Summary of study ST000074

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR000071. The data can be accessed directly via it's Project DOI: 10.21228/M8G01M This work is supported by NIH grant, U2C- DK119886.


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Study IDST000074
Study TitleGenetic effects of high fat diet on mouse fecal metabolomics
Study SummaryThis study includes 72 female mice with 4 mice from each of the 18 mice strains. Two mice from each strain were fed a high fat diet and two mice were fed a normal fat diet. The 36 mice fed a normal fat diet will serve as the controls. All mice are age 27.6 weeks or older at the time of sacrifice. UPLC-MS data was collected for all 72 samples.
University of North Carolina
DepartmentDiscovery and Analytical Sciences (DAS)
LaboratorySumner Lab
Last NameSumner
First NameSusan
AddressEastern Regional Comprehensive Metabolomics Resource Core, UNC Nutrition Research Institute, 500 Laureate Way, Kannapolis, NC, 28081
Submit Date2014-06-14
Num Groups36
Total Subjects72
Raw Data AvailableNo
Analysis Type DetailLC-MS
Release Date2015-06-01
Release Version1
Susan Sumner Susan Sumner application/zip

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Project ID:PR000071
Project DOI:doi: 10.21228/M8G01M
Project Title:Genetic effects of high fat diet on mouse fecal metabolomics
Project Type:Analysis of fecal metabolite profiles of genetically diverse BXD mouse strains receiving diets high or low in fat to elucidate the metabolism of the GI tract and obesity
Project Summary:We will study expression of a large series of metabolites in fecal samples from a genetically diverse set of mouse strains (BXD type). Separate cohorts of animals are maintained either on a 6% low fat diet or on a 60% high fat diet and at several points during normal life span. We predict that these dietary differences will have profound effects on the gastrointestinal (GI) tract and fecal metabolites, and that the differences will be influenced strongly by genetic factors. We also expect that fecal metabolite data will be a useful predictor of major outcome measures, such as fat mass, longevity, and mitochondrial state. All metabolite data will be entered into the GeneNetwork web service for genetic analysis of the effects of metabolites and diet on GI tract metabolism.
Institute:University of Tennessee
Department:Department of Anatomy and Neurobiology, Health Science Center
Last Name:Williams
First Name:Rovert
Address:855 Monroe Avenue, #515 LINK bldg, Memphis TN 38163 USA