Cholesterol and fatty acids regulate dynamic caveolin trafficking through the golgi complex and between the cell surface and lipid bodies

Caveolins are a crucial component of plasma membrane (PM) caveolae but have also been localized to intracellular compartments, including the Golgi complex and lipid bodies. Mutant caveolins associated with human disease show aberrant trafficking to the PM and Golgi accumulation. We now show that the Golgi pool of mainly newly synthesized protein is detergent-soluble and predominantly in a monomeric state, in contrast to the surface pool. Caveolin at the PM is not recognized by specific caveolin antibodies unless PM cholesterol is depleted. Exit from the Golgi complex of wild-type caveolin-1 or -3, but not vesicular stomatitis virus-G protein, is modulated by changing cellular cholesterol levels. In contrast, a muscular dystrophy-associated mutant of caveolin-3, Cav3P104L, showed increased accumulation in the Golgi complex upon cholesterol treatment. In addition, we demonstrate that in response to fatty acid treatment caveolin can follow a previously undescribed pathway from the PM to lipid bodies and can move from lipid bodies to the PM in response to removal of fatty acids. The results suggest that cholesterol is a rate-limiting component for caveolin trafficking. Changes in caveolin flux through the exocytic pathway can therefore be an indicator of cellular cholesterol and fatty acid levels.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (