MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Metabolomic profiling is an important tool for research into biological functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of experimental methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their reference spectra as well as their biological roles, locations, concentrations and raw data from metabolic experiments. Studies automatically receive a stable unique accession number that can be used as a publication reference (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.
Exciting funding initiatives are emerging in Europe and the US for metabolomics data production, storage, dissemination and analysis. This is based on a rich ecosystem of resources around the world, which has been build during the past ten years, including but not limited to resources such as MassBank in Japan and the Human Metabolome Database in Canada. Now, the European Bioinformatics Institute has launched MetaboLights, a database for metabolomics experiments and the associated metadata (http://www.ebi.ac.uk/metabolights). It is the first comprehensive, cross-species, cross-platform metabolomics database maintained by one of the major open access data providers in molecular biology. In October, the European COSMOS consortium will start its work on Metabolomics data standardization, publication and dissemination workflows. The NIH in the US is establishing 6–8 metabolomics services cores as well as a national metabolomics repository. This communication reports about MetaboLights as a new resource for Metabolomics research, summarises the related developments and outlines how they may consolidate the knowledge management in this third large omics field next to proteomics and genomics.
Metabolomics; Databases; ISA-Tab; ISA commons
Caenorhabditis elegans provides a genetically tractable model organism to investigate the network of genes involved in fat metabolism and how regulation is perturbed to produce the complex phenotype of obesity. C. elegans possess the full range of desaturases, including the Δ9 desaturases expressed by fat-5, fat-6 and fat-7. They regulate the biosynthesis of monounsaturated fatty acids, used for the synthesis of lipids including phospholipids, triglycerides and cholesteryl esters.
Liquid chromatography mass spectrometry (LC-MS), gas chromatography mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) spectroscopy were used to define the metabolome of all the possible knock-outs for the Δ9 desaturases, including for the first time intact lipids. Despite the genes having similar enzymatic roles, excellent discrimination was achievable for all single and viable double mutants highlighting the distinctive roles of fat-6 and fat-7, both expressing steroyl-CoA desaturases. The metabolomic changes extend to aqueous metabolites demonstrating the influence Δ9 desaturases have on regulating global metabolism and highlighting how comprehensive metabolomics is more discriminatory than classically used dyes for fat staining.
The propagation of metabolic changes across the network of metabolism demonstrates that modification of the Δ9 desaturases places C.elegans into a catabolic state compared with wildtype controls.
The nuclear receptors peroxisome proliferator-activated receptor γ (PPARγ) and peroxisome proliferator-activated receptor δ (PPARδ) play central roles in regulating metabolism in adipose tissue, as well as being targets for the treatment of insulin resistance. While the role of PPARγ in regulating insulin sensitivity has been well defined, research into PPARδ has been limited until recently due to a scarcity of selective PPARδ agonists.
The metabolic effects of PPARγ and PPARδ activation have been examined in vivo in white adipose tissue from ob/ob mice and in vitro in cultured 3T3-L1 adipocytes using 1H nuclear magnetic resonance spectroscopy and mass spectrometry metabolomics to understand the receptors' contrasting roles. These steady state measurements were supplemented with 13C-stable isotope substrate labeling to assess fluxes, in addition to respirometry and transcriptomic microarray analysis. The metabolic effects of the receptors were readily distinguished, with PPARγ activation characterized by increased fat storage, synthesis and elongation, while PPARδ activation caused increased fatty acid β-oxidation, tricarboxylic acid cycle rate and oxidation of extracellular branch chain amino acids. Stimulated glycolysis and increased fatty acid desaturation were common pathways for the agonists.
PPARγ and PPARδ restore insulin sensitivity through varying mechanisms. PPARδ activation increases total oxidative metabolism in white adipose tissue, a tissue not traditionally thought of as oxidative. However, the increased metabolism of branch chain amino acids may provide a mechanism for muscle atrophy, which has been linked to activation of this nuclear receptor. PPARδ has a role as an anti-obesity target and as an anti-diabetic, and hence may target both the cause and consequences of dyslipidemia.
Metabolomics is a rapidly developing functional genomic tool that has a wide range of applications in diverse fields in biology and medicine. However, unlike transcriptomics and proteomics there is currently no central repository for the depositing of data despite efforts by the Metabolomics Standard Initiative (MSI) to develop a standardised description of a metabolomic experiment.
