The Multi-Omic Milk (MuMi) Study: Leveraging the IMiC Platform and the CHILD Cohort to study human milk as a biological system and understand its composition, determinants and impacts on child health

项目摘要 意义：母乳（HM）已经进化了数百万年，以滋养和保护人类婴儿-然而， 我们对它的组成、变化和功能知之甚少。传统上，HM的研究集中在 然而，HM是一个复杂的生物系统，包括数千种成分， 它们相互作用并共同发挥作用。此外，虽然已知人乳成分受母体影响， 婴儿和环境因素，这些都是知之甚少，很少同时检查。解决 这些差距，我们的团队正在倡导一种多组学系统生物学方法来研究HM作为一个“系统内 一个系统”，反映牛奶本身是一个系统，嵌入“母乳婴儿”三元组。 方法：这笔赠款将利用和联合两个已建立的HM研究平台，调查HM及其 决定因素和健康影响之间的1600母婴二对使用一种新的多组学方法。的 国际牛奶成分（IMiC）联盟是一个由HM研究人员和数据科学家组成的网络， 为多组学HM研究建立基础设施。CHILD是一个正在进行的3600人的全国妊娠队列 2009-12年出生的孩子。我们的团队已经分析了1600个儿童人乳样品中的19种低聚糖，28 脂肪酸和数百种细菌我们现在将使用新的多组学HM增强丰富的CHILD数据集 分析（20种营养素，15种非营养性生物活性蛋白质和数千种代谢产物）并应用于 无监督的机器学习方法来识别离散的“乳糖型”（目标1）。接下来，我们将杠杆富人 确定与乳型成员资格相关的母亲、婴儿和环境因素的儿童数据和/或 单个HM组件（目标2）。最后，我们将使用机器学习方法来了解HM 组成影响婴儿和儿童时期的微生物组发育，生长，喘息和过敏 (Aim 3）。 创新：整合CHILD和IMiC平台将促进对HM作为一种 系统中的系统，并产生世界上最大和最深的表型母乳婴儿 数据集（n=1600个三联体，具有多组学乳汁特征和丰富的纵向孕产妇和婴儿元数据）。这 该项目将联合专家HM科学家，著名的儿科研究人员和数据科学家， 多组学方法的开发，使跨学科的MuMi团队处于无与伦比的地位， 在这个领域的新发现，并彻底改变了HM的研究和理解方式。

PROJECT SUMMARY Significance: Human milk (HM) has evolved over millions of years to nourish and protect human infants - yet we know surprisingly little about its composition, variation, and function. Traditionally, HM research has focused on individual HM components, yet HM is a complex biological system comprising thousands of components that interact and function in combination. Moreover, while HM composition is known to be affected by maternal, infant, and environmental factors, these are poorly understood and rarely examined simultaneously. To address these gaps, our team is championing a multi-omics systems biology approach to study HM as a “system within a system”, reflecting that milk itself is a system embedded within the “mother-milk-infant” triad. Approach: This grant will leverage and unite two established HM research platforms to investigate HM and its determinants and health impacts among 1600 mother-infant dyads using a novel multi-omic approach. The International Milk Composition (IMiC) Consortium is a network of HM researchers and data scientists with an established infrastructure for multi-omic HM research. CHILD is an ongoing national pregnancy cohort of 3600 children born in 2009-12. Our team has already analyzed 1600 CHILD HM samples for 19 oligosaccharides, 28 fatty acids, and hundreds of bacteria. We will now enhance the rich CHILD dataset with new multi-omic HM analyses (20 nutrients, 15 non-nutritive bioactive proteins and thousands of metabolites) and apply unsupervised machine learning methods to identify discrete ‘lactotypes’ (Aim 1). Next, we will leverage the rich CHILD data to identify maternal, infant and environmental factors associated with lactotype membership and/or individual HM components (Aim 2). Finally, we will use machine learning methods to understand how HM composition influences microbiome development, growth, wheezing and allergies during infancy and childhood (Aim 3). Innovation: Integrating the CHILD and IMiC platforms will facilitate unprecedented research on HM as a system-within-a-system and generate the world’s largest and most deeply-phenotyped mother-milk-infant dataset (n=1600 triads with multi-omic milk profiles and rich longitudinal maternal and infant metadata). This project will unite expert HM scientists, renowned pediatric researchers and data scientists at the forefront of multi-omic methods development, placing the interdisciplinary MuMi team in an unrivaled position to make novel discoveries in this space and revolutionize the way HM is studied and understood.