Mapping immune imprinting zones enables predictive vaccination optimization

Wei Yang; Kaichun Jin; Wei Li; Zhixing Ding; Houze Yu; Zumi Zhou; Wei Su; Xiao Zhu; Xupu Ma; Na Wan; Lei Xing; Yanli Chen; Xuanjing Yu; Xiaolu Tang; Taicheng Zhou; Jinzhong Lin; Hao Li; Jian Lu; Zijie Scott Zhang

doi:10.1016/j.celrep.2026.117271

Mapping immune imprinting zones enables predictive vaccination optimization

Cell Rep. 2026 Apr 16;45(4):117271. doi: 10.1016/j.celrep.2026.117271. Online ahead of print.

Authors

Wei Yang¹, Kaichun Jin², Wei Li³, Zhixing Ding⁴, Houze Yu⁵, Zumi Zhou⁶, Wei Su⁶, Xiao Zhu⁶, Xupu Ma⁵, Na Wan⁵, Lei Xing⁵, Yanli Chen⁷, Xuanjing Yu⁵, Xiaolu Tang², Taicheng Zhou⁸, Jinzhong Lin⁹, Hao Li⁴, Jian Lu¹⁰, Zijie Scott Zhang¹¹

Affiliations

¹ Bio-X Center for Interdisciplinary Innovation, Yunnan University, Yunnan, China; Yunnan Vaccine Laboratory, Yunnan, China. Electronic address: weiyang@ynu.edu.cn.
² State Key Laboratory of Gene Function and Modulation Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing, China; Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing, China.
³ Yunnan Vaccine Laboratory, Yunnan, China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China.
⁴ School of Information Science & Engineering, Yunnan University, Yunnan, China.
⁵ Bio-X Center for Interdisciplinary Innovation, Yunnan University, Yunnan, China; Southwest United Graduate School, Yunnan, China.
⁶ Yunnan Genvoo Biomedical Ltd., Yunnan, China.
⁷ Bio-X Center for Interdisciplinary Innovation, Yunnan University, Yunnan, China.
⁸ Central Lab and Liver Disease Research Center, The Affiliated Hospital of Yunnan University, Yunnan University, Yunnan, China.
⁹ State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China.
¹⁰ State Key Laboratory of Gene Function and Modulation Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing, China; Southwest United Graduate School, Yunnan, China; Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing, China. Electronic address: luj@pku.edu.cn.
¹¹ Bio-X Center for Interdisciplinary Innovation, Yunnan University, Yunnan, China; Yunnan Vaccine Laboratory, Yunnan, China; Southwest United Graduate School, Yunnan, China. Electronic address: zijiezhang@ynu.edu.cn.

PMID: 41999605
DOI: 10.1016/j.celrep.2026.117271

Abstract

Immune imprinting, where prior exposures shape antibody responses to variant antigens, remains a central obstacle to optimizing vaccination against evolving viruses. Here, we present DynaVac, a mathematical framework that models antigen-specific B cell dynamics, particularly the competition between memory and naive compartments across antigenic distances. Calibrated on neutralization titers from murine and human studies spanning diverse SARS-CoV-2 vaccine platforms, DynaVac accurately predicts antibody responses across complex heterologous and multivalent regimens. In silico simulations reveal three imprinting zones-protection, pitfall, and breakthrough-that determine when updated vaccinations amplify, are suppressed by, or bypass preexisting immunity. Unlike prior models limited to qualitative or single-exposure settings, DynaVac integrates empirical cross-neutralization matrices and enables prospective simulation of continuous booster responses across antigenic variants, dosages, and intervals. DynaVac also provides an actionable strategy for guiding real-time vaccine updates and strain selection. While DynaVac is calibrated on SARS-CoV-2, its structure is generalizable to other fast-evolving pathogens.

Keywords: CP: immunology; SARS-CoV-2 evolution; dynamic vaccination modeling; immune imprinting; immunogenicity evolution; vaccination strategies.