Carbohydrate enzyme gene clusters in human gut microbiome

项目摘要 人类肠道微生物组中的碳水化合物酶基因簇 希波克拉底在2,400年前说过：“让食物成为你的药物，让药物成为你的食物。现在众所周知 这在很大程度上是由于发生在人类肠道中的“饮食-微生物群-宿主”相互作用。特别是， 微生物降解碳水化合物可以产生多种代谢产物，这些代谢产物对 人体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， 该PI包括：（i）开发专门计算工具，以更好地对食物消化进行功能注释 微生物基因组和宏基因组，以及（ii）表征酶和其他遗传元件， 微生物、饮食和人类健康。R 01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（CGC）的功能注释 肠道微生物组PI在CAZyme生物信息学工具开发方面拥有超过10年的经验， 维护一个公认的CAZyme注释数据库和称为dbCAN的Web服务器 (http：//bcb.unl.edu/dbCAN2）。该项目旨在进一步开发dbCAN，以解决基本的 个性化营养问题：（i）肠道微生物是否能够利用特定类型的聚糖？(ii)一个人能 携带某些肠道微生物的人对个体化饮食作出反应（例如，益生元：膳食化合物， 对人体健康有益）。为了解决这些问题，新的CAZyme注释工具必须具有以下能力： 预测CAZymes的碳水化合物底物。 最近的研究发现，不同的CAZyme编码基因通常彼此共定位， 与其它基因（例如，那些编码糖转运蛋白、调节蛋白和信号蛋白的基因）在细菌中 基因组以形成CGC（也称为多糖利用基因座或普尔斯）。因此，新的基础 CGC的基因成员资格（或功能结构域组成）可用于 预测其碳水化合物底物（例如，木聚糖、果胶、葡聚糖等）。创新在于机器学习 方法将用于分析大量从文献中收集的实验表征的普尔斯， 提取的序列特征将用于建立有效的分类器，以预测和分类CGC， 新的基因组/宏基因组。预期的结果将是新颖和用户友好的开源计算机 程序，数据库和网络服务器，允许自动化的CGC识别和基板预测。的 重要的是，新的工具将有助于更多的普尔斯和他们的实验表征 在人类肠道微生物组中的碳水化合物底物（也在其他富含碳水化合物的环境中）。因此，我们认为， 该项目将为个性化营养的研究提供计算解决方案，例如，分析一个 一个人的肠道微生物组来预测这个人是否可以对含有某些益生元聚糖的饮食做出反应。

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.