New Tools for Understanding the Composition and Dynamics of Microbial Communities

描述(申请人提供)：居住在人体内的微生物数量比人体细胞多一个数量级，影响健康和疾病的许多方面，包括肥胖症、阴道病和克罗恩病。了解这种内源微生物区系正在成为了解人类基因组和遗传变异对健康和疾病的作用的努力的关键扩展。人类微生物组计划(HMP)将使用元基因组测序来描述大量健康个体的微生物群落。因此，迫切需要新的方法来解释序列数据以了解微生物群落的组成和动态。该项目将从生态学到进化生物学再到应用数学的各个学科联合起来，通过评估微生物多样性和变化的衡量标准，并创建所需的群落组成新指标，开发新的方法来了解哪些身体栖息地在微生物群落方面或多或少相似。这将使我们能够理解临床上相关的参数，如年龄、性别或特定身体栖息地的pH值如何影响这些群落，以及个体内微生物群落的变化动态、个体之间的传播以及人与环境之间的传播。该项目直接响应了人类微生物组项目数据计算分析新工具开发路线图RFA。这项建议的具体目标是：目标1.制定、表征和应用丰富的微生物群落多样性描述符。目的2.开发描述人类微生物群落随时间和空间变化的方法。目的3.开发新的方法来追踪生物在不同群落之间的流动。拟议工作的一些关键方面包括：制定估计人体栖息地微生物多样性的新统计方法；制定描述微生物群落多样性的丰富方法；通过大规模模拟并利用现有最大数据集对微生物群落进行详尽的验证，以经验地描述微生物群落的特征；开发利用标记基因和全元基因组数据追踪人体内微生物来源的新方法。关键成果包括帮助确定人类核心微生物群的程度，以及如何最好地对人类微生物多样性进行采样。开发的所有方法都将在开放源码许可证下提供，并将存放在氟氯烃淘汰管理计划数据分析和协调中心(DACC)。调查人员打算与参与氟氯烃淘汰管理计划的其他研究人员密切合作，以确保快速取得进展。公共卫生相关性：与人体相关的微生物群落在人类健康和疾病中发挥关键作用。该项目将提供有助于确定健康人体内微生物群落的性质和变异性的方法。这项工作将以这些人为基线，通过寻找与特定疾病状态相关的异常社区，为研究这些社区影响的广泛医疗条件铺平道路，从而允许开发新的诊断和治疗方法。

DESCRIPTION (provided by applicant): The microbes that inhabit human bodies outnumber the human cells by an order of magnitude, and impact many aspects of health and disease including obesity, vaginosis, and Crohn's disease. Understanding this endogenous microbiota is emerging as a key extension of efforts to understand the human genome and the role of genetic variation on health and disease. The Human Microbiome Project (HMP) will characterize microbial communities in a large number of individual healthy humans using metagenomic sequencing. Consequently, new methods for interpreting sequence data to understand microbial community composition and dynamics are urgently needed. This project unites disciplines ranging from ecology to evolutionary biology to applied mathematics, to develop new methods for understanding which body habitats are more or less similar in terms of their microbial communities, by evaluating measures of microbial diversity and change, and creating needed new metrics of community composition. This will enable understanding of how clinically relevant parameters such as age, sex, or the pH of specific body habitats affect these communities, and of how the dynamics of change in microbial communities within an individual, in transmission between individuals, and in transmission between humans and the environment. This project is directly responsive to the Roadmap RFA for Development of New tools for Computational Analysis of Human Microbiome Project Data. The specific aims of this proposal are: Aim 1. Develop, characterize, and apply enriched descriptors of microbial community diversity. Aim 2. Develop methods for describing how human microbial communities vary over time and space. Aim 3. Develop new methods for tracing the flow of organisms among different communities. Some key aspects of the proposed work are: the development of new statistical methods for estimating microbial diversity within a body habitat; development of enriched methods for describing microbial community diversity; exhaustive validation of methods for comparing microbial communities through large-scale simulations and by using the largest available data sets that characterize microbial communities empirically; and the development of new methods for tracing the sources of the microbes that inhabit the human body using both marker genes and whole-metagenome data. Key outcomes include the ability to help determine the extent to which there is a core human microbiome, and how best to sample human microbial diversity. All methods developed will be made available under open source licenses and will be deposited with the HMP Data Analysis and Coordination Center (DACC). The investigators intend to work closely with other researchers involved in the HMP in order to ensure rapid progress. PUBLIC HEALTH RELEVANCE: Microbial communities associated with the human body play critical roles in human health and disease. This project will provide methods that help establish the nature and variability of microbial communities in healthy human individuals. Using these individuals as a baseline, this work will will pave the way for studies of a wide range of medical conditions that these communities affect by looking for abnormal communities associated with specific disease states, allowing the development of new diagnostics and therapeutic approaches.