The Role of Syntrophic Bacteria in Methanogenic Metabolism in the Human Gut

描述(申请人提供)：人体肠道含有10-100万亿个微生物，这些微生物能够从我们饮食中无法消化的成分(如复杂的植物多糖)中获取营养/能量。合养(合作)代谢，即一种微生物产生另一种微生物生长所需的化合物，或移除抑制代谢反应进展的化合物，对我们的微生物从食物中提取卡路里的效率有很大影响。了解不同的人体肠道细菌如何适应厌氧降解过程的级联代谢相互作用，将有助于将群落组成信息与肠道生物反应器的功能和效率联系起来。这项工作的目标是使用来自人类粪便样本的元基因组(16S核糖体RNA或猎枪)序列和来自培养肠道分离株的基因组序列来预测微生物之间的相互作用，这些相互作用可以通过实验室实验进一步探索/验证。这项工作的重点是细菌与史密斯氏甲烷杆菌之间的相互作用，史密斯甲烷杆菌是人类肠道中最突出的古产甲烷菌。选择产甲烷古菌是因为：1)它们可以通过防止代谢产物如氢的积累来提高细菌发酵的效率；2)它们被认为是一个“基石”物种(即对群落组成和功能的影响比它们的普遍程度更高)；3)它们与其他环境中的特定合养细菌密切相关，例如污泥消化器，但目前还不清楚肠道中是否有类似的细菌。具体目标1的目标是使用来自人类粪便样本的元基因组序列数据来确定其流行率与史密斯分枝杆菌的存在/不存在相关的物种(系统类型)和基因。通过正在进行的肥胖对肠道微生物区系的影响的纵向研究收集的191个样本的初步分析，确定了27个细菌系统型，代表至少3个深层次的细菌谱系，这些细菌谱系似乎保守了导致与史密斯分枝杆菌共存的特征。具体目标2和3追求实验室和计算技术的组合，以确定这些细菌和史密斯分枝杆菌之间的共存是由共生作用还是共同的环境偏好驱动的。一些共同出现的系统类型来自于生物学特性完全未知的未培养血统。特殊目的2将提供关于这些未培养谱系的信息，并通过1)使用荧光原位杂交(FISH)显微镜确定它们是否与史密斯分枝杆菌形成结构复合体，以及2)使用流式细胞术对浓缩的细胞群体进行元基因组测序，来确定共生现象是否由共生驱动。具体目标3通过开发和应用基于代谢重建的技术来预测微生物之间的相互作用，包括合成和代谢生态位收敛，进一步探索共生模式的根本原因。最后，我将使用这一组合信息来设计针对灵知生菌小鼠的验证性实验。这项工作将有助于使用不断增长的人类肠道衍生序列集合来了解特定微生物是否以及如何相互作用，并将提供关于如何促进或阻止肠道中史密斯分枝杆菌活动的见解。这项提议是我对人类微生物组的博士和博士后研究的自然延伸。拟议的研究将进一步发展所需的技能，以实现我的目标，即建立一个拥有计算和实验室组成部分的独立研究小组。生物信息学的工作将我的经验与分析人类肠道的16S rRNA和基因组序列数据结合在一起，并将我的专业知识扩展到新的领域，如代谢网络建模。实验室部分借鉴了我对土壤中微生物进行独立于培养的分析的经验，并扩展了我在鱼类和流式细胞术方面的培训，以便从未培养的微生物谱系中产生基因组信息。在与人类受试者打交道方面的长期培训也将帮助我继续进行人类微生物组研究。我目前是科罗拉多大学博尔德分校Rob Knight博士的博士后，华盛顿大学基因组科学中心的Jeff Gordon博士为我提供了一个实现这些目标的绝佳环境。奈特实验室在开发利用高通量测序技术的进步来分析微生物群落所需的计算工具方面走在了前列，我可以在这个环境中与各种各样的学生、博士后和合作者互动，其中包括具有生物学(具有计算和/或实验室专业知识)、计算机科学(包括高性能计算和数据库设计)和应用数学背景的个人。戈登实验室对肠道微生物群落与营养疾病(肥胖和营养不良)的关联进行了开创性的研究，并应用灵知生菌小鼠模型来了解肠道微生物的相互作用。他们从人类肠道样本中产生大量的序列信息，这是这项拨款中提议的工作的核心。

DESCRIPTION (provided by applicant): The human gut harbors 10-100 trillion microorganisms that enable the harvest of nutrients/energy from otherwise undigestible components of our diet (e.g. complex plant polysaccharides). Syntrophic (cooperative) metabolism, where one microbe produces compounds that the other requires for growth or removes compounds that inhibit the progress of metabolic reactions, has a high impact on the efficiency at which our microbes extract calories from our food. Knowledge of how different human gut bacteria fit within the cascade of metabolic interactions of the anaerobic degradation process will help to relate community composition information to the function and efficiency of the gut bioreactor. The goal of this work is to use both metagenomic (16S ribosomal RNA or shotgun) sequences from human stool samples and genome sequences from cultured gut isolates to predict microbial interactions that can be further explored/verified with laboratory experiments. This work focuses on interactions between bacteria and Methanobrevibacter smithii, the most prominent archaeal methanogen in the human gut. Methanogenic archaea were chosen because 1) they can increase the efficiency of bacterial fermentation by preventing the accumulation of metabolic products such as hydrogen 2) they are thought to be a "keystone" species, (i.e. have a higher influence on community composition and function than their prevalence would suggest) and 3) they closely associate with specific syntrophic bacteria in other environments such as sludge digestors, but whether there is an analog in the gut is not known. The goal of Specific Aim 1 is to use metagenomic sequence data from human stool samples to identify species (phylotypes) and genes whose prevalence are correlated with the presence/absence of M. smithii. The preliminary analysis of 191 samples that were collected