# Systems Immunology

> **NIH NIH U19** · UNIVERSITY OF MARYLAND BALTIMORE · 2022 · $413,384

## Abstract

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 informati...

## Key facts

- **NIH application ID:** 10449298
- **Project number:** 5U19AI145825-02
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** John S Tsang
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $413,384
- **Award type:** 5
- **Project period:** 2021-07-12 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449298

## Citation

> US National Institutes of Health, RePORTER application 10449298, Systems Immunology (5U19AI145825-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10449298. Licensed CC0.

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