Collaborative Research: Novel Statistical Tools for Metagenomics and Metabolomics Data

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Accumulating evidence suggests that disrupting intestinal microbial ecosystems can cause many serious diseases including chronic diseases such as coronary heart disease (CHD), neurobehavioral diseases such as autism, inflammatory diseases such as inflammatory bowel disease (IBD) and etc. For example, the developing infant intestinal microbiome has been implicated in central myelination and the maturation and function of microglia (CNS immune cells), a core deficiency in Autism Spectrum Disorders. Thus, the study of the gut microbial distributions and their metabolites are very important to find new therapeutic targets for many diseases. However, compared to the huge amounts of medical research on human cells, our understanding of the microbial ecosystem is very limited: the biodiversity of them is barely studied, not to mention their interactions with the human host. This project will develop a suite of statistical theory and methods as well as computational tools to facilitate the understanding of the intestinal microbial ecosystem. The proposed methods are fast, efficient, and highly accurate. They can be widely applied to any metagenomic and metabolomic investigations. Graduate students will be trained by participating in research activities. The main goal of this project is to extend our knowledge of intestinal microbial ecosystem by developing novel quantitative methods for microbial species and their metabolites detection, identification, and quantification in various diseases. The sensitivity and specificity of our methods permit accurate detection of microbial species at very low coverage levels. This is a translational technology that should find substantial use in biomedical researches and drug developments. More specifically, the PIs shall develop methods for identifying microbial species especially unknown species, reducing error in metabolomic analysis, estimating microbial and metabolite distributions, quantifying microbial or metabolites distributional differences that are associated with diseases, integrating metagenomic and metabolomic analysis together to study the microbial ecosystem, building analytical models to link metabolite profiling with species profiling to understand how metabolites interact with genetic contents and eventually affect cell metabolism.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.