tetano
Editor, Senior Moderator
J Clin Invest
. 2025 Jul 15:e189300.
doi: 10.1172/JCI189300. Online ahead of print. Integrative mapping of pre-existing influenza immune landscapes predicts vaccine response
Stephanie Hao[SUP] 1 [/SUP], Ivan Tomic[SUP] 1 [/SUP], Benjamin B Lindsey[SUP] 2 [/SUP], Ya Jankey Jagne[SUP] 3 [/SUP], Katja Hoschler[SUP] 4 [/SUP], Adam Meijer[SUP] 5 [/SUP], Juan Manuel Carreño Quiroz[SUP] 6 [/SUP], Philip Meade[SUP] 6 [/SUP], Kaori Sano[SUP] 6 [/SUP], Chikondi Peno[SUP] 7 [/SUP], André G Costa-Martins[SUP] 8 [/SUP], Debby Bogaert[SUP] 7 [/SUP], Beate Kampmann[SUP] 9 [/SUP], Helder Nakaya[SUP] 8 [/SUP], Florian Krammer[SUP] 6 [/SUP], Thushan I de Silva[SUP] 2 [/SUP], Adriana Tomic[SUP] 1 [/SUP]
Affiliations
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 pre- and post-vaccination 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 three distinct immunophenotypic profiles driven by baseline immunity: (1) CD8 T-cell responders with strong pre-existing immunity boosting memory T-cell responses; (2) Mucosal responders with prior influenza A virus immunity developing robust mucosal IgA and subsequent influenza B virus seroconversion; and (3) Systemic, broad influenza A virus responders starting from immune naivety who mounted broad systemic antibody responses. Pathway analysis revealed how pre-existing 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).
Keywords: Adaptive immunity; Clinical Research; Immunology; Influenza; Vaccines; Virology.
. 2025 Jul 15:e189300.
doi: 10.1172/JCI189300. Online ahead of print. Integrative mapping of pre-existing influenza immune landscapes predicts vaccine response
Stephanie Hao[SUP] 1 [/SUP], Ivan Tomic[SUP] 1 [/SUP], Benjamin B Lindsey[SUP] 2 [/SUP], Ya Jankey Jagne[SUP] 3 [/SUP], Katja Hoschler[SUP] 4 [/SUP], Adam Meijer[SUP] 5 [/SUP], Juan Manuel Carreño Quiroz[SUP] 6 [/SUP], Philip Meade[SUP] 6 [/SUP], Kaori Sano[SUP] 6 [/SUP], Chikondi Peno[SUP] 7 [/SUP], André G Costa-Martins[SUP] 8 [/SUP], Debby Bogaert[SUP] 7 [/SUP], Beate Kampmann[SUP] 9 [/SUP], Helder Nakaya[SUP] 8 [/SUP], Florian Krammer[SUP] 6 [/SUP], Thushan I de Silva[SUP] 2 [/SUP], Adriana Tomic[SUP] 1 [/SUP]
Affiliations
- PMID: 40663396
- DOI: 10.1172/JCI189300
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 pre- and post-vaccination 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 three distinct immunophenotypic profiles driven by baseline immunity: (1) CD8 T-cell responders with strong pre-existing immunity boosting memory T-cell responses; (2) Mucosal responders with prior influenza A virus immunity developing robust mucosal IgA and subsequent influenza B virus seroconversion; and (3) Systemic, broad influenza A virus responders starting from immune naivety who mounted broad systemic antibody responses. Pathway analysis revealed how pre-existing 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).
Keywords: Adaptive immunity; Clinical Research; Immunology; Influenza; Vaccines; Virology.