The metabolomics core laboratory at the Duke Stedman Center deploys both targeted and non-targeted mass spectrometry (MS)-based metabolic profiling to provide a rare combination of broad coverage and analytical precision. The Duke team has been a leader in applying targeted MS-based metabolic profiling for understanding of disease and biological mechanisms. The role of the core in the current application will be to perform both targeted and non-targeted metabolomics assays on samples generated by Dr. Gordon in Project 1, and Drs. Heath and Klein in Project 2; in both instances this work continues previously established and productive collaborations between our groups. In the context of Project 1, Core A will measure metabolites produced by microbial communities taken from obese and lean twin pairs and transplanted into germ-free mice, and will also provide metabolomic profiles of serum, urine and tissues of the host animals. In the context of Project 2, Core A will provide metabolomic profiles of twin pairs with discordance for obesity that are further sub-divided into metabolically well and unwell subgroups. Obtaining metabolic profiles of the human subjects in collaboration with Project 2 provides a unique opportunity to investigate the impact of gut microbiota from those subjects on metabolism when transplanted into mice via Project 1. We hypothesize that disease-related metabolic signatures will be transmitted to germ-free mice by transplantation of microbial communities from obese and metabolically unhealthy subjects. Metabolic profiles of serum, urine and tissues will include targeted MS/MS and GC/MS-based assays of approximately 220 metabolites across 8 classes or modules of analytes that represent byproducts of core metabolic pathways. These analyses will be supplemented by non-targeted GC/MS-based analyses of a broader array of metabolites, including some unique to microbial metabolic pathways.
Studies by Core A will support the efforts of Projects 1 and 2 to understand the impact of gut microorganisms on host serum, urine and tissue metabolic profiles of the host. Defining the impact of the microbiota from obese, metabolically unhealthy versus lean healthy individuals on host metabolism has the potential to define new therapeutic strategies for intervention in obesity and related metabolic diseases and conditions.
| Chondronikola, Maria; Magkos, Faidon; Yoshino, Jun et al. (2018) Effect of Progressive Weight Loss on Lactate Metabolism: A Randomized Controlled Trial. Obesity (Silver Spring) 26:683-688 |
| Hillmann, Benjamin; Al-Ghalith, Gabriel A; Shields-Cutler, Robin R et al. (2018) Evaluating the Information Content of Shallow Shotgun Metagenomics. mSystems 3: |
| Janssen, Stefan; McDonald, Daniel; Gonzalez, Antonio et al. (2018) Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information. mSystems 3: |
| An, Jie; Wang, Liping; Patnode, Michael L et al. (2018) Physiological mechanisms of sustained fumagillin-induced weight loss. JCI Insight 3: |
| Wang, Hanghang; Muehlbauer, Michael J; O'Neal, Sara K et al. (2017) Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma. Metabolites 7: |
| Jiang, Lingjing; Amir, Amnon; Morton, James T et al. (2017) Discrete False-Discovery Rate Improves Identification of Differentially Abundant Microbes. mSystems 2: |
| Mark Welch, Jessica L; Hasegawa, Yuko; McNulty, Nathan P et al. (2017) Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc Natl Acad Sci U S A 114:E9105-E9114 |
| Newgard, Christopher B (2017) Metabolomics and Metabolic Diseases: Where Do We Stand? Cell Metab 25:43-56 |
| Morton, James T; Sanders, Jon; Quinn, Robert A et al. (2017) Balance Trees Reveal Microbial Niche Differentiation. mSystems 2: |
| Green, Jonathan M; Barratt, Michael J; Kinch, Michael et al. (2017) Food and microbiota in the FDA regulatory framework. Science 357:39-40 |
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