Predictive Modeling of Multidimensional Anti-Influenza IgG Repertoires

University Of Rochester, Rochester NY

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Abstract

Project Summary Annual immunization against in?uenza infection is one of the largest coordinated international public health ef- forts. Current in?uenza vaccination strategies elicit protection primarily through the generation of long lasting, type-speci?c, neutralizing anti-HA IgG antibodies. A major reason for the success of the seasonal in?uenza vac- cination is that it induces antibodies that bind to molecularly similar in?uenza subtypes. However, we currently lack both high throughput assays and multidimensional analytic methods to rapidly characterize comprehensive individual and population level IgG mediated immunity to a broad range of in?uenza strains, or to predict popula- tion immunity against new strains. The primary goal of this proposal is to develop an innovative multidimensional assay and quantitative modeling framework to characterize population level immunity to a large number of in- ?uenza strains, and predict responses to new in?uenza vaccines. In Aim 1, we will use a novel multiplex assay to measure pre-and post- vaccine anti-HA IgG reactivity against 50 in?uenza strains in 150 subjects. We then will test and validate a predictive model for vaccine induced changes in anti-HA IgG levels. In Aim 2 we will use the data from Aim 1 to estimate changes in population immunity pre- and post-standard seasonal in?uenza vacci- nation. Multidimensional scaling will be used to develop functional immune repertoire cartography, quantitatively modeling both the similarity of subject immune states and a model of vaccine-induced changes in the anti-HA IgG repertoire vector. Successful completion of these Aims will establish a new hybrid experimental-modeling method for predicting in?uenza vaccine responses and estimating population resistance to emerging in?uenza strains.