Random Forest Analysis of Untargeted Metabolomics Data Suggests Increased Use of Omega Fatty Acid Oxidation Pathway in Drosophila Melanogaster Larvae Fed a Medium Chain Fatty Acid Rich High-Fat Diet

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.

Multisite evaluation of phenotypic plasticity for specialized metabolites, some involved in carrot quality and disease resistance

“…Contrary to yield analysis which is univariate, the first hurdle encountered in metabolomic studies is the large number of variables, which requires sorting and prioritization to better study effects on involved compounds. Recognized thanks to its predictive accuracy as a classification method for complex traits genome-wide prediction [41], we used Random forest algorithm which is particularly adapted in metabolomic for biomarker identification [42][43][44]. The use of several contrasted growing locations allows to introduce environmental source of variation which is essential to assess variety stability for a given trait but may degrade algorithm predictive accuracy used for varietal marker characterization.…”

Comparative untargeted metabolomic profiling of induced mitochondrial fusion in pancreatic cancer

“…In order to ensure robustness in our feature selection process, we further developed three different pairwise models to identify metabolite markers for each group: a significance analysis of microarray (SAM) [11], PLS-DA variable importance in projection (VIP) [12], and a random forest (RF) [13] classification model. SAM identified 73 out of 234 metabolites in the Tet-On Mfn2 induced fusion group, 71 out of 245 metabolites in the indirect fusion group, and 74 out of 233 metabolites in the Leflunomide treated group as significantly altered based on an FDR < 0.05 and a corresponding delta of 0.39, 0.38, and 0.32 for the Tet-On Mfn2, sgDrp1, and Leflunomide groups respectively (Figure 4B).…”

UHPLC–MS/MS-Based Nontargeted Metabolomics Analysis Reveals Biomarkers Related to the Freshness of Chilled Chicken

“…As a machine learning method, the random forest model is a regression tree technique that uses bootstrap aggregation and randomization of predictors to achieve a high degree of predictive accuracy [ 27 ]. The random forest model has been widely used to analyze complex metabolomics data [ 28 , 29 , 30 , 31 , 32 , 33 ] and has its unique advantages. First, it is relatively robust to outliers and noise.…”