- MPC The Molecular Phenotyping Core will provide analytical tools to the MNORC investigators to elucidate molecular mechanisms of disease relevant to obesity and nutritional disorders including structural identification and quantification of metabolites, perform functional metabolic studies and integrate multi-omic datasets to provide biological context. These include identifying unknown metabolites, developing new methods for targeted metabolomics in a range of biological matrices, validating new biological response indicators and providing biological context to diverse metabolomic measurements by providing bioinformatics analysis and developing new bioinformatics tools. In addition to providing instrumental infrastructure, the Core staff will provide consultation and collaboration to apply metabolomics and data analytical platforms in nutrition and obesity research. The Molecular Phenotyping Core will optimize the efficiency and cost-effectiveness by providing these services to MNORC investigators through a centralized laboratory. In the past five years the core has provided standardized analytical techniques for the analysis of small molecule metabolites such as amino acid, lipid and nucleic acid metabolites in samples from murine, rodent and human tissues, plasma, and urine, and cultured cells. New offerings have included novel untargeted metabolomics platforms, metabolic flux analysis and data interrogation and bioinformatics tools which will enhance the infrastructure available for the MNORC investigators. Emphasis will also be placed on fulfilling the needs of Investigators of the MNORC who will gain maximum benefit from the power of molecular analysis. We are particularly interested in addressing three areas of need: first, applying the unique sensitivity and specificity of molecular Phenotyping techniques to broaden understanding of nutritional disorders ; second, the development of innovative techniques for the detection and structural elucidation of nutrition-related biomolecules; third, provide data integration and bioinformatic tools relevant to metabolomics studies; and fourt, providing training for graduate students and postdoctoral fellows with an interest in nutrition research. By centralizing and standardizing procedures, the Core provides a common set of analytical tools that will lead to a unified understanding of molecular mechanisms involved in physiologic and pathophysiologic processes underlying obesity and other nutrition related disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Center Core Grants (P30)
Project #
2P30DK089503-11
Application #
10045298
Study Section
Special Emphasis Panel (ZDK1)
Project Start
Project End
Budget Start
2020-07-01
Budget End
2021-06-30
Support Year
11
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Horowitz, Jeffrey F; Ortega, Juan F; Hinko, Alexander et al. (2018) Changes in markers for cardio-metabolic disease risk after only 1-2 weeks of a high saturated fat diet in overweight adults. PLoS One 13:e0198372
Peck, Bailey C E; Seeley, Randy J (2018) How does 'metabolic surgery' work its magic? New evidence for gut microbiota. Curr Opin Endocrinol Diabetes Obes 25:81-86
Isaman, Deanna J M; Rothberg, Amy E; Herman, William H (2018) The Effect of Attrition on Reported Diabetes Remission Rates Following Roux-en-Y Gastric Bypass: a Sensitivity Analysis. Obes Surg 28:1308-1312
Meng, Zhuo-Xian; Tao, Weiwei; Sun, Jingxia et al. (2018) Uncoupling Exercise Bioenergetics From Systemic Metabolic Homeostasis by Conditional Inactivation of Baf60 in Skeletal Muscle. Diabetes 67:85-97
Ward, Kristen M; Yeoman, Larisa; McHugh, Cora et al. (2018) Atypical Antipsychotic Exposure May Not Differentiate Metabolic Phenotypes of Patients with Schizophrenia. Pharmacotherapy 38:638-650
Tang, Ming; Gao, Chao; Goutman, Stephen A et al. (2018) Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering. Neuroinformatics :
Bagchi, Devika P; Forss, Isabel; Mandrup, Susanne et al. (2018) SnapShot: Niche Determines Adipocyte Character II. Cell Metab 27:266-266.e1
Arble, Deanna M; Evers, Simon S; Bozadjieva, Nadejda et al. (2018) Metabolic comparison of one-anastomosis gastric bypass, single-anastomosis duodenal-switch, Roux-en-Y gastric bypass, and vertical sleeve gastrectomy in rat. Surg Obes Relat Dis 14:1857-1867
Callaghan, Brian C; Xia, Rong; Reynolds, Evan et al. (2018) Better diagnostic accuracy of neuropathy in obesity: A new challenge for neurologists. Clin Neurophysiol 129:654-662
Zeng, Lixia; Mathew, Anna V; Byun, Jaeman et al. (2018) Myeloperoxidase-derived oxidants damage artery wall proteins in an animal model of chronic kidney disease-accelerated atherosclerosis. J Biol Chem 293:7238-7249

Showing the most recent 10 out of 342 publications