Center for Quantitative Biology: A focus on "omics", from organisms to single cells Supplement 1

Dartmouth College, Hanover NH

Investigators

Linked publications & trials

Abstract

The human gastrointestinal tract harbors a diverse and dense population of microbes termed the gut microbiota. This microbial population produces diverse metabolites that collectively influence human physiology. Two prominent classes of gut microbiota-dependent metabolites are the bioactive byproducts of tryptophan metabolism and bile acid metabolism. The complexity of the microbiota, its variation across individuals, and the diversity of tryptophan and bile acid metabolites produced has resulted in a knowledge gap surrounding the relationship between metabolite flux and host gene expression. Addressing this knowledge is challenging due to the breadth and depth of technical expertise required and, to surmount this, we have assembled an interdisciplinary team. Here, we propose a team research project that leverages multi-omic data generation from mice colonized with either a complex or defined microbiota in comparison with germfree mice lacking microbiota. We will develop and employ sophisticated new multi-dimensional data analysis methods to reveal the impact of microbiota-dependent metabolite flux on host gene expression. In projects 1 and 2, PIs Sundrud and Ross will generate quantitative metabolomic profiling data of diverse tryptophan and bile acid metabolites from conventionally colonized mice and gnotobiotic mice colonized with a defined microbiota consisting of 14 human-gut-derived isolates capable of producing diverse metabolites. Metagenomic and transcriptomic datasets including bulk and single-cell RNA sequencing will be generated from multiple tissues in each group, with a specific emphasis on analysis of intestinal immune cell populations. In projects 3 and 4, PIs Song and Hoen will develop and implement new methodologies for integrating these multi-omic data into models which map the relationships between metabolite abundances and host gene expression. Together, these projects represent new areas of collaborative research for the team and will establish comprehensive datasets and models for future collaborative grant applications.