New Tools for Understanding the Composition and Dynamics of Microbial Communities

描述（由申请人提供）：栖息在人体内的微生物数量超过人体细胞数量级，并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。了解这种内源性微生物群正在成为了解人类基因组以及遗传变异对健康和疾病的作用的关键延伸。人类微生物组计划（HMP）将使用宏基因组测序来表征大量健康人类个体中的微生物群落。因此，迫切需要解释序列数据以了解微生物群落组成和动态的新方法。该项目结合了从生态学到进化生物学再到应用数学的学科，通过评估微生物多样性和变化的措施，并创建所需的新的群落组成指标，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。这将有助于了解临床相关参数，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动态变化，个体之间的传播以及人类与环境之间的传播。该项目直接响应人类微生物组项目数据计算分析新工具开发路线图RFA。本提案的具体目标是：目标1。开发、表征和应用丰富的微生物群落多样性描述符。目标2.开发描述人类微生物群落如何随时间和空间变化的方法。目标3.开发新的方法来追踪不同社区之间的生物流动。拟议工作的一些关键方面是：制定新的统计方法，用于估计人体生境内的微生物多样性;制定更丰富的方法，用于描述微生物群落多样性;通过大规模模拟和利用现有最大的数据集，以经验为依据，对微生物群落进行比较，从而彻底验证各种方法;以及利用标记基因和整个宏基因组数据追踪人体微生物来源的新方法的开发。关键成果包括帮助确定核心人类微生物组的程度以及如何最好地对人类微生物多样性进行采样的能力。所有开发的方法都将在开源许可下提供，并将存放在HMP数据分析和协调中心（DACC）。研究人员打算与参与HMP的其他研究人员密切合作，以确保快速进展。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起着关键作用。该项目将提供有助于建立健康人类个体中微生物群落的性质和变异性的方法。以这些人为基准，这项工作将为研究这些社区影响的各种医疗状况铺平道路，寻找与特定疾病状态相关的异常社区，从而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.