Organization and operation of metabolic pathways in complex bacterial communities

在高通量测序创新的推动下，新兴的宏基因组学领域已经深刻地改变了我们对复杂微生物群落（微生物组）的理解。然而，虽然对社区组成的兴趣很大，但对这些社区内部的相互作用和相互依赖的了解却很少。如果我们要充分利用宏基因组学在生物修复和生物燃料等生物技术行业应用的新机会，确定和描述这些关系是关键。生物修复涉及应用微生物对被有毒碳氢化合物（如苯）污染的场所进行净化，并为更传统的净化方法提供了经济上可行的替代方案。仅在加拿大，修复污染土壤的市场价值就高达300亿美元。然而，尽管苯降解细菌自然存在于土壤中，为生物修复提供了一条潜在的途径，但人们发现，在更复杂的混合培养物之外培养它们具有挑战性。因此，苯降解细菌可能严重依赖其微生物组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组织和复杂的微生物群落的背景下运作。专注于实验上可调节的苯降解群落，我们将利用这些知识来产生对群落扰动的见解，从而为生物修复提供改进的策略。虽然雄心勃勃，但这项工作建立在基因组注释、代谢建模和宏基因组学的成功基础上。例如，在之前的工作中，我们对各种生物体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通量的主要途径。在补充工作中，我们正在开发创新的软件工具和管道，用于从元转录组数据集阐明微生物群落功能。我们提出了两个主要目标：目的1调查宏基因组数据集，以确定复杂群落的代谢相互依赖性。我们将首先构建计算管道，从下一代测序技术生成的宏基因组数据集中获得稳健的代谢重建。同时，分类信息的整合将使酶的功能归于特定的分类群。将该管道应用于宏基因组数据集，我们将描述各种微生物组中代谢功能的分配，这些微生物组越来越复杂。这项调查将产生跨微生物组代谢途径的组织和操作的第一个全局视图。目的2：苯降解菌群的代谢重建与分析。为了对微生物组内的代谢组织产生更多的机制见解，我们将重点研究一个表征良好的苯降解群落。将生成和处理元基因组和元转录组数据集，以获得该群落的详细代谢重建。然后，基于约束的建模将系统地探索分类群之间的路径通量，并检查引入额外物种对群落结构的影响。这些分析的预测将使用已建立的体内模型进行测试。除了对具有生物修复潜力的群落有更全面的了解外，我们期望这种跨学科的方法将为能够表征任何微生物组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.