SARS-CoV-2 evolution on a dynamic immune landscape

N. Alexia Raharinirina, Nils Gubela, Daniela Börnigen, Maureen Rebecca Smith, Djin-Ye Oh, Matthias Budt, Christin Mache, Claudia Schillings, Stephan Fuchs, Ralf Dürrwald, Thorsten Wolff, Martin Hölzer, Sofia Paraskevopoulou and Max Kleist ()
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N. Alexia Raharinirina: Freie Universität Berlin
Nils Gubela: Freie Universität Berlin
Daniela Börnigen: Robert Koch Institute
Maureen Rebecca Smith: Robert Koch Institute
Djin-Ye Oh: Robert Koch Institute
Matthias Budt: Robert Koch Institute
Christin Mache: Robert Koch Institute
Claudia Schillings: Freie Universität Berlin
Stephan Fuchs: Robert Koch Institute
Ralf Dürrwald: Robert Koch Institute
Thorsten Wolff: Robert Koch Institute
Martin Hölzer: Robert Koch Institute
Sofia Paraskevopoulou: Robert Koch Institute
Max Kleist: Freie Universität Berlin

Nature, 2025, vol. 639, issue 8053, 196-204

Abstract: Abstract Since the onset of the pandemic, many SARS-CoV-2 variants have emerged, exhibiting substantial evolution in the virus’ spike protein1, the main target of neutralizing antibodies2. A plausible hypothesis proposes that the virus evolves to evade antibody-mediated neutralization (vaccine- or infection-induced) to maximize its ability to infect an immunologically experienced population1,3. Because viral infection induces neutralizing antibodies, viral evolution may thus navigate on a dynamic immune landscape that is shaped by local infection history. Here we developed a comprehensive mechanistic model, incorporating deep mutational scanning data4,5, antibody pharmacokinetics and regional genomic surveillance data, to predict the variant-specific relative number of susceptible individuals over time. We show that this quantity precisely matched historical variant dynamics, predicted future variant dynamics and explained global differences in variant dynamics. Our work strongly suggests that the ongoing pandemic continues to shape variant-specific population immunity, which determines a variant’s ability to transmit, thus defining variant fitness. The model can be applied to any region by utilizing local genomic surveillance data, allows contextualizing risk assessment of variants and provides information for vaccine design.

Date: 2025
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DOI: 10.1038/s41586-024-08477-8

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