Abstract
Obesity is a complex disease, shaped by both genetic and environmental factors such as diet. In this study, we use untargeted metabolomics and Drosophila melanogaster to model how diet and genotype shape the metabolome of obese phenotypes. We used 16 distinct outbred genotypes of Drosophila larvae raised on normal (ND) and high-fat (HFD) diets, to produce three distinct phenotypic classes; genotypes that stored more triglycerides on a ND relative to the HFD, genotypes that stored more triglycerides on a HFD relative to ND, and genotypes that showed no change in triglyceride storage on either of the two diets. Using untargeted metabolomics we characterized 350 metabolites: 270 with definitive chemical IDs and 80 that were chemically unidentified. Using random forests, we determined metabolites that were important in discriminating between the HFD and ND larvae as well as between the triglyceride phenotypic classes. We found that flies fed on a HFD showed evidence of an increased use of omega fatty acid oxidation pathway, an alternative to the more commonly used beta fatty acid oxidation pathway. Additionally, we observed no correlation between the triglyceride storage phenotype and free fatty acid levels (laurate, caprate, caprylate, caproate), indicating that the distinct metabolic profile of fatty acids in high-fat diet fed Drosophila larvae does not propagate into triglyceride storage differences. However, dipeptides did show moderate differences between the phenotypic classes. We fit Gaussian graphical models (GGMs) of the metabolic profiles for HFD and ND flies to characterize changes in metabolic network structure between the two diets, finding the HFD to have a greater number of edges indicating that metabolome varies more across samples on a HFD. Taken together, these results show that, in the context of obesity, metabolomic profiles under distinct dietary conditions may not be reliable predictors of phenotypic outcomes in a genetically diverse population.
Highlights
A high-fat diet has been associated with many metabolic disease states such as diabetes, obesity, cardiovascular diseases, and metabolic syndrome [1,2,3,4,5,6,7]
We found that the edges detected in at least two of the Gaussian graphical models (GGMs) tended to link metabolites there were well correlated across all datasets (Figure 4a, Supplemental Table S6), even if they were not linked by an edge in one of the diet specific GGMs
There have been attempts to map the Drosophila metabolites in different tissues [57] and conditions [15,58,59,60,61,62,63,64] and predict possible metabolic pathways based on the annotated genome [65], a lack of a comprehensive Drosophila metabolome and metabolic pathways database based on metabolomics data has hindered the progress of untargeted metabolomics studies in Drosophila
Summary
A high-fat diet has been associated with many metabolic disease states such as diabetes, obesity, cardiovascular diseases, and metabolic syndrome [1,2,3,4,5,6,7]. Drosophila melanogaster has emerged as one of the important model organisms for evaluating the molecular and genetic mechanisms of these diseases [8,9,10,11,12,13,14]. Despite the extensive use of Drosophila in understanding human disease pathways the effects of diet on metabolic phenotypes requires further elucidation. We have employed an untargeted metabolomics approach to characterize the differences in the global metabolic profile of Drosophila melanogaster in different environmental states (diet) and in distinct phenotypic responses to diet for triglyceride storage (reaction norms). Untargeted metabolomics on a global scale can provide a “phenotypic readout” to identify altered biochemical pathways in diseases and help to elucidate the molecular mechanisms of novel biological processes [18]
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