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 #
5P01HL028481-28
Application #
8378151
Study Section
Heart, Lung, and Blood Initial Review Group (HLBP)
Project Start
Project End
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
28
Fiscal Year
2012
Total Cost
$222,622
Indirect Cost
$78,062

  Institution

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

  Publications

Miao, Zong; Alvarez, Marcus; Pajukanta, Päivi et al. (2018) ASElux: an ultra-fast and accurate allelic reads counter. Bioinformatics 34:1313-1320
Kurt, Zeyneb; Barrere-Cain, Rio; LaGuardia, Jonnby et al. (2018) Tissue-specific pathways and networks underlying sexual dimorphism in non-alcoholic fatty liver disease. Biol Sex Differ 9:46
Orozco, Luz D; Farrell, Colin; Hale, Christopher et al. (2018) Epigenome-wide association in adipose tissue from the METSIM cohort. Hum Mol Genet 27:1830-1846
Chella Krishnan, Karthickeyan; Kurt, Zeyneb; Barrere-Cain, Rio et al. (2018) Integration of Multi-omics Data from Mouse Diversity Panel Highlights Mitochondrial Dysfunction in Non-alcoholic Fatty Liver Disease. Cell Syst 6:103-115.e7
Freund, Malika Kumar; Burch, Kathryn S; Shi, Huwenbo et al. (2018) Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits. Am J Hum Genet 103:535-552
Pan, David Z; Garske, Kristina M; Alvarez, Marcus et al. (2018) Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS. Nat Commun 9:1512
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

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