Mapping host-microbiome interaction networks using integrative genomics

使用整合基因组学绘制宿主-微生物组相互作用网络

基本信息

批准号：
8745564
负责人：
John Tsang
金额：
$ 10.48万
依托单位：
NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/8745564
关键词：
Affect Animals Applied Genetics Area Autoimmunity Biological Assay Biological Markers Biological Process Body fat Breeding Chronic Collaborations Complex Computational Biology Custom DNA Data Data Analyses Disease Drug Targeting Dysplasia Environment Epithelial Epithelium Genetic Genomics Germ-Free Goals Host Defense Hypersensitivity Immune response Immune system Immunity Individual Inflammation Inflammatory Bowel Diseases Intestines Link Malignant Neoplasms Maps Metabolism Methods Microbe Modeling Mus Obese Mice Pathology Peripheral Phenotype Play Process Reading Relative (related person)Role Sampling Shapes Skin Source Surface Techniques Testing Transcript Variant base cost cyber infrastructure design gut microbiota interest member microbial microbiome pyrosequencing

Affect Animals Applied Genetics Area Autoimmunity Biological Assay Biological Markers Biological Process Body fat Breeding Chronic Collaborations Complex Computational Biology Custom DNA Data Data Analyses Disease Drug Targeting Dysplasia Environment Epithelial Epithelium Genetic Genomics Germ-Free Goals Host Defense Hypersensitivity Immune response Immune system Immunity Individual Inflammation Inflammatory Bowel Diseases Intestines Link Malignant Neoplasms Maps Metabolism Methods Microbe Modeling Mus Obese Mice Pathology Peripheral Phenotype Play Process Reading Relative (related person)Role Sampling Shapes Skin Source Surface Techniques Testing Transcript Variant base cost cyber infrastructure design gut microbiota interest member microbial microbiome pyrosequencing

展开

项目摘要

Our goal is to use the intestinal microbiota as a model and apply genetic and environmental perturbations to generate variations in gut microbiota (GM) composition and then use integrative genomics approaches to infer interactions among host and microbiota phenotypes. To reach our goal, we are developing enabling approaches in several critical areas: 1. Determine the best combinations of perturbation to use. We are considering both genetic and environmental perturbations. While existing data point to the importance of both environmental and genetic factors in shaping the microbiota, it is unclear, especially given the myriad confounding factors (e.g., cage and breeding environments), whether one is significantly more dominant than the other and what combined effects they exert together. Using mouse as a model, we are in the process of evaluating this and design appropriate perturbation and breeding strategies. 2. Develop inexpensive and high-throughput strategies to assay microbiome phenotypes and data analysis methods to process and analyze data generated from the individual data types. Phenotypes of interest include microbial composition and DNA and transcript contents. A first goal is to develop an Illumina-based approach to profile microbiota composition of complex samples (e.g., those from the gut). Existing culture-free techniques primarily use 454 pyrosequencing, which typically provides 10-20k reads per sample but still at a considerable cost per sample. Using the Illumina Hiseq platform would allow us to achieve significant deeper coverage (500k reads per sample) with lower costs ($20-30/sample). We have successfully developed a PCR amplicon based approach to sequence multiple 16s and genomics regions from samples. By using a barcoding and custom-primer strategy, we have conducted massively parallel phenotyping of microbiota composition of a number of intestinal, fecal and skin samples (in collaboration with Yasmine Belkaid). Some of these have already been sequenced by 454 to allow comparison. In collaboration with colleagues at the Computational Biology Branch of the Office of Cyber Infrastructure and Computational Biology, we are developing and testing Illumina-based data analysis methods for inferring microbial diversity and relative abundance.

我们的目标是将肠道菌群用作模型，并应用遗传和环境扰动来产生肠道菌群（GM）组成的变化，然后使用综合基因组学方法来推断宿主和微生物群表型之间的相互作用。为了实现我们的目标，我们正在在几个关键领域开发启用方法： 1。确定使用扰动的最佳组合。我们正在考虑遗传和环境扰动。尽管现有数据表明环境因素和遗传因素在塑造微生物群中的重要性，但尚不清楚，尤其是考虑到无数的混杂因素（例如笼子和育种环境），是否比另一个比另一个更重要的是占主导地位，以及它们将其施加的效果结合在一起。使用鼠标作为模型，我们正在评估它并设计适当的扰动和育种策略。 2。制定廉价和高通量策略，以测定微生物组表型和数据分析方法，以处理和分析从单个数据类型产生的数据。感兴趣的表型包括微生物组成以及DNA和转录含量。第一个目标是开发一种基于Illumina的方法来介绍复杂样品的微生物群组成（例如，肠道的样品）。现有的无培养技术主要使用454 pyrosequencing，通常提供10-20k的读取，但每个样品的成本仍然相当大。使用Illumina HISEQ平台将使我们能够以较低的成本（$ 20-30/样本）获得更深入的覆盖范围（每样本读数为50万）。我们已经成功地开发了一种基于PCR扩增子的方法，以从样品中序列多个16S和基因组区域。通过使用条形码和自定义提示策略，我们对许多肠道，粪便和皮肤样品的微生物群组成进行了大规模平行的表型（与Yasmine belkaid合作）。其中一些已经通过454测序以进行比较。与网络基础设施和计算生物学办公室计算生物学分支的同事合作，我们正在开发和测试基于Illumina的数据分析方法，以推断微生物多样性和相对丰度。