In this manuscript we describe how the MSI description has been applied to a published dataset involving the identification of cross-species metabolic biomarkers associated with type II diabetes. The study describes sample collection of urine from mice, rats and human volunteers, and the subsequent acquisition of data by high resolution 1H NMR spectroscopy. The metadata is described to demonstrate how the MSI descriptions could be applied in a manuscript and the spectra have also been made available for the mouse and rat studies to allow others to process the data.
The intention of this manuscript is to stimulate discussion as to whether the MSI description is sufficient to describe the metadata associated with metabolomic experiments and encourage others to make their data available to other researchers.
data standards; metabolomics repository; bioinformatics; NMR spectroscopy
The standardization of reporting of data promises to revolutionize biology by allowing community access to data generated in laboratories across the globe. This approach has already influenced genomics and transcriptomics. Projects that have previously been viewed as being too big to implement can now be distributed across multiple sites. There are now public databases for gene sequences, transcriptomic profiling and proteomic experiments. However, progress in the metabolomic community has seemed to falter recently, and whereas there are ontologies to describe the metadata for metabolomics there are still no central repositories for the datasets themselves. Here, we examine some of the challenges and potential benefits of further efforts towards data standardization in metabolomics and metabonomics.
RNA turnover is an essential element of cellular homeostasis and response to environmental change. Whether the ribonucleases that mediate RNA turnover can respond to cellular metabolic status is an unresolved question. Here we present evidence that the Krebs cycle metabolite citrate affects the activity of Escherichia coli polynucleotide phosphorylase (PNPase) and, conversely, that cellular metabolism is affected widely by PNPase activity. An E. coli strain that requires PNPase for viability has suppressed growth in the presence of increased citrate concentration. Transcriptome analysis reveals a PNPase-mediated response to citrate, and PNPase deletion broadly impacts on the metabolome. In vitro, citrate directly binds and modulates PNPase activity, as predicted by crystallographic data. Binding of metal-chelated citrate in the active site at physiological concentrations appears to inhibit enzyme activity. However, metal-free citrate is bound at a vestigial active site, where it stimulates PNPase activity. Mutagenesis data confirmed a potential role of this vestigial site as an allosteric binding pocket that recognizes metal-free citrate. Collectively, these findings suggest that RNA degradative pathways communicate with central metabolism. This communication appears to be part of a feedback network that may contribute to global regulation of metabolism and cellular energy efficiency.
Metabolism; Metabolomics; RNA Abundance; RNA Metabolism; RNA Turnover; RNA Degradation; Allosteric Regulation; Central Metabolism; Gene Expression; Polynucleotide Phosphorylase
Non-genotoxic carcinogens are notoriously difficult to identify as they do not damage DNA directly and have diverse modes of action, necessitating long term in vivo studies. The early effects of the classic rodent non-genotoxic hepatocarcinogen phenobarbital have been investigated in the Fisher rat using a combination of metabolomics and transcriptomics, to investige early stage mechanistic changes that are predictive of longer term pathology.
Liver and blood plasma were profiled across 14 days, and multivariate statistics used to identify perturbed pathways. Both metabolomics and transcriptomics detected changes in the liver which were dose dependent, even after one day of exposure. Integration of the two datasets associated perturbations with specific pathways. Hepatic glycogen was decreased due to a decrease in synthesis, and plasma triglycerides were decreased due to an increase in fatty acid uptake by the liver. Hepatic succinate was increased and this was associated with increased heme biosynthesis. Glutathione synthesis was also increased, presumably in response to oxidative stress. Liquid Chromatography Mass Spectrometry demonstrated a remodeling of lipid species, possibly resulting from proliferation of the smooth endoplasmic reticulum.
The data fusion of metabolomic and transcriptomic changes proved to be a highly sensitive approach for monitoring early stage changes in altered hepatic metabolism, oxidative stress and cytochrome P450 induction simultaneously. This approach is particularly useful in interpreting changes in metabolites such as succinate which are hubs of metabolism.
The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors and members of the nuclear receptor superfamily. The PPAR family consists of three members: PPARα, PPARγ, and PPARδ. PPARδ controls the transcription of genes involved in multiple physiological pathways, including cellular differentiation, lipid metabolism and energy homeostasis. The receptor is expressed almost ubiquitously, with high expression in liver and skeletal muscle. Although the physiological ligands of PPARδ remain undefined, a number of high affinity synthetic ligands have been developed for the receptor as a therapeutic target for type 2 diabetes mellitus, dyslipidemia and the metabolic syndrome.
In this study, the metabolic role of PPARδ activation has been investigated in liver, skeletal muscle, blood serum and white adipose tissue from ob/ob mice using a high affinity synthetic ligand and contrasted with PPARγ activation. To maximize the analytical coverage of the metabolome, 1H-nuclear magnetic resonance (1H-NMR) spectroscopy, gas chromatography-mass spectrometry (GC-MS) and ultra performance liquid chromatography-mass spectrometry (UPLC-MS) were used to examine metabolites from tissue extracts.
Analysis by multivariate statistics demonstrated that PPARδ activation profoundly affected glycolysis, gluconeogenesis, the TCA cycle and linoleic acid and α-linolenic acid essential fatty acid pathways.
Although activation of both PPARδ and PPARγ lead to increased insulin sensitivity and glucose tolerance, PPARδ activation was functionally distinct from PPARγ activation, and was characterized by increased hepatic and peripheral fatty acid oxidative metabolism, demonstrating the distinctive catabolic role of this receptor compared with PPARγ.
The metabolic effects of leptin may involve both centrally and peripherally mediated actions with a component of the central actions potentially independent of alterations in food intake. Ob/ob mice have significant abnormalities in lipid metabolism, correctable by leptin administration. We used ob/ob mice to study the relative importance of the subtypes of actions of leptin (central vs peripheral; food intake dependent vs independent) on lipid metabolism. Mice were treated for 3 days with leptin, either centrally (ICV) or peripherally (IP), and compared with mice pair-fed to the leptin-treated mice (PF) and with ad libitum fed controls (C). All treatment groups (ICV, IP, PF) showed indistinguishable changes in liver weight, hepatic steatosis, hepatic lipidomic profile and circulating free fatty acids, triglycerides and cholesterol lipoprotein profile. Changes in the expression of genes involved in lipogenesis and fatty acid oxidation in liver, muscle and white fat were broadly similar in IP, ICV and PF groups. Leptin (both ICV and IP) stimulated expression of both mitochondrial and peroxisomal acyl-coenzyme A oxidase (liver) and PPARα (skeletal muscle) to an extent not replicated by pair feeding. Leptin had profound effects on peripheral lipid metabolism but the majority were explained by its effects on food intake. Leptin had additional centrally mediated effects to increase the expression of a limited number of genes concerned with fatty acid oxidation. While we cannot exclude direct peripheral effects of leptin on certain aspects of lipid metabolism we were unable to detect any such effects on the parameters measured in this study.
Leptin; lipid metabolism; lipoprotein; lipogenesis; steatosis
Previous studies have shown that a combination of weight loss and fish oil supplementation reduce cardiovascular disease and diabetes risks by increasing adiponectin and reducing triacylglyceride concentrations, while weight loss alone significantly improves insulin sensitivity and reduces inflammation. Here, a metabolomic approach, using a combination of 1H-Nuclear Magnetic Resonance spectroscopy, and gas and liquid chromatography and mass spectrometry, was employed to elucidate the metabolic changes in blood plasma following weight loss and fish oil supplementation. The intervention study was conducted over 24 weeks, with 93 female subjects randomised to one of three groups. Two groups followed a 12-week weight loss program, followed by a 12-week weight maintenance period and were randomised to fish or placebo oil capsules; a control group did not follow the weight loss program and were given placebo oil capsules. Lipid profiles changed dramatically upon fish oil intake and subtly across the two weight loss groups. While the fish oil supplementation increased the proportion of various phospholipid species, previously reported reductions in total triacylglycerides (TAGs) upon fish oil intake were shown to be driven by a reduction in a specific subset of the measured TAGs. This remodelling of triglycerides may represent further beneficial effects of fish oil supplementation.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-009-0161-7) contains supplementary material, which is available to authorized users.
Metabolomics; Metabonomics weight loss; Omega-3 polyunsaturated fatty acids; PUFA; NMR; LC-MS; GC-MS; Triacylglycerol
Regulation between the fed and fasted states in mammals is partially controlled by peroxisome proliferator-activated receptor-α (PPAR-α). Expression of the receptor is high in the liver, heart and skeletal muscle, but decreases with age. A combined 1H nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry metabolomic approach has been used to examine metabolism in the liver, heart, skeletal muscle and adipose tissue in PPAR-α-null mice and wild-type controls during ageing between 3 and 13 months. For the PPAR-α-null mouse, multivariate statistics highlighted hepatic steatosis, reductions in the concentrations of glucose and glycogen in both the liver and muscle tissue, and profound changes in lipid metabolism in each tissue, reflecting known expression targets of the PPAR-α receptor. Hepatic glycogen and glucose also decreased with age for both genotypes. These findings indicate the development of age-related hepatic steatosis in the PPAR-α-null mouse, with the normal metabolic changes associated with ageing exacerbating changes associated with genotype. Furthermore, the combined metabolomic and multivariate statistics approach provides a robust method for examining the interaction between age and genotype.
