Predicting Resilience in the Human Microbiome

预测人类微生物组的弹性

• 批准号: 8741929
• 负责人: SUSAN P HOLMES
• 金额: $ 123.04万
• 依托单位: PALO ALTO VETERANS INSTIT FOR RESEARCH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-25 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8741929
• 关键词: Allergic Disease; Antibiotics; Attention; Characteristics; Chemicals; Chronic; Clinical; Clinical Research; Communities; Complex; Data; Data Set; Diet; Dimensions; Disease; Drug Metabolic Detoxication; Ecology; Ecosystem; Event; Exposure to; Future; Gene Expression; Gene Expression Profile; Genes; Genomics; Goals; Health; Human; Human Microbiome; Immune system; Immunity; Individual; Infection; Inflammatory; Intervention; Intestines; Investigation; Life; Machine Learning; Maintenance; Measurement; Medicine; Metabolism; Methods; Microbe; Monitor; Multivariate Analysis; Obesity; Output; Physiology; Play; Predisposition; Procedures; Property; Recovery; Research Design; Role; Sampling; Services; Statistical Methods; Taxon; Techniques; Testing; Time; Validation; abstracting; analytical method; data integration; environmental chemical; expectation; gut microbiota; human subject; innovation; mathematical methods; metabolomics; microbial; microbial community; microbiome; microorganism interaction; novel; nutrition; pathogen; public health relevance; resilience; response; theories; tool; urinary

DESCRIPTION (provided by applicant): Humans have co-evolved with complex, dynamic microbial communities that play essential roles in nutrition, metabolism, immunity, and numerous other aspects of human physiology. Hence, maintenance and recovery of key beneficial services by the microbiota in the face of disturbance is fundamental to health. Yet, stability and resilience vary in, and between individuals, and are poorly understood. Our goal is to identify features of the human microbiome that predict microbial community stability and resilience following disturbance. We propose an innovative large-scale clinical study design that will generate the necessary compositional and functional data from the most relevant ecosystem, i.e., humans! We will develop novel statistical and mathematical methods for data integration (sparse, non-linear multi-table methods), and test existing ecological theories and apply statistical learning strategies to allow data-driven investigation of ecological and clinical properties that determine and predict stability and/or resilience. The breadth and magnitude of this project's impact are significant: We envision tests to predict microbial community responses to disturbance, and procedures to stabilize or restore beneficial microbial interactions as needed. A predictive understanding of the stability and resilience of the gut microbiota will advance the rational practice of medicine. There are three key innovative aspects to our approach: 1) sequential perturbations of different types in a large number of human subjects sampled over time; 2) multiple compositional and functional measurements made on the same samples; and 3) novel data integration methods that incorporate all of the information. Aim 1. Profile the human microbiome before, during and after multiple forms of disturbance. One hundred subjects will each be sampled at 40 time points over a 34 week study period that encompasses two types of perturbation in each subject (dietary shift, and bowel cleansing or antibiotic). From each sample, we will determine taxonomic composition, genomic content, meta-transcriptome, and metabolomic profiles. Aim 2. Discover resilience: Develop non-linear approaches for complex data integration using sparse, multiple-table methods. We will develop a novel sparse, multiple-table approach for data integration and simultaneous analysis of diverse types of complex data over time. Aim 3. Explain resilience: Use statistical learning approaches to find the predictive features that characterize resilience. Using the multiple table approach, we will compare routine unperturbed dynamics within a community to the varied responses to a perturbation, define stable states, and identify common network features characteristic of resilient communities subjected to different forms of disturbance. Finally, we wil use validation techniques to confirm these candidate predictors of community resilience.

描述（由申请人提供）：人类与复杂、动态的微生物群落共同进化，这些微生物群落在营养、代谢、免疫和人体生理学的许多其他方面发挥重要作用。因此，微生物群在面临干扰时维持和恢复关键的有益服务对健康至关重要。然而，稳定性和复原力在个体内部和个体之间存在差异，人们对此知之甚少。我们的目标是确定人类微生物组的特征，这些特征可以预测微生物群落在干扰后的稳定性和恢复力。我们提出了一种创新的大规模临床研究设计，将从最相关的生态系统中生成必要的组成和功能数据，即，人类！ 我们将开发新的统计和数学方法进行数据整合（稀疏，非线性多表方法），并测试现有的生态理论，并应用统计学习策略，以允许数据驱动的生态和临床研究。 确定和预测稳定性和/或弹性的性质。该项目的影响的广度和幅度是显著的：我们设想测试来预测微生物群落对干扰的反应，以及根据需要稳定或恢复有益微生物相互作用的程序。对肠道微生物群的稳定性和弹性的预测性理解将促进医学的合理实践。我们的方法有三个关键的创新方面：1）随着时间的推移，在大量人类受试者中进行不同类型的连续扰动; 2）对相同样本进行多个成分和功能测量; 3）将所有信息整合在一起的新数据集成方法。目标1.在多种形式的干扰之前，期间和之后分析人体微生物组。在34周的研究期内，在40个时间点对100名受试者进行采样，研究期包括每名受试者的两种扰动类型（饮食改变和肠道清洁或抗生素）。从每个样本中，我们将确定分类组成，基因组含量，元转录组和代谢组学概况。目标2.发现弹性：使用稀疏、多表方法开发复杂数据集成的非线性方法。我们将开发一种新的稀疏的多表方法，用于数据集成和同时分析不同类型的复杂数据。目标3.解释弹性：使用统计学习方法来找到表征弹性的预测特征。使用多表的方法，我们将比较常规的未受干扰的动态在一个社区内的扰动的不同的反应，定义稳定状态，并确定共同的网络功能特性的弹性社区受到不同形式的干扰。最后，我们将使用验证技术来确认这些候选预测社区弹性。