Microbial metabolomics constitutes an integrated component of systems biology. By studying the complete set of metabolites within a microorganism and monitoring the global outcome of interactions between its development processes and the environment, metabolomics can potentially provide a more accurate snap shot of the actual physiological state of the cell. Recent advancement of technologies and post-genomic developments enable the study and analysis of metabolome. This unique contribution resulted in many scientific disciplines incorporating metabolomics as one of their “omics” platforms. This review focuses on metabolomics in microorganisms and utilizes selected topics to illustrate its impact on the understanding of systems microbiology. PMID:22379393
Cevallos-Cevallos, Juan Manuel; Reyes-De-Corcuera, José Ignacio
Metabolomics, the newest member of the omics techniques, has become an important tool in agriculture, pharmacy, and environmental sciences. Advances in compound extraction, separation, detection, identification, and data analysis have allowed metabolomics applications in food sciences including food processing, quality, and safety. This chapter discusses recent advances and applications of metabolomics in food science.
The Metabolomics and Epidemiology (MetEpi) Working Group promotes metabolomics analyses in population-based studies, as well as advancement in the field of metabolomics for broader biomedical and public health research.
Wishart, David S.; Mandal, Rupasri; Stanislaus, Avalyn; Ramirez-Gaona, Miguel
The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world’s largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer. PMID:26950159
Wishart, David S; Mandal, Rupasri; Stanislaus, Avalyn; Ramirez-Gaona, Miguel
The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world's largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer.
Liu, Xiaojing; Locasale, Jason W
Metabolomics generates a profile of small molecules that are derived from cellular metabolism and can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology. Technological advances have enabled rapid and increasingly expansive data acquisition with samples as small as single cells; however, substantial challenges in the field remain. In this primer we provide an overview of metabolomics, especially mass spectrometry (MS)-based metabolomics, which uses liquid chromatography (LC) for separation, and discuss its utilities and limitations. We identify and discuss several areas at the frontier of metabolomics. Our goal is to give the reader a sense of what might be accomplished when conducting a metabolomics experiment, now and in the near future.
Kuhlisch, Constanze; Pohnert, Georg
Chemical ecology elucidates the nature and role of natural products as mediators of organismal interactions. The emerging techniques that can be summarized under the concept of metabolomics provide new opportunities to study such environmentally relevant signaling molecules. Especially comparative tools in metabolomics enable the identification of compounds that are regulated during interaction situations and that might play a role as e.g. pheromones, allelochemicals or in induced and activated defenses. This approach helps overcoming limitations of traditional bioassay-guided structure elucidation approaches. But the power of metabolomics is not limited to the comparison of metabolic profiles of interacting partners. Especially the link to other -omics techniques helps to unravel not only the compounds in question but the entire biosynthetic and genetic re-wiring, required for an ecological response. This review comprehensively highlights successful applications of metabolomics in chemical ecology and discusses existing limitations of these novel techniques. It focuses on recent developments in comparative metabolomics and discusses the use of metabolomics in the systems biology of organismal interactions. It also outlines the potential of large metabolomics initiatives for model organisms in the field of chemical ecology.
Psychogios, Nikolaos; Hau, David D.; Peng, Jun; Guo, An Chi; Mandal, Rupasri; Bouatra, Souhaila; Sinelnikov, Igor; Krishnamurthy, Ramanarayan; Eisner, Roman; Gautam, Bijaya; Young, Nelson; Xia, Jianguo; Knox, Craig; Dong, Edison; Huang, Paul; Hollander, Zsuzsanna; Pedersen, Theresa L.; Smith, Steven R.; Bamforth, Fiona; Greiner, Russ; McManus, Bruce; Newman, John W.; Goodfriend, Theodore; Wishart, David S.
Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca. PMID:21359215
Lakshmanan, Viswanathan; Rhee, Kyu Y.; Daily, Johanna P.
Metabolomics has ushered in a novel and multi-disciplinary realm in biological research. It has provided researchers with a platform to combine powerful biochemical, statistical, computational, and bioinformatics techniques to delve into the mysteries of biology and disease. The application of metabolomics to study malaria parasites represents a major advance in our approach towards gaining a more comprehensive perspective on parasite biology and disease etiology. This review attempts to highlight some of the important aspects of the field of metabolomics, and its ongoing and potential future applications to malaria research. PMID:20970461
Paget, Timothy; Haroune, Nicolas; Bagchi, Sushmita; Jarroll, Edward
In this review, we examine the state-of-the-art technologies (gas and liquid chromatography, mass spectroscopy and nuclear magnetic resonance, etc.) in the well-established area of metabolomics especially as they relate to protozoan parasites.
Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically i...
Bouhifd, Mounir; Beger, Richard; Flynn, Thomas; Guo, Lining; Harris, Georgina; Hogberg, Helena; Kaddurah-Daouk, Rima; Kamp, Hennicke; Kleensang, Andre; Maertens, Alexandra; Odwin-DaCosta, Shelly; Pamies, David; Robertson, Donald; Smirnova, Lena; Sun, Jinchun; Zhao, Liang; Hartung, Thomas
Metabolomics promises a holistic phenotypic characterization of biological responses to toxicants. This technology is based on advanced chemical analytical tools with reasonable throughput, including mass-spectroscopy and NMR. Quality assurance, however - from experimental design, sample preparation, metabolite identification, to bioinformatics data-mining - is urgently needed to assure both quality of metabolomics data and reproducibility of biological models. In contrast to microarray-based transcriptomics, where consensus on quality assurance and reporting standards has been fostered over the last two decades, quality assurance of metabolomics is only now emerging. Regulatory use in safety sciences, and even proper scientific use of these technologies, demand quality assurance. In an effort to promote this discussion, an expert workshop discussed the quality assurance needs of metabolomics. The goals for this workshop were 1) to consider the challenges associated with metabolomics as an emerging science, with an emphasis on its application in toxicology and 2) to identify the key issues to be addressed in order to establish and implement quality assurance procedures in metabolomics-based toxicology. Consensus has still to be achieved regarding best practices to make sure sound, useful, and relevant information is derived from these new tools.
The COnsortium of METabolomics Studies (COMETS) is an extramural-intramural partnership that promotes collaboration among prospective cohort studies that follow participants for a range of outcomes and perform metabolomic profiling of individuals.
Guy, Charles; Kaplan, Fatma; Kopka, Joachim; Selbig, Joachim; Hincha, Dirk K
Plants possess inducible tolerance mechanisms that extend the temperature range for survival during acute temperature stress. The inducible mechanisms of cold acclimation and acquired thermotolerance involve highly complex processes. These include perception and signal transduction of non-optimal temperatures or their physical consequences on cellular components that program extensive modification of the transcriptome, proteome, metabolome and composition and physical structure of the cytoplasm, membranes and cell walls. Therefore, a systems biology approach will be necessary to advance the understanding of plant stress responses and tolerance mechanisms. One promise of systems biology is that it will greatly enhance our understanding of individual and collective functions and thereby provide a more holistic view of plant stress responses. Past studies have found that several metabolites that could functionally contribute to induced stress tolerance have been associated with stress responses. Recent metabolite-profiling studies have refocused attention on these and other potentially important components found in the 'temperature-stress metabolome'. These metabolomic studies have demonstrated that active reconfiguration of the metabolome is regulated in part by changes in gene expression initiated by temperature-stress-activated signaling and stress-related transcription factors. One aspect of metabolism that is consistent across all of the temperature-stress metabolomic studies to date is the prominent role of central carbohydrate metabolism, which seems to be a major feature of the reprogramming of the metabolome during temperature stress. Future metabolomic studies of plant temperature-stress responses should reveal additional metabolic pathways that have important functions in temperature-stress tolerance mechanisms.
Emara, Samy; Amer, Sara; Ali, Ahmed; Abouleila, Yasmine; Oga, April; Masujima, Tsutomu
The dynamics of a cell is always changing. Cells move, divide, communicate, adapt, and are always reacting to their surroundings non-synchronously. Currently, single-cell metabolomics has become the leading field in understanding the phenotypical variations between them, but sample volumes, low analyte concentrations, and validating gentle sample techniques have proven great barriers toward achieving accurate and complete metabolomics profiling. Certainly, advanced technologies such as nanodevices and microfluidic arrays are making great progress, and analytical techniques, such as matrix-assisted laser desorption ionization (MALDI), are gaining popularity with high-throughput methodology. Nevertheless, live single-cell mass spectrometry (LCSMS) values the sample quality and precision, turning once theoretical speculation into present-day applications in a variety of fields, including those of medicine, pharmaceutical, and agricultural industries. While there is still room for much improvement, it is clear that the metabolomics field is progressing toward analysis and discoveries at the single-cell level.
Beale, Michael H.; Ward, Jane L.; Baker, John M.
Modern ‘metabolomic’ methods allow us to compare levels of many structurally diverse compounds in an automated fashion across a large number of samples. This technology is ideally suited to screening of populations of plants, including trials where the aim is the determination of unintended effects introduced by GM. A number of metabolomic methods have been devised for the determination of substantial equivalence. We have developed a methodology, using [1H]-NMR fingerprinting, for metabolomic screening of plants and have applied it to the study of substantial equivalence of field-grown GM wheat. We describe here the principles and detail of that protocol as applied to the analysis of flour generated from field plots of wheat. Particular emphasis is given to the downstream data processing and comparison of spectra by multivariate analysis, from which conclusions regarding metabolome changes due to the GM can be assessed against the background of natural variation due to environment.
Frohnert, Brigitte I; Rewers, Marian J
Recent increases in the incidence of both type 1 (T1D) and type 2 diabetes (T2D) in children and adolescents point to the importance of environmental factors in the development of these diseases. Metabolomic analysis explores the integrated response of the organism to environmental changes. Metabolic profiling can identify biomarkers that are predictive of disease incidence and development, potentially providing insight into disease pathogenesis. This review provides an overview of the role of metabolomic analysis in diabetes research and summarizes recent research relating to the development of T1D and T2D in children. PMID:26420304
Nadella, K D; Marla, Soma S; Kumar, P Ananda
Metabolome refers to the complete set of metabolites synthesized through a series of multiple enzymatic steps from various biochemical pathways processing the information encrypted in the plant genome. Knowledge about synthesis and regulation of various plant metabolic substances has improved substantially with availability of Omics data originating from sequencing of plant genomes. Metabolic profiling of crops is increasingly becoming popular in assessing plant phenotypes and genetic diversity. Metabolic compositional changes vividly reflect the changes occurring during plant growth, development, and in response to stress. Hence, study of plant metabolic pathways, the interconnections between them in context of systems biology is increasingly becoming popular in identification of candidate genes. The present article reviews recent developments in analysis of plant metabolomics, available bioinformatics techniques and databases employed for comparative pathway analysis, metabolic QTLs, and their application in plants.
Primrose, Sandy; Draper, John; Elsom, Rachel; Kirkpatrick, Verity; Mathers, John C; Seal, Chris; Beckmann, Manfred; Haldar, Sumanto; Beattie, John H; Lodge, John K; Jenab, Mazda; Keun, Hector; Scalbert, Augustin
The present report summarises a workshop convened by the UK Food Standards Agency (Agency) on 25 March 2010 to discuss the current Agency's funded research on the use of metabolomics technologies in human nutrition research. The objectives of this workshop were to review progress to date, to identify technical challenges and ways of overcoming them, and to discuss future research priorities and the application of metabolomics in public health nutrition research and surveys. Results from studies nearing completion showed that by using carefully designed dietary and sampling regimens, it is possible to identify novel biomarkers of food intake that could not have been predicted from current knowledge of food composition. These findings provide proof-of-principle that the metabolomics approach can be used to develop new putative biomarkers of dietary intake. The next steps will be to validate these putative biomarkers, to develop rapid and inexpensive assays for biomarkers of food intake of high public health relevance, and to test their utility in population cohort studies and dietary surveys.
Bouatra, Souhaila; Aziat, Farid; Mandal, Rupasri; Guo, An Chi; Wilson, Michael R.; Knox, Craig; Bjorndahl, Trent C.; Krishnamurthy, Ramanarayan; Saleem, Fozia; Liu, Philip; Dame, Zerihun T.; Poelzer, Jenna; Huynh, Jessica; Yallou, Faizath S.; Psychogios, Nick; Dong, Edison; Bogumil, Ralf; Roehring, Cornelia; Wishart, David S.
Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing
Gummer, Joel P A; Krill, Christian; Du Fall, Lauren; Waters, Ormonde D C; Trengove, Robert D; Oliver, Richard P; Solomon, Peter S
Proteomics and transcriptomics are established functional genomics tools commonly used to study filamentous fungi. Metabolomics has recently emerged as another option to complement existing techniques and provide detailed information on metabolic regulation and secondary metabolism. Here, we describe broad generic protocols that can be used to undertake metabolomics studies in filamentous fungi.
Dettmer, Katja; Aronov, Pavel A.; Hammock, Bruce D.
This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed. PMID:16921475
Roessner, Ute; Beckles, Diane M
Soil salinity devastates agriculture. It reduces crop yields and makes arable land unsuitable for later use. Many species have evolved highly efficient strategies to sense, transduce, and build up tolerance to high salinity and even sensitive species have endogenous mechanism for coping with this stress. These underlying physiological and metabolic mechanisms can be unraveled using metabolomics. Here we describe detailed protocols of how to extract polar metabolites for analysis using GC-MS and LC-MS. We also touch briefly on considerations that should be taken into account when designing the experiment and how the resulting data may be analyzed and visualized in a biological context.
Simó, Carolina; Ibáñez, Clara; Valdés, Alberto; Cifuentes, Alejandro; García-Cañas, Virginia
Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs) making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade.
Simó, Carolina; Ibáñez, Clara; Valdés, Alberto; Cifuentes, Alejandro; García-Cañas, Virginia
Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs) making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade. PMID:25334064
Filla, Laura A; Edwards, James L
With a global prevalence of 9%, diabetes is the direct cause of millions of deaths each year and is quickly becoming a health crisis. Major long-term complications of diabetes arise from persistent oxidative stress and dysfunction in multiple metabolic pathways. The most serious complications involve vascular damage and include cardiovascular disease as well as microvascular disorders such as nephropathy, neuropathy, and retinopathy. Current clinical analyses like glycated hemoglobin and plasma glucose measurements hold some value as prognostic indicators of the severity of complications, but investigations into the underlying pathophysiology are still lacking. Advancements in biotechnology hold the key to uncovering new pathways and establishing therapeutic targets. Metabolomics, the study of small endogenous molecules, is a powerful toolset for studying pathophysiological processes and has been used to elucidate metabolic signatures of diabetes in various biological systems. Current challenges in the field involve correlating these biomarkers to specific complications to provide a better prediction of future risk and disease progression. This review will highlight the progress that has been made in the field of metabolomics including technological advancements, the identification of potential biomarkers, and metabolic pathways relevant to macro- and microvascular diabetic complications.
Kim, Sooah; Kim, Jungyeon; Yun, Eun Ju; Kim, Kyoung Heon
Metabolomics, one of the latest components in the suite of systems biology, has been used to understand the metabolism and physiology of living systems, including microorganisms, plants, animals and humans. Food metabolomics can be defined as the application of metabolomics in food systems, including food resources, food processing and diet for humans. The study of food metabolomics has increased gradually in the recent years, because food systems are directly related to nutrition and human health. This review describes the recent trends and applications of metabolomics to food systems, from farm to human, including food resource production, industrial food processing and food intake by humans.
Salek, Reza M; Neumann, Steffen; Schober, Daniel; Hummel, Jan; Billiau, Kenny; Kopka, Joachim; Correa, Elon; Reijmers, Theo; Rosato, Antonio; Tenori, Leonardo; Turano, Paola; Marin, Silvia; Deborde, Catherine; Jacob, Daniel; Rolin, Dominique; Dartigues, Benjamin; Conesa, Pablo; Haug, Kenneth; Rocca-Serra, Philippe; O'Hagan, Steve; Hao, Jie; van Vliet, Michael; Sysi-Aho, Marko; Ludwig, Christian; Bouwman, Jildau; Cascante, Marta; Ebbels, Timothy; Griffin, Julian L; Moing, Annick; Nikolski, Macha; Oresic, Matej; Sansone, Susanna-Assunta; Viant, Mark R; Goodacre, Royston; Günther, Ulrich L; Hankemeier, Thomas; Luchinat, Claudio; Walther, Dirk; Steinbeck, Christoph
Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.
Jewison, Timothy; Knox, Craig; Neveu, Vanessa; Djoumbou, Yannick; Guo, An Chi; Lee, Jacqueline; Liu, Philip; Mandal, Rupasri; Krishnamurthy, Ram; Sinelnikov, Igor; Wilson, Michael; Wishart, David S.
The Yeast Metabolome Database (YMDB, http://www.ymdb.ca) is a richly annotated ‘metabolomic’ database containing detailed information about the metabolome of Saccharomyces cerevisiae. Modeled closely after the Human Metabolome Database, the YMDB contains >2000 metabolites with links to 995 different genes/proteins, including enzymes and transporters. The information in YMDB has been gathered from hundreds of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the YMDB also contains an extensive collection of experimental intracellular and extracellular metabolite concentration data compiled from detailed Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR) metabolomic analyses performed in our lab. This is further supplemented with thousands of NMR and MS spectra collected on pure, reference yeast metabolites. Each metabolite entry in the YMDB contains an average of 80 separate data fields including comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, intracellular/extracellular concentrations, growth conditions and substrates, pathway information, enzyme data, gene/protein sequence data, as well as numerous hyperlinks to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of S. cervesiae's importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers, but also to yeast biologists, systems biologists, the industrial fermentation industry, as well as the beer, wine and spirit industry. PMID:22064855
Okazaki, Yozo; Saito, Kazuki
Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants.
Rubakhin, Stanislav S.; Lanni, Eric J.; Sweedler, Jonathan V.
The metabolome refers to the entire set of small molecules, or metabolites, within a biological sample. These molecules are involved in many fundamental intracellular functions and reflect the cell’s physiological condition. The ability to detect and identify metabolites and determine and monitor their amounts at the single cell level enables an exciting range of studies of biological variation and functional heterogeneity between cells, even within a presumably homogenous cell population. Significant progress has been made in the development and application of bioanalytical tools for single cell metabolomics based on mass spectrometry, microfluidics, and capillary separations. Remarkable improvements in the sensitivity, specificity, and throughput of these approaches enable investigation of multiple metabolites simultaneously in a range of individual cell samples. PMID:23246232
Shulaev, Vladimir; Cortes, Diego; Miller, Gad; Mittler, Ron
Stress in plants could be defined as any change in growth condition(s) that disrupts metabolic homeostasis and requires an adjustment of metabolic pathways in a process that is usually referred to as acclimation. Metabolomics could contribute significantly to the study of stress biology in plants and other organisms by identifying different compounds, such as by-products of stress metabolism, stress signal transduction molecules or molecules that are part of the acclimation response of plants. These could be further tested by direct measurements, correlated with changes in transcriptome and proteome expression and confirmed by mutant analysis. In this review, we will discuss recent application of metabolomics and system biology to the area of plant stress response. We will describe approaches such as metabolic profiling and metabolic fingerprinting as well as combination of different 'omics' platforms to achieve a holistic view of the plant response stress and conduct detailed pathway analysis.
Dixon, Richard A; Gang, David R; Charlton, Adrian J; Fiehn, Oliver; Kuiper, Harry A; Reynolds, Tracey L; Tjeerdema, Ronald S; Jeffery, Elizabeth H; German, J Bruce; Ridley, William P; Seiber, James N
Biological systems are exceedingly complex. The unraveling of the genome in plants and humans revealed fewer than the anticipated number of genes. Therefore, other processes such as the regulation of gene expression, the action of gene products, and the metabolic networks resulting from catalytic proteins must make fundamental contributions to the remarkable diversity inherent in living systems. Metabolomics is a relatively new approach aimed at improved understanding of these metabolic networks and the subsequent biochemical composition of plants and other biological organisms. Analytical tools within metabolomics including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy can profile the impact of time, stress, nutritional status, and environmental perturbation on hundreds of metabolites simultaneously resulting in massive, complex data sets. This information, in combination with transcriptomics and proteomics, has the potential to generate a more complete picture of the composition of food and feed products, to optimize crop trait development, and to enhance diet and health. Selected presentations from an American Chemical Society symposium held in March 2005 have been assembled to highlight the emerging application of metabolomics in agriculture.
Fitian, Asem I; Cabrera, Roniel
We elucidate major pathways of hepatocarcinogenesis and accurate diagnostic metabolomic biomarkers of hepatocellular carcinoma (HCC) identified by contemporary HCC metabolomics studies, and delineate a model HCC metabolomics study design. A literature search was carried out on Pubmed for HCC metabolomics articles published in English. All relevant articles were accessed in full text. Major search terms included “HCC”, “metabolomics”, “metabolomics”, “metabonomic” and “biomarkers”. We extracted clinical and demographic data on all patients and consolidated the lead candidate biomarkers, pathways, and diagnostic performance of metabolomic expression patterns reported by all studies in tables. Where reported, we also extracted and summarized the metabolites and pathways most highly associated with the development of cirrhosis in table format. Pathways of lysophospholipid, sphingolipid, bile acid, amino acid, and reactive oxygen species metabolism were most consistently associated with HCC in the cited works. Several studies also elucidate metabolic alterations strongly associated with cirrhosis, with γ-glutamyl peptides, bile acids, and dicarboxylic acids exhibiting the highest capacity for stratifying cirrhosis patients from appropriately matched controls. Collectively, global metabolomic profiles of the referenced works exhibit a promising diagnostic capacity for HCC at a capacity greater than that of conventional diagnostic biomarker alpha-fetoprotein. Metabolomics is a powerful strategy for identifying global metabolic signatures that exhibit potential to be leveraged toward the screening, diagnosis, and management of HCC. A streamlined study design and patient matching methodology may improve concordance among metabolomic datasets in future works. PMID:28105254
Lei, Shulei; Powers, Robert
Parkinson's disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergic neurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress are linked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolite changes in biofluids, cell lysates, tissues and tumors in order to correlate these metabolomic changes to a disease state. Thus, the application of metabolomics to investigate PD provides a systematic approach to understand the pathology of PD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This review will examine current research into PD mechanisms with a focus on mitochondrial dysfunction and oxidative stress. Neurotoxin-based PD animal models and the rationale for metabolomics studies in PD will also be discussed. The review will also explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis. PMID:26078917
Metabolomics is an “omic” science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism. PMID:24665364
García-Sevillano, Miguel Ángel; García-Barrera, Tamara; Gómez-Ariza, José Luis
Environmental metabolomics is an emerging field referred to the application of metabolomics to characterize the interactions of living organisms with their environment. In this sense, the importance of monitoring the effects of toxic metals on living organisms has increased as a consequence of natural changes and anthropogenic activities that have led to an increase of toxic metals levels in terrestrial and aquatic ecosystems. For this purpose, the use of metabolomics based on mass spectrometry to study metal toxicity is gaining importance in recent years. Environmental metabolomics can be used to: discover the mode of action (MOA) of toxic metals through controlled laboratory experiments; evaluate toxicity (biological adverse response to a substance), that may be useful in risk assessment; and develop new biomarkers (based in metabolome shifts discovered through controlled laboratory experiments) that may be applied in environmental biomonitoring (environmental realistic scenario). In this review, it is discussed how metabolomics based on mass spectrometry can be applied to study metal toxicity, considering the most important hallmarks related to metabolomic experiments. This article is protected by copyright. All rights reserved.
Fanos, V; Van den Anker, J; Noto, A; Mussap, M; Atzori, L
The newest 'omics' science is metabolomics, the latest offspring of genomics, considered the most innovative of the 'omics' sciences. Metabolomics, also called the 'new clinical biochemistry', is an approach based on the systematic study of the complete set of metabolites in a biological sample. The metabolome is considered the most predictive phenotype and is capable of considering epigenetic differences. It is so close to the phenotype that it can be considered the phenotype itself. In the last three years about 5000 papers have been listed in PubMed on this topic, but few data are available in the newborn. The aim of this review, after a description of background and technical procedures, is to analyse the clinical applications of metabolomics in neonatology, covering the following points: gestational age, postnatal age, type of delivery, zygosity, perinatal asphyxia, intrauterine growth restriction, prenatal inflammation and brain injury, respiratory, cardiovascular renal, metabolic diseases; sepsis, necrotizing enterocolitis and antibiotic treatment; nutritional studies on maternal milk and formula, pharma-metabolomics, long-term diseases. Pros and cons of metabolomics are also discussed. All this comes about with the non-invasive collection of a few drops of urine (exceptionally important for the neonate, especially those of low birth weight). Only time and large-scale studies to validate initial results will place metabolomics within neonatology. In any case, it is important for perinatologists to learn and understand this new technology to offer their patients the utmost in diagnostic and therapeutic opportunities.
Gooding, Jessica R.; Jensen, Mette V.; Newgard, Christopher B.
Metabolomics, the characterization of the set of small molecules in a biological system, is advancing research in multiple areas of islet biology. Measuring a breadth of metabolites simultaneously provides a broad perspective on metabolic changes as the islets respond dynamically to metabolic fuels, hormones, or environmental stressors. As a result, metabolomics has the potential to provide new mechanistic insights into islet physiology and pathophysiology. Here we summarize advances in our understanding of islet physiology and the etiologies of type-1 and type-2 diabetes gained from metabolomics studies. PMID:26116790
Metabolomics is the study of small molecules of both endogenous and exogenous origin, such as metabolic substrates and their products, lipids, small peptides, vitamins and other protein cofactors generated by metabolism, which are downstream from genes.
Castle, Arthur L; Fiehn, Oliver; Kaddurah-Daouk, Rima; Lindon, John C
Informatics standards and controlled vocabularies are essential for allowing information technology to help exchange, manage, interpret and compare large data collections. In a rapidly evolving field, the challenge is to work out how best to describe, but not prescribe, the use of these technologies and methods. A Metabolomics Standards Workshop was held by the US National Institutes of Health (NIH) to bring together multiple ongoing standards efforts in metabolomics with the NIH research community. The goals were to discuss metabolomics workflows (methods, technologies and data treatments) and the needs, challenges and potential approaches to developing a Metabolomics Standards Initiative that will help facilitate this rapidly growing field which has been a focus of the NIH roadmap effort. This report highlights specific aspects of what was presented and discussed at the 1st and 2nd August 2005 Metabolomics Standards Workshop.
Tawfike, Ahmed Fares; Viegelmann, Christina; Edrada-Ebel, Ruangelie
Metabolomic methods can be utilized to screen diverse biological sources of potentially novel and sustainable sources of antibiotics and pharmacologically-active drugs. Dereplication studies by high resolution Fourier transform mass spectrometry coupled to liquid chromatography (LC-HRFTMS) and nuclear magnetic resonance (NMR) spectroscopy can establish the chemical profile of endophytic and/or endozoic microbial extracts and their plant or animal sources. Identifying the compounds of interest at an early stage will aid in the isolation of the bioactive components. Therefore metabolite profiling is important for functional genomics and in the search for new pharmacologically active compounds. Using the tools of metabolomics through the employment of LC-HRFTMS as well as high resolution NMR will be a very efficient approach. Metabolomic profiling has found its application in screening extracts of macroorganisms as well as in the isolation and cultivation of suspected microbial producers of bioactive natural products.Metabolomics is being applied to identify and biotechnologically optimize the production of pharmacologically active secondary metabolites. The links between metabolome evolution during optimization and processing factors can be identified through metabolomics. Information obtained from a metabolomics dataset can efficiently establish cultivation and production processes at a small scale which will be finally scaled up to a fermenter system, while maintaining or enhancing synthesis of the desired compounds. MZmine (BMC Bioinformatics 11:395-399, 2010; http://mzmine.sourceforge.net/download.shtml ) and SIEVE ( http://www.vastscientific.com/resources/index.html ; Rapid Commun Mass Spectrom 22:1912-1918, 2008) softwares are utilized to perform differential analysis of sample populations to find significant expressed features of complex biomarkers between parameter variables. Metabolomes are identified with the aid of existing high resolution MS and NMR
As a complimentary tool to other omics platforms, metabolomics is increasingly being used bybiologists to study the dynamic response of biological systems (cells, tissues, or wholeorganisms) under diverse physiological or pathological conditions. Metabolomics deals with the quali...
Although lagging behind applications targeted to human endpoints, metabolomics offers great potential in environmental applications, including ecotoxicology. Indeed, the advantages of metabolomics (relative to other 'omic techniques) may be more tangible in ecotoxicology because...
Beyoğlu, Diren; Idle, Jeffrey R.
Summary The emergent discipline of metabolomics has attracted considerable research effort in hepatology. Here we review the metabolomic data for nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH), cirrhosis, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), alcoholic liver disease (ALD), hepatitis B and C, cholecystitis, cholestasis, liver transplantation and acute hepatotoxicity in animal models. A metabolomic window has permitted a view into the changing biochemistry occurring in the transitional phases between a healthy liver and hepatocellular carcinoma or cholangiocarcinoma. Whether provoked by obesity and diabetes, alcohol use or oncogenic viruses, the liver develops a core metabolomic phenotype (CMP) that involves dysregulation of bile acid and phospholipid homeostasis. The CMP commences at the transition between the healthy liver (Phase 0) and NAFLD/NASH, ALD or viral hepatitis (Phase 1). This CMP is maintained in the presence or absence of cirrhosis (Phase 2) and whether or not either HCC or CCA (Phase 3) develop. Inflammatory signalling in the liver triggers the appearance of the CMP. Many other metabolomic markers distinguish between Phases 0, 1, 2 and 3. A metabolic remodelling in HCC has been described but metabolomic data from all four Phases demonstrate that the Warburg shift from mitochondrial respiration to cytosolic glycolysis foreshadows HCC and may occur as early as Phase 1. The metabolic remodelling also involves an upregulation of fatty acid β-oxidation, also beginning in Phase 1. The storage of triglycerides in fatty liver provides high energy-yielding substrates for Phases 2 and 3 of liver pathology. The metabolomic window into hepatobiliary disease sheds new light on the systems pathology of the liver. PMID:23714158
Fanos, Vassilios; Antonucci, Roberto; Barberini, Luigi; Noto, Antonio; Atzori, Luigi
The youngest and more rapidly increasing "omic" discipline, called metabolomics, is the process of describing the phenotype of a cell, tissue or organism through the full complement of metabolites present. Metabolomics measure global sets of low molecular weight metabolites (including amino acids, organic acids, sugars, fatty acids, lipids, steroids, small peptides, vitamins, etc.), thus providing a "snapshot" of the metabolic status of a cell, tissue or organism in relation to genetic variations or external stimuli. The use of metabolomics appears to be a promising tool in neonatology. The management of sick newborns might improve if more information on perinatal/neonatal maturational processes and their metabolic background were available. Urine ("a window on the organism") is a biofluid particularly suitable for metabolomic analysis in neonatology because it may be collected by using simple, noninvasive techniques and because it may provide valuable diagnostic information. In this review, the authors report the few literature data on neonatal metabolomics, including their personal experience, in the following fields: intrauterine growth restriction, perinatal transition, asphyxia, brain injury and hypothermia, maternal milk evaluation, postnatal maturation, bronchiolitis, sepsis, patent ductus arteriosus, respiratory distress syndrome, nephrouropathies, metabolic diseases, antibiotic treatment, perinatal programming and long-term outcome in extremely low birth-weight infants.
Introduction/Objectives/Methods One of the biggest challenges in ecological risk assessment is determining the impact of multiple stressors on individual organisms and populations in ‘real world’ scenarios. Emerging ‘omic technologies, notably, metabolomics, provides an opportunity to address the uncertainties surrounding ecological risk assessment of multiple stressors. The objective of this study was to use a metabolomics biomarker approach to investigate the effect of multiple stressors on amphibian metamorphs. To this end, metamorphs of Rana pipiens (northern leopard frogs) were exposed to the insecticide Carbaryl (0.32 μg/L), a conspecific predator alarm call (Lithobates catesbeianus), Carbaryl and the predator alarm call, and a control with no stressor. In addition to metabolomic fingerprinting, we measured corticosterone levels in each treatment to assess general stress response. We analyzed relative abundances of endogenous metabolites collected in liver tissue with gas chromatography coupled with mass spectrometry. Support vector machine (SVM) methods with recursive feature elimination (RFE) were applied to rank the metabolomic profiles produced. Results/Conclusions SVM-RFE of the acquired metabolomic spectra demonstrated 85-96% classification accuracy among control and all treatment groups when using the top 75 ranked retention time bins. Biochemical fluxes observed in the groups exposed to carbaryl, predation threat, and the combined treatmen
Mercuro, Giuseppe; Bassareo, Pier P; Deidda, Martino; Cadeddu, Christian; Barberini, Luigi; Atzori, Luigi
The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end-products of cellular processes. Metabolomics is the systematic study of small-molecule metabolite profiles that specific cellular processes leave behind. RNA messenger gene expression data and proteomic analyses do not tell the whole story of what might be happening in a cell. Metabolic profiling, in turn, amplifies changes both in the proteome and the genome, and represents a more accurate approximation to the phenotype of an organism in health and disease. In this article, we have provided a description of metabolomics, in the presence of other, more familiar 'omics' disciplines, such as genomics and proteomics. In addition, we have reviewed the current rationale for metabolomics in cardiology, its basic methodology and the data actually available in human studies in this discipline. The discussed topics highlight the importance of being able to use the metabolomics information in order to understand disease mechanisms from a systems biology perspective as a noninvasive approach to diagnose, grade and treat cardiovascular diseases.
Zhang, Aihua; Sun, Hui; Wang, Zhigang; Sun, Wenjun; Wang, Ping; Wang, Xijun
Metabolomics represent a global understanding of metabolite complement of integrated living systems and dynamic responses to the changes of both endogenous and exogenous factors and has many potential applications and advantages for the research of complex systems. As a systemic approach, metabolomics adopts a "top-down" strategy to reflect the function of organisms from the end products of the metabolic network and to understand metabolic changes of a complete system caused by interventions in a holistic context. This property agrees with the holistic thinking of Traditional Chinese Medicine (TCM), a complex medical science, suggesting that metabolomics has the potential to impact our understanding of the theory behind the evidence-based Chinese medicine. Consequently, the development of robust metabolomic platforms will greatly facilitate, for example, the understanding of the action mechanisms of TCM formulae and the analysis of Chinese herbal (CHM) and mineral medicine, acupuncture, and Chinese medicine syndromes. This review summarizes some of the applications of metabolomics in special TCM issues with an emphasis on metabolic biomarker discovery.
Putnam, Joel G.
We combine cell assays and metabolomics to create a powerful tool, which emerges to elevate the identification of new control chemicals. We combined the use of bigheaded carp fry cell line with metabolite profiling to describe the dose response to thiram. Thiram is a registered pesticide commonly used as a fungicide in the field or as a seed protectant and is known to be toxic to fish. Seven concentrations of thiram were used to dose bighead carp fry cells and silver carp fry cells. We identified 700 metabolomic markers and 41 of those markers exhibited a dose response to thiram in the bighead carp fry cells. We identified 1590 metabolomic markers with 205 of those markers exhibited a dose response to thiram in the silver carp fry cells. When the metabolites of both cell lines are compared using volcano plots, 16 metabolomic markers were identified as significant. A smaller subset of metabolites indicate that a thiram specific metabolomic fingerprint exists that is not species specific, but instead toxin specific. Application of toxin fingerprints (toxin specific but species independent metabolites) can be used to address the cause of ecological significant events, such as mass fish kills.
Banoei, Mohammad Mehdi; Winston, Brent W.; Schraufnagel, Dean E.
Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases. PMID:26196272
Dromms, Robert A.; Styczynski, Mark P.
The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering. PMID:24957776
Courant, Frédérique; Antignac, Jean-Philippe; Dervilly-Pinel, Gaud; Le Bizec, Bruno
The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).
Rambla, José L; López-Gresa, M P; Bellés, J M; Granell, Antonio
Metabolomics is a powerful discipline aimed at a comprehensive and global analysis of the metabolites present in a cell, tissue, or organism, and to which increasing attention has been paid in the last few years. Given the high diversity in physical and chemical properties of plant metabolites, not a single method is able to analyze them all.Here we describe two techniques for the profiling of two quite different groups of metabolites: polar and semi-polar secondary metabolites, including many of those involved in plant response to biotic and abiotic stress, and volatile compounds, which include those responsible of most of our perception of food flavor. According to these techniques, polar and semi-polar metabolites are extracted in methanol, separated by liquid chromatography (UPLC), and detected by a UV-VIS detector (PDA) and a time-of-flight (ToF) mass spectrometer. Volatile compounds, on the other hand, are extracted by headspace solid phase microextraction (HS-SPME), and separated and detected by gas chromatography coupled to mass spectrometry (GC-MS).
Noctor, Graham; Lelarge-Trouverie, Caroline; Mhamdi, Amna
Oxidative stress resulting from increased availability of reactive oxygen species (ROS) is a key component of many responses of plants to challenging environmental conditions. The consequences for plant metabolism are complex and manifold. We review data on small compounds involved in oxidative stress, including ROS themselves and antioxidants and redox buffers in the membrane and soluble phases, and we discuss the wider consequences for plant primary and secondary metabolism. While metabolomics has been exploited in many studies on stress, there have been relatively few non-targeted studies focused on how metabolite signatures respond specifically to oxidative stress. As part of the discussion, we present results and reanalyze published datasets on metabolite profiles in catalase-deficient plants, which can be considered to be model oxidative stress systems. We emphasize the roles of ROS-triggered changes in metabolites as potential oxidative signals, and discuss responses that might be useful as markers for oxidative stress. Particular attention is paid to lipid-derived compounds, the status of antioxidants and antioxidant breakdown products, altered metabolism of amino acids, and the roles of phytohormone pathways.
Yau, Yunki; Leong, Rupert W; Zeng, Ming; Wasinger, Valerie C
Genome-wide studies in inflammatory bowel disease (IBD) have allowed us to understand Crohn's disease and ulcerative colitis as forms of related autoinflammatory disorders that arise from a multitude of pathogenic origins. Proteomics and metabolomics are the offspring of genomics that possess unprecedented possibilities to characterize unknown pathogenic pathways. It has been about a decade since proteomics was first applied to IBD, and 5 years for metabolomics. These techniques have yielded novel and potentially important findings, but turning these results into beneficial patient outcomes remains challenging. This review recounts the history and context of clinical IBD developments before and after proteomics and metabolomics IBD in this field, discusses the challenges in consolidating high complexity data with physiological understanding, and provides an outlook on the emerging principles that will help interface the bioanalytical laboratory with IBD prognosis.
Yan, Huifang; Ding, Mingzhu; Yuan, Yingjin
In order to study the inherent difference among terpenes producing yeasts from the point of metabolomics, we selected taxadiene producing yeasts as the model system. The changes of cellular metabolites during fermentation log phase of artificial functional yeasts were determined using metabolomics methods. The results represented that compared to W303-1A as a blank control, the metabolites in glycolysis, tricarboxylic acid cycle (TCA) cycle and several amino acids were influenced. And due to the changes of metabolites, the growth of cells was inhibited to a certain extent. Among the metabolites identified, citric acid content in taxadiene producing yeasts changed the most, the decreasing amplitude reached 90% or more. Therefore, citric acid can be a marker metabolite for the future study of artificial functional yeasts. The metabolomics analysis of taxadiene producing yeasts can provide more information in further studies on optimization of terpenes production in heterologous chassis.
Ahmed, Ishfaq; Roy, Badal C.; Khan, Salman A.; Septer, Seth; Umar, Shahid
Inflammatory Bowel Disease (IBD) is a multifactorial disorder that conceptually occurs as a result of altered immune responses to commensal and/or pathogenic gut microbes in individuals most susceptible to the disease. During Crohn’s Disease (CD) or Ulcerative Colitis (UC), two components of the human IBD, distinct stages define the disease onset, severity, progression and remission. Epigenetic, environmental (microbiome, metabolome) and nutritional factors are important in IBD pathogenesis. While the dysbiotic microbiota has been proposed to play a role in disease pathogenesis, the data on IBD and diet are still less convincing. Nonetheless, studies are ongoing to examine the effect of pre/probiotics and/or FODMAP reduced diets on both the gut microbiome and its metabolome in an effort to define the healthy diet in patients with IBD. Knowledge of a unique metabolomic fingerprint in IBD could be useful for diagnosis, treatment and detection of disease pathogenesis. PMID:27681914
Zheng, Hong; Clausen, Morten R; Dalsgaard, Trine K; Bertram, Hanne C
Dairy products are an important component in the Western diet and represent a valuable source of nutrients for humans. However, a reliable dairy intake assessment in nutrition research is crucial to correctly elucidate the link between dairy intake and human health. Metabolomics is considered a potential tool for assessment of dietary intake instead of traditional methods, such as food frequency questionnaires, food records, and 24-h recalls. Metabolomics has been successfully applied to discriminate between consumption of different dairy products under different experimental conditions. Moreover, potential metabolites related to dairy intake were identified, although these metabolites need to be further validated in other intervention studies before they can be used as valid biomarkers of dairy consumption. Therefore, this review provides an overview of metabolomics for assessment of dairy intake in order to better clarify the role of dairy products in human nutrition and health.
Guma, Monica; Tiziani, Stefano; Firestein, Gary S.
Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases. PMID:26935283
Rudell, David R; Mattheis, James P; Hertog, Maarten L A T M
Untargeted metabolic profiling was employed to characterize metabolomic changes associated with 'Granny Smith' apple superficial scald development following 1-MCP or DPA treatment. Partial least-squares discriminant analyses were used to link metabolites with scald, postharvest treatments, and storage duration. Models revealed metabolomic differentiation between untreated controls and fruit treated with DPA or 1-MCP within 1 week following storage initiation. Metabolic divergence between controls and DPA-treated fruit after 4 weeks of storage preceded scald symptom development by 2 months. alpha-Farnesene oxidation products with known associations to scald, including conjugated trienols, 6-methyl-5-hepten-2-one, and 6-methyl-5-hepten-2-ol, were associated with presymptomatic as well as scalded control fruit. Likewise, a large group of putative triterpenoids with mass spectral features similar to those of ursolic acid and beta-sitosterol were associated with control fruit and scald. Results demonstrate that extensive metabolomic changes associated with scald precede actual symptom development.
Tian, He; Lam, Sin Man; Shui, Guanghou
Metabolomics, which is based mainly on nuclear magnetic resonance (NMR), gas-chromatography (GC) or liquid-chromatography (LC) coupled to mass spectrometry (MS) analytical technologies to systematically acquire the qualitative and quantitative information of low-molecular-mass endogenous metabolites, provides a direct snapshot of the physiological condition in biological samples. As complements to transcriptomics and proteomics, it has played pivotal roles in agricultural and food science research. In this review, we discuss the capacities of NMR, GC/LC-MS in the acquisition of plant metabolome, and address the potential promise and diverse applications of metabolomics, particularly lipidomics, to investigate the responses of Arabidopsis thaliana, a primary plant model for agricultural research, to environmental stressors including heat, freezing, drought, and salinity. PMID:27869667
Guma, Monica; Tiziani, Stefano; Firestein, Gary S
Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.
The comprehensive experimental analysis of a metabolic constitution plays a central role in approaches of organismal systems biology. Quantifying the impact of a changing environment on the homeostasis of cellular metabolism has been the focus of numerous studies applying various metabolomics techniques. It has been proven that approaches which integrate different analytical techniques, e.g., LC-MS, GC-MS, CE-MS and H-NMR, can provide a comprehensive picture of a certain metabolic homeostasis. Identification of metabolic compounds and quantification of metabolite levels represent the groundwork for the analysis of regulatory strategies in cellular metabolism. This significantly promotes our current understanding of the molecular organization and regulation of cells, tissues and whole organisms. Nevertheless, it is demanding to elicit the pertinent information which is contained in metabolomics data sets. Based on the central dogma of molecular biology, metabolite levels and their fluctuations are the result of a directed flux of information from gene activation over transcription to translation and posttranslational modification. Hence, metabolomics data represent the summed output of a metabolic system comprising various levels of molecular organization. As a consequence, the inverse assignment of metabolomics data to underlying regulatory processes should yield information which—if deciphered correctly—provides comprehensive insight into a metabolic system. Yet, the deduction of regulatory principles is complex not only due to the high number of metabolic compounds, but also because of a high level of cellular compartmentalization and differentiation. Motivated by the question how metabolomics approaches can provide a representative view on regulatory biochemical processes, this article intends to present and discuss current metabolomics applications, strategies of data analysis and their limitations with respect to the interpretability in context of
Uppal, Karan; Walker, Douglas I.; Liu, Ken; Li, Shuzhao; Go, Young-Mi; Jones, Dean P.
“Sola dosis facit venenum.” These words of Paracelsus, “the dose makes the poison”, can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80–85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed. PMID:27629808
Cheng, Yidong; Yang, Xiao; Deng, Xiaheng; Zhang, Xiaolei; Li, Pengchao; Tao, Jun; Qin, Chao; Wei, Jifu; Lu, Qiang
Bladder cancer (BC) is the most common urological malignancy. Early diagnosis of BC is crucial to improve patient outcomes. Currently, metabolomics is a potential technique that can be used to detect BC. We reviewed current publications and synthesised the findings on BC and metabolomics, i.e. metabolite upregulation and downregulation. Fourteen metabolites (lactic acid, leucine, valine, phenylalanine, glutamate, histidine, aspartic acid, tyrosine, serine, uracil, hypoxanthine, carnitine, pyruvic acid and citric acid) were identified as potential biomarkers for BC. In conclusion, this systematic review presents new opportunities for the diagnosis of BC. PMID:26379905
Randhawa, Manpreet; Southall, Michael; Samaras, Samantha Tucker
It is very well known that exposure of skin to sun chronically accelerates the mechanism of aging as well as making it more susceptible toward skin cancer. This aspect of aging has been studied very well through genomics and proteomics tools. In this study we have used a metabolomic approach for the first time to determine the differences in the metabolome from full thickness skin biopsies from sun exposed and sun protected sites. We have primarily investigated the energy metabolism and the oxidative pathway in sun exposed skin. Biochemical pathway analysis revealed that energy metabolism in photoexposed skin is predominantly anaerobic. The study also validated the increased oxidative stress in skin.
Barnes, Stephen; Benton, H. Paul; Casazza, Krista; Cooper, Sara J.; Cui, Xiangqin; Du, Xiuxia; Engler, Jeffrey; Kabarowski, Janusz H.; Li, Shuzhao; Pathmasiri, Wimal; Prasain, Jeevan K.; Renfrow, Matthew B.; Tiwari, Hemant K.
The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program’s goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. PMID:27434804
Metabolomics is becoming a well-established tool for studying how organisms, such as fish, respond to various stressors. For example, the literature is rich with laboratory studies involving analysis of samples from organisms exposed to individual chemical toxicants. These studie...
Barkal, Layla J.; Theberge, Ashleigh B.; Guo, Chun-Jun; Spraker, Joe; Rappert, Lucas; Berthier, Jean; Brakke, Kenneth A.; Wang, Clay C. C.; Beebe, David J.; Keller, Nancy P.; Berthier, Erwin
The microbial secondary metabolome encompasses great synthetic diversity, empowering microbes to tune their chemical responses to changing microenvironments. Traditional metabolomics methods are ill-equipped to probe a wide variety of environments or environmental dynamics. Here we introduce a class of microscale culture platforms to analyse chemical diversity of fungal and bacterial secondary metabolomes. By leveraging stable biphasic interfaces to integrate microculture with small molecule isolation via liquid–liquid extraction, we enable metabolomics-scale analysis using mass spectrometry. This platform facilitates exploration of culture microenvironments (including rare media typically inaccessible using established methods), unusual organic solvents for metabolite isolation and microbial mutants. Utilizing Aspergillus, a fungal genus known for its rich secondary metabolism, we characterize the effects of culture geometry and growth matrix on secondary metabolism, highlighting the potential use of microscale systems to unlock unknown or cryptic secondary metabolites for natural products discovery. Finally, we demonstrate the potential for this class of microfluidic systems to study interkingdom communication between fungi and bacteria. PMID:26842393
Metabolomics is considered as a complementary tool to other omics platforms to provide a snapshot of the cellular biochemistry and physiology taking place at any instant. Metabolmics approaches have been widely used to provide comprehensive and quantitative analyses of the metabo...
We have developed NMR automation and cell quench methods for cell culture-based metabolomics to study chemical exposure and toxicity. Our flow automation method is robust and free of cross contamination. The direct cell quench method is rapid and effective. Cell culture-based met...
Muhlebach, Marianne S; Sha, Wei
Cystic fibrosis is a mono-genetic multi-system disease; however, respiratory manifestations cause the main morbidity and mortality where chronic bacterial infections lead to bronchiectasis and ultimately respiratory failure. Metabolomics allows a relatively complete snapshot of metabolic processes in a sample using different mass spectrometry methods. Sample types used for discovery of biomarkers or pathomechanisms in cystic fibrosis (CF) have included blood, respiratory secretions, and exhaled breath to date. Metabolomics has shown distinction of CF vs. non-CF for matrices of blood, exhaled breath, and respiratory epithelial cultures, each showing different pathways. Severity of lung disease has been addressed by studies in bronchoalveolar lavage and exhaled breath condensate showing separation by metabolites that the authors of each study related to inflammation; e.g., ethanol, acetone, purines. Lipidomics has been applied to blood and sputum samples showing associations with lung function and Pseudomonas aeruginosa infection status. Finally, studies of bacteria grown in vitro showed differences of bacterial metabolites to be associated with clinical parameters. Metabolomics, in the sense of global metabolomic profiling, is a powerful technique that has allowed discovery of pathways that had not previously been implicated in CF. These may include purines, mitochondrial pathways, and different aspects of glucose metabolism besides the known differences in lipid metabolism in CF. However, targeted studies to validate such potential metabolites and pathways of interest are necessary. Studies evaluating metabolites of bacterial origin are in their early stages. Thus further well-designed studies could be envisioned.
Weiss, Robert H; Kim, Kyoungmi
Metabolomics--the nontargeted measurement of all metabolites produced by the body--is beginning to show promise in both biomarker discovery and, in the form of pharmacometabolomics, in aiding the choice of therapy for patients with specific diseases. In its two basic forms (pattern recognition and metabolite identification), this developing field has been used to discover potential biomarkers in several renal diseases, including acute kidney injury (attributable to a variety of causes), autosomal dominant polycystic kidney disease and kidney cancer. NMR and gas chromatography or liquid chromatography, together with mass spectrometry, are generally used to separate and identify metabolites. Many hurdles need to be overcome in this field, such as achieving consistency in collection of biofluid samples, controlling for batch effects during the analysis and applying the most appropriate statistical analysis to extract the maximum amount of biological information from the data obtained. Pathway and network analyses have both been applied to metabolomic analysis, which vastly extends its clinical relevance and effects. In addition, pharmacometabolomics analyses, in which a metabolomic signature can be associated with a given therapeutic effect, are beginning to appear in the literature, which will lead to personalized therapies. Thus, metabolomics holds promise for early diagnosis, increased choice of therapy and the identification of new metabolic pathways that could potentially be targeted in kidney disease.
Metabolic profiling of 621 metabolites was employed to characterize metabolomic changes associated with ‘Granny Smith’ apple superficial scald development following 1-MCP or DPA treatment. Partial least squares-discriminant analyses were used to link metabolites with scald, postharvest treatments, ...
Rabinowitz, Joshua D; Aristilde, Ludmilla; Amador-Noguez, Daniel
Members of the genus Clostridium collectively have the ideal set of the metabolic capabilities for fermentative biofuel production: cellulose degradation, hydrogen production, and solvent excretion. No single organism, however, can effectively convert cellulose into biofuels. Here we developed, using metabolomics and isotope tracers, basic science knowledge of Clostridial metabolism of utility for future efforts to engineer such an organism. In glucose fermentation carried out by the biofuel producer Clostridium acetobutylicum, we observed a remarkably ordered series of metabolite concentration changes as the fermentation progressed from acidogenesis to solventogenesis. In general, high-energy compounds decreased while low-energy species increased during solventogenesis. These changes in metabolite concentrations were accompanied by large changes in intracellular metabolic fluxes, with pyruvate directed towards acetyl-CoA and solvents instead of oxaloacetate and amino acids. Thus, the solventogenic transition involves global remodeling of metabolism to redirect resources from biomass production into solvent production. In contrast to C. acetobutylicum, which is an avid fermenter, C. cellulolyticum metabolizes glucose only slowly. We find that glycolytic intermediate concentrations are radically different from fast fermenting organisms. Associated thermodynamic and isotope tracer analysis revealed that the full glycolytic pathway in C. cellulolyticum is reversible. This arises from changes in cofactor utilization for phosphofructokinase and an alternative pathway from phosphoenolpyruvate to pyruvate. The net effect is to increase the high-energy phosphate bond yield of glycolysis by 150% (from 2 to 5) at the expense of lower net flux. Thus, C. cellulolyticum prioritizes glycolytic energy efficiency over speed. Degradation of cellulose results in other sugars in addition to glucose. Simultaneous feeding of stable isotope-labeled glucose and unlabeled pentose sugars
Castillo-Peinado, L S; Luque de Castro, M D
The revulsive publications during the last years on the precariousness of forensic sciences worldwide have promoted the move of major steps towards improvement of this science. One of the steps (viz. a higher involvement of metabolomics in the new era of forensic analysis) deserves to be discussed under different angles. Thus, the characteristics of metabolomics that make it a useful tool in forensic analysis, the aspects in which this omics is so far implicit, but not mentioned in forensic analyses, and how typical forensic parameters such as the post-mortem interval or fingerprints take benefits from metabolomics are critically discussed in this review. The way in which the metabolomics-forensic binomial succeeds when either conventional or less frequent samples are used is highlighted here. Finally, the pillars that should support future developments involving metabolomics and forensic analysis, and the research required for a fruitful in-depth involvement of metabolomics in forensic analysis are critically discussed.
Misra, Biswapriya B; Assmann, Sarah M; Chen, Sixue
In conjunction with genomics, transcriptomics, and proteomics, plant metabolomics is providing large data sets that are paving the way towards a comprehensive and holistic understanding of plant growth, development, defense, and productivity. However, dilution effects from organ- and tissue-based sampling of metabolomes have limited our understanding of the intricate regulation of metabolic pathways and networks at the cellular level. Recent advances in metabolomics methodologies, along with the post-genomic expansion of bioinformatics knowledge and functional genomics tools, have allowed the gathering of enriched information on individual cells and single cell types. Here we review progress, current status, opportunities, and challenges presented by single cell-based metabolomics research in plants.
Crone, William J. K.; Vior, Natalia M.; Santos‐Aberturas, Javier; Schmitz, Lukas G.; Leeper, Finian J.
Abstract Bottromycin A2 is a structurally unique ribosomally synthesized and post‐translationally modified peptide (RiPP) that possesses potent antibacterial activity towards multidrug‐resistant bacteria. The structural novelty of bottromycin stems from its unprecedented macrocyclic amidine and rare β‐methylated amino acid residues. The N‐terminus of a precursor peptide (BtmD) is converted into bottromycin A2 by tailoring enzymes encoded in the btm gene cluster. However, little was known about key transformations in this pathway, including the unprecedented macrocyclization. To understand the pathway in detail, an untargeted metabolomic approach that harnesses mass spectral networking was used to assess the metabolomes of a series of pathway mutants. This analysis has yielded key information on the function of a variety of previously uncharacterized biosynthetic enzymes, including a YcaO domain protein and a partner protein that together catalyze the macrocyclization. PMID:27374993
Rizzato, Giovanni; Scalabrin, Elisa; Radaelli, Marta; Capodaglio, Gabriele; Piccolo, Oreste
The roots and rhizomes of licorice plants (genus Glycyrrhiza L.) are commercially employed, after processing, in confectionery production or as sweetening and flavouring agents in the food, tobacco and beer industries. G. glabra, G. inflata and G. uralensis are the most significant licorice species, often indistinctly used for different productions. Licorice properties are directly related to its chemical composition, which determines the commercial values and the quality of the derived products. In order to better understand the characteristics and properties of each species, a chemical characterization of three species of licorice (G. glabra, G. inflata, G. uralensis) is proposed, through an untargeted metabolomic approach and using high-resolution mass spectrometry. The statistical analysis reveals new possible markers for the analyzed species, and provides a reliable identification of a high number of metabolites, contributing to the characterization of Glycyrrhiza metabolome.
Crone, William J K; Vior, Natalia M; Santos-Aberturas, Javier; Schmitz, Lukas G; Leeper, Finian J; Truman, Andrew W
Bottromycin A2 is a structurally unique ribosomally synthesized and post-translationally modified peptide (RiPP) that possesses potent antibacterial activity towards multidrug-resistant bacteria. The structural novelty of bottromycin stems from its unprecedented macrocyclic amidine and rare β-methylated amino acid residues. The N-terminus of a precursor peptide (BtmD) is converted into bottromycin A2 by tailoring enzymes encoded in the btm gene cluster. However, little was known about key transformations in this pathway, including the unprecedented macrocyclization. To understand the pathway in detail, an untargeted metabolomic approach that harnesses mass spectral networking was used to assess the metabolomes of a series of pathway mutants. This analysis has yielded key information on the function of a variety of previously uncharacterized biosynthetic enzymes, including a YcaO domain protein and a partner protein that together catalyze the macrocyclization.
