Proteogenomic analysis of inflammation and dysbiosis in the infant gut

这个项目的目标是对一种易驯化和自然的- 正在发生的模式生态系统，新生儿肠道，以表征生态失调和 肠道发炎。 目前，我们缺乏对微生物区系和生物群之间关系的机械理解 炎症性疾病，如坏死性小肠结肠炎(NEC)和炎症性肠病。我们也缺乏 用于阐明哪些微生物生化过程在人体肠道中活跃的工具。在过去的十年里，我们 研究小组已经开发出将社区元基因组学与高通量质量相结合的方法 基于光谱的蛋白质组学分析，以菌株级别的分辨率准确识别蛋白质。一个 这一方法的重要组成部分是生物信息学工具的开发，使 整合和分析“经济”数据。最近，我们采用了这些方法来同时测量 时间序列婴儿粪便样本中的人类和微生物蛋白质。 在这里，我们建议检验早产儿肠道炎症是由异常引起的假设。 在微生物群落新陈代谢中。这个项目将利用样本和大量的元基因组数据 这是在NIH对患有和不患有NEC的婴儿的微生物区系进行的一项配套研究中获得的。的具体目标 本项目的目的是：1.研究婴儿出生后1个月肠道微生物群落功能 健康的婴儿，通过确定哪些微生物基因和代谢途径是最重要的 早期定居，特别注意从需氧向厌氧群落代谢的转变。 目的2.研究与肠道炎症相关的人类蛋白质的时间依赖性特征 新生儿的粪便样本，通过评估与肠道有关的人类蛋白质的丰度 微生物蛋白质组变化背景下的动态平衡、炎症和氧化还原生物学。目标3.测试 假设患有NEC的婴儿患炎症是由于延迟过渡到 通过比较人体和微生物蛋白质的时间模式，肠道中的厌氧微生物代谢 在患有和不患有NEC的婴儿中的表达为了确定生物失调是否先于炎症，这项工作将 依靠生物信息学战略分析大型时间序列数据集，这些数据集将部署在 GGKbase，一个新的知识库框架，将促进协作数据分析和共享 与科学界共享“技术”信息。 本研究以发育中的婴儿肠道为模型系统，揭示肠道的一般特征。 微生物群落功能，并阐明功能异常与炎症之间的关系。 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 人体和人体微生物群之间的关系的动力学。 1

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. 1