strain and the strains an individual was originally primed with can affect the cross-reactivity of the antibody response. Thus, understanding the antigenic relationships between influenza viruses that have circulated is important to both vaccinologists and immunologists.ResultsHere we develop a method of mapping antigenic relationships between influenza virus stains using a sequence-based antigenic distance approach (SBM). We used a modified version of the p-all-epitope sequence-based antigenic distance calculation, which determines the antigenic relatedness between strains using influenza hemagglutinin (HA) genetic coding sequence data and provide experimental validation of the p-all-epitope calculation. We calculated the antigenic distance between 4838 H1N1 viruses isolated from infected humans between 1918 and 2016. We demonstrate, for the first time, that sequence-based antigenic distances of H1N1 Influenza viruses can be accurately represented in 2-dimenstional antigenic cartography using classic multidimensional scaling. Additionally, the model correctly predicted decreases in cross-reactive antibody levels with 87% accuracy and was highly reproducible with even when small numbers of sequences were used.ConclusionThis work provides a highly accurate and precise bioinformatics tool that can be used to assess immune risk as well as design optimized vaccination strategies. SBM accurately estimated the antigenic relationship between strains using HA sequence data. Antigenic maps of H1N1 virus strains reveal that strains cluster antigenically similar to what has been reported for H3N2 viruses. Furthermore, we demonstrated that genetic variation differs across antigenic sites and discuss the implications.

Dataset Information

Characterizing SARS-CoV-2 neutralization profiles after bivalent boosting using antigenic cartography.

Publications

Characterizing SARS-CoV-2 neutralization profiles after bivalent boosting using antigenic cartography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets