The collections of microbes (i.e., microbiota) that inhabit the human intestine have profound effects on development, physiology and health. Alterations in the gut microbiota contribute to metabolic disorders including obesity and diabetes. Gut microbes affect our physiology at least in part by metabolizing bile acids (BAs). BAs are key nodes of metabolic communication between gut microbes and the host; they are synthesized in the host liver, have antimicrobial effects, facilitate the absorption of lipids, andact as hormones to modulate glucose homeostasis, lipid metabolism, energy expenditure, and intestinal motility. Gut microbes in turn metabolize BAs and regulate their synthesis and their influence on host physiology. However, the genes that modulate the composition of the gut microbiota and abundance of individual species of BAs remain largely unknown. We propose to combine the power of the Diversity Outbred (DO) mouse panel, a newly developed resource that contains as much genetic variation as the human population, with biochemical analyses of BAs and gut microbiota profiling to identify genes and pathways that modulate gut microbial composition and abundance of BAs, and are associated with disease susceptibility. The phenotypic diversity and high-resolution genetic mapping of the DO mice will direct our use of select gnotobiotic hosts of different genetic backgrounds to experimentally validate the contributions of these genes and pathways on gut microbial composition, abundance of BAs and disease susceptibility. The proposed studies are based on three central hypotheses: (i) host genetic variation alters microbiota composition; (ii) differences in microbiota composition result in changes in BA composition and BA-dependent host signaling; and (iii) altered BA signaling contributes to the development of metabolic disease. Our preliminary studies on a small cohort of DO mice have revealed an extraordinary level of phenotypic diversity of diabetes-related traits, fecal BAs and gut microbiota composition in response to a prolonged feeding of a western-style high-fat/high-sucrose diet. Our collective expertise in gnotobiotics and gut microbiome (Rey), nutrition, obesity and diabetes (Attie, Keller), metabolomics (Wang) and statistical genetics (Broman) will enable the discovery of novel genetically-driven host-microbe interactions that modulate the development of diet-induced metabolic disease.

Public Health Relevance

The collections of microbes that inhabit our intestines exert a profound influence on our metabolism and the development of metabolic diseases such as type-2 diabetes. This study is designed to reveal the contributions of genes and diets in selecting protective versus pathogenic microbes. The results from this study will be used to develop next- generation probiotics designed to prevent or mitigate the impact metabolic disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK108259-04
Application #
9545554
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Karp, Robert W
Project Start
2015-09-23
Project End
2020-08-31
Budget Start
2018-09-01
Budget End
2019-08-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Wisconsin Madison
Department
Microbiology/Immun/Virology
Type
Earth Sciences/Resources
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
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Skye, Sarah M; Zhu, Weifei; Romano, Kymberleigh A et al. (2018) Microbial Transplantation With Human Gut Commensals Containing CutC Is Sufficient to Transmit Enhanced Platelet Reactivity and Thrombosis Potential. Circ Res 123:1164-1176
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Kreznar, Julia H; Keller, Mark P; Traeger, Lindsay L et al. (2017) Host Genotype and Gut Microbiome Modulate Insulin Secretion and Diet-Induced Metabolic Phenotypes. Cell Rep 18:1739-1750
Romano, Kymberleigh A; Martinez-Del Campo, Ana; Kasahara, Kazuyuki et al. (2017) Metabolic, Epigenetic, and Transgenerational Effects of Gut Bacterial Choline Consumption. Cell Host Microbe 22:279-290.e7
Ye, Shuyun; Bacher, Rhonda; Keller, Mark P et al. (2017) Statistical Methods for Latent Class Quantitative Trait Loci Mapping. Genetics 206:1309-1317

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