Nutrigenomics of hepatic steatosis in a feline model: effect of monosodium glutamate, fructose, and Trans-fat feeding

Abstract

Nonalcoholic fatty liver disease begins with a relatively benign hepatic steatosis, often associated with increased adiposity, but may progress to a more severe nonalcoholic steatohepatitis with inflammation. A subset of these patients develops progressive fibrosis and ultimately cirrhosis. Various dietary components have been shown to contribute to the development of liver disease, including fat, sugars, and neonatal treatment with high doses of monosodium glutamate (MSG). However, rodent models of progressive disease have been disappointing, and alternative animal models of diet-induced liver disease would be desirable, particularly if they contribute to our knowledge of changes in gene expression as a result of dietary manipulation. The domestic cat has previously been shown to be an appropriate model for examining metabolic changes-associated human diseases such as diabetes. Our aim was therefore to compare changes in hepatic gene expression induced by dietary MSG, with that of a diet containing Trans-fat and high fructose corn syrup (HFCS), using a feline model. MSG treatment increased adiposity and promoted hepatic steatosis compared to control (P < 0.05). Exposure to Trans-fat and HFCS promoted hepatic fibrosis and markers of liver dysfunction. Affymetrix microarray analysis of hepatic gene expression showed that dietary MSG promoted the expression of genes involved in cholesterol and steroid metabolism. Conversely, Trans-fat and HFCS feeding promoted the expression of genes involved in lipolysis, glycolysis, liver damage/regeneration, and fibrosis. Our feline model examining gene-diet interactions (nutrigenomics) demonstrates how dietary MSG, Trans-fat, and HFCS may contribute to the development of hepatic steatosis.

Fig. 1

Effect of diet on hepatic morphology and triacylglycerol content. a Micrographs of hepatic tissue from control cats and those in diet group A, B, and C. Magnification is 10×; results are representative of at least 4 animals per diet group. b Higher magnification (100×) images show the differences in hepatic morphology induced by the diets. c Hepatic TAG content per diet group (mean ± SEM, P < 0.05)

Fig. 2

a Heatmap depicts the effect of dietary manipulation on the expression of genes involved in lipogenesis, lipolysis, gluconeogenesis, and glycolysis. Heatmap shows genes represented horizontally, and diet groups represented by the vertical rows: red color signals genes with increased expression while green indicated a reduction in expression, with a stringency ≥ ±1.5-fold change for the comparisons control versus diet A; control versus diet B, and diet B versus diet C. b Depicts the effect of dietary manipulation on the expression of genes involved in liver damage/regeneration, angiogenesis, fibrosis, inflammation, and proliferation (P < 0.01)

Fig. 2

a Heatmap depicts the effect of dietary manipulation on the expression of genes involved in lipogenesis, lipolysis, gluconeogenesis, and glycolysis. Heatmap shows genes represented horizontally, and diet groups represented by the vertical rows: red color signals genes with increased expression while green indicated a reduction in expression, with a stringency ≥ ±1.5-fold change for the comparisons control versus diet A; control versus diet B, and diet B versus diet C. b Depicts the effect of dietary manipulation on the expression of genes involved in liver damage/regeneration, angiogenesis, fibrosis, inflammation, and proliferation (P < 0.01)

Fig. 3

Correlation of the ratios from the microarray and real-time PCR data set. Genes that differed significantly (P < 0.01) in their regulation between the diet groups’ microarray analysis were selected and validated with the same samples by real-time PCR analysis. Ratios of expressions between the diet comparisons calculated from the microarray data set correlated well with the ratio calculated from the real-time PCR data (r = 0.78, P < 0.0001)

Fig. 4

Bar chart represents the most significant biological functions (a) and toxicology analysis (b) of genes deregulated by diet for comparisons control versus diet A (red bars), control versus diet B (blue bars), and diet C versus diet B (green bars). x-axis represents—log of the P value denoting significance

Fig. 5

Ingenuity pathway analysis was used to create a network of biologically relevant genes regulated in response to diet for comparisons control versus diet A, control versus diet B, and diet C versus diet B. The network is displayed graphically as nodes representing genes and edges (the biological relationship between the genes). Different shaped nodes represent the functional class of the gene product. The position of the node represents the subcellular localization of the gene product. A total of 38 genes were represented in the network, with a number of focus genes with functions relating to transcription, growth and proliferation, and lipid metabolism