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

• 批准号: 8556047
• 负责人: John Tsang
• 金额: $ 41.92万
• 依托单位: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
• 依托单位国家: 美国
• 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/8556047
• 关键词: Affect; Applied Genetics; Area; Autoimmunity; Biological Assay; Biological Markers; Body fat; Breeding; Chronic; Collaborations; Complex; Computational Biology; Computing Methodologies; Custom; DNA; Data; Data Analyses; Disease; Drug Delivery Systems; Dysplasia; Environment; Epithelial; Epithelium; Genetic; Genomics; Germ-Free; Goals; Host Defense; Hypersensitivity; Immune response; Immune system; Immunity; Individual; Inflammation; Inflammatory Bowel Diseases; Intestines; Life; Link; Malignant Neoplasms; Maps; Metabolism; Methods; Microbe; Modeling; Mus; Obese Mice; Pathology; Peripheral; Phenotype; Play; Process; Reading; Regulation; Relative (related person); Role; Sampling; Shapes; Skin; Source; Surface; Techniques; Transcript; Variant; base; cost; cyber infrastructure; design; gut microbiota; interest; member; microbial; microbial host; microbiome

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 and regulation 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 PCR pipelines to amplify 16s regions of multiple samples. By using a barcoding and custom-primer strategy, we are conducting massively parallel 16s phenotyping of microbiota composition of samples that have already been sequenced by 454 to facilitate comparison. In collaboration with colleagues at the Computational Biology Branch of the Office of Cyber Infrastructure and Computational Biology, we are developing Illumina-based data analysis methods for inferring microbial diversity and relative abundance. 3. Develop computational methods to infer interactions among host and microbiota phenotypes by integrating host and microbial data. Before we have all the data generated, we are in the process of utilizing publicly available data along with the pilot data we are generating to start developing these methods.

我们的目标是使用肠道菌群作为模型，并应用遗传和环境扰动来产生肠道菌群（GM）组成的变化，然后使用整合基因组学方法来推断宿主和微生物群中的相互作用和调节。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）。我们已经成功开发了PCR管道来扩大多个样品的16S区域。通过使用条形码和自定义提示策略，我们正在对样品的微生物群组成进行大规模平行的16S表型，这些表型已经通过454测序，以促进比较。与网络基础设施和计算生物学办公室的计算生物学分支机构的同事合作，我们正在开发基于Illumina的数据分析方法，用于推断微生物多样性和相对丰度。 3。开发计算方法，通过整合宿主和微生物数据来推断宿主和微生物型表型之间的相互作用。在生成所有数据之前，我们正在利用公开可用的数据以及我们正在生成的试点数据开始开发这些方法。