Integrative Mapping of Pre-existing Immune Landscapes for Vaccine Response Prediction

Area of Science:

• Immunology
• Vaccinology
• Computational Biology

Background:

• Predicting individual vaccine responses is challenging due to immune system complexity.
• Systems vaccinology requires integrated analysis of multi-omics data for comprehensive understanding.

Purpose of the Study:

• To develop and validate immunaut, an open-source R package for analyzing and predicting immunological outcomes post-vaccination.
• To identify distinct immunophenotypic responder profiles following live attenuated influenza vaccine (LAIV) administration.

Main Methods:

• Utilized a comprehensive dataset from 244 Gambian children vaccinated with LAIV.
• Integrated humoral, mucosal, cellular, transcriptomic, and microbiological parameters.
• Applied dimensionality reduction, clustering, and predictive modeling to identify responder profiles and determinants.

Main Results:

• Delineated three distinct immunophenotypes: CD8 T-cell responders, Mucosal responders, and Systemic broad influenza A virus responders.
• Identified key baseline determinants including pre-existing immunity, mucosal integrity, and prior viral exposure.
• Demonstrated how distinct immunological landscapes shape vaccine response trajectories.

Conclusions:

• Immunaut provides a holistic, data-driven framework for precision vaccinology.
• Understanding baseline immunological features is crucial for predicting vaccine efficacy.
• The immunaut package is a versatile resource for optimizing immunization strategies in diverse populations.