Global molecular changes in rat livers treated with RXR agonists: a comparison using transcriptomics and proteomics

Abstract

The ability of the retinoid X receptors (RXRs) specific agonists (targretin [TRG] and UAB30) to alter rat liver gene and protein expression was determined using Affymetrix Exon arrays and high-performance liquid chromatography – tandem mass spectrometry (LC-MS/MS). TRG profoundly increases triglycerides levels while UAB30 does not. The expression patterns of transcripts or proteins from rat liver treated with TRG or UAB-30 were different from controls and each other. There were six times more gene transcripts identified than proteins. Differentially expressed RNAs or proteins were mapped into known gene ontology (GO) categories and GeneGo Metacore (KEGG) pathway maps. The GO categories which were highly overrepresented with differentially expressed RNAs (P < 10−16) were also overrepresented at the protein level. This high concordance of GO Terms was achieved despite the fact that typically ≤1/3 of the elements identified by gene expression were identified by proteomics. Within these GO categories, the magnitude of alterations induced by RXR agonists at the transcript and protein levels were correlated. When GO categories with moderate overrepresentation (10−5 < P < 10−9) were examined, there was greater discordance between the transcript and protein data. Examination of KEGG pathway maps with highly significant changes at both the protein and the RNA levels showed that the individual proteins/genes altered were often the same and changes were of similar magnitude; while KEGG pathways showed limited statistical significance and exhibited minimal overlap. Finally, metabolomics analysis of liver and serum identified altered expression of metabolites related to fatty acid oxidation and bile acid metabolism that were consistent with transcript/protein changes. We observed significant concordance between genomics and proteomics implying either can identify pathways modulated and can indirectly predict resulting physiologic effects.