Systems Immunology

项目4 -母婴系统免疫学-摘要 在全世界每年约300万新生儿死亡中，因感染导致的婴儿死亡约占20%。是 越来越明显的是，双管齐下的战略可以非常有效地降低婴儿死亡率： 在怀孕期间进行免疫接种，以保护出生后头几个月的新生儿， 最高，2）婴儿免疫接种，以提供随后的早期生命免疫。设计有效的疫苗是 这是具有挑战性的，因为人们对诱导保护性免疫的规则知之甚少。许多因素也 导致个体和人群之间的疫苗应答变异性，包括年龄、性别、遗传学， 预先存在的豁免权。特别是，环境、暴露历史和其他变量可以建立 影响反应的基线免疫“设定点”，正如我们在人类中使用的多种疫苗所显示的那样， 强调了浆细胞样树突状细胞-I型干扰素（INF-1）轴作为设定点的重要性。 在孕妇和婴儿中，疫苗应答变异性研究得尤其不足。怀孕和婴儿期 伴随着免疫和生理参数的动态变化， 定义了这些过程如何影响免疫设定点，包括IFN-I途径和随后的先天性和 母亲对疫苗的适应性反应以及由此产生的对婴儿的免疫转移代表了一种主要的 知识差距;转移的免疫力，如母体抗体（Abs）如何影响婴儿的设定点， 疫苗的反应仍然未知。解决这些差距对于设计改进的疫苗至关重要， 母婴二分体在这里，我们建议全面测量单个外周免疫细胞的状态 在怀孕期间（目标1）和婴儿期（目标2）接种疫苗前（基线）和接种疫苗后， 使用多模式单细胞分析技术来揭示基线设定点和早期响应 母婴二分体血清学结果的细胞预测因素和决定因素。机器学习将 用于将单细胞表型与项目1-3中测量的“系统血清学”参数联系起来，包括 妊娠期间的抗体反应，从母亲转移到婴儿的抗体水平和库，以及抗体 婴儿接种疫苗前后的情况。抗体功能超出滴度，例如糖基化、亚类、Fc 受体结合和效应子功能将包括在这些分析中。平行研究和机制 使用单细胞和空间组织在小鼠模型中进行解剖（由NIH Intramural Program资助“实物”） 将整合成像方法。预期的结果是发现和理解 免疫细胞中的转录和表观遗传回路和表型，特别是那些沿着IFN-1轴的， 来预测和协调母婴间的血清学反应。这些信息将填补关键 知识差距，使设计有效的疫苗，专门为怀孕和婴儿。

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.