Systems Immunology

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PROJECT 4 – MATERNAL-INFANT SYSTEMS IMMUNOLOGY – ABSTRACT Infant mortality due to infection accounts for ~20% of the ~3 million neonatal deaths per year worldwide. It is increasingly apparent that a two-pronged strategy can be highly effective in reducing infant mortality: 1) maternal immunization during pregnancy to protect newborns during the first months of life when infection vulnerability is the highest, 2) infant immunizations to provide subsequent early-life immunity. Designing effective vaccines is challenging because the rules for inducing protective immunity are poorly understood. Many factors also contribute to vaccine response variability across individuals and populations, including age, sex, genetics, and pre-existing immunity. In particular, the environment, exposure history, and other variables can establish baseline immune “set points” that impact responses, as we have shown for multiple vaccines in humans that highlighted the importance of the plasmacytoid dendritic cell—Type I Interferon (INF-I) axis as a set point. Vaccine response variability is particularly understudied in pregnant women and infants. Pregnancy and infancy are accompanied by dynamic changes in immune and physiologic parameters that are only beginning to be defined. How these processes impact immune set points including the IFN-I pathway and subsequent innate and adaptive responses to vaccines in the mother and the resulting transferred immunity to infants represent a major knowledge gap; how transferred immunity such as maternal antibodies (Abs) impacts infant set points to shape vaccine responses remains unknown. Addressing these gaps are critical for designing improved vaccines for the maternal-infant dyad. Here, we propose to comprehensively measure the state of single peripheral immune cells before (at baseline) and after vaccination during pregnancy (Aim 1) and infancy (Aim 2) at unprecedented resolution using multi-modal single cell profiling technologies to uncover baseline set point and early-response cellular predictors and determinants of serological outcomes in the maternal-infant dyad. Machine learning will be used to link single-cell phenotypes with “systems serology” parameters measured in Projects 1-3, including Ab responses during pregnancy, the level and repertoire of Abs transferred from mothers to infants, and Ab profiles of infants pre- and post-vaccination. Ab features beyond titers such as glycosylation, subclasses, Fc receptor binding and effector functions will be included in these analyses. Parallel studies and mechanistic dissection in mouse models (funded “in kind” by the NIH Intramural Program) using single cell and spatial tissue imaging approaches will be integrated. The anticipated outcome is the discovery and understanding of transcriptional and epigenetic circuits and phenotypes in immune cells, particularly those along the IFN-I axis, that predict and orchestrate serological responses in the mother-infant dyad. This information will fill in critical knowledge gaps to enable the design of effective vaccines specifically for pregnancy and infancy.