The Biochemical and Metabolic Profiling Core provides sensitive and accurate in vitro biochemical assays and in vivo metabolic/physiologic assays for the analysis of selected biomarkers relevant to lipid and glucose metabolism. The methods and assays described below have all been fully implemented and used successfully by our experienced personnel for several years. This Core has served as a vital component of the PPG for the past 20 years. The assays performed by the Biochemical and Metabolic Profiling Core can be broken down into four broad categories: 1). Analysis of plasma samples;2) Analysis of tissue samples;3) In vivo metabolic/physiologic tests. 4) Specialized biochemical and metabolic analysis designed to address specific project needs. The assays and methods we will use are listed below. The assays in categories 1-3 which will be used on a routine basis have a more detailed description of the methodology in the """"""""Method Details"""""""" at the end of this section.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Program Projects (P01)
Project #
5P01HL028481-29
Application #
8502729
Study Section
Heart, Lung, and Blood Initial Review Group (HLBP)
Project Start
Project End
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
29
Fiscal Year
2013
Total Cost
$211,936
Indirect Cost
$74,315
Name
University of California Los Angeles
Department
Type
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Singh, Rajan; Parveen, Meher; Basgen, John M et al. (2016) Increased Expression of Beige/Brown Adipose Markers from Host and Breast Cancer Cells Influence Xenograft Formation in Mice. Mol Cancer Res 14:78-92
Sul, Jae Hoon; Bilow, Michael; Yang, Wen-Yun et al. (2016) Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models. PLoS Genet 12:e1005849
Baldán, Ángel; de Aguiar Vallim, Thomas Q (2016) miRNAs and High-Density Lipoprotein metabolism. Biochim Biophys Acta 1861:2053-2061
Agrawal, Rahul; Noble, Emily; Vergnes, Laurent et al. (2016) Dietary fructose aggravates the pathobiology of traumatic brain injury by influencing energy homeostasis and plasticity. J Cereb Blood Flow Metab 36:941-53
Kang, Eun Yong; Martin, Lisa; Mangul, Serghei et al. (2016) Discovering SNPs Regulating Human Gene Expression Using Allele Specific Expression from RNA-Seq Data. Genetics :
Gusev, Alexander; Ko, Arthur; Shi, Huwenbo et al. (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245-52
Kang, Eun Yong; Park, Yurang; Li, Xiao et al. (2016) ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis. G3 (Bethesda) 6:1793-8
Joo, Jong Wha J; Kang, Eun Yong; Org, Elin et al. (2016) Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure. Genetics 204:1379-1390
Ribas, Vicent; Drew, Brian G; Zhou, Zhenqi et al. (2016) Skeletal muscle action of estrogen receptor α is critical for the maintenance of mitochondrial function and metabolic homeostasis in females. Sci Transl Med 8:334ra54
Salehi, Pezhman; Myint, Anthony; Kim, Young J et al. (2016) Genome-Wide Association Analysis Identifies Dcc as an Essential Factor in the Innervation of the Peripheral Vestibular System in Inbred Mice. J Assoc Res Otolaryngol 17:417-31

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