This project is a continuation of a project in the original cycle of our PPG two decades ago. The concept is to apply the power of mouse genetics to understand, in vivo, the complex physiologic, cellular and molecular interactions that contribute to cardiovascular disease and the metabolic syndrome. During the current grant, we positionally cloned a gene in mouse that results in a combined hyperlipidemic phenotype. The gene proved to correspond to thioredoxin interacting protein (Txnip), a poorly understood protein that binds to and inactivates thioredoxin. Our recent studies and those of other laboratories have now implicated Txnip in fundamental regulation of both lipid and glucose metabolism. We will now examine the mechanisms involved using our mouse model. Another protein that we identified in genetic studies in mice is apolipoprotein All (apoAII). We have shown that the levels of this protein, ranging from null (apoAII knockout) to a few mg/dl (strain SM) to approximately 20 mg/dl (strain C57BL/6) to approximately 30 mg/dl (strain BALB/c) to approximately 100 mg/dl (apoAII transgenic) have ;a continuous, significant impact on insulin resistance, triglyceride levels, body fat, atherosclerosis and inflammatory properties of HDL. We now propose to further pursue the causal interactions involved.
The third aim i s to positionally clone a novel gene in mouse that contributes to combined hyperlipidemia and atherosclerosis. The last aim is to study in mouse a gene that was positionally cloned in the current grant in studies of Finnish familial combined hyperlipidemic pedigrees. The gene, USF1, will be examined in knockout and transgenic mouse models on a variety of backgrounds. Project I will interact with each of the other projects and with all the cores.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Program Projects (P01)
Project #
5P01HL028481-23
Application #
7344752
Study Section
Heart, Lung, and Blood Initial Review Group (HLBP)
Project Start
2007-02-01
Project End
2010-01-31
Budget Start
2007-02-01
Budget End
2008-01-31
Support Year
23
Fiscal Year
2007
Total Cost
$508,608
Indirect Cost
Name
University of California Los Angeles
Department
Type
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
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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