functional genomics; hepatic steatosis; metabonomics; obesity
Functional genomic studies are dominated by transcriptomic approaches, in part reflecting the vast amount of information that can be obtained, the ability to amplify mRNA and the availability of commercially standardized functional genomic DNA microarrays and related techniques. This can be contrasted with proteomics, metabolomics and metabolic flux analysis (fluxomics), which have all been much slower in development, despite these techniques each providing a unique viewpoint of what is happening in the overall biological system. Here, we give an overview of developments in these fields 'downstream' of the transcriptome by considering the characterization of one particular, but widely used, mouse model of human disease. The mdx mouse is a model of Duchenne muscular dystrophy (DMD) and has been widely used to understand the progressive skeletal muscle wasting that accompanies DMD, and more recently the associated cardiomyopathy, as well as to unravel the roles of the other isoforms of dystrophin, such as those found in the brain. Studies using proteomics, metabolomics and fluxomics have characterized perturbations in calcium homeostasis in dystrophic skeletal muscle, provided an understanding of the role of dystrophin in skeletal muscle regeneration, and defined the changes in substrate energy metabolism in the working heart. More importantly, they all point to perturbations in proteins, metabolites and metabolic fluxes reflecting mitochondrial energetic alterations, even in the early stage of the dystrophic pathology. Philosophically, these studies also illustrate an important lesson relevant to both functional genomics and the mouse phenotyping in that the knowledge generated has advanced our understanding of cell biology and physiological organization as much as it has advanced our understanding of the disease.
To date most global approaches to functional genomics have centred on genomics, transcriptomics and proteomics. However, since a number of high-profile publications, interest in metabolomics, the global profiling of metabolites in a cell, tissue or organism, has been rapidly increasing. A range of analytical techniques, including 1H NMR spectroscopy, gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS), Fourier Transform mass spectrometry (FT–MS), high performance liquid chromatography (HPLC) and electrochemical array (EC-array), are required in order to maximize the number of metabolites that can be identified in a matrix. Applications have included phenotyping of yeast, mice and plants, understanding drug toxicity in pharmaceutical drug safety assessment, monitoring tumour treatment regimes and disease diagnosis in human populations. These successes are likely to be built on as other analytical and bioinformatic approaches are developed to fully exploit the information obtained in metabolic profiles. To assist in this process, databases of metabolomic data will be necessary to allow the passage of information between laboratories. In this prospective review, the capabilities of metabolomics in the field of medicine will be assessed in an attempt to predict the impact this ‘Cinderella approach’ will have at the ‘functional genomic ball’.
phenotyping; transcriptomics; proteomics; genomics; pattern recognition; bioinformatics
There is an increased reliance on genetically modified organisms as a functional genomic tool to elucidate the role of genes and their protein products. Despite this, many models do not express the expected phenotype thought to be associated with the gene or protein. There is thus an increased need to further define the phenotype resultant from a genetic modification to understand how the transcriptional or proteomic network may conspire to alter the expected phenotype. This is best typified by the description of the silent phenotype in genetic manipulations of yeast. High-resolution proton nuclear magnetic resonance ((1)H NMR) spectroscopy provides an ideal mechanism for the profiling of metabolites within biofluids, tissue extracts or, with recent advances, intact tissues. These metabolic datasets can be readily mined using a range of pattern recognition techniques, including hierarchical cluster analysis, principal components analysis, partial least squares and neural networks, with the combined approach being termed metabolomics. This review describes the application of NMR-based metabolomics or metabonomics to genetic and chemical interventions in a number of different species, demonstrating the versatility of such an approach, as well as suggesting how it may be integrated with other "omic" technologies.
To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open ‘data commoning’ culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared ‘Investigation-Study-Assay’ framework to support that vision.
Changes in energy metabolism of the cells are common to many kinds of tumors and are considered a hallmark of cancer. Gas chromatography followed by time-of-flight mass spectrometry (GC-TOFMS) is a well-suited technique to investigate the small molecules in the central metabolic pathways. However, the metabolic changes between invasive carcinoma and normal breast tissues were not investigated in a large cohort of breast cancer samples so far.