Sud, Manish; Fahy, Eoin; Cotter, Dawn; Azam, Kenan; Vadivelu, Ilango; Burant, Charles; Edison, Arthur; Fiehn, Oliver; Higashi, Richard; Nair, K. Sreekumaran; Sumner, Susan; Subramaniam, Shankar
The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world. PMID:26467476
Sud, Manish; Fahy, Eoin; Cotter, Dawn; Azam, Kenan; Vadivelu, Ilango; Burant, Charles; Edison, Arthur; Fiehn, Oliver; Higashi, Richard; Nair, K Sreekumaran; Sumner, Susan; Subramaniam, Shankar
The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world.
online 31 October 2015 Keywords: Transcriptomics Metabolomics Blood systems biology Personalized medicine Data integrationMolecular analysis of blood...samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology . Recent developments have opened new...article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Contents1. An overdue review of “blood systems biology
Lee, W-N. Paul; Wahjudi, Paulin N.; Xu, Jun; Go, Vay Liang
Tracer-based metabolomics is a systems biology tool that combines advances in tracer methodology for physiological studies, high throughput “-omics” technologies and constraint based modeling of metabolic networks. It is different from the commonly known metabolomics or metabonomics in that it is a targeted approach based on a metabolic network model in cells. Because of its complexity, it is the least understood among the various “-omics”. In this review, the development of concepts and practices of tracer-based metabolomics is traced from the early application of radioactive isotopes in metabolic studies to the recent application of stable isotopes and isotopomer analysis using mass spectrometry; and from the modeling of biochemical reactions using flux analysis to the recent theoretical formulation of the constraint based modeling. How these newer experimental methods and concepts of constraint-based modeling approaches can be applied to metabolic studies is illustrated by examples of studies in determining metabolic responses of cells to pharmacological agents and nutrient environment changes. PMID:20713038
Hsu, Ping-Ching; Lan, Renny S; Brasky, Theodore M; Marian, Catalin; Cheema, Amrita K; Ressom, Habtom W; Loffredo, Christopher A; Pickworth, Wallace B; Shields, Peter G
Smoking-related biomarkers for lung cancer and other diseases are needed to enhance early detection strategies and to provide a science base for tobacco product regulation. An untargeted metabolomics approach by ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF MS) totaling 957 assays was used in a novel experimental design where 105 current smokers smoked two cigarettes 1 h apart. Blood was collected immediately before and after each cigarette allowing for within-subject replication. Dynamic changes of the metabolomic profiles from smokers' four blood samples were observed and biomarkers affected by cigarette smoking were identified. Thirty-one metabolites were definitively shown to be affected by acute effect of cigarette smoking, uniquely including menthol-glucuronide, the reduction of glutamate, oleamide, and 13 glycerophospholipids. This first time identification of a menthol metabolite in smokers' blood serves as proof-of-principle for using metabolomics to identify new tobacco-exposure biomarkers, and also provides new opportunities in studying menthol-containing tobacco products in humans. Gender and race differences also were observed. Network analysis revealed 12 molecules involved in cancer, notably inhibition of cAMP. These novel tobacco-related biomarkers provide new insights to the effects of smoking which may be important in carcinogenesis but not previously linked with tobacco-related diseases. © 2016 Wiley Periodicals, Inc.
Zivkovic, Angela M.; German, J. Bruce
Purpose of review The current rise in diet-related diseases continues to be one of the most significant health problems facing both the developed and the developing world. The use of metabolomics – the accurate and comprehensive measurement of a significant fraction of important metabolites in accessible biological fluids – for the assessment of nutritional status, is a promising way forward. The basic toolset, targets, and knowledge are all being developed in the emerging field of metabolomics, yet important knowledge and technology gaps will need to be addressed in order to bring such assessment to practice. Recent findings Dysregulation within the principal metabolic organs (e.g. intestine, adipose, skeletal muscle, liver) are at the center of a diet-disease paradigm that includes metabolic syndrome, type 2 diabetes, and obesity. The assessment of both essential nutrient status, and the more comprehensive systemic metabolic response to dietary, lifestyle, and environmental influences (e.g. metabolic phenotype) are necessary for the evaluation of status in individuals that can identify the multiple targets of intervention needed to address metabolic disease. Summary The first proofs of principle building the knowledge to bring actionable metabolic diagnostics to practice through metabolomics are now appearing. PMID:19584717
Cacciatore, Stefano; Loda, Massimo
Metabolomics is the systemic study of all small molecules (metabolites) and their concentration as affected by pathological and physiological alterations or environmental or other factors. Metabolic alterations represent a “window” on the complex interactions between genetic expression, enzyme activity, and metabolic reactions. Techniques, including nuclear magnetic resonance spectroscopy, mass spectrometry, Fourier-transform infrared, and Raman spectroscopy, have led to significant advances in metabolomics. The field is shifting from feasibility studies to biological and clinical applications. Fields of application range from cancer biology to stem cell research and assessment of xenobiotics and drugs in tissues and single cells. Cross-validation across high-throughput platforms has allowed findings from expression profiling to be confirmed with metabolomics. Specific genetic alterations appear to drive unique metabolic programs. These, in turn, can be used as biomarkers of genetic subtypes of prostate cancer or as discovery tools for therapeutic targeting of metabolic enzymes. Thus, metabolites in blood may serve as biomarkers of tumor state, including inferring driving oncogenes. Novel applications such as these suggest that metabolic profiling may be utilized in refining personalized medicine. PMID:26014591
Schrimpe-Rutledge, Alexandra C.; Codreanu, Simona G.; Sherrod, Stacy D.; McLean, John A.
Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies—specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.
AWARD NUMBER: W81XWH-13-1-0493 TITLE: Psychosocial Stress and Ovarian Cancer Risk: Metabolomics and...SUBTITLE Psychosocial Stress and Ovarian Cancer Risk: Metabolomics and Perceived Stress 5a. CONTRACT NUMBER Perceived Stress...relationship between stress and ovarian cancer has never been evaluated in humans. In our analysis of self-reported stress and risk of ovarian cancer , we
Marques, Ana Patrícia; Serralheiro, Maria Luisa; Ferreira, António E. N.; Freire, Ana Ponces; Cordeiro, Carlos; Silva, Marta Sousa
Metabolomics is a key discipline in systems biology, together with genomics, transcriptomics, and proteomics. In this omics cascade, the metabolome represents the biochemical products that arise from cellular processes and is often regarded as the final response of a biological system to environmental or genetic changes. The overall screening…
project. The cumulative data is currently being analyzed with the clinical data in the partnering PIs facility. 15. SUBJECT TERMS Kidney cancer ...American Association of Cancer Research Annual Meeting, Washington, DC, April 2013. Conclusions: We have developed a combined proteomics, metabolomics ...1 AD_________________ Award Number: W81XWH-10-1-0173 TITLE: Tissue and Metabolomic Biomarkers of
Moseley, Hunter N B
Error analysis plays a fundamental role in describing the uncertainty in experimental results. It has several fundamental uses in metabolomics including experimental design, quality control of experiments, the selection of appropriate statistical methods, and the determination of uncertainty in results. Furthermore, the importance of error analysis has grown with the increasing number, complexity, and heterogeneity of measurements characteristic of 'omics research. The increase in data complexity is particularly problematic for metabolomics, which has more heterogeneity than other omics technologies due to the much wider range of molecular entities detected and measured. This review introduces the fundamental concepts of error analysis as they apply to a wide range of metabolomics experimental designs and it discusses current methodologies for determining the propagation of uncertainty in appropriate metabolomics data analysis. These methodologies include analytical derivation and approximation techniques, Monte Carlo error analysis, and error analysis in metabolic inverse problems. Current limitations of each methodology with respect to metabolomics data analysis are also discussed.
Rogers, Angela J; Matthay, Michael A
A better understanding of the pathogenesis and the resolution of the acute respiratory distress syndrome (ARDS) is needed. Although some progress has been made with the use of protein biomarkers and candidate gene studies in understanding the pathobiology of ARDS, we propose that new studies that measure the chemical breakdown products of cellular metabolism (metabolomics) may provide new insights into ARDS, in part because metabolomics targets a later point in the genomics cascade than is possible with studies of DNA, RNA, and protein biomarkers. Technological advances have made large-scale metabolomic profiling increasingly feasible. Metabolomic approaches have already achieved novel insights in nonpulmonary diseases such as diabetes mellitus and malignancy, as well as in sepsis, a major risk factor for developing ARDS. Metabolomic profiling is a promising approach to identify novel pathways in both patients at risk for developing ARDS as well as in the early phase of established ARDS.
Martínez-Arranz, Ibon; Mayo, Rebeca; Pérez-Cormenzana, Miriam; Mincholé, Itziar; Salazar, Lorena; Alonso, Cristina; Mato, José M
Metabolomics research has evolved considerably, particularly during the last decade. Over the course of this evolution, the interest in this 'omic' discipline is now more evident than ever. However, the future of metabolomics will depend on its capability to find biomarkers. For that reason, data mining constitutes a challenging task in metabolomics workflow. This work has been designed in support of the research article entitled "Enhancing metabolomics research through data mining", which proposed a methodological data handling guideline. An aging research in healthy population was used as a guiding thread to illustrate this process. Here we provide a further interpretation of the obtained statistical results. We also focused on the importance of graphical visualization tools as a clue to understand the most common univariate and multivariate data analyses applied in metabolomics.
Carnicer, Marc; Canelas, André B; Ten Pierick, Angela; Zeng, Zhen; van Dam, Jan; Albiol, Joan; Ferrer, Pau; Heijnen, Joseph J; van Gulik, Walter
Accurate, reliable and reproducible measurement of intracellular metabolite levels has become important for metabolic studies of microbial cell factories. A first critical step for metabolomic studies is the establishment of an adequate quenching and washing protocol, which ensures effective arrest of all metabolic activity and removal of extracellular metabolites, without causing leakage of metabolites from the cells. Five different procedures based on cold methanol quenching and cell separation by filtration were tested for metabolomics of Pichia pastoris regarding methanol content and temperature of the quenching solution as key parameters. Quantitative evaluation of these protocols was carried out through mass balance analysis, based on metabolite measurements in all sample fractions, those are whole broth, quenched and washed cells, culture filtrate and quenching and washing solution. Finally, the optimal method was used to study the time profiles of free amino acid and central carbon metabolism intermediates in glucose-limited chemostat cultures. Acceptable recoveries (>90%) were obtained for all quenching procedures tested. However, quenching at -27°C in 60% v/v methanol performed slightly better in terms of leakage minimization. We could demonstrate that five residence times under glucose limitation are enough to reach stable intracellular metabolite pools. Moreover, when comparing P. pastoris and S. cerevisiae metabolomes, under the same cultivation conditions, similar metabolite fingerprints were found in both yeasts, except for the lower glycolysis, where the levels of these metabolites in P. pastoris suggested an enzymatic capacity limitation in that part of the metabolism. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0308-1) contains supplementary material, which is available to authorized users.
Clendinen, Chaevien S; Pasquel, Christian; Ajredini, Ramadan; Edison, Arthur S
The many advantages of (13)C NMR are often overshadowed by its intrinsically low sensitivity. Given that carbon makes up the backbone of most biologically relevant molecules, (13)C NMR offers a straightforward measurement of these compounds. Two-dimensional (13)C-(13)C correlation experiments like INADEQUATE (incredible natural abundance double quantum transfer experiment) are ideal for the structural elucidation of natural products and have great but untapped potential for metabolomics analysis. We demonstrate a new and semiautomated approach called INETA (INADEQUATE network analysis) for the untargeted analysis of INADEQUATE data sets using an in silico INADEQUATE database. We demonstrate this approach using isotopically labeled Caenorhabditis elegans mixtures.
Smirnov, Kirill S; Maier, Tanja V; Walker, Alesia; Heinzmann, Silke S; Forcisi, Sara; Martinez, Inés; Walter, Jens; Schmitt-Kopplin, Philippe
The review highlights the role of metabolomics in studying human gut microbial metabolism. Microbial communities in our gut exert a multitude of functions with huge impact on human health and disease. Within the meta-omics discipline, gut microbiome is studied by (meta)genomics, (meta)transcriptomics, (meta)proteomics and metabolomics. The goal of metabolomics research applied to fecal samples is to perform their metabolic profiling, to quantify compounds and classes of interest, to characterize small molecules produced by gut microbes. Nuclear magnetic resonance spectroscopy and mass spectrometry are main technologies that are applied in fecal metabolomics. Metabolomics studies have been increasingly used in gut microbiota related research regarding health and disease with main focus on understanding inflammatory bowel diseases. The elucidated metabolites in this field are summarized in this review. We also addressed the main challenges of metabolomics in current and future gut microbiota research. The first challenge reflects the need of adequate analytical tools and pipelines, including sample handling, selection of appropriate equipment, and statistical evaluation to enable meaningful biological interpretation. The second challenge is related to the choice of the right animal model for studies on gut microbiota. We exemplified this using NMR spectroscopy for the investigation of cross-species comparison of fecal metabolite profiles. Finally, we present the problem of variability of human gut microbiota and metabolome that has important consequences on the concepts of personalized nutrition and medicine.
Washio, Jumpei; Takahashi, Nobuhiro
Oral diseases are known to be closely associated with oral biofilm metabolism, while cancer tissue is reported to possess specific metabolism such as the 'Warburg effect'. Metabolomics might be a useful method for clarifying the whole metabolic systems that operate in oral biofilm and oral cancer, however, technical limitations have hampered such research. Fortunately, metabolomics techniques have developed rapidly in the past decade, which has helped to solve these difficulties. In vivo metabolomic analyses of the oral biofilm have produced various findings. Some of these findings agreed with the in vitro results obtained in conventional metabolic studies using representative oral bacteria, while others differed markedly from them. Metabolomic analyses of oral cancer tissue not only revealed differences between metabolomic profiles of cancer and normal tissue, but have also suggested a specific metabolic system operates in oral cancer tissue. Saliva contains a variety of metabolites, some of which might be associated with oral or systemic disease; therefore, metabolomics analysis of saliva could be useful for identifying disease-specific biomarkers. Metabolomic analyses of the oral biofilm, oral cancer, and saliva could contribute to the development of accurate diagnostic, techniques, safe and effective treatments, and preventive strategies for oral and systemic diseases.
Cox, Daniel G.; Oh, Joonseok; Keasling, Adam; Colson, Kim
Background Metabolomics is a well-established rapidly developing research field involving quantitative and qualitative metabolite assessment within biological systems. Recent improvements in metabolomics technologies reveal the unequivocal value of metabolomics tools in natural products discovery, gene-function analysis, systems biology and diagnostic platforms. Scope of review We review of some of the prominent metabolomics methodologies employed in data acquisition and analysis of natural products and disease-related biomarkers. Major conclusions This review demonstrates that metabolomics represents a highly adaptable technology with diverse applications ranging from environmental toxicology to disease diagnosis. Metabolomic analysis is shown to provide a unique snapshot of the functional genetic status of an organism by examining its biochemical profile, with relevance toward resolving phylogenetic associations involving horizontal gene transfer and distinguishing subgroups of genera possessing high genetic homology, as well as an increasing role in both elucidating biosynthetic transformations of natural products and detecting preclinical biomarkers of numerous disease states. General significance This review expands the interest in multiplatform combinatorial metabolomic analysis. The applications reviewed range from phylogenetic assignment, biosynthetic transformations of natural products, and the detection of preclinical biomarkers. PMID:25151044
Su, L Joseph; Fiehn, Oliver; Maruvada, Padma; Moore, Steven C; O'Keefe, Stephen J; Wishart, David S; Zanetti, Krista A
The NIH has made a significant commitment through the NIH Common Fund's Metabolomics Program to build infrastructure and capacity for metabolomics research, which should accelerate the field. Given this investment, it is the ideal time to start planning strategies to capitalize on the infrastructure being established. An obvious gap in the literature relates to the effective use of metabolomics in large-population studies. Although published reports from population-based studies are beginning to emerge, the number to date remains relatively small. Yet, there is great potential for using metabolomics in population-based studies to evaluate the effects of nutritional, pharmaceutical, and environmental exposures (the "exposome"); conduct risk assessments; predict disease development; and diagnose diseases. Currently, the majority of the metabolomics studies in human populations are in nutrition or nutrition-related fields. This symposium provided a timely venue to highlight the current state-of-science on the use of metabolomics in population-based research. This session provided a forum at which investigators with extensive experience in performing research within large initiatives, multi-investigator grants, and epidemiology consortia could stimulate discussion and ideas for population-based metabolomics research and, in turn, improve knowledge to help devise effective methods of health research.
Metabolomics is a strategy for analysis, and quantification of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scientific research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10–100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and specific instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism. PMID:27900235
Fearnley, Liam G; Inouye, Michael
Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology. PMID:27118561
Yang, Fengmin; Du, Jie; Zhang, Hong; Ruan, Guorui; Xiang, Junfeng; Wang, Lixia; Sun, Hongxia; Guan, Aijiao; Shen, Gang; Liu, Yan; Guo, Xiaomeng; Li, Qian; Tang, Yalin
Burkitt lymphoma (BL) is a rare and highly aggressive type of non-Hodgkin lymphoma. The mortality rate of BL patients is very high due to the rapid growth rate and frequent systemic spread of the disease. A better understanding of the pathogenesis, more sensitive diagnostic tools and effective treatment methods for BL are essential. Metabolomics, an important aspect of systems biology, allows the comprehensive analysis of global, dynamic and endogenous biological metabolites based on their nuclear magnetic resonance (NMR) and mass spectrometry (MS). It has already been used to investigate the pathogenesis and discover new biomarkers for disease diagnosis and prognosis. In this study, we analyzed differences of serum metabolites in BL mice and normal mice by NMR-based metabolomics. We found that metabolites associated with energy metabolism, amino acid metabolism, fatty acid metabolism and choline phospholipid metabolism were altered in BL mice. The diagnostic potential of the metabolite differences was investigated in this study. Glutamate, glycerol and choline had a high diagnostic accuracy; in contrast, isoleucine, leucine, pyruvate, lysine, α-ketoglutarate, betaine, glycine, creatine, serine, lactate, tyrosine, phenylalanine, histidine and formate enabled the accurate differentiation of BL mice from normal mice. The discovery of abnormal metabolism and relevant differential metabolites may provide useful clues for developing novel, noninvasive approaches for the diagnosis and prognosis of BL based on these potential biomarkers. PMID:28129369
Rigobello-Masini, Marilda; Penteado, José Carlos Pires; Masini, Jorge Cesar
Since "omics" techniques emerged, plant studies, from biochemistry to ecology, have become more comprehensive. Plant proteomics and metabolomics enable the construction of databases that, with the help of genomics and informatics, show the data obtained as a system. Thus, all the constituents of the system can be seen with their interactions in both space and time. For instance, perturbations in a plant ecosystem as a consequence of application of herbicides or exposure to pollutants can be predicted by using information gathered from these databases. Analytical chemistry has been involved in this scientific evolution. Proteomics and metabolomics are emerging fields that require separation, identification, and quantification of proteins, peptides, and small molecules of metabolites in complex biological samples. The success of this work relies on efficient chromatographic and electrophoretic techniques, and on mass spectrometric detection. This paper reviews recent developments in the use of monolithic columns, focusing on their applications in "top-down" and "bottom-up" approaches, including their use as supports for immobilization of proteolytic enzymes and their use in two-dimensional and multidimensional chromatography. Whereas polymeric columns have been predominantly used for separation of proteins and polypeptides, silica-based monoliths have been more extensively used for separation of small molecules of metabolites. Representative applications in proteomics and in analysis of plant metabolites are given and summarized in tables.
Insulin resistance associated with type 2 diabetes mellitus (T2DM), obesity, and atherosclerosis is a global health problem. A portfolio of abnormalities of metabolic and vascular homeostasis accompanies T2DM and obesity, which are believed to conspire to lead to accelerated atherosclerosis and premature death. The complexity of metabolic changes in the diseases presents challenges for a full understanding of the molecular pathways contributing to the development of these diseases. The recent advent of new technologies in this area termed “Metabolomics” may aid in comprehensive metabolic analysis of these diseases. Therefore, metabolomics has been extensively applied to the metabolites of T2DM, obesity, and atherosclerosis not only for the assessment of disease development and prognosis, but also for the biomarker discovery of disease diagnosis. Herein, we summarize the recent applications of metabolomics technology and the generated datasets in the metabolic profiling of these diseases, in particular, the applications of these technologies to these diseases at the cellular, animal models, and human disease levels. In addition, we also extensively discuss the mechanisms linking the metabolic profiling in insulin resistance, T2DM, obesity, and atherosclerosis, with a particular emphasis on potential roles of increased production of reactive oxygen species (ROS) and mitochondria dysfunctions. PMID:24252331
Menon, Smrithi S.; Uppal, Medha; Randhawa, Subeena; Cheema, Mehar S.; Aghdam, Nima; Usala, Rachel L.; Ghosh, Sanchita P.; Cheema, Amrita K.; Dritschilo, Anatoly
Human exposure to ionizing radiation (IR) disrupts normal metabolic processes in cells and organs by inducing complex biological responses that interfere with gene and protein expression. Conventional dosimetry, monitoring of prodromal symptoms, and peripheral lymphocyte counts are of limited value as organ- and tissue-specific biomarkers for personnel exposed to radiation, particularly, weeks or months after exposure. Analysis of metabolites generated in known stress-responsive pathways by molecular profiling helps to predict the physiological status of an individual in response to environmental or genetic perturbations. Thus, a multi-metabolite profile obtained from a high-resolution mass spectrometry-based metabolomics platform offers potential for identification of robust biomarkers to predict radiation toxicity of organs and tissues resulting from exposures to therapeutic or non-therapeutic IR. Here, we review the status of radiation metabolomics and explore applications as a standalone technology, as well as its integration in systems biology, to facilitate a better understanding of the molecular basis of radiation response. Finally, we draw attention to the identification of specific pathways that can be targeted for the development of therapeutics to alleviate or mitigate harmful effects of radiation exposure. PMID:26870697
Studies in premature infants and animals show that carbohydrate malabsorption and gut microbiota colonisation are key elements for triggering necrotizing enterocolitis (NEC). Our aim was to determine how dietary carbohydrate composition affects the metabolomic profile and whether unique metabolite s...
Several fundamental requirements must be met so that NMR-based metabolomics and the related technique of metabonomics can be formally adopted into environmental monitoring and chemical risk assessment. Here we report an intercomparison exercise which has evaluated the effectivene...
Metabolomics may unravel important biological pathways involved in the pathophysiology of childhood obesity. We aimed to 1) identify metabolites that differ significantly between nonobese and obese Hispanic children; 2) collapse metabolites into principal components (PCs) associated with obesity and...
Metabolomics datasets, by definition, comprise of measurements of large numbers of metabolites. Both technical (analytical) and biological factors will induce variation within these measurements that is not consistent across all metabolites. Consequently, criteria are required to...
NASA’s Human Research Program releases “Metabolomics: You Are What You Eat” video to highlight its Twins Study which uses omics to study Mark and Scott Kelly’s metabolites. Omics is an evolving fie...
The US EPA is tasked with protecting not only humans, but also ecosystems from potentially harmful effects of chemical pollutants. Although lagging behind applications targeted to human endpoints, metabolomics offers great potential in ecotoxicology. Indeed, the advantages of met...
Metabolomics is becoming well-established for studying chemical contaminant-induced alterations to normal biological function. For example, the literature contains a wealth of laboratory-based studies involving analysis of samples from organisms exposed to individual chemical tox...
Introduction/Objectives/Methods One of the biggest challenges in ecological risk assessment is determining the impact of multiple stressors on individual organisms and populations in ‘real world’ scenarios. Emerging ‘omic technologies, notably, metabolomics, pr...
Ivanisevic, Julijana; Siuzdak, Gary
This special edition of the Journal of Neuroimmune Pharmacology focuses on the leading edge of metabolomics in brain metabolism research. The topics covered include a metabolomic field overview and the challenges in neuroscience metabolomics. The workflow and utility of different analytical platforms to profile complex biological matrices that include biofluids, brain tissue and cells, are shown in several case studies. These studies demonstrate how global and targeted metabolite profiling can be applied to distinguish disease stages and to understand the effects of drug action on the central nervous system (CNS). Finally, we discuss the importance of metabolomics to advance the understanding of brain function that includes ligand-receptor interactions and new insights into the mechanisms of CNS disorders.
Zhang, Bo; Powers, Robert
Infectious diseases can be difficult to cure, especially if the pathogen forms a biofilm. After decades of extensive research into the morphology, physiology and genomics of biofilm formation, attention has recently been directed toward the analysis of the cellular metabolome in order to understand the transformation of a planktonic cell to a biofilm. Metabolomics can play an invaluable role in enhancing our understanding of the underlying biological processes related to the structure, formation and antibiotic resistance of biofilms. A systematic view of metabolic pathways or processes responsible for regulating this 'social structure' of microorganisms may provide critical insights into biofilm-related drug resistance and lead to novel treatments. This review will discuss the development of NMR-based metabolomics as a technology to study medically relevant biofilms. Recent advancements from case studies reviewed in this manuscript have shown the potential of metabolomics to shed light on numerous biological problems related to biofilms.
Salek, Reza M; Haug, Kenneth; Steinbeck, Christoph
With ever-increasing amounts of metabolomics data produced each year, there is an even greater need to disseminate data and knowledge produced in a standard and reproducible way. To assist with this a general purpose, open source metabolomics repository, MetaboLights, was launched in 2012. To promote a community standard, initially culminated as metabolomics standards initiative (MSI), COordination of Standards in MetabOlomicS (COSMOS) was introduced. COSMOS aims to link life science e-infrastructures within the worldwide metabolomics community as well as develop and maintain open source exchange formats for raw and processed data, ensuring better flow of metabolomics information.
Hall, Robert D; de Maagd, Ruud A
Metabolomics separates and detects small molecules and helps determine the composition of plant materials. This makes it appear to be a possible contributor to environmental risk assessment (ERA) of transgenic plants. Here we argue that, despite important advances in the technology, limited annotation and our limited knowledge of the role of metabolites in plant-environment interactions means that metabolomics is not yet ripe for ERA.
Fan, Teresa W-M.; Lorkiewicz, Pawel; Sellers, Katherine; Moseley, Hunter N.B.; Higashi, Richard M.; Lane, Andrew N.
Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), during the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly cancer. The metabolome is the functional readout of the genome, functional genome, and proteome; it is also an integral partner in molecular regulations for homeostasis. The interrogation of the metabolome, or metabolomics, is now being applied to numerous diseases, largely by metabolite profiling for biomarker discovery, but also in pharmacology and therapeutics. Recent advances in stable isotope tracer-based metabolomic approaches enable unambiguous tracking of individual atoms through compartmentalized metabolic networks directly in human subjects, which promises to decipher the complexity of the human metabolome at an unprecedented pace. This knowledge will revolutionize our understanding of complex human diseases, clinical diagnostics, as well as individualized therapeutics and drug response. In this review, we focus on the use of stable isotope tracers with metabolomics technologies for understanding metabolic network dynamics in both model systems and in clinical applications. Atom-resolved isotope tracing via the two major analytical platforms, NMR and MS, has the power to determine novel metabolic reprogramming in diseases, discover new drug targets, and facilitates ADME studies. We also illustrate new metabolic tracer-based imaging technologies, which enable direct visualization of metabolic processes in vivo. We further outline current practices and future requirements for biochemoinformatics development, which is an integral part of translating stable isotope-resolved metabolomics into clinical reality. PMID:22212615
Stringer, Kathleen A.; McKay, Ryan T.; Karnovsky, Alla; Quémerais, Bernadette; Lacy, Paige
Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a “snapshot” in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision
Nägele, Thomas; Mair, Andrea; Sun, Xiaoliang; Fragner, Lena; Teige, Markus; Weckwerth, Wolfram
High-throughput molecular analysis has become an integral part in organismal systems biology. In contrast, due to a missing systematic linkage of the data with functional and predictive theoretical models of the underlying metabolic network the understanding of the resulting complex data sets is lacking far behind. Here, we present a biomathematical method addressing this problem by using metabolomics data for the inverse calculation of a biochemical Jacobian matrix, thereby linking computer-based genome-scale metabolic reconstruction and in vivo metabolic dynamics. The incongruity of metabolome coverage by typical metabolite profiling approaches and genome-scale metabolic reconstruction was solved by the design of superpathways to define a metabolic interaction matrix. A differential biochemical Jacobian was calculated using an approach which links this metabolic interaction matrix and the covariance of metabolomics data satisfying a Lyapunov equation. The predictions of the differential Jacobian from real metabolomic data were found to be correct by testing the corresponding enzymatic activities. Moreover it is demonstrated that the predictions of the biochemical Jacobian matrix allow for the design of parameter optimization strategies for ODE-based kinetic models of the system. The presented concept combines dynamic modelling strategies with large-scale steady state profiling approaches without the explicit knowledge of individual kinetic parameters. In summary, the presented strategy allows for the identification of regulatory key processes in the biochemical network directly from metabolomics data and is a fundamental achievement for the functional interpretation of metabolomics data.
Oldiges, Marco; Lütz, Stephan; Pflug, Simon; Schroer, Kirsten; Stein, Nadine; Wiendahl, Christiane
In recent years, metabolomics developed to an accepted and valuable tool in life sciences. Substantial improvements of analytical hardware allow metabolomics to run routinely now. Data are successfully used to investigate genotype-phenotype relations of strains and mutants. Metabolomics facilitates metabolic engineering to optimise mircoorganisms for white biotechnology and spreads to the investigation of biotransformations and cell culture. Metabolomics serves not only as a source of qualitative but also quantitative data of intra-cellular metabolites essential for the model-based description of the metabolic network operating under in vivo conditions. To collect reliable metabolome data sets, culture and sampling conditions, as well as the cells' metabolic state, are crucial. Hence, application of biochemical engineering principles and method standardisation efforts become important. Together with the other more established omics technologies, metabolomics will strengthen its claim to contribute to the detailed understanding of the in vivo function of gene products, biochemical and regulatory networks and, even more ambitious, the mathematical description and simulation of the whole cell in the systems biology approach. This knowledge will allow the construction of designer organisms for process application using biotransformation and fermentative approaches making effective use of single enzymes, whole microbial and even higher cells.
Nägele, Thomas; Mair, Andrea; Sun, Xiaoliang; Fragner, Lena; Teige, Markus; Weckwerth, Wolfram
High-throughput molecular analysis has become an integral part in organismal systems biology. In contrast, due to a missing systematic linkage of the data with functional and predictive theoretical models of the underlying metabolic network the understanding of the resulting complex data sets is lacking far behind. Here, we present a biomathematical method addressing this problem by using metabolomics data for the inverse calculation of a biochemical Jacobian matrix, thereby linking computer-based genome-scale metabolic reconstruction and in vivo metabolic dynamics. The incongruity of metabolome coverage by typical metabolite profiling approaches and genome-scale metabolic reconstruction was solved by the design of superpathways to define a metabolic interaction matrix. A differential biochemical Jacobian was calculated using an approach which links this metabolic interaction matrix and the covariance of metabolomics data satisfying a Lyapunov equation. The predictions of the differential Jacobian from real metabolomic data were found to be correct by testing the corresponding enzymatic activities. Moreover it is demonstrated that the predictions of the biochemical Jacobian matrix allow for the design of parameter optimization strategies for ODE-based kinetic models of the system. The presented concept combines dynamic modelling strategies with large-scale steady state profiling approaches without the explicit knowledge of individual kinetic parameters. In summary, the presented strategy allows for the identification of regulatory key processes in the biochemical network directly from metabolomics data and is a fundamental achievement for the functional interpretation of metabolomics data. PMID:24695071
Zhang, Ai-hua; Sun, Hui; Wang, Xi-jun
Discovery of clinically relevant biomarkers for diseases has revealed metabolomics has potential advantages that classical diagnostic approaches do not. The great asset of metabolomics is that it enables assessment of global metabolic profiles of biofluids and discovery of biomarkers distinguishing disease status, with the possibility of enhancing clinical diagnostics. Most current clinical chemistry tests rely on old technology, and are neither sensitive nor specific for a particular disease. Clinical diagnosis of major neurological disorders, for example Alzheimer's disease and Parkinson's disease, on the basis of current clinical criteria is unsatisfactory. Emerging metabolomics is a powerful technique for discovering novel biomarkers and biochemical pathways to improve diagnosis, and for determination of prognosis and therapy. Identifying multiple novel biomarkers for neurological diseases has been greatly enhanced with recent advances in metabolomics that are more accurate than routine clinical practice. Cerebrospinal fluid (CSF), which is known to be a rich source of small-molecule biomarkers for neurological and neurodegenerative diseases, and is in close contact with diseased areas in neurological disorders, could potentially be used for disease diagnosis. Metabolomics will drive CSF analysis, facilitate and improve the development of disease treatment, and result in great benefits to public health in the long-term. This review covers different aspects of CSF metabolomics and discusses their significance in the postgenomic era, emphasizing the potential importance of endogenous small-molecule metabolites in this emerging field.
Shin, Jae M.; Kamarajan, Pachiyappan; Fenno, J. Christopher; Rickard, Alexander H.; Kapila, Yvonne L.
Metabolomics is used in systems biology to enhance the understanding of complex disease processes, such as cancer. Head and neck cancer (HNC) is an epithelial malignancy that arises in the upper aerodigestive tract and affects more than half a million people worldwide each year. Recently, significant effort has focused on integrating multiple “omics” technologies for oncological research. In particular, research has been focused on identifying tumor-specific metabolite profiles using different sample types (biological fluids, cells and tissues) and a variety of metabolomic platforms and technologies. With our current understanding of molecular abnormalities of HNC, the addition of metabolomic studies will enhance our knowledge of the pathogenesis of this disease and potentially aid in the development of novel strategies to prevent and treat HNC. In this review, we summarize the proposed hypotheses and conclusions from publications that reported findings on the metabolomics of HNC. In addition, we address the potential influence of host-microbe metabolomics in cancer. From a systems biology perspective, the integrative use of genomics, transcriptomics and proteomics will be extremely important for future translational metabolomic-based research discoveries. PMID:27877135
Anđelković, Boban; Vujisić, Ljubodrag; Vučković, Ivan; Tešević, Vele; Vajs, Vlatka; Gođevac, Dejan
Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and UV spectroscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400m, originating from P. nigra and P. x euramericana buds. Samples collected at 400-500m were of mixed origin, with variable amounts of all detected metabolites.
Ivanisevic, Julijana; Epstein, Adrian A; Kurczy, Michael E; Benton, Paul H; Uritboonthai, Winnie; Fox, Howard S; Boska, Michael D; Gendelman, Howard E; Siuzdak, Gary
Historically, studies of brain metabolism have been based on targeted analyses of a limited number of metabolites. Here we present an untargeted mass spectrometry-based metabolomic strategy that has successfully uncovered differences in a broad array of metabolites across anatomical regions of the mouse brain. The NSG immunodeficient mouse model was chosen because of its ability to undergo humanization leading to numerous applications in oncology and infectious disease research. Metabolic phenotyping by hydrophilic interaction liquid chromatography and nanostructure imaging mass spectrometry revealed both water-soluble and lipid metabolite patterns across brain regions. Neurochemical differences in metabolic phenotypes were mainly defined by various phospholipids and several intriguing metabolites including carnosine, cholesterol sulfate, lipoamino acids, uric acid, and sialic acid, whose physiological roles in brain metabolism are poorly understood. This study helps define regional homeostasis for the normal mouse brain to give context to the reaction to pathological events.
Ivanisevic, Julijana; Epstein, Adrian; Kurczy, Michael E.; Benton, H. Paul; Uritboonthai, Winnie; Fox, Howard S.; Boska, Michael D.; Gendelman, Howard E.; Siuzdak, Gary
SUMMARY Historically, studies of brain metabolism have been based on targeted analyses of a limited number of metabolites. Here we present a novel untargeted mass spectrometry-based metabolomics approach that has successfully uncovered differences in broad array of metabolites across anatomical regions of the mouse brain. The NSG immunodeficient mouse model was chosen because of its ability to undergo humanization leading to numerous applications in oncology and infectious disease research. Metabolic phenotyping by hydrophilic interaction liquid chromatography and nanostructure imaging mass spectrometry revealed unique water-soluble and lipid metabolite patterns between brain regions. Neurochemical differences in metabolic phenotypes were mainly defined by various phospholipids and several intriguing metabolites including carnosine, cholesterol sulfate, lipoamino acids, uric and sialic acid whose physiological roles in brain metabolism are poorly understood. This study lays important groundwork by defining regional homeostasis for the normal mouse brain to give context to the reaction to pathological events. PMID:25457182
Schaub, Jochen; Schiesling, Carola; Reuss, Matthias; Dauner, Michael
Metabolome analysis, the analysis of large sets of intracellular metabolites, has become an important systems analysis method in biotechnological and pharmaceutical research. In metabolic engineering, the integration of metabolome data with fluxome and proteome data into large-scale mathematical models promises to foster rational strategies for strain and cell line improvement. However, the development of reproducible sampling procedures for quantitative analysis of intracellular metabolite concentrations represents a major challenge, accomplishing (i) fast transfer of sample, (ii) efficient quenching of metabolism, (iii) quantitative metabolite extraction, and (iv) optimum sample conditioning for subsequent quantitative analysis. In addressing these requirements, we propose an integrated sampling procedure. Simultaneous quenching and quantitative extraction of intracellular metabolites were realized by short-time exposure of cells to temperatures < or =95 degrees C, where intracellular metabolites are released quantitatively. Based on these findings, we combined principles of heat transfer with knowledge on physiology, for example, turnover rates of energy metabolites, to develop an optimized sampling procedure based on a coiled single tube heat exchanger. As a result, this sampling procedure enables reliable and reproducible measurements through (i) the integration of three unit operations into a one unit operation, (ii) the avoidance of any alteration of the sample due to chemical reagents in quenching and extraction, and (iii) automation. A sampling frequency of 5 s(-)(1) and an overall individual sample processing time faster than 30 s allow observing responses of intracellular metabolite concentrations to extracellular stimuli on a subsecond time scale. Recovery and reliability of the unit operations were analyzed. Impact of sample conditioning on subsequent IC-MS analysis of metabolites was examined as well. The integrated sampling procedure was validated
Vizcaino, Maria I.; Crawford, Jason M.
This chapter provides step-by-step methods for building secondary metabolic pathway-targeted molecular networks to assess microbial natural product biosynthesis at a systems level and to aid in downstream natural product discovery efforts. Methods described include high-resolution mass spectrometry (HRMS)-based comparative metabolomics, pathway-targeted tandem MS (MS/MS) molecular networking, and isotopic labeling for the elucidation of natural products encoded by orphan biosynthetic pathways. The metabolomics network workflow covers the following six points: (1) method development, (2) bacterial culture growth and organic extraction, (3) HRMS data acquisition and analysis, (4) pathway-targeted MS/MS data acquisition, (5) mass spectral network building, and (6) network enhancement. This chapter opens with a discussion on the practical considerations of natural product extraction, chromatographic processing, and enhanced detection of the analytes of interest within complex organic mixtures using liquid chromatography (LC)-HRMS. Next, we discuss the utilization of a chemometric platform, focusing on Agilent Mass Profiler Professional software, to run MS-based differential analysis between sample groups and controls to acquire a unique set of molecular features that are dependent on the presence of a secondary metabolic pathway. Using this unique list of molecular features, the chapter then details targeted MS/MS acquisition for subsequent pathway-dependent network clustering through the online Global Natural Products Social Molecular Networking (GnPS) platform. Genetic information, ionization intensities, isotopic labeling, and additional experimental data can be mapped onto the pathway-dependent network, facilitating systems biosynthesis analyses. The finished product will provide a working molecular network to assess experimental perturbations and guide novel natural product discoveries. PMID:26831709
As a complimentary tool to other omics platforms, metabolomics is increasingly being used by biologists to study the dynamic response of biological systems (cells, tissues, or whole organisms) under diverse physiological or pathological conditions. Metabolomics deals with the qu...
Metabolomics, the comprehensive analysis of metabolites in a biological system, provides detailed information about the biochemical/physiological condition of the test system, and of changes affected by anthropogenic chemicals. Metabolomic analysis is used in many fields, ranging...
Although lagging behind applications targeted to human endpoints, metabolomics offers great potential in environmental applications, including ecotoxicology. Indeed, the advantages of metabolomics (relative to other ‘omic techniques) may be more tangible in ecotoxicology because...
enforcement agencies. Given the above information, strategies that are able to determine cultivar and provenance of an extract from R. communis...consideration was metabolomics. Metabolomics is the study of the metabolome of an organism. The metabolome can be defined as the pool of extractable chemistry...these analyses were then further analysed using Principal Component Analysis (PCA). For HPLC-UV analysis, the seed extract from seven R. communis
Kim, Kyoungmi; Aronov, Pavel; Zakharkin, Stanislav O; Anderson, Danielle; Perroud, Bertrand; Thompson, Ian M; Weiss, Robert H
Renal cell carcinoma (RCC) accounts for 11,000 deaths per year in the United States. When detected early, generally serendipitously by imaging conducted for other reasons, long term survival is generally excellent. When detected with symptoms, prognosis is poor. Under these circumstances, a screening biomarker has the potential for substantial public health benefit. The purpose of this study was to evaluate the utility of urine metabolomics analysis for metabolomic profiling, identification of biomarkers, and ultimately for devising a urine screening test for RCC. Fifty urine samples were obtained from RCC and control patients from two institutions, and in a separate study, urine samples were taken from 13 normal individuals. Hydrophilic interaction chromatography-mass spectrometry was performed to identify small molecule metabolites present in each sample. Cluster analysis, principal components analysis, linear discriminant analysis, differential analysis, and variance component analysis were used to analyze the data. Previous work is extended to confirm the effectiveness of urine metabolomics analysis using a larger and more diverse patient cohort. It is now shown that the utility of this technique is dependent on the site of urine collection and that there exist substantial sources of variation of the urinary metabolomic profile, although group variation is sufficient to yield viable biomarkers. Surprisingly there is a small degree of variation in the urinary metabolomic profile in normal patients due to time since the last meal, and there is little difference in the urinary metabolomic profile in a cohort of pre- and postnephrectomy (partial or radical) renal cell carcinoma patients, suggesting that metabolic changes associated with RCC persist after removal of the primary tumor. After further investigations relating to the discovery and identity of individual biomarkers and attenuation of residual sources of variation, our work shows that urine metabolomics
Aasly, Jan O.; White, Linda R.; Matson, Wayne R.; Henchcliffe, Claire; Beal, M. Flint; Bogdanov, Mikhail
Background Mutations in LRRK2 gene represent the most common known genetic cause of Parkinson's disease (PD). Methodology/Principal Findings We used metabolomic profiling to identify biomarkers that are associated with idiopathic and LRRK2 PD. We compared plasma metabolomic profiles of patients with PD due to the G2019S LRRK2 mutation, to asymptomatic family members of these patients either with or without G2019S LRRK2 mutations, and to patients with idiopathic PD, as well as non-related control subjects. We found that metabolomic profiles of both idiopathic PD and LRRK2 PD subjects were clearly separated from controls. LRRK2 PD patients had metabolomic profiles distinguishable from those with idiopathic PD, and the profiles could predict whether the PD was secondary to LRRK2 mutations or idiopathic. Metabolomic profiles of LRRK2 PD patients were well separated from their family members, but there was a slight overlap between family members with and without LRRK2 mutations. Both LRRK2 and idiopathic PD patients showed significantly reduced uric acid levels. We also found a significant decrease in levels of hypoxanthine and in the ratios of major metabolites of the purine pathway in plasma of PD patients. Conclusions/Significance These findings show that LRRK2 patients with the G2019S mutation have unique metabolomic profiles that distinguish them from patients with idiopathic PD. Furthermore, asymptomatic LRRK2 carriers can be separated from gene negative family members, which raises the possibility that metabolomic profiles could be useful in predicting which LRRK2 carriers will eventually develop PD. The results also suggest that there are aberrations in the purine pathway in PD which may occur upstream from uric acid. PMID:19847307
Yoshida, Masaru; Nishiumi, Shin; Azuma, Takeshi
The field of omics involves comprehensive investigations based on genomics, transcriptomics, proteomics, and metabolomics, and omics studies have developed rapidly. Metabolomics, metabolome analysis, involves technology to analyze the concentrations of low-molecular-weight metabolites comprehensively, and has recently rapidly developed along with improvements in analytical technology. Therefore, metabolome analysis is just beginning to be applied to not only food science and environmental research fields but also medical research. In the medical research field, especially, metabolome analysis plays an important role in novel disease biomarker discovery. The metabolome represents the endpoint of the omics cascade and, therefore, is considered to be closer to the phenotype. In addition, there is also a possibility that the metabolome is affected by exogenous factors such as environmental and food factors, as well as endogenous factors such as DNA/mRNA/protein. Therefore, metabolome analysis can be a useful approach for discovering effective biomarkers. Here, we explain the characteristics of metabolome analysis, and also outline metabolome analysis using a liquid chromatograph mass spectrometer (LC-MS), gas chromatograph mass spectrometer (GC-MS), capillary electrophoresis mass spectrometer (CE-MS), and matrix-assisted laser desorption ionization mass spectrometer (MALDI-MS). Then, we describe the findings of studies that used metabolome analysis in an attempt to discover biomarker candidates for pancreatic cancer, and discuss metabolome analysis-based disease diagnosis.
Nagana Gowda, G. A.; Raftery, Daniel
The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact bio-specimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory.
Parsons, Helen M; Ekman, Drew R; Collette, Timothy W; Viant, Mark R
Metabolomics datasets, by definition, comprise of measurements of large numbers of metabolites. Both technical (analytical) and biological factors will induce variation within these measurements that is not consistent across all metabolites. Consequently, criteria are required to assess the reproducibility of metabolomics datasets that are derived from all the detected metabolites. Here we calculate spectrum-wide relative standard deviations (RSDs; also termed coefficient of variation, CV) for ten metabolomics datasets, spanning a variety of sample types from mammals, fish, invertebrates and a cell line, and display them succinctly as boxplots. We demonstrate multiple applications of spectral RSDs for characterising technical as well as inter-individual biological variation: for optimising metabolite extractions, comparing analytical techniques, investigating matrix effects, and comparing biofluids and tissue extracts from single and multiple species for optimising experimental design. Technical variation within metabolomics datasets, recorded using one- and two-dimensional NMR and mass spectrometry, ranges from 1.6 to 20.6% (reported as the median spectral RSD). Inter-individual biological variation is typically larger, ranging from as low as 7.2% for tissue extracts from laboratory-housed rats to 58.4% for fish plasma. In addition, for some of the datasets we confirm that the spectral RSD values are largely invariant across different spectral processing methods, such as baseline correction, normalisation and binning resolution. In conclusion, we propose spectral RSDs and their median values contained herein as practical benchmarks for metabolomics studies.
Wang, Pengcheng; Wang, Qiuhong; Yang, Bingyou; Zhao, Shan; Kuang, Haixue
Traditional Chinese medicine (TCM) has played important roles in health protection and disease treatment for thousands of years in China and has gained the gradual acceptance of the international community. However, many intricate issues, which cannot be explained by traditional methods, still remain, thus, new ideas and technologies are needed. As an emerging system biology technology, the holistic view adopted by metabolomics is similar to that of TCM, which allows us to investigate TCM with complicated conditions and multiple factors in depth. In this paper, we tried to give a timely and comprehensive update about the methodology progression of metabolomics, as well as its applications, in different fields of TCM studies including quality control, processing, safety and efficacy evaluation. The herbs investigated by metabolomics were selected for detailed examination, including Anemarrhena asphodeloides Bunge, Atractylodes macrocephala Kidd, Pinellia ternate, etc.; furthermore, some valuable results have been obtained and summarized. In conclusion, although the study of metabolomics is at the early phase and requires further scrutiny and validation, it still provides bright prospects to dissect the synergistic action of multiple components from TCM. Overall, with the further development of analytical techniques, especially multi-analysis techniques, we expect that metabolomics will greatly promote TCM research and the establishment of international standards, which is beneficial to TCM modernization.
Gowda, G.A. Nagana; Raftery, Daniel
The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact biospecimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory. PMID:26476597
Shen, Chong; Sun, Zeyu; Chen, Deying; Su, Xiaoling; Jiang, Jing; Li, Gonghui; Lin, Biaoyang; Yan, Jiajun
Early detection is vital to improve the overall survival rate of bladder cancer (BCa) patients, yet there is a lack of a reliable urine-based assay for early detection of BCa. Urine metabolites represented a potential rich source of biomarkers for BCa. This study aimed to develop a metabolomics approach for high coverage discovery and identification of metabolites in urine samples. Urine samples from 23 early stage BCa patients and 21 healthy volunteers with minimum sample preparations were analyzed by a short 30 min UPLC-HRMS method. We detected and quantified over 9000 unique UPLC-HRMS features, which is more than four times than about 2000 features detected in previous urine metabolomic studies. Furthermore, multivariate OPLS-DA classification models were established to differentiate urine samples from bladder cancer cohort and normal health cohort. We identified three BCa-upregulated metabolites: nicotinuric acid, trehalose, AspAspGlyTrp, and three BCa-downregulated metabolites: inosinic acid, ureidosuccinic acid, GlyCysAlaLys. Finally, analysis of six post-surgery BCa urine samples showed that these BCa-metabolomic features reverted to normal state after tumor removal, suggesting that they reflected metabolomic features associated with BCa. ROC analyses using two linear regression models to combine the identified markers showed a high diagnostic performance for detecting BCa with AUC (area under the ROC curve) values of 0.919 to 0.934. In summary, we developed a high coverage metabolomic approach that has potential for biomarker discovery in cancers.
Hocher, Berthold; Adamski, Jerzy
Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.
Mushtaq, Mian Yahya; Marçal, Rosilene Moretti; Champagne, Danielle L; van der Kooy, Frank; Verpoorte, Robert; Choi, Young Hae
We applied an acute stress model to zebra fish in order to measure the changes in the metabolome due to biological stress. This was done by submitting the fish to fifteen minutes of acute confinement (netting) stress, and then five minutes for the open field and light/dark field tests. A polar extract of the zebra fish was then subjected to (1)H nuclear magnetic spectroscopy. Multivariate data analysis of the spectra showed a clear separation associated to a wide range of metabolites between zebra fish that were submitted to open field and light/dark field tests. Alanine, taurine, adenosine, creatine, lactate, and histidine were high in zebra fish to which the light/dark field test was applied, regardless of stress, while acetate and isoleucine/lipids appeared to be higher in zebra fish exposed to the open field test. These results show that any change in the environment, even for a small period of time, has a noticeable physiological impact. This research provides an insight of how different mechanisms are activated under different environments to maintain the homeostasis of the body. It should also contribute to establish zebra fish as a model for metabolomics studies.
Breunig, Jeffrey S; Hackett, Sean R; Rabinowitz, Joshua D; Kruglyak, Leonid
Metabolism, the conversion of nutrients into usable energy and biochemical building blocks, is an essential feature of all cells. The genetic factors responsible for inter-individual metabolic variability remain poorly understood. To investigate genetic causes of metabolome variation, we measured the concentrations of 74 metabolites across ~ 100 segregants from a Saccharomyces cerevisiae cross by liquid chromatography-tandem mass spectrometry. We found 52 quantitative trait loci for 34 metabolites. These included linkages due to overt changes in metabolic genes, e.g., linking pyrimidine intermediates to the deletion of ura3. They also included linkages not directly related to metabolic enzymes, such as those for five central carbon metabolites to ira2, a Ras/PKA pathway regulator, and for the metabolites, S-adenosyl-methionine and S-adenosyl-homocysteine to slt2, a MAP kinase involved in cell wall integrity. The variant of ira2 that elevates metabolite levels also increases glucose uptake and ethanol secretion. These results highlight specific examples of genetic variability, including in genes without prior known metabolic regulatory function, that impact yeast metabolism.
Breunig, Jeffrey S.; Hackett, Sean R.; Rabinowitz, Joshua D.; Kruglyak, Leonid
Metabolism, the conversion of nutrients into usable energy and biochemical building blocks, is an essential feature of all cells. The genetic factors responsible for inter-individual metabolic variability remain poorly understood. To investigate genetic causes of metabolome variation, we measured the concentrations of 74 metabolites across 100 segregants from a Saccharomyces cerevisiae cross by liquid chromatography-tandem mass spectrometry. We found 52 quantitative trait loci for 34 metabolites. These included linkages due to overt changes in metabolic genes, e.g., linking pyrimidine intermediates to the deletion of ura3. They also included linkages not directly related to metabolic enzymes, such as those for five central carbon metabolites to ira2, a Ras/PKA pathway regulator, and for the metabolites, S-adenosyl-methionine and S-adenosyl-homocysteine to slt2, a MAP kinase involved in cell wall integrity. The variant of ira2 that elevates metabolite levels also increases glucose uptake and ethanol secretion. These results highlight specific examples of genetic variability, including in genes without prior known metabolic regulatory function, that impact yeast metabolism. PMID:24603560
Baars, Oliver; Zhang, Xinning
In this study, we performed a detailed characterization of the siderophore metabolome, or “chelome,” of the agriculturally important and widely studied model organism Azotobacter vinelandii. Using a new high-resolution liquid chromatography-mass spectrometry (LC-MS) approach, we found over 35 metal-binding secondary metabolites, indicative of a vast chelome in A. vinelandii. These include vibrioferrin, a siderophore previously observed only in marine bacteria. Quantitative analyses of siderophore production during diazotrophic growth with different sources and availabilities of Fe showed that, under all tested conditions, vibrioferrin was present at the highest concentration of all siderophores and suggested new roles for vibrioferrin in the soil environment. Bioinformatic searches confirmed the capacity for vibrioferrin production in Azotobacter spp. and other bacteria spanning multiple phyla, habitats, and lifestyles. Moreover, our studies revealed a large number of previously unreported derivatives of all known A. vinelandii siderophores and rationalized their origins based on genomic analyses, with implications for siderophore diversity and evolution. Together, these insights provide clues as to why A. vinelandii harbors multiple siderophore biosynthesis gene clusters. Coupled with the growing evidence for alternative functions of siderophores, the vast chelome in A. vinelandii may be explained by multiple, disparate evolutionary pressures that act on siderophore production. PMID:26452553
Baars, Oliver; Zhang, Xinning; Morel, François M M; Seyedsayamdost, Mohammad R
In this study, we performed a detailed characterization of the siderophore metabolome, or "chelome," of the agriculturally important and widely studied model organism Azotobacter vinelandii. Using a new high-resolution liquid chromatography-mass spectrometry (LC-MS) approach, we found over 35 metal-binding secondary metabolites, indicative of a vast chelome in A. vinelandii. These include vibrioferrin, a siderophore previously observed only in marine bacteria. Quantitative analyses of siderophore production during diazotrophic growth with different sources and availabilities of Fe showed that, under all tested conditions, vibrioferrin was present at the highest concentration of all siderophores and suggested new roles for vibrioferrin in the soil environment. Bioinformatic searches confirmed the capacity for vibrioferrin production in Azotobacter spp. and other bacteria spanning multiple phyla, habitats, and lifestyles. Moreover, our studies revealed a large number of previously unreported derivatives of all known A. vinelandii siderophores and rationalized their origins based on genomic analyses, with implications for siderophore diversity and evolution. Together, these insights provide clues as to why A. vinelandii harbors multiple siderophore biosynthesis gene clusters. Coupled with the growing evidence for alternative functions of siderophores, the vast chelome in A. vinelandii may be explained by multiple, disparate evolutionary pressures that act on siderophore production.
Rischer, Heiko; Oksman-Caldentey, Kirsi-Marja
In Europe the commercialization of food derived from genetically modified plants has been slow because of the complex regulatory process and the concerns of consumers. Risk assessment is focused on potential adverse effects on humans and the environment, which could result from unintended effects of genetic modifications: unintended effects are connected to changes in metabolite levels in the plants. One of the major challenges is how to analyze the overall metabolite composition of GM plants in comparison to conventional cultivars, and one possible solution is offered by metabolomics. The ultimate aim of metabolomics is the identification and quantification of all small molecules in an organism; however, a single method enabling complete metabolome analysis does not exist. Given a comprehensive extraction method, a hierarchical strategy--starting with global fingerprinting and followed by complementary profiling attempts--is the most logical and economic approach to detect unintended effects in GM crops.
Chinnaiyan, Prakash; Kensicki, Elizabeth; Bloom, Gregory; Prabhu, Antony; Sarcar, Bhaswati; Kahali, Soumen; Eschrich, Steven; Qu, Xiaotao; Forsyth, Peter; Gillies, Robert
Although considerable progress has been made toward understanding glioblastoma biology through large-scale genetic and protein expression analyses, little is known about the underlying metabolic alterations promoting their aggressive phenotype. We conducted global metabolomic profiling on patient-derived glioma specimens and identified specific metabolic programs differentiating low- and high-grade tumors, with the metabolic signature of glioblastoma reflecting accelerated anabolic metabolism. When coupled with transcriptional profiles, we identified the metabolic phenotype of the mesenchymal subtype to consist of accumulation of the glycolytic intermediate phosphoenolpyruvate and decreased pyruvate kinase activity. Unbiased hierarchical clustering of metabolomic profiles identified three subclasses, which we term energetic, anabolic, and phospholipid catabolism with prognostic relevance. These studies represent the first global metabolomic profiling of glioma, offering a previously undescribed window into their metabolic heterogeneity, and provide the requisite framework for strategies designed to target metabolism in this rapidly fatal malignancy. PMID:23026133
Hong, Jun; Yang, Litao; Zhang, Dabing; Shi, Jianxin
As genomes of many plant species have been sequenced, demand for functional genomics has dramatically accelerated the improvement of other omics including metabolomics. Despite a large amount of metabolites still remaining to be identified, metabolomics has contributed significantly not only to the understanding of plant physiology and biology from the view of small chemical molecules that reflect the end point of biological activities, but also in past decades to the attempts to improve plant behavior under both normal and stressed conditions. Hereby, we summarize the current knowledge on the genetic and biochemical mechanisms underlying plant growth, development, and stress responses, focusing further on the contributions of metabolomics to practical applications in crop quality improvement and food safety assessment, as well as plant metabolic engineering. We also highlight the current challenges and future perspectives in this inspiring area, with the aim to stimulate further studies leading to better crop improvement of yield and quality.
