The Southeast Resource Center for Integrated Metabolomics (SECIM) integrates existing strengths to create a comprehensive resource for basic and clinical scientists to obtain state-of-the-art metabolomics data and analyses. The SECIM Administrative Core is directed by Prof Arthur Edison, who will provide oversight of the entire project. The SECIM Program Coordinator, Dr. Eric Milgram, will oversee day-to-day operations, manage and optimize the overall workflow, serve as the primary interface and point of contact with SECIM users, and provide technical assistance to the mass spectrometry technical cores 1 and 3. Dr. Milgram has 60% of his effort in this core and 40% in the Bioinformatics Core to provide a seamless interface between the SECIM user portal, pipeline, and analysis. The Administrative Core provides oversight to SECIM through the establishment of 3 standing committees: the SECIM-Executive Committee (SECIM-EC), the Internal Advisory Committee (lAC), and the External Advisory Committee (EAC). SECIM-EC is comprised of SECIM leadership and is responsible for operational and budgetary decisions. The lAC is comprised of faculty representatives from colleges or centers at UF who have a stake in SECIM and will provide input about services;this group also covers the vast majority of types of metabolomics applications, ranging from agriculture to basic science to clinical science. The EAC is comprised of international metabolomics experts who will advise on technical and strategic matters and challenge SECIM to constantly improve. The Administrative Core will also develop and implement a comprehensive business plan, designed to move SECIM into full cost recovery in years 6 and beyond. Alicia Turner is the SECIM Chief Business Development Officer, and she has developed a model that serves as the starting budget for SECIM. During the first 3 years of operation, Ms. Turner will improve the model as real user data are recorded and true costs determined. She will train a financial assistant, who will implement the model in a full cost recovery auxiliary in the CTSI in year 6.

Public Health Relevance

The major goal of metabolomics is the measurement and characterization of metabolites from living organisms, including people. Small molecule metabolites are sensitive to disease, treatment, and many environmental factors, and they can be used as indicators of disease in diagnosis or as markers to assess the outcome of treatment.

Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Florida
Department
Type
DUNS #
City
Gainesville
State
FL
Country
United States
Zip Code
32611
Hattori, Ayuna; Tsunoda, Makoto; Konuma, Takaaki et al. (2017) Cancer progression by reprogrammed BCAA metabolism in myeloid leukaemia. Nature 545:500-504
Le Guennec, Adrien; Tayyari, Fariba; Edison, Arthur S (2017) Alternatives to Nuclear Overhauser Enhancement Spectroscopy Presat and Carr-Purcell-Meiboom-Gill Presat for NMR-Based Metabolomics. Anal Chem 89:8582-8588
Ulmer, Candice Z; Koelmel, Jeremy P; Ragland, Jared M et al. (2017) LipidPioneer : A Comprehensive User-Generated Exact Mass Template for Lipidomics. J Am Soc Mass Spectrom 28:562-565
Markley, John L; Br├╝schweiler, Rafael; Edison, Arthur S et al. (2017) The future of NMR-based metabolomics. Curr Opin Biotechnol 43:34-40
Reed, Laura K; Baer, Charles F; Edison, Arthur S (2017) Considerations when choosing a genetic model organism for metabolomics studies. Curr Opin Chem Biol 36:7-14
Liu, Haiyan; Garrett, Timothy J; Su, Zhihua et al. (2017) UHPLC-Q-Orbitrap-HRMS-based global metabolomics reveal metabolome modifications in plasma of young women after cranberry juice consumption. J Nutr Biochem 45:67-76
DeRatt, Barbara N; Ralat, Maria A; Lysne, Vegard et al. (2017) Metabolomic Evaluation of the Consequences of Plasma Cystathionine Elevation in Adults with Stable Angina Pectoris. J Nutr 147:1658-1668
Koelmel, Jeremy P; Kroeger, Nicholas M; Gill, Emily L et al. (2017) Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation. J Am Soc Mass Spectrom 28:908-917
Koelmel, Jeremy P; Ulmer, Candice Z; Jones, Christina M et al. (2017) Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation. Biochim Biophys Acta 1862:766-770
Wang, Cheng; He, Lidong; Li, Da-Wei et al. (2017) Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry. J Proteome Res 16:3774-3786

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