A cohort of 271 breast cancer and 98 normal tissue samples was investigated using GC-TOFMS-based metabolomics. A total number of 468 metabolite peaks could be detected; out of these 368 (79%) were significantly changed between cancer and normal tissues (p<0.05 in training and validation set). Furthermore, 13 tumor and 7 normal tissue markers were identified that separated cancer from normal tissues with a sensitivity and a specificity of >80%. Two-metabolite classifiers, constructed as ratios of the tumor and normal tissues markers, separated cancer from normal tissues with high sensitivity and specificity. Specifically, the cytidine-5-monophosphate / pentadecanoic acid metabolic ratio was the most significant discriminator between cancer and normal tissues and allowed detection of cancer with a sensitivity of 94.8% and a specificity of 93.9%.
For the first time, a comprehensive metabolic map of breast cancer was constructed by GC-TOF analysis of a large cohort of breast cancer and normal tissues. Furthermore, our results demonstrate that spectrometry-based approaches have the potential to contribute to the analysis of biopsies or clinical tissue samples complementary to histopathology.
Breast cancer; Metabolomics; Gas chromatography; Mass spectrometry; Cancer detection
Mice lacking Peroxisome Proliferator-Activated Receptor γ2 (PPARγ2) have unexpectedly normal glucose tolerance and mild insulin resistance. Mice lacking PPARγ2 were found to have elevated levels of Lipocalin prostaglandin D synthase (L-PGDS) expression in BAT and subcutaneous white adipose tissue (WAT). To determine if induction of L-PGDS was compensating for a lack of PPARγ2, we crossed L-PGDS KO mice to PPARγ2 KO mice to generate Double Knock Out mice (DKO). Using DKO mice we demonstrated a requirement of L-PGDS for maintenance of subcutaneous WAT (scWAT) function. In scWAT, DKO mice had reduced expression of thermogenic genes, the de novo lipogenic program and the lipases ATGL and HSL. Despite the reduction in markers of lipolysis in scWAT, DKO mice had a normal metabolic rate and elevated serum FFA levels compared to L-PGDS KO alone. Analysis of intra-abdominal white adipose tissue (epididymal WAT) showed elevated expression of mRNA and protein markers of lipolysis in DKO mice, suggesting that DKO mice may become more reliant on intra-abdominal WAT to supply lipid for oxidation. This switch in depot utilisation from subcutaneous to epididymal white adipose tissue was associated with a worsening of whole organism metabolic function, with DKO mice being glucose intolerant, and having elevated serum triglyceride levels compared to any other genotype. Overall, L-PGDS and PPARγ2 coordinate to regulate carbohydrate and lipid metabolism.
Mutations in a number of genes have been linked to inherited dilated cardiomyopathy (DCM). However, such mutations account for only a small proportion of the clinical cases emphasising the need for alternative discovery approaches to uncovering novel pathogenic mutations in hitherto unidentified pathways. Accordingly, as part of a large-scale N-ethyl-N-nitrosourea mutagenesis screen, we identified a mouse mutant, Python, which develops DCM. We demonstrate that the Python phenotype is attributable to a dominant fully penetrant mutation in the dynamin-1-like (Dnm1l) gene, which has been shown to be critical for mitochondrial fission. The C452F mutation is in a highly conserved region of the M domain of Dnm1l that alters protein interactions in a yeast two-hybrid system, suggesting that the mutation might alter intramolecular interactions within the Dnm1l monomer. Heterozygous Python fibroblasts exhibit abnormal mitochondria and peroxisomes. Homozygosity for the mutation results in the death of embryos midway though gestation. Heterozygous Python hearts show reduced levels of mitochondria enzyme complexes and suffer from cardiac ATP depletion. The resulting energy deficiency may contribute to cardiomyopathy. This is the first demonstration that a defect in a gene involved in mitochondrial remodelling can result in cardiomyopathy, showing that the function of this gene is needed for the maintenance of normal cellular function in a relatively tissue-specific manner. This disease model attests to the importance of mitochondrial remodelling in the heart; similar defects might underlie human heart muscle disease.
Heart disease is very common. Some cases of heart disease are strongly influenced by lifestyle and diet, whereas others have a strong genetic component. A certain form of heart failure, known as dilated cardiomyopathy (DCM) quite often runs in families suggesting that a defective gene or genes underlie this disease. We describe a new mouse mutant called “Python” which suffers from a heart disease similar to DCM. We were able to pinpoint the defective gene responsible for the disease. This gene is normally involved in the division of mitochondria, the “power plants” of the cell that generate one of the main energy supplies for the cell. This is a unique model that implicates a new gene and mechanism of disease for further investigation.