Preez, Ilse du; Luies, Laneke; Loots, Du Toit
Numerous studies have contributed to our current understanding of the complex biology of pulmonary tuberculosis and subsequently provided solutions to its control or eradication. Metabolomics, a newcomer to the Omics research domain, has significantly contributed to this understanding by identifying biomarkers originating from the disease-associated metabolome adaptations of both the microbe and host. These biomarkers have shed light on previously unknown disease mechanisms, many of which have been implemented toward the development of improved diagnostic strategies. In this review, we will discuss the role that metabolomics has played in tuberculosis research to date, with a specific focus on new biomarker identification, and how these have contributed to improved disease characterization and diagnostics, and their potential clinical applications.
Vallverdú-Queralt, Anna; Medina-Remón, Alexander; Casals-Ribes, Isidre; Amat, Mercedes; Lamuela-Raventós, Rosa Maria
The agronomic environments in which tomatoes are cultivated potentially affect the levels of antioxidants and other metabolites in commercial products. In this study, biochemical and metabolomic techniques were used to assess the differences between ketchups produced by organic and conventional systems. An untargeted metabolomic approach using QToF-MS was used to identify those nutrients that have the greatest impact on the overall metabolomic profile of organic ketchups as compared to conventional ones. Individual polyphenols were quantified using LC-ESI-QqQ. This multifaceted approach revealed that the agronomic environment in which tomatoes are grown induces alterations in the content of antioxidant capacity, phenolics, and other metabolites in ketchups. Organic cultivation was found to provide tomatoes and tomato-derived products with a significantly higher content of antioxidant microconstituents, whereas glutamylphenylalanine and N-malonyltryptophan were detected only in conventional ketchups.
Ceglarek, Uta; Leichtle, Alexander; Brügel, Mathias; Kortz, Linda; Brauer, Romy; Bresler, Kristin; Thiery, Joachim; Fiedler, Georg Martin
'Clinical metabolomics' aims at evaluating and predicting health and disease risk in an individual by investigating metabolic signatures in body fluids or tissues, which are influenced by genetics, epigenetics, environmental exposures, diet, and behaviour. Powerful analytical techniques like liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) offers a rapid, effective and economical way to analyze metabolic alterations of pre-defined target metabolites in biological samples. Novel hyphenated technical approaches like the combination of tandem mass spectrometry combined with linear ion trap (QTrap mass spectrometry) combines both identification and quantification of known and unknown metabolic targets. We describe new concepts and developments of mass spectrometry based multi-target metabolome profiling in the field of clinical diagnostics and research. Particularly, the experiences from newborn screening provided important insights about the diagnostic potential of metabolite profiling arrays and directs to the clinical aim of predictive, preventive and personalized medicine by metabolomics.
Reed, Laura K; Baer, Charles F; Edison, Arthur S
Model organisms are important in many areas of chemical biology. In metabolomics, model organisms can provide excellent samples for methods development as well as the foundation of comparative phylometabolomics, which will become possible as metabolomics applications expand. Comparative studies of conserved and unique metabolic pathways will help in the annotation of metabolites as well as provide important new targets of investigation in biology and biomedicine. However, most chemical biologists are not familiar with genetics, which needs to be considered when choosing a model organism. In this review we summarize the strengths and weaknesses of several genetic systems, including natural isolates, recombinant inbred lines, and genetic mutations. We also discuss methods to detect targets of selection on the metabolome.
Wang, Zhiyi; Ma, Jianshe; Zhang, Meiling; Wen, Congcong; Huang, Xueli; Sun, Fa; Wang, Shuanghu; Hu, Lufeng; Lin, Guanyang; Wang, Xianqin
Paraquat is one of the most widely used herbicides in the world and is highly toxic to humans and animals. In this study, we developed a serum metabolomic method based on GC/MS to evaluate the effects of acute paraquat poisoning on rats. Pattern recognition analysis, including both principal component analysis and partial least squares-discriminate analysis revealed that acute paraquat poisoning induced metabolic perturbations. Compared with the control group, the level of octadecanoic acid, L-serine, L-threonine, L-valine, and glycerol in the acute paraquat poisoning group (36 mg/kg) increased, while the levels of hexadecanoic acid, D-galactose, and decanoic acid decreased. These findings provide an overview of systematic responses to paraquat exposure and metabolomic insight into the toxicological mechanism of paraquat. Our results indicate that metabolomic methods based on GC/MS may be useful to elucidate the mechanism of acute paraquat poisoning through the exploration of biomarkers.
Okazaki, Yozo; Saito, Kazuki
Metabolomics is widely employed to monitor the cellular metabolic state and assess the quality of plant-derived foodstuffs because it can be used to manage datasets that include a wide range of metabolites in their analytical samples. In this review, we discuss metabolomics research on rice in order to elucidate the overall regulation of the metabolism as it is related to the growth and mechanisms of adaptation to genetic modifications and environmental stresses such as fungal infections, submergence, and oxidative stress. We also focus on phytochemical genomics studies based on a combination of metabolomics and quantitative trait locus (QTL) mapping techniques. In addition to starch, rice produces many metabolites that also serve as nutrients for human consumers. The outcomes of recent phytochemical genomics studies of diverse natural rice resources suggest there is potential for using further effective breeding strategies to improve the quality of ingredients in rice grains.
Benton, H. Paul; Ivanisevic, Julijana; Mahieu, Nathaniel G.; Kurczy, Michael E.; Johnson, Caroline H.; Franco, Lauren; Rinehart, Duane; Valentine, Elizabeth; Gowda, Harsha; Ubhi, Baljit K.; Tautenhahn, Ralf; Gieschen, Andrew; Fields, Matthew W.; Patti, Gary J.; Siuzdak, Gary
An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. We can analyze large profiling datasets and simultaneously obtain structural identifications, as a result of this unique integration. Furthermore, validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.
Vincent, Isabel M.; Ehmann, David E.; Mills, Scott D.; Perros, Manos
Deciphering the mode of action (MOA) of new antibiotics discovered through phenotypic screening is of increasing importance. Metabolomics offers a potentially rapid and cost-effective means of identifying modes of action of drugs whose effects are mediated through changes in metabolism. Metabolomics techniques also collect data on off-target effects and drug modifications. Here, we present data from an untargeted liquid chromatography-mass spectrometry approach to identify the modes of action of eight compounds: 1-[3-fluoro-4-(5-methyl-2,4-dioxo-pyrimidin-1-yl)phenyl]-3-[2-(trifluoromethyl)phenyl]urea (AZ1), 2-(cyclobutylmethoxy)-5′-deoxyadenosine, triclosan, fosmidomycin, CHIR-090, carbonyl cyanide m-chlorophenylhydrazone (CCCP), 5-chloro-2-(methylsulfonyl)-N-(1,3-thiazol-2-yl)-4-pyrimidinecarboxamide (AZ7), and ceftazidime. Data analysts were blind to the compound identities but managed to identify the target as thymidylate kinase for AZ1, isoprenoid biosynthesis for fosmidomycin, acyl-transferase for CHIR-090, and DNA metabolism for 2-(cyclobutylmethoxy)-5′-deoxyadenosine. Changes to cell wall metabolites were seen in ceftazidime treatments, although other changes, presumably relating to off-target effects, dominated spectral outputs in the untargeted approach. Drugs which do not work through metabolic pathways, such as the proton carrier CCCP, have no discernible impact on the metabolome. The untargeted metabolomics approach also revealed modifications to two compounds, namely, fosmidomycin and AZ7. An untreated control was also analyzed, and changes to the metabolome were seen over 4 h, highlighting the necessity for careful controls in these types of studies. Metabolomics is a useful tool in the analysis of drug modes of action and can complement other technologies already in use. PMID:26833150
Vincent, Isabel M; Ehmann, David E; Mills, Scott D; Perros, Manos; Barrett, Michael P
Deciphering the mode of action (MOA) of new antibiotics discovered through phenotypic screening is of increasing importance. Metabolomics offers a potentially rapid and cost-effective means of identifying modes of action of drugs whose effects are mediated through changes in metabolism. Metabolomics techniques also collect data on off-target effects and drug modifications. Here, we present data from an untargeted liquid chromatography-mass spectrometry approach to identify the modes of action of eight compounds: 1-[3-fluoro-4-(5-methyl-2,4-dioxo-pyrimidin-1-yl)phenyl]-3-[2-(trifluoromethyl)phenyl]urea (AZ1), 2-(cyclobutylmethoxy)-5'-deoxyadenosine, triclosan, fosmidomycin, CHIR-090, carbonyl cyanidem-chlorophenylhydrazone (CCCP), 5-chloro-2-(methylsulfonyl)-N-(1,3-thiazol-2-yl)-4-pyrimidinecarboxamide (AZ7), and ceftazidime. Data analysts were blind to the compound identities but managed to identify the target as thymidylate kinase for AZ1, isoprenoid biosynthesis for fosmidomycin, acyl-transferase for CHIR-090, and DNA metabolism for 2-(cyclobutylmethoxy)-5'-deoxyadenosine. Changes to cell wall metabolites were seen in ceftazidime treatments, although other changes, presumably relating to off-target effects, dominated spectral outputs in the untargeted approach. Drugs which do not work through metabolic pathways, such as the proton carrier CCCP, have no discernible impact on the metabolome. The untargeted metabolomics approach also revealed modifications to two compounds, namely, fosmidomycin and AZ7. An untreated control was also analyzed, and changes to the metabolome were seen over 4 h, highlighting the necessity for careful controls in these types of studies. Metabolomics is a useful tool in the analysis of drug modes of action and can complement other technologies already in use.
Zordoky, Beshay N.; Sung, Miranda M.; Ezekowitz, Justin; Mandal, Rupasri; Han, Beomsoo; Bjorndahl, Trent C.; Bouatra, Souhaila; Anderson, Todd; Oudit, Gavin Y.; Wishart, David S.; Dyck, Jason R. B.
Background Heart failure (HF) with preserved ejection fraction (HFpEF) is increasingly recognized as an important clinical entity. Preclinical studies have shown differences in the pathophysiology between HFpEF and HF with reduced ejection fraction (HFrEF). Therefore, we hypothesized that a systematic metabolomic analysis would reveal a novel metabolomic fingerprint of HFpEF that will help understand its pathophysiology and assist in establishing new biomarkers for its diagnosis. Methods and Results Ambulatory patients with clinical diagnosis of HFpEF (n = 24), HFrEF (n = 20), and age-matched non-HF controls (n = 38) were selected for metabolomic analysis as part of the Alberta HEART (Heart Failure Etiology and Analysis Research Team) project. 181 serum metabolites were quantified by LC-MS/MS and 1H-NMR spectroscopy. Compared to non-HF control, HFpEF patients demonstrated higher serum concentrations of acylcarnitines, carnitine, creatinine, betaine, and amino acids; and lower levels of phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins. Medium and long-chain acylcarnitines and ketone bodies were higher in HFpEF than HFrEF patients. Using logistic regression, two panels of metabolites were identified that can separate HFpEF patients from both non-HF controls and HFrEF patients with area under the receiver operating characteristic (ROC) curves of 0.942 and 0.981, respectively. Conclusions The metabolomics approach employed in this study identified a unique metabolomic fingerprint of HFpEF that is distinct from that of HFrEF. This metabolomic fingerprint has been utilized to identify two novel panels of metabolites that can separate HFpEF patients from both non-HF controls and HFrEF patients. Clinical Trial Registration ClinicalTrials.gov NCT02052804 PMID:26010610
Griffiths, William J.; Hornshaw, Martin; Woffendin, Gary; Baker, Sharon F.; Lockhart, Andrew; Heidelberger, Sibylle; Gustafsson, Magnus; Sjövall, Jan; Wang, Yuqin
While the proteome defines the expressed gene products, the metabolome results from reactions controlled by such gene products. Plasma represents an accessible “window” to the metabolome both in regard of availability and content. The wide range of the plasma metabolome, in terms of molecular diversity and abundance, makes its comprehensive analysis challenging. Here we demonstrate an analytical method designed to target one region of the metabolome i.e. oxysterols. Since the discovery of their biological activity as ligands to nuclear receptors there has been a reawakening of interest in oxysterols and their analysis. In addition, the oxysterols, 24S- and 27-hydroxycholesterol, are currently under investigation as potential biomarkers associated with neurodegenerative disorders such as Alzheimer's disease and multiple sclerosis; widespread analysis of these lipids in clinical studies will require the development of robust, sensitive and rapid analytical techniques. In this communication we present results of an investigation of the oxysterols content of human plasma using a newly developed high-performance liquid chromatography – mass spectrometry (HPLC-MS) method incorporating charge-tagging and high-resolution MS. The method has allowed the identification in plasma of monohydroxylated cholesterol molecules, 7α-, 24S- and 27-hydroxycholesterol; the cholestenetriol 7α,27-dihydroxycholesterol; and 3β-hydroxycholest-5-en-27-oic acid and its metabolite and 3β,7α-dihydroxycholest-5-en-27-oic acid. The methodology described is also applicable for the analysis of other sterols in plasma i.e. cholesterol, 7-dehydrocholesterol, and desmosterol, as well as cholesterol 5,6-seco-sterols and steroid hormones. Although involving derivatisation, sample preparation is straight forward and chromatographic analysis rapid (17 min), while the MS method offers high sensitivity (ng/mL of sterol in plasma, or pg on-column) and specificity. The methodology is suitable for
Astarita, Giuseppe; Langridge, James
Nutritional research is undergoing a remarkable transformation driven by new technological tools. Because of the complexity of the components present in food and how they may interact with the biochemical networks of living organisms, nutrition cannot be considered a reductionist discipline. More holistic approaches, which are capable of gathering comprehensive, high-throughput amounts of data, appear to best enhance our understanding of the role of food in health and disease. In this context, global metabolite analysis, or 'metabolomics', is becoming an appealing research tool for nutrigenomics and nutrigenetics scientists. The purpose of the present review is to highlight some potential applications of metabolomics in nutrition research.
Martien, Julia I; Amador-Noguez, Daniel
Biofuel production from plant biomass is a promising source of renewable energy . However, efficient biofuel production involves the complex task of engineering high-performance microorganisms, which requires detailed knowledge of metabolic function and regulation. This review highlights the potential of mass-spectrometry-based metabolomic analysis to guide rational engineering of biofuel-producing microbes. We discuss recent studies that apply knowledge gained from metabolomic analyses to increase the productivity of engineered pathways, characterize the metabolism of emerging biofuel producers, generate novel bioproducts, enable utilization of lignocellulosic feedstock, and improve the stress tolerance of biofuel producers.
Wang, Lv; Wu, Ning; Zhao, Tai-Yun; Li, Jin
Drug addiction places a significant burden on society and individuals. Proteomics and metabolomics approaches pave the road for searching potential biomarkers to assist the diagnosis and treatment. This review summarized putative drug addiction-related biomarkers in proteomics and metabolomics studies and discussed challenges and prospects in future studies. Alterations of several hundred proteins and metabolites were reported when exposure to abused drug, which enriched in energy metabolism, oxidative stress response, protein modification and degradation, synaptic function and neurotrasmission, etc. Hsp70, peroxiredoxin-6 and α- and β-synuclein, as well as n-methylserotonin and purine metabolites, were promising as potential biomarker for drug addiction.
Mathew, Anna V; Zeng, Lixia; Byun, Jaeman; Pennathur, Subramaniam
Atherosclerotic cardiovascular disease is the leading cause of death in patients with chronic kidney disease (CKD), but the mechanisms underlying vascular disease has not been fully understood. As the nitrogen donor in nitric oxide (NO·) synthesis, arginine and its metabolic products are integrally linked to vascular health and information. We hypothesized that derangements in this pathway could explain, in part, increased atherosclerotic risk in CKD. We developed a targeted metabolomic platform to profile quantitatively arginine metabolites in plasma by liquid chromatography tandem mass spectrometry (LC/MS). Male low-density lipoprotein receptor defcient (LDLr−/−) mice at age 6 weeks were subjected to sham or 5/6 nephrectomy surgery to induce CKD. Subsequently, the animals were maintained on high fat diet for 24 weeks. Targeted metabolomic analysis of arginine metabolites in plasma was performed by isotope dilution LC/MS including asymmetric dimethyl arginine (ADMA), symmetric dimethyl arginine (SDMA), N-mono-methylarginine (NMMA), arginine and citrulline. Although elevated plasma levels of ADMA and SDMA were found in the CKD mice, only higher ADMA level correlated with degree of atherosclerosis. No significant differences were noted in levels of NMMA between the groups. CKD mice had high levels of citrulline and arginine, but ADMA levels had no correlation with either of these metabolites. These fndings strongly implicate altered arginine methylation and accumulation of ADMA, may in part contribute to CKD accelerated atherosclerosis. It raises the possibility that interrupting pathways that generate ADMA or enhance its metabolism may have therapeutic potential in mitigating atherosclerosis. PMID:26778898
Alonso, Cristina; Agirrezabal, Ion; Kotelnikova, Ekaterina; Zubizarreta, Irati; Pulido-Valdeolivas, Irene; Saiz, Albert; Comabella, Manuel; Montalban, Xavier; Villar, Luisa; Alvarez-Cermeño, Jose Carlos; Fernández, Oscar; Alvarez-Lafuente, Roberto; Arroyo, Rafael; Castro, Azucena
Objective: To identify differences in the metabolomic profile in the serum of patients with multiple sclerosis (MS) compared to controls and to identify biomarkers of disease severity. Methods: We studied 2 cohorts of patients with MS: a retrospective longitudinal cohort of 238 patients and 74 controls and a prospective cohort of 61 patients and 41 controls with serial serum samples. Patients were stratified into active or stable disease based on 2 years of prospective assessment accounting for presence of clinical relapses or changes in disability measured with the Expanded Disability Status Scale (EDSS). Metabolomic profiling (lipids and amino acids) was performed by ultra-high-performance liquid chromatography coupled to mass spectrometry in serum samples. Data analysis was performed using parametric methods, principal component analysis, and partial least square discriminant analysis for assessing the differences between cases and controls and for subgroups based on disease severity. Results: We identified metabolomics signatures with high accuracy for classifying patients vs controls as well as for classifying patients with medium to high disability (EDSS >3.0). Among them, sphingomyelin and lysophosphatidylethanolamine were the metabolites that showed a more robust pattern in the time series analysis for discriminating between patients and controls. Moreover, levels of hydrocortisone, glutamic acid, tryptophan, eicosapentaenoic acid, 13S-hydroxyoctadecadienoic acid, lysophosphatidylcholines, and lysophosphatidylethanolamines were associated with more severe disease (non-relapse-free or increase in EDSS). Conclusions: We identified metabolomic signatures composed of hormones, lipids, and amino acids associated with MS and with a more severe course. PMID:28180139
Hoque, Md. Aminul; Shahjaman, Md.; Islam, S. M. Shahinul; Mollah, Md. Nurul Haque
Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease. PMID:28293630
Metabolomics is a systems biology discipline wherein abundances of endogenous metabolites from biological samples are identified and quantitatively measured across a large range of metabolites and/or a large number of samples. Since all developmental, physiological and response to the environment ph...
Kapranas, Apostolos; Snart, Charles J. P.; Williams, Huw; Hardy, Ian C. W.; Barrett, David A.
Metabolomics studies of low-biomass organisms, such as small insects, have previously relied on the pooling of biological samples to overcome detection limits, particularly using NMR. We show that the differentiation of metabolite profiles of individual 1 mg parasitoid wasps of different ages is possible when using a modified sample preparation and a combination of untargeted NMR and LC-MS based metabolomics. Changes were observed between newly emerged and older wasps in glycerolipids, amino acids and circulatory sugars. This advance in chemical profiling has important implications for the study of the behaviour and ecology of parasitoids and many other species of small organisms because predictions and observations are typically made at the level of the individual. Thus, the metabolomic state of low-biomass individuals can now be related to their behaviour and ecological performance. We discuss specifically the utility of age-related metabolomic profiling but our new approach can be applied to a wide range of biological research. PMID:27713504
Paglia, Giuseppe; Williams, Jonathan P; Menikarachchi, Lochana; Thompson, J Will; Tyldesley-Worster, Richard; Halldórsson, Skarphédinn; Rolfsson, Ottar; Moseley, Arthur; Grant, David; Langridge, James; Palsson, Bernhard O; Astarita, Giuseppe
Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC-TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches.
Vasilopoulou, Catherine G.; Margarity, Marigoula; Klapa, Maria I.
Metabolism being a fundamental part of molecular physiology, elucidating the structure and regulation of metabolic pathways is crucial for obtaining a comprehensive perspective of cellular function and understanding the underlying mechanisms of its dysfunction(s). Therefore, quantifying an accurate metabolic network activity map under various physiological conditions is among the major objectives of systems biology in the context of many biological applications. Especially for CNS, metabolic network activity analysis can substantially enhance our knowledge about the complex structure of the mammalian brain and the mechanisms of neurological disorders, leading to the design of effective therapeutic treatments. Metabolomics has emerged as the high-throughput quantitative analysis of the concentration profile of small molecular weight metabolites, which act as reactants and products in metabolic reactions and as regulatory molecules of proteins participating in many biological processes. Thus, the metabolic profile provides a metabolic activity fingerprint, through the simultaneous analysis of tens to hundreds of molecules of pathophysiological and pharmacological interest. The application of metabolomics is at its standardization phase in general, and the challenges for paving a standardized procedure are even more pronounced in brain studies. In this review, we support the value of metabolomics in brain research. Moreover, we demonstrate the challenges of designing and setting up a reliable brain metabolomic study, which, among other parameters, has to take into consideration the sex differentiation and the complexity of brain physiology manifested in its regional variation. We finally propose ways to overcome these challenges and design a study that produces reproducible and consistent results. PMID:27252656
Iron (Fe) deficiency is an important agricultural concern leading to lower yields and crop quality. A better understanding of the condition, at the metabolome level, could contribute to the design of strategies to ameliorate Fe deficiency problems. Fe-sufficient and Fe-deficient soybean leaf extract...
We have developed a research program in metabolomics that involves numerous partners across EPA, other federal labs, academia, and the private sector. A primary goal is to develop metabolite-based markers that can be used by EPA in ecological exposure and risk assessments. We are...
Booth, Sean C.; Weljie, Aalim M.; Turner, Raymond J.
Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before. PMID:24688685
One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders. PMID:26453695
High-throughput metabolomic assays that allow simultaneous targeted screening of hundreds of metabolites have recently become available in kit form. Such assays provide a window into understanding changes to biochemical pathways due to chemical exposure or disease, and are usefu...
Mauri-Capdevila, Gerard; Jove, Mariona; Suarez-Luis, Idalmis; Portero-Otin, Manuel; Purroy, Francisco
The study of biomarkers related with ischaemic stroke is becoming increasingly more important as a way to further our knowledge of the pathophysiological changes that occur in cerebrovascular disease and to make it easier to reach an early diagnosis. Within this field, metabolomics offers a novel approach. The field is defined as the study of the small-molecule metabolites derived from cell metabolism. Its interest lies in the fact that, using a biological sample, it offers a snapshot of the cellular changes that are taking place. Today, the application of metabolomics requires a complex methodology that includes the application of laboratory separation techniques, multivariant statistical analyses and the use of bioinformatic tools. A number of studies conducted within the field of cardiovascular disease have focused on the application of this approach. In recent years there has been a steady growth in the number of publications referring to the metabolic changes related with ischaemic stroke, both in animal models and in patients. Metabolomics makes it possible to obtain the profiles of metabolites that identify patients who have suffered an ischaemic stroke. Furthermore, since studies have been carried out that relate certain metabolites with the most common causations of ischaemic stroke, metabolomics may eventually play a significant role in the study of cryptogenic stroke. The most exhaustive knowledge of the changes in the metabolic pathways involved in cerebrovascular disease could lay the foundations for the development of new neuroprotector strategies.
Ohta, Erika; Nakayama, Yasumune; Mukai, Yukio; Bamba, Takeshi; Fukusaki, Eiichiro
The budding yeast Saccharomyces cerevisiae is widely used for brewing and ethanol production. The ethanol sensitivity of yeast cells is still a serious problem during ethanol fermentation, and a variety of genetic approaches (e.g., random mutant screening under selective pressure of ethanol) have been developed to improve ethanol tolerance. In this study, we developed a strategy for improving ethanol tolerance of yeast cells based on metabolomics as a high-resolution quantitative phenotypic analysis. We performed gas chromatography-mass spectrometry analysis to identify and quantify 36 compounds on 14 mutant strains including knockout strains for transcription factor and metabolic enzyme genes. A strong relation between metabolome of these mutants and their ethanol tolerance was observed. Data mining of the metabolomic analysis showed that several compounds (such as trehalose, valine, inositol and proline) contributed highly to ethanol tolerance. Our approach successfully detected well-known ethanol stress related metabolites such as trehalose and proline thus, to further prove our strategy, we focused on valine and inositol as the most promising target metabolites in our study. Our results show that simultaneous deletion of LEU4 and LEU9 (leading to accumulation of valine) or INM1 and INM2 (leading to reduction of inositol) significantly enhanced ethanol tolerance. This study shows the potential of the metabolomic approach to identify target genes for strain improvement of S. cerevisiae with higher ethanol tolerance.
Misra, Biswapriya B.; de Armas, Evaldo; Tong, Zhaohui; Chen, Sixue
Anthropogenic CO2 presently at 400 ppm is expected to reach 550 ppm in 2050, an increment expected to affect plant growth and productivity. Paired stomatal guard cells (GCs) are the gate-way for water, CO2, and pathogen, while mesophyll cells (MCs) represent the bulk cell-type of green leaves mainly for photosynthesis. We used the two different cell types, i.e., GCs and MCs from canola (Brassica napus) to profile metabolomic changes upon increased CO2 through supplementation with bicarbonate (HCO3-). Two metabolomics platforms enabled quantification of 268 metabolites in a time-course study to reveal short-term responses. The HCO3- responsive metabolomes of the cell types differed in their responsiveness. The MCs demonstrated increased amino acids, phenylpropanoids, redox metabolites, auxins and cytokinins, all of which were decreased in GCs in response to HCO3-. In addition, the GCs showed differential increases of primary C-metabolites, N-metabolites (e.g., purines and amino acids), and defense-responsive pathways (e.g., alkaloids, phenolics, and flavonoids) as compared to the MCs, indicating differential C/N homeostasis in the cell-types. The metabolomics results provide insights into plant responses and crop productivity under future climatic changes where elevated CO2 conditions are to take center-stage. PMID:26641455
Naz, Shama; Vallejo, Maria; García, Antonia; Barbas, Coral
Non-targeted metabolomics is the hypothesis generating, global unbiased analysis of all the small-molecule metabolites present within a biological system, under a given set of conditions. It includes several common steps such as selection of biological samples, sample pre-treatment, analytical conditions set-up, acquiring data, data analysis by chemometrics, database search and biological interpretation. Non-targeted metabolomics offers the potential for a holistic approach in the area of biomedical research in order to improve disease diagnosis and to understand its pathological mechanisms. Various analytical methods have been developed based on nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) coupled with different separation techniques. The key points in any analytical method development are the validation of every step to get a reliable and reproducible result and non-targeted metabolomics is not beyond this criteria, although analytical challenges are completely new and different to target methods. This review paper will describe the available validation strategies that are being used and as well will recommend some steps to consider during a non-targeted metabolomics analytical method development.
Type 2 diabetes mellitus (T2DM) develops over many years, providing an opportunity to consider early prognostic tools that guide interventions to thwart disease. Advancements in analytical chemistry enable quantitation of hundreds of metabolites in biofluids and tissues (metabolomics), providing in...
Although widely used in the study of rodent toxicity responses to assess human risk, metabolomics is now finding utility in toxicity assessments in a wide variety of other organisms including environmentally relevant small fish species such as fathead minnow (FHM) and medaka. To...
Corona, Giuseppe; Polesel, Jerry; Fratino, Lucia; Miolo, Gianmaria; Rizzolio, Flavio; Crivellari, Diana; Addobbati, Riccardo; Cervo, Silvia; Toffoli, Giuseppe
Metabolome analysis has emerged as a powerful technique for detecting and define specific physio-pathological phenotypes. In this investigation the diagnostic potential of metabolomics has been applied to better characterize the multiple biochemical alterations that concur in the definition of the frailty phenotype observed in elderly breast cancer patients. The study included 89 women with breast cancer (range 70-97 years) classified as Fit (n = 49), Unfit (n = 23), or Frail (n = 17) according to comprehensive geriatric assessment. The serum metabolomic profile was performed by tandem mass spectrometry and included different classes of metabolites such as amino acids, acylcarnitines, sphingo-, and glycerol-phospolipids. ANOVA was applied to identify the metabolites differing significantly among Fit, Unfit, and Frail patients. In patients carrying the frail phenotype, the amino acid perturbations involve serine, tryptophan, hydroxyproline, histidine, its derivate 3-methyl-hystidine, cystine, and β-aminoisobutyric acid. With regard to lipid metabolism, the frailty phenotype was characterized by a decrease of a wide number of glycerol- and sphingo-phospholipid metabolites. These metabolomics biomarkers may give a further insight into the biochemical processes involved in the development of frailty in breast cancer patients. Moreover, they might be useful to refine the comprehensive geriatric assessment model.
Ganti, Sheila; Taylor, Sandra L; Abu Aboud, Omran; Yang, Joy; Evans, Christopher; Osier, Michael V; Alexander, Danny C; Kim, Kyoungmi; Weiss, Robert H
Metabolomics is increasingly being used in cancer biology for biomarker discovery and identification of potential novel therapeutic targets. However, a systematic metabolomics study of multiple biofluids to determine their interrelationships and to describe their use as tumor proxies is lacking. Using a mouse xenograft model of kidney cancer, characterized by subcapsular implantation of Caki-1 clear cell human kidney cancer cells, we examined tissue, serum, and urine all obtained simultaneously at baseline (urine) and at, or close to, animal sacrifice (urine, tissue, and plasma). Uniform metabolomics analysis of all three "matrices" was accomplished using gas chromatography- and liquid chromatography-mass spectrometry. Of all the metabolites identified (267 in tissue, 246 in serum, and 267 in urine), 89 were detected in all 3 matrices, and the majority was altered in the same direction. Heat maps of individual metabolites showed that alterations in serum were more closely related to tissue than was urine. Two metabolites, cinnamoylglycine and nicotinamide, were concordantly and significantly (when corrected for multiple testing) altered in tissue and serum, and cysteine-glutathione disulfide showed the highest change (232.4-fold in tissue) of any metabolite. On the basis of these and other considerations, three pathways were chosen for biologic validation of the metabolomic data, resulting in potential therapeutic target identification. These data show that serum metabolomics analysis is a more accurate proxy for tissue changes than urine and that tryptophan degradation (yielding anti-inflammatory metabolites) is highly represented in renal cell carcinoma, and support the concept that PPAR-α antagonism may be a potential therapeutic approach for this disease.
Background To determine how diets high in saturated fat could increase polyp formation in the mouse model of intestinal neoplasia, Apc Min/+ , we conducted large-scale metabolome analysis and association study of colon and small intestine polyp formation from plasma and liver samples of Apc Min/+ vs. wild-type littermates, kept on low vs. high-fat diet. Label-free mass spectrometry was used to quantify untargeted plasma and acyl-CoA liver compounds, respectively. Differences in contrasts of interest were analyzed statistically by unsupervised and supervised modeling approaches, namely Principal Component Analysis and Linear Model of analysis of variance. Correlation between plasma metabolite concentrations and polyp numbers was analyzed with a zero-inflated Generalized Linear Model. Results Plasma metabolome in parallel to promotion of tumor development comprises a clearly distinct profile in Apc Min/+ mice vs. wild type littermates, which is further altered by high-fat diet. Further, functional metabolomics pathway and network analyses in Apc Min/+ mice on high-fat diet revealed associations between polyp formation and plasma metabolic compounds including those involved in amino-acids metabolism as well as nicotinamide and hippuric acid metabolic pathways. Finally, we also show changes in liver acyl-CoA profiles, which may result from a combination of Apc Min/+ -mediated tumor progression and high fat diet. The biological significance of these findings is discussed in the context of intestinal cancer progression. Conclusions These studies show that high-throughput metabolomics combined with appropriate statistical modeling and large scale functional approaches can be used to monitor and infer changes and interactions in the metabolome and genome of the host under controlled experimental conditions. Further these studies demonstrate the impact of diet on metabolic pathways and its relation to intestinal cancer progression. Based on our results, metabolic signatures
Yang, Liu; Yu, Qing-Tao; Ge, Ya-Zhong; Zhang, Wen-Song; Fan, Yong; Ma, Chung-Wah; Liu, Qun; Qi, Lian-Wen
Ginseng occupies a prominent position in the list of best-selling natural products worldwide. Asian ginseng (Panax ginseng) and American ginseng (Panax quinquefolius) show different properties and medicinal applications in pharmacology, even though the main active constituents of them are both thought to be ginsenosides. Metabolomics is a promising method to profile entire endogenous metabolites and monitor their fluctuations related to exogenous stimulus. Herein, an untargeted metabolomics approach was applied to study the overall urine metabolic differences between Asian ginseng and American ginseng in mice. Metabolomics analyses were performed using gas chromatography-mass spectrometry (GC-MS) together with multivariate statistical data analysis. A total of 21 metabolites related to D-glutamine and D-glutamate metabolism, glutathione metabolism, TCA cycle and glyoxylate and dicarboxylate metabolism, differed significantly under the Asian ginseng treatment; 34 metabolites mainly associated with glyoxylate and dicarboxylate metabolism, TCA cycle and taurine and hypotaurine metabolism, were significantly altered after American ginseng treatment. Urinary metabolomics reveal that Asian ginseng and American ginseng can benefit organism physiological and biological functions via regulating multiple metabolic pathways. The important pathways identified from Asian ginseng and American ginseng can also help to explore new therapeutic effects or action targets so as to broad application of these two ginsengs. PMID:27991533
Carneiro, Sónia; Pereira, Rui; Rocha, Isabel
Metabolome sample preparation is one of the key factors in metabolomics analyses. The quality of the metabolome data will depend on the suitability of the experimental procedures to the cellular system (e.g., yeast cells) and the analytical performance. Here, we summarize a protocol for metabolome analysis of yeast cells using gas chromatography-mass spectrometry (GC-MS). First, the main phases of a metabolomics analysis are identified: sample preparation, metabolite extraction, and analysis. We also provide an overview on different methods used to quench samples and extract intracellular metabolites from yeast cells. This protocol provides a detailed description of a GC-MS-based analysis of yeast metabolome, in particular for metabolites containing amino and/or carboxyl groups, which represent most of the compounds participating in the central carbon metabolism.
Alonso, Arnald; Marsal, Sara; Julià, Antonio
Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile – the metabolome – has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed. PMID:25798438
Ramalingam, Abirami; Kudapa, Himabindu; Pazhamala, Lekha T.; Weckwerth, Wolfram; Varshney, Rajeev K.
The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several
Ramalingam, Abirami; Kudapa, Himabindu; Pazhamala, Lekha T; Weckwerth, Wolfram; Varshney, Rajeev K
The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several
Haznadar, Majda; Maruvada, Padma; Mette, Eliza; Milner, John; Moore, Steven C; Nicastro, Holly L; Sampson, Joshua N; Su, L Joseph; Verma, Mukesh; Zanetti, Krista A
Metabolomics platforms allow for the measurement of hundreds to thousands of unique small chemical entities, as well as offer extensive coverage of metabolic markers related to obesity, diet, smoking, and other exposures of high interest to health scientists. Nevertheless, its potential use as a tool in population-based study design has not been fully explored. As the field of metabolomics continues to mature, and in part, accelerate through the National Institutes of Health (NIH) investment of ≤65 million in the Common Fund's Metabolomics Program (https://common fund.nih.gov/metabolomics/index), it is time to consider those challenges most pertinent to epidemiologic studies.
Shah, Svati H.; Newgard, Christopher B.
The genetic architecture underlying the heritability of cardiovascular disease (CVD) is incompletely understood. Metabolomics is an emerging technology platform that has shown early success in identifying biomarkers and mechanisms of common, chronic diseases. Integration of metabolomics, genetics and other ‘omics’ platforms in a systems biology approach holds potential for elucidating novel genetic markers and mechanisms for CVD. We review important studies that have utilized metabolomic profiling in cardiometabolic diseases, approaches for integrating metabolomics with genetics and other molecular profiling platforms, and key studies showing the potential for such studies in deciphering CVD genetics, biomarkers and mechanisms. PMID:25901039
Pedersen, Helle Krogh; Gudmundsdottir, Valborg; Nielsen, Henrik Bjørn; Hyotylainen, Tuulia; Nielsen, Trine; Jensen, Benjamin A H; Forslund, Kristoffer; Hildebrand, Falk; Prifti, Edi; Falony, Gwen; Le Chatelier, Emmanuelle; Levenez, Florence; Doré, Joel; Mattila, Ismo; Plichta, Damian R; Pöhö, Päivi; Hellgren, Lars I; Arumugam, Manimozhiyan; Sunagawa, Shinichi; Vieira-Silva, Sara; Jørgensen, Torben; Holm, Jacob Bak; Trošt, Kajetan; Kristiansen, Karsten; Brix, Susanne; Raes, Jeroen; Wang, Jun; Hansen, Torben; Bork, Peer; Brunak, Søren; Oresic, Matej; Ehrlich, S Dusko; Pedersen, Oluf
Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.
Darghouth, D; Koehl, B; Junot, C; Roméo, P-H
Metabolic signatures of specialized circulating hematopoietic cells in physiological or human hematological diseases start to be described. We use a simple and highly reproductive extraction method of erythrocytes metabolites coupled with a liquid chromatography-mass spectrometry based metabolites profiling method to determine metabolomes of normal and sickle cell erythrocytes. Sickle cell erythrocytes and normal erythrocytes metabolomes display major differences in glycolysis, in glutathione, in ascorbate metabolisms and in metabolites associated to membranes turnover. In addition, the amounts of metabolites derived from urea cycle and NO metabolism that partly take place within erythrocyte were different between normal and sickle cell erythrocytes. These results show that metabolic profiling of red blood cell diseases can now be determined and might indicate new biomarkers that can be used for the follow-up of sickle cell patients.
Audoin, Coralie; Cocandeau, Vincent; Thomas, Olivier P.; Bruschini, Adrien; Holderith, Serge; Genta-Jouve, Grégory
Ultra-high pressure liquid chromatography coupled to high resolution mass spectrometry (UHPLC-MS/MS) analysis of the organic extract obtained from the Mediterranean zoanthid Parazoanthus axinellae yielded to the identification of five new parazoanthines F-J. The structures were fully determined by comparison of fragmentation patterns with those of previously isolated parazoathines and MS/MS spectra simulation of in silico predicted compounds according to the metabolome consistency. The absolute configuration of the new compounds has been assigned using on-line electronic circular dichroism (UHPLC-ECD). We thus demonstrated the potential of highly sensitive hyphenated techniques to characterize the structures of a whole family of natural products within the metabolome of a marine species. Minor compounds can be characterized using these techniques thus avoiding long isolation processes that may alter the structure of the natural products. These results are also of interest to identify putative bioactive compounds present at low concentration in a complex mixture. PMID:24957034
Audoin, Coralie; Cocandeau, Vincent; Thomas, Olivier P; Bruschini, Adrien; Holderith, Serge; Genta-Jouve, Grégory
Ultra-high pressure liquid chromatography coupled to high resolution mass spectrometry (UHPLC-MS/MS) analysis of the organic extract obtained from the Mediterranean zoanthid Parazoanthus axinellae yielded to the identification of five new parazoanthines F-J. The structures were fully determined by comparison of fragmentation patterns with those of previously isolated parazoathines and MS/MS spectra simulation of in silico predicted compounds according to the metabolome consistency. The absolute configuration of the new compounds has been assigned using on-line electronic circular dichroism (UHPLC-ECD). We thus demonstrated the potential of highly sensitive hyphenated techniques to characterize the structures of a whole family of natural products within the metabolome of a marine species. Minor compounds can be characterized using these techniques thus avoiding long isolation processes that may alter the structure of the natural products. These results are also of interest to identify putative bioactive compounds present at low concentration in a complex mixture.
Lokhov, Petr; Archakov, Alexander
Abstract We live in exciting times with the prospects of postgenomics diagnostics. Metabolomics is a novel “omics” data-intensive science that is accelerating the development of postgenomics diagnostics, particularly with use of accessible peripheral tissue compartments. Metabolomics involves the study of a comprehensive set of low molecular weight substances (metabolites) present in biological systems. The metabolite profiles represent the molecular phenotype of biological systems and reflect the information encoded at the genomic level and implemented at the transcriptomic and proteomic levels. Analysis of the human blood metabolite profile is a universal and highly promising tool for clinical postgenomics applications because it reflects both the endogenous and exogenous (environmental) factors influencing an individual organism. This article presents a critical synthesis and original analysis of both the technical implementation of metabolic profiling of blood and statistical analysis of metabolite profiles for effective disease diagnostics and risk assessment in the present postgenomics era. PMID:24044364
Nakabayashi, Ryo; Saito, Kazuki
The advent of the genome-editing era greatly increases the opportunities for synthetic biology research that aims to enhance production of potentially useful bioactive metabolites in heterologous hosts. A wide variety of sulfur (S)-containing metabolites (S-metabolites) are known to possess bioactivities and health-promoting properties, but finding them and their chemical assignment using mass spectrometry-based metabolomics has been difficult. In this review, we highlight recent advances on the targeted metabolomic analysis of S-metabolites (S-omics) in plants using ultrahigh resolution mass spectrometry. The use of exact mass and signal intensity differences between (32)S-containing monoisotopic ions and counterpart (34)S isotopic ions exploits an entirely new method to characterize S-metabolites. Finally, we discuss the availability of S-omics for synthetic biology.
Rai, Amit; Umashankar, Shivshankar; Swarup, Sanjay
Metabolomics is one of the most recent additions to the functional genomics approaches. It involves the use of analytical chemistry techniques to provide high-density data of metabolic profiles. Data is then analyzed using advanced statistics and databases to extract biological information, thus providing the metabolic phenotype of an organism. Large variety of metabolites produced by plants through the complex metabolic networks and their dynamic changes in response to various perturbations can be studied using metabolomics. Here, we describe the basic features of plant metabolic diversity and analytical methods to describe this diversity, which includes experimental workflows starting from experimental design, sample preparation, hardware and software choices, combined with knowledge extraction methods. Finally, we describe a scenario for using these workflows to identify differential metabolites and their pathways from complex biological samples.
Laparre, Jérôme; Kaabia, Zied; Mooney, Mark; Buckley, Tom; Sherry, Mark; Le Bizec, Bruno; Dervilly-Pinel, Gaud
Urine stability during storage is essential in metabolomics to avoid misleading conclusions or erroneous interpretations. Facing the lack of comprehensive studies on urine metabolome stability, the present work performed a follow-up of potential modifications in urinary chemical profile using LC-HRMS on the basis of two parameters: the storage temperature (+4 °C, -20 °C, -80 °C and freeze-dried stored at -80 °C) and the storage duration (5-144 days). Both HILIC and RP chromatographies have been implemented in order to globally monitor the urinary metabolome. Using an original data processing associated to univariate and multivariate data analysis, our study confirms that chemical profiles of urine samples stored at +4 °C are very rapidly modified, as observed for instance for compounds such as:N-acetyl Glycine, Adenosine, 4-Amino benzoic acid, N-Amino diglycine, creatine, glucuronic acid, 3-hydroxy-benzoic acid, pyridoxal, l-pyroglutamic acid, shikimic acid, succinic acid, thymidine, trigonelline and valeryl-carnitine, while it also demonstrates that urine samples stored at -20 °C exhibit a global stability over a long period with no major modifications compared to -80 °C condition. This study is the first to investigate long term stability of urine samples and report potential modifications in the urinary metabolome, using both targeted approach monitoring individually a large number (n > 200) of urinary metabolites and an untargeted strategy enabling assessing for global impact of storage conditions.
Background Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling. Results We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models using a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. The generated model of mycorrhizal metabolome predicts that the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose. Conclusions The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems. PMID:21569493
Li, Yihong; Li, Shanshan; Ai, Guomin; Wang, Weishan; Zhang, Buchang; Yang, Keqian
Streptomycetes produce many antibiotics and are important model microorgansims for scientific research and antibiotic production. Metabolomics is an emerging technological platform to analyze low molecular weight metabolites in a given organism qualitatively and quantitatively. Compared to other Omics platform, metabolomics has greater advantage in monitoring metabolic flux distribution and thus identifying key metabolites related to target metabolic pathway. The present work aims at establishing a rapid, accurate sample preparation protocol for metabolomics analysis in streptomycetes. In the present work, several sample preparation steps, including cell quenching time, cell separation method, conditions for metabolite extraction and metabolite derivatization were optimized. Then, the metabolic profiles of Streptomyces coelicolor during different growth stages were analyzed by GC-MS. The optimal sample preparation conditions were as follows: time of low-temperature quenching 4 min, cell separation by fast filtration, time of freeze-thaw 45 s/3 min and the conditions of metabolite derivatization at 40 degrees C for 90 min. By using this optimized protocol, 103 metabolites were finally identified from a sample of S. coelicolor, which distribute in central metabolic pathways (glycolysis, pentose phosphate pathway and citrate cycle), amino acid, fatty acid, nucleotide metabolic pathways, etc. By comparing the temporal profiles of these metabolites, the amino acid and fatty acid metabolic pathways were found to stay at a high level during stationary phase, therefore, these pathways may play an important role during the transition between the primary and secondary metabolism. An optimized protocol of sample preparation was established and applied for metabolomics analysis of S. coelicolor, 103 metabolites were identified. The temporal profiles of metabolites reveal amino acid and fatty acid metabolic pathways may play an important role in the transition from primary to
Zhu, Mingzhi; Liu, Ting; Guo, Mingquan
Lotus (Nelumbo nucifera), which is distributed widely throughout Asia, Australia and North America, is an aquatic perennial that has been cultivated for over 2,000 years. It is very stimulating that almost all parts of lotus have been consumed as vegetable as well as food, especially the seeds. Except for the nutritive values of lotus, there has been increasing interest in its potential as functional food due to its rich secondary metabolites, such as flavonoids and alkaloids. Not only have these metabolites greatly contributed to the biological process of lotus seeds, but also have been reported to possess multiple health-promoting effects, including antioxidant, anti-amnesic, anti-inflammatory, and anti-tumor activities. Thus, comprehensive metabolomic profiling of these metabolites is of key importance to help understand their biological activities, and other chemical biology features. In this context, this review will provide an update on the current technological platforms, and workflow associated with metabolomic studies on lotus seeds, as well as insights into the application of metabolomics for the improvement of food safety and quality, assisting breeding, and promotion of the study of metabolism and pharmacokinetics of lotus seeds; meanwhile it will also help explore new perspectives and outline future challenges in this fast-growing research subject. PMID:27379154
Armitage, Emily G; Barbas, Coral
Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted.
Background Hepatic lipidosis or fatty liver disease is a major metabolic disorder of high-producing dairy cows that compromises animal performance and, hence, causes heavy economic losses worldwide. This syndrome, occurring during the critical transition from gestation to early lactation, leads to an impaired health status, decreased milk yield, reduced fertility and shortened lifetime. Because the prevailing clinical chemistry parameters indicate advanced liver damage independently of the underlying disease, currently, hepatic lipidosis can only be ascertained by liver biopsy. We hypothesized that the condition of fatty liver disease may be accompanied by an altered profile of endogenous metabolites in the blood of affected animals. Results To identify potential small-molecule biomarkers as a novel diagnostic alternative, the serum samples of diseased dairy cows were subjected to a targeted metabolomics screen by triple quadrupole mass spectrometry. A subsequent multivariate test involving principal component and linear discriminant analyses yielded 29 metabolites (amino acids, phosphatidylcholines and sphingomyelines) that, in conjunction, were able to distinguish between dairy cows with no hepatic lipidosis and those displaying different stages of the disorder. Conclusions This proof-of-concept study indicates that metabolomic profiles, including both amino acids and lipids, distinguish hepatic lipidosis from other peripartal disorders and, hence, provide a promising new tool for the diagnosis of hepatic lipidosis. By generating insights into the molecular pathogenesis of hepatic lipidosis, metabolomics studies may also facilitate the prevention of this syndrome. PMID:24888604
Adkins, D E; McClay, J L; Vunck, S A; Batman, A M; Vann, R E; Clark, S L; Souza, R P; Crowley, J J; Sullivan, P F; van den Oord, E J C G; Beardsley, P M
Behavioral sensitization has been widely studied in animal models and is theorized to reflect neural modifications associated with human psychostimulant addiction. While the mesolimbic dopaminergic pathway is known to play a role, the neurochemical mechanisms underlying behavioral sensitization remain incompletely understood. In this study, we conducted the first metabolomics analysis to globally characterize neurochemical differences associated with behavioral sensitization. Methamphetamine (MA)-induced sensitization measures were generated by statistically modeling longitudinal activity data for eight inbred strains of mice. Subsequent to behavioral testing, nontargeted liquid and gas chromatography-mass spectrometry profiling was performed on 48 brain samples, yielding 301 metabolite levels per sample after quality control. Association testing between metabolite levels and three primary dimensions of behavioral sensitization (total distance, stereotypy and margin time) showed four robust, significant associations at a stringent metabolome-wide significance threshold (false discovery rate, FDR <0.05). Results implicated homocarnosine, a dipeptide of GABA and histidine, in total distance sensitization, GABA metabolite 4-guanidinobutanoate and pantothenate in stereotypy sensitization, and myo-inositol in margin time sensitization. Secondary analyses indicated that these associations were independent of concurrent MA levels and, with the exception of the myo-inositol association, suggest a mechanism whereby strain-based genetic variation produces specific baseline neurochemical differences that substantially influence the magnitude of MA-induced sensitization. These findings demonstrate the utility of mouse metabolomics for identifying novel biomarkers, and developing more comprehensive neurochemical models, of psychostimulant sensitization.
Edison, Arthur S.; Hall, Robert D.; Junot, Christophe; Karp, Peter D.; Kurland, Irwin J.; Mistrik, Robert; Reed, Laura K.; Saito, Kazuki; Salek, Reza M.; Steinbeck, Christoph; Sumner, Lloyd W.; Viant, Mark R.
Model organisms are an essential component of biological and biomedical research that can be used to study specific biological processes. These organisms are in part selected for facile experimental study. However, just as importantly, intensive study of a small number of model organisms yields important synergies as discoveries in one area of science for a given organism shed light on biological processes in other areas, even for other organisms. Furthermore, the extensive knowledge bases compiled for each model organism enable systems-level understandings of these species, which enhance the overall biological and biomedical knowledge for all organisms, including humans. Building upon extensive genomics research, we argue that the time is now right to focus intensively on model organism metabolomes. We propose a grand challenge for metabolomics studies of model organisms: to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model organisms, and to relate organism metabolic pathways within the context of evolutionary metabolomics, i.e., phylometabolomics. These efforts should focus on a series of established model organisms in microbial, animal and plant research. PMID:26891337
Silva, Catarina L.; Perestrelo, Rosa; Silva, Pedro; Tomás, Helena; Câmara, José S.
Breast cancer (BC) remains the most prevalent oncologic pathology in women, causing huge psychological, economic and social impacts on our society. Currently, the available diagnostic tools have limited sensitivity and specificity. Metabolome analysis has emerged as a powerful tool for obtaining information about the biological processes that occur in organisms, and is a useful platform for discovering new biomarkers or make disease diagnosis using different biofluids. Volatile organic compounds (VOCs) from the headspace of cultured BC cells and normal human mammary epithelial cells, were collected by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography combined with mass spectrometry (GC–MS), thus defining a volatile metabolomic signature. 2-Pentanone, 2-heptanone, 3-methyl-3-buten-1-ol, ethyl acetate, ethyl propanoate and 2-methyl butanoate were detected only in cultured BC cell lines. Multivariate statistical methods were used to verify the volatomic differences between BC cell lines and normal cells in order to find a set of specific VOCs that could be associated with BC, providing comprehensive insight into VOCs as potential cancer biomarkers. The establishment of the volatile fingerprint of BC cell lines presents a powerful approach to find endogenous VOCs that could be used to improve the BC diagnostic tools and explore the associated metabolomic pathways. PMID:28256598
Beatty, Perrin H.; Klein, Matthias S.; Fischer, Jeffrey J.; Lewis, Ian A.; Muench, Douglas G.; Good, Allen G.
A comprehensive understanding of plant metabolism could provide a direct mechanism for improving nitrogen use efficiency (NUE) in crops. One of the major barriers to achieving this outcome is our poor understanding of the complex metabolic networks, physiological factors, and signaling mechanisms that affect NUE in agricultural settings. However, an exciting collection of computational and experimental approaches has begun to elucidate whole-plant nitrogen usage and provides an avenue for connecting nitrogen-related phenotypes to genes. Herein, we describe how metabolomics, computational models of metabolism, and flux balance analysis have been harnessed to advance our understanding of plant nitrogen metabolism. We introduce a model describing the complex flow of nitrogen through crops in a real-world agricultural setting and describe how experimental metabolomics data, such as isotope labeling rates and analyses of nutrient uptake, can be used to refine these models. In summary, the metabolomics/computational approach offers an exciting mechanism for understanding NUE that may ultimately lead to more effective crop management and engineered plants with higher yields. PMID:27735856
Xu, Yong-Jiang; Luo, Feifei; Gao, Qiang; Shang, Yanfang; Wang, Chengshu
The interactions between insects and pathogenic fungi are complex. We employed metabolomic techniques to profile insect metabolic dynamics upon infection by the pathogenic fungus Beauveria bassiana. Silkworm larvae were infected with fungal spores and microscopic observations demonstrated that the exhaustion of insect hemocytes was coupled with fungal propagation in the insect body cavity. Metabolomic analyses revealed that fungal infection could significantly alter insect energy and nutrient metabolisms as well as the immune defense responses, including the upregulation of carbohydrates, amino acids, fatty acids, and lipids, but the downregulation of eicosanoids and amines. The insect antifeedant effect of the fungal infection was evident with the reduced level of maclurin (a component of mulberry leaves) in infected insects but elevated accumulations in control insects. Insecticidal and cytotoxic mycotoxins like oosporein and beauveriolides were also detected in insects at the later stages of infection. Taken together, the metabolomics data suggest that insect immune responses are energy-cost reactions and the strategies of nutrient deprivation, inhibition of host immune responses, and toxin production would be jointly employed by the fungus to kill insects. The data obtained in this study will facilitate future functional studies of genes and pathways associated with insect-fungus interactions.
Gray, Elizabeth; Larkin, James R.; Claridge, Tim D. W.; Talbot, Kevin; Sibson, Nicola R.; Turner, Martin R.
Neurochemical biomarkers are urgently sought in ALS. Metabolomic analysis of cerebrospinal fluid (CSF) using proton nuclear magnetic resonance (1H-NMR) spectroscopy is a highly sensitive method capable of revealing nervous system cellular pathology. The 1H-NMR CSF metabolomic signature of ALS was sought in a longitudinal cohort. Six-monthly serial collection was performed in ALS patients across a range of clinical sub-types (n = 41) for up to two years, and in healthy controls at a single time-point (n = 14). A multivariate statistical approach, partial least squares discriminant analysis, was used to determine differences between the NMR spectra from patients and controls. Significantly predictive models were found using those patients with at least one year's interval between recruitment and the second sample. Glucose, lactate, citric acid and, unexpectedly, ethanol were the discriminating metabolites elevated in ALS. It is concluded that 1H-NMR captured the CSF metabolomic signature associated with derangements in cellular energy utilization connected with ALS, and was most prominent in comparisons using patients with longer disease duration. The specific metabolites identified support the concept of a hypercatabolic state, possibly involving mitochondrial dysfunction specifically. Endogenous ethanol in the CSF may be an unrecognized novel marker of neuronal tissue injury in ALS. PMID:26121274
Heaney, Liam M; Deighton, Kevin; Suzuki, Toru
Metabolomics incorporates the study of metabolites that are produced and released through physiological processes at both the systemic and cellular levels. Biological compounds at the metabolite level are of paramount interest in the sport and exercise sciences, although research in this field has rarely been referred to with the global 'omics terminology. Commonly studied metabolites in exercise science are notably within cellular pathways for adenosine triphosphate production such as glycolysis (e.g., pyruvate and lactate), β-oxidation of free fatty acids (e.g., palmitate) and ketone bodies (e.g., β-hydroxybutyrate). Non-targeted metabolomic technologies are able to simultaneously analyse the large numbers of metabolites present in human biological samples such as plasma, urine and saliva. These analytical technologies predominately employ nuclear magnetic resonance spectroscopy and chromatography coupled to mass spectrometry. Performing experiments based on non-targeted methods allows for systemic metabolite changes to be analysed and compared to a particular physiological state (e.g., pre-/post-exercise) and provides an opportunity to prospect for metabolite signatures that offer beneficial information for translation into an exercise science context, for both elite performance and public health monitoring. This narrative review provides an introduction to non-targeted metabolomic technologies and discusses current and potential applications in sport and exercise science.
Larsen, Peter E.; Dai, Yang
Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.
