Proteogenomic analysis of inflammation and dysbiosis in the infant gut

该项目的目标是进行时间序列蛋白质组学分析的一个易于处理的和自然的- 发生模式生态系统，新生儿肠道，以表征生态失调和 肠道炎症 目前，我们缺乏对微生物群与 炎性疾病，如坏死性小肠结肠炎（NEC）和炎性肠病。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。在过去的十年里，我们 一个研究小组已经开发出了将社区宏基因组学与高通量质量 基于光谱的蛋白质组学分析，以菌株级分辨率准确鉴定蛋白质。一个 这种方法的一个重要组成部分是开发生物信息学工具， “omic”数据的整合和分析。最近，我们采用这些方法同时测量 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，炎症在早产儿肠道是由畸变触发 微生物群落代谢中的作用。这个项目将利用样本和大量的宏基因组数据 这是在NIH对患有和没有NEC的婴儿的微生物群的同伴研究中获得的。的具体目标 该项目是：目标1。在出生后的第一个月内表征肠道微生物群落功能， 健康婴儿，通过确定哪些微生物基因和代谢途径是最重要的， 早期定殖，特别注意从有氧代谢到厌氧代谢的转变。 目标2.表征与肠道炎症相关的人类蛋白质的时间依赖性特征， 新生儿粪便样本，通过评估与肠道相关的人类蛋白质丰度， 在微生物蛋白质组变化的背景下，体内平衡、炎症和氧化还原生物学。目标3：测试 假设NEC的婴儿由于延迟过渡到炎症， 厌氧微生物代谢在肠道中，通过比较人类和微生物蛋白质的时间模式 在有和没有NEC的婴儿中表达，以确定微生态失调是否先于炎症。 依靠生物信息学策略来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， “omic”信息与科学界。 本研究使用发育中的婴儿肠道作为模型系统，以揭示肠道的一般特征， 微生物群落功能，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了解 人体和人体微生物组之间关系的动态。 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