Organization and operation of metabolic pathways in complex bacterial communities

在高通量测序创新的推动下，新生的元基因组学领域已经深刻地改变了我们对复杂微生物群落(微生物群)的理解。然而，尽管许多人的兴趣集中在社区组成上，但关于这些社区内的相互作用和相互依存关系的了解要少得多。如果我们要充分利用生物修复和生物燃料等生物技术行业中元基因组学应用的新机会，识别和描述这些关系是关键。生物修复涉及应用微生物来净化被苯等有毒碳氢化合物污染的场所，并提供了一种经济上可行的替代更传统的净化方法。仅在加拿大，修复受污染土壤的市场被认为价值300亿美元。然而，虽然自然存在于土壤中的苯降解菌为生物修复提供了一条潜在的途径，但它们被发现对在更复杂的混合培养物外培养具有挑战性。因此，出现了一种新的范例，即苯降解菌可能严重依赖其微生物组的成员来提供必要的代谢功能。 在这里，我们提出了一个研究计划，旨在阐明代谢活动是如何在复杂的微生物群落中组织和运作的。聚焦于一个实验上可降解苯的群落，我们将利用这一知识来洞察群落的扰动，为生物修复提供改进的策略。尽管雄心勃勃，但这项工作建立在基因组注释、代谢建模和元基因组学方面的既定成功的基础上。例如，在以前的工作中，我们对各种生物的代谢能力进行了系统的调查，并采用了复杂的数学建模框架来确定代谢流量的主要途径。在补充工作中，我们正在开发创新的软件工具和管道，用于从元转录数据集阐明微生物群落功能。我们提出两个主要的目标： 目的1调查元基因组数据集，以确定代谢与复杂群落的相互依赖关系。我们将首先构建计算管道，从通过下一代测序技术生成的元基因组数据集中获得稳健的代谢重建。同时，分类学信息的整合将允许将酶功能归因于特定的类群。将这条管道应用于元基因组数据集，我们将表征代谢功能在各种日益复杂的微生物群中的划分。这项调查将首次对跨微生物组的代谢途径的组织和运行进行全球观察。 目的2苯降解菌群落的代谢重建与分析。为了对微生物体内新陈代谢的组织产生更多的机械性见解，我们将专注于一个具有良好特征的苯降解群落。将生成并处理元基因组和元翻译数据集，以得出群落的详细新陈代谢重建。然后，基于约束的建模将系统地探索分类群之间的途径通量，并研究引入更多物种对群落结构的影响。来自这些分析的预测将使用已建立的体内模型进行测试。 除了获得对具有生物修复潜力的群落的更全面的了解外，我们预计这种跨学科的方法将为能够表征任何微生物组的代谢潜力的新的快速通道策略奠定基础。

The nascent field of metagenomics, driven by innovations in high-throughput sequencing, has profoundly transformed our understanding of complex microbial communities (microbiomes). However, while much interest has focused on community composition, much less is known concerning the interactions and co-dependencies within these communities. Identifying and characterizing these relationships is key if we are to fully exploit emerging opportunities for metagenomics applications in biotechnology industries such as bioremediation and biofuels. Bioremediation involves the application of microbes to decontaminate sites polluted with toxic hydrocarbons such as benzene and offers an economically viable alternative to more traditional decontamination methods. In Canada alone, the market for the remediation of contaminated soil is thought to be worth $30 billion. However, while benzene degrading bacteria naturally present in soil, offer a potential route for bioremediation, they have been found challenging to culture outside more complex mixed cultures. Consequently there is an emerging paradigm that benzene degrading bacteria may be critically dependent on members of their microbiome to provide essential metabolic functions. Here we propose a program of research aimed at elucidating how metabolic activities are organized and operate within the context of complex microbial communities. Focusing on an experimentally amenable benzene degrading community, we will exploit this knowledge to yield insights into community perturbations that offer improved strategies for bioremediation. Although ambitious, this work builds on established successes in genome annotation, metabolic modeling and metagenomics. For example, in previous work, we have performed systematic surveys of the metabolic capacities for a variety of organisms and adopted sophisticated mathematical modeling frameworks to identify major routes of metabolic flux. In complementary work, we are developing innovative software tools and pipelines for elucidating microbial community function from meta-transcriptomic datasets. We propose two major objaectives: OBJECTIVE 1 Survey metagenomic datasets to identify metabolic co-dependencies with complex communities. We will first construct computational pipelines that derive robust metabolic reconstructions from metagenomic datasets generated through next generation sequencing technology. At the same time, integration of taxonomic information will allow enzymatic functions to be ascribed to specific taxa. Applying this pipeline to metagenomic datasets, we will characterize the partitioning of metabolic functions across a variety of microbiomes of increasing complexity. This survey will yield a first global view of the organization and operation of metabolic pathways across microbiomes. OBJECTIVE 2 Metabolic reconstruction and analysis of a benzene degrading community. To yield more mechanistic insights into the organization of metabolism within a microbiome, we will focus on a well characterized benzene degrading community. Metagenomic and metatranscriptomic datasets will be generated and processed to derive a detailed metabolic reconstruction of the community. Constraints based modeling will then systematically explore pathway fluxes between taxa and examine the impact of introducing additional species on community structure. Predictions from these analyses will be tested using an established in vivo model. In addition to deriving a more complete understanding of a community with bioremediation potential, we expect this interdisciplinary approach will lay the foundation for new fast-track strategies capable of characterizing the metabolic potential of any microbiome.