Silva, Catarina L; Perestrelo, Rosa; Silva, Pedro; Tomás, Helena; Câmara, José S
Breast cancer (BC) remains the most prevalent oncologic pathology in women, causing huge psychological, economic and social impacts on our society. Currently, the available diagnostic tools have limited sensitivity and specificity. Metabolome analysis has emerged as a powerful tool for obtaining information about the biological processes that occur in organisms, and is a useful platform for discovering new biomarkers or make disease diagnosis using different biofluids. Volatile organic compounds (VOCs) from the headspace of cultured BC cells and normal human mammary epithelial cells, were collected by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography combined with mass spectrometry (GC-MS), thus defining a volatile metabolomic signature. 2-Pentanone, 2-heptanone, 3-methyl-3-buten-1-ol, ethyl acetate, ethyl propanoate and 2-methyl butanoate were detected only in cultured BC cell lines. Multivariate statistical methods were used to verify the volatomic differences between BC cell lines and normal cells in order to find a set of specific VOCs that could be associated with BC, providing comprehensive insight into VOCs as potential cancer biomarkers. The establishment of the volatile fingerprint of BC cell lines presents a powerful approach to find endogenous VOCs that could be used to improve the BC diagnostic tools and explore the associated metabolomic pathways.
Zhang, Yinfei; Wang, Yu; Ding, Zhaotang; Wang, Hui; Song, Lubin; Jia, Sisi; Ma, Dexin
The research of physiological responses to Zn stress in plants has been extensively studied. However, the ionomics and metabolomics responses of plants to Zn stress remain largely unknown. In present study, the nutrient elements were identified involved in ion homeostasis and metabolomics changes related to Zn deficiency or excess in tea plants. Nutrient element analysis demonstrated that the concentrations of Zn affected the ion-uptake in roots and the nutrient element transportation to leaves, leading to the different distribution of P, S, Al, Ca, Fe and Cu in the tea leaves or roots. Metabolomics analysis revealed that Zn deficiency or excess differentially influenced the metabolic pathways in the tea leaves. More specifically, Zn deficiency affected the metabolism of carbohydrates, and Zn excess affected flavonoids metabolism. Additionally, the results showed that both Zn deficiency and Zn excess led to reduced nicotinamide levels, which speeded up NAD(+) degradation and thus reduced energy metabolism. Furthermore, element-metabolite correlation analysis illustrated that Zn contents in the tea leaves were positively correlated with organic acids, nitrogenous metabolites and some carbohydrate metabolites, and negatively correlated with the metabolites involved in secondary metabolism and some other carbohydrate metabolites. Meanwhile, metabolite-metabolite correlation analysis demonstrated that organic acids, sugars, amino acids and flavonoids played dominant roles in the regulation of the tea leaf metabolism under Zn stress. Therefore, the conclusion should be drawn that the tea plants responded to Zn stress by coordinating ion-uptake and regulation of metabolism of carbohydrates, nitrogenous metabolites, and flavonoids.
Larsen, Peter E.; Dai, Yang
Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less
Laine, Jessica E; Bailey, Kathryn A; Olshan, Andrew F; Smeester, Lisa; Drobná, Zuzana; Stýblo, Miroslav; Douillet, Christelle; García-Vargas, Gonzalo; Rubio-Andrade, Marisela; Pathmasiri, Wimal; McRitchie, Susan; Sumner, Susan J; Fry, Rebecca C
Prenatal inorganic arsenic (iAs) exposure is associated with health effects evident at birth and later in life. An understanding of the relationship between prenatal iAs exposure and alterations in the neonatal metabolome could reveal critical molecular modifications, potentially underpinning disease etiologies. In this study, nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was used to identify metabolites in neonate cord serum associated with prenatal iAs exposure in participants from the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort, in Gómez Palacio, Mexico. Through multivariable linear regression, ten cord serum metabolites were identified as significantly associated with total urinary iAs and/or iAs metabolites, measured as %iAs, %monomethylated arsenicals (MMAs), and %dimethylated arsenicals (DMAs). A total of 17 metabolites were identified as significantly associated with total iAs and/or iAs metabolites in cord serum. These metabolites are indicative of changes in important biochemical pathways such as vitamin metabolism, the citric acid (TCA) cycle, and amino acid metabolism. These data highlight that maternal biotransformation of iAs and neonatal levels of iAs and its metabolites are associated with differences in neonate cord metabolomic profiles. The results demonstrate the potential utility of metabolites as biomarkers/indicators of in utero environmental exposure.
Edison, Arthur S; Hall, Robert D; Junot, Christophe; Karp, Peter D; Kurland, Irwin J; Mistrik, Robert; Reed, Laura K; Saito, Kazuki; Salek, Reza M; Steinbeck, Christoph; Sumner, Lloyd W; Viant, Mark R
Model organisms are an essential component of biological and biomedical research that can be used to study specific biological processes. These organisms are in part selected for facile experimental study. However, just as importantly, intensive study of a small number of model organisms yields important synergies as discoveries in one area of science for a given organism shed light on biological processes in other areas, even for other organisms. Furthermore, the extensive knowledge bases compiled for each model organism enable systems-level understandings of these species, which enhance the overall biological and biomedical knowledge for all organisms, including humans. Building upon extensive genomics research, we argue that the time is now right to focus intensively on model organism metabolomes. We propose a grand challenge for metabolomics studies of model organisms: to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model organisms, and to relate organism metabolic pathways within the context of evolutionary metabolomics, i.e., phylometabolomics. These efforts should focus on a series of established model organisms in microbial, animal and plant research.
Wang, Jeffrey H; Byun, Jaeman; Pennathur, Subramaniam
Phenotypic expression of renal diseases encompasses a complex interaction between genetic, environmental, and local tissue factors. The level of complexity requires integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate metabolites from biological samples. The small molecules represent the end result of complexity of biological processes in a given cell, tissue, or organ, and thus form attractive candidates to understand disease phenotypes. Metabolites represent a diverse group of low-molecular-weight structures including lipids, amino acids, peptides, nucleic acids, and organic acids, which makes comprehensive analysis a difficult analytical challenge. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled separation, characterization, detection, and quantification of such chemically diverse structures. Continued development of bioinformatics and analytical strategies will accelerate widespread use and integration of metabolomics into systems biology. Here, we will discuss analytical and bioinformatic techniques and highlight recent studies that use metabolomics in understanding pathophysiology of disease processes.
Mussap, Michele; Antonucci, Roberto; Noto, Antonio; Fanos, Vassilios
Metabolomics consists of the quantitative analysis of a large number of low molecular mass metabolites involving substrates or products in metabolic pathways existing in all living systems. The analysis of the metabolic profile detectable in a human biological fluid allows to instantly identify changes in the composition of endogenous and exogenous metabolites caused by the interaction between specific physiopathological states, gene expression, and environment. In pediatrics and neonatology, metabolomics offers new encouraging perspectives for the improvement of critically ill patient outcome, for the early recognition of metabolic profiles associated with the development of diseases in the adult life, and for delivery of individualized medicine. In this view, nutrimetabolomics, based on the recognition of specific cluster of metabolites associated with nutrition and pharmacometabolomics, based on the capacity to personalize drug therapy by analyzing metabolic modifications due to therapeutic treatment may open new frontiers in the prevention and in the treatment of pediatric and neonatal diseases. This review summarizes the most relevant results published in the literature on the application of metabolomics in pediatric and neonatal clinical settings. However, there is the urgent need to standardize physiological and preanalytical variables, analytical methods, data processing, and result presentation, before establishing the definitive clinical value of results.
Rhee, Eugene P.; Yang, Qiong; Yu, Bing; Liu, Xuan; Cheng, Susan; Deik, Amy; Pierce, Kerry A.; Bullock, Kevin; Ho, Jennifer E.; Levy, Daniel; Florez, Jose C.; Kathiresan, Sek; Larson, Martin G.; Vasan, Ramachandran S.; Clish, Clary B.; Wang, Thomas J.; Boerwinkle, Eric; O'Donnell, Christopher J.; Gerszten, Robert E.
The study of rare variants may enhance our understanding of the genetic determinants of the metabolome. Here, we analyze the association between 217 plasma metabolites and exome variants on the Illumina HumanExome Beadchip in 2,076 participants in the Framingham Heart Study, with replication in 1,528 participants of the Atherosclerosis Risk in Communities Study. We identify an association between GMPS and xanthosine using single variant analysis and associations between HAL and histidine, PAH and phenylalanine, and UPB1 and ureidopropionate using gene-based tests (P<5 × 10−8 in meta-analysis), highlighting novel coding variants that may underlie inborn errors of metabolism. Further, we show how an examination of variants across the spectrum of allele frequency highlights independent association signals at select loci and generates a more integrated view of metabolite heritability. These studies build on prior metabolomics genome wide association studies to provide a more complete picture of the genetic architecture of the plasma metabolome. PMID:27453504
Zhang, Yumin; Zhang, Siwen; Wang, Guixia
Diabetic kidney disease (DKD) is generally characterized by increasing albuminuria in diabetic patients; however, few biomarkers are available to facilitate early diagnosis of this disease. The application of metabolomics has shown promises addressing this need. In this review, we conducted a search about metabolomic biomarkers in DKD patients through MEDLINE, EMBASE, and Cochrane Database up to the end of March, 2015. 12 eligible studies were selected and evaluated subsequently through the use of QUADOMICS, a quality assessment tool. 7 of the 12 included studies were classified as 'high quality'. We also recorded specific study characteristics including participants' characteristics, metabolomic techniques, sample types, and significantly altered metabolites between DKD and control groups. Products of lipid metabolisms including esterified and non-esterified fatty acids, carnitines, phospholipids and metabolites involved in branch-chained amino acids and aromatic amino acids metabolisms were frequently affected biomarkers of DKD. Other differential metabolites were also found, while some of their associations with DKD were unclear. Further more studies are required to test these findings in larger, diverse ethnic populations with elaborate study designs, and finally we could translate them into the benefits of DKD patients.
MacMillan, Heath A.; Knee, Jose M.; Dennis, Alice B.; Udaka, Hiroko; Marshall, Katie E.; Merritt, Thomas J. S.; Sinclair, Brent J.
Cold tolerance is a key determinant of insect distribution and abundance, and thermal acclimation can strongly influence organismal stress tolerance phenotypes, particularly in small ectotherms like Drosophila. However, there is limited understanding of the molecular and biochemical mechanisms that confer such impressive plasticity. Here, we use high-throughput mRNA sequencing (RNA-seq) and liquid chromatography – mass spectrometry (LC-MS) to compare the transcriptomes and metabolomes of D. melanogaster acclimated as adults to warm (rearing) (21.5 °C) or cold conditions (6 °C). Cold acclimation improved cold tolerance and led to extensive biological reorganization: almost one third of the transcriptome and nearly half of the metabolome were differentially regulated. There was overlap in the metabolic pathways identified via transcriptomics and metabolomics, with proline and glutathione metabolism being the most strongly-supported metabolic pathways associated with increased cold tolerance. We discuss several new targets in the study of insect cold tolerance (e.g. dopamine signaling and Na+-driven transport), but many previously identified candidate genes and pathways (e.g. heat shock proteins, Ca2+ signaling, and ROS detoxification) were also identified in the present study, and our results are thus consistent with and extend the current understanding of the mechanisms of insect chilling tolerance. PMID:27357258
Hajduk, Joanna; Klupczynska, Agnieszka; Dereziński, Paweł; Matysiak, Jan; Kokot, Piotr; Nowak, Dorota M.; Gajęcka, Marzena; Nowak-Markwitz, Ewa; Kokot, Zenon J.
The aim of this pilot study was to apply a novel combined metabolomic and proteomic approach in analysis of gestational diabetes mellitus. The investigation was performed with plasma samples derived from pregnant women with diagnosed gestational diabetes mellitus (n = 18) and a matched control group (n = 13). The mass spectrometry-based analyses allowed to determine 42 free amino acids and low molecular-weight peptide profiles. Different expressions of several peptides and altered amino acid profiles were observed in the analyzed groups. The combination of proteomic and metabolomic data allowed obtaining the model with a high discriminatory power, where amino acids ethanolamine, l-citrulline, l-asparagine, and peptide ions with m/z 1488.59; 4111.89 and 2913.15 had the highest contribution to the model. The sensitivity (94.44%) and specificity (84.62%), as well as the total group membership classification value (90.32%) calculated from the post hoc classification matrix of a joint model were the highest when compared with a single analysis of either amino acid levels or peptide ion intensities. The obtained results indicated a high potential of integration of proteomic and metabolomics analysis regardless the sample size. This promising approach together with clinical evaluation of the subjects can also be used in the study of other diseases. PMID:26694367
Benton, H. Paul; Ivanisevic, Julijana; Mahieu, Nathaniel G.; ...
An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. We can analyze large profiling datasets and simultaneously obtain structural identifications, as a result of this unique integration. Furthermore, validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometrymore » data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.« less
Yang, Kai; Xia, Bairong; Wang, Wenjie; Cheng, Jinlong; Yin, Mingzhu; Xie, Hongyu; Li, Junnan; Ma, Libing; Yang, Chunyan; Li, Ang; Fan, Xin; Dhillon, Harman S.; Hou, Yan; Lou, Ge; Li, Kang
Cervical cancer (CC) still remains a common and deadly malignancy among females in developing countries. More accurate and reliable diagnostic methods/biomarkers should be discovered. In this study, we performed a comprehensive analysis of metabolomics (285 samples) and transcriptomics (52 samples) on the potential diagnostic implication and metabolic characteristic description in cervical cancer. Sixty-two metabolites were different between CC and normal controls (NOR), in which 5 metabolites (bilirubin, LysoPC(17:0), n-oleoyl threonine, 12-hydroxydodecanoic acid and tetracosahexaenoic acid) were selected as candidate biomarkers for CC. The AUC value, sensitivity (SE), and specificity (SP) of these 5 biomarkers were 0.99, 0.98 and 0.99, respectively. We further analysed the genes in 7 significantly enriched pathways, of which 117 genes, that were expressed differentially, were mainly involved in catalytic activity. Finally, a fully connected network of metabolites and genes in these pathways was built, which can increase the credibility of our selected metabolites. In conclusion, our biomarkers from metabolomics could set a path for CC diagnosis and screening. Our results also showed that variables of both transcriptomics and metabolomics were associated with CC. PMID:28225065
This presentation gives a brief introduction to EPA's computational toxicology program and the Athens Lab's role in it. The talk also covered a brief introduction to metabolomics; advantages/disadvanage of metabolomics for toxicity assessment; goals of the EPA Athens metabolomics...
Llorach, Rafael; Garrido, Ignacio; Monagas, Maria; Urpi-Sarda, Mireia; Tulipani, Sara; Bartolome, Begona; Andres-Lacueva, Cristina
Almond, as a part of the nut family, is an important source of biological compounds, and specifically, almond skins have been considered an important source of polyphenols, including flavan-3-ols and flavonols. Polyphenol metabolism may produce several classes of metabolites that could often be more biologically active than their dietary precursor and could also become a robust new biomarker of almond polyphenol intake. In order to study urinary metabolome modifications during the 24 h after a single dose of almond skin extract, 24 volunteers (n = 24), who followed a polyphenol-free diet for 48 h before and during the study, ingested a dietary supplement of almond skin phenolic compounds (n = 12) or a placebo (n = 12). Urine samples were collected before ((-2)-0 h) and after (0-2 h, 2-6 h, 6-10 h, and 10-24 h) the intake and were analyzed by liquid chromatography-mass spectrometry (LC-q-TOF) and multivariate statistical analysis (principal component analysis (PCA) and orthogonal projection to latent structures (OPLS)). Putative identification of relevant biomarkers revealed a total of 34 metabolites associated with the single dose of almond extract, including host and, in particular, microbiota metabolites. As far as we know, this is the first time that conjugates of hydroxyphenylvaleric, hydroxyphenylpropionic, and hydroxyphenylacetic acids have been identified in human samples after the consumption of flavan-3-ols through a metabolomic approach. The results showed that this non-targeted approach could provide new intake biomarkers, contributing to the development of the food metabolome as an important part of the human urinary metabolome.
Meller, Sebastian; Meyer, Hellmuth-A; Bethan, Bianca; Dietrich, Dimo; Maldonado, Sandra González; Lein, Michael; Montani, Matteo; Reszka, Regina; Schatz, Philipp; Peter, Erik; Stephan, Carsten; Jung, Klaus; Kamlage, Beate; Kristiansen, Glen
Integrated analysis of metabolomics, transcriptomics and immunohistochemistry can contribute to a deeper understanding of biological processes altered in cancer and possibly enable improved diagnostic or prognostic tests. In this study, a set of 254 metabolites was determined by gas-chromatography/liquid chromatography-mass spectrometry in matched malignant and non-malignant prostatectomy samples of 106 prostate cancer (PCa) patients. Transcription analysis of matched samples was performed on a set of 15 PCa patients using Affymetrix U133 Plus 2.0 arrays. Expression of several proteins was immunohistochemically determined in 41 matched patient samples and the association with clinico-pathological parameters was analyzed by an integrated data analysis. These results further outline the highly deregulated metabolism of fatty acids, sphingolipids and polyamines in PCa. For the first time, the impact of the ERG translocation on the metabolome was demonstrated, highlighting an altered fatty acid oxidation in TMPRSS2-ERG translocation positive PCa specimens. Furthermore, alterations in cholesterol metabolism were found preferentially in high grade tumors, enabling the cells to create energy storage. With this integrated analysis we could not only confirm several findings from previous metabolomic studies, but also contradict others and finally expand our concepts of deregulated biological pathways in PCa.
Tubers of potato (Solanum tuberosum L. cv. Estima) genetically modified (GM) to reduce polyphenol oxidase (PPO) activity and enzymatic discolouration were assessed for changes in the metabolome using Liquid Chromatography-Mass Spectrometry (LC-MS) and Gas Chromatography (GC)-MS. Metabolome changes ...
McRae, C.; Sharma, V.; Fisher, J.
Background. This paper presents the literature on biomarkers of in vitro fertilisation (IVF) outcome, demonstrating the progression of these studies towards metabolite profiling, specifically metabolomics. The need for more, and improved, metabolomics studies in the field of assisted conception is discussed. Methods. Searches were performed on ISI Web of Knowledge SM for literature associated with biomarkers of oocyte and embryo quality, and biomarkers of IVF outcome in embryo culture medium, follicular fluid (FF), and blood plasma in female mammals. Results. Metabolomics in the field of female reproduction is still in its infancy. Metabolomics investigations of embryo culture medium for embryo selection have been the most common, but only within the last five years. Only in 2012 has the first metabolomics investigation of FF for biomarkers of oocyte quality been reported. The only metabolomics studies of human blood plasma in this context have been aimed at identifying women with polycystic ovary syndrome (PCOS). Conclusions. Metabolomics is becoming more established in the field of assisted conception, but the studies performed so far have been preliminary and not all potential applications have yet been explored. With further improved metabolomics studies, the possibility of identifying a method for predicting IVF outcome may become a reality. PMID:25763388
Metabolomics is now being widely used to obtain complementary information to genomic and proteomic studies. Among the various approaches used in metabolomics, NMR spectroscopy is particularly powerful, in part because it is relatively non-selective, and is amenable to the study o...
Measurement of changes in endogenous metabolites via 1H-NMR-based metabolomics has shown great potential for assessing organisms exposed to environmental pollutants, and thus could aid the efforts of risk assessors. However, to date, the application of metabolomics to ecologi...
Steinbeck, Christoph; Conesa, Pablo; Haug, Kenneth; Mahendraker, Tejasvi; Williams, Mark; Maguire, Eamonn; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Salek, Reza M; Griffin, Julian L
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.
This Think Tank identified resources that can be used collaboratively across prospective cohorts; developed strategies to leverage resources for advancing the use of metabolomics in prospective cohort studies; identified the best strategies for performing analyses using metabolomics data across multiple studies; and, established a collaborative group that will identify and tackle research projects that cannot be effectively investigated by one independent group.
McGhie, Tony K; Rowan, Daryl D
Metabolomics, comprehensive metabolite analysis, is finding increasing application as a tool to measure and enable the manipulation of the phytochemical content of foods, to identify the measures of dietary intake, and to understand human and animal responses to phytochemicals in the diet. Recent applications of metabolomics directed toward understanding the role of phytochemicals in food and nutrition are reviewed.
Metabolites are involved in a diverse range of intracellular processes, including a cell’s response to a changing extracellular environment. Using single-cell capillary electrophoresis coupled to electrospray ionization mass spectrometry, we investigated how placing individual identified neurons in culture affects their metabolic profile. First, glycerol-based cell stabilization was evaluated using metacerebral neurons from Aplysia californica; the measurement error was reduced from ∼24% relative standard deviation to ∼6% for glycerol-stabilized cells compared to those isolated without glycerol stabilization. In order to determine the changes induced by culturing, 14 freshly isolated and 11 overnight-cultured neurons of two metabolically distinct cell types from A. californica, the B1 and B2 buccal neurons, were characterized. Of the more than 300 distinctive cell-related signals detected, 35 compounds were selected for their known biological roles and compared among each measured cell. Unsupervised multivariate and statistical analysis revealed robust metabolic differences between these two identified neuron types. We then compared the changes induced by overnight culturing; metabolite concentrations were distinct for 26 compounds in the cultured B1 cells. In contrast, culturing had less influence on the metabolic profile of the B2 neurons, with only five compounds changing significantly. As a result of these culturing-induced changes, the metabolic composition of the B1 neurons became indistinguishable from the cultured B2 cells. This observation suggests that the two cell types differentially regulate their in vivo or in vitro metabolomes in response to a changing environment. PMID:23077722
Bovo, S; Mazzoni, G; Calò, D G; Galimberti, G; Fanelli, F; Mezzullo, M; Schiavo, G; Scotti, E; Manisi, A; Samoré, A B; Bertolini, F; Trevisi, P; Bosi, P; Dall'Olio, S; Pagotto, U; Fontanesi, L
Metabolomics has opened new possibilities to investigate metabolic differences among animals. In this study, we applied a targeted metabolomic approach to deconstruct the pig sex metabolome as defined by castrated males and entire gilts. Plasma from 545 performance-tested Italian Large White pigs (172 castrated males and 373 females) sampled at about 160 kg live weight were analyzed for 186 metabolites using the Biocrates AbsoluteIDQ p180 Kit. After filtering, 132 metabolites (20 AA, 11 biogenic amines, 1 hexose, 13 acylcarnitines, 11 sphingomyelins, 67 phosphatidylcholines, and 9 lysophosphatidylcholines) were retained for further analyses. The multivariate approach of the sparse partial least squares discriminant analysis was applied, together with a specifically designed statistical pipeline, that included a permutation test and a 10 cross-fold validation procedure that produced stability and effect size statistics for each metabolite. Using this approach, we identified 85 biomarkers (with metabolites from all analyzed chemical families) that contributed to the differences between the 2 groups of pigs ( < 0.05 at the stability statistic test). All acylcarnitines and almost all biogenic amines were higher in castrated males than in gilts. Metabolites involved in tryptophan catabolism had the largest differences (i.e., delta = 20% for serotonin) between castrated males (higher) and gilts (lower). The level of several AA (Ala, Arg, Gly, His, Lys, Ser, Thr, and Trp) was higher in gilts (delta was from approximately 1.0 to approximately 4.8%) whereas products of AA catabolism (taurine, 2-aminoadipic acid, and methionine sulfoxide) were higher in castrated males (delta was approximately 5.0-6.0%), suggesting a metabolic shift in castrated males toward energy storage and lipid production. Similar general patterns were observed for most sphingomyelins, phosphatidylcholines, and lysophosphatidylcholines. Metabolomic pathway analysis and pathway enrichment identified
Li, Jinling; Xu, Weitong; Liang, Yibiao; Wang, Hui
Metabolomics is a powerful emerging tool for the identification of biomarkers and the exploration of metabolic pathways in a high-throughput manner. As an administration site for percutaneous absorption, the skin has a variety of metabolic enzymes, except other than hepar. However, technologies to fully detect dermal metabolites remain lacking. Skin metabolomics studies have mainly focused on the regulation of dermal metabolites by drugs or on the metabolism of drugs themselves. Skin metabolomics techniques include collection and preparation of skin samples, data collection, data processing and analysis. Furthermore, studying dermal metabolic effects via metabolomics can provide novel explanations for the pathogenesis of some dermatoses and unique insights for designing targeted prodrugs, promoting drug absorption and controlling drug concentration. This paper reviews current progress in the field of skin metabolomics, with a specific focus on dermal drug delivery systems and dermatosis.
Chokkathukalam, Achuthanunni; Kim, Dong-Hyun; Barrett, Michael P; Breitling, Rainer; Creek, Darren J
The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics. PMID:24568354
Yu, Tao; Wang, Yongtao; Zhang, Huizhen; Johnson, Caroline H; Jiang, Yiming; Li, Xiangjun; Wu, Zeming; Liu, Tian; Krausz, Kristopher W; Yu, Aiming; Gonzalez, Frank J; Huang, Min; Bi, Huichang
Mycoplasma contamination is a common problem in cell culture and can alter cellular functions. Since cell metabolism is either directly or indirectly involved in every aspect of cell function, it is important to detect changes to the cellular metabolome after mycoplasma infection. In this study, liquid chromatography mass spectrometry (LC/MS)-based metabolomics was used to investigate the effect of mycoplasma contamination on the cellular metabolism of human pancreatic carcinoma cells (PANC-1). Multivariate analysis demonstrated that mycoplasma contamination induced significant metabolic changes in PANC-1 cells. Twenty-three metabolites were identified and found to be involved in arginine and purine metabolism and energy supply. This study demonstrates that mycoplasma contamination significantly alters cellular metabolite levels, confirming the compelling need for routine checking of cell cultures for mycoplasma contamination, particularly when used for metabolomics studies. Graphical abstract Metabolomics reveals mycoplasma contamination changes the metabolome of PANC-1 cells.
Brown, Jeffrey N; Samuelsson, Linda; Bernardi, Giuliana; Gooneratne, Ravi; Larsson, D G Joakim
The common pasture earthworm Aporrectodea caliginosa has often been neglected in environmental metabolomics in favor of species easily bred in the laboratory. The present study assigns aqueous metabolites in A. caliginosa using high-resolution 1- and 2-dimensional nuclear magnetic resonance (NMR) spectroscopy. In total, 51 aqueous metabolites were identified, including typical amino acids (alanine, leucine, asparagine, phenylalanine), sugars (maltose, glucose), the dominant earthworm-specific 2-hexyl-5-ethyl-furansulfonate, and several previously unreported metabolites (oxoglutarate, putrescine). Examining the lesser-known earthworm lipid metabolome showed various lipid fatty acyl chains, cholesterol, and phosphatidylcholine. To briefly test if the NMR metabolomic techniques could differentiate A. caliginosa from different sites, earthworms were collected from 2 adjacent farms. Orthogonal partial least squares discriminant analysis detected metabolomic differences, suggesting the worms from the 2 sites differed in their energy metabolism, as indicated by altered levels of alanine, glutamine, glutamate, malate, fumarate, and lipids. Evidence of greater utilization of lipid energy reserves and onset of protein catabolism was also present. While the precise cause of the metabolomic differences could not be determined, the results show the potential of this species for further environmental metabolomic studies.
Mervaala, Eero; Biala, Agnieszka; Merasto, Saara; Lempiäinen, Juha; Mattila, Ismo; Martonen, Essi; Eriksson, Ove; Louhelainen, Marjut; Finckenberg, Piet; Kaheinen, Petri; Muller, Dominik N; Luft, Friedrich C; Lapatto, Risto; Oresic, Matej
Angiotensin II (Ang II) induces mitochondrial dysfunction. We tested whether Ang II alters the "metabolomic" profile. We harvested hearts from 8-week-old double transgenic rats harboring human renin and angiotensinogen genes (dTGRs) and controls (Sprague-Dawley), all with or without Ang II type 1 receptor (valsartan) blockade. We used gas chromatography coupled with time-of-flight mass spectrometry to detect 247 intermediary metabolites. We used a partial least-squares discriminate analysis and identified 112 metabolites that differed significantly after corrections (false discovery rate q <0.05). We found great differences in the use of fatty acids as an energy source, namely, decreased levels of octanoic, oleic, and linoleic acids in dTGR (all P<0.01). The increase in cardiac hypoxanthine levels in dTGRs suggested an increase in purine degradation, whereas other changes supported an increased ketogenic amino acid tyrosine level, causing energy production failure. The metabolomic profile of valsartan-treated dTGRs more closely resembled Sprague-Dawley rats than untreated dTGRs. Mitochondrial respiratory chain activity of cytochrome C oxidase was decreased in dTGRs, whereas complex I and complex II were unaltered. Mitochondria from dTGR hearts showed morphological alterations suggesting increased mitochondrial fusion. Cardiac expression of the redox-sensitive and the cardioprotective metabolic sensor sirtuin 1 was increased in dTGRs. Interestingly, valsartan changed the level of 33 metabolites and induced mitochondrial biogenesis in Sprague-Dawley rats. Thus, distinct patterns of cardiac substrate use in Ang II-induced cardiac hypertrophy are associated with mitochondrial dysfunction. The finding underscores the importance of Ang II in the regulation of mitochondrial biogenesis and cardiac metabolomics, even in healthy hearts.
Parman, Toufan; Bunin, Deborah I.; Ng, Hanna H.; McDunn, Jonathan E.; Wulff, Jacob E.; Wang, Abraham; Swezey, Robert; Rasay, Laura; Fairchild, David G.; Kapetanovic, Izet M.; Green, Carol E.
Pentamethyl-6-chromanol (PMCol), a chromanol-type compound related to vitamin E, was proposed as an anticancer agent with activity against androgen-dependent cancers. In repeat dose-toxicity studies in rats and dogs, PMCol caused hepatotoxicity, nephrotoxicity, and hematological effects. The objectives of this study were to determine the mechanisms of the observed toxicity and identify sensitive early markers of target organ injury by integrating classical toxicology, toxicogenomics, and metabolomic approaches. PMCol was administered orally to male Sprague-Dawley rats at 200 and 2000 mg/kg daily for 7 or 28 days. Changes in clinical chemistry included elevated alanine aminotransferase, total bilirubin, cholesterol and triglycerides—indicative of liver toxicity that was confirmed by microscopic findings (periportal hepatocellular hydropic degeneration and cytomegaly) in treated rats. Metabolomic evaluations of liver revealed time- and dose-dependent changes, including depletion of total glutathione and glutathione conjugates, decreased methionine, and increased S-adenosylhomocysteine, cysteine, and cystine. PMCol treatment also decreased cofactor levels, namely, FAD and increased NAD(P)+. Microarray analysis of liver found that differentially expressed genes were enriched in the glutathione and cytochrome P450 pathways by PMCol treatment. Reverse transcription-polymerase chain reaction of six upregulated genes and one downregulated gene confirmed the microarray results. In conclusion, the use of metabolomics and toxicogenomics demonstrates that chronic exposure to high doses of PMCol induces liver damage and dysfunction, probably due to both direct inhibition of glutathione synthesis and modification of drug metabolism pathways. Depletion of glutathione due to PMCol exposure ultimately results in a maladaptive response, increasing the consumption of hepatic dietary antioxidants and resulting in elevated reactive oxygen species levels associated with hepatocellular
Cai, Qingpo; Alvarez, Jessica A; Kang, Jian; Yu, Tianwei
Untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry (LC-MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC-MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.
Frediani, Jennifer K.; Jones, Dean P.; Tukvadze, Nestan; Uppal, Karan; Sanikidze, Eka; Kipiani, Maia; Tran, ViLinh T.; Hebbar, Gautam; Walker, Douglas I.; Kempker, Russell R.; Kurani, Shaheen S.; Colas, Romain A.; Dalli, Jesmond; Tangpricha, Vin; Serhan, Charles N.; Blumberg, Henry M.; Ziegler, Thomas R.
We aimed to characterize metabolites during tuberculosis (TB) disease and identify new pathophysiologic pathways involved in infection as well as biomarkers of TB onset, progression and resolution. Such data may inform development of new anti-tuberculosis drugs. Plasma samples from adults with newly diagnosed pulmonary TB disease and their matched, asymptomatic, sputum culture-negative household contacts were analyzed using liquid chromatography high-resolution mass spectrometry (LC-MS) to identify metabolites. Statistical and bioinformatics methods were used to select accurate mass/charge (m/z) ions that were significantly different between the two groups at a false discovery rate (FDR) of q<0.05. Two-way hierarchical cluster analysis (HCA) was used to identify clusters of ions contributing to separation of cases and controls, and metabolomics databases were used to match these ions to known metabolites. Identity of specific D-series resolvins, glutamate and Mycobacterium tuberculosis (Mtb)-derived trehalose-6-mycolate was confirmed using LC-MS/MS analysis. Over 23,000 metabolites were detected in untargeted metabolomic analysis and 61 metabolites were significantly different between the two groups. HCA revealed 8 metabolite clusters containing metabolites largely upregulated in patients with TB disease, including anti-TB drugs, glutamate, choline derivatives, Mycobacterium tuberculosis-derived cell wall glycolipids (trehalose-6-mycolate and phosphatidylinositol) and pro-resolving lipid mediators of inflammation, known to stimulate resolution, efferocytosis and microbial killing. The resolvins were confirmed to be RvD1, aspirin-triggered RvD1, and RvD2. This study shows that high-resolution metabolomic analysis can differentiate patients with active TB disease from their asymptomatic household contacts. Specific metabolites upregulated in the plasma of patients with active TB disease, including Mtb-derived glycolipids and resolvins, have potential as biomarkers
Wolfender, Jean-Luc; Rudaz, Serge; Choi, Young Hae; Kim, Hye Kyong
Metabolomics is playing an increasingly important role in plant science. It aims at the comprehensive analysis of the plant metabolome which consists both of primary and secondary metabolites. The goal of metabolomics is ultimately to identify and quantify this wide array of small molecules in biological samples. This new science is included in several systems biology approaches and is based primarily on the unbiased acquisition of mass spectrometric (MS) or nuclear magnetic resonance (NMR) data from carefully selected samples. This approach provides the most ''functional'' information of the 'omics' technologies of a given organism since metabolites are the end products of the cellular regulatory processes. The application of state-of-the-art data mining, that includes various untargeted and targeted multivariate data analysis methods, to the vast amount of data generated by this data-driven approach leads to sample classification and the identification of relevant biomarkers. The biological areas that have been successfully studied by this holistic approach include global metabolite composition assessment, mutant and phenotype characterisation, taxonomy, developmental processes, stress response, interaction with the environment, quality control assessment, lead finding and mode of action of botanicals. This review summarises the main MS- and NMR-based approaches that are used to perform these studies and discusses the potential and current limitations of the various methods. The intent is not to provide an exhaustive overview of the field, which has grown considerably over the past decade, but to summarise the main strategies that are used and to discuss the potential and limitations of the different approaches as well as future trends.
Denery, Judith R; Nunes, Ashlee A K; Dickerson, Tobin J
Large-scale proteomic and metabolomic technologies are increasingly gaining attention for their use in the diagnosis of human disease. In order to ensure the statistical power of relevant markers, such analyses must incorporate a large number of representative samples. While in a best-case scenario these samples are collected through a study design that is specifically tailored for the desired analysis, often studies must rely upon the analysis of large numbers of previously banked samples that may or may not have complete and accurate documentation of their associated collection and storage methods. In this study, several human blood matrices were analyzed and compared for the quality of metabolomic output. The sample types that were tested include plasma prepared with a variety of anticoagulants and serum collected by venipuncture and capillary blood collection protocols. Analysis with liquid chromatography-mass spectrometry (LC-MS) revealed only subtle differences between the various plasma preparation methods. Differences between the serum and plasma samples appear to be largely peptide/protein-based and are consistent with the biological distinction of the two matrices. Interestingly, the small molecule lysophosphatidylinositol was found to be in higher abundance in plasma, as a possible consequence of the effect of the intrinsic clotting cascade on adjacent metabolic pathways. Comparison of the small-molecule profiles of the capillary- and venipuncture-collected samples revealed 23 statistically significant compound differences between these sample types. Most of these features can be attributed to surfactants and detergents used to pretreat the skin in order to maintain the sterility of sample collection. However, several have identical mass and molecular formulas as endogenous human metabolites and could be erroneously attributed to actual metabolic perturbations. Understanding the extent of these matrix effects is important for control of systematic bias
Background Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species. Results To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins. Conclusion Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization
Sun, Xiaoliang; Länger, Bettina; Weckwerth, Wolfram
Inferring dynamics of metabolic networks directly from metabolomics data provides a promising way to elucidate the underlying mechanisms of biological systems, as reported in our previous studies (Weckwerth, 2011; Sun and Weckwerth, 2012; Nägele et al., 2014) by a differential Jacobian approach. The Jacobian is solved from an overdetermined system of equations as JC + CJT = −2D, called Lyapunov Equation in its generic form,1 where J is the Jacobian, C is the covariance matrix of metabolomics data, and D is the fluctuation matrix. Lyapunov Equation can be further simplified as the linear form Ax = b. Frequently, this linear equation system is ill-conditioned, i.e., a small variation in the right side b results in a big change in the solution x, thus making the solution unstable and error-prone. At the same time, inaccurate estimation of covariance matrix and uncertainties in the fluctuation matrix bring biases to the solution x. Here, we first reviewed common approaches to circumvent the ill-conditioned problems, including total least squares, Tikhonov regularization, and truncated singular value decomposition. Then, we benchmarked these methods on several in silico kinetic models with small to large perturbations on the covariance and fluctuation matrices. The results identified that the accuracy of the reverse Jacobian is mainly dependent on the condition number of A, the perturbation amplitude of C, and the stiffness of the kinetic models. Our research contributes a systematical comparison of methods to inversely solve Jacobian from metabolomics data. PMID:26636075
Kim, Kyoungmi; Mall, Christine; Taylor, Sandra L; Hitchcock, Stacie; Zhang, Chen; Wettersten, Hiromi I; Jones, A Daniel; Chapman, Arlene; Weiss, Robert H
While metabolomics has tremendous potential for diagnostic biomarker and therapeutic target discovery, its utility may be diminished by the variability that occurs due to environmental exposures including diet and the influences of the human circadian rhythm. For successful translation of metabolomics findings into the clinical setting, it is necessary to exhaustively define the sources of metabolome variation. To address these issues and to measure the variability of urinary and plasma metabolomes throughout the day, we have undertaken a comprehensive inpatient study in which we have performed non-targeted metabolomics analysis of blood and urine in 26 volunteers (13 healthy subjects with no known disease and 13 healthy subjects with autosomal dominant polycystic kidney disease not taking medication). These individuals were evaluated in a clinical research facility on two separate occasions, over three days, while on a standardized, weight-based diet. Subjects provided pre- and post-prandial blood and urine samples at the same time of day, and all samples were analyzed by "fast lane" LC-MS-based global metabolomics. The largest source of variability in blood and urine metabolomes was attributable to technical issues such as sample preparation and analysis, and less variability was due to biological variables, meals, and time of day. Higher metabolome variability was observed after the morning as compared to the evening meal, yet day-to-day variability was minimal and urine metabolome variability was greater than that of blood. Thus we suggest that blood and urine are suitable biofluids for metabolomics studies, though nontargeted mass spectrometry alone may not offer sufficient precision to reveal subtle changes in the metabolome. Additional targeted analyses may be needed to support the data from nontargeted mass spectrometric analyses. In light of these findings, future metabolomics studies should consider these sources of variability to allow for appropriate
Rosas, Herminia D; Doros, Gheorghe; Bhasin, Swati; Thomas, Beena; Gevorkian, Sona; Malarick, Keith; Matson, Wayne; Hersch, Steven M
Objective Huntington’s disease (HD) is a rare neurodegenerative disease caused by the expansion of an N-terminal repeat in the huntingtin protein. The protein is expressed in all cells in the body; hence, peripheral tissues, such as blood, may recapitulate processes in the brain. The plasma metabolome may provide a window into active processes that influence brain health and a unique opportunity to noninvasively identify processes that may contribute to neurodegeneration. Alterations in metabolic pathways in brain have been shown to profoundly impact HD. Therefore, identification and quantification of critical metabolomic perturbations could provide novel biomarkers for disease onset and disease progression. Methods We analyzed the plasma metabolomic profiles from 52 premanifest (PHD), 102 early symptomatic HD, and 140 healthy controls (NC) using liquid chromatography coupled with a highly sensitive electrochemical detection platform. Results Alterations in tryptophan, tyrosine, purine, and antioxidant pathways were identified, including many related to energetic and oxidative stress and derived from the gut microbiome. Multivariate statistical modeling demonstrated mutually distinct metabolomic profiles, suggesting that the processes that determine onset were likely distinct from those that determine progression. Gut microbiome-derived metabolites particularly differentiated the PHD metabolome, while the symptomatic HD metabolome was increasingly influenced by metabolites that may reflect mutant huntingtin toxicity and neurodegeneration. Interpretation Understanding the complex changes in the delicate balance of the metabolome and the gut microbiome in HD, and how they relate to disease onset, progression, and phenotypic variability in HD are critical questions for future research. PMID:26273688
Amathieu, Roland; Triba, Mohamed Nawfal; Goossens, Corentine; Bouchemal, Nadia; Nahon, Pierre; Savarin, Philippe; Le Moyec, Laurence
Metabolomics is defined as the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. It is an “omics” technique that is situated downstream of genomics, transcriptomics and proteomics. Metabolomics is recognized as a promising technique in the field of systems biology for the evaluation of global metabolic changes. During the last decade, metabolomics approaches have become widely used in the study of liver diseases for the detection of early biomarkers and altered metabolic pathways. It is a powerful technique to improve our pathophysiological knowledge of various liver diseases. It can be a useful tool to help clinicians in the diagnostic process especially to distinguish malignant and non-malignant liver disease as well as to determine the etiology or severity of the liver disease. It can also assess therapeutic response or predict drug induced liver injury. Nevertheless, the usefulness of metabolomics is often not understood by clinicians, especially the concept of metabolomics profiling or fingerprinting. In the present work, after a concise description of the different techniques and processes used in metabolomics, we will review the main research on this subject by focusing specifically on in vitro proton nuclear magnetic resonance spectroscopy based metabolomics approaches in human studies. We will first consider the clinical point of view enlighten physicians on this new approach and emphasis its future use in clinical “routine”. PMID:26755887
Fanos, Vassilios; Barberini, Luigi; Antonucci, Roberto; Atzori, Luigi
The 'omics' technologies represent analytical approaches that have a holistic view on molecules such as genes, transcripts, proteins and metabolites constituting a cell, tissue or organism. The profiling of genes, transcripts and proteins has been referred to as genomics, transcriptomics and proteomics. Finally, there is the youngest and most rapidly increasing of the "omics" disciplines: metabolomics. Metabolomics appears to be a new, very useful tool in neonatology, especially in the fields of pharma-metabolomics and nutri- metabolomics. Since it appears to be predictive and preventive, it can be considered the 'new clinical chemistry' for personalized neonatal medicine. At present, the use of metabolomics in neonatology is still in the pioneering phase. In clinical practice, only a limited number of metabolites are routinely measured in the biofluids of newborns by conventional analytical methods to study the metabolic status of the organism. However, the management of sick or preterm newborns might be improved if more information on perinatal/ neonatal maturational processes and their metabolic background were available. The aim of this review, after a general introduction on pharma-metabolomics, is to present the potential of NMR-based metabolomic analysis of newbom urine in neonatology in the field of pharmacology.
Abu Bakar, Mohamad Hafizi; Sarmidi, Mohamad Roji; Cheng, Kian-Kai; Ali Khan, Abid; Suan, Chua Lee; Zaman Huri, Hasniza; Yaakob, Harisun
Metabolomic studies on obesity and type 2 diabetes mellitus have led to a number of mechanistic insights into biomarker discovery and comprehension of disease progression at metabolic levels. This article reviews a series of metabolomic studies carried out in previous and recent years on obesity and type 2 diabetes, which have shown potential metabolic biomarkers for further evaluation of the diseases. Literature including journals and books from Web of Science, Pubmed and related databases reporting on the metabolomics in these particular disorders are reviewed. We herein discuss the potential of reported metabolic biomarkers for a novel understanding of disease processes. These biomarkers include fatty acids, TCA cycle intermediates, carbohydrates, amino acids, choline and bile acids. The biological activities and aetiological pathways of metabolites of interest in driving these intricate processes are explained. The data from various publications supported metabolomics as an effective strategy in the identification of novel biomarkers for obesity and type 2 diabetes. Accelerating interest in the perspective of metabolomics to complement other fields in systems biology towards the in-depth understanding of the molecular mechanisms underlying the diseases is also well appreciated. In conclusion, metabolomics can be used as one of the alternative approaches in biomarker discovery and the novel understanding of pathophysiological mechanisms in obesity and type 2 diabetes. It can be foreseen that there will be an increasing research interest to combine metabolomics with other omics platforms towards the establishment of detailed mechanistic evidence associated with the disease processes.
Chen, Dan-Qian; Chen, Hua; Chen, Lin; Tang, Dan-Dan; Miao, Hua; Zhao, Ying-Yong
Natural product plays a vital role in disease prevention and treatment since the appearance of civilization, but the toxicity severely hinders its wide use. In order to avoid toxic effect as far as possible and use natural product safely, more comprehensive understandings of toxicity are urgently required. Since the metabolome represents the physiological or pathological status of organisms, metabolomics-based toxicology is of significance to observe potential injury before toxins have caused physiological or pathological damages. Metabolomics-based toxicology can evaluate toxicity and identify toxicological biomarker of natural product, which is helpful to guide clinical medication and reduce adverse drug reactions. In the past decades, dozens of metabolomic researches have been implemented on toxicity evaluation, toxicological biomarker identification and potential mechanism exploration of nephrotoxicity, hepatotoxicity, cardiotoxicity and central nervous system toxicity induced by pure compounds, extracts and compound prescriptions. In this paper, metabolomic technology, sample preparation, data process and analysis, and metabolomics-based toxicological research of natural product are reviewed, and finally, the potential problems and further perspectives in toxicological metabolomic investigations of natural product are discussed.
Amathieu, Roland; Triba, Mohamed Nawfal; Goossens, Corentine; Bouchemal, Nadia; Nahon, Pierre; Savarin, Philippe; Le Moyec, Laurence
Metabolomics is defined as the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. It is an "omics" technique that is situated downstream of genomics, transcriptomics and proteomics. Metabolomics is recognized as a promising technique in the field of systems biology for the evaluation of global metabolic changes. During the last decade, metabolomics approaches have become widely used in the study of liver diseases for the detection of early biomarkers and altered metabolic pathways. It is a powerful technique to improve our pathophysiological knowledge of various liver diseases. It can be a useful tool to help clinicians in the diagnostic process especially to distinguish malignant and non-malignant liver disease as well as to determine the etiology or severity of the liver disease. It can also assess therapeutic response or predict drug induced liver injury. Nevertheless, the usefulness of metabolomics is often not understood by clinicians, especially the concept of metabolomics profiling or fingerprinting. In the present work, after a concise description of the different techniques and processes used in metabolomics, we will review the main research on this subject by focusing specifically on in vitro proton nuclear magnetic resonance spectroscopy based metabolomics approaches in human studies. We will first consider the clinical point of view enlighten physicians on this new approach and emphasis its future use in clinical "routine".
Li, Kefeng; Wang, Xu; Pidatala, Venkataramana R; Chang, Chi-Peng; Cao, Xiaohong
Quantitative metabolomics (qMetabolomics) is a powerful tool for understanding the intricate metabolic processes involved in plant abiotic stress responses. qMetabolomics is hindered by the limited coverage and high cost of isotopically labeled standards. In this study, we first selected 271 metabolites which might play important roles in abiotic stress responses as the targets and established a comprehensive LC-MS/MS based qMetabolomic method. We then developed a novel metabolic labeling method using E. coli-Saccharomyces cerevisiae two-step cultivation for the production of uniformly (13)C-labeled metabolites as internal standards. Finally, we applied the developed qMetabolomic method to investigate the influence of Pb stress on maize root metabolism. The absolute concentration of 226 metabolites in maize roots was accurately quantified in a single run within 30 min. Our study also revealed that glycolysis, purine, pyrimidine, and phospholipids were the main metabolic pathways in maize roots involved in Pb stress response. To our knowledge, this is the most comprehensive qMetabolomic method for plant metabolomics thus far. We developed a simple and inexpensive metabolic labeling method which dramatically expanded the availability of uniformly (13)C labeled metabolites. Our findings also provided new insights of maize metabolic responses to Pb stress.
Klein, Matthias S.; Shearer, Jane
Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine. PMID:26636104
Kafsack, Björn F C; Llinás, Manuel
The application of metabolomics, the global analysis of metabolite levels, to the study of protozoan parasites has become an important tool for understanding the host-parasite relationship and holds promise for the development of direly needed therapeutics and improved diagnostics. Research advances over the past decade have opened the door for a systems biology approach to protozoan parasites with metabolomics, providing a crucial readout of metabolic activity. In this review, we highlight recent metabolomic approaches to protozoan parasites, including metabolite profiling, integration with genomics, transcription, and proteomic analysis, and the use of metabolic fingerprints for the diagnosis of parasitic infections.
McNamara, Laura E.; Sjöström, Terje; Meek, R. M. Dominic; Oreffo, Richard O. C.; Su, Bo; Dalby, Matthew J.; Burgess, Karl E. V.
Metabolomics is a method for investigation of changes in the global metabolite profile of cells. This paper discusses the technical application of the approach, considering metabolite extraction, separation, mass spectrometry and data interpretation. A particular focus is on the application of metabolomics to the study of stem cell physiology in the context of biomaterials and regenerative medicine. Case studies are used to illustrate key points, focusing on the use of metabolomics in the examination of mesenchymal stem cell responses to titania-nanopillared substrata designed for orthopaedic applications. PMID:22628210
Wanichthanarak, Kwanjeera; Fan, Sili; Grapov, Dmitry; Barupal, Dinesh Kumar; Fiehn, Oliver
Similar to genomic and proteomic platforms, metabolomic data acquisition and analysis is becoming a routine approach for investigating biological systems. However, computational approaches for metabolomic data analysis and integration are still maturing. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. With the number of options provided in each analysis module, it also supports data analysis of other 'omic' families. The toolbox is an R-based web application, and it is freely available at http://kwanjeeraw.github.io/metabox/ under the GPL-3 license.
Grapov, Dmitry; Barupal, Dinesh Kumar
Similar to genomic and proteomic platforms, metabolomic data acquisition and analysis is becoming a routine approach for investigating biological systems. However, computational approaches for metabolomic data analysis and integration are still maturing. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. With the number of options provided in each analysis module, it also supports data analysis of other ‘omic’ families. The toolbox is an R-based web application, and it is freely available at http://kwanjeeraw.github.io/metabox/ under the GPL-3 license. PMID:28141874
Kafsack, Björn F.C.; Llinás, Manuel
The application of metabolomics, the global analysis of metabolite levels, to the study of protozoan parasites has become an important tool for understanding the host/parasite relationship and holds promise for the development of direly needed therapeutics and improved diagnostics. Research advances over the past decade have opened the door for a systems biology approach to protozoan parasites with metabolomics providing a crucial readout of metabolic activity. In this review we highlight recent metabolomic approaches to protozoan parasites, including metabolite profiling, integration with genomics, transcription, and proteomic analysis, as well as the use of metabolic fingerprints for the diagnosis of parasitic infections. PMID:20159614
Background Human cerebral spinal fluid (CSF) is known to be a rich source of small molecule biomarkers for neurological and neurodegenerative diseases. In 2007, we conducted a comprehensive metabolomic study and performed a detailed literature review on metabolites that could be detected (via metabolomics or other techniques) in CSF. A total of 308 detectable metabolites were identified, of which only 23% were shown to be routinely identifiable or quantifiable with the metabolomics technologies available at that time. The continuing advancement in analytical technologies along with the growing interest in CSF metabolomics has led us to re-visit the human CSF metabolome and to re-assess both its size and the level of coverage than can be achieved with today's technologies. Methods We used five analytical platforms, including nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), direct flow injection-mass spectrometry (DFI-MS/MS) and inductively coupled plasma-mass spectrometry (ICP-MS) to perform quantitative metabolomics on multiple human CSF samples. This experimental work was complemented with an extensive literature review to acquire additional information on reported CSF compounds, their concentrations and their disease associations. Results NMR, GC-MS and LC-MS methods allowed the identification and quantification of 70 CSF metabolites (as previously reported). DFI-MS/MS allowed the quantification of 78 metabolites (6 acylcarnitines, 13 amino acids, hexose, 42 phosphatidylcholines, 2 lyso-phosphatidylcholines and 14 sphingolipids), while ICP-MS provided quantitative results for 33 metal ions in CSF. Literature analysis led to the identification of 57 more metabolites. In total, 476 compounds have now been confirmed to exist in human CSF. Conclusions The use of improved metabolomic and other analytical techniques has led to a 54% increase in the known size of the human CSF metabolome
Jiménez-Girón, Ana; Muñoz-González, Irene; Martín-Álvarez, Pedro J.; Moreno-Arribas, María Victoria; Bartolomé, Begoña
Dietary polyphenols, including red wine phenolic compounds, are extensively metabolized during their passage through the gastrointestinal tract; and their biological effects at the gut level (i.e., anti-inflammatory activity, microbiota modulation, interaction with cells, among others) seem to be due more to their microbial-derived metabolites rather than to the original forms found in food. In an effort to improve our understanding of the biological effects that phenolic compounds exert at the gut level, this paper summarizes the changes observed in the human fecal metabolome after an intervention study consisting of a daily consumption of 250 mL of wine during four weeks by healthy volunteers (n = 33). It assembles data from two analytical approaches: (1) UPLC-ESI-MS/MS analysis of phenolic metabolites in fecal solutions (targeted analysis); and (2) UHPLC-TOF MS analysis of the fecal solutions (non-targeted analysis). Both approaches revealed statistically-significant changes in the concentration of several metabolites as a consequence of the wine intake. Similarity and complementarity between targeted and non-targeted approaches in the analysis of the fecal metabolome are discussed. Both strategies allowed the definition of a complex metabolic profile derived from wine intake. Likewise, the identification of endogenous markers could lead to new hypotheses to unravel the relationship between moderate wine consumption and the metabolic functionality of gut microbiota. PMID:25532710
Toya, Yoshihiro; Shimizu, Hiroshi
Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and (13)C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering.
Righetti, Laura; Rubert, Josep; Galaverna, Gianni; Folloni, Silvia; Ranieri, Roberto; Stranska-Zachariasova, Milena; Hajslova, Jana; Dall'Asta, Chiara
Hulled, or ancient, wheats were the earliest domesticated wheats by mankind and the ancestors of current wheats. Their cultivation drastically decreased during the 1960s; however, the increasing demand for a healthy and equilibrated diet led to rediscovering these grains. Our aim was to use a non-targeted metabolomic approach to discriminate and characterize similarities and differences between ancient Triticum varieties. For this purpose, 77 hulled wheat samples from three different varieties were collected: Garfagnana T. turgidum var. dicoccum L. (emmer), ID331 T. monococcum L. (einkorn) and Rouquin T. spelta L. (spelt). The ultra high performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-QTOF) metabolomics approach highlighted a pronounced sample clustering according to the wheat variety, with an excellent predictability (Q²), for all the models built. Fifteen metabolites were tentatively identified based on accurate masses, isotopic pattern, and product ion spectra. Among these, alkylresorcinols (ARs) were found to be significantly higher in spelt and emmer, showing different homologue composition. Furthermore, phosphatidylcholines (PC) and lysophosphatidylcholines (lysoPC) levels were higher in einkorn variety. The results obtained in this study confirmed the importance of ARs as markers to distinguish between Triticum species and revealed their values as cultivar markers, being not affected by the environmental influences.
Burghardt, Kyle J.; Evans, Simon J.; Wiese, Kristen M.; Ellingrod, Vicki L.
Background Second generation antipsychotic (SGA) use in bipolar disorder is common and has proven effective in short-term trials. There continues to be a lack of understanding of the mechanisms underlying many of their positive and negative effects in bipolar disorder. This study aimed to describe the metabolite profiles of bipolar subjects treated with SGAs by comparing to metabolite profiles of bipolar subjects treated with lithium, and schizophrenia subjects treated with SGAs. Methods Cross-sectional, fasting untargeted serum metabolomic profiling was conducted in 82 subjects diagnosed with bipolar I disorder (n=30 on SGAs and n=32 on lithium) or schizophrenia (n=20). Metabolomic profiles of bipolar subjects treated with SGAs were compared to bipolar subjects treated with lithium and schizophrenia subjects treated with SGAs using multivariate methods. Results Partial lease square discriminant analysis (PLS-DA) plots showed separation between bipolar subjects treated with SGAs, bipolar subjects treated with lithium, or schizophrenia subjects treated with SGAs. Top influential metabolite features were associated with several pathways including that of polyunsaturated fatty acids, pyruvate, glucose and branched chain amino acids. Conclusions The findings from this study require further validation in pre and post treated bipolar and schizophrenia subjects, but suggest that the pharmacometabolome may be diagnosis specific. PMID:26314700
Macintyre, Lynsey; Zhang, Tong; Viegelmann, Christina; Martinez, Ignacio Juarez; Cheng, Cheng; Dowdells, Catherine; Abdelmohsen, Usama Ramadam; Gernert, Christine; Hentschel, Ute; Edrada-Ebel, RuAngelie
Marine invertebrate-associated symbiotic bacteria produce a plethora of novel secondary metabolites which may be structurally unique with interesting pharmacological properties. Selection of strains usually relies on literature searching, genetic screening and bioactivity results, often without considering the chemical novelty and abundance of secondary metabolites being produced by the microorganism until the time-consuming bioassay-guided isolation stages. To fast track the selection process, metabolomic tools were used to aid strain selection by investigating differences in the chemical profiles of 77 bacterial extracts isolated from cold water marine invertebrates from Orkney, Scotland using liquid chromatography-high resolution mass spectrometry (LC-HRMS) and nuclear magnetic resonance (NMR) spectroscopy. Following mass spectrometric analysis and dereplication using an Excel macro developed in-house, principal component analysis (PCA) was employed to differentiate the bacterial strains based on their chemical profiles. NMR 1H and correlation spectroscopy (COSY) were also employed to obtain a chemical fingerprint of each bacterial strain and to confirm the presence of functional groups and spin systems. These results were then combined with taxonomic identification and bioassay screening data to identify three bacterial strains, namely Bacillus sp. 4117, Rhodococcus sp. ZS402 and Vibrio splendidus strain LGP32, to prioritize for scale-up based on their chemically interesting secondary metabolomes, established through dereplication and interesting bioactivities, determined from bioassay screening.
