Proteogenomic analysis of inflammation and dysbiosis in the infant gut

描述(由申请人提供)：该项目的目标是对一个易于处理和自然发生的模式生态系统--新生儿肠道--进行时间序列蛋白质组学分析，以确定生态失调和肠道炎症之间的联系。在… 目前，我们缺乏对微生物区系与炎症性疾病之间的关系的机械理解，如坏死性小肠结肠炎(NEC)和炎症性肠病。我们也缺乏工具来阐明哪些微生物生化过程在人类肠道中是活跃的。在过去的十年里，我们的研究小组开发了将社区元基因组学与基于高通量质谱学的蛋白质组分析相结合的方法，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，使之能够整合和分析“组学”数据。最近，我们采用这些方法来同时检测时间序列婴儿粪便样本中的人类和微生物蛋白质。在这里，我们建议检验这一假说，即早产儿肠道炎症是由微生物群落代谢异常引发的。该项目将利用NIH对患有和不患有NEC的婴儿的微生物区系进行的配套研究中获得的样本和大量元基因组数据。本项目的具体目标是：目标1.健康婴儿出生后第一个月肠道微生物群落功能的特征 通过确定在早期定居期间哪些微生物基因和代谢途径是最重要的，特别注意从需氧向无氧群落代谢的转变，对婴儿进行监测。目的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的丰度，来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。目的3.通过比较患有和不患有NEC的婴儿体内人类和微生物蛋白表达的时间模式，以确定是否在炎症之前发生了生物失调，来测试患有NEC的婴儿由于肠道向厌氧微生物代谢的延迟转变而患上炎症的假设。这项工作将依赖于生物信息学策略来分析大型时间序列数据集，该策略将部署在GGKbase的背景下，GGKbase是一个新的知识库框架，将促进与科学界的协作数据分析和“基因组”信息的共享。本研究以发育中的婴幼儿肠道为模型系统，揭示肠道微生物群落功能的一般特征，阐明肠道微生物群落功能异常与炎症的关系。我们的结果和我们开发的信息学工具将有助于更好地理解人体和人类微生物群之间的动态关系。

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.