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Integrative mapping of preexisting influenza immune landscapes predicts vaccine response
Stephanie Hao, … , Thushan I. de Silva, Adriana Tomic
Stephanie Hao, … , Thushan I. de Silva, Adriana Tomic
Published July 15, 2025
Citation Information: J Clin Invest. 2025;135(18):e189300. https://doi.org/10.1172/JCI189300.
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Clinical Research and Public Health Clinical Research Immunology Virology

Integrative mapping of preexisting influenza immune landscapes predicts vaccine response

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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

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Figure 1

Immune response landscape mapping of LAIV reveals distinct immunophenotypic groups.

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Immune response landscape mapping of LAIV reveals distinct immunophenoty...
(A) Cohort overview depicting all features used for unsupervised machine learning analysis: 244 children (24–59 months of age) vaccinated with LAIV; mucosal and blood samples collected on day 0 (prevaccination) and day 21 (postvaccination). Vaccine-induced immune responses calculated as fold-change relative to prevaccination levels. (B) Workflow schematic for automated clustering pipeline applying t-SNE dimensionality reduction, KNN graph construction, and Louvain community detection to identify distinct immunophenotypic clusters. (C and D) Louvain resolution sweep results used to assess cluster stability and select optimal number of clusters. (C) Modularity score plotted against Louvain resolution parameter, colored by number of clusters identified (3–6). High modularity indicates well-separated clusters. Red diamond indicates selected clustering parameters. (D) Number of clusters identified plotted against Louvain resolution parameter, colored by modularity score. Stability of 3-cluster solution (red diamond) is observed across range where modularity is maximal (Q ≈ 0.717). (E) Clustered t-SNE plot of fold-change data (post/pre-LAIV) revealing 3 distinct LAIV response phenotypes: group 1 (green, n = 82), group 2 (orange, n = 88), and group 3 (purple, n = 74). (t-SNE parameters: perplexity: 30; exaggeration factor: 4; max iterations: 10,000; theta: 0; eta: 500; K: 60 for KNN graph; final silhouette score: 0.40). (F and G) Clustering patterns overlaid with demographic factors on t-SNE map. (F) Clustering by sex (female, green; male, orange). (G) Clustering by study year (2017, green; 2018, orange). (H) Heatmap and hierarchical clustering display fold-change data for key immune features across 3 clusters (columns: groups 2, 1, and 3 from left to right). Rows represent immune features, clustered using Euclidean distance and Ward’s D2 method. Heatmap cells are colored based on scaled FC values from –1 (blue, low FC) to 1 (red, high FC). The top color bar indicates responder groups (group 1, green; group 2, orange; group 3, purple). Side color bars indicate qualitative response classifications derived from assays: HAI (purple: high, dark; low, light), IgA (orange: high, dark; low, light), CD4+ T cell (blue: high, dark; low, light), and CD8+ T cell (green: high, dark; low, light). Column cluster ordering optimized for visual clarity.

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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