Davies, Sarah K; Ang, Joo Ern; Revell, Victoria L; Holmes, Ben; Mann, Anuska; Robertson, Francesca P; Cui, Nanyi; Middleton, Benita; Ackermann, Katrin; Kayser, Manfred; Thumser, Alfred E; Raynaud, Florence I; Skene, Debra J
Sleep restriction and circadian clock disruption are associated with metabolic disorders such as obesity, insulin resistance, and diabetes. The metabolic pathways involved in human sleep, however, have yet to be investigated with the use of a metabolomics approach. Here we have used untargeted and targeted liquid chromatography (LC)/MS metabolomics to examine the effect of acute sleep deprivation on plasma metabolite rhythms. Twelve healthy young male subjects remained in controlled laboratory conditions with respect to environmental light, sleep, meals, and posture during a 24-h wake/sleep cycle, followed by 24 h of wakefulness. Two-hourly plasma samples collected over the 48 h period were analyzed by LC/MS. Principal component analysis revealed a clear time of day variation with a significant cosine fit during the wake/sleep cycle and during 24 h of wakefulness in untargeted and targeted analysis. Of 171 metabolites quantified, daily rhythms were observed in the majority (n = 109), with 78 of these maintaining their rhythmicity during 24 h of wakefulness, most with reduced amplitude (n = 66). During sleep deprivation, 27 metabolites (tryptophan, serotonin, taurine, 8 acylcarnitines, 13 glycerophospholipids, and 3 sphingolipids) exhibited significantly increased levels compared with during sleep. The increased levels of serotonin, tryptophan, and taurine may explain the antidepressive effect of acute sleep deprivation and deserve further study. This report, to our knowledge the first of metabolic profiling during sleep and sleep deprivation and characterization of 24 h rhythms under these conditions, offers a novel view of human sleep/wake regulation.
Dove, Alistair D M; Leisen, Johannes; Zhou, Manshui; Byrne, Jonathan J; Lim-Hing, Krista; Webb, Harry D; Gelbaum, Leslie; Viant, Mark R; Kubanek, Julia; Fernández, Facundo M
In a search for biomarkers of health in whale sharks and as exploration of metabolomics as a modern tool for understanding animal physiology, the metabolite composition of serum in six whale sharks (Rhincodon typus) from an aquarium collection was explored using (1)H nuclear magnetic resonance (NMR) spectroscopy and direct analysis in real time (DART) mass spectrometry (MS). Principal components analysis (PCA) of spectral data showed that individual animals could be resolved based on the metabolite composition of their serum and that two unhealthy individuals could be discriminated from the remaining healthy animals. The major difference between healthy and unhealthy individuals was the concentration of homarine, here reported for the first time in an elasmobranch, which was present at substantially lower concentrations in unhealthy whale sharks, suggesting that this metabolite may be a useful biomarker of health status in this species. The function(s) of homarine in sharks remain uncertain but it likely plays a significant role as an osmolyte. The presence of trimethylamine oxide (TMAO), another well-known protective osmolyte of elasmobranchs, at 0.1-0.3 mol L(-1) was also confirmed using both NMR and MS. Twenty-three additional potential biomarkers were identified based on significant differences in the frequency of their occurrence between samples from healthy and unhealthy animals, as detected by DART MS. Overall, NMR and MS provided complementary data that showed that metabolomics is a useful approach for biomarker prospecting in poorly studied species like elasmobranchs.
Dove, Alistair D. M.; Leisen, Johannes; Zhou, Manshui; Byrne, Jonathan J.; Lim-Hing, Krista; Webb, Harry D.; Gelbaum, Leslie; Viant, Mark R.; Kubanek, Julia; Fernández, Facundo M.
In a search for biomarkers of health in whale sharks and as exploration of metabolomics as a modern tool for understanding animal physiology, the metabolite composition of serum in six whale sharks (Rhincodon typus) from an aquarium collection was explored using 1H nuclear magnetic resonance (NMR) spectroscopy and direct analysis in real time (DART) mass spectrometry (MS). Principal components analysis (PCA) of spectral data showed that individual animals could be resolved based on the metabolite composition of their serum and that two unhealthy individuals could be discriminated from the remaining healthy animals. The major difference between healthy and unhealthy individuals was the concentration of homarine, here reported for the first time in an elasmobranch, which was present at substantially lower concentrations in unhealthy whale sharks, suggesting that this metabolite may be a useful biomarker of health status in this species. The function(s) of homarine in sharks remain uncertain but it likely plays a significant role as an osmolyte. The presence of trimethylamine oxide (TMAO), another well-known protective osmolyte of elasmobranchs, at 0.1–0.3 mol L−1 was also confirmed using both NMR and MS. Twenty-three additional potential biomarkers were identified based on significant differences in the frequency of their occurrence between samples from healthy and unhealthy animals, as detected by DART MS. Overall, NMR and MS provided complementary data that showed that metabolomics is a useful approach for biomarker prospecting in poorly studied species like elasmobranchs. PMID:23166652
Ishikawa, Shigeo; Sugimoto, Masahiro; Kitabatake, Kenichiro; Sugano, Ayako; Nakamura, Marina; Kaneko, Miku; Ota, Sana; Hiwatari, Kana; Enomoto, Ayame; Soga, Tomoyoshi; Tomita, Masaru; Iino, Mitsuyoshi
The objective of this study was to explore salivary metabolite biomarkers by profiling both saliva and tumor tissue samples for oral cancer screening. Paired tumor and control tissues were obtained from oral cancer patients and whole unstimulated saliva samples were collected from patients and healthy controls. The comprehensive metabolomic analysis for profiling hydrophilic metabolites was conducted using capillary electrophoresis time-of-flight mass spectrometry. In total, 85 and 45 metabolites showed significant differences between tumor and matched control samples, and between salivary samples from oral cancer and controls, respectively (P < 0.05 correlated by false discovery rate); 17 metabolites showed consistent differences in both saliva and tissue-based comparisons. Of these, a combination of only two biomarkers yielded a high area under receiver operating characteristic curves (0.827; 95% confidence interval, 0.726–0.928, P < 0.0001) for discriminating oral cancers from controls. Various validation tests confirmed its high generalization ability. The demonstrated approach, integrating both saliva and tumor tissue metabolomics, helps eliminate pseudo-molecules that are coincidentally different between oral cancers and controls. These combined salivary metabolites could be the basis of a clinically feasible method of non-invasive oral cancer screening. PMID:27539254
Couch, Robin D; Dailey, Allyson; Zaidi, Fatima; Navarro, Karl; Forsyth, Christopher B; Mutlu, Ece; Engen, Phillip A; Keshavarzian, Ali
Studies have shown that excessive alcohol consumption impacts the intestinal microbiota composition, causing disruption of homeostasis (dysbiosis). However, this observed change is not indicative of the dysbiotic intestinal microbiota function that could result in the production of injurious and toxic products. Thus, knowledge of the effects of alcohol on the intestinal microbiota function and their metabolites is warranted, in order to better understand the role of the intestinal microbiota in alcohol associated organ failure. Here, we report the results of a differential metabolomic analysis comparing volatile organic compounds (VOC) detected in the stool of alcoholics and non-alcoholic healthy controls. We performed the analysis with fecal samples collected after passage as well as with samples collected directly from the sigmoid lumen. Regardless of the approach to fecal collection, we found a stool VOC metabolomic signature in alcoholics that is different from healthy controls. The most notable metabolite alterations in the alcoholic samples include: (1) an elevation in the oxidative stress biomarker tetradecane; (2) a decrease in five fatty alcohols with anti-oxidant property; (3) a decrease in the short chain fatty acids propionate and isobutyrate, important in maintaining intestinal epithelial cell health and barrier integrity; (4) a decrease in alcohol consumption natural suppressant caryophyllene; (5) a decrease in natural product and hepatic steatosis attenuator camphene; and (6) decreased dimethyl disulfide and dimethyl trisulfide, microbial products of decomposition. Our results showed that intestinal microbiota function is altered in alcoholics which might promote alcohol associated pathologies.
Righetti, Laura; Rubert, Josep; Galaverna, Gianni; Folloni, Silvia; Ranieri, Roberto; Stranska-Zachariasova, Milena; Hajslova, Jana; Dall’Asta, Chiara
Hulled, or ancient, wheats were the earliest domesticated wheats by mankind and the ancestors of current wheats. Their cultivation drastically decreased during the 1960s; however, the increasing demand for a healthy and equilibrated diet led to rediscovering these grains. Our aim was to use a non-targeted metabolomic approach to discriminate and characterize similarities and differences between ancient Triticum varieties. For this purpose, 77 hulled wheat samples from three different varieties were collected: Garfagnana T. turgidum var. dicoccum L. (emmer), ID331 T. monococcum L. (einkorn) and Rouquin T. spelta L. (spelt). The ultra high performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-QTOF) metabolomics approach highlighted a pronounced sample clustering according to the wheat variety, with an excellent predictability (Q2), for all the models built. Fifteen metabolites were tentatively identified based on accurate masses, isotopic pattern, and product ion spectra. Among these, alkylresorcinols (ARs) were found to be significantly higher in spelt and emmer, showing different homologue composition. Furthermore, phosphatidylcholines (PC) and lysophosphatidylcholines (lysoPC) levels were higher in einkorn variety. The results obtained in this study confirmed the importance of ARs as markers to distinguish between Triticum species and revealed their values as cultivar markers, being not affected by the environmental influences. PMID:27472322
Vasilev, Nikolay; Boccard, Julien; Lang, Gerhard; Grömping, Ulrike; Fischer, Rainer; Goepfert, Simon; Rudaz, Serge; Schillberg, Stefan
Multiple factors act simultaneously on plants to establish complex interaction networks involving nutrients, elicitors and metabolites. Metabolomics offers a better understanding of complex biological systems, but evaluating the simultaneous impact of different parameters on metabolic pathways that have many components is a challenging task. We therefore developed a novel approach that combines experimental design, untargeted metabolic profiling based on multiple chromatography systems and ionization modes, and multiblock data analysis, facilitating the systematic analysis of metabolic changes in plants caused by different factors acting at the same time. Using this method, target geraniol compounds produced in transgenic tobacco cell cultures were grouped into clusters based on their response to different factors. We hypothesized that our novel approach may provide more robust data for process optimization in plant cell cultures producing any target secondary metabolite, based on the simultaneous exploration of multiple factors rather than varying one factor each time. The suitability of our approach was verified by confirming several previously reported examples of elicitor–metabolite crosstalk. However, unravelling all factor–metabolite networks remains challenging because it requires the identification of all biochemically significant metabolites in the metabolomics dataset. PMID:27853298
Finkelstein, Julia L.; Pressman, Eva K.; Cooper, Elizabeth M.; Kent, Tera R.; Bar, Haim Y.
Vitamin D is linked to a number of adverse pregnancy outcomes through largely unknown mechanisms. This study was conducted to examine the role of vitamin D status in metabolomic profiles in a group of 30 pregnant, African American adolescents (17.1 ± 1.1 years) at midgestation (26.8 ± 2.8 weeks), in 15 adolescents with 25-hydroxy vitamin D (25(OH)D) ≥20 ng/mL, and in 15 teens with 25(OH)D <20 ng/mL. Serum metabolomic profiles were examined using gas chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry. A novel hierarchical mixture model was used to evaluate differences in metabolite profiles between low and high groups. A total of 326 compounds were identified and included in subsequent statistical analyses. Eleven metabolites had significantly different means between the 2 vitamin D groups, after correcting for multiple hypothesis testing: pyridoxate, bilirubin, xylose, and cholate were higher, and leukotrienes, 1,2-propanediol, azelate, undecanedioate, sebacate, inflammation associated complement component 3 peptide (HWESASXX), and piperine were lower in serum from adolescents with 25(OH)D ≥20 ng/mL. Lower maternal vitamin D status at midgestation impacted serum metabolic profiles in pregnant adolescents. PMID:25367051
Ko, Bong-Kuk; Ahn, Hyuk-Jin; van den Berg, Frans; Lee, Cherl-Ho; Hong, Young-Shick
Soy sauce, a well-known seasoning in Asia and throughout the world, consists of many metabolites that are produced during fermentation or aging and that have various health benefits. However, their comprehensive assessment has been limited due to targeted or instrumentally specific analysis. This paper presents for the first time a metabolic characterization of soy sauce, especially that aged up to 12 years, to obtain a global understanding of the metabolic variations through (1)H NMR spectroscopy coupled with multivariate pattern recognition techniques. Elevated amino acids and organic acids and the consumption of carbohydrate were associated with continuous involvement of microflora in aging for 12 years. In particular, continuous increases in the levels of betaine were found during aging for up to 12 years, demonstrating that microbial- or enzyme-related metabolites were also coupled with osmotolerant or halophilic bacteria present during aging. This work provides global insights into soy sauce through a (1)H NMR-based metabolomic approach that enhances the current understanding of the holistic metabolome and allows assessment of soy sauce quality.
Jenkins, Helen; Johnson, Helen; Kular, Baldeep; Wang, Trevor; Hardy, Nigel
Over recent years, a number of initiatives have proposed standard reporting guidelines for functional genomics experiments. Associated with these are data models that may be used as the basis of the design of software tools that store and transmit experiment data in standard formats. Central to the success of such data handling tools is their usability. Successful data handling tools are expected to yield benefits in time saving and in quality assurance. Here, we describe the collection of datasets that conform to the recently proposed data model for plant metabolomics known as ArMet (architecture for metabolomics) and illustrate a number of approaches to robust data collection that have been developed in collaboration between software engineers and biologists. These examples also serve to validate ArMet from the data collection perspective by demonstrating that a range of software tools, supporting data recording and data upload to central databases, can be built using the data model as the basis of their design. PMID:15888680
Clendinen, Chaevien S; Lee-McMullen, Brittany; Williams, Caroline M; Stupp, Gregory S; Vandenborne, Krista; Hahn, Daniel A; Walter, Glenn A; Edison, Arthur S
(13)C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality (13)C NMR spectra obtained using a custom (13)C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D (13)C and (1)H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful (13)C-(13)C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of (13)C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The (13)C and (1)H data together led to 15 matches in the database compared to just 7 using (1)H alone, and the (13)C correlated peak lists had far fewer false positives than the (1)H generated lists. In addition, the (13)C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the D. melanogaster extracts and mouse serum.
Stupp, Gregory S; Clendinen, Chaevien S; Ajredini, Ramadan; Szewc, Mark A; Garrett, Timothy; Menger, Robert F; Yost, Richard A; Beecher, Chris; Edison, Arthur S
We demonstrate the global metabolic analysis of Caenorhabditis elegans stress responses using a mass-spectrometry-based technique called isotopic ratio outlier analysis (IROA). In an IROA protocol, control and experimental samples are isotopically labeled with 95 and 5% (13)C, and the two sample populations are mixed together for uniform extraction, sample preparation, and LC-MS analysis. This labeling strategy provides several advantages over conventional approaches: (1) compounds arising from biosynthesis are easily distinguished from artifacts, (2) errors from sample extraction and preparation are minimized because the control and experiment are combined into a single sample, (3) measurement of both the molecular weight and the exact number of carbon atoms in each molecule provides extremely accurate molecular formulas, and (4) relative concentrations of all metabolites are easily determined. A heat-shock perturbation was conducted on C. elegans to demonstrate this approach. We identified many compounds that significantly changed upon heat shock, including several from the purine metabolism pathway. The metabolomic response information by IROA may be interpreted in the context of a wealth of genetic and proteomic information available for C. elegans . Furthermore, the IROA protocol can be applied to any organism that can be isotopically labeled, making it a powerful new tool in a global metabolomics pipeline.
Sanchez, Diego H; Schwabe, Franziska; Erban, Alexander; Udvardi, Michael K; Kopka, Joachim
Water limitation has become a major concern for agriculture. Such constraints reinforce the urgent need to understand mechanisms by which plants cope with water deprivation. We used a non-targeted metabolomic approach to explore plastic systems responses to non-lethal drought in model and forage legume species of the Lotus genus. In the model legume Lotus. japonicus, increased water stress caused gradual increases of most of the soluble small molecules profiled, reflecting a global and progressive reprogramming of metabolic pathways. The comparative metabolomic approach between Lotus species revealed conserved and unique metabolic responses to drought stress. Importantly, only few drought-responsive metabolites were conserved among all species. Thus we highlight a potential impediment to translational approaches that aim to engineer traits linked to the accumulation of compatible solutes. Finally, a broad comparison of the metabolic changes elicited by drought and salt acclimation revealed partial conservation of these metabolic stress responses within each of the Lotus species, but only few salt- and drought-responsive metabolites were shared between all. The implications of these results are discussed with regard to the current insights into legume water stress physiology.
Xia, Jianguo; Sinelnikov, Igor V; Han, Beomsoo; Wishart, David S
MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources. First introduced in 2009, MetaboAnalyst has experienced more than a 50X growth in user traffic (>50 000 jobs processed each month). In order to keep up with the rapidly increasing computational demands and a growing number of requests to support translational and systems biology applications, we performed a substantial rewrite and major feature upgrade of the server. The result is MetaboAnalyst 3.0. By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, we have been able substantially improve its performance, capacity and user interactivity. Three new modules have also been added including: (i) a module for biomarker analysis based on the calculation of receiver operating characteristic curves; (ii) a module for sample size estimation and power analysis for improved planning of metabolomics studies and (iii) a module to support integrative pathway analysis for both genes and metabolites. In addition, popular features found in existing modules have been significantly enhanced by upgrading the graphical output, expanding the compound libraries and by adding support for more diverse organisms.
Liang, Qun; Liu, Han; Wang, Cong; Li, Binbing
Hepatocarcinoma (HCC) is one of the deadliest cancers in the world and represents a significant disease burden. Better biomarkers are needed for early detection of HCC. Metabolomics was applied to urine samples obtained from HCC patients to discover noninvasive and reliable biomarkers for rapid diagnosis of HCC. Metabolic profiling was performed by LC-Q-TOF-MS in conjunction with multivariate data analysis, machine learning approaches, ingenuity pathway analysis and receiver-operating characteristic curves were used to select the metabolites which were used for the noninvasive diagnosis of HCC. Fifteen differential metabolites contributing to the complete separation of HCC patients from matched healthy controls were identified involving several key metabolic pathways. More importantly, five marker metabolites were effective for the diagnosis of human HCC, achieved a sensitivity of 96.5% and specificity of 83% respectively, could significantly increase the diagnostic performance of the metabolic biomarkers. Overall, these results illustrate the power of the metabolomics technology which has the potential as a non-invasive strategies and promising screening tool to evaluate the potential of the metabolites in the early diagnosis of HCC patients at high risk and provides new insight into pathophysiologic mechanisms. PMID:26805550
Xie, Guoxiang; Su, Mingming; Li, Peng; Gu, Xue; Yan, Chao; Qiu, Yunping; Li, Houkai; Jia, Wei
A new approach for the metabolomic study of urinary samples using pressurized CEC (pCEC) with gradient elution is proposed as an alternative chromatographic separation tool with higher degree of resolution, selectivity, sensitivity, and efficiency. The pCEC separation of urinary samples was performed on a RP column packed with C(18), 5 microm particles with an ACN/water mobile phase containing TFA. The effects of the acid modifiers, applied voltage, mobile phase, and detection wavelength were systematically evaluated using eight spiked standards, as well as urine samples. A typical analytical trial of urine samples from Sprague Dawley (S.D.) rats exposed to high-energy diet was carried out following sample pretreatment. Significant differences in urinary metabolic profiles were observed between the high energy diet-induced obesity rats and the healthy control rats at the 6th wk postdose. Multivariate statistical analysis revealed the differential metabolites in response to the diet, which were partially validated with the putative standards. This work suggests that such a pCEC-based separation and analysis method may provide a new and cost-effective platform for metabolomic study uniquely positioned between the conventional chromatographic tools such as HPLC, and hyphenated analytical techniques such as LC-MS.
Xu, Huajun; Zheng, Xiaojiao; Qian, Yingjun; Guan, Jian; Yi, Hongliang; Zou, Jianyin; Wang, Yuyu; Meng, Lili; Zhao, Aihua; Yin, Shankai; Jia, Wei
Few clinical studies have explored altered urinary metabolite levels in patients with obstructive sleep apnea (OSA). Thus, we applied a metabolomics approach to analyze urinary metabolites in three groups of participants: patients with polysomnography (PSG)-confirmed OSA, simple snorers (SS), and normal subjects. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and gas chromatography coupled with time-of-flight mass spectrometry were used. A total of 21 and 31 metabolites were differentially expressed in the SS and OSA groups, respectively. Patients with OSA had 18 metabolites different from those with SS. Of the 56 metabolites detected among the 3 groups, 24 were consistently higher or lower. A receiver operator curve analysis revealed that the combination of 4-hydroxypentenoic acid, arabinose, glycochenodeoxycholate-3-sulfate, isoleucine, serine, and xanthine produced a moderate diagnostic score with a sensitivity (specificity) of 75% (78%) for distinguishing OSA from those without OSA. The combination of 4-hydroxypentenoic acid, 5-dihydrotestosterone sulfate, serine, spermine, and xanthine distinguished OSA from SS with a sensitivity of 85% and specificity of 80%. Multiple metabolites and metabolic pathways associated with SS and OSA were identified using the metabolomics approach, and the altered metabolite signatures could potentially serve as an alternative diagnostic method to PSG. PMID:27480913
Kurczy, Michael E; Forsberg, Erica M; Thorgersen, Michael P; Poole, Farris L; Benton, H Paul; Ivanisevic, Julijana; Tran, Minerva L; Wall, Judy D; Elias, Dwayne A; Adams, Michael W W; Siuzdak, Gary
Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using (15)NO3 as a nitrogen source. In contrast to a control (Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee (Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2 strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. The use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.
Macintyre, Lynsey; Zhang, Tong; Viegelmann, Christina; Juarez Martinez, Ignacio; Cheng, Cheng; Dowdells, Catherine; Abdelmohsen, Usama Ramadan; Gernert, Christine; Hentschel, Ute; Edrada-Ebel, RuAngelie
Marine invertebrate-associated symbiotic bacteria produce a plethora of novel secondary metabolites which may be structurally unique with interesting pharmacological properties. Selection of strains usually relies on literature searching, genetic screening and bioactivity results, often without considering the chemical novelty and abundance of secondary metabolites being produced by the microorganism until the time-consuming bioassay-guided isolation stages. To fast track the selection process, metabolomic tools were used to aid strain selection by investigating differences in the chemical profiles of 77 bacterial extracts isolated from cold water marine invertebrates from Orkney, Scotland using liquid chromatography-high resolution mass spectrometry (LC-HRMS) and nuclear magnetic resonance (NMR) spectroscopy. Following mass spectrometric analysis and dereplication using an Excel macro developed in-house, principal component analysis (PCA) was employed to differentiate the bacterial strains based on their chemical profiles. NMR 1H and correlation spectroscopy (COSY) were also employed to obtain a chemical fingerprint of each bacterial strain and to confirm the presence of functional groups and spin systems. These results were then combined with taxonomic identification and bioassay screening data to identify three bacterial strains, namely Bacillus sp. 4117, Rhodococcus sp. ZS402 and Vibrio splendidus strain LGP32, to prioritize for scale-up based on their chemically interesting secondary metabolomes, established through dereplication and interesting bioactivities, determined from bioassay screening. PMID:24905482
Ganti, Sheila; Weiss, Robert H
Renal cell carcinoma (RCC) is one of the few human cancers whose incidence is increasing. The disease regularly progresses asymptomatically and is frequently metastatic upon presentation, thereby necessitating the development of an early method of detection. A metabolomic approach for biomarker detection using urine as a biofluid is appropriate since the tumor is located in close proximity to the urinary space. By comparing the composition of urine from individuals with RCC to control individuals, differences in metabolite composition of this biofluid can be identified, and these data can be utilized to create a clinically applicable and, possibly, bedside assay. Recent studies have shown that sample handling and processing greatly influences the variability seen in the urinary metabolome of both cancer and control patients. Once a standard method of collection is developed, identifying metabolic derangements associated with RCC will also lead to the investigation of novel targets for therapeutic intervention. The objective of this review is to discuss existing methods for sample collection, processing, data analysis, and recent findings in this emerging field.
Gu, Eun-Ji; Kim, Dong Wook; Jang, Gwang-Ju; Song, Seong Hwa; Lee, Jae-In; Lee, Sang Bong; Kim, Bo-Min; Cho, Yeongrae; Lee, Hyeon-Jeong; Kim, Hyun-Jin
We investigated the metabolite profile of soybean sprouts at 0, 1, 2, 3, and 4days after germination using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-MS (LC-MS) to understand the relationship between germination and nutritional quality. Data were analyzed by partial least squares-discriminant analysis (PLS-DA), and sprout samples were separated successfully using their PLS-DA scores. Fifty-eight metabolites, including macromolecular derivatives related to energy production, amino acids, myo-inositol metabolites, phytosterols, antioxidants, isoflavones, and soyasaponins, contributed to the separation. Amino acids, myo-inositol metabolites, isoflavone aglycones, B soyasaponins, antioxidants, and phytosterols, associated with health benefits and/or taste quality, increased with germination time while isoflavone glycosides and DDMP soyasaponins decreased. Based on these metabolites, the metabolomic pathway associated with energy production in soybean sprouts is suggested. Our data suggest that sprouting is a useful processing step to improve soybean nutritional quality, and metabolomic analysis is useful in understanding nutritional change during sprouting.
Couch, Robin D.; Dailey, Allyson; Zaidi, Fatima; Navarro, Karl; Forsyth, Christopher B.; Mutlu, Ece; Engen, Phillip A.; Keshavarzian, Ali
Studies have shown that excessive alcohol consumption impacts the intestinal microbiota composition, causing disruption of homeostasis (dysbiosis). However, this observed change is not indicative of the dysbiotic intestinal microbiota function that could result in the production of injurious and toxic products. Thus, knowledge of the effects of alcohol on the intestinal microbiota function and their metabolites is warranted, in order to better understand the role of the intestinal microbiota in alcohol associated organ failure. Here, we report the results of a differential metabolomic analysis comparing volatile organic compounds (VOC) detected in the stool of alcoholics and non-alcoholic healthy controls. We performed the analysis with fecal samples collected after passage as well as with samples collected directly from the sigmoid lumen. Regardless of the approach to fecal collection, we found a stool VOC metabolomic signature in alcoholics that is different from healthy controls. The most notable metabolite alterations in the alcoholic samples include: (1) an elevation in the oxidative stress biomarker tetradecane; (2) a decrease in five fatty alcohols with anti-oxidant property; (3) a decrease in the short chain fatty acids propionate and isobutyrate, important in maintaining intestinal epithelial cell health and barrier integrity; (4) a decrease in alcohol consumption natural suppressant caryophyllene; (5) a decrease in natural product and hepatic steatosis attenuator camphene; and (6) decreased dimethyl disulfide and dimethyl trisulfide, microbial products of decomposition. Our results showed that intestinal microbiota function is altered in alcoholics which might promote alcohol associated pathologies. PMID:25751150
13C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality 13C NMR spectra obtained using a custom 13C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D 13C and 1H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful 13C–13C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of 13C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The 13C and 1H data together led to 15 matches in the database compared to just 7 using 1H alone, and the 13C correlated peak lists had far fewer false positives than the 1H generated lists. In addition, the 13C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the D. melanogaster extracts and mouse serum. PMID:25140385
Ivanisevic, Julijana; Elias, Darlene; Deguchi, Hiroshi; Averell, Patricia M.; Kurczy, Michael; Johnson, Caroline H.; Tautenhahn, Ralf; Zhu, Zhengjiang; Watrous, Jeramie; Jain, Mohit; Griffin, John; Patti, Gary J.; Siuzdak, Gary
The human circulatory system consists of arterial blood that delivers nutrients to tissues, and venous blood that removes the metabolic by-products. Although it is well established that arterial blood generally has higher concentrations of glucose and oxygen relative to venous blood, a comprehensive biochemical characterization of arteriovenous differences has not yet been reported. Here we apply cutting-edge, mass spectrometry-based metabolomic technologies to provide a global characterization of metabolites that vary in concentration between the arterial and venous blood of human patients. Global profiling of paired arterial and venous plasma from 20 healthy individuals, followed up by targeted analysis made it possible to measure subtle (<2 fold), yet highly statistically significant and physiologically important differences in water soluble human plasma metabolome. While we detected changes in lactic acid, alanine, glutamine, and glutamate as expected from skeletal muscle activity, a number of unanticipated metabolites were also determined to be significantly altered including Krebs cycle intermediates, amino acids that have not been previously implicated in transport, and a few oxidized fatty acids. This study provides the most comprehensive assessment of metabolic changes in the blood during circulation to date and suggests that such profiling approach may offer new insights into organ homeostasis and organ specific pathology. PMID:26244428
Kurczy, Michael E.; Forsberg, Erica M.; Thorgersen, Michael P.; Poole, Farris L.; Benton, H. Paul; Ivanisevic, Julijana; Tran, Minerva L.; Wall, Judy D.; Elias, Dwayne A.; Adams, Michael W. W.; Siuzdak, Gary
Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using 15NO3 as a nitrogen source. In contrast to a control (Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee (Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2 strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. In conclusion, the use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.
Kurczy, Michael E.; Forsberg, Erica M.; Thorgersen, Michael P.; ...
Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using 15NO3 as a nitrogen source. In contrast to a control (Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee (Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2 strains conserve nitrogen-containingmore » metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. In conclusion, the use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.« less
Szewc, Mark A.; Garrett, Timothy; Menger, Robert F.; Yost, Richard A.; Beecher, Chris; Edison, Arthur S.
We demonstrate the global metabolic analysis of Caenorhabditis elegans stress responses using a mass spectrometry-based technique called Isotopic Ratio Outlier Analysis (IROA). In an IROA protocol, control and experimental samples are isotopically labeled with 95% and 5% 13C, and the two sample populations are mixed together for uniform extraction, sample preparation, and LC-MS analysis. This labeling strategy provides several advantages over conventional approaches: 1) compounds arising from biosynthesis are easily distinguished from artifacts, 2) errors from sample extraction and preparation are minimized because the control and experiment are combined into a single sample, 3) measurement of both the molecular weight and the exact number of carbon atoms in each molecule provides extremely accurate molecular formulae, and 4) relative concentrations of all metabolites are easily determined. A heat shock perturbation was conducted on C. elegans to demonstrate this approach. We identified many compounds that significantly changed upon heat shock, including several from the purine metabolism pathway, which we use to demonstrate the approach. The metabolomic response information by IROA may be interpreted in the context of a wealth of genetic and proteomic information available for C. elegans. Furthermore, the IROA protocol can be applied to any organism that can be isotopically labeled, making it a powerful new tool in a global metabolomics pipeline. PMID:24274725
Peng, Jun; Chen, Yi-Ting; Chen, Chien-Lun; Li, Liang
Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and
Peng, Jun; Guo, Kevin; Xia, Jianguo; Zhou, Jianjun; Yang, Jing; Westaway, David; Wishart, David S; Li, Liang
Because of a limited volume of urine that can be collected from a mouse, it is very difficult to apply the common strategy of using multiple analytical techniques to analyze the metabolites to increase the metabolome coverage for mouse urine metabolomics. We report an enabling method based on differential isotope labeling liquid chromatography mass spectrometry (LC-MS) for relative quantification of over 950 putative metabolites using 20 μL of urine as the starting material. The workflow involves aliquoting 10 μL of an individual urine sample for ¹²C-dansylation labeling that target amines and phenols. Another 10 μL of aliquot was taken from each sample to generate a pooled sample that was subjected to ¹³C-dansylation labeling. The ¹²C-labeled individual sample was mixed with an equal volume of the ¹³C-labeled pooled sample. The mixture was then analyzed by LC-MS to generate information on metabolite concentration differences among different individual samples. The interday repeatability for the LC-MS runs was assessed, and the median relative standard deviation over 4 days was 5.0%. This workflow was then applied to a metabolomic biomarker discovery study using urine samples obtained from the TgCRND8 mouse model of early onset familial Alzheimer's disease (FAD) throughout the course of their pathological deposition of beta amyloid (Aβ). It was showed that there was a distinct metabolomic separation between the AD prone mice and the wild type (control) group. As early as 15-17 weeks of age (presymptomatic), metabolomic differences were observed between the two groups, and after the age of 25 weeks the metabolomic alterations became more pronounced. The metabolomic changes at different ages corroborated well with the phenotype changes in this transgenic mice model. Several useful candidate biomarkers including methionine, desaminotyrosine, taurine, N1-acetylspermidine, and 5-hydroxyindoleacetic acid were identified. Some of them were found in previous
Kopp, Florian; Komatsu, Toru; Nomura, Daniel K.; Trauger, Sunia A.; Thomas, Jason R.; Siuzdak, Gary; Simon, Gabriel M.; Cravatt, Benjamin F.
GDE1 is a mammalian glycerophosphodiesterase (GDE) implicated by in vitro studies in the regulation of glycerophopho-inositol (GroPIns) and possibly other glycerophospho (GroP) metabolites. Here, we show using untargeted metabolomics that GroPIns is profoundly (> 20-fold) elevated in brain tissue from GDE1(-/-) mice. Furthermore, two additional GroP-metabolites not previously identified in eukaryotic cells, glycerophospho-serine (GroPSer) and glycerophospho-glycerate (GroPGate), were also highly elevated in GDE1(-/-) brains. Enzyme assays with synthetic GroP-metabolites confirmed that GroPSer and GroPGate are direct substrates of GDE1. Interestingly, our metabolomic profiles also revealed that serine (both L-and D-) levels were significantly reduced in brains of GDE1 (-/-) mice. These findings designate GroPSer as a previously unappreciated reservoir for free serine in the nervous system and suggest that GDE1, through recycling serine from GroPSer, may impact D-serine-dependent neural signaling processes in vivo. PMID:20797612
Deda, Olga; Gika, Helen G.; Taitzoglou, Ioannis; Raikos, Νikolaos; Theodoridis, Georgios
Aging is an inevitable condition leading to health deterioration and death. Regular physical exercise can moderate the metabolic phenotype changes of aging. However, only a small number of metabolomics-based studies provide data on the effect of exercise along with aging. Here, urine and whole blood samples from Wistar rats were analyzed in a longitudinal study to explore metabolic alterations due to exercise and aging. The study comprised three different programs of exercises, including a life-long protocol which started at the age of 5 months and ended at the age of 21 months. An acute exercise session was also evaluated. Urine and whole blood samples were collected at different time points and were analyzed by LC-MS/MS (Liquid Chromatography–tandem Mass Spectrometry). Based on their metabolic profiles, samples from trained and sedentary rats were differentiated. The impact on the metabolome was found to depend on the length of exercise period with acute exercise also showing significant changes. Metabolic alterations due to aging were equally pronounced in sedentary and trained rats in both urine and blood analyzed samples. PMID:28241477
Sumner, Lloyd W.; Lei, Zhentian; Nikolau, Basil J.; Saito, Kazuki
Plant metabolomics has matured and modern plant metabolomics has accelerated gene discoveries and the elucidation of a variety of plant natural product biosynthetic pathways. This study highlights specific examples of the discovery and characterization of novel genes and enzymes associated with the biosynthesis of natural products such as flavonoids, glucosinolates, terpenoids, and alkaloids. Additional examples of the integration of metabolomics with genome-based functional characterizations of plant natural products that are important to modern pharmaceutical technology are also reviewed. This article also provides a substantial review of recent technical advances in mass spectrometry imaging, nuclear magnetic resonance imaging, integrated LC-MS-SPE-NMR for metabolite identifications, and x-ray crystallography of microgram quantities for structural determinations. The review closes with a discussion on the future prospects of metabolomics related to crop species and herbal medicine.
Prosser, Gareth A; Larrouy-Maumus, Gerald; de Carvalho, Luiz Pedro S
Recent technological advances in accurate mass spectrometry and data analysis have revolutionized metabolomics experimentation. Activity-based and global metabolomic profiling methods allow simultaneous and rapid screening of hundreds of metabolites from a variety of chemical classes, making them useful tools for the discovery of novel enzymatic activities and metabolic pathways. By using the metabolome of the relevant organism or close species, these methods capitalize on biological relevance, avoiding the assignment of artificial and non-physiological functions. This review discusses state-of-the-art metabolomic approaches and highlights recent examples of their use for enzyme annotation, discovery of new metabolic pathways, and gene assignment of orphan metabolic activities across diverse biological sources. PMID:24829223
Ernst, Madeleine; Silva, Denise Brentan; Silva, Ricardo Roberto; Vêncio, Ricardo Z N; Lopes, Norberto Peporine
Covering: up to 2013. Plant metabolomics is a relatively recent research field that has gained increasing interest in the past few years. Up to the present day numerous review articles and guide books on the subject have been published. This review article focuses on the current applications and limitations of the modern mass spectrometry techniques, especially in combination with electrospray ionisation (ESI), an ionisation method which is most commonly applied in metabolomics studies. As a possible alternative to ESI, perspectives on matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-MS) in metabolomics studies are introduced, a method which still is not widespread in the field. In metabolomics studies the results must always be interpreted in the context of the applied sampling procedures as well as data analysis. Different sampling strategies are introduced and the importance of data analysis is illustrated in the example of metabolic network modelling.
Metabolomics involves the application of advanced analytical and statistical tools to profile changes in levels of endogenous metabolites in tissues and biofluids resulting from disease onset, stress, or chemical exposure. Nuclear Magnetic Resonance (NMR) spectroscopy-based meta...
Recently, metabolomics, or the quantitative measurement of a broad spectrum of metabolic responses of living systems in response to disease onset or genetic modification, has been employed to enable rapid identification of the mechanisms of toxicity for compounds of environmental...
This presentation, Using Metabolomics with Neonatal Blood Spots to Discover Causes of Childhood Leukemia, was given at the NIEHS/EPA Children's Centers 2016 Webinar Series: Exposome held on May 11, 2016.
Sumner, Lloyd W.; Lei, Zhentian; Nikolau, Basil J.; ...
Plant metabolomics has matured and modern plant metabolomics has accelerated gene discoveries and the elucidation of a variety of plant natural product biosynthetic pathways. This study highlights specific examples of the discovery and characterization of novel genes and enzymes associated with the biosynthesis of natural products such as flavonoids, glucosinolates, terpenoids, and alkaloids. Additional examples of the integration of metabolomics with genome-based functional characterizations of plant natural products that are important to modern pharmaceutical technology are also reviewed. This article also provides a substantial review of recent technical advances in mass spectrometry imaging, nuclear magnetic resonance imaging, integrated LC-MS-SPE-NMR formore » metabolite identifications, and x-ray crystallography of microgram quantities for structural determinations. The review closes with a discussion on the future prospects of metabolomics related to crop species and herbal medicine.« less
Normal Raman spectroscopy was evaluated as a metabolomic tool for assessing the impacts of exposure to environmental contaminants, using rat urine collected during the course of a toxicological study. Specifically, one of three triazole fungicides, myclobutanil, propiconazole or ...
Johanningsmeier, Suzanne D; Harris, G Keith; Klevorn, Claire M
It is now well documented that the diet has a significant impact on human health and well-being. However, the complete set of small molecule metabolites present in foods that make up the human diet and the role of food production systems in altering this food metabolome are still largely unknown. Metabolomic platforms that rely on nuclear magnetic resonance (NMR) and mass spectrometry (MS) analytical technologies are being employed to study the impact of agricultural practices, processing, and storage on the global chemical composition of food; to identify novel bioactive compounds; and for authentication and region-of-origin classifications. This review provides an overview of the current terminology, analytical methods, and compounds associated with metabolomic studies, and provides insight into the application of metabolomics to generate new knowledge that enables us to produce, preserve, and distribute high-quality foods for health promotion.
Aretz, Ina; Meierhofer, David
Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology. PMID:27128910
Triazoles are a class of fungicides widely used in both pharmaceutical and agricultural applications. These compounds elicit a variety of toxic effects including disruption of normal metabolic processes such as steroidogenesis. Metabolomics is used to measure dynamic changes in e...
Wettersten, Hiromi I; Weiss, Robert H
Metabolomics is one of the relative newcomers of the omics techniques and is likely the one most closely related to actual real-time disease pathophysiology. Hence, it has the power to yield not only specific biomarkers but also insight into the pathophysiology of disease. Despite this power, metabolomics as applied to kidney disease is still in its early adolescence and has not yet reached the mature stage of clinical application, i.e., specific biomarker and therapeutic target discovery. On the other hand, the insight gained from hints into what makes these diseases tick, as is evident from the metabolomics pathways which have been found to be altered in kidney cancer, are now beginning to bear fruit in leading to potential therapeutic targets. It is quite likely that, with greater numbers of clinical materials and with more investigators jumping into the field, metabolomics may well change the course of kidney disease research.
Das, Shamik; Jackson, William P.; Prasain, Jeevan K.; Hanna, Ann; Bailey, Sarah K.; Tucker, J. Allan; Bae, Sejong; Wilson, Landon S.; Samant, Rajeev S.; Barnes, Stephen; Shevde, Lalita A.
The tumor suppressor protein Merlin is proteasomally degraded in breast cancer. We undertook an untargeted metabolomics approach to discern the global metabolomics profile impacted by Merlin in breast cancer cells. We discerned specific changes in glutathione metabolites that uncovered novel facets of Merlin in impacting the cancer cell metabolome. Concordantly, Merlin loss increased oxidative stress causing aberrant activation of Hedgehog signaling. Abrogation of GLI-mediated transcription activity compromised the aggressive phenotype of Merlin-deficient cells indicating a clear dependence of cells on Hedgehog signaling. In breast tumor tissues, GLI1 expression enhanced tissue identification and discriminatory power of Merlin, cumulatively presenting a powerful substantiation of the relationship between these two proteins. We have uncovered, for the first time, details of the tumor cell metabolomic portrait modulated by Merlin, leading to activation of Hedgehog signaling. Importantly, inhibition of Hedgehog signaling offers an avenue to target the vulnerability of tumor cells with loss of Merlin. PMID:28112165
Use of metabolomics in laboratory studies for chemical toxicity evaluation is fast becoming an established technique in environmental science, displaying excellent sensitivity, physiological relevance, and providing valuable information regarding toxic mode(s)-of-action. These qu...
Fukushima, Atsushi; Kusano, Miyako
Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology. PMID:23577015
This research project combines the use of whole organism endpoints, genomic, proteomic and metabolomic approaches, and computational modeling in a systems biology approach to 1) identify molecular indicators of exposure and biomarkers of effect to EDCs representing several modes/...
Do Canto, Luisa Matos; Marian, Catalin; Varghese, Rency S.; Ahn, Jaeil; Da Cunha, Patricia A.; Willey, Shawna; Sidawy, Mary; Rone, Janice D.; Cheema, Amrita K.; Luta, George; Nezami ranjbar, Mohammad R.; Ressom, Habtom W.; Haddad, Bassem R.
Identification of new biomarkers for breast cancer remains critical in order to enhance early detection of the disease and improve its prognosis. Towards this end, we performed an untargeted metabolomic analysis of breast ductal fluid using an ultra-performance liquid chromatography coupled with a quadrupole time-of-light (UPLC-QTOF) mass spectrometer. We investigated the metabolomic profiles of breast tumors using ductal fluid samples collected by ductal lavage (DL). We studied fluid from both the affected breasts and the unaffected contralateral breasts (as controls) from 43 women with confirmed unilateral breast cancer. Using this approach, we identified 1560 ions in the positive mode and 538 ions in the negative mode after preprocessing of the UPLC-QTOF data. Paired t-tests applied on these data matrices identified 209 ions (positive and negative modes combined) with significant change in intensity level between affected and unaffected control breasts (adjusted P-values <0.05). Among these, 83 ions (39.7%) showed a fold change (FC) >1.2 and 66 ions (31.6%) were identified with putative compound names. The metabolites that we identified included endogenous metabolites such as amino acid derivatives (N-Acetyl-DL-tryptophan) or products of lipid metabolism such as N-linoleoyl taurine, trans-2-dodecenoylcarnitine, lysophosphatidylcholine LysoPC(18:2(9Z,12Z)), glycerophospholipids PG(18:0/0:0), and phosphatidylserine PS(20:4(5Z,8Z,11Z,14Z). Generalized LASSO regression further selected 21 metabolites when race, menopausal status, smoking, grade and TNM stage were adjusted for. A predictive conditional logistic regression model, using the LASSO selected 21 ions, provided diagnostic accuracy with the area under the curve of 0.956 (sensitivity/specificity of 0.907/0.884). This is the first study that shows the feasibility of conducting a comprehensive metabolomic profiling of breast tumors using breast ductal fluid to detect changes in the cellular microenvironment of
Walker, Douglas I; Uppal, Karan; Zhang, Luoping; Vermeulen, Roel; Smith, Martyn; Hu, Wei; Purdue, Mark P; Tang, Xiaojiang; Reiss, Boris; Kim, Sungkyoon; Li, Laiyu; Huang, Hanlin; Pennell, Kurt D; Jones, Dean P; Rothman, Nathaniel; Lan, Qing
Background: Occupational exposure to trichloroethylene (TCE) has been linked to adverse health outcomes including non-Hodgkin’s lymphoma and kidney and liver cancer; however, TCE’s mode of action for development of these diseases in humans is not well understood. Methods: Non-targeted metabolomics analysis of plasma obtained from 80 TCE-exposed workers [full shift exposure range of 0.4 to 230 parts-per-million of air (ppma)] and 95 matched controls were completed by ultra-high resolution mass spectrometry. Biological response to TCE exposure was determined using a metabolome-wide association study (MWAS) framework, with metabolic changes and plasma TCE metabolites evaluated by dose-response and pathway enrichment. Biological perturbations were then linked to immunological, renal and exposure molecular markers measured in the same population. Results: Metabolic features associated with TCE exposure included known TCE metabolites, unidentifiable chlorinated compounds and endogenous metabolites. Exposure resulted in a systemic response in endogenous metabolism, including disruption in purine catabolism and decreases in sulphur amino acid and bile acid biosynthesis pathways. Metabolite associations with TCE exposure included uric acid (β = 0.13, P-value = 3.6 × 10−5), glutamine (β = 0.08, P-value = 0.0013), cystine (β = 0.75, P-value = 0.0022), methylthioadenosine (β = −1.6, P-value = 0.0043), taurine (β = −2.4, P-value = 0.0011) and chenodeoxycholic acid (β = −1.3, P-value = 0.0039), which are consistent with known toxic effects of TCE, including immunosuppression, hepatotoxicity and nephrotoxicity. Correlation with additional exposure markers and physiological endpoints supported known disease associations. Conclusions: High-resolution metabolomics correlates measured occupational exposure to internal dose and metabolic response, providing insight into molecular mechanisms of exposure
Rivas-Ubach, Albert; Barbeta, Adrià; Sardans, Jordi; Guenther, Alex; Ogaya, Romà; Oravec, Michal; Urban, Otmar; Peñuelas, Josep
Soils provide physical support, water, and nutrients to terrestrial plants. Upper soil layers are crucial for forest dynamics, especially under drought conditions, because many biological processes occur there and provide support, water and nutrients to terrestrial plants. We postulated that tree size and overall plant function manifested in the metabolome composition, the total set of metabolites, were dependent on the depth of upper soil layers and on water availability. We sampled leaves for stoichiometric and metabolomic analyses once per season from differently sized Quercus ilex trees under natural and experimental drought conditions as projected for the coming decades. Different sized trees had different metabolomes and plots with shallower soils had smaller trees. Soil moisture of the upper soil did not explain the tree size and smaller trees did not show higher concentrations of biomarker metabolites related to drought stress. However, the impact of drought treatment on metabolomes was higher in smaller trees in shallower soils. Our results suggested that tree size was more dependent on the depth of the upper soil layers, which indirectly affect the metabolomes of the trees, than on the moisture content of the upper soil layers. Metabolomic profiling of Q. ilex supported the premise that water availability in the upper soil layers was not necessarily correlated with tree size. The higher impact of drought on trees growing in shallower soils nevertheless indicates a higher vulnerability of small trees to the future increase in frequency, intensity, and duration of drought projected for the Mediterranean Basin and other areas. Metabolomics has proven to be an excellent tool detecting significant metabolic changes among differently sized individuals of the same species and it improves our understanding of the connection between plant metabolomes and environmental variables such as soil depth and moisture content.
Xia, Jianguo; Psychogios, Nick; Young, Nelson; Wishart, David S.
Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca PMID:19429898
Jones, Oliver A H; Sdepanian, Stephanie; Lofts, Steven; Svendsen, Claus; Spurgeon, David J; Maguire, Mahon L; Griffin, Julian L
Metabolic profiling can be used to assess the changes in biochemical profiles of soil communities living in contaminated sites. The term "community metabolomics" is proposed for the application of metabolomics techniques to the study of the entire community of a soil sample. The authors anticipate the present study to be a starting point for the use of this technique to assess how communities respond to factors such as pollution and climate change.
Beger, Richard D.; Sun, Jinchun; Schnackenberg, Laura K.
Hepatotoxicity and nephrotoxicity are two major reasons that drugs are withdrawn post-market, and hence it is of major concern to both the FDA and pharmaceutical companies. The number of cases of serious adverse effects (SAEs) in marketed drugs has climbed faster than the number of total drug prescriptions issued. In some cases, preclinical animal studies fail to identify the potential toxicity of a new chemical entity (NCE) under development. The current clinical chemistry biomarkers of liver and kidney injury are inadequate in terms of sensitivity and/or specificity, prompting the need to discover new translational specific biomarkers of organ injury. Metabolomics along with genomics and proteomics technologies have the capability of providing translational diagnostic and prognostic biomarkers specific for early stages of liver and kidney injury. Metabolomics has several advantages over the other omics platforms such as ease of sample preparation, data acquisition and use of biofluids collected through minimally invasive procedures in preclinical and clinical studies. The metabolomics platform is reviewed with particular emphasis on applications involving drug-induced hepatotoxicity and nephrotoxicity. Analytical platforms for metabolomics, chemometrics for mining metabolomics data and the applications of the metabolomics technologies are covered in detail with emphasis on recent work in the field.
Vernocchi, Pamela; Del Chierico, Federica; Putignani, Lorenza
The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies. PMID:27507964
Guo, Lining; Milburn, Michael V.; Ryals, John A.; Lonergan, Shaun C.; Mitchell, Matthew W.; Wulff, Jacob E.; Alexander, Danny C.; Evans, Anne M.; Bridgewater, Brandi; Miller, Luke; Gonzalez-Garay, Manuel L.; Caskey, C. Thomas
Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care. PMID:26283345
Jiménez-Girón, Ana; Ibáñez, Clara; Cifuentes, Alejandro; Simó, Carolina; Muñoz-González, Irene; Martín-Álvarez, Pedro J; Bartolomé, Begoña; Moreno-Arribas, M Victoria
Faecal metabolome contains information on the metabolites found in the intestine, from which knowledge about the metabolic function of the gut microbiota can be obtained. Changes in the metabolomic profile of faeces reflect, among others, changes in the composition and activity of the intestinal microorganisms. In an effort to improve our understanding of the biological effects that phenolic compounds (including red wine polyphenols) exert at the gut level, in this foodomic study we have undertaken a metabolome characterization of human faeces after moderate consumption of red wine by healthy subjects for 4 weeks. Namely, a nontargeted metabolomic approach based on the use of UHPLC-TOF MS was developed to achieve the maximum metabolite information on 82 human faecal samples. After data processing and statistical analysis, 37 metabolites were related to wine intake, from which 20 could be tentatively or completely identified, including the following: (A) wine compounds, (B) microbial-derived metabolites of wine polyphenols, and (C) endogenous metabolites and/or others derived from other nutrient pathways. After wine consumption, faecal metabolome was fortified in flavan-3-ols metabolites. Also, of relevance was the down regulation of xanthine and bilirubin-derived metabolites such as urobilinogen and stercobilin after moderate wine consumption. As far as we know, this is the first study of the faecal metabolome after wine intake.
López-Bascón, M A; Priego-Capote, F; Peralbo-Molina, A; Calderón-Santiago, M; Luque de Castro, M D
Major threats in metabolomics clinical research are biases in sampling and preparation of biological samples. Bias in sample collection is a frequently forgotten aspect responsible for uncontrolled errors in metabolomics analysis. There is a great diversity of blood collection tubes for sampling serum or plasma, which are widely used in metabolomics analysis. Most of the existing studies dealing with the influence of blood collection on metabolomics analysis have been restricted to comparison between plasma and serum. However, polymeric gel tubes, which are frequently proposed to accelerate the separation of serum and plasma, have not been studied. In the present research, samples of serum or plasma collected in polymeric gel tubes were compared with those taken in conventional tubes from a metabolomics perspective using an untargeted GC-TOF/MS approach. The main differences between serum and plasma collected in conventional tubes affected to critical pathways such as the citric acid cycle, metabolism of amino acids, fructose and mannose metabolism and that of glycerolipids, and pentose and glucuronate interconversion. On the other hand, the polymeric gel only promoted differences at the metabolite level in serum since no critical differences were observed between plasma collected with EDTA tubes and polymeric gel tubes. Thus, the main changes were attributable to serum collected in gel and affected to the metabolism of amino acids such as alanine, proline and threonine, the glycerolipids metabolism, and two primary metabolites such as aconitic acid and lactic acid. Therefore, these metabolite changes should be taken into account in planning an experimental protocol for metabolomics analysis.
Kale, Namrata S; Haug, Kenneth; Conesa, Pablo; Jayseelan, Kalaivani; Moreno, Pablo; Rocca-Serra, Philippe; Nainala, Venkata Chandrasekhar; Spicer, Rachel A; Williams, Mark; Li, Xuefei; Salek, Reza M; Griffin, Julian L; Steinbeck, Christoph
MetaboLights is the first general purpose, open-access database repository for cross-platform and cross-species metabolomics research at the European Bioinformatics Institute (EMBL-EBI). Based upon the open-source ISA framework, MetaboLights provides Metabolomics Standard Initiative (MSI) compliant metadata and raw experimental data associated with metabolomics experiments. Users can upload their study datasets into the MetaboLights Repository. These studies are then automatically assigned a stable and unique identifier (e.g., MTBLS1) that can be used for publication reference. The MetaboLights Reference Layer associates metabolites with metabolomics studies in the archive and is extensively annotated with data fields such as structural and chemical information, NMR and MS spectra, target species, metabolic pathways, and reactions. The database is manually curated with no specific release schedules. MetaboLights is also recommended by journals for metabolomics data deposition. This unit provides a guide to using MetaboLights, downloading experimental data, and depositing metabolomics datasets using user-friendly submission tools.
Aguiar-Pulido, Vanessa; Huang, Wenrui; Suarez-Ulloa, Victoria; Cickovski, Trevor; Mathee, Kalai; Narasimhan, Giri
Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from multiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes. PMID:27199545
Kumar, Alok; Ghosh, Devlina; Singh, R. L.
Amyotrophic lateral sclerosis (ALS) is one of the most common motor neurodegenerative disorders, primarily affecting upper and lower motor neurons in the brain, brainstem, and spinal cord, resulting in paralysis due to muscle weakness and atrophy. The majority of patients die within 3–5 years of symptom onset as a consequence of respiratory failure. Due to relatively fast progression of the disease, early diagnosis is essential. Metabolomics offer a unique opportunity to understand the spatiotemporal metabolic crosstalks through the assessment of body fluids and tissue. So far, one of the most challenging issues related to ALS is to understand the variation of metabolites in body fluids and CNS with the progression of disease. In this paper we will review the changes in metabolic profile in response to disease progression condition and also see the therapeutic implication of various drugs in ALS patients. PMID:26317018
Barnes, V M; Ciancio, S G; Shibly, O; Xu, T; Devizio, W; Trivedi, H M; Guo, L; Jönsson, T J
Periodontitis is a chronic inflammatory disease characterized by tissue destruction. In the diseased oral environment, saliva has primarily been considered to act as a protectant by lubricating the tissue, mineralizing the bones, neutralizing the pH, and combating microbes. To understand the metabolic role that saliva plays in the diseased state, we performed untargeted metabolomic profiling of saliva from healthy and periodontitic individuals. Several classes of biochemicals, including dipeptide, amino acid, carbohydrate, lipids, and nucleotide metabolites, were altered, consistent with increased macromolecular degradation of proteins, triacylglycerol, glycerolphospholipids, polysaccharides, and polynucleotides in the individuals with periodontal disease. These changes partially reflected the enhanced host-bacterial interactions in the diseased state as supported by increased levels of bacterially modified amino acids and creatine metabolite. More importantly, the increased lipase, protease, and glycosidase activities associated with periodontitis generated a more favorable energy environment for oral bacteria, potentially exacerbating the disease state.
Chen, Daian; Ye, Yangfang; Chen, Juanjuan; Yan, Xiaojun
Crab paste is regularly consumed by people in the coastal area of China. The fermentation time plays a key role on the quality of crab paste. Here, we investigated the dynamic evolution of metabolite profile of crab paste during fermentation by combined use of NMR spectroscopy and multivariate data analysis. Our results showed that crab paste quality was significantly affected by fermentation. The quality change was manifested in the decline of lactate, betaine, taurine, trimethylamine-N-oxide, trigonelline, inosine, adenosine diphosphate, and 2-pyridinemethanol, and in the fluctuation of a range of amino acids as well as in the accumulation of glutamate, sucrose, formate, acetate, trimethylamine, and hypoxanthine. Trimethylamine production and its increased level with fermentation could be considered as a freshness index of crab paste. These results contribute to quality assessment of crab paste and confirm the metabolomics technique as a useful tool to provide important information on the crab paste quality.
