Collaborative Research: Identification of Immunomodulatory Microbiota Metabolites Grant uri icon


  • Lee/Jayaraman 1264502/1264526 The overall goal of this research is to identify bioactive metabolites generated by the gut microbiota that impact the inflammation of adipose tissue in obesity. The human gastrointestinal (GI) tract is colonized by hundreds of trillions bacteria belonging to ~1,000 species that are collectively termed the microbiota. Alterations in the microbiota composition and/or function (dysbiosis) are correlated to a growing number of metabolic disorders, including obesity. Chronic, low-grade inflammation of adipose tissue is robustly associated with obesity, and also underlies the development of insulin resistance and the metabolic syndrome. There is growing evidence that gut dysbiosis leads to inflammation in mesenteric adipose tissue. However, the molecular mediators and mechanisms of their actions remain poorly understood. This work hypothesizes that microbiota-derived metabolites are important modulators of host adipose tissue inflammation. Identifying these microbiota metabolites has been extremely difficult, because a majority of the commensal bacteria in the gut are poorly characterized and many of these bacteria cannot be grown in culture. As microbes are capable of performing metabolic reactions not available to the host, and metabolites synthesized by one species can be further modified by another species, the biotransformation space accessible to the microbiota is vast. To overcome these challenges, this project investigates a novel bioinformatics-metabolomics approach enabling focused and quantitative exploration of gut microbiota metabolites. The results of the bioinformatics and metabolomics analyses will be used to establish a physiological basis for in vitro experiments on the mechanisms whereby microbiota metabolites influence adipose tissue inflammation in obesity. The expected outcome of this project is to identify specific metabolites that can be unequivocally sourced to the gut microbiota and are present in host adipose tissue, and to determine their immunomodulatory properties in the context of adipose tissue inflammation in obesity. Broader Impact This research is novel in that few studies have explored the role for microbiota metabolites in the development of chronic body fat inflammation in obesity. The proposed work will identify and quantify bacterial metabolites whose levels may be altered under conditions of obesity and influence the state of inflammation. This research has transformative potential, both methodologically as well as discovery-wise. The proposed experiments could pave the way for a general methodology for measuring bioactive chemicals that are naturally present in the body, but are produced by bacteria, rather than the body. The discovery of naturally resident bacterial metabolites with anti-inflammatory properties could lead to new, safe treatment modalities for obesity as an inflammatory disease. The proposed project is highly interdisciplinary, and provides a unique opportunity to train students in cutting-edge research at the interface of several different fields in engineering and life science. To create research opportunities for underrepresented minorities, the proposal includes a plan for a joint summer internship program. Two minority students from Texas A&M will be recruited each year to intern in the lead investigator''s laboratory at Tufts. In addition, the investigators will integrate the proposed research into ongoing educational and outreach efforts at their respective institutions by recruiting undergraduate students to participate in open-ended projects from the proposed work and incorporating the methodologies and findings into existing courses in Metabolic Engineering and Systems Biology. Due to the interdisciplinary nature of the project, this award by the Biotechnology, Biochemical, and Biomass Engineering Program of the CBET Division is co-funded by the Systems and Synthetic Biology Program of the Division of Molecular and Cellular Biology.

date/time interval

  • 2013 - 2017