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.

项目概要 母乳对婴儿健康有许多益处，但造成这些益处的成分元素 为每个婴儿塑造独特的牛奶“配方”的好处和基本分子机制仍然存在 基因组学的进步率先探索了其他人体组织的细胞和分子生物学。 提供了理解生物系统的强大策略，但在牛奶研究中尚未得到充分利用。是时候了 母乳研究的新时代专注于母乳细胞基因组学的深入研究， 此外，母乳塑造成分与母亲临床和行为因素的相互作用。 牛奶成分的正常变异尚未确定牛奶的规范变异范围。 是下一代母乳强化技术的关键，可满足早产儿的营养需求 和其他感兴趣的临床人群 拟议项目的主要目标是产生一个 在健康母亲及其足月婴儿的背景下对母乳的系统层面的看法。 拟议的工作直接响应 NIH/NICHD RFA-022-020，“母乳作为生物系统”。 研究利用了来自 400 名母婴的丰富表型队列的现有数据、牛奶和粪便样本 成对的，并且基于令人信服的初步数据，识别出塑造牛奶的新基因序列变异 基因表达以及乳汁代谢组、脂质组和微生物组变异与婴儿生长的关系 具体目标 1：确定影响母乳基因的母亲遗传和临床因素。 我们将鉴定牛奶转录组的新遗传决定因素并评估潜力。 妊娠体重增加、饮食和其他单细胞因素对这些遗传关联的改变。 RNA 测序将为表征细胞类型组成提供额外的必要信息 具体目标 2：描述规范母乳生物系统的主要特征及其 该项目扩展了队列中现有的牛奶组学数据，包括牛奶。 微生物组、寡糖、代谢组学和脂质组学，与基因组数据整合 根据目标 1 制作。已建立的新颖的机器学习技术将用于表征 母乳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： 确定牛奶生物系统与婴儿肠道微生物组和健康的变化之间的关系。 网络将与婴儿生长、身体成分和从出生到 6 岁的肠道微生物组变化保持一致 统计和机器学习技术将在上述队列的 400 名婴儿后代中进行几个月的研究。 定义与正常婴儿生长和肠道微生物组一致的乳汁系统变异范围 对于 150 名婴儿的子集，我们还将纳入创新的早期生命认知评估。 最后，我们将开发一个在线门户来可视化结果数据，从而实现 其他研究人员调查特定特征和兴趣关系。