Computational Metabolomics of Gut Microbiota Metabolites

描述（由申请人提供）：本提案的目标是建立一个新的计算代谢组学平台，能够有效地探索胃肠道（GI）中的细菌代谢物。越来越明显的是，微生物衍生的代谢物在人类胃肠道炎症和免疫调节的背景下介导重要信号。尽管有强烈的兴趣，但在胃肠道中只有少数生物活性微生物群代谢物被确定。一个主要的挑战是胃肠道中存在的代谢物的光谱非常复杂，因为微生物群可以进行各种各样的生物转化反应，包括那些不存在于哺乳动物宿主中的反应。传统的方法，如分离和培养单个细菌和鉴定这些培养物中产生的代谢物，并没有取得太大的成功，因为胃肠道中的许多细菌物种不能在标准的实验室条件下培养。此外，这种方法也没有考虑细菌之间的社区水平的相互作用，也没有考虑宿主与细菌之间的相互作用。因此，需要替代的发现方法。我们的方法是将微生物群建模为一个代谢网络，并采用概率搜索来确定选定的代谢物可能的生物转化产物，这些代谢物可以明确地归因于细菌。一个重要的新发展是通过宿主的一系列异种转化酶来捕获宿主的贡献。由于许多这些酶表现出高度的底物灵活性，因此将开发基于模式匹配的算法来增强基于反应定义的概率搜索。为了建立概念验证，我们计划通过对粪便培养样品进行有针对性的质谱测量来验证预测的代谢物，并表征确认代谢物的生物活性。我们的具体目标如下。在Aim 1中，我们将建立胃肠道微生物群的代谢网络模型，以便对细菌生物转化产物进行集中预测。我们将通过开发一种途径分析算法来分析网络模型，根据相关酶在胃肠道微生物群中表达的可能性来预测细菌代谢物并对其进行排名。我们将通过分析小鼠粪便培养物作为胃肠道微生物群的替代实验系统来验证模型预测。在Aim 2中，我们将增加Aim 1的搜索算法，通过已知CYP生物转化的模式识别分析计算出可能的宿主修饰预测。与Aim 1一样，我们将使用培养的肝细胞作为肝脏的替代系统，对模型预测进行实验验证。这些研究预计将证明计算代谢途径分析对目标代谢组学的显著益处，并提供一种普遍适用的方法来识别对人类健康有益的生物活性微生物群代谢物。

DESCRIPTION (provided by applicant): The goal of this proposal is to build a novel, computational metabolomics platform enabling efficient exploration of bacterial metabolites in the gastrointestinal (GI) tract. It is becoming increasingly evident that microbiota- derived metabolites mediate important signals in the context of inflammation and immunomodulation in the human GI tract. Despite intense interest, only a handful of bioactive microbiota metabolites in the GI tract have been identified. One major challenge is that the spectrum of metabolites present in the GI tract is extremely complex, as the microbiota can carry out a diverse range of biotransformation reactions, including those that are not present in the mammalian host. Classical approaches such as isolating and culturing individual bacteria and identifying metabolites produced in these cultures has not yielded much success, as many bacterial species in the GI tract cannot be cultured under standard laboratory conditions. Moreover, this approach also does not account for community-level interactions between the bacteria nor the interactions between host and bacteria. Thus, alternate methods of discovery are needed. Our approach is to model the microbiota as a metabolic network, and employ a probabilistic search to identify possible biotransformation products of selected metabolites that can be unambiguously attributed to bacteria. A critical new development is to capture the contributions of the host organism through its array of xenobiotic transformation enzymes. Since many of these enzymes exhibit a high degree of substrate flexibility, an algorithm based on pattern matching will be developed to augment the probabilistic search based on reaction definitions. To establish proof-of-concept, we plan to validate the predicted metabolites by performing targeted mass spectrometry measurements on fecal culture samples and characterize the bioactivity of the confirmed metabolites. Our specific aims are as follows. In Aim 1, we will build a metabolic network model of GI tract microbiota to enable focused predictions on bacterial biotransformation products. We will analyze the network model by developing a pathway analysis algorithm to predict and rank bacterial metabolites based on the likelihood that the relevant enzymes are expressed in the GI tract microbiota. We will validate the model predictions by analyzing murine fecal cultures as a surrogate experimental system for the GI tract microbiota. In Aim 2, we will augment the search algorithm of Aim 1 with predictions on probable host modifications computed from pattern recognition analysis of known CYP biotransformations. As in Aim 1, we will perform experimental validation of the model predictions using cultured hepatocytes as a surrogate system for the liver. These studies are expected to demonstrate the significant benefits of computational metabolic pathway analysis for targeted metabolomics, and provide a generally applicable methodology for identifying bioactive microbiota metabolites that are beneficial to human health.