Transcriptome signature for dietary fructose-specific changes in rat renal cortex: A quantitative approach to physiological relevance

Fructose consumption causes metabolic diseases and renal injury primarily in the renal cortex where fructose is metabolized. Analyzing gene differential expression induced by dietary manipulation is challenging. The effects may depend on the base diet and primary changes likely induce secondary or higher order changes that are difficult to capture by conventional univariate transcriptome analyses. We hypothesized that dietary fructose induces a genetic program in the kidney cortex that favors lipogenesis and gluconeogenesis. To test this, we analyzed renal cortical transcriptomes of rats on normal- and high-salt base diets supplemented with fructose. Both sets of data were analyzed using the Characteristic Direction method to yield fructose-induced gene vectors of associated differential expression values. A fructose-specific “signature” of 139 genes differentially expressed was extracted from the 2 diet vectors by a new algorithm that takes into account a gene’s rank and standard deviation of its differential expression value. Of these genes, 97 were annotated and the top 34 accounted for 80% of the signal in the annotated signature. The genes were predominantly proximal tubule–specific, coding for metabolic enzymes or transporters. Cosine similarity of signature genes in the two fructose-induced vectors was >0.78. These 139 genes of the fructose signature contributed 27% and 38% of total differential expression on normal- and high- salt diet, respectively. Principal Component Analysis showed that the individual animals could be grouped according to diet. The fructose signature contained a greater enrichment of Gene Ontology processes related to nutrition and metabolism of fructose than two univariate analysis methods. The major feature of the fructose signature is a change in metabolic programs of the renal proximal tubule consistent with gluconeogenesis and de-novo lipogenesis. This new “signature” constitutes a new metric to bridge the gap between physiological phenomena and differential expression profile.