Dietary and Microbial Reprogramming of Intestinal Microbiota-Produced Metabolites

描述(由申请人提供)：生活在人类肠道微生物区系中的微生物具有新的和定义不清的代谢能力。这个微生物生态系统产生的许多代谢物被人类宿主吸收，并最终被肾脏排泄。当肾脏衰竭时，这种溶质会积聚，并在维持透析的患者的血浆中发现非常高水平的“尿毒症”溶质的相当大一部分。对甲酚硫酸盐(PCS)、吲哚硫酸盐(IS)和三甲胺-N-氧化物(TMAO)是尿毒症的三种主要溶质，它们依赖于饮食衍生分子的微生物代谢。每种微生物在产生的数量上都表现出个体间的差异，这表明微生物群产生这些分子的能力不同。PC和IS一直与肾脏患者的不良结局有关，而TMAO与人类的心血管事件有关。这项研究的最终目标是了解人类肠道微生物区系如何被调节以减少这些化合物的产生。主要目的是阐明(I)作为PCS、IS和TMAO产生的关键贡献者的微生物基因和物种；(Ii)饮食对这些溶质产生的影响；以及(Iii)通过给予其他肠道衍生物种将高产量微生物区系重新编程为低产量表型的最佳方法。在Aim 1中，将使用一个新的机器学习软件ClusterFinder来查询~900个已测序的肠道微生物区系基因组，以预测与PCS、IS或TMAO生成有关的基因和基因盒。预测将在纯培养和活体中得到验证，使用单物种和多物种定植的诺生菌小鼠模型。基因预测将通过基因缺失或异源表达得到基因验证。在AIM中，将确定2名健康的杂食性人类，他们的尿中TMAO、PCS和IS的产量分别为稳定的高低。与溶质产生表型相关的微生物组编码基因和分类群将通过使用基于16S rRNA的微生物区系计数、元基因组学和元转录组学分析这些个体的粪便样本来确定。目标3将阐述饮食是否影响TMAO、PCS的产生，或者正在使用目标1中确认的细菌联合体和人源化小鼠模型，该模型由目标2中确定的高和低TMAO、PCS或IS的高和低产生TMAO、PCS或IS的无菌前小鼠和人类粪便微生物群组成。饮食将包括高纤维与低纤维，高蛋白质与低蛋白质，或L肉碱补充与素食。TMAO、PCS和/或IS产量的减少将与微生物区系组成和功能的变化有关。目的4将确定在人源化小鼠中重新编程微生物群以减少TMAO、PCS和IS生产的最有效方法，方法是移植完整的微生物群、供体微生物群产生的培养物或模式菌株代表。移植前抗生素的使用和强化饮食的影响将作为帮助微生物区系重新编程的方法进行测试。

DESCRIPTION (provided by applicant): The microbes that live in the human gut microbiota possess novel and ill-defined metabolic capabilities. Many of the metabolites produced by this microbial ecosystem are absorbed by the human host and ultimately excreted by the kidneys. Such solutes accumulate when the kidneys fail and comprise a significant portion of the "uremic" solutes found at very high level in the plasma of patients maintained on dialysis. p-cresol sulfate (PCS), indoxyl sulfate (IS), and trimethylamine-N-oxide (TMAO) are three prominent uremic solutes that depend upon microbial metabolism of diet-derived molecules. Each exhibits inter-individual variability in the quantities produced, suggesting that microbiotas differ in their ability to produce these molecules. PCS and IS have been associated with poor outcomes in renal patients and TMAO has been linked to cardiovascular events in humans. The ultimate goal of this research is to understand how the human gut microbiota may be modulated to decrease the production of these compounds. The main objectives are to elucidate (i) the microbial genes and species that are the key contributors to PCS, IS, and TMAO production; (ii) the impact of diet on the production of these solutes; and (iii) the best method for reprogramming a high-producing microbiota to a low-producing phenotype via administration of other gut-derived species. In Aim 1 a new machine learning software, ClusterFinder, will be used to query ~900 sequenced gut microbiota genomes to predict genes and gene cassettes that contribute to PCS, IS, or TMAO generation. Predictions will be validated in pure culture and in vivo, using a gnotobiotic mouse model in single and multiple species colonizations. Gene predictions will be genetically validated using gene deletion or heterologous expression. In Aim 2 healthy omnivorous humans with stable high and low urinary TMAO, PCS and IS production will be identified. Microbiome-encoded genes and taxa associated with solute production phenotypes will be determined by analyzing stool samples for these individuals using 16S rRNA-based microbiota enumerations, metagenomics, and metatranscriptomics. Aim 3 will address whether diet affects the production of TMAO, PCS, or IS using either gnotobiotic mice colonized with bacterial consortia validated in Aim 1, and a humanized mouse model, consisting of ex-germ-free mice colonized with the human fecal microbiota identified in Aim 2 of high- and low-producers of TMAO, PCS, or IS. Diets will include high vs. low fiber, high vs. low protein, or L-carnitine supplemented vs. vegetarian. Decreases in TMAO, PCS and/or IS production will be associated with changes in microbiota composition and function. Aim 4 will identify the most effective method for microbiota reprogramming in humanized mice to decrease TMAO, PCS and IS production using transplants of an intact microbiota, donor microbiota generated culture collections, or type strain representatives. The use of antibiotics before transplant and the influence of dietary reinforcement will be tested as methods of aiding microbiota reprogramming.