Abstract

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.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Program Projects (P01)
Project #
5P01HL028481-27
Application #
8300888
Study Section
Heart, Lung, and Blood Initial Review Group (HLBP)
Project Start
Project End
Budget Start
2011-07-01
Budget End
2012-06-30
Support Year
27
Fiscal Year
2011
Total Cost
$222,622
Indirect Cost

  Institution

Name
University of California Los Angeles
Department
Type
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095

  Publications

Small, Kerrin S; Todor?evi?, Marijana; Civelek, Mete et al. (2018) Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition. Nat Genet 50:572-580
Mangul, Serghei; Yang, Harry Taegyun; Strauli, Nicolas et al. (2018) ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues. Genome Biol 19:36
Cantor, Rita; Navarro, Linda; Pan, Calvin (2018) Identifying fenofibrate responsive CpG sites. BMC Proc 12:43
Rahmani, Elior; Schweiger, Regev; Shenhav, Liat et al. (2018) BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference. Genome Biol 19:141
Goldberg, Ira J; Reue, Karen; Abumrad, Nada A et al. (2018) Deciphering the Role of Lipid Droplets in Cardiovascular Disease: A Report From the 2017 National Heart, Lung, and Blood Institute Workshop. Circulation 138:305-315
Hui, Simon T; Kurt, Zeyneb; Tuominen, Iina et al. (2018) The Genetic Architecture of Diet-Induced Hepatic Fibrosis in Mice. Hepatology 68:2182-2196
Kang, Eun Yong; Lee, Cue Hyunkyu; Furlotte, Nicholas A et al. (2018) An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures. Genetics 209:685-698
Seldin, Marcus M; Koplev, Simon; Rajbhandari, Prashant et al. (2018) A Strategy for Discovery of Endocrine Interactions with Application to Whole-Body Metabolism. Cell Metab 27:1138-1155.e6
Lang, Jennifer M; Pan, Calvin; Cantor, Rita M et al. (2018) Impact of Individual Traits, Saturated Fat, and Protein Source on the Gut Microbiome. MBio 9:
Cherlin, Svetlana; Wang, Maggie Haitian; Bickeböller, Heike et al. (2018) Detecting responses to treatment with fenofibrate in pedigrees. BMC Genet 19:64

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