Nikiforov, Andrey; Kulikova, Veronika; Ziegler, Mathias
Abstract The metabolism of NAD has emerged as a key regulator of cellular and organismal homeostasis. Being a major component of both bioenergetic and signaling pathways, the molecule is ideally suited to regulate metabolism and major cellular events. In humans, NAD is synthesized from vitamin B3 precursors, most prominently from nicotinamide, which is the degradation product of all NAD-dependent signaling reactions. The scope of NAD-mediated regulatory processes is wide including enzyme regulation, control of gene expression and health span, DNA repair, cell cycle regulation and calcium signaling. In these processes, nicotinamide is cleaved from NAD+ and the remaining ADP-ribosyl moiety used to modify proteins (deacetylation by sirtuins or ADP-ribosylation) or to generate calcium-mobilizing agents such as cyclic ADP-ribose. This review will also emphasize the role of the intermediates in the NAD metabolome, their intra- and extra-cellular conversions and potential contributions to subcellular compartmentalization of NAD pools. PMID:25837229
Bingol, Ahmet K.; Bruschweiler-Li, Lei; Li, Dawei; Zhang, Bo; Xie, Mouzhe; Bruschweiler, Rafael
NMR is a very powerful tool for the identification of known and unknown (or unnamed) metabolites in complex mixtures as encountered in metabolomics. Known compounds can be reliably identified using 2D NMR methods, such as 13C-1H HSQC, for which powerful web servers with databases are available for semi-automated analysis. For the identification of unknown compounds, new combinations of NMR with MS have been developed recently that make synergistic use of the mutual strengths of the two techniques. The use of chemical additives to the NMR tube, such as reactive agents, paramagnetic ions, or charged silica nanoparticles, permit the identification of metabolites with specific physical chemical properties. In the following sections, we give an overview of some of the recent advances in metabolite identification and discuss remaining challenges.
Wang, Ningli; Wei, Jianteng; Liu, Yewei; Pei, Dong; Hu, Qingping; Wang, Yu; Di, Duolong
Oxidative stress has a close relationship with various pathologic physiology phenomena and the potential biomarkers of oxidative stress may provide evidence for clinical diagnosis or disease prevention. Metabolomics was employed to identify the potential biomarkers of oxidative stress. High-performance liquid chromatography-diode array detector, mass spectrometry and partial least squares discriminate analysis were used in this study. The 10, 15 and 13 metabolites were considered to discriminate the model group, vitamin E-treated group and l-glutathione-treated group, respectively. Some of them have been identified, namely, malic acid, vitamin C, reduced glutathione and tryptophan. Identification of other potential biomarkers should be conducted and their physiological significance also needs to be elaborated.
Nakabayashi, Ryo; Saito, Kazuki
Plants are considered to biosynthesize specialized (traditionally called secondary) metabolites to adapt to environmental stresses such as biotic and abiotic stresses. The majority of specialized metabolites induced by abiotic stress characteristically exhibit antioxidative activity in vitro, but their function in vivo is largely yet to be experimentally confirmed. In this review, we highlight recent advances in the identification of the role of abiotic stress-responsive specialized metabolites with an emphasis on flavonoids. Integrated 'omics' analysis, centered on metabolomics with a series of plant resources differing in their flavonoid accumulation, showed experimentally that flavonoids play a major role in antioxidation in vivo. In addition, the results also suggest the role of flavonoids in the vacuole. To obtain more in-depth insights, chemical and biological challenges need to be addressed for the identification of unknown specialized metabolites and their in vivo functions.
Gravel, Simon-Pierre; Avizonis, Daina; St-Pierre, Julie
The tumor microenvironment is a complex and heterogeneous milieu in which cancer cells undergo metabolic reprogramming to fuel their growth. Cancer cell lines grown in vitro using traditional culture methods represent key experimental models to gain a mechanistic understanding of tumor biology. This protocol describes the use of gas chromatography-mass spectrometry (GC-MS) to assess metabolic changes in cancer cells grown under varied levels of oxygen and nutrients that may better mimic the tumor microenvironment. Intracellular metabolite changes, metabolite uptake and release, as well as stable isotope ((13)C) tracer analyses are done in a single experimental setup to provide an integrated understanding of metabolic adaptation. Overall, this chapter describes some essential tools and methods to perform comprehensive metabolomics analyses.
Gordon, Benjamin R.; Leggat, William
Symbioses play an important role within the marine environment. Among the most well known of these symbioses is that between coral and the photosynthetic dinoflagellate, Symbiodinium spp. Understanding the metabolic relationships between the host and the symbiont is of the utmost importance in order to gain insight into how this symbiosis may be disrupted due to environmental stressors. Here we summarize the metabolites related to nutritional roles, diel cycles and the common metabolites associated with the invertebrate-Symbiodinium relationship. We also review the more obscure metabolites and toxins that have been identified through natural products and biomarker research. Finally, we discuss the key role that metabolomics and functional genomics will play in understanding these important symbioses. PMID:21116405
Bingol, Kerem; Zhang, Fengli; Bruschweiler-Li, Lei; Bruschweiler, Rafael
A customized metabolomics NMR database, TOCCATA, is introduced, which uses 13C chemical shift information for the reliable identification of metabolites, their spin systems and isomeric states. TOCCATA, whose information was derived from information of the BMRB and HMDB databases and the literature, currently contains 463 compounds and 801 spin systems and it can be used through a publicly accessible web server at http://spinportal.magnet.fsu.edu/toccata/webquery.html. TOCCATA allows the identification of metabolites in the sub-mM concentration range from 13C-13C TOCSY experiments of complex mixtures, which is demonstrated for an E.coli cell lysate, a carbohydrate mixture, and an amino acid mixture, all of which were uniformly 13C-labeled. PMID:23016498
Nikiforov, Andrey; Kulikova, Veronika; Ziegler, Mathias
The metabolism of NAD has emerged as a key regulator of cellular and organismal homeostasis. Being a major component of both bioenergetic and signaling pathways, the molecule is ideally suited to regulate metabolism and major cellular events. In humans, NAD is synthesized from vitamin B3 precursors, most prominently from nicotinamide, which is the degradation product of all NAD-dependent signaling reactions. The scope of NAD-mediated regulatory processes is wide including enzyme regulation, control of gene expression and health span, DNA repair, cell cycle regulation and calcium signaling. In these processes, nicotinamide is cleaved from NAD(+) and the remaining ADP-ribosyl moiety used to modify proteins (deacetylation by sirtuins or ADP-ribosylation) or to generate calcium-mobilizing agents such as cyclic ADP-ribose. This review will also emphasize the role of the intermediates in the NAD metabolome, their intra- and extra-cellular conversions and potential contributions to subcellular compartmentalization of NAD pools.
Jones, Janice; Gunst, Phillip R.; Kacerovsky, Marian; Fortunato, Stephen J.; Saade, George R.; Basraon, Sanmaan
Objective: To identify metabolic changes associated with early spontaneous preterm birth (PTB; <34 weeks) and term births, using high-throughput metabolomics of amniotic fluid (AF) in African American population. Method: In this study, AF samples retrieved from spontaneous PTB (<34 weeks [n = 25]) and normal term birth (n = 25) by transvaginal amniocentesis at the time of labor prior to delivery were subjected to metabolomics analysis. Equal volumes of samples were subjected to a standard solvent extraction method and analyzed using gas chromatography/mass spectrometry (MS) and liquid chromatography/MS/MS. Biochemicals were identified through matching of ion features to a library of biochemical standards. After log transformation and imputation of minimum observed values for each compound, t test, correlation tests, and false discovery rate corrections were used to identify differentially regulated metabolites. Data were controlled for clinical/demographic variables and medication during pregnancy. Results: Of 348 metabolites measured in AF samples, 121 metabolites had a gestational age effect and 116 differed significantly between PTB and term births. A majority of significantly altered metabolites could be classified into 3 categories, namely, (1) liver function, (2) fatty acid and coenzyme A (CoA) metabolism, and (3) histidine metabolism. The signature of altered liver function was apparent in many cytochrome P450-related pathways including bile acids, steroids, xanthines, heme, and phase II detoxification of xenobiotics with the largest fold change seen with pantothenol, a CoA synthesis inhibitor that was 8-fold more abundant in PTB. Conclusion: Global metabolic profiling of AF revealed alteration in hepatic metabolites involving xenobiotic detoxification and CoA metabolism in PTB. Maternal and/or fetal hepatic function differences may be developmentally related and its contribution PTB as a cause or effect of PTB is still unclear. PMID:24440995
Nakabayashi, Ryo; Hashimoto, Kei; Toyooka, Kiminori; Saito, Kazuki
Streamlining the processes that reveal heteroatom-containing metabolites and their biosynthetic genes is essential in integrated metabolomics studies. These metabolites are especially targeted for their potential pharmaceutical activities. By using a Fourier-transform ion cyclotron resonance-mass spectrometry (FTICR-MS) instrument, we provide top-down targeted metabolomic analyses using ultrahigh-resolution liquid chromatography-mass spectrometry (LC-MS), high-resolution matrix-assisted laser desorption/ionization (MALDI), and high-resolution imaging mass spectrometry (IMS) with (15)N labeling of nitrogen-containing metabolites. In this study, we efficiently extract known and unknown chemicals and spatial information from the medicinal plant Catharanthus roseus, which sources several cancer drugs. The ultrahigh-resolution LC-MS analysis showed that the molecular formula of 65 N-metabolites were identified using the petals, peduncles, leaves, petioles, stems, and roots of the non- and (15)N-labeled Catharanthus plants. The high resolution MALDI analysis showed the molecular formula of 64 N-metabolites using the petals, leaves, and stems of the non- and (15)N-labeled Catharanthus. The chemical assignments using molecular formulas stored in databases identified known and unknown metabolites. The comparative analyses using the assigned metabolites revealed that most of the organ-specific ions are derived from unknown N-metabolites. The high-resolution IMS analysis characterized the spatial accumulation patterns of 32 N-metabolites using the buds, leaves, stems, and roots in Catharanthus. The comparative analysis using the non- and (15)N-labeled IMS data showed the same spatial accumulation patterns of a non- and (15)N-labeled metabolite in the organs, showing that top-down analysis can be performed even in IMS analysis.
Sato, Shigeru; Arita, Masanori; Soga, Tomoyoshi; Nishioka, Takaaki; Tomita, Masaru
Background To elucidate the interaction of dynamics among modules that constitute biological systems, comprehensive datasets obtained from "omics" technologies have been used. In recent plant metabolomics approaches, the reconstruction of metabolic correlation networks has been attempted using statistical techniques. However, the results were unsatisfactory and effective data-mining techniques that apply appropriate comprehensive datasets are needed. Results Using capillary electrophoresis mass spectrometry (CE-MS) and capillary electrophoresis diode-array detection (CE-DAD), we analyzed the dynamic changes in the level of 56 basic metabolites in plant foliage (Oryza sativa L. ssp. japonica) at hourly intervals over a 24-hr period. Unsupervised clustering of comprehensive metabolic profiles using Kohonen's self-organizing map (SOM) allowed classification of the biochemical pathways activated by the light and dark cycle. The carbon and nitrogen (C/N) metabolism in both periods was also visualized as a phenotypic linkage map that connects network modules on the basis of traditional metabolic pathways rather than pairwise correlations among metabolites. The regulatory networks of C/N assimilation/dissimilation at each time point were consistent with previous works on plant metabolism. In response to environmental stress, glutathione and spermidine fluctuated synchronously with their regulatory targets. Adenine nucleosides and nicotinamide coenzymes were regulated by phosphorylation and dephosphorylation. We also demonstrated that SOM analysis was applicable to the estimation of unidentifiable metabolites in metabolome analysis. Hierarchical clustering of a correlation coefficient matrix could help identify the bottleneck enzymes that regulate metabolic networks. Conclusion Our results showed that our SOM analysis with appropriate metabolic time-courses effectively revealed the synchronous dynamics among metabolic modules and elucidated the underlying biochemical
Biancone, Luigi; Bussolino, Stefania; Merugumala, Sai; Tezza, Sara; D’Addio, Francesca; Ben Nasr, Moufida; Valderrama-Vasquez, Alessandro; Usuelli, Vera; De Zan, Valentina; El Essawy, Basset; Venturini, Massimo; Secchi, Antonio; De Cobelli, Francesco; Lin, Alexander; Chandraker, Anil; Fiorina, Paolo
Background Alteration of certain metabolites may play a role in the pathophysiology of renal allograft disease. Methods To explore metabolomic abnormalities in individuals with a failing kidney allograft, we analyzed by liquid chromatography-mass spectrometry (LC-MS/MS; for ex vivo profiling of serum and urine) and two dimensional correlated spectroscopy (2D COSY; for in vivo study of the kidney graft) 40 subjects with varying degrees of chronic allograft dysfunction stratified by tertiles of glomerular filtration rate (GFR; T1, T2, T3). Ten healthy non-allograft individuals were chosen as controls. Results LC-MS/MS analysis revealed a dose-response association between GFR and serum concentration of tryptophan, glutamine, dimethylarginine isomers (asymmetric [A]DMA and symmetric [S]DMA) and short-chain acylcarnitines (C4 and C12), (test for trend: T1-T3 = p<0.05; p = 0.01; p<0.001; p = 0.01; p = 0.01; p<0.05, respectively). The same association was found between GFR and urinary levels of histidine, DOPA, dopamine, carnosine, SDMA and ADMA (test for trend: T1-T3 = p<0.05; p<0.01; p = 0.001; p<0.05; p = 0.001; p<0.001; p<0.01, respectively). In vivo 2D COSY of the kidney allograft revealed significant reduction in the parenchymal content of choline, creatine, taurine and threonine (all: p<0.05) in individuals with lower GFR levels. Conclusions We report an association between renal function and altered metabolomic profile in renal transplant individuals with different degrees of kidney graft function. PMID:28052095
Denery, Judith R.; Nunes, Ashlee A. K.; Hixon, Mark S.; Dickerson, Tobin J.; Janda, Kim D.
Background Development of robust, sensitive, and reproducible diagnostic tests for understanding the epidemiology of neglected tropical diseases is an integral aspect of the success of worldwide control and elimination programs. In the treatment of onchocerciasis, clinical diagnostics that can function in an elimination scenario are non-existent and desperately needed. Due to its sensitivity and quantitative reproducibility, liquid chromatography-mass spectrometry (LC-MS) based metabolomics is a powerful approach to this problem. Methodology/Principal Findings Analysis of an African sample set comprised of 73 serum and plasma samples revealed a set of 14 biomarkers that showed excellent discrimination between Onchocerca volvulus–positive and negative individuals by multivariate statistical analysis. Application of this biomarker set to an additional sample set from onchocerciasis endemic areas where long-term ivermectin treatment has been successful revealed that the biomarker set may also distinguish individuals with worms of compromised viability from those with active infection. Machine learning extended the utility of the biomarker set from a complex multivariate analysis to a binary format applicable for adaptation to a field-based diagnostic, validating the use of complex data mining tools applied to infectious disease biomarker discovery and diagnostic development. Conclusions/Significance An LC-MS metabolomics-based diagnostic has the potential to monitor the progression of onchocerciasis in both endemic and non-endemic geographic areas, as well as provide an essential tool to multinational programs in the ongoing fight against this neglected tropical disease. Ultimately this technology can be expanded for the diagnosis of other filarial and/or neglected tropical diseases. PMID:20957145
Lee, Sang-Hyun; Kim, Sooah; Kwon, Min-A; Jung, Young Hoon; Shin, Yong-An; Kim, Kyoung Heon
Well-established metabolome sample preparation is a prerequisite for reliable metabolomic data. For metabolome sampling of a Gram-positive strict anaerobe, Clostridium acetobutylicum, fast filtration and metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v) at -20°C under anaerobic conditions has been commonly used. This anaerobic metabolite processing method is laborious and time-consuming since it is conducted in an anaerobic chamber. Also, there have not been any systematic method evaluation and development of metabolome sample preparation for strict anaerobes and Gram-positive bacteria. In this study, metabolome sampling and extraction methods were rigorously evaluated and optimized for C. acetobutylicum by using gas chromatography/time-of-flight mass spectrometry-based metabolomics, in which a total of 116 metabolites were identified. When comparing the atmospheric (i.e., in air) and anaerobic (i.e., in an anaerobic chamber) processing of metabolome sample preparation, there was no significant difference in the quality and quantity of the metabolomic data. For metabolite extraction, pure methanol at -20°C was a better solvent than acetonitrile/methanol/water (2:2:1, v/v/v) at -20°C that is frequently used for C. acetobutylicum, and metabolite profiles were significantly different depending on extraction solvents. This is the first evaluation of metabolite sample preparation under aerobic processing conditions for an anaerobe. This method could be applied conveniently, efficiently, and reliably to metabolome analysis for strict anaerobes in air.
Song, Ya-Nan; Dong, Shu; Wei, Bin; Liu, Ping; Zhang, Yong-Yu; Su, Shi-Bing
Aims To investigate mechanisms and altered pathways of gypenoside against carbon tetrachloride (CCl4)-induced liver fibrosis based on integrative analysis of proteomics and metabolomics data. Methods CCl4-induced liver fibrosis rats were administrated gypenoside. The anti-fibrosis effects were evaluated by histomorphology and liver hydroxyproline (Hyp) content. Protein profiling and metabolite profiling of rats liver tissues were examined by isobaric tags for relative and absolute quantitation (iTRAQ) approach and gas chromatography-mass spectrometer (GC-MS) technology. Altered pathways and pivotal proteins and metabolites were searched by integrative analysis of proteomics and metabolomics data. The levels of some key proteins in altered pathways were determined by western blot. Results Histopathological changes and Hyp content in gypenoside group had significant improvements (P<0.05). Compared to liver fibrosis model group, we found 301 up-regulated and 296 down-regulated proteins, and 9 up-regulated and 8 down-regulated metabolites in gypenoside group. According to integrative analysis, some important pathways were found, including glycolysis or gluconeogenesis, fructose and mannose metabolism, glycine, serine and threonine metabolism, lysine degradation, arginine and proline metabolism, glutathione metabolism, and sulfur metabolism. Furthermore, the levels of ALDH1B1, ALDH2 and ALDH7A1 were found increased and restored to normal levels after gypenoside treated (P<0.05). Conclusions Gypenoside inhibited CCl4-induced liver fibrosis, which may be involved in the alteration of glycolysis metabolism and the protection against the damage of aldehydes and lipid peroxidation by up-regulating ALDH. PMID:28291813
Singh, Digar; Son, Su Y.; Lee, Choong H.
The trophic interactions of entomopathogenic fungi in different ecological niches viz., soil, plants, or insect themselves are effectively regulated by their maneuvered metabolomes and the plethora of metabotypes. In this article, we discuss a holistic framework of co-evolutionary metabolomes and metabotypes to model the interactions of biocontrol fungi especially with mycosed insects. Conventionally, the studies involving fungal biocontrol mechanisms are reported in the context of much aggrandized fungal entomotoxins while the adaptive response mechanisms of host insects are relatively overlooked. The present review asserts that the selective pressure exerted among the competing or interacting species drives alterations in their overall metabolomes which ultimately implicates in corresponding metabotypes. Quintessentially, metabolomics offers a most generic and tractable model to assess the fungal-insect antagonism in terms of interaction biomarkers, biosynthetic pathway plasticity, and their co-evolutionary defense. The fungi chiefly rely on a battery of entomotoxins viz., secondary metabolites falling in the categories of NRP’s (non-ribosomal peptides), PK’s (polyketides), lysine derive alkaloids, and terpenoids. On the contrary, insects overcome mycosis through employing different layers of immunity manifested as altered metabotypes (phenoloxidase activity) and overall metabolomes viz., carbohydrates, lipids, fatty acids, amino acids, and eicosanoids. Here, we discuss the recent findings within conventional premise of fungal entomotoxicity and the evolution of truculent immune response among host insect. The metabolomic frameworks for fungal–insect interaction can potentially transmogrify our current comprehensions of biocontrol mechanisms to develop the hypervirulent biocontrol strains with least environmental concerns. Moreover, the interaction metabolomics (interactome) in complementation with other -omics cascades could further be applied to address
Takayama, Takahiro; Mochizuki, Toshiki; Todoroki, Kenichiro; Min, Jun Zhe; Mizuno, Hajime; Inoue, Koichi; Akatsu, Hiroyasu; Noge, Ichiro; Toyo'oka, Toshimasa
Chiral metabolites are found in a wide variety of living organisms and some of them are understood to be physiologically active compounds and biomarkers. However, the overall analysis of chiral metabolomics is quite difficult due to the high number of metabolites, the significant diversity in their physicochemical properties, and concentration range from metabolite-to-metabolite. To solve this difficulty, we developed a novel approach for chiral metabolomics fingerprinting and chiral metabolomics extraction, which is based on the labeling of a pair of enantiomers of chiral derivatization reagents (i.e., DMT-(S,R)-Pro-OSu and DMT-3(S,R)-Apy) and precursor ion scan chromatography of the derivatives. The multivariate statistics is also required for this strategy. The proposed procedures were evaluated by the detection of a diagnostic marker (i.e., d-lactic acid) using the saliva of diabetic patients. This method was used for the determination of biomarker candidates of chiral amines and carboxyls in Alzheimer's disease (AD) brain homogenates. As the results, l-phenylalanine (L-Phe) and l-lactic acid (L-LA) were identified as the decreased and increased biomarker candidates in the AD brain, respectively. Therefore, the proposed approach seems to be helpful for the determination of non-target chiral metabolomics possessing amines and carboxyls.
Southam, Andrew D; Lange, Anke; Al-Salhi, Raghad; Hill, Elizabeth M; Tyler, Charles R; Viant, Mark R
Environmental metabolomics is increasingly used to investigate organismal responses to complex chemical mixtures, including waste water effluent (WWE). In parallel, increasingly sensitive analytical methods are being used in metabolomics studies, particularly mass spectrometry. This introduces a considerable, yet overlooked, challenge that high analytical sensitivity will not only improve the detection of endogenous metabolites in biological specimens but also exogenous chemicals. If these often unknown xenobiotic features are not removed from the "biological" dataset, they will bias the interpretation and could lead to incorrect conclusions about the biotic response. Here we illustrate and validate a novel workflow classifying the origin of peaks detected in biological samples as: endogenous, xenobiotics, or metabolised xenobiotics. The workflow is demonstrated using direct infusion mass spectrometry-based metabolomic analysis of testes from roach exposed to different concentrations of a complex WWE. We show that xenobiotics and their metabolic products can be detected in roach testes (including triclosan, chloroxylenol and chlorophene), and that these compounds have a disproportionately high level of statistical significance within the total (bio)chemical changes induced by the WWE. Overall we have demonstrated that this workflow extracts more information from an environmental metabolomics study of complex mixture exposures than was possible previously.
Ortmayr, Karin; Hann, Stephan
Efficient and robust separation methods are indispensable in modern LC-MS based metabolomics, where high-resolution mass spectrometers are challenged by isomeric and isobaric metabolites. The optimization of chromatographic separation hence remains an invaluable tool in the comprehensive analysis of the chemically diverse intracellular metabolome. While it is widely accepted that a single method with comprehensive metabolome coverage does not exist, the potential of combining different chromatographic selectivities in two-dimensional liquid chromatography is underestimated in the field. Here, we introduce a novel separation system combining reversed-phase and porous graphitized carbon liquid chromatography in a heart-cut on-line two-dimensional setup for mass spectrometry. The proposed experimental setup can be readily implemented using standard HPLC equipment with only one additional HPLC pump and a two-position six-port valve. The method proved to be robust with excellent retention time stability (average 0.4%) even in the presence of biological matrix. Testing the presented approach on a test mixture of 82 relevant intracellular metabolites, the number of metabolites that are retained could be doubled as compared to reversed-phase liquid chromatography alone. The presented work further demonstrates how the distinct selectivity of porous graphitized carbon complements reversed-phase liquid chromatography and extends the metabolome coverage of conventional LC-MS based methods in metabolomics to biologically important, but analytically challenging compound groups such as sugar phosphates. Both metabolic profiling and metabolic fingerprinting benefit from this method's increased separation capabilities that enhance sample throughput and the biological information content of LC-MS data. An inter-platform comparison with GC- and LC-tandem MS analyses confirmed the validity of the presented two-dimensional approach in the analysis of yeast cell extracts from P
Liebeke, Manuel; Dörries, Kirsten; Meyer, Hanna; Lalk, Michael
The field of metabolomics has become increasingly important in the context of functional genomics. Together with other "omics" data, the investigation of the metabolome is an essential part of systems biology. Beside the analysis of human and animal biofluids, the investigation of the microbial physiology by methods of metabolomics has gained increased attention. For example, the analysis of metabolic processes during growth or virulence factor expression is crucially important to understand pathogenesis of bacteria. Common bioanalytical techniques for metabolome analysis include liquid and gas chromatographic methods coupled to mass spectrometry (LC-MS and GC-MS) and spectroscopic approaches such as NMR. In order to achieve metabolome data representing the physiological status of a microorganism, well-verified protocols for sampling and analysis are necessary. This chapter presents a detailed protocol for metabolome analysis of the Gram-positive bacterium Staphylococcus aureus. A detailed manual for cell sampling and metabolite extraction is given, followed by the description of the analytical procedures GC-MS and LC-MS. The advantages and limitations of each experimental setup are discussed. Here, a guideline specified for S. aureus metabolomics and information for important protocol steps are presented, to avoid common pitfalls in microbial metabolome analysis.
Rivas-Ubach, Albert; Hódar, José A.; Sardans, Jordi; Kyle, Jennifer E.; Kim, Young-Mo; Oravec, Michal; Urban, Otmar; Guenther, Alex; Peñuelas, Josep
The debate whether the coevolution of plants and insects or macroevolutionary processes (phylogeny) is the main driver determining the arsenal of molecular defensive compounds of plants remains unresolved. Attacks by herbivorous insects affect not only the composition of defensive compounds in plants but the entire metabolome (the set of molecular metabolites), including defensive compounds. Metabolomes are the final products of genotypes and are directly affected by macroevolutionary processes, so closely related species should have similar metabolomic compositions and may respond in similar ways to attacks by folivores. We analyzed the elemental compositions and metabolomes of needles from Pinus pinaster, P. nigra and P. sylvestris to determine if these closely related Pinus species with different coevolutionary histories with the caterpillars of the processionary moth respond similarly to attacks by this lepidopteran. All pines had different metabolomes and metabolic responses to herbivorous attack. The metabolomic variation among the pine species and the responses to folivory reflected their macroevolutionary relationships, with P. pinaster having the most divergent metabolome. The concentrations of phenolic metabolites were generally not higher in the attacked trees, which had lower concentrations of terpenes, suggesting that herbivores avoid individuals with high concentrations of terpenes. Our results suggest that macroevolutionary history plays important roles in the metabolomic responses of these pine species to folivory, but plant-insect coevolution probably constrains those responses. Combinations of different evolutionary factors and trade-offs are likely responsible for the different responses of each species to folivory, which is not necessarily exclusively linked to plant-insect coevolution.
Su, Qiao; Guan, Tianbing; Lv, Haitao
Uropathogenic Escherichia coli (UPEC) growth in women’s bladders during urinary tract infection (UTI) incurs substantial chemical exchange, termed the “interactive metabolome”, which primarily accounts for the metabolic costs (utilized metabolome) and metabolic donations (excreted metabolome) between UPEC and human urine. Here, we attempted to identify the individualized interactive metabolome between UPEC and human urine. We were able to distinguish UPEC from non-UPEC by employing a combination of metabolomics and genetics. Our results revealed that the interactive metabolome between UPEC and human urine was markedly different from that between non-UPEC and human urine, and that UPEC triggered much stronger perturbations in the interactive metabolome in human urine. Furthermore, siderophore biosynthesis coordinately modulated the individualized interactive metabolome, which we found to be a critical component of UPEC virulence. The individualized virulence-associated interactive metabolome contained 31 different metabolites and 17 central metabolic pathways that were annotated to host these different metabolites, including energetic metabolism, amino acid metabolism, and gut microbe metabolism. Changes in the activities of these pathways mechanistically pinpointed the virulent capability of siderophore biosynthesis. Together, our findings provide novel insights into UPEC virulence, and we propose that siderophores are potential targets for further discovery of drugs to treat UPEC-induced UTI. PMID:27076285
Klawitter, Jelena; Chonchol, Michel; Bassett, Candace J.; Racine, Matthew L.; Seals, Douglas R.
Background and objectives Metabolomics is a relatively new field of “-omics” research, focusing on high-throughput identification of small molecular weight metabolites. Diet has both acute and chronic effects on metabolic profiles; however, alterations in response to dietary sodium restriction (DSR) are completely unknown. The goal of this study was to explore changes in urine metabolites in response to DSR, as well as their association with previously reported improvements in vascular function with DSR. Design, setting, participants, & measurements Using stored urine samples from a 10-week randomized placebo-controlled crossover study of DSR in 17 middle-aged/older adults (six men and 11 women; mean age 62±8 years) who had moderately elevated systolic BP (130–159 mmHg) and were otherwise healthy, a liquid chromatography/mass spectrometry–based analysis of 289 metabolites was performed. This study identified metabolites that were significantly altered between the typical (153±29 mmol/d) and low (70±29 mmol/d) sodium conditions, as well as their baseline (typical sodium) association with responsiveness to previously reported improvements in vascular endothelial function (brachial artery flow-mediated dilation) and large elastic artery stiffness (aortic pulse wave velocity). Results Of the 289 metabolites surveyed, 10 were significantly altered (nine were upregulated and one was downregulated) during the low sodium condition, and eight of these exceeded our prespecified clinically significant threshold of a >40% change. These metabolites were involved in biologic pathways broadly related to cardiovascular risk, nitric oxide production, oxidative stress, osmotic regulation, and metabolism. One metabolite, serine, was independently (positively) associated with previously reported improvements in the primary vascular outcome of brachial artery flow-mediated dilation. Conclusions This proof-of-concept study provides the first evidence that DSR is a stimulus
Yu, Bing; Zheng, Yan; Nettleton, Jennifer A.; Alexander, Danny; Coresh, Josef
Background and objectives Novel biomarkers that more accurately reflect kidney function and predict future CKD are needed. The human metabolome is the product of multiple physiologic or pathophysiologic processes and may provide novel insight into disease etiology and progression. This study investigated whether estimated kidney function would be associated with multiple metabolites and whether selected metabolomic factors would be independent risk factors for incident CKD. Design, setting, participants, & measurements In total, 1921 African Americans free of CKD with a median of 19.6 years follow-up among the Atherosclerosis Risk in Communities Study were included. A total of 204 serum metabolites quantified by untargeted gas chromatography–mass spectrometry and liquid chromatography–mass spectrometry was analyzed by both linear regression for the cross-sectional associations with eGFR (specified by the Chronic Kidney Disease Epidemiology Collaboration equation) and Cox proportional hazards model for the longitudinal associations with incident CKD. Results Forty named and 34 unnamed metabolites were found to be associated with eGFR specified by the Chronic Kidney Disease Epidemiology Collaboration equation with creatine and 3-indoxyl sulfate showing the strongest positive (2.8 ml/min per 1.73 m2 per +1 SD; 95% confidence interval, 2.1 to 3.5) and negative association (−14.2 ml/min per 1.73 m2 per +1 SD; 95% confidence interval, −17.0 to −11.3), respectively. Two hundred four incident CKD events with a median follow-up time of 19.6 years were included in the survival analyses. Higher levels of 5-oxoproline (hazard ratio, 0.70; 95% confidence interval, 0.60 to 0.82) and 1,5-anhydroglucitol (hazard ratio, 0.68; 95% confidence interval, 0.58 to 0.80) were significantly related to lower risk of incident CKD, and the associations did not appreciably change when mutually adjusted. Conclusions These data identify a large number of metabolites associated with
Mena-Bravo, A; Luque de Castro, M D
Sweat is a biofluid with present scant use as clinical sample. This review tries to demonstrate the advantages of sweat over other biofluids such as blood or urine for routine clinical analyses and the potential when related to metabolomics. With this aim, critical discussion of sweat samplers and equipment for analysis of target compounds in this sample is made. Well established routine analyses in sweat as is that to diagnose cystic fibrosis, and the advantages and disadvantages of sweat versus urine or blood for doping control have also been discussed. Methods for analytes such as essential metals and xenometals, ethanol and electrolytes in sweat in fact constitute target metabolomics approaches or belong to any metabolomics subdiscipline such as metallomics, ionomics or xenometabolomics. The higher development of biomarkers based on genomics or proteomics as omics older than metabolomics is discussed and also the potential role of metabolomics in systems biology taking into account its emergent implementation. Normalization of the volume of sampled sweat constitutes a present unsolved shortcoming that deserves investigation. Foreseeable trends in this area are outlined.
Creek, Darren J.; Nijagal, Brunda; Kim, Dong-Hyun; Rojas, Federico; Matthews, Keith R.
In vitro culture methods underpin many experimental approaches to biology and drug discovery. The modification of established cell culture methods to make them more biologically relevant or to optimize growth is traditionally a laborious task. Emerging metabolomic technology enables the rapid evaluation of intra- and extracellular metabolites and can be applied to the rational development of cell culture media. In this study, untargeted semiquantitative and targeted quantitative metabolomic analyses of fresh and spent media revealed the major nutritional requirements for the growth of bloodstream form Trypanosoma brucei. The standard culture medium (HMI11) contained unnecessarily high concentrations of 32 nutrients that were subsequently removed to make the concentrations more closely resemble those normally found in blood. Our new medium, Creek's minimal medium (CMM), supports in vitro growth equivalent to that in HMI11 and causes no significant perturbation of metabolite levels for 94% of the detected metabolome (<3-fold change; α = 0.05). Importantly, improved sensitivity was observed for drug activity studies in whole-cell phenotypic screenings and in the metabolomic mode of action assays. Four-hundred-fold 50% inhibitory concentration decreases were observed for pentamidine and methotrexate, suggesting inhibition of activity by nutrients present in HMI11. CMM is suitable for routine cell culture and offers important advantages for metabolomic studies and drug activity screening. PMID:23571546
Park, Seokjae; Sadanala, Krishna Chaitanya; Kim, Eun-Kyoung
Obesity and diabetes arise from an intricate interplay between both genetic and environmental factors. It is well recognized that obesity plays an important role in the development of insulin resistance and diabetes. Yet, the exact mechanism of the connection between obesity and diabetes is still not completely understood. Metabolomics is an analytical approach that aims to detect and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics to the identification of disease biomarkers, with a number of well-known biomarkers identified. Metabolomics is a potent approach to unravel the intricate relationships between metabolism, obesity and progression to diabetes and, at the same time, has potential as a clinical tool for risk evaluation and monitoring of disease. Moreover, metabolomics applications have revealed alterations in the levels of metabolites related to obesity-associated diabetes. This review focuses on the part that metabolomics has played in elucidating the roles of metabolites in the regulation of systemic metabolism relevant to obesity and diabetes. It also explains the possible metabolic relation and association between the two diseases. The metabolites with altered profiles in individual disorders and those that are specifically and similarly altered in both disorders are classified, categorized and summarized. PMID:26072981
Yoon, Changshin; Yoon, Dahye; Cho, Junghee; Kim, Siwon; Lee, Heonho; Choi, Hyeonsoo; Kim, Suhkmann
Proton nuclear magnetic resonance ((1)H-NMR) spectroscopy was used to study the response of zebrafish (Danio rerio) to increasing concentrations of bisphenol A (4,4'-(propane-2,2-diyl)diphenol, BPA). Orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to detect aberrant metabolomic profiles after 72 h of BPA exposure at all levels tested (0.01, 0.1, and 1.0 mg/L). The OPLS-DA score plots showed that BPA exposure caused significant alterations in the metabolome. The metabolomic changes in response to BPA exposure generally exhibited nonlinear patterns, with the exception of reduced levels of several metabolites, including glutamine, inosine, lactate, and succinate. As the level of BPA exposure increased, individual metabolite patterns indicated that the zebrafish metabolome was subjected to severe oxidative stress. Interestingly, ATP levels increased significantly at all levels of BPA exposure. In the present study, we demonstrated the applicability of (1)H-NMR-based metabolomics to identify the discrete nature of metabolic changes.
Patel, Seema; Ahmed, Shadab
Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.
Sakurai, Nozomu; Ara, Takeshi; Enomoto, Mitsuo; Motegi, Takeshi; Morishita, Yoshihiko; Kurabayashi, Atsushi; Iijima, Yoko; Ogata, Yoshiyuki; Nakajima, Daisuke; Suzuki, Hideyuki; Shibata, Daisuke
A metabolome--the collection of comprehensive quantitative data on metabolites in an organism--has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study on Jatropha curcas is presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data.
Srivastava, Anubhav; Kowalski, Greg M.; Callahan, Damien L.; Meikle, Peter J.; Creek, Darren J.
This is a perspective from the peer session on stable isotope labelling and fluxomics at the Australian & New Zealand Metabolomics Conference (ANZMET) held from 30 March to 1 April 2016 at La Trobe University, Melbourne, Australia. This report summarizes the key points raised in the peer session which focused on the advantages of using stable isotopes in modern metabolomics and the challenges in conducting flux analyses. The session highlighted the utility of stable isotope labelling in generating reference standards for metabolite identification, absolute quantification, and in the measurement of the dynamic activity of metabolic pathways. The advantages and disadvantages of different approaches of fluxomics analyses including flux balance analysis, metabolic flux analysis and kinetic flux profiling were also discussed along with the use of stable isotope labelling in in vivo dynamic metabolomics. A number of crucial technical considerations for designing experiments and analyzing data with stable isotope labelling were discussed which included replication, instrumentation, methods of labelling, tracer dilution and data analysis. This report reflects the current viewpoint on the use of stable isotope labelling in metabolomics experiments, identifying it as a great tool with the potential to improve biological interpretation of metabolomics data in a number of ways. PMID:27706078
Wood, Paul L
Metabolomics research has the potential to provide biomarkers for the detection of disease, for subtyping complex disease populations, for monitoring disease progression and therapy, and for defining new molecular targets for therapeutic intervention. These potentials are far from being realized because of a number of technical, conceptual, financial, and bioinformatics issues. Mass spectrometry provides analytical platforms that address the technical barriers to success in metabolomics research; however, the limited commercial availability of analytical and stable isotope standards has created a bottleneck for the absolute quantitation of a number of metabolites. Conceptual and financial factors contribute to the generation of statistically under-powered clinical studies, whereas bioinformatics issues result in the publication of a large number of unidentified metabolites. The path forward in this field involves targeted metabolomics analyses of large control and patient populations to define both the normal range of a defined metabolite and the potential heterogeneity (eg, bimodal) in complex patient populations. This approach requires that metabolomics research groups, in addition to developing a number of analytical platforms, build sufficient chemistry resources to supply the analytical standards required for absolute metabolite quantitation. Examples of metabolomics evaluations of sulfur amino-acid metabolism in psychiatry, neurology, and neuro-oncology and of lipidomics in neurology will be reviewed.
Rinehart, Duane; Epstein, Adrian; Kurczy, Michael E.; Boska, Michael D.; Gendelman, Howard E.
Heat maps are a commonly used visualization tool for metabolomic data where the relative abundance of ions detected in each sample is represented with color intensity. A limitation of applying heat maps to global metabolomic data, however, is the large number of ions that have to be displayed and the lack of information provided about important metabolomic parameters such as m/z and retention time. Here we address these challenges by introducing the interactive cluster heat map in the data-processing software XCMS Online. XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process, statistically evaluate, and visualize mass-spectrometry based metabolomic data. An interactive heat map is provided for all data processed by XCMS Online. The heat map is clickable, allowing users to zoom and explore specific metabolite metadata (EICs, Box-and-whisker plots, mass spectra) that are linked to the METLIN metabolite database. The utility of the XCMS interactive heat map is demonstrated on metabolomic data set generated from different anatomical regions of the mouse brain. PMID:26195918
Fischer, Roman; Bowness, Paul; Kessler, Benedikt M
Proteomic research facilities and laboratories are facing increasing demands for the integration of biological data from multiple ‘-OMICS’ approaches. The aim to fully understand biological processes requires the integrated study of genomes, proteomes and metabolomes. While genomic and proteomic workflows are different, the study of the metabolome overlaps significantly with the latter, both in instrumentation and methodology. However, chemical diversity complicates an easy and direct access to the metabolome by mass spectrometry (MS). The present review provides an introduction into metabolomics workflows from the viewpoint of proteomic researchers. We compare the physicochemical properties of proteins and peptides with metabolites/small molecules to establish principle differences between these analyte classes based on human data. We highlight the implications this may have on sample preparation, separation, ionisation, detection and data analysis. We argue that a typical proteomic workflow (nLC-MS) can be exploited for the detection of a number of aliphatic and aromatic metabolites, including fatty acids, lipids, prostaglandins, di/tripeptides, steroids and vitamins, thereby providing a straightforward entry point for metabolomics-based studies. Limitations and requirements are discussed as well as extensions to the LC-MS workflow to expand the range of detectable molecular classes without investing in dedicated instrumentation such as GC-MS, CE-MS or NMR. PMID:24155035
Trushina, Eugenia; Mielke, Michelle M.
The pathophysiological changes associated with Alzheimer’s Disease (AD) begin decades before the emergence of clinical symptoms. Understanding the early mechanisms associated with AD pathology is, therefore, especially important for identifying disease-modifying therapeutic targets. While the majority of AD clinical trials to date have focused on anti-amyloid-beta (Aβ) treatments, other therapeutic approaches may be necessary. The ability to monitor changes in cellular networks that include both Aβ and non-Aβ pathways is essential to advance our understanding of the etiopathogenesis of AD and subsequent development of cognitive symptoms and dementia. Metabolomics is a powerful tool that detects perturbations in the metabolome, a pool of metabolites that reflects changes downstream of genomic, transcriptomic and proteomic fluctuations, and represents an accurate biochemical profile of the organism in health and disease. The application of metabolomics could help to identify biomarkers for early AD diagnosis, to discover novel therapeutic targets, and to monitor therapeutic response and disease progression. Moreover, given the considerable parallel between mouse and human metabolism, the use of metabolomics provides ready translation of animal research into human studies for accelerated drug design. In this review, we will summarize current progress in the application of metabolomics in both animal models and in humans to further understanding of the mechanisms involved in AD pathogenesis. PMID:23816564
Xia, Jianguo; Wishart, David S
MetaboAnalyst (http://www.metaboanalyst.ca) is a comprehensive Web application for metabolomic data analysis and interpretation. MetaboAnalyst handles most of the common metabolomic data types from most kinds of metabolomics platforms (MS and NMR) for most kinds of metabolomics experiments (targeted, untargeted, quantitative). In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst also supports a number of data analysis and data visualization tasks using a range of univariate, multivariate methods such as PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), heatmap clustering and machine learning methods. MetaboAnalyst also offers a variety of tools for metabolomic data interpretation including MSEA (metabolite set enrichment analysis), MetPA (metabolite pathway analysis), and biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAnalyst 3.0), followed by eight detailed protocols. © 2016 by John Wiley & Sons, Inc.
Cambiaghi, Alice; Ferrario, Manuela; Masseroli, Marco
Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different 'omics' data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCore(TM), MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other 'omics' data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration.
Cao, Hongxin; Zhang, Aihua; Zhang, Huamin; Sun, Hui; Wang, Xijun
Metabolomics provides an opportunity to develop the systematic analysis of the metabolites and has been applied to discovering biomarkers and perturbed pathways which can clarify the action mechanism of traditional Chinese medicines (TCM). TCM is a comprehensive system of medical practice that has been used to diagnose, treat and prevent illnesses more than 3000 years. Metabolomics represents a powerful approach that provides a dynamic picture of the phenotype of biosystems through the study of endogenous metabolites, and its methods resemble those of TCM. Recently, metabolomics tools have been used for facilitating interactional effects of both Western medicine and TCM. We describe a protocol for investigating how metabolomics can be used to open up 'dialogue' between Chinese and Western medicine, and facilitate lead compound discovery and development from TCM. Metabolomics will bridge the cultural gap between TCM and Western medicine and improve development of integrative medicine, and maximally benefiting the human.
Demine, Stéphane; Reddy, Nagabushana; Renard, Patricia; Raes, Martine; Arnould, Thierry
Mitochondrial dysfunction(s) (MDs) can be defined as alterations in the mitochondria, including mitochondrial uncoupling, mitochondrial depolarization, inhibition of the mitochondrial respiratory chain, mitochondrial network fragmentation, mitochondrial or nuclear DNA mutations and the mitochondrial accumulation of protein aggregates. All these MDs are known to alter the capacity of ATP production and are observed in several pathological states/diseases, including cancer, obesity, muscle and neurological disorders. The induction of MDs can also alter the secretion of several metabolites, reactive oxygen species production and modify several cell-signalling pathways to resolve the mitochondrial dysfunction or ultimately trigger cell death. Many metabolites, such as fatty acids and derived compounds, could be secreted into the blood stream by cells suffering from mitochondrial alterations. In this review, we summarize how a mitochondrial uncoupling can modify metabolites, the signalling pathways and transcription factors involved in this process. We describe how to identify the causes or consequences of mitochondrial dysfunction using metabolomics (liquid and gas chromatography associated with mass spectrometry analysis, NMR spectroscopy) in the obesity and insulin resistance thematic. PMID:25257998
Zhang, Meiling; Bao, Shihui; Lin, Feiou; Lin, Yingying; Zhang, Lijing; Xu, Mengzhi; Huang, Xueli; Wen, Congcong; Hu, Lufeng; Lin, Guanyang
This study aimed to evaluate the effect of Datura stramonium on rats by examining the differences in urine and serum metabolites between Datura stramonium groups and control group. SIMCA-P+22.214.171.124 software was used for partial least-squares discriminant analysis (PLS-DA) to screen for the differential metabolites. Fifteen metabolites in urine including malonic acid, pentanedioic acid, D-xylose, D-ribose, xylulose, azelaic acid, threitol, glycine, butanoic acid, D-mannose, D-gluconic acid, galactonic acid, myo-inositol, octadecanoic acid, pseudouridine and ten metabolites in serum including alanine, butanedioic acid, L-methionine, propanedioic acid, hexadecanoic acid, D-fructose, tetradecanoic acid, D-glucose, D-galactose, oleic acid were selected as the characteristic metabolites. The PLS-DA scores plot indicated that serum and urine metabolites have a variety of changes among low dose group, high dose group and control group. These metabolites were related with amino metabolism, lipid metabolism and energy metabolism. The result reflected the relationship between metabolites in rat fluid and Datura stramonium spectra. Potential differences in metabolites and metabolic pathway analysis showed that the establishment of urine and serum metabolomics methods for further evaluating drug has great significance.
Zhang, Meiling; Bao, Shihui; Lin, Feiou; Lin, Yingying; Zhang, Lijing; Xu, Mengzhi; Huang, Xueli; Wen, Congcong; Hu, Lufeng; Lin, Guanyang
This study aimed to evaluate the effect of Datura stramonium on rats by examining the differences in urine and serum metabolites between Datura stramonium groups and control group. SIMCA-P+126.96.36.199 software was used for partial least-squares discriminant analysis (PLS-DA) to screen for the differential metabolites. Fifteen metabolites in urine including malonic acid, pentanedioic acid, D-xylose, D-ribose, xylulose, azelaic acid, threitol, glycine, butanoic acid, D-mannose, D-gluconic acid, galactonic acid, myo-inositol, octadecanoic acid, pseudouridine and ten metabolites in serum including alanine, butanedioic acid, L-methionine, propanedioic acid, hexadecanoic acid, D-fructose, tetradecanoic acid, D-glucose, D-galactose, oleic acid were selected as the characteristic metabolites. The PLS-DA scores plot indicated that serum and urine metabolites have a variety of changes among low dose group, high dose group and control group. These metabolites were related with amino metabolism, lipid metabolism and energy metabolism. The result reflected the relationship between metabolites in rat fluid and Datura stramonium spectra. Potential differences in metabolites and metabolic pathway analysis showed that the establishment of urine and serum metabolomics methods for further evaluating drug has great significance. PMID:26885052
Sherman, Mara H.; Yu, Ruth T.; Tseng, Tiffany W.; Sousa, Cristovao M.; Liu, Sihao; Truitt, Morgan L.; He, Nanhai; Ding, Ning; Liddle, Christopher; Atkins, Annette R.; Leblanc, Mathias; Collisson, Eric A.; Asara, John M.; Kimmelman, Alec C.; Downes, Michael; Evans, Ronald M.
A fibroinflammatory stromal reaction cooperates with oncogenic signaling to influence pancreatic ductal adenocarcinoma (PDAC) initiation, progression, and therapeutic outcome, yet the mechanistic underpinning of this crosstalk remains poorly understood. Here we show that stromal cues elicit an adaptive response in the cancer cell including the rapid mobilization of a transcriptional network implicated in accelerated growth, along with anabolic changes of an altered metabolome. The close overlap of stroma-induced changes in vitro with those previously shown to be regulated by oncogenic Kras in vivo suggests that oncogenic Kras signaling—a hallmark and key driver of PDAC—is contingent on stromal inputs. Mechanistically, stroma-activated cancer cells show widespread increases in histone acetylation at transcriptionally enhanced genes, implicating the PDAC epigenome as a presumptive point of convergence between these pathways and a potential therapeutic target. Notably, inhibition of the bromodomain and extraterminal (BET) family of epigenetic readers, and of Bromodomain-containing protein 2 (BRD2) in particular, blocks stroma-inducible transcriptional regulation in vitro and tumor progression in vivo. Our work suggests the existence of a molecular “AND-gate” such that tumor activation is the consequence of mutant Kras and stromal cues, providing insight into the role of the tumor microenvironment in the origin and treatment of Ras-driven tumors. PMID:28096419
Fan, Teresa W-M.; Lane, Andrew N.; Higashi, Richard M.
An important component of this methodology is to assess the role of the tumor microenvironment on tumor growth and survival. To tackle this problem, we have adapted the original approach of Warburg 1, by combining thin tissue slices with Stable Isotope Resolved Metabolomics (SIRM) to determine detailed metabolic activity of human tissues. SIRM enables the tracing of metabolic transformations of source molecules such as glucose or glutamine over defined time periods, and is a requirement for detailed pathway tracing and flux analysis. In our approach, we maintain freshly resected tissue slices (both cancerous and non- cancerous from the same organ of the same subject) in cell culture media, and treat with appropriate stable isotope-enriched nutrients, e.g. 13C6-glucose or 13C5, 15N2 -glutamine. These slices are viable for at least 24 h, and make it possible to eliminate systemic influence on the target tissue metabolism while maintaining the original 3D cellular architecture. It is therefore an excellent pre-clinical platform for assessing the effect of therapeutic agents on target tissue metabolism and their therapeutic efficacy on individual patients 2,3. PMID:27158639
Gerstl, Matthias P; Ruckerbauer, David E; Mattanovich, Diethard; Jungreuthmayer, Christian; Zanghellini, Jürgen
Elementary flux modes (EFMs) are non-decomposable steady-state pathways in metabolic networks. They characterize phenotypes, quantify robustness or identify engineering targets. An EFM analysis (EFMA) is currently restricted to medium-scale models, as the number of EFMs explodes with the network's size. However, many topologically feasible EFMs are biologically irrelevant. We present thermodynamic EFMA (tEFMA), which calculates only the small(er) subset of thermodynamically feasible EFMs. We integrate network embedded thermodynamics into EFMA and show that we can use the metabolome to identify and remove thermodynamically infeasible EFMs during an EFMA without losing biologically relevant EFMs. Calculating only the thermodynamically feasible EFMs strongly reduces memory consumption and program runtime, allowing the analysis of larger networks. We apply tEFMA to study the central carbon metabolism of E. coli and find that up to 80% of its EFMs are thermodynamically infeasible. Moreover, we identify glutamate dehydrogenase as a bottleneck, when E. coli is grown on glucose and explain its inactivity as a consequence of network embedded thermodynamics. We implemented tEFMA as a Java package which is available for download at https://github.com/mpgerstl/tEFMA.
Yu, Bing; Li, Alexander H.; Metcalf, Ginger A.; Muzny, Donna M.; Morrison, Alanna C.; White, Simon; Mosley, Thomas H.; Gibbs, Richard A.; Boerwinkle, Eric
The metabolome is a collection of small molecules resulting from multiple cellular and biological processes that can act as biomarkers of disease, and African-Americans exhibit high levels of genetic diversity. Exome sequencing of a sample of deeply phenotyped African-Americans allowed us to analyze the effects of annotated loss-of-function (LoF) mutations on 308 serum metabolites measured by untargeted liquid and gas chromatography coupled with mass spectrometry. In an independent sample, we identified and replicated four genes harboring six LoF mutations that significantly affected five metabolites. These sites were related to a 19 to 45% difference in geometric mean metabolite levels, with an average effect size of 25%. We show that some of the affected metabolites are risk predictors or diagnostic biomarkers of disease and, using the principle of Mendelian randomization, are in the causal pathway of disease. For example, LoF mutations in SLCO1B1 elevate the levels of hexadecanedioate, a fatty acid significantly associated with increased blood pressure levels and risk of incident heart failure in both African-Americans and an independent sample of European-Americans. We show that SLCO1B1 LoF mutations significantly increase the risk of incident heart failure, thus implicating the metabolite in the causal pathway of disease. These results reveal new avenues into gene function and the understanding of disease etiology by integrating -omic technologies into a deeply phenotyped population study. PMID:27602404
Eckert, Andrew J; Wegrzyn, Jill L; Cumbie, W Patrick; Goldfarb, Barry; Huber, Dudley A; Tolstikov, Vladimir; Fiehn, Oliver; Neale, David B
The metabolome of a plant comprises all small molecule metabolites, which are produced during cellular processes. The genetic basis for metabolites in nonmodel plants is unknown, despite frequently observed correlations between metabolite concentrations and stress responses. A quantitative genetic analysis of metabolites in a nonmodel plant species is thus warranted. Here, we use standard association genetic methods to correlate 3563 single nucleotide polymorphisms (SNPs) to concentrations of 292 metabolites measured in a single loblolly pine (Pinus taeda) association population. A total of 28 single locus associations were detected, representing 24 and 20 unique SNPs and metabolites, respectively. Multilocus Bayesian mixed linear models identified 2998 additional associations for a total of 1617 unique SNPs associated to 255 metabolites. These SNPs explained sizeable fractions of metabolite heritabilities when considered jointly (56.6% on average) and had lower minor allele frequencies and magnitudes of population structure as compared with random SNPs. Modest sets of SNPs (n = 1-23) explained sizeable portions of genetic effects for many metabolites, thus highlighting the importance of multi-SNP models to association mapping, and exhibited patterns of polymorphism consistent with being linked to targets of natural selection. The implications for association mapping in forest trees are discussed.
Milne, Stephen B.; Mathews, Thomas P.; Myers, David S.; Ivanova, Pavlina T.; Brown, H. Alex
Metabolomics is a rapidly growing field of research used in the identification and quantification of the small molecule metabolites within an organism, thereby providing insights into cell metabolism and bioenergetics as well as processes important in clinical medicine, such as disposition of pharmaceutical compounds. It offers comprehensive information on thousands of low molecular weight compounds (<1500 Da) that represent a wide range of pathways and intermediary metabolism. Due to its vast expansion in the last two decades mass spectrometry has become an indispensable tool in “omic” analyses. The use of different ionization techniques such as the more traditional electrospray (ESI) and matrix-assisted laser desorption (MALDI), as well as recently popular desorption electrospray ionization (DESI), has allowed the analysis of a wide range of biomolecules (e.g. peptides, proteins, lipids and sugars), and their imaging and analysis in the original sample environment in a workup free fashion. An overview of the current state of the methodology is given, as well as examples of application. PMID:23442130
Locasale, Jason W.; Melman, Tamar; Song, Susan; Yang, Xuemei; Swanson, Kenneth D.; Cantley, Lewis C.; Wong, Eric T.; Asara, John M.
Cerebrospinal fluid is routinely collected for the diagnosis and monitoring of patients with neurological malignancies. However, little is known as to how its constituents may change in a patient when presented with a malignant glioma. Here, we used a targeted mass-spectrometry based metabolomics platform using selected reaction monitoring with positive/negative switching and profiled the relative levels of over 124 polar metabolites present in patient cerebrospinal fluid. We analyzed the metabolic profiles from 10 patients presenting malignant gliomas and seven control patients that did not present malignancy to test whether a small sample size could provide statistically significant signatures. We carried out multiple unbiased forms of classification using a series of unsupervised techniques and identified metabolic signatures that distinguish malignant glioma patients from the control patients. One subtype identified contained metabolites enriched in citric acid cycle components. Newly diagnosed patients segregated into a different subtype and exhibited low levels of metabolites involved in tryptophan metabolism, which may indicate the absence of an inflammatory signature. Together our results provide the first global assessment of the polar metabolic composition in cerebrospinal fluid that accompanies malignancy, and demonstrate that data obtained from high throughput mass spectrometry technology may have suitable predictive capabilities for the identification of biomarkers and classification of neurological diseases. PMID:22240505
Jové, Mariona; Maté, Ianire; Naudí, Alba; Mota-Martorell, Natalia; Portero-Otín, Manuel; De la Fuente, Mónica; Pamplona, Reinald
A molecular description of the mechanisms by which aging is produced is still very limited. Here, we have determined the plasma metabolite profile by using high-throughput metabolome profiling technologies of 150 healthy humans ranging from 30 to 100 years of age. Using a nontargeted approach, we detected 2,678 metabolite species in plasma, and the multivariate analyses separated perfectly two groups indicating a specific signature for each gender. In addition, there is a set of gender-shared metabolites, which change significantly during aging with a similar tendency. Among the identified molecules, we found vitamin D2-related compound, phosphoserine (40:5), monoacylglyceride (22:1), diacylglyceride (33:2), and resolvin D6, all of them decreasing with the aging process. Finally, we found three molecules that directly correlate with age and seven that inversely correlate with age, independently of gender. Among the identified molecules (6 of 10 according to exact mass and retention time), we found a proteolytic product (l-γ-glutamyl-l-leucine), which increased with age. On the contrary, a hydroxyl fatty acid (25-hydroxy-hexacosanoic), a polyunsaturated fatty acid (eicosapentaenoic acid), two phospholipids (phosphocholine [42:9]and phosphoserine [42:3]) and a prostaglandin (15-keto-prostaglandin F2α) decreased with aging. These results suggest that lipid species and their metabolism are closely linked to the aging process.
