Milk-Omics: Systems Biology of Human Milk and Its Links to Maternal and Infant Health

项目摘要 人乳赋予婴儿许多健康益处，但负责这些益处的组成元素 为每个婴儿塑造独特牛奶“配方”的基本分子机制仍然存在， 缺乏基因组学的进步开创了探索其他人体组织的细胞和分子生物学 提供了强大的策略来了解生物系统，但在牛奶研究中未得到充分利用。是时候 对于母乳研究的新时代，重点是对母体乳腺细胞基因组学的深入研究， 与母亲的临床和行为因素的相互作用，在塑造牛奶成分。此外， 奶成分的正常变化尚待确定。概述牛奶中标准偏差的范围 是下一代母乳强化技术的关键，以支持早产儿的营养需求 和其他感兴趣的临床人群。拟议项目的主要目标是 在健康母亲及其足月婴儿的背景下，从系统层面看待母乳。因此， 建议的工作直接响应NIH/NICHD RFA-022-020，“人乳作为一个生物系统”。的 一项研究利用了来自400名母婴的丰富表型队列的现有数据、乳汁和粪便标本， 是基于令人信服的初步数据确定新的基因序列变异塑造牛奶 基因表达，以及牛奶代谢组学、脂质组学和微生物组学变化与婴儿生长的关系 和认知。具体目标1：确定塑造母乳基因的母体遗传和临床因素 表情我们将确定牛奶转录组的新的遗传决定因素， 妊娠期体重增加、饮食和其他临床因素对这些遗传关联的修饰。单细胞 RNA测序将为细胞类型组成的表征提供额外的必要信息 在人奶中。具体目标2：描述标准人乳生物系统的关键特征及其 相互影响。该项目扩展了现有的牛奶组学数据，包括牛奶 微生物组学、低聚糖、代谢组学和脂质组学，与基因组数据相结合 根据目标1生产。已建立和新颖的机器学习技术将用于表征 人乳的这些关键特征之间的相互作用网络和相关结构。具体目标3： 确定牛奶生物系统如何与婴儿肠道微生物组和健康的变化相关。牛奶多'omic 网络将与婴儿的生长，身体组成和肠道微生物组的变化，从出生到6 来自上述队列的400名婴儿后代的月龄。统计和机器学习技术将 确定与正常婴儿生长和肠道微生物组一致的乳汁系统变化范围 发展对于150名婴儿的子集，我们还将纳入创新的早期生活认知评估 进行探索性分析。最后，我们将开发一个在线门户，用于可视化结果数据， 其他研究人员调查感兴趣的具体特征和关系。

PROJECT SUMMARY Human milk confers numerous infant health benefits, yet the compositional elements responsible for these benefits and the fundamental molecular mechanisms shaping the unique milk “recipe” for each infant remains lacking. Genomic advances pioneered to probe the cellular and molecular biology of other human tissues provide powerful strategies to understand biological systems, but are underutilized in milk research.. It is time for a new era of human milk research focused on deep interrogation of maternal mammary cell genomics, in interaction with maternal clinical and behavioral factors, in shaping milk composition. Furthermore, the scope of normal variation in milk composition is yet to be established. Outlining the scope of normative variation in milk is key to next-generation human milk fortification techniques to support the nutritional needs of preterm infants and other clinical populations of interest. The primary objective of the proposed project is to generate a systems-level view of human milk in the context of healthy mothers and their term infants. As such, the proposed work is directly responsive to NIH/NICHD RFA-022-020, “Human Milk as a Biological System”. The study leverages existing data, milk, and fecal specimens from a richly phenotyped cohort of 400 mother-infant dyads, and is based on compelling preliminary data identifying novel genetic sequence variation shaping milk gene expression, and relationships of milk metabolomic, lipidomic, and microbiomic variation to infant growth and cognition. Specific Aim 1: to identify maternal genetic and clinical factors that shape human milk gene expression. We will identify novel genetic determinants of the milk transcriptome and assess potential modification of these genetic associations by gestational weight gain, diet, and other clinical factors. Single-cell RNA-sequencing will provide additional necessary information for characterization of the cell type composition within human milk. Specific Aim 2: to describe key features of the normative human milk biosystem and their interactions with one another. The project expands on existing milk omics data from the cohort, including milk microbiomes, oligosaccharides, metabolomics, and lipidomics, to be integrated with the genomic data produced under Aim 1. Established and novel machine learning techniques will be used to characterize interaction networks and correlational structures among these key features of human milk. Specific Aim 3: to establish how the milk biosystem is related to variation in infant gut microbiomes and health. Milk multi-‘omic networks will be aligned with infant growth, body composition, and gut microbiome variation from birth to 6 months in the 400 infant offspring from the above cohort. Statistical and machine learning techniques will define the scope of milk system variation consistent with normal infant growth and gut microbiome development. For a subset of 150 infants, we will also incorporate innovative early life cognitive assessments for exploratory analyses. Finally, we will develop an online portal for visualizing the resulting data, enabling other researchers to investigate specific features and relationships of interest.