Summary/Abstract Although vaccination is the most effective measure for influenza prevention, there is considerable variation in the responses to influenza vaccines that is influenced by factors such as age, sex, and obesity level. Major advances in predicting and analyzing the cellular and molecular basis of vaccine responses are being made possible by the application of high-dimensional experimental and computational approaches that comprise Systems Vaccinology. This framework is yielding predictive molecular signatures for influenza vaccine immunogenicity and protection. However, there remains a considerable knowledge gap in delineation of cellular and molecular pathways that affect the responses to advanced-generation influenza vaccines in older or obese individuals. To gain new insights into the cellular and molecular states that underlie variation of influenza vaccine responses in older, healthy weight or obese individuals we propose to perform deep molecular and genomic profiling of immune cell states after screening for extreme responders. Our approach, focused on extremes of individual vaccine responses, draws upon successful prior use of such a framework in analyzing genetic basis of extreme phenotypic variability. We propose in Aim 1 to elucidate latent factors and B cell genomic states underlying weak or robust immunogenicity of the advanced-generation seasonal influenza vaccine within healthy weight older individuals using deep molecular profiling and interpretable machine learning as well as computational genomics. Aim 2 will delineate latent factors and infer molecular mechanisms by which obesity distinctively affects influenza vaccine immunogenicity based on high-dimensional and multi-scale profiling of the immune responses as in Aim 1. Uncovering new molecular markers and pathways will spur tailored vaccine design that addresses specific impairments in vulnerable individuals. Our team brings together strong expertise in three complementary and essential disciplines that comprise vaccinology, immunology and systems biology.