Using Common Fund Datasets to Illuminate Drug-Microbial Interactions

使用共同基金数据集阐明药物-微生物相互作用

• 批准号: 10777339
• 负责人: Soha Hassoun
• 金额: $ 30.07万
• 依托单位: TUFTS UNIVERSITY MEDFORD
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10777339
• 关键词: Acetylation; Adverse drug effect; Asthma; Bacteria; Biochemical; Brain; Carbohydrates; Cells; Clinical; Communities; Complex; Data; Data Set; Databases; Decarboxylation; Diabetes Mellitus; Disease; Docking; Drug Catalogs; Drug Interactions; Drug Targeting; Enzyme Interaction; Enzymes; Exhibits; Experimental Designs; Fermentation; Funding; Funding Opportunities; Future; Gastrointestinal tract structure; Genes; Genome; Goals; Health; Human; Human Microbiome; Hydrolysis; Individual; Investigation; Knowledge; Link; Liver; Lung; Malignant Neoplasms; Measures; Metabolic; Metabolic Biotransformation; Metabolism; Methods; Molecular; Nutrient; Obesity; Participant; Pharmaceutical Preparations; Pharmacologic Actions; Pilot Projects; Process; Proteins; Reaction; Research; Research Design; Resources; Role; Shotguns; Site; Techniques; Testing; Toxic effect; United States National Institutes of Health; Work; bacterial community; cohort; deacylation; deep learning; deep learning model; dehalogenation; dehydroxylation; demethylation; design; dietary; drug metabolism; experimental study; gastrointestinal; gut microbes; gut microbiome; gut microbiota; insight; machine learning method; microbial; microbial community; microbial products; microbiome composition; microbiota; microorganism interaction; novel; patient population; prebiotics; predictive tools; screening; tool; web site; whole genome

PROJECT SUMMARY/ABSTRACT Each human is, on average, colonized by 1014 microbial cells that mostly reside in the gastrointestinal track. Research in the last two decades has uncovered the central role of this microbial community in human health and disease. A pressing challenge, however, is the lack of understanding of microbial drug metabolism. Experimental studies, clinical observations, and anecdotal examples demonstrate that microbial enzymes alter drugs through common enzymatic transformations such as reduction, hydrolysis, dehydroxylation, demethylation, and others. Despite progress, there lacks a systematic approach for the discovery and analysis for such transformations, thus hindering the design and interpretation of experimental studies. There is therefore a need to establish workflows to explore such transformations. We investigate in this proposal microbial drug metabolism at the molecular and community levels. We are proposing to use data from two Common Fund data sets to conduct this investigation. Illuminating the Druggable Genome (IDG) catalogues drugs and their pharmacologic action, while the NIH Human Microbiome Project (HMP) provides detailed gut microbial data for cohorts. We are also proposing to use our deep-learning tools to predict the likelihood of interaction between microbial enzymes and drugs (Aim 1), and to predict putative derivative products due to this interaction (Aim 2). Our tools (CSI for Aim 1, and GNN-SOM and PROXIMAL for Aim 2) have already been validated on other datasets and in other studies, and they will be adapted for microbial enzymes and drugs based on data culled from IDG and HMP and other resources. The workflows established in Aims 1 and 2 will be utilized to conduct a pilot study (Aim 3) to investigate the extent of functional redundancy towards drugs within microbial communities of healthy individuals that are culled from HMP. The strength of our Approach therefore lies in: i) adapting novel, state-of-the-art deep-learning models to predict microbial enzyme promiscuity on drugs, ii) providing biochemically explainable drug products, and iii) exploring how drug microbial metabolism is a function of microbial community composition. The Significance of this research is that it provides an explainable hypothesis of microbial drug metabolism. The work is impactful as it will enable further studies, such as exploring the functional redundancy of a microbial community towards drugs (as planned in Aim 3) and designing and interpreting experimental studies involving the impact of the gut microbiota on drugs. The proposed work is appropriate for this funding opportunity as it curates and annotates data using novel deep-learning approaches and creates a previously unexplored link between the HMP and IDG. 1

项目摘要/摘要 平均而言，每个人都有1014个微生物细胞定植，这些微生物细胞大多位于胃肠道。 过去二十年的研究揭示了这种微生物群落在人类健康中的核心作用 和疾病。然而，一个紧迫的挑战是缺乏对微生物药物代谢的了解。 实验研究、临床观察和轶事例证表明，微生物酶可以改变 药物通过常见的酶转化，如还原，水解，脱羟基， 去甲基化等。尽管取得了进展，但缺乏系统的方法来发现和分析 对于这种转变，因此阻碍了实验研究的设计和解释。因此，有 需要建立工作流来探索这种转变。 在这个方案中，我们在分子和社区水平上研究微生物的药物代谢。我们是 提议使用来自两个共同基金数据集的数据进行这项调查。照亮可用药 基因组(IDG)编目药物及其药理作用，而NIH人类微生物组计划 (HMP)为队列提供详细的肠道微生物数据。我们还提议使用我们的深度学习工具来 预测微生物酶与药物相互作用的可能性(目标1)，并预测假定 这种相互作用导致的衍生产品(目标2)。我们的工具(用于AIM 1的CSI和用于 目标2)已经在其他数据集和其他研究中得到验证，它们将适用于微生物 酶和药物基于从IDG和HMP和其他资源中挑选的数据。已建立的工作流程 在目标1和目标2中，将进行试点研究(目标3)，以调查功能冗余的程度 对于健康个体微生物群落内的药物，这些药物是从HMP中剔除的。 因此，我们方法的优势在于：i)采用新颖、最先进的深度学习模型来预测 微生物酶在药物上的混杂，ii)提供可生物化学解释的药物产品，以及iii)探索 药物微生物代谢如何成为微生物群落组成的函数。这件事的意义 研究表明，它为微生物药物新陈代谢提供了一个可解释的假说。这项工作很有影响力，因为它 将使进一步的研究成为可能，例如探索微生物群落对药物的功能冗余 (按照目标3中的计划)以及设计和解释涉及肠道影响的实验研究 毒品上的微生物区系。拟议的工作适合这一筹资机会，因为它策划和注释了 数据使用新的深度学习方法，并在医疗保健计划和国际数据集团之间建立了一种以前未曾探索过的联系。 1