Integrative mapping of preexisting influenza immune landscapes predicts vaccine response
Series / Report no.
Open Access
Type
Journal Article
Article
Article
Language
en
Date
2025-07-15
Research Projects
Organizational Units
Journal Issue
Title
Integrative mapping of preexisting influenza immune landscapes predicts vaccine response
Translated Title
Published in
J Clin Invest 2025; 135(18):e189300
Abstract
BACKGROUNDPredicting 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.METHODSUsing 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.RESULTSThe 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.CONCLUSIONOur 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.FUNDINGWellcome Trust (110058/Z/15/Z); Bill & Melinda Gates Foundation (INV-004222); HIC-Vac Consortium; NIAID (R21 AI151917); NIAID CEIRR Network (75N93021C00045).