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Computational Metabolomics of Gut Microbiota Metabolites

肠道微生物代谢物的计算代谢组学

基本信息

批准号：
8794445
负责人：
KYONGBUM LEE
金额：
$ 21.32万
依托单位：
TUFTS UNIVERSITY MEDFORD
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-02-01 至 2017-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8794445
关键词：
Accounting Algorithms Anabolism Animals Anti-Inflammatory Agents Anti-inflammatory Aromatic Amino Acids Bacteria Biochemical Pathway Cataloging Catalogs Cells Chemicals Colitis Communities Complex Cytochrome P450 Data Development Diet Disease Endocrine Disruptors Enzymes Epithelial Cells Exhibits Family Foundations Gastrointestinal tract structure Gene Dosage Genes Genome Goals Health Hepatocyte Human In Vitro Individual Indoles Inflammation Inflammatory Inflammatory Bowel Diseases Intestines Knowledge Laboratories Learning Lipids Liver Malignant Neoplasms Mammals Mass Spectrum Analysis Measurement Mediating Metabolic Metabolic Biotransformation Metabolic Pathway Metabolism Methodology Methods Modality Modeling Modification Molecular Mus Nature Organism Pathway Analysis Pathway interactions Pattern Pattern Recognition Pharmaceutical Preparations Phase Polychlorinated Biphenyls Property Reaction Route Sampling Signal Transduction Site Source Specific qualifier value System Testing Tryptophan Tryptophanase Uncertainty Validation Work Xenobiotics base bisphenol A design diphenyl environmental chemical flexibility gut microbiota immunoregulation interest metabolic engineering metabolomics microbial microbial genome microbiome network models novel operation screening success

项目摘要

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.

描述（由申请人提供）：该提案的目标是建立一个新的计算代谢组学平台，能够有效探索胃肠道（GI）中的细菌代谢物。越来越明显的是，微生物群衍生的代谢物在人胃肠道中的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，但只有少数胃肠道中的生物活性微生物群代谢物被确定。一个主要挑战是胃肠道中存在的代谢物谱极其复杂，因为微生物群可以进行各种各样的生物转化反应，包括哺乳动物宿主中不存在的那些。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产物，并没有取得很大的成功，因为胃肠道中的许多细菌物种不能在标准实验室条件下培养。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群建模为代谢网络，并采用概率搜索来识别可以明确归因于细菌的选定代谢物的可能生物转化产物。一个关键的新发展是通过其异生转化酶阵列捕获宿主生物体的贡献。由于许多这些酶表现出高度的底物的灵活性，将开发一种基于模式匹配的算法，以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靶向质谱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群的代谢网络模型，以实现对细菌生物转化产物的集中预测。我们将通过开发一种途径分析算法来分析网络模型，以根据相关酶在胃肠道微生物群中表达的可能性来预测和排名细菌代谢物。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。在目标2中，我们将增加目标1的搜索算法与预测可能的主机修改计算模式识别分析已知的生物转化。与目标1相同，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。这些研究有望证明计算代谢途径分析对靶向代谢组学的显著益处，并为识别有益于人类健康的生物活性微生物群代谢物提供普遍适用的方法。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Pathways and functions of gut microbiota metabolism impacting host physiology.

DOI：
10.1016/j.copbio.2015.08.015
发表时间：
2015-12
期刊：
Current opinion in biotechnology
影响因子：
7.7
作者：
Krishnan S;Alden N;Lee K
通讯作者：
Lee K

Gut Microbiota-Derived Tryptophan Metabolites Modulate Inflammatory Response in Hepatocytes and Macrophages.

肠道菌群衍生的色氨酸代谢产物调节肝细胞和巨噬细胞中的炎症反应。

DOI：
10.1016/j.celrep.2018.03.109
发表时间：
2018-04-24
期刊：
Cell reports
影响因子：
8.8
作者：
Krishnan S;Ding Y;Saedi N;Choi M;Sridharan GV;Sherr DH;Yarmush ML;Alaniz RC;Jayaraman A;Lee K
通讯作者：
Lee K

PROXIMAL: a method for Prediction of Xenobiotic Metabolism.