through an ongoing longitudinal study of the effects of obesity on the gut microbiota, has identified 27 bacterial phylotypes, representing at least 3 deep bacterial lineages that appear to have conserved the traits that lead to co-occurrence with M. smithii. Specific Aims 2 and 3 pursue a combination of laboratory and computational techniques to determine whether the co- occurrence between these bacteria and M. smithii is driven by syntrophy or by shared environmental preferences. Some of the co-occurring phylotypes are from uncultured lineages whose biological properties are completely unknown. Specific Aim 2 will yield information on these uncultured lineages and determine whether co-occurrence was driven by syntrophy by 1) microscopic determination of whether they form structured complexes with M. smithii using Fluorescence In Situ Hybridization (FISH) and 2) metagenomic sequencing of cell populations that were concentrated using flow cytometry. Specific Aim 3 further explores the underlying cause of co-occurrence patterns by developing and applying metabolic reconstruction-based techniques to predict interactions between microbes, including syntrophy and metabolic niche convergence. Finally, I will use this combined information to design confirmatory experiments in gnotobiotic mice. This work will facilitate the use of the growing collection of human-gut derived sequences to understand whether and how particular microbes interact, and will provide insights as to how to promote or discourage the activity M. smithii in the gut. This proposal is a natural extension of my Ph.D. and post-doctoral studies of the human microbiome. The proposed research will further develop the skills needed to achieve my goal of developing an independent research group with both computational and laboratory components. The bioinformatics work integrates my experience with analysis of 16S rRNA and genomic sequence data from the human gut, and extends my expertise into new areas, such as metabolic network modeling. The laboratory component draws upon my experience in performing culture-independent analysis of microbes in soil, and extends my training in FISH and flow-cytometry, for the generation of genomic information from uncultured microbial lineages. Extended training in working with human subjects will also help me to continue to perform human microbiome research. My current position as a post-doc with Dr. Rob Knight at the University of Colorado at Boulder, and the co-mentoring that I receive from Dr. Jeff Gordon from the Center for Genome Sciences at Washington University provide an excellent environment in which to reach these goals. The Knight lab is on the forefront of generating the computational tools required to utilize advances in high-throughput sequencing for the analysis of microbial communities, and is an environment where I can interact with a diverse collection of students, post-docs, and collaborators including individuals with backgrounds in biology (with computational and/or laboratory expertise), computer science (including high performance computing and database design), and applied math. The Gordon lab performs ground-breaking research on the association of the gut microbial community with diseases of nutrition (obesity and malnutrition) and the application of gnotobiotic mouse models to understanding microbial interactions in the gut. They produce massive amounts of sequence information from human gut samples that is central to the work proposed in this grant.