权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10709555
负责人：
Ran Blekhman
金额：
$ 66.68万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
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/10709555
关键词：
Acceleration Behavioral Biological Birth Body Composition Cells Cellular biology Clinical Cognition Cohort Studies Communities Complex Data 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 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 United States National Institutes of Health Variant Visualization Visualization software Woman Work 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

项目摘要

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.

项目总结