Carbohydrate enzyme gene clusters in human gut microbiome

项目总结 人体肠道微生物组中的碳水化合物酶基因簇 希波克拉底在2400年前就说过：“让食物成为你的药，让药成为你的食物”。现在大家都知道 这在很大程度上是由于人类肠道中发生的“饮食-微生物-宿主”相互作用。特别是， 微生物降解碳水化合物可以产生各种代谢物，这些代谢物对 人类健康。作为内布拉斯加州食品健康中心的生物信息学研究员， PI包括：(1)开发专门计算工具，以便更好地对食物消化进行功能注释 微生物基因组和元基因组，以及(Ii)表征酶和其他连接的遗传元素 微生物、饮食和人类健康。该R01项目的目标是开发一套生物信息学工具 用于人类碳水化合物活性酶(CAZyme)和CAZyme基因簇(CGCs)的功能注释 肠道微生物群。PI在CAZyme生物信息学工具开发方面拥有10多年的经验，并且 维护一个公认的CAZyme批注数据库和名为DBCAN的Web服务器 (http://bcb.unl.edu/dbCAN2).)该项目旨在进一步开发数据库CAN，以解决基本问题 个性化营养问题：(I)肠道微生物能够利用特定类型的多糖吗？(Ii)一个人是否可以 携带某些肠道微生物会对个性化的饮食产生反应(例如，益生元：具有 对人类健康有益)？要解决这些问题，新的CAZyme标注工具必须能够 预测CAZymes的碳水化合物底物。 最近的研究发现，不同的CAZyme编码基因经常是相互共定位的，并且 与细菌中的其他基因(例如，编码糖转运体、调节器和信号蛋白的基因) 基因组形成CGCs(也称为多糖利用基因座或PULs)。因此，新的 工具开发是CGC的基因成员(或功能结构域组成)可用于 预测其碳水化合物底物(如木聚糖、果胶、葡聚糖等)。创新之处在于机器学习 将使用方法来分析从文献中精选的大量实验表征的PULS， 提取的序列特征将用于构建有效的分类器来预测和分类CGCS 新的基因组/元基因组。预期的结果将是新奇的和用户友好的开源计算机 程序、数据库和网络服务器，允许自动识别CGCs和底物预测。这个 重要的是，新的工具将促进更多的PULS和它们的实验表征 人体肠道微生物群中的碳水化合物底物(也存在于其他碳水化合物丰富的环境中)。因此， 该项目将为个性化营养研究提供计算解决方案，例如，分析 人的肠道微生物群，以预测此人是否能对含有某些益生菌多糖的饮食做出反应。

PROJECT SUMMARY Carbohydrate enzyme gene clusters in human gut microbiome Hippocrates said ~2,400 years ago: “Let food be thy medicine and medicine be thy food”. It is now well known that this is largely due to the “diet-microbiota-host” interactions that happen in the human gut. In particular, microbial degradation of carbohydrates can produce a variety of metabolites, which have a profound impact on human health. As a bioinformatics researcher in the Nebraska Food for Health Center, the long-term interests of the PI include: (i) develop specialized computational tools for better functional annotation of food-digesting microbial genomes and metagenomes, and (ii) characterize enzymes and other genetic elements that connect microbes, diets, and human health. The objective of this R01 project is to develop a suite of bioinformatics tools for functional annotation of carbohydrate active enzyme (CAZyme) and CAZyme gene clusters (CGCs) in human gut microbiome. The PI has over 10 years of experience in CAZyme bioinformatics tool development, and maintains a well-recognized CAZyme annotation database and web server called dbCAN (http://bcb.unl.edu/dbCAN2). This project aims to further dbCAN development to address fundamental personalized nutrition questions: (i) is a gut microbe able to utilize a specific type of glycan? (ii) can a person carrying certain gut microbes respond to an individualized diet (e.g., prebiotics: dietary compounds that are beneficial to human health)? To address these questions, new CAZyme annotation tools must have the ability to predict the carbohydrate substrates of CAZymes. Recent research has found that different CAZyme encoding genes are often co-localized with each other and with other genes (e.g., those encoding sugar transporters, regulators, and signaling proteins) in bacterial genomes to form CGCs (also known as polysaccharide utilization loci or PULs). Thus, the foundation of the new tool development is that the gene membership (or functional domain composition) of a CGC can be used to predict its carbohydrate substrates (e.g., xylans, pectins, glucans, etc.). The innovation is that machine learning approaches will be used to analyze a large number of experimentally characterized PULs curated from literature, and the extracted sequence features will be used to build effective classifiers to predict and classify CGCs in new genomes/metagenomes. The expected outcome will be novel and user-friendly open source computer programs, databases, and web servers that allow automated CGCs identification and substrate predictions. The significance is that the new tools will facilitate the experimental characterization of more PULs and their carbohydrate substrates in human gut microbiome (also in other carbohydrate rich environments). Therefore, this project will contribute computational solutions to the research of personalized nutrition, e.g., analyze a person's gut microbiome to predict if this person can respond to diets containing certain prebiotic glycans.