{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T05:54:03Z","timestamp":1775886843355,"version":"3.50.1"},"posted":{"date-parts":[[2025,1,23]]},"group-title":"Immunology","reference-count":47,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Predicting individual vaccine responses remains a significant challenge due to the complexity and variability of immune processes. To address this gap, we developed\n                  <jats:italic>immunaut<\/jats:italic>\n                  , an open-source, data-driven framework implemented as an R package specifically designed for all systems vaccinologists seeking to analyze and predict immunological outcomes across diverse vaccination settings. Leveraging one of the most comprehensive live attenuated influenza vaccine (LAIV) datasets to date - 244 Gambian children enrolled in a phase 4 immunogenicity study -\n                  <jats:italic>immunaut<\/jats:italic>\n                  integrates humoral, mucosal, cellular, transcriptomic, and microbiological parameters collected before and after vaccination, providing an unprecedentedly holistic view of LAIV-induced immunity. Through advanced dimensionality reduction, clustering, and predictive modeling, immunaut identifies distinct immunophenotypic responder profiles and their underlying baseline determinants. In this study,\n                  <jats:italic>immunaut<\/jats:italic>\n                  delineated three immunophenotypes: (1) CD8 T-cell responders, marked by strong baseline mucosal immunity and extensive prior influenza virus exposure that boosts memory CD8 T-cell responses, without generating influenza virus-specific antibody responses; (2) Mucosal responders, characterized by pre-existing systemic influenza A virus immunity (specifically to H3N2) and stable epithelial integrity, leading to potent mucosal IgA expansions and subsequent seroconversion to influenza B virus; and (3) Systemic, broad influenza A virus responders, who start with relatively naive immunity and leverage greater initial viral replication to drive broad systemic antibody responses against multiple influenza A virus variants beyond those included in the LAIV vaccine. By integrating pathway-level analysis, model-derived contribution scores, and hierarchical decision rules,\n                  <jats:italic>immunaut<\/jats:italic>\n                  elucidates how distinct immunological landscapes shape each response trajectory and how key baseline features, including pre-existing immunity, mucosal preparedness, and cellular support, dictate vaccine outcomes. Collectively, these findings emphasize the power of integrative, predictive frameworks to advance precision vaccinology, and highlight\n                  <jats:italic>immunaut<\/jats:italic>\n                  as a versatile, community-available resource for optimizing immunization strategies across diverse populations and vaccine platforms.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>One-Sentence Summary<\/jats:title>\n                  <jats:p>\n                    Using one of the most comprehensive LAIV datasets compiled to date,\n                    <jats:italic>immunaut<\/jats:italic>\n                    , an integrative machine learning framework, identifies distinct immunophenotypic responder groups shaped by baseline immune landscapes, advancing precision vaccinology and guiding more effective, personalized immunization strategies.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Graphical abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Immunaut<\/jats:italic>\n                    , an automated framework for mapping and predicting vaccine response immunotypes. Step 1 outlines the identification of vaccine response outcomes using pre- and post-vaccination data integration across immune features, including antibodies, flu-specific T-cells, and immunophenotyping at mucosal and systemic sites. Clustering methods define the vaccine response landscape, stability, and validation through t-SNE-based visualization. Step 2 leverages an automated machine learning modeling approach, to enhance the accuracy and interpretability of vaccine response predictions, enabling stratification and targeted intervention strategies for personalized vaccine immunogenicity.\n                  <\/jats:p>\n                  <jats:fig id=\"ufig1\" position=\"float\" orientation=\"portrait\" fig-type=\"figure\">\n                    <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"634302v1_ufig1\" position=\"float\" orientation=\"portrait\"\/>\n                  <\/jats:fig>\n                <\/jats:sec>","DOI":"10.1101\/2025.01.22.634302","type":"posted-content","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T07:20:18Z","timestamp":1737616818000},"source":"Crossref","is-referenced-by-count":0,"title":["Integrative Mapping of Pre-existing Immune Landscapes for Vaccine Response Prediction"],"prefix":"10.64898","author":[{"given":"Stephanie","family":"Hao","sequence":"first","affiliation":[{"name":"Atomic lab, The National Emerging Infectious Diseases Laboratories (NEIDL), Boston University; 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