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An integrated reverse functional genomic and metabolic approach to understanding orotic acid-induced fatty liver

¹ Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, United Kingdom
² MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College London, Hammersmith Hospital, London W12 ONN, United Kingdom
³ Metabolic Research Laboratory, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford OX3 9JL, United Kingdom
⁴ Biomedical Sciences, Faculty of Medicine, Imperial College London, Exhibition Road, South Kensington, London, United Kingdom
⁵ Genetics and Genomics Research Institute, Faculty of Medicine, Imperial College London, South Kensington, Armstrong Road, London SW7 2AZ, United Kingdom

In functional genomics, DNA microarrays for gene expression profiling are increasingly being used to provide insights into biological function or pathology. To better understand the significance of the multiple transcriptional changes across a time period, the temporal changes in phenotype must be described. Orotic acid-induced fatty liver disease was investigated at the transcriptional and metabolic levels using microarrays and metabolic profiling in two strains of rats. High-resolution ¹H-NMR spectroscopic analysis of liver tissue indicated that Kyoto rats compared with Wistar rats are predisposed to the insult. Metabolite analysis and gene expression profiling following orotic acid treatment identified perturbed metabolic pathways, including those involved in fatty acid, triglyceride, and phospholipid synthesis, ß-oxidation, altered nucleotide, methyl donor, and carbohydrate metabolism, and stress responses. Multivariate analysis and statistical bootstrapping were used to investigate co-responses with transcripts involved in metabolism and stress responses. This reverse functional genomic strategy highlighted the relationship between changes in the transcription of stearoyl-CoA desaturase 1 and those of other lipid-related transcripts with changes in NMR-derived lipid profiles. The results suggest that the integration of ¹H-NMR and gene expression data sets represents a robust method for identifying a focused line of research in a complex system.

transcriptomics; metabolomics/metabonomics; DNA microarray; system biology; bioinformatics

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