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.
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.