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Abstract
BACKGROUND Predicting individual vaccine responses is a substantial public health challenge. We developed Immunaut, an open-source, data-driven framework for systems vaccinologists to analyze and predict immunological outcomes across diverse vaccination settings, beyond traditional assessments.METHODS Using a comprehensive live attenuated influenza vaccine (LAIV) dataset from 244 Gambian children, Immunaut integrated prevaccination and postvaccination humoral, mucosal, cellular, and transcriptomic data. Through advanced modeling, our framework provided a holistic, systems-level view of LAIV-induced immunity.RESULTS The analysis identified 3 distinct immunophenotypic profiles driven by baseline immunity: (a) CD8+ T cell responders with strong preexisting immunity boosting memory T cell responses; (b) mucosal responders with prior influenza A virus immunity developing robust mucosal IgA and subsequent influenza B virus seroconversion; and (c) systemic, broad influenza A virus responders starting from immune naivety who mounted broad systemic antibody responses. Pathway analysis revealed how preexisting immune landscapes and baseline features, such as mucosal preparedness and cellular support, quantitatively dictate vaccine outcomes.CONCLUSION Our findings emphasize the power of integrative, predictive frameworks for advancing precision vaccinology. The Immunaut framework is a valuable resource for deciphering vaccine response heterogeneity and can be applied to optimize immunization strategies across diverse populations and vaccine platforms.FUNDING Wellcome Trust (110058/Z/15/Z); Bill & Melinda Gates Foundation (INV-004222); HIC-Vac Consortium; NIAID (R21 AI151917); NIAID CEIRR Network (75N93021C00045).
Authors
Stephanie Hao, Ivan Tomic, Benjamin B. Lindsey, Ya Jankey Jagne, Katja Hoschler, Adam Meijer, Juan Manuel Carreño Quiroz, Philip Meade, Kaori Sano, Chikondi Peno, André G. Costa-Martins, Debby Bogaert, Beate Kampmann, Helder Nakaya, Florian Krammer, Thushan I. de Silva, Adriana Tomic
Usage data is cumulative from July 2025 through June 2026.
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ISSN: 0021-9738 (print), 1558-8238 (online)