Computational Metabolomics of Gut Microbiota Metabolites

描述(由申请人提供)：这项提议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，微生物区系衍生的代谢物在人类胃肠道的炎症和免疫调节背景下介导了重要的信号。尽管引起了强烈的兴趣，但在胃肠道中只发现了少数具有生物活性的微生物区系代谢物。一个主要的挑战是，胃肠道中存在的代谢物的光谱极其复杂，因为微生物区系可以进行一系列不同的生物转化反应，包括那些不存在于哺乳动物宿主中的反应。分离和培养单个细菌和鉴定在这些培养物中产生的代谢物等经典方法没有取得太大成功，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群落水平的相互作用，也没有考虑宿主和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区系建模为代谢网络，并使用概率搜索来确定选定代谢物的可能生物转化产物，这些代谢物可以明确地归因于细菌。一个关键的新发展是通过寄主有机体的一系列异源转化酶来捕捉寄主有机体的贡献。由于这些酶中的许多都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本进行有针对性的质谱学测量来验证预测的代谢物，并表征确认的代谢物的生物活性。我们的具体目标如下。在目标1中，我们将建立胃肠道微生物区系的代谢网络模型，以实现对细菌生物转化产物的重点预测。我们将通过开发一种路径分析算法来分析网络模型，该算法基于相关酶在胃肠道微生物区系中表达的可能性来预测细菌代谢物并对其进行排序。我们将通过分析小鼠粪便培养作为胃肠道微生物区系的替代实验系统来验证模型预测。在目标2中，我们将用从已知CYP生物转化的模式识别分析中计算出的对可能的宿主修改的预测来增强目标1的搜索算法。正如在目标1中一样，我们将使用培养的肝细胞作为肝脏的替代系统来对模型预测进行实验验证。这些研究有望证明计算代谢途径分析对靶向代谢组学的显著好处，并为鉴定有益于人类健康的生物活性微生物区系代谢物提供一种普遍适用的方法学。

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.