Larry Parnell, USDA Human Nutrition Research Center on Aging, Tufts University
Small molecules ingested as components of the diet or metabolites thereof elicit responses of genes and proteins with implications toward both the short- and long-term health status of the individual. Nutrigenetics seeks to elucidate the effect of genetic variation on the interaction between diet and disease. Realization of the goal of nutrigenetics (i.e, personalized nutrition) demands utilization of multiple forms of genomics-based data geared toward the individual, particularly genotypes and gene expression. Variations in gene control regions are increasingly viewed as central to mild phenotypic variations observed between individuals. Promoter variants of both the hepatic lipase (LIPC) and the cholesteryl ester transfer protein (CETP) genes have been shown to associate with HDL-cholesterol (HDL-C) concentrations, the former depending on the amount of fat in the diet. In addition, extensive population studies revealed several gene-nutrient interactions. In the case of the apolipoprotein A1 ?75G>A variation, the amount of PUFA in the diet affects the association in women between genotype and HDL-C levels: A allele, high PUFA, high HDL-C; G/G genotype, high PUFA, low HDL-C. To gain more from these results, we seek to combine our genotyping and gene-diet interaction data with gene-reporter assays in order to assemble data needed to run in silico simulations of dietary response to genes relevant to obesity and lipid metabolism. Central to this is the influence of several SNPs located in the promoters of the apolipoprotein A5 (APOA5) and LIPC genes. These promoters with different alleles have been cloned in front of the firefly luciferase gene. By measuring luc activity in the extracts of transfected HepG2 hepatic cell cultures, we are able to assign changes in promoter activity due to the presence of the variant. Reporter gene expression is stimulated by saturated or unsaturated, n-3 or n-6 fatty acids, compounds found in the diet and known to influence activity of such transcription factors as PPARA, PPARG, HNF4 and LXR. Such measurements allow for the prediction of the gene response to diet.