Proteogenomic analysis of inflammation and dysbiosis in the infant gut

婴儿肠道炎症和生态失调的蛋白质组学分析

• 批准号: 8802879
• 负责人: Jillian Banfield
• 金额: $ 37.34万
• 依托单位: UT-BATTELLE, LLC-OAK RIDGE NATIONAL LAB
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-06 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8802879
• 关键词: Adult; Aerobic; Anti-Inflammatory Agents; Anti-inflammatory; Attention; Biochemical Process; Bioinformatics; Biological Markers; Biological Models; Biology; Carbon; Communities; Companions; Crohn' s disease; Data; Data Analyses; Data Set; Development; Disease; Ecosystem; Epithelial; Fermentation; Generations; Genes; Goals; Health; Human; Human Microbiome; Human body; Hydrogen; Immune response; Infant; Inflammation; Inflammatory; Inflammatory Bowel Diseases; Inflammatory Response; Inflammatory disease of the intestine; Informatics; Intestinal Diseases; Intestines; Investigation; Life; Link; Maintenance; Mass Spectrum Analysis; Measurement; Measures; Metabolic Pathway; Metabolism; Metagenomics; Methods; Microbe; Modeling; Monitor; Natural Immunity; Necrotizing Enterocolitis; Newborn Infant; Nitrogen; Onset of illness; Organism; Oxidation-Reduction; Pathogenesis; Pathway interactions; Pattern; Premature Infant; Production; Proteins; Proteome; Proteomics; Research; Resolution; Respiration; Sampling; Series; Study models; Sulfur Metabolism Pathway; System; Testing; Time; United States National Institutes of Health; Virulence Factors; Volatile Fatty Acids; Work; base; improved; interest; intestinal homeostasis; knowledge base; link protein; microbial; microbial colonization; microbial community; microbiome; novel; protein expression; tool

DESCRIPTION (provided by applicant): The goal of this project is to conduct time-series proteomic analyses of a tractable and naturally- occurring model ecosystem, the newborn intestinal tract, in order to characterize links between dysbiosis and intestinal inflammation. At present, we lack a mechanistic understanding of the relationships between the microbiota and inflammatory disorders such as necrotizing enterocolitis (NEC) and inflammatory bowel disease. We also lack tools to clarify which microbial biochemical processes are active in the human gut. Over the past decade, our research group has developed methods to pair community metagenomics with high-throughput mass spectrometry-based proteomic analyses to accurately identify proteins with strain-level resolution. An important component of this approach has been the development of bioinformatics tools that enable the integration and analysis of "omic" data. Recently, we adapted these methods to simultaneously measure human and microbial proteins in time series infant fecal samples. Here, we propose to test the hypothesis that inflammation in the premature infant gut is triggered by aberrations in microbial community metabolism. This project will leverage samples and a large amount of metagenomic data obtained in a companion NIH study of the microbiota in babies with and without NEC. The specific Aims of this project are: Aim 1. Characterize gut microbial community function during the first month of life in healthy infants, by determining which microbial genes and metabolic pathways are most important during early colonization, with specific attention to the transition from aerobic to anaerobic community metabolism. Aim 2. Characterize time-dependent signatures of human proteins linked to intestinal inflammation in fecal samples from newborn infants, by evaluating the abundances of human proteins linked to intestinal homeostasis, inflammation, and redox biology in the context of changes in the microbial proteome. Aim 3. Test the hypothesis that babies with NEC developed inflammation as a consequence of a delayed transition to anaerobic microbial metabolism in the gut, by comparing temporal patterns of human and microbial protein expression in babies with and without NEC to determine if dysbiosis precedes inflammation This work will rely on a bioinformatics strategy for analysis of large time series datasets that will deployed in the context of GGKbase, a novel knowledgebase framework that will facilitate collaborative data analysis and sharing of "omic" information with the scientific community. This research uses the developing infant gut as a model system to uncover general features of gut microbial community function, and to clarify the relationships between aberrant function and inflammation. Our results and the informatics tools that we develop will contribute to an improved understanding of the dynamics of the relationship between the human body and the human microbiome.

描述（由申请人提供）：本项目的目标是对易处理的自然发生的模型生态系统新生儿肠道进行时间序列蛋白质组学分析，以表征生态失调和肠道炎症之间的联系。 在 目前，我们缺乏对微生物群与炎症性疾病如坏死性小肠结肠炎（NEC）和炎症性肠病之间关系的机制理解。我们也缺乏工具来澄清哪些微生物生化过程在人类肠道中是活跃的。在过去的十年中，我们的研究小组已经开发出将社区宏基因组学与基于高通量质谱的蛋白质组学分析配对的方法，以准确地识别具有应变水平分辨率的蛋白质。这一方法的一个重要组成部分是开发生物信息学工具，以便能够整合和分析“组学”数据。最近，我们采用这些方法来同时测量时间序列婴儿粪便样品中的人类和微生物蛋白质。 在这里，我们建议测试的假设，即炎症在早产儿肠道是由微生物群落代谢的畸变。该项目将利用样本和大量的宏基因组数据，这些数据是在NIH对患有和不患有NEC的婴儿的微生物群的研究中获得的。该项目的具体目标是：目标1。在健康婴儿出生后的第一个月内表征肠道微生物群落功能 婴儿，通过确定哪些微生物基因和代谢途径是最重要的早期殖民，特别注意从有氧到厌氧社区代谢的过渡。目标2.通过在微生物蛋白质组变化的背景下评估与肠道稳态、炎症和氧化还原生物学相关的人类蛋白质丰度，表征新生儿粪便样本中与肠道炎症相关的人类蛋白质的时间依赖性特征。目标3.通过比较有和没有NEC的婴儿中人类和微生物蛋白质表达的时间模式，以确定微生态失调是否先于炎症，测试NEC婴儿由于肠道中厌氧微生物代谢的延迟过渡而发生炎症的假设。这项工作将依赖于生物信息学策略，用于分析将在GGKbase背景下部署的大型时间序列数据集，一个新的知识库框架，将促进协作数据分析和与科学界分享“omic”信息。 本研究以发育中的婴儿肠道为模型系统，揭示肠道微生物群落功能的一般特征，并阐明异常功能与炎症之间的关系。我们的研究结果和我们开发的信息学工具将有助于更好地理解人体与人体微生物组之间关系的动态。