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

乳汁组学：母乳的系统生物学及其与母婴健康的联系

• 批准号: 10531465
• 负责人: Ran Blekhman
• 金额: $ 73.22万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-23 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10531465
• 关键词: Behavioral; Biological; Birth; Body Composition; Cells; Clinical; Cognition; Cohort Studies; Communities; Complex; Data; Data Analyses; Data Set; Development; Diet; Educational workshop; Elements; Exclusive Breastfeeding; Gene Expression; Genetic; Genetic Determinism; Genomics; Goals; Growth; Health Benefit; Human; Human Milk; Individual; Infant; Infant Development; Infant Health; Life; Link; Lipids; Machine Learning; Maternal Health; Maternal Physiology; Metabolic dysfunction; Milk; Milk Substitutes; Modification; Molecular; Molecular and Cellular Biology; Mothers; Multiomic Data; National Institute of Child Health and Human Development; Nutritional Support; Oligosaccharides; Participant; Phenotype; Physical activity; Population; Premature Infant; Recipe; Research; Research Personnel; Sampling; Shapes; Source; Specimen; Structure; System; Systems Biology; Techniques; Time; Triad Acrylic Resin; United States National Institutes of Health; Variant; Visualization software; Woman; Work; base; biological systems; cell type; clinically significant; cognitive testing; cohort; cytokine; data quality; data visualization; donor milk; fecal microbiome; fortification; genetic association; genomic data; gestational weight gain; gut health; gut microbiome; human tissue; infant gut microbiome; innovation; interest; lipidomics; mammary; metabolomics; microbiome research; milk microbiome; multiple omics; next generation; novel; offspring; precision nutrition; single-cell RNA sequencing; statistical and machine learning; transcriptome; web portal

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.

项目摘要 人牛奶承认了许多婴儿的健康益处，但是为此负责的复合元素 益处和基本分子机制为每个婴儿塑造独特的牛奶“食谱” 缺乏。基因组进步率先探测其他人体组织的细胞和分子生物学 提供强大的策略来了解生物系统，但在牛奶研究中未充分利用。 对于人类牛奶研究的新时代，重点是对母亲乳腺细胞基因组学的深入询问， 与母体临床和行为因素的相互作用，在牛奶组成中。此外， 牛奶成分的正常变化尚未确定。概述牛奶正常变化的范围 是下一代人牛奶强化技术的关键，以支持早产儿的营养需求 和其他感兴趣的临床人群。拟议项目的主要目的是生成一个 在健康的母亲及其学期婴儿的背景下，系统级人类牛奶的视野。因此， 拟议的工作直接响应NIH/NICHD RFA-022-020，“作为生物系统的人牛奶”。这 研究利用了400个母亲的表型，利用现有的数据，牛奶和粪便标本 二元组，基于引人注目的初步数据，以识别新的遗传序列变异形成牛奶 基因表达以及牛奶代谢组学，脂质组和微生物差异与婴儿生长的关系 和认知。特定目的1：确定塑造人牛奶基因的母体遗传和临床因素 表达。我们将确定牛奶转录组和评估潜力的新遗传决定剂 通过妊娠体重增加，饮食和其他临床因素来修改这些遗传关联。单细胞 RNA测序将为表征细胞类型组成提供其他必要信息 在人牛奶中。特定目的2：描述普通人牛奶生物系统及其其关键特征 互相互动。该项目扩展了从同类中的现有牛奶法数据，包括牛奶 微生物组，寡糖，代谢组学和脂肪组学，与基因组数据集成 在AIM 1下生产。建立和新颖的机器学习技术将用于表征 这些关键特征之间的相互作用网络和相关结构。特定目标3： 确定牛奶生物系统与婴儿肠道微生物组和健康的变化如何相关。牛奶多' 网络将与婴儿的生长，身体成分和从出生到6的肠道微生物组变化保持一致 上述队列的400个婴儿后代的月份。统计和机器学习技术将 定义与正常婴儿生长和肠道微生物组一致的牛奶系统变异范围 发展。对于150名婴儿的子集，我们还将结合创新的早期生活认知评估 用于探索性分析。最后，我们将开发一个在线门户网站，以可视化所得数据，启用 其他研究人员研究特定特征和感兴趣的关系。