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      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

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          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.

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          Innovation: Metabolomics: the apogee of the omics trilogy.

          Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.
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            Organization of GC/MS and LC/MS metabolomics data into chemical libraries

            Background Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Qu antify I ndividual C omponents in a S ample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. Results LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. Conclusion The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.
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              High-dimensional graphs and variable selection with the Lasso

              The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately for each node in the graph and is hence equivalent to variable selection for Gaussian linear models. We show that the proposed neighborhood selection scheme is consistent for sparse high-dimensional graphs. Consistency hinges on the choice of the penalty parameter. The oracle value for optimal prediction does not lead to a consistent neighborhood estimate. Controlling instead the probability of falsely joining some distinct connectivity components of the graph, consistent estimation for sparse graphs is achieved (with exponential rates), even when the number of variables grows as the number of observations raised to an arbitrary power.
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                Author and article information

                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                31 December 2018
                January 2019
                : 9
                : 1
                : 5
                Affiliations
                Department of Biological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA; jkaicher@ 123456crimson.ua.edu (J.K.A.); lreed1@ 123456ua.edu (L.K.R.)
                Author notes
                [* ]Correspondence: vhoza@ 123456crimson.ua.edu
                Article
                metabolites-09-00005
                10.3390/metabo9010005
                6359074
                30602659
                0723a4b9-76e2-4866-923e-6ca7a2e0e358
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 09 November 2018
                : 27 December 2018
                Categories
                Article

                untargeted metabolomics,drosophila melanogaster,random forest,gaussian graphical models

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