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

合养细菌在人类肠道产甲烷代谢中的作用

• 批准号: 8655670
• 负责人: Catherine Lozupone
• 金额: $ 11.75万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8655670
• 关键词: Acetates; Affect; Alcohols; Archaea; Area; Automobile Driving; Bacteria; Biochemical Pathway; Bioinformatics; Biological; Biology; Bioreactors; Calories; Carbon Dioxide; Cells; Collection; Colorado; Communities; Complex; Computational Technique; Data; Diet; Disease; Doctor of Philosophy; Environment; Environmental sludge; Feces; Fermentation; Flow Cytometry; Fluorescent in Situ Hybridization; Food; Formates; Generations; Genes; Genome; Genomics; Gnotobiotic; Goals; Grant; Growth; Harvest; Health; High Performance Computing; Human; Human Genome; Human Microbiome; Hydrogen; Individual; Individual Differences; Knowledge; Laboratories; Lead; Link; Longitudinal Studies; Malnutrition; Mentors; Metabolic; Metabolism; Metagenomics; Methanobacteria; Methanobrevibacter; Microbe; Microscopic; Modeling; Mus; Nature; Nutrient; Nutritional Requirements; Obesity; Organism; Pattern; Plants; Polysaccharides; Population; Positioning Attribute; Postdoctoral Fellow; Prevalence; Process; Property; RNA, Ribosomal, 16S; Reaction; Recording of previous events; Relative (related person); Research; Ribosomal RNA; Role; Sampling; Science; Sequence Analysis; Shotgun Sequencing; Shotguns; Soil; Structure; Students; Surveys; Taxon; Techniques; Testing; Training; Universities; Volatile Fatty Acids; Washington; Work; analog; base; combat; computer science; computerized tools; database design; design; experience; genome sequencing; gut microbiota; human data; human subject; insight; interest; member; metagenomic sequencing; microbial; microbial community; microbiome; microorganism; microorganism interaction; mouse model; network models; nutrition; preference; prevent; reconstruction; research study; skills; trait

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.

描述（由申请人提供）：人类肠道蕴藏着 10-100 万亿个微生物，这些微生物能够从我们饮食中其他难以消化的成分（例如复杂的植物多糖）中获取营养/能量。互养（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去除抑制代谢反应进程的化合物，对微生物从食物中提取热量的效率有很大影响。了解不同的人类肠道细菌如何适应厌氧降解过程的代谢相互作用级联，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人类粪便样本中的宏基因组（16S 核糖体 RNA 或鸟枪法）序列和培养的肠道分离株的基因组序列来预测微生物相互作用，并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和史密斯甲烷短杆菌（人类肠道中最重要的古菌产甲烷菌）之间的相互作用。选择产甲烷古菌是因为 1) 它们可以通过防止氢气等代谢产物的积累来提高细菌发酵的效率 2) 它们被认为是“关键”物种（即对群落组成和功能的影响比它们的流行表明）和3）它们与其他环境（例如污泥消化器）中的特定互养细菌密切相关，但肠道中是否存在类似物尚不清楚。具体目标 1 的目标是使用人类粪便样本的宏基因组序列数据来识别其流行程度与 M. smithii 的存在/不存在相关的物种（系统发育型）和基因。通过对肥胖对肠道微生物群影响的持续纵向研究收集的 191 个样本进行了初步分析，确定了 27 种细菌系统型，代表至少 3 个深层细菌谱系，这些谱系似乎保留了导致共现的特征与M. smithii。具体目标 2 和 3 追求实验室和计算技术的结合，以确定这些细菌和 M. smithii 之间的共存是否是由共养或共同的环境偏好驱动的。一些共存的系统型来自未培养的谱系，其生物学特性完全未知。具体目标 2 将产生有关这些未培养谱系的信息，并通过 1) 使用荧光原位杂交 (FISH) 显微测定它们是否与 M. smithii 形成结构化复合物和 2) 细胞宏基因组测序来确定共现是否由互养驱动使用流式细胞术浓缩的群体。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（包括共养和代谢生态位收敛），进一步探索共现模式的根本原因。最后，我将利用这些综合信息在限生小鼠中设计验证性实验。这项工作将有助于利用不断增多的人类肠道衍生序列来了解特定微生物是否相互作用以及如何相互作用，并将提供有关如何促进或抑制肠道中史密斯氏菌活性的见解。这个提案是我的博士学位的自然延伸。以及人类微生物组的博士后研究。拟议的研究将进一步发展实现我的目标所需的技能，即建立一个具有计算和实验室组成部分的独立研究小组。生物信息学工作整合了我对 16S rRNA 和人类肠道基因组序列数据分析的经验，并将我的专业知识扩展到新领域，例如代谢网络建模。实验室部分借鉴了我对土壤中微生物进行独立培养分析的经验，并扩展了我在 FISH 和流式细胞术方面的培训，以便从未培养的微生物谱系中生成基因组信息。与人类受试者合作的扩展培训也将帮助我继续进行人类微生物组研究。我目前在科罗拉多大学博尔德分校与 Rob Knight 博士一起担任博士后职位，以及华盛顿大学基因组科学中心 Jeff Gordon 博士的共同指导，为我提供了一个极好的环境。达到这些目标。奈特实验室处于生成利用高通量测序的进步来分析微生物群落所需的计算工具的最前沿，并且是一个我可以与各种学生、博士后和合作者互动的环境，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）的关系进行了开创性的研究，并应用无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序列信息，这是本次资助中提出的工作的核心。