Carbohydrate enzyme gene clusters in human gut microbiome

人类肠道微生物组中的碳水化合物酶基因簇

• 批准号: 10398795
• 负责人: Yanbin Yin
• 金额: $ 30.21万
• 依托单位: UNIVERSITY OF NEBRASKA LINCOLN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10398795
• 关键词: Address; Algorithms; Bacterial Genome; Bacteroidetes; Bioinformatics; Carbohydrates; Data; Databases; Development; Diet; Dietary Fiber; Environment; Enzymes; Family; Firmicutes; Food; Foundations; Gene Cluster; Genes; Genome; Genomics; Glucans; Glycosides; Health; Health Food; Human; Human Microbiome; Link; Literature; Machine Learning; Mannans; Medicine; Metabolic; Metabolic Diseases; Microbe; Nebraska; Other Genetics; Outcome; Pectins; Persons; Physiology; Polysaccharides; Proteins; Proteobacteria; Publishing; Research; Research Personnel; Sampling; Signaling Protein; Starch; System; Testing; Training; Work; Xylans; annotation system; base; bioinformatics tool; computer program; computerized tools; contig; dietary; experience; genetic element; gut metagenome; gut microbes; gut microbiome; host microbiota; innovation; interest; machine learning method; metagenome; metatranscriptome; microbial; microbial community; microbial genome; microbiome; microbiome sequencing; novel; nutrition; open source; prebiotics; predictive tools; prevent; programs; sugar; supervised learning; therapeutic development; tool; tool development; user-friendly; web server

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.

项目摘要 人肠道微生物组中的碳水化合物酶基因簇 希波克拉底说，〜2400年前：“让食物成为你的药物，是你的食物”。现在众所周知 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。尤其， 碳水合物的微生物降解会产生多种代谢产物，这对 人类健康。作为内布拉斯加州卫生中心食品中的生物信息学研究者，长期利益的长期利益 PI包括：（i）开发了专门的计算工具，以更好地对食物消化的功能注释 微生物基因组和宏基因组，（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项目的目的是开发一套生物信息学工具 在人类中的碳氢化活性酶（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，并且 维护一个公认的Cazyme注释数据库和名为DBCAN的Web服务器 （http://bcb.unl.edu/dbcan2）。该项目旨在进一步开发以解决基本 个性化营养问题：（i）能够使用特定类型的聚糖的肠道微生物？ （ii）一个人可以 携带某些肠道微生物对个性化饮食有反应（例如，益生元： 对人类健康有益）？要解决这些问题，新的Cazyme注释工具必须具有能力 预测cazymes的碳水化合物底物。 最近的研究发现，不同编码基因的不同cazyme通常彼此共定位，并且 与细菌中的其他基因（例如那些编码糖转运蛋白，调节蛋白和信号蛋白的那些基因） 形成CGC的基因组（也称为局部或脉冲的多糖利用率）。那是新的基础 工具开发是CGC的基因成员资格（或功能域组成）可用于 预测其碳氢化物底物（例如，木，果胶，葡萄糖等）。创新是机器学习 方法将用于分析大量从文献中策划的实验表征的脉冲， 并且提取的序列特征将用于构建有效的分类器，以预测和对CGC进行分类 新的基因组/宏基因组。预期的结果将是新颖且用户友好的开源计算机 程序，数据库和Web服务器允许自动化的CGC标识和底物预测。这 意义在于，新工具将促进更多脉冲及其的实验表征 人肠道微生物组中的碳水化合物底物（在其他碳水化合物富含环境中）。所以， 该项目将为个性化营养的研究贡献计算解决方案，例如分析A 人的肠道微生物组可以预测此人是否可以对含有某些益生元聚糖的饮食做出反应。