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

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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.