Identifying population-level variation in cross-sectional and longitudinal HMP st

描述（由申请人提供）：在理解人类微生物组结构中的个体内和个体间变异如何影响人类表型的重要性方面存在根本性差距。这种差距的持续存在是有问题的，因为它妨碍了将这种变化与宿主健康变化联系起来的能力。部分问题在于过度依赖微生物群范围内的相似性指标，而不是基于人群的指标。这类似于使用微阵列技术来比较E.大肠杆菌基因在指数期与稳定期的表达，而不解决单个基因表达的变化。然而，缺乏一个定量框架来帮助分析来自横截面和纵向研究的人口数据。长期目标是了解塑造人体微生物组结构和功能的机制。该提案的目的是开发强大的计算工具，这些工具经过优化以分析大型序列集合，但对于不是生物信息学专家的典型研究人员来说是可以访问的。具体而言，该提案将满足开发计算工具的需求，使HMP科学家能够确定“某一地点微生物组的变异是否与人类表型有关，例如疾病。“这项提议将开发强大的计算工具，这些工具经过优化，可以分析大型序列集合，但对于不是生物信息学专家的典型研究人员来说，这些工具是可以访问的。这一提议的理由是即将发布来自一些HMP示范项目的数据，这些项目正在进行横截面和纵向采样，但已经意识到他们在确定微生物组的特定变化与人类表型之间的统计学可靠联系方面的能力有限。基于先前与HMP研究者的广泛经验和互动，该目标将通过追求三个具体目标来实现：1）在mothur软件包中实施和传播计算工具; 2）开发将微生物组中的受试者间变化与健康变化相关联的工具;以及3）开发将微生物组的动态与健康变化相关联的工具。在拟议的研究中开发的每个工具将使用模拟数据进行验证，并使用HMP生成的序列数据进行评估。这项研究是创新的，因为它建立在一个已经强大的工具集合，用于描述流行的mothur软件包中的社区“零件清单”，并将创建一个强大的统计工具集，用于评估时间变化以及这种变化如何与健康相关。这项研究意义重大，因为它将通过将社区和人口水平的动态与人类健康的变化联系起来，提高我们推进HMP目标的能力。 公共卫生相关性：拟议的研究与公共卫生有关，因为允许将微生物组的变化与健康联系起来的工具的产生将使科学家能够确定微生物组中负责肥胖，细菌性阴道病，肠易激综合征和癌症等疾病的微生物种群。因此，拟议的研究与NIH的使命有关，即促进创新研究战略，提高国家预防和治疗疾病的能力。

DESCRIPTION (provided by applicant): There is a fundamental gap in understanding the significance of how intra- and inter-personal variation in the structure of the human microbiome affects human phenotypes. Continued existence of this gap is problematic because it impedes the ability to relate this variation with changes in host health. Part of the problem is the over-reliance on microbiome-wide metrics of similarity instead of population-based metrics. This is similar to using microarray technology to compare the overall differences of E. coli gene expression in exponential versus stationary phase without addressing the change in expression of individual genes. Yet, a quantitative framework to aid in the analysis of population data from cross- sectional and longitudinal studies is lacking. The long-term goal is to understand the mechanisms that shape the structure and function of the human microbiome. The objective of this proposal is to develop robust computational tools that are optimized to analyze large sequence collections, yet are accessible to the typical investigator that is not an expert in bioinformatics. Specifically, this proposal will fulfill the stated need to develop computational tools that enable HMP-scientists to determine whether "variation in the microbiome at a site can be related to human phenotypes, such as disease." This proposal will develop robust computational tools that are optimized to analyze large sequence collections, yet are accessible to the typical investigator that is not an expert in bioinformatics. The rationale for this proposal is the imminent release of data from a number of HMP Demonstration Projects that are pursuing cross-sectional and longitudinal sampling, but have realized that they are limited in their ability to identify statistically robust linkages between specific changes in the microbiome with human phenotypes. Building upon extensive previous experience and interactions with HMP investigators, the objective will be achieved by pursuing three specific aims: 1) implement and disseminate computational tools in the mothur software package; 2) develop tools to correlate inter- subject variation in the microbiome with variation in health; and 3) develop tools to connect the dynamics of the microbiome with changes in health. Each of the tools developed in the proposed research will be validated using simulated data and evaluated using HMP-generated sequence data. This research is innovative because it builds upon an already strong collection of tools for describing a community's "parts list" within the popular mothur software package and will create a robust set of statistical tools for assessing temporal variation and how that variation is related to health. The proposed research is significant because it will advance our ability to advance the goals of the HMP by relating community and population-level dynamics to changes in human health. PUBLIC HEALTH RELEVANCE: The proposed research is relevant to public health because the generation of tools that allow one to link changes in the microbiome to health will allow scientists to identify microbial populations within the microbiome that are responsible for diseases such as obesity, bacterial vaginosis, irritable bowel disorders, and cancer. Therefore, the proposed research is relevant to the part of the NIH's mission related to fostering innovative research strategies that improve the nation's ability to prevent and treat disease.