High-resolution analysis of diversity and variation in the human microbiome

人类微生物组多样性和变异的高分辨率分析

基本信息

批准号：
7991431
负责人：
Eric John Alm
金额：
$ 21万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-06-15 至 2012-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7991431
关键词：
Address Age Bacterial Model Base Sequence Benchmarking Biological Cells Classification Clinical Research Collaborations Communities Complex Computer software Data Data Set Diet Disease Excision Genetic Variation Genome Goals Health Human Human Genome Project Human Microbiome Individual Institutes Laboratories Length Light Medical Metagenomics Methods Microbe Modeling Molecular Patients Pattern Phenotype Population Preparation Process Protocols documentation Public Health Reading Research Research Personnel Resolution Ribosomal RNA Risk Factors Role Running Sampling Structure Surveys Taxon Technology Testing Time United States National Institutes of Health Variant Work analytical method base computer code computerized tools cost design genome sequencing improved insight mathematical model meetings member microbial microbial community microbiome new technology next generation novel open source public health relevance rRNA Genes tool

项目摘要

DESCRIPTION (provided by applicant): This project will result in new technologies for high-throughput 16S rRNA sequencing of microbial communities. Understanding the role of the human microbiome in health and disease is an emerging field, and has been targeted as a major NIH Roadmap Initiative. Microbial community analysis by 16S rRNA sequencing is a key component of microbiome studies, together with whole genome sequencing and metagenomics. This project will establish and optimize (i) a suite of computational tools that are capable of identifying populations specifically associated with host phenotypes (broadly defined to include disease state, diet, age, risk factors, etc.), and predicting host phenotype based on microbiome data, and (ii) an experimental approach to generating partial 16S rRNA sequences that is orders of magnitude less expensive than conventional methods thus enabling unprecedented resolution in microbiome comparisons. The analytical method is an extension of previously successful modeling of bacterial population structure in environmental samples, but will be adapted to the specific requirements of microbiome research (e.g., high variation between individuals). The details of the sequencing method (efficient sample multiplexing, removal of amplification primers, and optimization of PCR conditions/primers to reduce bias), and the data generated will be generalizable to most next- generation sequencing technologies, although the proposed work will focus on the Illumina platform because of its currently favorable cost-capability attributes. A further scientific aim of this project is to use ultra-deep sequencing to expand coverage of an ongoing clinical study of IBD patients. This project will be done in tight collaboration with the Broad Institute, a major sequencing center currently involved in the Human Microbiome Project, and tools will be widely disseminated so that results obtained can have an immediate impact on the field. PUBLIC HEALTH RELEVANCE: Understanding the role of the microbiome in human health and disease is a major NIH Roadmap Initiative. The proposed work will directly impact researchers engaged in human microbiome studies by lowering the cost of deep 16S rRNA community sampling by orders of magnitude to ~$35 for >30,000X coverage of a sample, thus bringing personalized microbiome analysis within reach. In addition, the computational tools that will be developed as part of this project will help to uncover meaningful medical and biological insight from the massive data sets enabled by the proposed experimental platform and other ongoing studies.

描述（由申请人提供）：该项目将产生用于微生物群落高通量 16S rRNA 测序的新技术。了解人类微生物组在健康和疾病中的作用是一个新兴领域，并已被列为 NIH 路线图的一项主要计划。通过 16S rRNA 测序进行微生物群落分析，以及全基因组测序和宏基因组学，是微生物组研究的关键组成部分。该项目将建立和优化（i）一套计算工具，能够识别与宿主表型（广义上定义为包括疾病状态、饮食、年龄、风险因素等）具体相关的群体，并根据宿主表型预测宿主表型。微生物组数据，以及 (ii) 生成部分 16S rRNA 序列的实验方法，该方法比传统方法便宜几个数量级，从而在微生物组比较中实现前所未有的分辨率。该分析方法是先前成功的环境样本中细菌种群结构建模的延伸，但将适应微生物组研究的特定要求（例如个体之间的高度变异）。测序方法的细节（有效的样本复用、扩增引物的去除以及 PCR 条件/引物的优化以减少偏差）以及生成的数据将可推广到大多数下一代测序技术，尽管拟议的工作将集中于Illumina 平台因其目前有利的成本能力属性而被选中。该项目的另一个科学目标是利用超深度测序来扩大正在进行的 IBD 患者临床研究的覆盖范围。该项目将与布罗德研究所（目前参与人类微生物组项目的主要测序中心）密切合作完成，工具将得到广泛传播，以便获得的结果能够对该领域产生直接影响。公共卫生相关性：了解微生物组在人类健康和疾病中的作用是 NIH 路线图的一项主要举措。拟议的工作将直接影响从事人类微生物组研究的研究人员，将深度 16S rRNA 群落采样的成本降低几个数量级至约 35 美元，样本覆盖率超过 30,000 倍，从而使个性化微生物组分析触手可及。此外，作为该项目的一部分开发的计算工具将有助于从拟议的实验平台和其他正在进行的研究所支持的海量数据集中揭示有意义的医学和生物学见解。