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

项目总结 意义：人类乳汁(HM)经过数百万年的进化以滋养和保护人类婴儿--然而 令人惊讶的是，我们对其组成、变异和功能知之甚少。传统上，HM研究一直专注于 在单个HM组件上，HM是一个由数千个组件组成的复杂生物系统 它们相互作用，共同发挥作用。此外，虽然已知HM的组成受母体的影响， 婴儿和环境因素，这些因素了解很少，很少同时进行检查。致信地址 对于这些差距，我们的团队正在倡导一种多组学的系统生物学方法，将HM作为一个“内部系统”来研究 一个系统“，反映出牛奶本身就是一个嵌入”母亲-牛奶-婴儿“三位一体的系统。 方法：这笔赠款将利用并联合两个成熟的HM研究平台来研究HM及其 用一种新的多组学方法对1600名母婴双生子的决定因素和健康影响进行研究。这个 国际牛奶成分(IMIC)联盟是由HM研究人员和数据科学家组成的网络，拥有 建立了多组体HM研究的基础设施。CHILD是一个正在进行的全国怀孕队列3600人 2009-12年出生的儿童。我们的团队已经分析了1600个儿童HM样本中的19种寡糖，28种 脂肪酸和数以百计的细菌。现在，我们将使用新的多组体HM增强富集子数据集 分析(20种营养素、15种非营养性生物活性蛋白和数千种代谢物)和应用 识别离散“乳型”的无监督机器学习方法(目标1)。接下来，我们将利用富人 儿童数据，以确定与乳型成员和/或乳型相关的产妇、婴儿和环境因素 单独的卫生管理组成部分(目标2)。最后，我们将使用机器学习方法来理解HM 婴儿和儿童时期的组成影响微生物群的发育、生长、喘息和过敏 (目标3)。 创新：整合儿童和IMIC平台将促进对HM作为一种 系统内系统，并产生世界上最大和最深表型的母乳婴儿 数据集(n=1600个三联体，具有多组乳汁特征和丰富的母婴纵向元数据)。这 该项目将联合HM专家科学家、著名儿科研究人员和处于前沿的数据科学家 多组学方法开发，将跨学科的MUMI团队置于无与伦比的地位，以使 在这个空间中的新发现，并革命性地研究和理解HM的方式。

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.