Chronology, Maternal Determinants, and Impact of Feeding Mode on Human Milk: A Systems Biology and Ecological Approach

母乳(HM)是一种“活组织”：其组成随时间变化，包括昼夜波动， 满足婴儿不断变化的需求，并对婴儿的“生物钟”和生理学进行编程。尽管 它的特殊重要性，对母体HM的时间生物学和系统生物学知之甚少 影响这一生物学的因素，以及其对正常婴儿发育做出贡献的机制。 婴儿的输入(通过哺乳)如何影响HM生物学也是未知的，这是一个关键的问题，因为几乎 饲喂的普遍做法表示(即：泵送)HM。因此，我们的总体假设是，HM是一个 具有明显的日变化和经向变化的生态系统，受母体固定的 和可修改的因素，以及婴儿的输入，导致不同的HM生物动力学，取决于是否 婴儿主要是母乳喂养，而不是表达的HM。我们的HM研究人员和跨学科团队 计算生物学家将收集每日纵向和全天的HM样本，并从120个样本中收集大量元数据 只在产后1-4个月期间给健康婴儿喂食HM的母亲：主要是60名母亲 喂养ATB组(ATB组)和单独表达HM的母亲60例(Express组)。多元组学分析 平台将用于表征HM激素(胰岛素、瘦素、皮质醇)、大量营养素、微生物区系、 低聚糖、细胞因子和免疫球蛋白，将HM作为一个生态系统进行研究。这部新奇的高- 分辨率采样与尖端的分析和建模技术相结合，支持这些目标： 1)检测HM组成的日变化和纵向变化，并进行比较 ATB和EXPRESS组之间的轨迹。时间序列模型和最新分析 将利用各种方法来推断潜在的HM动力学和识别时间轨迹，这是第一次， 确定HM组成中的纵向和24小时时间模式。然后我们将确定这些 当婴儿缺乏进入HM系统的生物反馈时，通过比较以下轨迹，轨迹会有所不同 ATB VS Express组。 2)确定HM组成和时间模式的母体决定因素。除了……之外 将利用社会人口和健康因素、手机应用程序和可穿戴设备来捕获 关于HM表达模式、母亲饮食、压力、体力活动和睡眠模式的细粒度数据。这些 将通过贝叶斯监督与随时间共同变化的HM组件的“分支”相链接 机器学习算法。该算法将区分孕妇因素和母体因素之间的关系 HM的组成和动态在ATB和Express组之间有所不同。 这项工作将阐明HM对婴儿健康和发育的潜在贡献机制，允许 对喂养ATB或表达的HM的婴儿和依赖于 表达了HM。这项研究也为科学界提供了一种变革性的未来资源。

Human milk (HM) is a “live tissue”: its composition changes over time including diurnal fluctuations, answering the evolving needs of the infant and programming infants' “biological clock” and physiology. Despite its extraordinary importance, little is known about the chronobiology and system biology of HM, the maternal factors that impact this biology, and the mechanisms that underlie its contribution to normal infant development. It is also unknown how infant input (via suckling) influences HM biology, a crucial question given the almost universal practice of feeding expressed (ie: pumped) HM. As such, our over-arching hypothesis is that HM is an ecological system that harbors distinct diurnal and longitudinal variation that is impacted by both maternal fixed and modifiable factors, and by infant input, resulting in different HM biological dynamics depending on if the infant is primarily fed at-the-breast vs expressed HM. Our interdisciplinary team of HM researchers and computational biologists will collect daily longitudinal and diurnal HM samples, and extensive metadata from 120 mothers exclusively feeding HM to their healthy infants between 1-4 months postpartum: 60 mothers primarily feeding ATB (ATB group) and 60 mothers exclusively expressing HM (Express group). Multi-omics analytical platforms will be used to characterize HM hormones (insulin, leptin, cortisol), macronutrients, microbiota, oligosaccharides, cytokines, and immunoglobulins to study HM as an ecological system. This novel high- resolution sampling combined with cutting-edge analytical and modeling techniques power these aims: 1) Detect temporal changes in HM composition diurnally and longitudinally and compare these trajectories between ATB vs express groups. Time-series models and state-of-the-art analytic approaches will be utilized to infer latent HM dynamics and identify temporal trajectories, for the first time, identifying longitudinal and 24-hour temporal patterns in HM composition. We will then determine how these trajectories differ when infant “biofeedback” into the HM system is lacking by comparing trajectories between ATB vs Express Groups. 2) Identify maternal determinants of HM composition and temporal patterns. In addition to sociodemographic and health factors, mobile phone apps and wearable devices will be utilized to capture granular data on HM expression patterns, maternal diet, stress, physical activity and sleep patterns. These will be linked with “clades” of HM components that co-vary together over time via Bayesian supervised machine learning algorithms. The algorithm will distinguish if relationships between maternal factors and HM composition and dynamics differs between ATB vs Express groups. This work will elucidate mechanisms underlying HM contribution to infant health and development, allowing for optimized feeding practices for infants fed ATB or expressed HM, and premature infants dependent on expressed HM. This study also represents a transformative future resource for the scientific community.