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An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines.

ABSTRACT: INTRODUCTION:It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites. OBJECTIVES:Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines. METHODS:We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the 'exometabolome' or metabolic footprint). RESULTS:Our method measures some 4000-5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other. CONCLUSION:Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.

SUBMITTER: Wright Muelas M

PROVIDER: S-EPMC7541387 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines.

Wright Muelas Marina M Roberts Ivayla I Mughal Farah F O'Hagan Steve S Day Philip J PJ Kell Douglas B DB

Metabolomics : Official journal of the Metabolomic Society 20201007 10

<h4>Introduction</h4>It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites.<h4>Objectives</h4>Blood s ...[more]

PMID: 33026554

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Project description:Untargeted metabolomics aims at measuring the entire set of metabolites in a wide range of biological samples. However, due to the high chemical diversity of metabolites that range from small to large and more complex molecules (i.e., amino acids/carbohydrates vs. phospholipids/gangliosides), the identification and characterization of the metabolome remain a major bottleneck. The first step of this process consists of searching the experimental monoisotopic mass against databases, thus resulting in a highly redundant/complex list of candidates. Despite the progress in this area, researchers are still forced to manually explore the resulting table in order to prioritize the most likely identifications for further biological interpretation or confirmation with standards. Here, we present TurboPutative (https://proteomics.cnic.es/TurboPutative/), a flexible and user-friendly web-based platform composed of four modules (Tagger, REname, RowMerger, and TPMetrics) that streamlines data handling, classification, and interpretability of untargeted LC-MS-based metabolomics data. Tagger classifies the different compounds and provides preliminary insights into the biological system studied. REname improves putative annotation handling and visualization, allowing the recognition of isomers and equivalent compounds and redundant data removal. RowMerger reduces the dataset size, facilitating the manual comparison among annotations. Finally, TPMetrics combines different datasets with feature intensity and relevant information for the researcher and calculates a score based on adduct probability and feature correlations, facilitating further identification, assessment, and interpretation of the results. The TurboPutative web application allows researchers in the metabolomics field that are dealing with massive datasets containing multiple putative annotations to reduce the number of these entries by 80%-90%, thus facilitating the extrapolation of biological knowledge and improving metabolite prioritization for subsequent pathway analysis. TurboPutative comprises a rapid, automated, and customizable workflow that can also be included in programmed bioinformatics pipelines through its RESTful API services. Users can explore the performance of each module through demo datasets supplied on the website. The platform will help the metabolomics community to speed up the arduous task of manual data curation that is required in the first steps of metabolite identification, improving the generation of biological knowledge.

Project description:BackgroundThe mammalian brain is organized into regions with specific biological functions and properties. These regions have distinct transcriptomes, but little is known whether they may also differ in their metabolome. The metabolome, a collection of small molecules or metabolites, is at the intersection of the genetic background of a given cell or tissue and the environmental influences that affect it. Thus, the metabolome directly reflects information about the physiologic state of a biological system under a particular condition. The objective of this study was to investigate whether various brain regions have diverse metabolome profiles, similarly to their genetic diversity. The answer to this question would suggest that not only the genome but also the metabolome may contribute to the functional diversity of brain regions.MethodsWe investigated the metabolome of four regions of the mouse brain that have very distinct functions: frontal cortex, hippocampus, cerebellum, and olfactory bulb. We utilized gas- and liquid- chromatography mass spectrometry platforms and identified 215 metabolites.ResultsPrincipal component analysis, an unsupervised multivariate analysis, clustered each brain region based on its metabolome content, thus providing the unique metabolic profile of each region. A pathway-centric analysis indicated that olfactory bulb and cerebellum had most distinct metabolic profiles, while the cortical parenchyma and hippocampus were more similar in their metabolome content. Among the notable differences were distinct oxidative-anti-oxidative status and region-specific lipid profiles. Finally, a global metabolic connectivity analysis using the weighted correlation network analysis identified five hub metabolites that organized a unique metabolic network architecture within each examined brain region. These data indicate the diversity of global metabolome corresponding to specialized regional brain function and provide a new perspective on the underlying properties of brain regions.ConclusionIn summary, we observed many differences in the metabolome among the various brain regions investigated. All four brain regions in our study had a unique metabolic signature, but the metabolites came from all categories and were not pathway-centric.

Dataset Information

An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines.

Publications

An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines.

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