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Systems immune profiling of variant-specific vaccination against SARS-CoV-2
Lei Peng; Jonathan Park; Zhenhao Fang; Xiaoyu Zhou; Matthew Dong; Qiancheng Xiong; Chenxiang Lin; Sidi Chen.
Affiliation
  • Lei Peng; Yale University
  • Jonathan Park; Yale University
  • Zhenhao Fang; Yale University
  • Xiaoyu Zhou; Yale University
  • Matthew Dong; Yale University
  • Qiancheng Xiong; Yale University
  • Chenxiang Lin; Yale University
  • Sidi Chen; Yale University
Preprint in English | bioRxiv | ID: ppbiorxiv-471028
Journal article
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ABSTRACT
Lipid-nanoparticle(LNP)-mRNA vaccines offer protection against COVID-19. However, multiple variant lineages caused widespread breakthrough infections. There is no report on variant-specific vaccines to date. Here, we generated LNP-mRNAs specifically encoding wildtype, B.1.351 and B.1.617 SARS-CoV-2 spikes, and systematically studied their immune responses in animal models. All three LNP-mRNAs induced potent antibody responses in mice. However, WT-LNP-mRNA vaccination showed reduced neutralization against B.1.351 and B.1.617; and B.1.617-specific vaccination showed differential neutralization. All three vaccine candidates elicited antigen-specific CD8 and CD4 T cell responses. Single cell transcriptomics of B.1.351-LNP-mRNA and B.1.617-LNP-mRNA vaccinated animals revealed a systematic landscape of immune cell populations and global gene expression. Variant-specific vaccination induced a systemic increase in reactive CD8 T cell population, with a strong signature of transcriptional and translational machineries in lymphocytes. BCR-seq and TCR-seq unveiled repertoire diversity and clonal expansions in vaccinated animals. These data provide direct systems immune profiling of variant-specific LNP-mRNA vaccination in vivo.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: bioRxiv Type of study: Systematic review Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: bioRxiv Type of study: Systematic review Language: English Year: 2021 Document type: Preprint
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