Computational Metabolomics of Gut Microbiota Metabolites

这项提案的目标是建立一个新的，计算代谢组学平台，使有效的探索 细菌代谢物在胃肠道（GI）中的作用。越来越明显的是，微生物群- 衍生代谢物介导炎症和免疫调节背景下的重要信号， 人类胃肠道。尽管有强烈的兴趣，但只有少数胃肠道中的生物活性微生物群代谢物具有 被识别。一个主要的挑战是存在于胃肠道中的代谢物的谱是极其复杂的。 复杂，因为微生物群可以进行各种生物转化反应，包括那些 并不存在于哺乳动物宿主中。传统的方法，如分离和培养单个细菌 鉴定这些培养物中产生的代谢物并没有取得多大成功，因为许多细菌 在标准实验室条件下不能培养胃肠道中的细菌。此外，这种方法还 不能解释细菌之间的群落水平的相互作用，也不能解释宿主和 细菌因此，需要替代的发现方法。我们的方法是将微生物群建模为 代谢网络，并采用概率搜索来识别可能的生物转化产物， 选择的代谢物，可以明确归因于细菌。一个关键的新发展是捕捉 宿主生物体通过其异生物质转化酶的阵列的贡献。因为许多 这些酶表现出高度的底物灵活性，基于模式匹配的算法将 开发用于基于反应定义来增强概率搜索。为了建立概念验证，我们 计划通过对粪便进行靶向质谱测量来验证预测的代谢物 培养样品并表征确认代谢物的生物活性。我们的具体目标如下。 在目标1中，我们将建立一个胃肠道微生物群的代谢网络模型，以实现对以下方面的集中预测： 细菌生物转化产物。我们将通过开发路径分析来分析网络模型 算法来预测和排名细菌代谢物的基础上的可能性，相关酶是 在胃肠道菌群中表达。我们将通过分析小鼠粪便培养物来验证模型预测 作为胃肠道菌群的替代实验系统。在目标2中，我们将增强搜索算法 的目标1与预测可能的主机修改计算从模式识别分析已知的 生物转化。与目标1一样，我们将使用以下方法对模型预测进行实验验证： 培养的肝细胞作为肝脏的替代系统。预计这些研究将证明 计算代谢途径分析对靶向代谢组学的显著益处，并提供了一个 用于鉴定对人类有益的生物活性微生物群代谢物的普遍适用的方法 健康

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.