||5U24CA268153-02 Interpret this number
||Univ Of North Carolina Chapel Hill
||Metabolomics and Clinical Assays Center
Abstract (Metabolomics and Clinical Assay Center, MCAC)
Determining how individuals differ in their metabolism, and in their response to dietary intake, is critical to
developing personalized intervention strategies for preventing and delaying the onset of chronic diseases such
as obesity, diabetes, cardiovascular disease, and cancer. The MCAC will a) acquire and process high quality
targeted and untargeted metabolomics data, b) prioritize, predict, and confirm the identity of unknown peaks, c)
provide CLIA certified clinical assays, d) collaborate with the Common Fund Data Ecosystem, e) construct a data
infrastructure which ensures FAIRness and enables interoperability of the data with other Common Fund data
sets, and f) collaboratively work with the NIH Common Fund Nutrition for Precision Health (NPH) Consortium.
The MCAC brings an outstanding team of investigators from 3 UNC Systems Universities that are co-located on
the North Carolina Research Campus (NCRC) and Duke University. Dr. Susan Sumner (UNC Chapel Hill,
Nutrition Research Institute, NCRC, Untargeted Metabolomics) will serve as the PI with support from expert
scientists who specialize in nutrition and targeted metabolomics of host metabolism (Dr. Christopher Newgard,
Director, Sarah W. Stedman Nutrition and Metabolism Center and Duke Molecular Physiology Institute), dietary
interventions and targeted phytochemical analysis (Dr. Colin Kay, North Carolina State University, NCRC), CLIA
certified clinical assays (Dr. Steven Cotten, UNCCH), and Computational Metabolomics (Dr. Xiuxia Du, UNC
Charlotte, NCRC). Our team provides a unique combination of long-standing expertise in metabolomics
technologies, coupled with deep knowledge of nutrition, metabolic physiology, and chronic disease mechanisms.
We are experienced with the application of targeted and untargeted metabolomics in large-scale clinical and
epidemiology studies, including in other NIH Consortia. We have used metabolomics to define metabolic
signatures and pathways associated with dietary intake, nutrition assessments, demographics, lifestyle factors,
microbial populations, genetics, transcriptomics, clinical assays, and clinical phenotypes of health and wellness.
We have developed comprehensive informatics capabilities for targeted and untargeted metabolomics and
exposome research. We have developed an online mass spectral knowledge base resource for prioritizing and
predicting unknown metabolites by leveraging publicly available data. Our high quality MCAC datasets produced
under fine-tuned protocols with quality control and quality assurance metrics, will be essential for success of the
NPH Consortium. The MCAC will provide data and expert biological interpretation in exploration of the
heterogeneity in metabolism among study subjects, providing a roadmap that will help explain why individuals
differ in their metabolic responses to dietary interventions, and what this portends for future disease risk. The
MCAC will provide a robust data set to the Artificial Intelligence for Multimodal Data Modeling and Bioinformatics
Center for use in development of algorithms to predict individual dietary responses that can ultimately be
translated for design of targeted dietary interventions to improve health and quality of life.
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, Strathmann F.G.
, Barrett F.S.
, Labay L.
, Mullally J.
, Sherwood A.M.
, Wiegand F.
Clinical chemistry, 2023-04-03; 69(4), p. 319-326.
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, Korpi-Steiner N.
, Cotten S.W.
Clinical chemistry, 2023-02-01; 69(2), p. 202-204.