Cuperlovic-Culf, Miroslava; Ferguson, Dean; Culf, Adrian; Morin, Pier; Touaibia, Mohamed
Glioblastoma multiforme (GBM) is the most common form of malignant glioma, characterized by unpredictable clinical behaviors that suggest distinct molecular subtypes. With the tumor metabolic phenotype being one of the hallmarks of cancer, we have set upon to investigate whether GBMs show differences in their metabolic profiles. 1H NMR analysis was performed on metabolite extracts from a selection of nine glioblastoma cell lines. Analysis was performed directly on spectral data and on relative concentrations of metabolites obtained from spectra using a multivariate regression method developed in this work. Both qualitative and quantitative sample clustering have shown that cell lines can be divided into four groups for which the most significantly different metabolites have been determined. Analysis shows that some of the major cancer metabolic markers (such as choline, lactate, and glutamine) have significantly dissimilar concentrations in different GBM groups. The obtained lists of metabolic markers for subgroups were correlated with gene expression data for the same cell lines. Metabolic analysis generally agrees with gene expression measurements, and in several cases, we have shown in detail how the metabolic results can be correlated with the analysis of gene expression. Combined gene expression and metabolomics analysis have shown differential expression of transporters of metabolic markers in these cells as well as some of the major metabolic pathways leading to accumulation of metabolites. Obtained lists of marker metabolites can be leveraged for subtype determination in glioblastomas. PMID:22528487
Eckel-Mahan, Kristin L; Patel, Vishal R; Mohney, Robert P; Vignola, Katie S; Baldi, Pierre; Sassone-Corsi, Paolo
The circadian clock governs a large array of physiological functions through the transcriptional control of a significant fraction of the genome. Disruption of the clock leads to metabolic disorders, including obesity and diabetes. As food is a potent zeitgeber (ZT) for peripheral clocks, metabolites are implicated as cellular transducers of circadian time for tissues such as the liver. From a comprehensive dataset of over 500 metabolites identified by mass spectrometry, we reveal the coordinate clock-controlled oscillation of many metabolites, including those within the amino acid and carbohydrate metabolic pathways as well as the lipid, nucleotide, and xenobiotic metabolic pathways. Using computational modeling, we present evidence of synergistic nodes between the circadian transcriptome and specific metabolic pathways. Validation of these nodes reveals that diverse metabolic pathways, including the uracil salvage pathway, oscillate in a circadian fashion and in a CLOCK-dependent manner. This integrated map illustrates the coherence within the circadian metabolome, transcriptome, and proteome and how these are connected through specific nodes that operate in concert to achieve metabolic homeostasis.
Lima, Marta R M; Diaz, Sílvia O; Lamego, Inês; Grusak, Michael A; Vasconcelos, Marta W; Gil, Ana M
Iron (Fe) deficiency is an important agricultural concern that leads to lower yields and crop quality. A better understanding of the condition at the metabolome level could contribute to the design of strategies to ameliorate Fe-deficiency problems. Fe-sufficient and Fe-deficient soybean leaf extracts and whole leaves were analyzed by liquid (1)H nuclear magnetic resonance (NMR) and high-resolution magic-angle spinning NMR spectroscopy, respectively. Overall, 30 compounds were measurable and identifiable (comprising amino and organic acids, fatty acids, carbohydrates, alcohols, polyphenols, and others), along with 22 additional spin systems (still unassigned). Thus, metabolite differences between treatment conditions could be evaluated for different compound families simultaneously. Statistically relevant metabolite changes upon Fe deficiency included higher levels of alanine, asparagine/aspartate, threonine, valine, GABA, acetate, choline, ethanolamine, hypoxanthine, trigonelline, and polyphenols and lower levels of citrate, malate, ethanol, methanol, chlorogenate, and 3-methyl-2-oxovalerate. The data indicate that the main metabolic impacts of Fe deficiency in soybean include enhanced tricarboxylic acid cycle activity, enhanced activation of oxidative stress protection mechanisms and enhanced amino acid accumulation. Metabolites showing accumulation differences in Fe-starved but visually asymptomatic leaves could serve as biomarkers for early detection of Fe-deficiency stress.
Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei
Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.
Abdel-Farid, I.B.; Sheded, M.G.; Mohamed, E.A.
Metabolomic profiling of different parts (leaves, flowers and pods) of Acacia species (Acacia nilotica, Acacia seyal and Acacia laeta) was evaluated. The multivariate data analyses such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were used to differentiate the distribution of plant metabolites among different species or different organs of the same species. A.nilotica was characterized with a high content of saponins and A.seyal was characterized with high contents of proteins, phenolics, flavonoids and anthocyanins. A.laeta had a higher content of carbohydrates than A. nilotica and A. seyal. On the basis of these results, total antioxidant capacity, DPPH free radical scavenging activity and reducing power of the methanolic extracts of studied parts were evaluated. A.nilotica and A.seyal extracts showed less inhibitory concentration 50 (IC50) compared to A.laeta extracts which means that these two species have the strongest radical scavenging activity whereas A. laeta extracts have the lowest radical scavenging activity. A positive correlation between saponins and flavonoids with total antioxidant capacity and DPPH radical scavenging activity was observed. Based on these results, the potentiality of these plants as antioxidants was discussed. PMID:25313274
He, Y; Yu, Z; Giegling, I; Xie, L; Hartmann, A M; Prehn, C; Adamski, J; Kahn, R; Li, Y; Illig, T; Wang-Sattler, R; Rujescu, D
Schizophrenia is a severe complex mental disorder affecting 0.5–1% of the world population. To date, diagnosis of the disease is mainly based on personal and thus subjective interviews. The underlying molecular mechanism of schizophrenia is poorly understood. Using targeted metabolomics we quantified and compared 103 metabolites in plasma samples from 216 healthy controls and 265 schizophrenic patients, including 52 cases that do not take antipsychotic medication. Compared with healthy controls, levels of five metabolites were found significantly altered in schizophrenic patients (P-values ranged from 2.9 × 10−8 to 2.5 × 10−4) and in neuroleptics-free probands (P-values ranging between 0.006 and 0.03), respectively. These metabolites include four amino acids (arginine, glutamine, histidine and ornithine) and one lipid (PC ae C38:6) and are suggested as candidate biomarkers for schizophrenia. To explore the genetic susceptibility on the associated metabolic pathways, we constructed a molecular network connecting these five aberrant metabolites with 13 schizophrenia risk genes. Our result implicated aberrations in biosynthetic pathways linked to glutamine and arginine metabolism and associated signaling pathways as genetic risk factors, which may contribute to patho-mechanisms and memory deficits associated with schizophrenia. This study illustrated that the metabolic deviations detected in plasma may serve as potential biomarkers to aid diagnosis of schizophrenia. PMID:22892715
Al-Ani, Bahjat; Fitzpatrick, Martin; Al-Nuaimi, Hamad; Coughlan, Alice M.; Hickey, Fionnuala B.; Pusey, Charles D.; Savage, Caroline; Benton, Christopher M.; O’Brien, Eóin C.; O’Toole, Declan; Mok, Ken H.; Young, Stephen P.; Little, Mark A.
Current biomarkers of renal disease in systemic vasculitis lack predictive value and are insensitive to early damage. To identify novel biomarkers of renal vasculitis flare, we analysed the longitudinal urinary metabolomic profile of a rat model of anti-neutrophil cytoplasmic antibody (ANCA) vasculitis. Wistar-Kyoto (WKY) rats were immunised with human myeloperoxidase (MPO). Urine was obtained at regular intervals for 181 days, after which relapse was induced by re-challenge with MPO. Urinary metabolites were assessed in an unbiased fashion using nuclear magnetic resonance (NMR) spectroscopy, and analysed using partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLS-R). At 56 days post-immunisation, we found that rats with vasculitis had a significantly different urinary metabolite profile than control animals; the observed PLS-DA clusters dissipated between 56 and 181 days, and re-emerged with relapse. The metabolites most altered in rats with active or relapsing vasculitis were trimethylamine N-oxide (TMAO), citrate and 2-oxoglutarate. Myo-inositol was also moderately predictive. The key urine metabolites identified in rats were confirmed in a large cohort of patients using liquid chromatography–mass spectrometry (LC-MS). Hypocitraturia and elevated urinary myo-inositol remained associated with active disease, with the urine myo-inositol:citrate ratio being tightly correlated with active renal vasculitis. PMID:27905491
Patterson, Andrew D.; Lanz, Christian; Gonzalez, Frank J.; Idle, Jeffrey R.
Radiation metabolomics can be defined as the global profiling of biological fluids to uncover latent, endogenous small molecules whose concentrations change in a dose-response manner following exposure to ionizing radiation. In response to the potential threat of nuclear or radiological terrorism, the Center for High-Throughput Minimally Invasive Radiation Biodosimetry (CMCR) was established to develop field-deployable biodosimeters based, in principle, on rapid analysis by mass spectrometry of readily and easily obtainable biofluids. In this review, we briefly summarize radiation biology and key events related to actual and potential nuclear disasters, discuss the important contributions the field of mass spectrometry has made to the field of radiation metabolomics, and summarize current discovery efforts to use mass spectrometry-based metabolomics to identify dose-responsive urinary constituents, and ultimately to build and deploy a noninvasive high-throughput biodosimeter. PMID:19890938
Gonzalez-Riano, Carolina; Tapia-González, Silvia; García, Antonia; Muñoz, Alberto; DeFelipe, Javier; Barbas, Coral
Understanding the human brain is the ultimate goal in neuroscience, but this is extremely challenging in part due to the fact that brain tissue obtained from autopsy is practically the only source of normal brain tissue and also since changes at different levels of biological organization (genetic, molecular, biochemical, anatomical) occur after death due to multiple mechanisms. Here we used metabolomic and anatomical techniques to study the possible relationship between post-mortem time (PT)-induced changes that may occur at both the metabolomics and anatomical levels in the same brains. Our experiments have mainly focused on the hippocampus of the mouse. We found significant metabolomic changes at 2 h PT, whereas the integrity of neurons and glia, at the anatomical/ neurochemical level, was not significantly altered during the first 5 h PT for the majority of histological markers.
Yuan, Jie; Doucette, Christopher D; Fowler, William U; Feng, Xiao-Jiang; Piazza, Matthew; Rabitz, Herschel A; Wingreen, Ned S; Rabinowitz, Joshua D
Despite extensive study of individual enzymes and their organization into pathways, the means by which enzyme networks control metabolite concentrations and fluxes in cells remains incompletely understood. Here, we examine the integrated regulation of central nitrogen metabolism in Escherichia coli through metabolomics and ordinary-differential-equation-based modeling. Metabolome changes triggered by modulating extracellular ammonium centered around two key intermediates in nitrogen assimilation, α-ketoglutarate and glutamine. Many other compounds retained concentration homeostasis, indicating isolation of concentration changes within a subset of the metabolome closely linked to the nutrient perturbation. In contrast to the view that saturated enzymes are insensitive to substrate concentration, competition for the active sites of saturated enzymes was found to be a key determinant of enzyme fluxes. Combined with covalent modification reactions controlling glutamine synthetase activity, such active-site competition was sufficient to explain and predict the complex dynamic response patterns of central nitrogen metabolites. PMID:19690571
Dove, Alistair D M
This essay explores the potential of metabolomics for exotic animal research in a zoological setting. Metabolomics is a suite of analytical tools aimed at gaining a holistic understanding of animal metabolism without prior knowledge of the compounds to be measured. These metabolic fingerprints can be used to define normal metabolism for an unstudied species, to characterize the metabolic deviation of diseased animals from the normal state over time, to identify biomarker compounds that best capture such deviations, and to measure the metabolic impact of clinical and nutritional interventions. Two approaches, nuclear magnetic resonance (NMR) and mass spectrometry (MS) provide large amounts of complimentary pure and applied biological data. Metabolomic methods hold great potential for researchers, clinicians, and nutritionists studying exotic and aquatic animals because they can produce a huge data return on research effort, and because they do not require much a priori knowledge of the animals' metabolism, which is so often then case in zoological settings.
Misra, Biswapriya B.; Acharya, Biswa R.; Granot, David; Assmann, Sarah M.; Chen, Sixue
Guard cells represent a unique single cell-type system for the study of cellular responses to abiotic and biotic perturbations that affect stomatal movement. Decades of effort through both classical physiological and functional genomics approaches have generated an enormous amount of information on the roles of individual metabolites in stomatal guard cell function and physiology. Recent application of metabolomics methods has produced a substantial amount of new information on metabolome control of stomatal movement. In conjunction with other “omics” approaches, the knowledge-base is growing to reach a systems-level description of this single cell-type. Here we summarize current knowledge of the guard cell metabolome and highlight critical metabolites that bear significant impact on future engineering and breeding efforts to generate plants/crops that are resistant to environmental challenges and produce high yield and quality products for food and energy security. PMID:26042131
Shao, Tiejuan; Shao, Li; Li, Haichang; Xie, Zhijun; He, Zhixing; Wen, Chengping
This study employed microbiome and metabolome analysis to explore the fecal signatures of gout patients. Fecal samples from 52 male individuals (26 healthy controls and 26 gout patients) were analyzed by (1)H NMR spectroscopy and Illumina Miseq sequencing. The signatures of microbiome showed being up-regulation of opportunistic pathogens, such as Bacteroides, Porphyromonadaceae Rhodococcus, Erysipelatoclostridium and Anaerolineaceae. The signatures of metabolome were some altered metabolites which may involve uric acid excretion, purine metabolism, and inflammatory responses. Meanwhile, the correlation between discrepant metabolites and microbial taxa indicated that they could be the combined signatures of gout. This study suggests that the combined analysis of the fecal microbiome and metabolome may effectively characterize diseases.
Misra, Biswapriya B; Acharya, Biswa R; Granot, David; Assmann, Sarah M; Chen, Sixue
Guard cells represent a unique single cell-type system for the study of cellular responses to abiotic and biotic perturbations that affect stomatal movement. Decades of effort through both classical physiological and functional genomics approaches have generated an enormous amount of information on the roles of individual metabolites in stomatal guard cell function and physiology. Recent application of metabolomics methods has produced a substantial amount of new information on metabolome control of stomatal movement. In conjunction with other "omics" approaches, the knowledge-base is growing to reach a systems-level description of this single cell-type. Here we summarize current knowledge of the guard cell metabolome and highlight critical metabolites that bear significant impact on future engineering and breeding efforts to generate plants/crops that are resistant to environmental challenges and produce high yield and quality products for food and energy security.
Allwood, J William; Clarke, Andrew; Goodacre, Royston; Mur, Luis A J
One of the most well-characterised plant pathogenic interactions involves Arabidopsis thaliana and the bacteria Pseudomonas syringae pathovar tomato (Pst). The standard Pst inoculation procedure involves infiltration of large populations of bacteria into plant leaves which means that metabolite changes cannot be readily assigned to the host or pathogen. A plant cell-pathogen co-culture based approach has been developed where the plant and pathogen cells are separated after 12h of co-culture via differential filtering and centrifugation. Fourier transform infrared (FT-IR) spectroscopy was employed to assess the intracellular metabolomes (metabolic fingerprints) of both host and pathogen and their extruded (extracellular) metabolites (metabolic footprints) under conditions relevant to disease and resistance. We propose that this system will enable the metabolomic profiling of the separated host and pathogen (i.e. 'dual metabolomics') and will facilitate the modelling of reciprocal responses.
Hivert, MF; Perng, W; Watkins, S; Newgard, CB; Kenny, LC; Kristal, BS; Patti, ME; Isganaitis, E; DeMeo, DL; Oken, E; Gillman, MW
In this review, we discuss the potential role of metabolomics to enhance understanding of obesity-related developmental origins of health and disease (DOHaD). We first provide an overview of common techniques and analytical approaches to help interested investigators dive into this relatively novel field. Next, we describe how metabolomics may capture exposures that are notoriously difficult to quantify, and help to further refine phenotypes associated with excess adiposity and related metabolic sequelae over the life course. Together, these data can ultimately help to elucidate mechanisms that underlie fetal metabolic programming. Finally, we review current gaps in knowledge and identify areas where the field of metabolomics is likely to provide insights into mechanisms linked to DOHaD in human populations. PMID:25631626
Chen, Ya-zhou; Pang, Qiu-Ying; He, Yan; Zhu, Ning; Branstrom, Isabel; Yan, Xiu-Feng; Chen, Sixue
To understand plant molecular networks of glucosinolate metabolism, perturbation of aliphatic glucosinolate biosynthesis was established using inducible RNA interference (RNAi) in Arabidopsis. Two RNAi lines were chosen for examining global protein and metabolite changes using complementary proteomics and metabolomics approaches. Proteins involved in metabolism including photosynthesis and hormone metabolism, protein binding, energy, stress, and defense showed marked responses to glucosinolate perturbation. In parallel, metabolomics revealed major changes in the levels of amino acids, carbohydrates, peptides, and hormones. The metabolomics data were correlated with the proteomics results and revealed intimate molecular connections between cellular pathways/processes and glucosinolate metabolism. This study has provided an unprecedented view of the molecular networks of glucosinolate metabolism and laid a foundation towards rationale glucosinolate engineering for enhanced defense and quality.
Tenenboim, Hezi; Brotman, Yariv
Many aspects of the way plants protect themselves against pathogen attack, or react upon such an attack, are realized by metabolites. The ambitious aim of metabolomics, namely the identification and annotation of the entire cellular metabolome, still poses a considerable challenge due to the high diversity of the metabolites in the cell. Recent advances in analytical methods and data analysis have resulted in improved sensitivity, accuracy, and capacity, allowing the analysis of several hundreds or even thousands of compounds within one sample. Investigators have only recently begun to acknowledge and harness the power of metabolomics to elucidate key questions in the study of plant biotic interactions; we review trends and developments in the field.
Bozek, Katarzyna; Wei, Yuning; Yan, Zheng; Liu, Xiling; Xiong, Jieyi; Sugimoto, Masahiro; Tomita, Masaru; Pääbo, Svante; Pieszek, Raik; Sherwood, Chet C.; Hof, Patrick R.; Ely, John J.; Steinhauser, Dirk; Willmitzer, Lothar; Bangsbo, Jens; Hansson, Ola; Call, Josep; Giavalisco, Patrick; Khaitovich, Philipp
Metabolite concentrations reflect the physiological states of tissues and cells. However, the role of metabolic changes in species evolution is currently unknown. Here, we present a study of metabolome evolution conducted in three brain regions and two non-neural tissues from humans, chimpanzees, macaque monkeys, and mice based on over 10,000 hydrophilic compounds. While chimpanzee, macaque, and mouse metabolomes diverge following the genetic distances among species, we detect remarkable acceleration of metabolome evolution in human prefrontal cortex and skeletal muscle affecting neural and energy metabolism pathways. These metabolic changes could not be attributed to environmental conditions and were confirmed against the expression of their corresponding enzymes. We further conducted muscle strength tests in humans, chimpanzees, and macaques. The results suggest that, while humans are characterized by superior cognition, their muscular performance might be markedly inferior to that of chimpanzees and macaque monkeys. PMID:24866127
Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations. PMID:26213431
T'kindt, Ruben; Storme, Michael; Deforce, Dieter; Van Bocxlaer, Jan
The metabolomics goal, the unbiased relative quantification of all metabolites in a biological system, still lacks a universal analytical approach. In the LC-MS line of approach, one of the major problems encountered is the polar nature of a large group of (plant) metabolites. Here, we investigate the potential of hydrophilic interaction chromatography (HILIC) and compare its qualities with extended polarity RP chromatography. Two opposite LC phase compositions (Atlantis dC18 vs. TSKgel Amide-80) are compared in a plant metabolomics setting. Both performed equally well with regard to retentive capacities, but variation in peak area was about 5% higher for the HILIC approach. Focussing on matrix effects (ME) on the other hand, it was observed that this well-known problem in RP LC-MS metabolomics was not reduced on using hydrophilic interaction chromatography.
Lucarelli, Giuseppe; Rutigliano, Monica; Galleggiante, Vanessa; Giglio, Andrea; Palazzo, Silvano; Ferro, Matteo; Simone, Cristiano; Bettocchi, Carlo; Battaglia, Michele; Ditonno, Pasquale
Metabolomic profiling offers a powerful methodology for understanding the perturbations of biochemical systems occurring during a disease process. During neoplastic transformation, prostate cells undergo metabolic reprogramming to satisfy the demands of growth and proliferation. An early event in prostate cell transformation is the loss of capacity to accumulate zinc. This change is associated with a higher energy efficiency and increased lipid biosynthesis for cellular proliferation, membrane formation and cell signaling. Moreover, recent studies have shown that sarcosine, an N-methyl derivative of glycine, was significantly increased during disease progression from normal to localized to metastatic prostate cancer. Mapping the metabolomic profiles to their respective biochemical pathways showed an upregulation of androgen-induced protein synthesis, an increased amino acid metabolism and a perturbation of nitrogen breakdown pathways, along with high total choline-containing compounds and phosphocholine levels. In this review, the role of emerging biomarkers is summarized, based on the current understanding of the prostate cancer metabolome.
Shao, Tiejuan; Shao, Li; Li, Haichang; Xie, Zhijun; He, Zhixing; Wen, Chengping
This study employed microbiome and metabolome analysis to explore the fecal signatures of gout patients. Fecal samples from 52 male individuals (26 healthy controls and 26 gout patients) were analyzed by 1H NMR spectroscopy and Illumina Miseq sequencing. The signatures of microbiome showed being up-regulation of opportunistic pathogens, such as Bacteroides, Porphyromonadaceae Rhodococcus, Erysipelatoclostridium and Anaerolineaceae. The signatures of metabolome were some altered metabolites which may involve uric acid excretion, purine metabolism, and inflammatory responses. Meanwhile, the correlation between discrepant metabolites and microbial taxa indicated that they could be the combined signatures of gout. This study suggests that the combined analysis of the fecal microbiome and metabolome may effectively characterize diseases. PMID:28270806
Tuttolomondo, Teresa; Martinelli, Federico; Mariotti, Lorenzo; Leto, Claudio; Maggio, Antonella; La Bella, Salvatore
Although Origanum vulgare (L.) has been deeply analysed at phytochemical level, poor knowledge is available regarding non-volatile compounds such as lipids. The aim of this work was to characterise five wild Sicilian Origanum ecotypes from an agronomic, metabolomic and lipidomic perspective. Serradifalco presented higher dry weight and inflorescences/plant than the others while Favara had a significantly higher number of branches per plant and more extensive flowered stratum. Metabolomic analysis, performed with LC-MS-TOF, allowed a preliminary characterisation of the non-volatile metabolome of the five oregano ecotypes Origanum vulgare ssp. hirtum. Twenty-five metabolites were identified belonging to organic acids, amino acids, lysophosphatidylcholines, carnithines, nucleic bases and lysophosphatidylethanolamines. Lipidomic analysis identified 115 polar plant membrane glycerolipid species. Thirteen of them were differentially present in the two chosen ecotypes. The role of these metabolites in plant physiology from a qualitative and pharmacological point of view was discussed.
Marrachelli, Vannina G.; Rentero, Pilar; Mansego, María L.; Morales, Jose Manuel; Galan, Inma; Pardo-Tendero, Mercedes; Martinez, Fernando; Martin-Escudero, Juan Carlos; Briongos, Laisa; Chaves, Felipe Javier; Redon, Josep; Monleon, Daniel
Background To identify metabolomic and genomic markers associated with the presence of clustering of cardiometabolic risk factors (CMRFs) from a general population. Methods and Findings One thousand five hundred and two subjects, Caucasian, > 18 years, representative of the general population, were included. Blood pressure measurement, anthropometric parameters and metabolic markers were measured. Subjects were grouped according the number of CMRFs (Group 1: <2; Group 2: 2; Group 3: 3 or more CMRFs). Using SNPlex, 1251 SNPs potentially associated to clustering of three or more CMRFs were analyzed. Serum metabolomic profile was assessed by 1H NMR spectra using a Brucker Advance DRX 600 spectrometer. From the total population, 1217 (mean age 54±19, 50.6% men) with high genotyping call rate were analysed. A differential metabolomic profile, which included products from mitochondrial metabolism, extra mitochondrial metabolism, branched amino acids and fatty acid signals were observed among the three groups. The comparison of metabolomic patterns between subjects of Groups 1 to 3 for each of the genotypes associated to those subjects with three or more CMRFs revealed two SNPs, the rs174577_AA of FADS2 gene and the rs3803_TT of GATA2 transcription factor gene, with minimal or no statistically significant differences. Subjects with and without three or more CMRFs who shared the same genotype and metabolomic profile differed in the pattern of CMRFS cluster. Subjects of Group 3 and the AA genotype of the rs174577 had a lower prevalence of hypertension compared to the CC and CT genotype. In contrast, subjects of Group 3 and the TT genotype of the rs3803 polymorphism had a lower prevalence of T2DM, although they were predominantly males and had higher values of plasma creatinine. Conclusions The results of the present study add information to the metabolomics profile and to the potential impact of genetic factors on the variants of clustering of cardiometabolic risk factors
Lim, Yan Wei; Mak, Tytus D.; Whiteson, Katrine; Conrad, Douglas; Rohwer, Forest; Dorrestein, Pieter
Background. Cystic fibrosis (CF) is a genetic disease that results in chronic infections of the lungs. CF patients experience intermittent pulmonary exacerbations (CFPE) that are associated with poor clinical outcomes. CFPE involves an increase in disease symptoms requiring more aggressive therapy. Methods. Longitudinal sputum samples were collected from 11 patients (n = 44 samples) to assess the effect of exacerbations on the sputum metabolome using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The data was analyzed with MS/MS molecular networking and multivariate statistics. Results. The individual patient source had a larger influence on the metabolome of sputum than the clinical state (exacerbation, treatment, post-treatment, or stable). Of the 4,369 metabolites detected, 12% were unique to CFPE samples; however, the only known metabolites significantly elevated at exacerbation across the dataset were platelet activating factor (PAF) and a related monacylglycerophosphocholine lipid. Due to the personalized nature of the sputum metabolome, a single patient was followed for 4.2 years (capturing four separate exacerbation events) as a case study for the detection of personalized biomarkers with metabolomics. PAF and related lipids were significantly elevated during CFPEs of this patient and ceramide was elevated during CFPE treatment. Correlating the abundance of bacterial 16S rRNA gene amplicons to metabolomics data from the same samples during a CFPE demonstrated that antibiotics were positively correlated to Stenotrophomonas and Pseudomonas, while ceramides and other lipids were correlated with Streptococcus, Rothia, and anaerobes. Conclusions. This study identified PAF and other inflammatory lipids as potential biomarkers of CFPE, but overall, the metabolome of CF sputum was patient specific, supporting a personalized approach to molecular detection of CFPE onset. PMID:27602256
Beyoğlu, Diren; Imbeaud, Sandrine; Maurhofer, Olivier; Bioulac-Sage, Paulette; Zucman-Rossi, Jessica; Dufour, Jean-François; Idle, Jeffrey R.
Hepatocellular carcinoma (HCC) is one of the commonest causes of death from cancer. A plethora of metabolomic investigations of HCC have yielded molecules in biofluids that are both up- and downregulated but no real consensus has emerged regarding exploitable biomarkers for early detection of HCC. We report here a different approach, a combined transcriptomics and metabolomics study of energy metabolism in HCC. A panel of 31 pairs of HCC tumors and corresponding non-tumor liver tissues from the same patients was investigated by gas chromatography-mass spectrometry (GCMS) based metabolomics. HCC was characterized by approximately two-fold depletion of glucose, glycerol 3- and 2-phosphate, malate, alanine, myo-inositol, and linoleic acid. Data are consistent with a metabolic remodeling involving a four-fold increase in glycolysis over mitochondrial oxidative phosphorylation. A second panel of 59 HCC that had been typed by transcriptomics and classified in G1 to G6 subgroups was also subjected to GCMS tissue metabolomics. No differences in glucose, lactate, alanine, glycerol 3-phosphate, malate, myo-inositol or stearic acid tissue concentrations were found, suggesting that the Wnt/β-catenin pathway activated by CTNNB1 mutation in subgroups G5 and G6 did not exhibit specific metabolic remodeling. However, subgroup G1 had markedly reduced tissue concentrations of 1-stearoylglycerol, 1-palmitoylglycerol, and palmitic acid, suggesting that the high serum α-fetoprotein phenotype of G1, associated with the known overexpression of lipid catabolic enzymes, could be detected through metabolomics as increased lipid catabolism. Conclusion Tissue metabolomics yielded precise biochemical information regarding HCC tumor metabolic remodeling from mitochondrial oxidation to aerobic glycolysis and the impact of molecular subtypes on this process. PMID:23463346
Rocca-Serra, Philippe; Salek, Reza M; Arita, Masanori; Correa, Elon; Dayalan, Saravanan; Gonzalez-Beltran, Alejandra; Ebbels, Tim; Goodacre, Royston; Hastings, Janna; Haug, Kenneth; Koulman, Albert; Nikolski, Macha; Oresic, Matej; Sansone, Susanna-Assunta; Schober, Daniel; Smith, James; Steinbeck, Christoph; Viant, Mark R; Neumann, Steffen
Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.
Lima, Ana Rita; Bastos, Maria de Lourdes; Carvalho, Márcia; Guedes de Pinho, Paula
Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA) levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.
Kumar, Deepak; Gupta, Ashish; Nath, Kavindra
Advances in the application of NMR spectroscopy-based metabolomic profiling of prostate cancer comprises a potential tactic for understanding the impaired biochemical pathways arising due to a disease evolvement and progression. This technique involves qualitative and quantitative estimation of plethora of small molecular weight metabolites of body fluids or tissues using state-of-the-art chemometric methods delivering an important platform for translational research from basic to clinical, to reveal the pathophysiological snapshot in a single step. This review summarizes the present arrays and recent advancements in NMR-based metabolomics and a glimpse of currently used medical imaging tactics, with their role in clinical diagnosis of prostate cancer.
Catchpole, Gareth S.; Beckmann, Manfred; Enot, David P.; Mondhe, Madhav; Zywicki, Britta; Taylor, Janet; Hardy, Nigel; Smith, Aileen; King, Ross D.; Kell, Douglas B.; Fiehn, Oliver; Draper, John
There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome “fingerprinting” to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars. PMID:16186495
Today, the technology of ‘targeted’ based metabolomics is pivotal in the clinical analysis workflow as it provides information of metabolic phenotyping (metabotypes) by enhancing our understanding of metabolism of complex diseases, biomarker discovery for disease development, progression, treatment, and drug function and assessment. This review is focused on surveying and providing a gap analysis on metabolic phenotyping with a focus on targeted based metabolomics from an instrumental, technical point-of-view discussing the state-of-the-art instrumentation, pre- to post- analytical aspects as well as an overall future necessity for biomarker discovery and future (pre-) clinical routine application. PMID:28149265
Catchpole, Gareth S; Beckmann, Manfred; Enot, David P; Mondhe, Madhav; Zywicki, Britta; Taylor, Janet; Hardy, Nigel; Smith, Aileen; King, Ross D; Kell, Douglas B; Fiehn, Oliver; Draper, John
There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome "fingerprinting" to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.
Wu, Changsheng; Kim, Hye Kyong; van Wezel, Gilles P; Choi, Young Hae
Metabolomics is a high throughput analytical technique used to globally measure low molecular weight metabolites, allowing simultaneous metabolic comparison of different biological samples and thus highlighting differentially produced compounds as potential biomarkers. Although microbes are renowned as prolific sources of antibiotics, the traditional approach for new anti-infectives discovery is time-consuming and labor-intensive. In this review, the use of NMR- or MS-based metabolomics is proposed as an efficient approach to find antimicrobials in microbial single- or co-cultures.
López-Rituerto, Eva; Savorani, Francesco; Avenoza, Alberto; Busto, Jesús H; Peregrina, Jesús M; Engelsen, Søren Balling
In this study, La Rioja wine terroir was investigated by the use of (1)H NMR metabolomics on must and wine samples. Rioja is a small wine region in central northern Spain which can geographically be divided into three subareas (Rioja Alta, Rioja Baja, and Rioja Alavesa). The winemaking process from must, through alcoholic and malolactic fermentation, was followed by NMR metabolomics and chemometrics of nine wineries in the Rioja subareas (terroirs). Application of interval extended canonical variate analysis (iECVA) showed discriminative power between wineries which are geographically very close. Isopentanol and isobutanol compounds were found to be key biomarkers for this differentiation.
Heyman, Heino M.; Zhang, Xing; Tang, Keqi; Baker, Erin Shammel; Metz, Thomas O.
Metabolomics is the quantitative analysis of all metabolites in a given sample. Due to the chemical complexity of the metabolome, optimal separations are required for comprehensive identification and quantification of sample constituents. This chapter provides an overview of both conventional and advanced separations methods in practice for reducing the complexity of metabolite extracts delivered to the mass spectrometer detector, and covers gas chromatography (GC), liquid chromatography (LC), capillary electrophoresis (CE), supercritical fluid chromatography (SFC) and ion mobility spectrometry (IMS) separation techniques coupled with mass spectrometry (MS) as both uni-dimensional and as multi-dimensional approaches.
Jutley, Gurpreet Singh; Young, Stephen P
There is an overwhelming need for a simple, reliable tool that aids clinicians in diagnosing, assessing disease activity and treating rheumatic conditions. Identification of biomarkers in partially understood inflammatory disorders has long been sought after as the Holy Grail of Rheumatology. Given the complex nature of inflammatory conditions, it has been difficult to earmark the potential biomarkers. Metabolomics, however, is promising in providing new insights into inflammatory conditions and also identifying such biomarkers. Metabolomic studies have generally revealed increased energy requirements for by-products of a hypoxic environment, leading to a characteristic metabolic fingerprint. Here, we discuss the significance of such studies and their potential as a biomarker.
Jun, Hee-jin; Lee, Ji Hae; Kim, Jiyoung; Jia, Yaoyao; Kim, Kyoung Heon; Hwang, Kwang Yeon; Yun, Eun Ju; Do, Kyoung-Rok; Lee, Sung-Joon
We investigated the hypotriglyceridemic mechanism of action of linalool, an aromatic monoterpene present in teas and fragrant herbs. Reporter gene and time-resolved fluorescence resonance energy transfer assays demonstrated that linalool is a direct ligand of PPARα. Linalool stimulation reduced cellular lipid accumulation regulating PPARα-responsive genes and significantly induced FA oxidation, and its effects were markedly attenuated by silencing PPARα expression. In mice, the oral administration of linalool for 3 weeks reduced plasma TG concentrations in Western-diet-fed C57BL/6J mice (31%, P < 0.05) and human apo E2 mice (50%, P < 0.05) and regulated hepatic PPARα target genes. However, no such effects were seen in PPARα-deficient mice. Transcriptome profiling revealed that linalool stimulation rewired global gene expression in lipid-loaded hepatocytes and that the effects of 1 mM linalool were comparable to those of 0.1 mM fenofibrate. Metabolomic analysis of the mouse plasma revealed that the global metabolite profiles were significantly distinguishable between linalool-fed mice and controls. Notably, the concentrations of saturated FAs were significantly reduced in linalool-fed mice. These findings suggest that the appropriate intake of a natural aromatic compound could exert beneficial metabolic effects by regulating a cellular nutrient sensor. PMID:24752549
Lee, Yong Jian; Mynampati, Kalyan; Drautz, Daniela; Arumugam, Krithika; Williams, Rohan; Schuster, Stephan; Kjelleberg, Staffan; Swarup, Sanjay
The aquatic rhizosphere is a region around the roots of aquatic plants. Many studies focusing on terrestrial rhizosphere have led to a good understanding of the interactions between the roots, its exudates and its associated rhizobacteria. The rhizosphere of free-floating roots, however, is a different habitat that poses several additional challenges, including rapid diffusion rates of signals and nutrient molecules, which are further influenced by the hydrodynamic forces. These can lead to rapid diffusion and complicates the studying of diffusible factors from both plant and/or rhizobacterial origins. These plant systems are being increasingly used for self purification of water bodies to provide sustainable solution. A better understanding of these processes will help in improving their performance for ecological engineering of freshwater systems. The same principles can also be used to improve the yield of hydroponic cultures. Novel toolsets and approaches are needed to investigate the processes occurring in the aquatic rhizosphere. We are interested in understanding the interaction between root exudates and the complex microbial communities that are associated with the roots, using a systems biology approach involving metabolomics and metagenomics. With this aim, we have developed a RhizoFlowCell (RFC) system that provides a controlled study of aquatic plants, observed the root biofilms, collect root exudates and subject the rhizosphere system to changes in various chemical or physical perturbations. As proof of concept, we have used RFC to test the response of root exudation patterns of Pandanus amaryllifolius after exposure to the pollutant naphthalene. Complexity of root exudates in the aquatic rhizosphere was captured using this device and analysed using LC-qTOF-MS. The highly complex metabolomic profile allowed us to study the dynamics of the response of roots to varying levels of naphthalene. The metabolic profile changed within 5mins after spiking with
Wu, Gary D
The human gut contains a vast number of microorganisms known collectively as the gut microbiota. Despite its importance in maintaining the health of the host, growing evidence suggests the gut microbiota may also be an important factor in the pathogenesis of various diseases, a number of which have shown a rapid increase in incidence over the past few decades. In some of these diseases, such as inflammatory bowel disease (IBD), the microbiota is dysbiotic with an abnormal community structure and decrease in diversity. If the dysbiotic microbiota plays a role in disease pathogenesis, interventions that modify its composition to make it more similar to the microbiota observed in health, might be a strategy to treat certain disease processes. Indeed, the high-level efficacy of fecal microbiota transplantation in the treatment of refractory Clostridia difficile infection supports this notion as proof-of-principle. The composition of the microbiota can be influenced by many factors including age, genetics, host environment, and diet. With respect to the later, diet has an impact upon both the composition and function of the microbiota. There are epidemiologic data associating diet with the development of IBD as well as evidence that diet can influence both the form and function of the microbiome in a manner that impacts upon the development of intestinal inflammation. Based on this evidence, studies are now underway to examine the effect of defined formula diets, an effective therapeutic modality in Crohn's disease, on both the gut microbiome and its metabolome as a therapeutic probe with the hope of better defining the 'healthy' diet in patients with IBD.
Sansbury, Brian E.; De Martino, Angelica M.; Xie, Zhengzhi; Brooks, Alan C.; Brainard, Robert E.; Watson, Lewis J.; DeFilippis, Andrew P.; Cummins, Timothy D.; Harbeson, Matthew A.; Brittian, Kenneth R.; Prabhu, Sumanth D.; Bhatnagar, Aruni; Jones, Steven P.; Hill, Bradford G.
Background Cardiac hypertrophy and heart failure are associated with metabolic dysregulation and a state of chronic energy deficiency. Although several disparate changes in individual metabolic pathways have been described, there has been no global assessment of metabolomic changes in hypertrophic and failing hearts in vivo. Here, we investigated the impact of pressure overload and infarction on myocardial metabolism. Methods and Results Male C57BL/6J mice were subjected to transverse aortic constriction (TAC) or permanent coronary occlusion (myocardial infarction; MI). A combination of LC/MS/MS and GC/MS techniques was used to measure 288 metabolites in these hearts. Both TAC and MI were associated with profound changes in myocardial metabolism affecting up to 40% of all metabolites measured. Prominent changes in branched amino acids acids (BCAAs) were observed after 1 week of TAC and 5 days after MI. Changes in BCAAs after MI were associated with myocardial insulin resistance. Longer duration of TAC and MI led to a decrease in purines, acylcarnitines, fatty acids and several lysolipid and sphingolipid species, but a marked increase in pyrimidines as well as ascorbate, heme and other indices of oxidative stress. Cardiac remodeling and contractile dysfunction in hypertrophied hearts were associated also with large increases in myocardial, but not plasma, levels of the polyamines putrescine and spermidine as well as the collagen breakdown product prolylhydroxyproline. Conclusions These findings reveal extensive metabolic remodeling common to both hypertrophic and failing hearts that are indicative of extensive extracellular matrix remodeling, insulin resistance and perturbations in amino acid, lipid and nucleotide metabolism. PMID:24762972
Cheung, William; Keski-Rahkonen, Pekka; Assi, Nada; Ferrari, Pietro; Freisling, Heinz; Rinaldi, Sabina; Slimani, Nadia; Zamora-Ros, Raul; Rundle, Milena; Frost, Gary; Gibbons, Helena; Carr, Eibhlin; Brennan, Lorraine; Cross, Amanda J; Pala, Valeria; Panico, Salvatore; Sacerdote, Carlotta; Palli, Domenico; Tumino, Rosario; Kühn, Tilman; Kaaks, Rudolf; Boeing, Heiner; Floegel, Anna; Mancini, Francesca; Boutron-Ruault, Marie-Christine; Baglietto, Laura; Trichopoulou, Antonia; Naska, Androniki; Orfanos, Philippos; Scalbert, Augustin
Background: Meat and fish intakes have been associated with various chronic diseases. The use of specific biomarkers may help to assess meat and fish intake and improve subject classification according to the amount and type of meat or fish consumed.Objective: A metabolomic approach was applied to search for biomarkers of meat and fish intake in a dietary intervention study and in free-living subjects from the European Prospective Investigation into Cancer and Nutrition (EPIC) study.Design: In the dietary intervention study, 4 groups of 10 subjects consumed increasing quantities of chicken, red meat, processed meat, and fish over 3 successive weeks. Twenty-four-hour urine samples were collected during each period and analyzed by high-resolution liquid chromatography-mass spectrometry. Signals characteristic of meat or fish intake were replicated in 50 EPIC subjects for whom a 24-h urine sample and 24-h dietary recall were available and who were selected for their exclusive intake or no intake of any of the 4 same foods.Results: A total of 249 mass spectrometric features showed a positive dose-dependent response to meat or fish intake in the intervention study. Eighteen of these features best predicted intake of the 4 food groups in the EPIC urine samples on the basis of partial receiver operator curve analyses with permutation testing (areas under the curve ranging between 0.61 and 1.0). Of these signals, 8 metabolites were identified. Anserine was found to be specific for chicken intake, whereas trimethylamine-N-oxide showed good specificity for fish. Carnosine and 3 acylcarnitines (acetylcarnitine, propionylcarnitine, and 2-methylbutyrylcarnitine) appeared to be more generic indicators of meat and meat and fish intake, respectively.Conclusion: The meat and fish biomarkers identified in this work may be used to study associations between meat and fish intake and disease risk in epidemiologic studies. This trial was registered at clinicaltrials.gov as NCT01684917.
Reinke, Stacey N; Gallart-Ayala, Héctor; Gómez, Cristina; Checa, Antonio; Fauland, Alexander; Naz, Shama; Kamleh, Muhammad Anas; Djukanović, Ratko; Hinks, Timothy S C; Wheelock, Craig E
In this study, we sought to determine whether asthma has a metabolic profile and whether this profile is related to disease severity.We characterised the serum from 22 healthy individuals and 54 asthmatics (12 mild, 20 moderate, 22 severe) using liquid chromatography-high-resolution mass spectrometry-based metabolomics. Selected metabolites were confirmed by targeted mass spectrometry assays of eicosanoids, sphingolipids and free fatty acids.We conclusively identified 66 metabolites; 15 were significantly altered with asthma (p≤0.05). Levels of dehydroepiandrosterone sulfate, cortisone, cortisol, prolylhydroxyproline, pipecolate and N-palmitoyltaurine correlated significantly (p<0.05) with inhaled corticosteroid dose, and were further shifted in individuals treated with oral corticosteroids. Oleoylethanolamide increased with asthma severity independently of steroid treatment (p<0.001). Multivariate analysis revealed two patterns: 1) a mean difference between controls and patients with mild asthma (p=0.025), and 2) a mean difference between patients with severe asthma and all other groups (p=1.7×10(-4)). Metabolic shifts in mild asthma, relative to controls, were associated with exogenous metabolites (e.g. dietary lipids), while those in moderate and severe asthma (e.g. oleoylethanolamide, sphingosine-1-phosphate, N-palmitoyltaurine) were postulated to be involved in activating the transient receptor potential vanilloid type 1 (TRPV1) receptor, driving TRPV1-dependent pathogenesis in asthma.Our findings suggest that asthma is characterised by a modest systemic metabolic shift in a disease severity-dependent manner, and that steroid treatment significantly affects metabolism.
Baran, Richard; Brodie, Eoin L.; Mayberry-Lewis, Jazmine; Nunes Da Rocha, Ulisses; Bowen, Benjamin P.; Karaoz, Ulas; Cadillo-Quiroz, Hinsby; Garcia-Pichel, Ferran; Northen, Trent R.
Biological soil crusts (BSCs) are communities of organisms inhabiting the upper layer of soil in arid environments. BSCs persist in a dessicated dormant state for extended periods of time and experience pulsed periods of activity facilitated by infrequent rainfall. Microcoleus vaginatus, a non-diazotrophic filamentous cyanobacterium, is the key primary producer in BSCs in the Colorado Plateau and is an early pioneer in colonizing arid environments. Over decades, BSCs proceed through developmental stages with increasing complexity of constituent microorganisms and macroscopic properties. Metabolic interactions among BSC microorganisms probably play a key role in determining the community dynamics and cycling of carbon and nitrogen. However, these metabolic interactions have not been studied systematically. Towards this goal, exometabolomic analysis was performed using liquid chromatography coupled to tandem mass spectrometry on biological soil crust pore water and spent media of key soil bacterial isolates. Comparison of spent vs. fresh media was used to determine uptake or release of metabolites by specific microbes. To link pore water experiments with isolate studies, metabolite extracts of authentic soil were used as supplements for isolate exometabolomic profiling. Our soil metabolomics methods detected hundreds of metabolites from soils including many novel compounds. Overall, Microcoleus vaginatus was found to release and utilize a broad range of metabolites. Many of these metabolites were also taken up by heterotrophs but there were surprisingly few metabolites uptaken by all isolates. This points to a competition for a small set of central metabolites and specialization of individual heterotrophs towards a diverse pool of available organic nutrients. Overall, these data suggest that understanding the substrate specialization of biological soil crust bacteria can help link community structure to nutrient cycling.
Alves, Zélia; Melo, André; Figueiredo, Ana Raquel; Coimbra, Manuel A.; Gomes, Ana C.; Rocha, Sílvia M.
Winemaking is a highly industrialized process and a number of commercial Saccharomyces cerevisiae strains are used around the world, neglecting the diversity of native yeast strains that are responsible for the production of wines peculiar flavours. The aim of this study was to in-depth establish the S. cerevisiae volatile metabolome and to assess inter-strains variability. To fulfill this objective, two indigenous strains (BT2652 and BT2453 isolated from spontaneous fermentation of grapes collected in Bairrada Appellation, Portugal) and two commercial strains (CSc1 and CSc2) S. cerevisiae were analysed using a methodology based on advanced multidimensional gas chromatography (HS-SPME/GC×GC-ToFMS) tandem with multivariate analysis. A total of 257 volatile metabolites were identified, distributed over the chemical families of acetals, acids, alcohols, aldehydes, ketones, terpenic compounds, esters, ethers, furan-type compounds, hydrocarbons, pyrans, pyrazines and S-compounds. Some of these families are related with metabolic pathways of amino acid, carbohydrate and fatty acid metabolism as well as mono and sesquiterpenic biosynthesis. Principal Component Analysis (PCA) was used with a dataset comprising all variables (257 volatile components), and a distinction was observed between commercial and indigenous strains, which suggests inter-strains variability. In a second step, a subset containing esters and terpenic compounds (C10 and C15), metabolites of particular relevance to wine aroma, was also analysed using PCA. The terpenic and ester profiles express the strains variability and their potential contribution to the wine aromas, specially the BT2453, which produced the higher terpenic content. This research contributes to understand the metabolic diversity of indigenous wine microflora versus commercial strains and achieved knowledge that may be further exploited to produce wines with peculiar aroma properties. PMID:26600152
Kumari, Asha; Das, Paromita; Parida, Asish Kumar; Agarwal, Pradeep K.
Halophytes are plants which naturally survive in saline environment. They account for ∼1% of the total flora of the world. They include both dicots and monocots and are distributed mainly in arid, semi-arid inlands and saline wet lands along the tropical and sub-tropical coasts. Salinity tolerance in halophytes depends on a set of ecological and physiological characteristics that allow them to grow and flourish in high saline conditions. The ability of halophytes to tolerate high salt is determined by the effective coordination between various physiological processes, metabolic pathways and protein or gene networks responsible for delivering salinity tolerance. The salinity responsive proteins belong to diverse functional classes such as photosynthesis, redox homeostasis; stress/defense, carbohydrate and energy metabolism, protein metabolism, signal transduction and membrane transport. The important metabolites which are involved in salt tolerance of halophytes are proline and proline analog (4-hydroxy-N-methyl proline), glycine betaine, pinitol, myo-inositol, mannitol, sorbitol, O-methylmucoinositol, and polyamines. In halophytes, the synthesis of specific proteins and osmotically active metabolites control ion and water flux and support scavenging of oxygen radicals under salt stress condition. The present review summarizes the salt tolerance mechanisms of halophytes by elucidating the recent studies that have focused on proteomic, metabolomic, and ionomic aspects of various halophytes in response to salinity. By integrating the information from halophytes and its comparison with glycophytes could give an overview of salt tolerance mechanisms in halophytes, thus laying down the pavement for development of salt tolerant crop plants through genetic modification and effective breeding strategies. PMID:26284080
Clendinen, Chaevien S.; Stupp, Gregory S.; Wang, Bing; Garrett, Timothy J.; Edison, Arthur S.
Abstract: Background Isotopic Ratio Outlier Analysis (IROA) is an untargeted metabolomics method that uses stable isotopic labeling and LC-HRMS for identification and relative quantification of metabolites in a biological sample under varying experimental conditions. Objective We demonstrate a method using high-sensitivity 13C NMR to identify an unknown metabolite isolated from fractionated material from an IROA LC-HRMS experiment. Methods IROA samples from the nematode Caenorhabditis elegans were fractionated using LC-HRMS using 5 repeated injections and collecting 30 sec fractions. These were concentrated and analyzed by 13C NMR. Results We isotopically labeled samples of C. elegans and collected 2 adjacent LC fractions. By HRMS, one contained at least 2 known metabolites, phenylalanine and inosine, and the other contained tryptophan and an unknown feature with a monoisotopic mass of m/z 380.0742 [M+H]+. With NMR, we were able to easily verify the known compounds, and we then identified the spin system networks responsible for the unknown resonances. After searching the BMRB database and comparing the molecular formula from LC-HRMS, we determined that the fragments were a modified anthranilate and a glucose modified by a phosphate. We then performed quantum chemical NMR chemical shift calculations to determine the most likely isomer, which was 3’-O-phospho-β-D-glucopyranosyl-anthranilate. This compound had previously been found in the same organism, validating our approach. Conclusion We were able to dereplicate previously known metabolites and identify a metabolite that was not in databases by matching resonances to NMR databases and using chemical shift calculations to determine the correct isomer. This approach is efficient and can be used to identify unknown compounds of interest using the same material used for IROA. PMID:28090435
Malm, Linus; Tybring, Gunnel; Moritz, Thomas; Landin, Britta; Galli, Joakim
Handling and processing of blood can significantly alter the molecular composition and consistency of biobank samples and can have a major impact on the identification of biomarkers. It is thus crucial to identify tools to determine the quality of samples to be used in biomarker discovery studies. In this study, a non-targeted gas chromatography/time-of-flight mass spectrometry (GC-TOFMS) metabolomic strategy was used with the aim of identifying quality markers for serum and plasma biobank collections lacking proper documentation of preanalytical handling. The effect of postcentrifugation delay was examined in serum stored in tubes with gel separation plugs and ethylenediaminetetraacetic acid (EDTA) plasma in tubes with or without gel separation plugs. The change in metabolic pattern was negligible in all sample types processed within 3 hours after centrifugation regardless of whether the samples were kept at 4°C or 22°C. After 8 and 24 hours postcentrifugation delay before aliquoting, there was a pronounced increase in the number of affected metabolites, as well as in the magnitude of the observed changes. No protective effect on the metabolites was observed in gel-separated EDTA plasma samples. In a separate series of experiments, lactate and glucose levels were determined in plasma to estimate the effect of precentrifugation delay. This separate experiment indicates that the lactate to glucose ratio may serve as a marker to identify samples with delayed time to centrifugation. Although our data from the untargeted GC-TOFMS analysis did not identify any specific markers, we conclude that plasma and serum metabolic profiles remain quite stable when plasma and serum are centrifuged and separated from the blood cells within 3 hours.
Clish, Clary B.; Ghorbani, Anahita; Larson, Martin G.; Elmariah, Sammy; McCabe, Elizabeth; Yang, Qiong; Cheng, Susan; Pierce, Kerry; Deik, Amy; Souza, Amanda L.; Farrell, Laurie; Domos, Carly; Yeh, Robert W.; Palacios, Igor; Rosenfield, Kenneth; Vasan, Ramachandran S.; Florez, Jose C.; Wang, Thomas J.; Fox, Caroline S.
Metabolomic approaches have begun to catalog the metabolic disturbances that accompany CKD, but whether metabolite alterations can predict future CKD is unknown. We performed liquid chromatography/mass spectrometry–based metabolite profiling on plasma from 1434 participants in the Framingham Heart Study (FHS) who did not have CKD at baseline. During the following 8 years, 123 individuals developed CKD, defined by an estimated GFR of <60 ml/min per 1.73 m2. Numerous metabolites were associated with incident CKD, including 16 that achieved the Bonferroni-adjusted significance threshold of P≤0.00023. To explore how the human kidney modulates these metabolites, we profiled arterial and renal venous plasma from nine individuals. Nine metabolites that predicted CKD in the FHS cohort decreased more than creatinine across the renal circulation, suggesting that they may reflect non–GFR-dependent functions, such as renal metabolism and secretion. Urine isotope dilution studies identified citrulline and choline as markers of renal metabolism and kynurenic acid as a marker of renal secretion. In turn, these analytes remained associated with incident CKD in the FHS cohort, even after adjustment for eGFR, age, sex, diabetes, hypertension, and proteinuria at baseline. Addition of a multimarker metabolite panel to clinical variables significantly increased the c-statistic (0.77–0.83, P<0.0001); net reclassification improvement was 0.78 (95% confidence interval, 0.60 to 0.95; P<0.0001). Thus, the addition of metabolite profiling to clinical data may significantly improve the ability to predict whether an individual will develop CKD by identifying predictors of renal risk that are independent of estimated GFR. PMID:23687356
Smilowitz, Jennifer T.; O’Sullivan, Aifric; Barile, Daniela; German, J. Bruce; Lönnerdal, Bo; Slupsky, Carolyn M.
Breast milk delivers nutrition and protection to the developing infant. There has been considerable research on the high-molecular-weight milk components; however, low-molecular-weight metabolites have received less attention. To determine the effect of maternal phenotype and diet on the human milk metabolome, milk collected at day 90 postpartum from 52 healthy women was analyzed by using proton nuclear magnetic resonance spectroscopy. Sixty-five milk metabolites were quantified (mono-, di-, and oligosaccharides; amino acids and derivatives; energy metabolites; fatty acids and associated metabolites; vitamins, nucleotides, and derivatives; and others). The biological variation, represented as the percentage CV of each metabolite, varied widely (4–120%), with several metabolites having low variation (<20%), including lactose, urea, glutamate, myo-inositol, and creatinine. Principal components analysis identified 2 clear groups of participants who were differentiable on the basis of milk oligosaccharide concentration and who were classified as secretors or nonsecretors of fucosyltransferase 2 (FUT2) gene products according to the concentration of 2′-fucosyllactose, lactodifucotetraose, and lacto-N-fucopentaose I. Exploration of the interrelations between the milk sugars by using Spearman rank correlations revealed significant positive and negative associations, including positive correlations between fucose and products of the FUT2 gene and negative correlations between fucose and products of the fucosyltransferase 3 (FUT3) gene. The total concentration of milk oligosaccharides was conserved among participants (%CV = 18%), suggesting tight regulation of total oligosaccharide production; however, concentrations of specific oligosaccharides varied widely between participants (%CV = 30.4–84.3%). The variability in certain milk metabolites suggests possible roles in infant or infant gut microbial development. This trial was registered at clinicaltrials.gov as NCT
Smilowitz, Jennifer T; O'Sullivan, Aifric; Barile, Daniela; German, J Bruce; Lönnerdal, Bo; Slupsky, Carolyn M
Breast milk delivers nutrition and protection to the developing infant. There has been considerable research on the high-molecular-weight milk components; however, low-molecular-weight metabolites have received less attention. To determine the effect of maternal phenotype and diet on the human milk metabolome, milk collected at day 90 postpartum from 52 healthy women was analyzed by using proton nuclear magnetic resonance spectroscopy. Sixty-five milk metabolites were quantified (mono-, di-, and oligosaccharides; amino acids and derivatives; energy metabolites; fatty acids and associated metabolites; vitamins, nucleotides, and derivatives; and others). The biological variation, represented as the percentage CV of each metabolite, varied widely (4-120%), with several metabolites having low variation (<20%), including lactose, urea, glutamate, myo-inositol, and creatinine. Principal components analysis identified 2 clear groups of participants who were differentiable on the basis of milk oligosaccharide concentration and who were classified as secretors or nonsecretors of fucosyltransferase 2 (FUT2) gene products according to the concentration of 2'-fucosyllactose, lactodifucotetraose, and lacto-N-fucopentaose I. Exploration of the interrelations between the milk sugars by using Spearman rank correlations revealed significant positive and negative associations, including positive correlations between fucose and products of the FUT2 gene and negative correlations between fucose and products of the fucosyltransferase 3 (FUT3) gene. The total concentration of milk oligosaccharides was conserved among participants (%CV = 18%), suggesting tight regulation of total oligosaccharide production; however, concentrations of specific oligosaccharides varied widely between participants (%CV = 30.4-84.3%). The variability in certain milk metabolites suggests possible roles in infant or infant gut microbial development. This trial was registered at clinicaltrials.gov as NCT01817127.
