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.

项目概要 人类肠道微生物组中的碳水化合物酶基因簇 大约 2,400 年前，希波克拉底说过：“让食物成为你的药物，让药物成为你的食物”，这句话现在已众所周知。 这主要是由于人类肠道中发生的“饮食-微生物-宿主”相互作用。 微生物降解碳水化合物可产生多种代谢产物，对人体产生深远的影响 作为内布拉斯加州健康食品中心的生物信息学研究员，我们的长远利益是 PI 包括：（i）开发专门的计算工具，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）表征酶和其他连接微生物的遗传元件 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 (CGC) 的功能注释 PI 在 CAZyme 生物信息学工具开发方面拥有 10 多年的经验，并且 维护着一个广受认可的 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 dbCAN 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聚糖吗？(ii) 人可以吗？ 携带某些肠道微生物对个体化饮食有反应（例如，益生元：膳食化合物 有益于人类健康）？为了解决这些问题，新的 CAZyme 注释工具必须具备以下能力： 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，并且 与细菌中的其他基因（例如编码糖转运蛋白、调节蛋白和信号蛋白的基因） 基因组形成 CGC（也称为多糖利用位点或 PUL），从而为新的基础奠定了基础。 工具开发的重点是 CGC 的基因成员资格（或功能域组成）可用于 预测其碳水化合物底物（例如木聚糖、果胶、葡聚糖等），其创新在于机器学习。 方法将用于分析从文献中整理的大量经过实验表征的 PUL， 提取的序列特征将用于构建有效的分类器来预测和分类 CGC 新的基因组/宏基因组将是新颖且用户友好的开源计算机。 允许自动 CGC 识别和底物预测的程序、数据库和网络服务器。 重要的是，新工具将有助于更多 PUL 及其特性的实验表征 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）。 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 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。