Integrating time, function, phylogenetics and genome dynamics to redefine microbial diversity

由于DNA测序技术和生物信息学的进步，我们比以往任何时候都更了解微生物和微生物群落。过去30年的工作揭示了地球上每个栖息地中微生物的惊人多样性，研究人员现在正试图将这种多样性与废水反应器和人类肠道等不同环境中的生态系统功能联系起来。了解特定栖息地（微生物组）中的微生物开辟了新的应用，包括减轻人类疾病和修复受污染的场地。 尽管这场“微生物革命”有很大的希望，但研究人员很难深入了解微生物组。微生物物种的显著多样性，在单个环境样本中可以超过1,000种，是理解系统生态学的重大障碍。许多栖息地还包含数百万个不同的微生物基因，这一数字使人类基因组中的20，000 - 25，000个基因相形见绌。然而，最关键的限制因素是，当我们检查微生物群落时，我们不知道要寻找什么样的模式。假设物种等自然分组的方法不可避免地会错过数据中的重要模式，我们需要用新的微生物多样性表示来扩展我们的微生物生物学工具箱。 我的研究项目涉及微生物组的多样性和进化。该提案包括两个互补的项目，将提高我们解构和描述微生物群落的能力。首先，我们将开发新的生态模型，将单个基因视为物种，并将生物体表示为这些基因相互作用的整个生态系统。这种“基因生态学”模型将使我们能够将多样性、扩散和竞争等传统概念映射到微生物基因组的研究中。第二，我们将制定新的生物多样性单位的定义，这些单位的基础是随着时间的推移，在多个栖息地的共同出现，类似的功能和生态作用，以及传统的分类相似性。这些新的定义将产生大量的候选单位，我们将开发新的方法来识别和解释与特定生态问题最相关的候选单位。 微生物基因生态学将更好地了解微生物组中基因产物之间的复杂相互作用，扩大的候选单位集将帮助我们更好地评估微生物群落组成的变化。总之，这些技术将告诉我们微生物群落的哪些成员扮演着哪些角色，群落的不同成员如何相互作用，以及群落如何应对环境的变化。这种理解将是必不可少的，因为我们试图开发微生物解决方案，以解决人类健康和环境中的一系列问题。

Thanks to advances in DNA sequencing technology and bioinformatics, we know more about microorganisms and microbial communities than ever before. Work in the last thirty years has revealed a stunning diversity of microbes in every habitat on Earth, and researchers are now trying to connect this diversity to ecosystem function in settings as varied as wastewater reactors and the human gut. Understanding the microbes in a given habitat (the microbiome) opens up new applications including mitigation of human disease and remediation of contaminated sites. Although this “microbial revolution” has great promise, researchers struggle to understand the microbiome in depth. The remarkable diversity of microbial species, which can exceed 1,000 in a single environmental sample, is a significant barrier to understanding the ecology of a system. Many habitats also contain millions of distinct microbial genes, a number that dwarfs the 20,000-25,000 genes in the human genome. However, the most critical limiting factor is that we don't know what patterns to look for when we examine microbial communities. Methods that presuppose natural groupings such as species inevitably miss important patterns in the data, and we need to expand our microbiomics toolbox with new representations of microbial diversity. My research program is concerned with diversity and evolution of the microbiome. This proposal encompasses two complementary projects that will advance our ability to deconstruct and describe microbial communities. First, we will develop new ecological models that treat individual genes as species, and represent an organism as an entire ecosystem in which these genes interact. This “gene ecology” model will allow us to map traditional concepts such as diversity, dispersal, and competition into the study of microbial genomes. Second, we will develop new definitions of biodiversity units that are based on covariance through time, co-occurrence in multiple habitats, similar functions and ecological roles, as well as traditional taxonomic similarity. These new definitions will produce a prohibitively large number of candidate units, and we will develop new approaches to identify and interpret the candidates that are most relevant to a particular ecological question. Microbial gene ecology will provide a better understanding of the complex interactions between gene products in the microbiome, and an expanded set of candidate units will help us better evaluate changes in microbial community composition. Taken together, these techniques will tell us which members of a microbial community are playing which roles, how different members of the community are interacting with one another, and how communities are likely to respond to changes in their environment. This understanding will be essential as we try to develop microbial solutions to a host of problems in human health and the environment.