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，因感染死亡的婴儿约占20%。它是 越来越明显的是，双管齐下的战略在降低婴儿死亡率方面可以非常有效：1)产妇 怀孕期间进行免疫接种，以保护新生儿在生命的头几个月感染易感性 最高的，2)婴儿免疫，以提供随后的早期免疫。设计有效的疫苗是 具有挑战性，因为人们对诱导保护性免疫的规则知之甚少。许多因素也 导致疫苗反应在个人和人群中的变异性，包括年龄、性别、遗传和 预先存在的豁免权。特别是，环境、暴露历史和其他变量可以确定 影响反应的基线免疫“设定点”，正如我们在人类多种疫苗中所显示的那样 强调了浆细胞样树突状细胞-I型干扰素(INF-I)轴作为设定点的重要性。 对孕妇和婴儿的疫苗反应可变性研究尤其不足。孕期和婴儿期 伴随着免疫和生理参数的动态变化，这些参数才刚刚开始 已定义。这些过程如何影响免疫设定点，包括干扰素-I途径和随后的先天和 母亲对疫苗的适应性反应和由此导致的婴儿免疫转移是一个主要的 知识差距；母源抗体(Abs)等转移免疫如何影响婴儿设定点形状 疫苗的反应仍不清楚。解决这些差距对于设计改进的疫苗是至关重要的 母婴二分体。在这里，我们建议综合衡量单个外周免疫细胞的状态 在孕期(目标1)和婴儿期(目标2)接种疫苗前(基线)和接种后(目标2)前所未有 使用多模式单细胞分析技术进行解析以发现基线设定点和早期响应 母婴二联体血清学结局的细胞学预测因子和决定因子。机器学习将会 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 孕期抗体反应、母婴转移抗体水平和库及抗体 婴儿接种前和接种后的情况。超出滴度的AB特征，如糖基化、亚类、Fc 受体结合和效应器功能将包括在这些分析中。平行研究和机械论 使用单细胞和空间组织对小鼠模型(由美国国立卫生研究院内部计划资助)进行解剖 成像方法将被整合。预期的结果是发现和理解 免疫细胞的转录和表观遗传回路和表型，特别是沿着干扰素-I轴的那些细胞， 预测和协调母婴二联体的血清学反应。此信息将填写关键字 知识差距，使专门为怀孕和婴儿设计有效疫苗成为可能。

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.