Using Common Fund Datasets to Illuminate Drug-Microbial Interactions

项目总结/摘要 平均而言，每个人都有1014个微生物细胞，这些细胞主要位于胃肠道中。 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，临床观察和轶事例子表明，微生物酶改变 药物通过常见的酶转化，如还原，水解，脱羟基， 去甲基化和其他。尽管取得了进展，但缺乏系统的方法来发现和分析 这种转变，从而阻碍了设计和解释的实验研究。因此存在 需要建立工作流程来探索这种转变。 我们在这个提案中调查微生物药物代谢的分子和社区水平。我们 建议使用来自两个共同基金数据集的数据来进行这项调查。照亮可药用的 基因组（IDG）对药物及其药理作用进行了分类，而NIH人类微生物组计划 (HMP)提供了队列的详细肠道微生物数据。我们还建议使用我们的深度学习工具， 预测微生物酶和药物之间相互作用的可能性（目标1），并预测假定的 由于这种相互作用而产生的衍生产品（目标2）。我们的工具（CSI用于目标1，GNN-SOM和PROXIMAL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 酶和药物的基础上挑选的数据从IDG和HMP和其他资源。建立的工作流程 将利用目标1和目标2中的方法进行试点研究（目标3），以调查功能冗余的程度 从HMP中挑选的健康个体的微生物群落中的药物。 因此，我们方法的优势在于：i）采用新颖的、最先进的深度学习模型来预测 微生物酶对药物的混杂，ii）提供生物化学上可解释的药物产品，以及iii）探索 药物微生物代谢是微生物群落组成的函数。其意义 它提供了一个可解释的微生物药物代谢假说。这项工作是有影响力的，因为它 将使进一步的研究，如探索功能冗余的微生物群落对药物 (as计划在目标3）和设计和解释实验研究，涉及肠道的影响 微生物对药物的影响拟议的工作是适合这个资助机会，因为它策划和注释 使用新的深度学习方法收集数据，并在HMP和IDG之间建立了以前未探索的联系。 1

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