Tulipani, Sara; Mora-Cubillos, Ximena; Jáuregui, Olga; Llorach, Rafael; García-Fuentes, Eduardo; Tinahones, Francisco J; Andres-Lacueva, Cristina
Although LC-MS untargeted metabolomics continues to expand into exiting research domains, methodological issues have not been solved yet by the definition of unbiased, standardized and globally accepted analytical protocols. In the present study, the response of the plasma metabolome coverage to specific methodological choices of the sample preparation (two SPE technologies, three sample-to-solvent dilution ratios) and the LC-ESI-MS data acquisition steps of the metabolomics workflow (four RP columns, four elution solvent combinations, two solvent quality grades, postcolumn modification of the mobile phase) was investigated in a pragmatic and decision tree-like performance evaluation strategy. Quality control samples, reference plasma and human plasma from a real nutrimetabolomic study were used for intermethod comparisons. Uni- and multivariate data analysis approaches were independently applied. The highest method performance was obtained by combining the plasma hybrid extraction with the highest solvent proportion during sample preparation, the use of a RP column compatible with 100% aqueous polar phase (Atlantis T3), and the ESI enhancement by using UHPLC-MS purity grade methanol as both organic phase and postcolumn modifier. Results led to the following considerations: submit plasma samples to hybrid extraction for removal of interfering components to minimize the major sample-dependent matrix effects; avoid solvent evaporation following sample extraction if loss in detection and peak shape distortion of early eluting metabolites are not noticed; opt for a RP column for superior retention of highly polar species when analysis fractionation is not feasible; use ultrahigh quality grade solvents and "vintage" analytical tricks such as postcolumn organic enrichment of the mobile phase to enhance ESI efficiency. The final proposed protocol offers an example of how novel and old-fashioned analytical solutions may fruitfully cohabit in untargeted metabolomics
Propiconazole is a mouse hepatotumorigenic fungicide and has been the subject of recent mechanistic investigations on its carcinogenic mechanism of action. The goals of this study were: 1. To identify metabolomic changes induced in the liver by increasing doses of propiconazole i...
Pan, Xiaobei; Nasaruddin, Muhammad Bin; Elliott, Christopher T; McGuinness, Bernadette; Passmore, Anthony P; Kehoe, Patrick G; Hölscher, Christian; McClean, Paula L; Graham, Stewart F; Green, Brian D
The pathogenesis of Alzheimer's disease (AD) is complex involving multiple contributing factors. The extent to which AD pathology affects the metabolome is still not understood nor is it known how disturbances change as the disease progresses. For the first time, we have profiled longitudinally (6, 8, 10, 12, and 18 months) both the brain and plasma metabolome of APPswe/PS1deltaE9 double transgenic and wild-type mice. A total of 187 metabolites were quantified using a targeted metabolomic methodology. Multivariate statistical analysis produced models that distinguished APPswe/PS1deltaE9 from wild-type mice at 8, 10, and 12 months. Metabolic pathway analysis found perturbed polyamine metabolism in both brain and blood plasma. There were other disturbances in essential amino acids, branched-chain amino acids, and also in the neurotransmitter serotonin. Pronounced imbalances in phospholipid and acylcarnitine homeostasis were evident in 2 age groups. AD-like pathology, therefore, affects greatly on both the brain and blood metabolomes, although there appears to be a clear temporal sequence whereby changes to brain metabolites precede those in blood.
Foliar stomatal movements are critical for regulating plant water status and gas exchange. Elevated carbon dioxide (CO2) concentrations are known to induce stomatal closure. However, current knowledge on CO2 signal transduction in stomatal guard cells is limited. Here we report the metabolomic respo...
Enomoto, Mitsuo; Morishita, Yoshihiko; Kurabayashi, Atsushi; Iijima, Yoko; Ogata, Yoshiyuki; Nakajima, Daisuke; Suzuki, Hideyuki; Shibata, Daisuke
A metabolome—the collection of comprehensive quantitative data on metabolites in an organism—has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study on Jatropha curcas is presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data. PMID:24949426
Gas chromatography-mass spectrometry (GC-MS)-based metabolomics is ideal for identifying and quantitating small molecular metabolites (<650 daltons), including small acids, alcohols, hydroxyl acids, amino acids, sugars, fatty acids, sterols, catecholamines, drugs, and toxins, often using chemical derivatization to make these compounds volatile enough for gas chromatography. This unit shows that on GC-MS- based metabolomics easily allows integrating targeted assays for absolute quantification of specific metabolites with untargeted metabolomics to discover novel compounds. Complemented by database annotations using large spectral libraries and validated, standardized standard operating procedures, GC-MS can identify and semi-quantify over 200 compounds per study in human body fluids (e.g., plasma, urine or stool) samples. Deconvolution software enables detection of more than 300 additional unidentified signals that can be annotated through accurate mass instruments with appropriate data processing workflows, similar to liquid chromatography-MS untargeted profiling (LC-MS). Hence, GC-MS is a mature technology that not only uses classic detectors (‘quadrupole’) but also target mass spectrometers (‘triple quadrupole’) and accurate mass instruments (‘quadrupole-time of flight’). This unit covers the following aspects of GC-MS-based metabolomics: (i) sample preparation from mammalian samples, (ii) acquisition of data, (iii) quality control, and (iv) data processing. PMID:27038389
Turroni, Silvia; Fiori, Jessica; Rampelli, Simone; Schnorr, Stephanie L.; Consolandi, Clarissa; Barone, Monica; Biagi, Elena; Fanelli, Flaminia; Mezzullo, Marco; Crittenden, Alyssa N.; Henry, Amanda G.; Brigidi, Patrizia; Candela, Marco
The recent characterization of the gut microbiome of traditional rural and foraging societies allowed us to appreciate the essential co-adaptive role of the microbiome in complementing our physiology, opening up significant questions on how the microbiota changes that have occurred in industrialized urban populations may have altered the microbiota-host co-metabolic network, contributing to the growing list of Western diseases. Here, we applied a targeted metabolomics approach to profile the fecal metabolome of the Hadza of Tanzania, one of the world’s few remaining foraging populations, and compared them to the profiles of urban living Italians, as representative of people in the post-industrialized West. Data analysis shows that during the rainy season, when the diet is primarily plant-based, Hadza are characterized by a distinctive enrichment in hexoses, glycerophospholipids, sphingolipids, and acylcarnitines, while deplete in the most common natural amino acids and derivatives. Complementary to the documented unique metagenomic features of their gut microbiome, our findings on the Hadza metabolome lend support to the notion of an alternate microbiome configuration befitting of a nomadic forager lifestyle, which helps maintain metabolic homeostasis through an overall scarcity of inflammatory factors, which are instead highly represented in the Italian metabolome. PMID:27624970
Shi, Yaolong; Wang, Dehua
Mongolian gerbils (Meriones unguiculatus) have evolved a wide thermoneutral zone (26.5-38.9 °C) and high upper critical temperature, and appear to have a high tolerance for heat exposure. Here, we use a metabolomic approach to measure global metabolite profiles for gerbils between lower (27 °C) and upper critical temperatures (38 °C) to investigate the role of metabolomic characterization in maintaining basal metabolic rates within a wide thermoneutral zone. We found that in serum and liver, 14 and 19 metabolites were significantly altered, respectively. In the aerobic respiration-related tricarboxylic cycle (TCA), 5 intermediates (isocitric acid, cis-aconitic acid, α-ketoglutaric acid, fumaric acid and malic acid) were increased in serum in 38 °C animals; however, no such increase was found in the liver. A stable level of hepatic TCA cycle intermediates may be related to the steady state of aerobic respiration at 38 °C. Metabolomic results also revealed that acute heat exposure caused increased oxidative stress and low molecular weight antioxidants in Mongolian gerbils. Increased methionine and 2-hydroxybutyrate suggest an accelerated synthesis of glutathione. Increased urate and its precursors, inosine and hypoxanthine, were detected at 38 °C. Glucuronate, threonate and oxalate involved in ascorbate synthesis and degradation were increased in serum at 38 °C. In conclusion, although dramatic metabolomic variation was found, a stable hepatic TCA cycle may contribute to maintaining a constant basal metabolic rate within a wide thermoneutral zone in Mongolian gerbils.
Metabolomics is being increasingly used for diagnosis of asymptomatic/difficult-to-diagnose diseases in humans including parasitic (i.e. protozoan, schistosomal), viral (i.e. cytomegalovirus), bacterial (i.e. cystic fibrosis caused by Pseudomonas), genetic (i.e. autism) and cancer (i.e. gastric canc...
There are likely a large number of compounds that constitute the peanut seed metabolome that have yet to be elucidated. Although the proximate composition and nutrients such as vitamins and minerals are well known, the composition of many other small molecule metabolites present have not been syste...
: It is known that environmental factors can affect the biosynthesis of leaf metabolites. Similarly, specific pairwise plant-microbe interactions modulate specifically the plant’s metabolome by stimulating production of phytoalexins and other defense-related compounds. However, there is no informati...
Zhao, Dongying; Han, Lianshu; He, Zhengjuan; Zhang, Jun; Zhang, Yongjun
Early detection is the most effective way to improve the clinical outcome of biliary atresia (BA). Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve early diagnosis. The aim of this study is to find the potential biomarkers to distinguish BA from neonatal hepatitis syndrome (NHS) by using a metabolomics method. We comprehensively analyzed the serum metabolites in a total of 124 blood samples from patients with BA or neonatal hepatitis syndrome (NHS) and from normal individuals using advanced metabolomic approaches, and found that the levels of glutarylcarnitine (C5DC) significantly increased in the BA group while the levels of threonine (Thr) significantly rose in the NHS group comparing with the other groups. The levels of glutamic acid (Glu) in the BA group were significantly elevated compared to those in the NHS group, but still lower than the hyperbilirubinemia and normal controls. The levels of propionyl carnitine (C3), isovaleryl carnitine (C5) and glutamine (Gln) were reduced in the BA group compared to those in the NHS group, but still higher than the hyperbilirubinemia and normal controls. This study demonstrates the possibility of metabolomics as non-invasive biomarkers for the early detection of BA and also provides new insight into pathophysiologic mechanisms for BA. PMID:24416443
Patti, Gary J.; Tautenhahn, Ralf; Fonslow, Bryan R.; ...
Global metabolomics has emerged as a powerful tool to interrogate cellular biochemistry at the systems level by tracking alterations in the levels of small molecules. One approach to define cellular dynamics with respect to this dysregulation of small molecules has been to consider metabolic flux as a function of time. While flux measurements have proven effective for model organisms, acquiring multiple time points at appropriate temporal intervals for many sample types (e.g., clinical specimens) is challenging. As an alternative, meta-analysis provides another strategy for delineating metabolic cause and effect perturbations. That is, the combination of untargeted metabolomic data from multiplemore » pairwise comparisons enables the association of specific changes in small molecules with unique phenotypic alterations. We recently developed metabolomic software called metaXCMS to automate these types of higher order comparisons. Here we discuss the potential of metaXCMS for analyzing proteomic datasets and highlight the biological value of combining meta-results from both metabolomic and proteomic analyses. The combined meta-analysis has the potential to facilitate efforts in functional genomics and the identification of metabolic disruptions related to disease pathogenesis.« less
Lokhov, Petr G; Balashova, Elena E; Voskresenskaya, Anna A; Trifonova, Oxana P; Maslov, Dmitry L; Archakov, Alexander I
In metabolomics, a large number of small molecules can be detected in a single run. However, metabolomic data do not include the absolute concentrations of each metabolite. Generally, mass spectrometry analyses provide metabolite concentrations that are derived from mass peak intensities, and the peak intensities are strictly dependent on the type of mass spectrometer used, as well as the technical characteristics, options and protocols applied. To convert mass peak intensities to actual concentrations, calibration curves have to be generated for each metabolite, and this represents a significant challenge depending on the number of metabolites that are detected and involved in metabolome-based diagnostics. To overcome this limitation, and to facilitate the development of diagnostic tests based on metabolomics, mass peak intensities may be expressed in quintiles. The present study demonstrates the advantage of this approach. The examples of diagnostic signatures, which were designed in accordance to this approach, are provided for lung and prostate cancer (leading causes of mortality due to cancer in developed countries) and impaired glucose tolerance (which precedes type 2 diabetes, the most common endocrinology disease worldwide).
Schmidt, Brian; Ebrahim, Ali; Metz, Thomas O.; Adkins, Joshua N.; Palsson, Bernard O.; Hyduke, Daniel R.
Motivation: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been reported. Results: GIMMME (Gene Inactivation Moderated by Metabolism, Metabolomics, and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics, and intracellular metabolomics data. GIMMME establishes metabolite utilization requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions, and also provides calculations of the turnover (production / consumption) flux of metabolites. GIMMME was employed to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. Availability: GIMMME has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/
Kirkwood, Jay S; Legette, LeeCole L; Miranda, Cristobal L; Jiang, Yuan; Stevens, Jan F
Mild, mitochondrial uncoupling increases energy expenditure and can reduce the generation of reactive oxygen species (ROS). Activation of cellular, adaptive stress response pathways can result in an enhanced capacity to reduce oxidative damage. Together, these strategies target energy imbalance and oxidative stress, both underlying factors of obesity and related conditions such as type 2 diabetes. Here we describe a metabolomics-driven effort to uncover the anti-obesity mechanism(s) of xanthohumol (XN), a prenylated flavonoid from hops. Metabolomics analysis of fasting plasma from obese, Zucker rats treated with XN revealed decreases in products of dysfunctional fatty acid oxidation and ROS, prompting us to explore the effects of XN on muscle cell bioenergetics. At low micromolar concentrations, XN acutely increased uncoupled respiration in several different cell types, including myocytes. Tetrahydroxanthohumol also increased respiration, suggesting electrophilicity did not play a role. At higher concentrations, XN inhibited respiration in a ROS-dependent manner. In myocytes, time course metabolomics revealed acute activation of glutathione recycling and long term induction of glutathione synthesis as well as several other changes indicative of short term elevated cellular stress and a concerted adaptive response. Based on these findings, we hypothesize that XN may ameliorate metabolic syndrome, at least in part, through mitochondrial uncoupling and stress response induction. In addition, time course metabolomics appears to be an effective strategy for uncovering metabolic events that occur during a stress response.
Mahrous, Engy A.; Farag, Mohamed A.
Today, most investigations of the plant metabolome tend to be based on either nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS), with or without hyphenation with chromatography. Although less sensitive than MS, NMR provides a powerful complementary technique for the identification and quantification of metabolites in plant extracts. NMR spectroscopy, well appreciated by phytochemists as a particularly information-rich method, showed recent paradigm shift for the improving of metabolome(s) structural and functional characterization and for advancing the understanding of many biological processes. Furthermore, two dimensional NMR (2D NMR) experiments and the use of chemometric data analysis of NMR spectra have proven highly effective at identifying novel and known metabolites that correlate with changes in genotype or phenotype. In this review, we provide an overview of the development of NMR in the field of metabolomics with special focus on 2D NMR spectroscopic techniques and their applications in phytomedicines quality control analysis and drug discovery from natural sources, raising more attention at its potential to reduce the gap between the pace of natural products research and modern drug discovery demand. PMID:25685540
The pharmaceutical industry has significantly contributed to improving human health. Drugs have been attributed to both increasing life expectancy and decreasing health care costs. Unfortunately, there has been a recent decline in the creativity and productivity of the pharmaceutical industry. This is a complex issue with many contributing factors resulting from the numerous mergers, increase in out-sourcing, and the heavy dependency on high-throughput screening (HTS). While a simple solution to such a complex problem is unrealistic and highly unlikely, the inclusion of metabolomics as a routine component of the drug discovery process may provide some solutions to these problems. Specifically, as the binding affinity of a chemical lead is evolved during the iterative structure-based drug design process, metabolomics can provide feedback on the selectivity and the in vivo mechanism of action. Similarly, metabolomics can be used to evaluate and validate HTS leads. In effect, metabolomics can be used to eliminate compounds with potential efficacy and side effect problems while prioritizing well-behaved leads with druglike characteristics. PMID:24588729
Hines, Adam; Yeung, Wai Ho; Craft, John; Brown, Margaret; Kennedy, Jill; Bignell, John; Stentiford, Grant D; Viant, Mark R
Omics technologies are increasingly being used to monitor organismal responses to environmental stressors. Previous studies have shown that species identification, an appreciation of life history traits, and organism phenotype (e.g., gender) are essential for the accurate interpretation of omics data from field samples. As marine mussels are increasingly being used in ecotoxicogenomics and monitoring, a technique to determine mussel gender throughout their annual reproductive cycle is urgently needed. This study examines four methods for sex determination in the two mussel species found in the United Kingdom, Mytilus edulis and Mytilus galloprovincialis, and their hybrid. Each of these methods-histology, a lipid-based assay, a new reverse transcriptase polymerase chain reaction (RT-PCR) assay, and nuclear magnetic resonance (NMR)-based metabolomics-initially was evaluated using sexually mature ("ripe") mussels whose gender was clearly distinguishable using histology. The methods subsequently were tested on spawned ("spent") mussels. For ripe animals, all techniques yielded high classification accuracies: histology, 100%; RT-PCR, 94.6%; lipid analysis, 90.6%; and metabolomics, 89.5%. The gender of spent animals, however, could not be determined by histology (0%) or lipid analysis (55.6%), but RT-PCR (100%) and metabolomics (88.9%) both proved to be successful. In addition, the RT-PCR, metabolomics, and lipid-based methods identified animals of mixed sex. Our findings highlight the application of a novel RT-PCR method as a robust technique for gender determination of ripe and spent mussels.
Kirkwood, Jay S.; Legette, LeeCole L.; Miranda, Cristobal L.; Jiang, Yuan; Stevens, Jan F.
Mild, mitochondrial uncoupling increases energy expenditure and can reduce the generation of reactive oxygen species (ROS). Activation of cellular, adaptive stress response pathways can result in an enhanced capacity to reduce oxidative damage. Together, these strategies target energy imbalance and oxidative stress, both underlying factors of obesity and related conditions such as type 2 diabetes. Here we describe a metabolomics-driven effort to uncover the anti-obesity mechanism(s) of xanthohumol (XN), a prenylated flavonoid from hops. Metabolomics analysis of fasting plasma from obese, Zucker rats treated with XN revealed decreases in products of dysfunctional fatty acid oxidation and ROS, prompting us to explore the effects of XN on muscle cell bioenergetics. At low micromolar concentrations, XN acutely increased uncoupled respiration in several different cell types, including myocytes. Tetrahydroxanthohumol also increased respiration, suggesting electrophilicity did not play a role. At higher concentrations, XN inhibited respiration in a ROS-dependent manner. In myocytes, time course metabolomics revealed acute activation of glutathione recycling and long term induction of glutathione synthesis as well as several other changes indicative of short term elevated cellular stress and a concerted adaptive response. Based on these findings, we hypothesize that XN may ameliorate metabolic syndrome, at least in part, through mitochondrial uncoupling and stress response induction. In addition, time course metabolomics appears to be an effective strategy for uncovering metabolic events that occur during a stress response. PMID:23673658
Jové, Mariona; Portero-Otín, Manuel; Naudí, Alba; Ferrer, Isidre; Pamplona, Reinald
Neurons in the mature human central nervous system (CNS) perform a wide range of motor, sensory, regulatory, behavioral, and cognitive functions. Such diverse functional output requires a great diversity of CNS neuronal and non-neuronal populations. Metabolomics encompasses the study of the complete set of metabolites/low-molecular-weight intermediates (metabolome), which are context-dependent and vary according to the physiology, developmental state, or pathologic state of the cell, tissue, organ, or organism. Therefore, the use of metabolomics can help to unravel the diversity-and to disclose the specificity-of metabolic traits and their alterations in the brain and in fluids such as cerebrospinal fluid and plasma, thus helping to uncover potential biomarkers of aging and neurodegenerative diseases. Here, we review the current applications of metabolomics in studies of CNS aging and certain age-related neurodegenerative diseases such as Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis. Neurometabolomics will increase knowledge of the physiologic and pathologic functions of neural cells and will place the concept of selective neuronal vulnerability in a metabolic context.
Background Analysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data. Results A novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis. Conclusions Fusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach. PMID:21696593
Niedenführ, Sebastian; ten Pierick, Angela; van Dam, Patricia T N; Suarez-Mendez, Camilo A; Nöh, Katharina; Wahl, S Aljoscha
Fluxomics and metabolomics are crucial tools for metabolic engineering and biomedical analysis to determine the in vivo cellular state. Especially, the application of (13)C isotopes allows comprehensive insights into the functional operation of cellular metabolism. Compared to single MS, tandem mass spectrometry (MS/MS) provides more detailed and accurate measurements of the metabolite enrichment patterns (tandem mass isotopomers), increasing the accuracy of metabolite concentration measurements and metabolic flux estimation. MS-type data from isotope labeling experiments is biased by naturally occurring stable isotopes (C, H, N, O, etc.). In particular, GC-MS(/MS) requires derivatization for the usually non-volatile intracellular metabolites introducing additional natural isotopes leading to measurements that do not directly represent the carbon labeling distribution. To make full use of LC- and GC-MS/MS mass isotopomer measurements, the influence of natural isotopes has to be eliminated (corrected). Our correction approach is analyzed for the two most common applications; (13)C fluxomics and isotope dilution mass spectrometry (IDMS) based metabolomics. Natural isotopes can have an impact on the calculated flux distribution which strongly depends on the substrate labeling and the actual flux distribution. Second, we show that in IDMS based metabolomics natural isotopes lead to underestimated concentrations that can and should be corrected with a nonlinear calibration. Our simulations indicate that the correction for natural abundance in isotope based fluxomics and quantitative metabolomics is essential for correct data interpretation.
Propiconazole is a mouse hepatotumorigenic fungicide and has been the subject of recent investigations into its carcinogenic mechanism of action. The goals of this study were: 1. To identify metabolomic changes induced in the liver by increasing doses of propiconazole in mice; 2...
Preharvest calcium application has been shown to increase broccoli microgreen yield and extend shelf life. Here we investigated the effect of calcium application on its metabolome using ultra high-performance liquid chromatography (UHPLC) tandem with mass spectrometry (HRMS). The data collected were...
It is now well documented that the diet has a significant impact on human health and well-being. However, the complete set of small molecule metabolites present in foods that make up the human diet and the role of food production systems in altering this food metabolome are still largely unknown. Me...
Shen, Sensen; Weng, Rui; Li, Linnan; Xu, Xinyuan; Bai, Yu; Liu, Huwei
Autophagy-related protein 7 (Atg7) is essential in the formation of the autophagophore and is indispensable for autophagy induction. Autophagy will exist in lower level or even be blocked in cells without Atg7. Even though the possible signaling pathways of Atg7 have been proposed, the metabolomic responses under acute starvation in cells with and without Atg7 have not been elucidated. This study therefore was designed and aimed to reveal the metabolomics of Atg7-dependent autophagy through metabolomic analysis of Atg7−/− mouse embryonic fibroblast cells (MEFs) and wild-type MEFs along with the starvation time. 30 significantly altered metabolites were identified in response to nutrient stress, which were mainly associated with amino acid, energy, carbohydrate, and lipid metabolism. For the wild-type MEFs, the induction of autophagy protected cell survival with some up-regulated lipids during the first two hours’ starvation, while the subsequent apoptosis resulted in the decrease of cell viability after four hours’ starvation. For the Atg7−/− MEFs, apoptosis perhaps led to the deactivation of tricarboxylic acid (TCA) cycle due to the lack of autophagy, which resulted in the immediate drop of cellular viability under starvation. These results contributed to the metabolomic study and provided new insights into the mechanism associated with Atg7-dependent autophagy. PMID:27703171
A crucial step in metabolomic analysis of cellular extracts is the cell quenching process. The conventional method first uses trypsin to detach cells from their growth surface. This inevitably changes the profile of cellular metabolites since the detachment of cells from the extr...
Huan, Tao; Li, Liang
Generating precise and accurate quantitative information on metabolomic changes in comparative samples is important for metabolomics research where technical variations in the metabolomic data should be minimized in order to reveal biological changes. We report a method and software program, IsoMS-Quant, for extracting quantitative information from a metabolomic data set generated by chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). Unlike previous work of relying on mass spectral peak ratio of the highest intensity peak pair to measure relative quantity difference of a differentially labeled metabolite, this new program reconstructs the chromatographic peaks of the light- and heavy-labeled metabolite pair and then calculates the ratio of their peak areas to represent the relative concentration difference in two comparative samples. Using chromatographic peaks to perform relative quantification is shown to be more precise and accurate. IsoMS-Quant is integrated with IsoMS for picking peak pairs and Zero-fill for retrieving missing peak pairs in the initial peak pairs table generated by IsoMS to form a complete tool for processing CIL LC-MS data. This program can be freely downloaded from the www.MyCompoundID.org web site for noncommercial use.
Zhang, Aihua; Sun, Hui; Wang, Xijun
Metabolomics is a promising "omics" field in systems biology; its objective is comprehensive analysis of low-molecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could result in earlier intervention and provide valuable insights into the mechanisms of diseases. Because of the possible discovery of clinically relevant biomarkers, metabolomics has potential advantages that routine approaches to clinical diagnosis do not. Monitoring specific metabolite levels in serum, the most commonly used biofluid in metabolomics, has become an important way of detecting the early stages of a disease. Serum is a readily accessible and informative biofluid, making it ideal for early detection of a wide range of diseases, and analysis of serum has several advantages over analysis of other biofluids. Metabolite profiles of serum can be regarded as important indicators of physiological and pathological states and may aid understanding of the mechanism of disease occurrence and progression on the metabolic level, and provide information enabling identification of early and differential metabolic markers of disease. Analysis of these crucial metabolites in serum has become important in monitoring the state of biological organisms and is widely used for diagnosis of disease. Emerging metabolomics will drive serum analysis, facilitate and improve the development of disease treatments, and provide great benefits for public health in the long-term.
Rushton, Paul J.
Understanding how plants respond to water deficit is important in order to develop crops tolerant to drought. In this study, we compare two large metabolomics datasets where we employed a nontargeted metabolomics approach to elucidate metabolic pathways perturbed by progressive dehydration in tobacco and soybean plants. The two datasets were created using the same strategy to create water deficit conditions and an identical metabolomics pipeline. Comparisons between the two datasets therefore reveal common responses between the two species, responses specific to one of the species, responses that occur in both root and leaf tissues, and responses that are specific to one tissue. Stomatal closure is the immediate response of the plant and this did not coincide with accumulation of abscisic acid. A total of 116 and 140 metabolites were observed in tobacco leaves and roots, respectively, while 241 and 207 were observed in soybean leaves and roots, respectively. Accumulation of metabolites is significantly correlated with the extent of dehydration in both species. Among the metabolites that show increases that are restricted to just one plant, 4-hydroxy-2-oxoglutaric acid (KHG) in tobacco roots and coumestrol in soybean roots show the highest tissue-specific accumulation. The comparisons of these two large nontargeted metabolomics datasets provide novel information and suggest that KHG will be a useful marker for drought stress for some members of Solanaceae and coumestrol for some legume species. PMID:28127554
Information on crop genotype- and phenotype-metabolite associations can be of value to trait development as well as to food security and safety. The unique study presented here assessed seed metabolomic and ionomic diversity in a soybean lineage representing ~35 years of breeding (launch years 1972-...
There is a pressing need to increase the throughput of NMR analysis in fields such as metabolomics and drug discovery. Direct injection (DI) NMR automation is recognized to have the potential to meet this need due to its suitability for integration with the 96-well plate format. ...
Investigating the metabolome provides the evaluation of all cellular processes occuring while accounting for environmental influence and may provide additional information for selection criteria to fully evolve. Blood samples and body condition measurements were acquired from 68, first-parity gilts ...
Del Bove, Marzia; Lattanzi, Monia; Rellini, Paolo; Pelliccia, Cristina; Fatichenti, Fabrizio; Cardinali, Gianluigi
Debaryomyces hansenii is one of the yeast species most frequently isolated from cheese and salty foods, however little is known about the phenotypic and molecular variability of its strains. In order to explore the possibilities of a large study on its biodiversity, some D. hansenii strains were selectively isolated from pecorino cheese sampled in ten different Italian regions. All isolates were identified as D. hansenii on the basis of conventional and molecular taxonomic analysis. The D1/D2 domain sequences of the 26S-rDNA did not show any variation, confirming the extreme homogeneity of this species. PCR-duplex-RAPD banding patterns analyzed with PCoA showed interesting clustering related to the geographic areas of isolation, although some overlapping between strains derived from different districts could be observed. A FTIR (Fourier Transform Infrared Spectroscopy) metabolomic fingerprint produced groupings weakly related to those observed with RAPD and less associated with the isolation locales. The discriminatory power of metabolomic fingerprint was able to discriminate strains otherwise considered identical. This preliminary study showed that, in spite of the homogeneity at the 26S-rDNA level, the D. hansenii strains exhibit high molecular and metabolomic variability somehow linked to the places of isolation. Further studies will be necessary to better investigate on the link between terroir and strain variability, as well as on the relation between genotypic and metabolomic fingerprints.
The rumen plays a central role in the efficiency of digestion in ruminants. To identify potential differences in rumen function that lead to differences in feed efficiency, rumen metabolomic analysis by ultra-performance liquid chromatography/ time-of-flight mass spectrometry (MS) and multivariate/u...
Du, Chao-Chao; Yang, Manjun; Li, Min-Yi; Yang, Jun; Peng, Bo; Li, Hui; Peng, Xuan-Xian
Crucial metabolites that modulate hosts' metabolome to eliminate bacterial pathogens have been documented, but the metabolic mechanisms are largely unknown. The present study explores the metabolic mechanism for L-leucine-induced metabolome to eliminate Streptococcus iniae in tilapia. GC-MS based metabolomics was used to investigate tilapia liver metabolic profile in the presence of exogenous L-leucine. Thirty-seven metabolites of differential abundance were determined, and eleven metabolic pathways were enriched. Pattern recognition analysis identified serine and proline as crucial metabolites, which are the two metabolites identified in survived tilapias during S. iniae infection, suggesting the two metabolites play crucial roles in L-leucine-induced elimination of the pathogen by the host. Exogenous L-serine reduces mortality of tilapias infected by S. iniae, providing a robust proof for supporting the conclusion. Furthermore, exogenous serine elevates expression of genes Il-1β and Il-8 in tilapia spleen, but not TNFα, CXCR4 and Mx, suggesting the metabolite promotes a phagocytosis role of macrophages, which is consistent with the finding that L-leucine promotes macrophages to kill both Gram-positive and negative bacterial pathogens. Therefore, the ability of phagocytosis enhanced by exogenous L-leucine is partly attributed to elevation of serine. These results demonstrate a metabolic mechanism by which exogenous L-leucine modulates tilapias' metabolome to enhance innate immunity and eliminate pathogens.
Sotton, Benoît; Paris, Alain; Le Manach, Séverine; Blond, Alain; Lacroix, Gérard; Millot, Alexis; Duval, Charlotte; Qiao, Qin; Catherine, Arnaud; Marie, Benjamin
Cyanobacterial blooms induce important ecological constraints for aquatic organisms and strongly impact the functioning of aquatic ecosystems. In the past decades, the effects of the cyanobacterial secondary metabolites, so called cyanotoxins, have been extensively studied in fish. However, many of these studies have used targeted approaches on specific molecules, which are thought to react to the presence of these specific cyanobacterial compounds. Since a few years, untargeted metabolomic approaches provide a unique opportunity to evaluate the global response of hundreds of metabolites at a glance. In this way, our study provides the first utilization of metabolomic analyses in order to identify the response of fish exposed to bloom-forming cyanobacteria. Three relevant fish species of peri-urban lakes of the European temperate regions were exposed for 96h either to a microcystin (MC)-producing or to a non-MC-producing strain of Microcystis aeruginosa and metabolome changes were characterized in the liver of fish. The results suggest that a short-term exposure to those cyanobacterial biomasses induces metabolome changes without any response specificity linked to the fish species considered. Candidate metabolites are involved in energy metabolism and antioxidative response, which could potentially traduce a stress response of fish submitted to cyanobacteria. These results are in agreement with the already known information and could additionally bring new insights about the molecular interactions between cyanobacteria and fish.
Jorge, Tiago F; Rodrigues, João A; Caldana, Camila; Schmidt, Romy; van Dongen, Joost T; Thomas-Oates, Jane; António, Carla
Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.
Sun, C X; Gao, X X; Li, M Q; Fu, J Q; Zhang, Y L
Environmentally inducible phenotypic plasticity is a major player in plant responses to climate change. However, metabolic responses and their role in determining the phenotypic plasticity of plants that are subjected to temperature variations remain poorly understood. The metabolomic profiles and metabolite levels in the leaves of three maize inbred lines grown in different temperature conditions were examined with a nuclear magnetic resonance metabolomic technique. The relationship of functional traits to metabolome profiles and the metabolic mechanism underlying temperature variations were then explored. A comparative analysis showed that during heat and cold stress, maize plants shared common plastic responses in biomass accumulation, carbon, nitrogen, sugars, some amino acids and compatible solutes. We also found that the plastic response of maize plants to heat stress was different from that under cold stress, mainly involving biomass allocation, shikimate and its aromatic amino acid derivatives, and other non-polar metabolites. The plastic responsiveness of functional traits of maize lines to temperature variations was low, while the metabolic responsiveness in plasticity was high, indicating that functional and metabolic plasticity may play different roles in maize plant adaptation to temperature variations. A linear regression analysis revealed that the maize lines could adapt to growth temperature variations through the interrelation of plastic responses in the metabolomes and functional traits, such as biomass allocation and the status of carbon and nitrogen. We provide valuable insight into the plastic response strategy of maize plants to temperature variations that will permit the optimisation of crop cultivation in an increasingly variable environment.
Gibon, Yves; Rolin, Dominique
Experiments involve the deliberate variation of one or more factors in order to provoke responses, the identification of which then provides the first step towards functional knowledge. Because environmental, biological, and/or technical noise is unavoidable, biological experiments usually need to be designed. Thus, once the major sources of experimental noise have been identified, individual samples can be grouped, randomised, and/or pooled. Like other 'omics approaches, metabolomics is characterised by the numbers of analytes largely exceeding sample number. While this unprecedented singularity in biology dramatically increases false discovery, experimental error can nevertheless be decreased in plant metabolomics experiments. For this, each step from plant cultivation to data acquisition needs to be evaluated in order to identify the major sources of error and then an appropriate design can be produced, as with any other experimental approach. The choice of technology, the time at which tissues are harvested, and the way metabolism is quenched also need to be taken into consideration, as they decide which metabolites can be studied. A further recommendation is to document data and metadata in a machine readable way. The latter should also describe every aspect of the experiment. This should provide valuable hints for future experimental design and ultimately give metabolomic data a second life. To facilitate the identification of critical steps, a list of items to be considered before embarking on time-consuming and costly metabolomic experiments is proposed.
Wang, Jia-Bo; Pu, Shi-Biao; Sun, Ying; Li, Zhong-Feng; Niu, Ming; Yan, Xian-Zhong; Zhao, Yan-Ling; Wang, Li-Feng; Qin, Xue-Mei; Ma, Zhi-Jie; Zhang, Ya-Ming; Li, Bao-Sen; Luo, Sheng-Qiang; Gong, Man; Sun, Yong-Qiang; Zou, Zheng-Sheng; Xiao, Xiao-He
Autoimmune hepatitis (AIH) is often confused with other liver diseases because of their shared nonspecific symptoms and serological and histological overlap. This study compared the plasma metabolomic profiles of patients with AIH, primary biliary cirrhosis (PBC), PBC/AIH overlap syndrome (OS), and drug-induced liver injury (DILI) with those of healthy subjects to identify potential biomarkers of AIH. Metabolomic profiling and biomarker screening were performed using proton nuclear magnetic resonance spectroscopy ((1)H NMR) coupled with a partial least-squares discriminant analysis. Compared with the levels in healthy volunteers and other liver disease patients, AIH patients exhibited relatively high levels of plasma pyruvate, lactate, acetate, acetoacetate, and glucose. Such metabolites are typically related to energy metabolism alterations and may be a sign of metabolic conversion to the aerobic glycolysis phenotype of excessive immune activation. Increased aromatic amino acids and decreased branched-chain amino acids were found in the plasma of AIH patients. The whole NMR profiles were stepwise-reduced, and nine metabolomic biomarkers having the greatest significance in the discriminant analysis were obtained. The diagnostic utility of the selected metabolites was assessed, and these biomarkers achieved good sensitivity, specificity, and accuracy (all above 93%) in distinguishing AIH from PBC, DILI, and OS. This report is the first to present the metabolic phenotype of AIH and the potential utility of (1)H NMR metabolomics in the diagnosis of AIH.
The field of metabonomics/metabolomics involves observing endogenous metabolites from organisms that change in response to exposure to a stressor or chemical of interest. Methods are being developed for measuring the Raman spectra of low-concentration metabolites in urine. The ...
Falegan, Oluyemi S; Vogel, Hans J; Hittel, Dustin S; Koch, Lauren G; Britton, Steven L; Hepple, Russ T; Shearer, Jane
Advancing age is associated with declines in maximal oxygen consumption. Declines in aerobic capacity not only contribute to the aging process but also are an independent risk factor for morbidity, cardiovascular disease, and all-cause mortality. Although statistically convincing, the relationships between aerobic capacity, aging, and disease risk remain largely unresolved. To this end, we employed sensitive, system-based metabolomics approach to determine whether enhanced aerobic capacity could mitigate some of the changes seen in the plasma metabolomic profile associated with aging. Metabolomic profiles of plasma samples obtained from young (13 month) and old (26 month) rats bred for low (LCR) or high (HCR) running capacity using proton nuclear magnetic resonance spectroscopy ((1)H NMR) were examined. Results demonstrated strong profile separation in old and low aerobic capacity rats, whereas young and high aerobic capacity rat models were less predictive. Significantly differential metabolites between the groups include taurine, acetone, valine, and trimethylamine-N-oxide among other metabolites, specifically citrate, succinate, isovalerate, and proline, were differentially increased in older HCR animals compared with their younger counterparts. When interactions between age and aerobic capacity were examined, results demonstrated that enhanced aerobic capacity could mitigate some but not all age-associated alterations in the metabolomic profile.
Kuligowski, Julia; Pérez-Guaita, David; Sánchez-Illana, Ángel; León-González, Zacarías; de la Guardia, Miguel; Vento, Máximo; Lock, Eric F; Quintás, Guillermo
Metabolic profiling is increasingly being used for understanding biological processes but there is no single analytical technique that provides a complete quantitative or qualitative profiling of the metabolome. Data fusion (i.e. joint analysis of data from multiple sources) has the potential to circumvent this issue facilitating knowledge discovery and reliable biomarker identification. Another field of application of data fusion is the simultaneous analysis of metabolomic changes through several biofluids or tissues. However, metabolomics typically deals with large datasets, with hundreds to thousands of variables and the identification of shared and individual factors or structures across multiple sources is challenging due to the high variable to sample ratios and differences in intensity and noise range. In this work we apply a recent method, Joint and Individual Variation Explained (JIVE), for the integrated unsupervised analysis of metabolomic profiles from multiple data sources. This method separates the shared patterns among data sources (i.e. the joint structure) from the individual structure of each data source that is unrelated to the joint structure. Two examples are described to show the applicability of JIVE for the simultaneous analysis of multi-source data using: (i) plasma samples subjected to different analytical techniques, sample treatment and measurement conditions; and (ii) plasma and urine samples subjected to liquid chromatography-mass spectrometry measured using two ionization conditions.
Somsen, Govert W.; de Jong, Gerhardus J.
CE–MS can be considered a useful analytical technique for the global profiling of (highly) polar and charged metabolites in various samples. Over the past few years, significant advancements have been made in CE–MS approaches for metabolomics studies. In this paper, which is a follow‐up of a previous review paper covering the years 2012–2014 (Electrophoresis 2015, 36, 212–224), recent CE–MS strategies developed for metabolomics covering the literature from July 2014 to June 2016 are outlined. Attention will be paid to new CE–MS approaches for the profiling of anionic metabolites and the potential of SPE coupled to CE–MS is also demonstrated. Representative examples illustrate the applicability of CE–MS in the fields of biomedical, clinical, microbial, plant, and food metabolomics. A complete overview of recent CE–MS‐based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, general conclusions and perspectives are given. PMID:27718257
Kirwan, Jennifer A; Weber, Ralf J M; Broadhurst, David I; Viant, Mark R
Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment.
The American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.) Research Workshop, "Using Nutrigenomics and Metabolomics in Clinical Nutrition Research," was held on January 21, 2012, in Orlando, Florida. The conference brought together experts in human nutrition who use nutrigenomic and meta...
Metabolomics analysis offers insight into the metabolic and physiological state of biological samples. Blood samples and body condition measurements were acquired from 68, first-parity gilts at post-farrowing and weaning. Twenty gilts were retrospectively selected with similar (P >= 0.4475) number o...
Vázquez-Fresno, Rosa; Llorach, Rafael; Marinic, Jelena; Tulipani, Sara; Garcia-Aloy, Mar; Espinosa-Martos, Irene; Jiménez, Esther; Rodríguez, Juan Miguel; Andres-Lacueva, Cristina
Infectious mastitis is a common condition among lactating women, with staphylococci and streptococci being the main aetiological agents. In this context, some lactobacilli strains isolated from breast milk appear to be particularly effective for treating mastitis and, therefore, constitute an attractive alternative to antibiotherapy. A (1)H NMR-based metabolomic approach was applied to detect metabolomic differences after consuming a probiotic strain (Lactobacillus salivarius PS2) in women with mastitis. 24h urine of women with lactational mastitis was collected at baseline and after 21 days of probiotic (PB) administration. Multivariate analysis (OSC-PLS-DA and hierarchical clustering) showed metabolome differences after PB treatment. The discriminant metabolites detected at baseline were lactose, and ibuprofen and acetaminophen (two pharmacological drugs commonly used for mastitis pain), while, after PB intake, creatine and the gut microbial co-metabolites hippurate and TMAO were detected. In addition, a voluntary desertion of the pharmacological drugs ibuprofen and acetaminophen was observed after probiotic administration. The application of NMR-based metabolomics enabled the identification of the overall effects of probiotic consumption among women suffering from mastitis and highlighted the potential of this approach in evaluating the outcomes of probiotics consumption. To our knowledge, this is the first time that this approach has been applied in women with mastitis during lactation.
Zhao, Ying-Yong; Lin, Rui-Chao
In the last decade, proteomics and metabolomics have contributed substantially to our understanding of different diseases. Proteomics and metabolomics aims to comprehensively identify proteins and metabolites to gain insight into the cellular signaling pathways underlying disease and to discover novel biomarkers for screening, early detection and diagnosis, as well as for determining prognoses and predicting responses to specific treatments. For comprehensive analysis of cellular proteins and metabolites, analytical methods of wider dynamic range higher resolution and good sensitivity are required. Ultra performance liquid chromatography-mass spectrometry(Elevated Energy) (UPLC-MS(E)) is currently one of the most versatile techniques. UPLC-MS(E) is an established technology in proteomics studies and is now expanding into metabolite research. MS(E) was used for simultaneous acquisition of precursor ion information and fragment ion data at low and high collision energy in one analytical run, providing similar information to conventional MS(2). In this review, UPLC-MS(E) application in proteomics and metabolomics was highlighted to assess protein and metabolite changes in different diseases, including cancer, neuropsychiatric pharmacology studies from clinical trials and animal models. In addition, the future prospects for complete proteomics and metabolomics are discussed.
Bonneau, E; Tétreault, N; Robitaille, R; Boucher, A; De Guire, V
Organ transplantation is the treatment of choice for many end stage diseases. The development and appropriate use of new immunosupressants have considerably improved the outcome of patients in the last decades. However, noninvasive, sensitive and specific biomarkers for early detection of complications leading to graft dysfunction are still needed. Current transplantation monitoring mostly relies on non-specific biochemical tests whereas diagnosis of rejection is generally based on invasive procedures such as biopsies. New approaches based on large scale profiling of body fluids and tissues are needed to address the complexity and multifactorial aspect of organ transplantation complications. Metabolomics aim to characterize and quantify the metabolome, which is the collection of the low-molecular weight compounds rising from metabolic pathways. Extracted from tissues or detected in body fluids, the small molecules are measured using nuclear magnetic resonance spectroscopy or mass spectrometry. By profiling the downstream products of cellular activity, metabolomics is most likely to represent the immediate cellular response to stresses. Diagnostic applications have been proposed in cancer, cardiovascular diseases, kidney diseases, neurological diseases and many more. This review will focus on the potential applications of metabolomics in organ transplantation including follow up of graft function recovery, diagnostic of alloimmune rejection as well as monitoring of immunosuppressant toxicity.
Lee, Jang-Eun; Lee, Bum-Jin; Chung, Jin-Oh; Kim, Hak-Nam; Kim, Eun-Hee; Jung, Sungheuk; Lee, Hyosang; Lee, Sang-Jun; Hong, Young-Shick
Numerous factors such as geographical origin, cultivar, climate, cultural practices, and manufacturing processes influence the chemical compositions of tea, in the same way as growing conditions and grape variety affect wine quality. However, the relationships between these factors and tea chemical compositions are not well understood. In this study, a new approach for non-targeted or global analysis, i.e., metabolomics, which is highly reproducible and statistically effective in analysing a diverse range of compounds, was used to better understand the metabolome of Camellia sinensis and determine the influence of environmental factors, including geography, climate, and cultural practices, on tea-making. We found a strong correlation between environmental factors and the metabolome of green, white, and oolong teas from China, Japan, and South Korea. In particular, multivariate statistical analysis revealed strong inter-country and inter-city relationships in the levels of theanine and catechin derivatives found in green and white teas. This information might be useful for assessing tea quality or producing distinct tea products across different locations, and highlights simultaneous identification of diverse tea metabolites through an NMR-based metabolomics approach.
Alexandre-Gouabau, Marie-Cécile; Courant, Frédérique; Le Gall, Gwénaëlle; Moyon, Thomas; Darmaun, Dominique; Parnet, Patricia; Coupé, Bérengère; Antignac, Jean-Philippe
Intrauterine growth restriction (IUGR), along with postnatal growth trajectory, is closely linked with metabolic diseases and obesity at adulthood. The present study reports the time-dependent metabolomic response of male offspring of rat dams exposed to maternal adequate protein diet during pregnancy and lactation (CC) or protein deprivation during pregnancy only (IUGR with rapid catch-up growth, RC) or through pregnancy and lactation (IUGR with slow postnatal growth, RR). Plasma LC-HRMS metabolomic fingerprints for 8 male rats per group, combined with multivariate statistical analysis (PLS-DA and HCA), were used to study the impact of IUGR and postnatal growth velocity on the offspring metabolism in early life (until weaning) and once they reached adulthood (8 months). Compared with CC rats, RR pups had clear-cut alterations in plasma metabolome during suckling, but none at adulthood; in contrast, in RC pups, alterations in metabolome were minimal in early life but more pronounced in the long run. In particular, our results pinpoint transient alterations in proline, arginine, and histidine in RR rats, compared to CC rats, and persistent differences in tyrosine and carnitine, compared to RC rats at adulthood. These findings suggest that the long-term deregulation in feeding behavior and fatty acid metabolism in IUGR rats depends on postnatal growth velocity.
Rodrigues, Daniela; Jerónimo, Carmen; Henrique, Rui; Belo, Luís; de Lourdes Bastos, Maria; de Pinho, Paula Guedes; Carvalho, Márcia
Metabolomics has recently proved to be useful in the area of biomarker discovery for cancers in which early diagnostic and prognostic biomarkers are urgently needed, as is the case of bladder cancer (BC). This article presents a comprehensive review of the literature on the metabolomic studies on BC, highlighting metabolic pathways perturbed in this disease and the altered metabolites as potential biomarkers for BC detection. Current disease model systems used in the study of BC metabolome include in vitro-cultured cancer cells, ex vivo neoplastic bladder tissues and biological fluids, mainly urine but also blood serum/plasma, from BC patients. The major advantages and drawbacks of each model system are discussed. Based on available data, it seems that BC metabolic signature is mainly characterized by alterations in metabolites related to energy metabolic pathways, particularly glycolysis, amino acid and fatty acid metabolism, known to be crucial for cell proliferation, as well as glutathione metabolism, known to be determinant in maintaining cellular redox balance. In addition, purine and pyrimidine metabolism as well as carnitine species were found to be altered in BC. Finally, it is emphasized that, despite the progress made in respect to novel biomarkers for BC diagnosis, there are still some challenges and limitations that should be addressed in future metabolomic studies to ensure their translatability to clinical practice.
Rodrigues, Daniela; Monteiro, Márcia; Jerónimo, Carmen; Henrique, Rui; Belo, Luís; Bastos, Maria de Lourdes; Guedes de Pinho, Paula; Carvalho, Márcia
Metabolomics, an emerging field of "omics" sciences, has caught wide scientific attention in the area of biomarker research for cancers in which early diagnostic biomarkers have the potential to greatly improve patient outcome, such as renal cell carcinoma (RCC). Metabolomic approaches have been successfully applied to various human RCC model systems, mostly ex vivo neoplastic renal tissues and biofluids (urine and serum) from patients with RCC. Importantly, in contrast to other cancers, only a few studies have addressed the RCC metabolome using cancer cell culture-based in vitro models. Herein, we first carried out a comprehensive review of current metabolomic data in RCC, with emphasis on metabolite disturbances and dysregulated metabolic pathways identified in each of these experimental models. We then critically analyzed the consistency of evidence in this field and whether metabolites found altered in tumor cell and tissue microenvironment are reflected in biofluids, which constitute the rationale underlying the translation of discovered metabolic biomarkers into noninvasive diagnostic tools. Finally, dominant metabolic pathways and promising metabolites as biomarkers for diagnosis and prognosis of RCC are outlined.
Johnson, Sean R.; Lange, Bernd Markus
Various databases have been developed to aid in assigning structures to spectral peaks observed in metabolomics experiments. In this review article, we discuss the utility of currently available open-access spectral and chemical databases for natural products discovery. We also provide recommendations on how the research community can contribute to further improvements. PMID:25789275
Martinez-Lozano Sinues, Pablo; Tarokh, Leila; Li, Xue; Kohler, Malcolm; Brown, Steven A.; Zenobi, Renato; Dallmann, Robert
Circadian clocks play a significant role in the correct timing of physiological metabolism, and clock disruption might lead to pathological changes of metabolism. One interesting method to assess the current state of metabolism is metabolomics. Metabolomics tries to capture the entirety of small molecules, i.e. the building blocks of metabolism, in a given matrix, such as blood, saliva or urine. Using mass spectrometric approaches we and others have shown that a significant portion of the human metabolome in saliva and blood exhibits circadian modulation; independent of food intake or sleep/wake rhythms. Recent advances in mass spectrometry techniques have introduced completely non-invasive breathprinting; a method to instantaneously assess small metabolites in human breath. In this proof-of-principle study, we extend these findings about the impact of circadian clocks on metabolomics to exhaled breath. As previously established, our method allows for real-time analysis of a rich matrix during frequent non-invasive sampling. We sampled the breath of three healthy, non-smoking human volunteers in hourly intervals for 24 hours during total sleep deprivation, and found 111 features in the breath of all individuals, 36–49% of which showed significant circadian variation in at least one individual. Our data suggest that real-time mass spectrometric "breathprinting" has high potential to become a useful tool to understand circadian metabolism, and develop new biomarkers to easily and in real-time assess circadian clock phase and function in experimental and clinical settings. PMID:25545545
Costa, Christopher; Maraschin, Marcelo; Rocha, Miguel
Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as nuclear magnetic resonance, gas or liquid chromatography, mass spectrometry, infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.
Genomic techniques (transcriptomics, proteomics, and metabolomics) have the potential to significantly improve the way chemical risk is managed in the 21st century. Indeed, a significant amount of research has been devoted to the use of these techniques to screen chemicals for h...
Quanbeck, Stephanie M.; Brachova, Libuse; Campbell, Alexis A.; Guan, Xin; Perera, Ann; He, Kun; Rhee, Seung Y.; Bais, Preeti; Dickerson, Julie A.; Dixon, Philip; Wohlgemuth, Gert; Fiehn, Oliver; Barkan, Lenore; Lange, Iris; Lange, B. Markus; Lee, Insuk; Cortes, Diego; Salazar, Carolina; Shuman, Joel; Shulaev, Vladimir; Huhman, David V.; Sumner, Lloyd W.; Roth, Mary R.; Welti, Ruth; Ilarslan, Hilal; Wurtele, Eve S.; Nikolau, Basil J.
Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs). A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs. PMID:22645570
Rankin, Naomi J; Preiss, David; Welsh, Paul; Sattar, Naveed
Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact. PMID:27789671
Rankin, Naomi J; Preiss, David; Welsh, Paul; Sattar, Naveed
Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact.
Suhre, Karsten; Meisinger, Christa; Döring, Angela; Altmaier, Elisabeth; Belcredi, Petra; Gieger, Christian; Chang, David; Milburn, Michael V.; Gall, Walter E.; Weinberger, Klaus M.; Mewes, Hans-Werner; Hrabé de Angelis, Martin; Wichmann, H.-Erich; Kronenberg, Florian; Adamski, Jerzy; Illig, Thomas
Background Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study. Methodology/Principal Findings 40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid). Conclusions/Significance Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population. PMID:21085649
Wu, Gary D; Compher, Charlene; Chen, Eric Z; Smith, Sarah A; Shah, Rachana D; Bittinger, Kyle; Chehoud, Christel; Albenberg, Lindsey G; Nessel, Lisa; Gilroy, Erin; Star, Julie; Weljie, Aalim M; Flint, Harry J; Metz, David C; Bennett, Michael J; Li, Hongzhe; Bushman, Frederic D; Lewis, James D
Objective The consumption of an agrarian diet is associated with a reduced risk for many diseases associated with a ‘Westernised’ lifestyle. Studies suggest that diet affects the gut microbiota, which subsequently influences the metabolome, thereby connecting diet, microbiota and health. However, the degree to which diet influences the composition of the gut microbiota is controversial. Murine models and studies comparing the gut microbiota in humans residing in agrarian versus Western societies suggest that the influence is large. To separate global environmental influences from dietary influences, we characterised the gut microbiota and the host metabolome of individuals consuming an agrarian diet in Western society. Design and results Using 16S rRNA-tagged sequencing as well as plasma and urinary metabolomic platforms, we compared measures of dietary intake, gut microbiota composition and the plasma metabolome between healthy human vegans and omnivores, sampled in an urban USA environment. Plasma metabolome of vegans differed markedly from omnivores but the gut microbiota was surprisingly similar. Unlike prior studies of individuals living in agrarian societies, higher consumption of fermentable substrate in vegans was not associated with higher levels of faecal short chain fatty acids, a finding confirmed in a 10-day controlled feeding experiment. Similarly, the proportion of vegans capable of producing equol, a soy-based gut microbiota metabolite, was less than that was reported in Asian societies despite the high consumption of soy-based products. Conclusions Evidently, residence in globally distinct societies helps determine the composition of the gut microbiota that, in turn, influences the production of diet-dependent gut microbial metabolites. PMID:25431456
Laiakis, Evagelia C.; Mak, Tytus D.; Anizan, Sebastien; Amundson, Sally A.; Barker, Christopher A.; Wolden, Suzanne L.; Brenner, David J.; Fornace, Albert J.
The emergence of the threat of radiological terrorism and other radiological incidents has led to the need for development of fast, accurate and noninvasive methods for detection of radiation exposure. The purpose of this study was to extend radiation metabolomic biomarker discovery to humans, as previous studies have focused on mice. Urine was collected from patients undergoing total body irradiation at Memorial Sloan-Kettering Cancer Center prior to hematopoietic stem cell transplantation at 4–6 h postirradiation (a single dose of 1.25 Gy) and 24 h (three fractions of 1.25 Gy each). Global metabolomic profiling was obtained through analysis with ultra performance liquid chromatography coupled to time-of-flight mass spectrometry (TOFMS). Prior to further analyses, each sample was normalized to its respective creatinine level. Statistical analysis was conducted by the nonparametric Kolmogorov-Smirnov test and the Fisher’s exact test and markers were validated against pure standards. Seven markers showed distinct differences between pre- and post-exposure samples. Of those, trimethyl-l-lysine and the carnitine conjugates acetylcarnitine, decanoylcarnitine and octanoylcarnitine play an important role in the transportation of fatty acids across mitochondria for subsequent fatty acid β-oxidation. The remaining metabolites, hypoxanthine, xanthine and uric acid are the final products of the purine catabolism pathway, and high levels of excretion have been associated with increased oxidative stress and radiation induced DNA damage. Further analysis revealed sex differences in the patterns of excretion of the markers, demonstrating that generation of a sex-specific metabolomic signature will be informative and can provide a quick and reliable assessment of individuals in a radiological scenario. This is the first radiation metabolomics study in human urine laying the foundation for the use of metabolomics in biodosimetry and providing confidence in biomarker