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.