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Zhang X, Su Y, Lane AN, Stromberg AJ, Fan TWM, Wang C. Bayesian kinetic modeling for tracer-based metabolomic data. BMC Bioinformatics 2023; 24:108. [PMID: 36949395 PMCID: PMC10035190 DOI: 10.1186/s12859-023-05211-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 02/24/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups. RESULTS We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux . CONCLUSIONS Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.
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Affiliation(s)
- Xu Zhang
- Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, 40536, USA.
| | - Ya Su
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, 23220, USA
| | - Andrew N Lane
- Markey Cancer Center, University of Kentucky, Lexington, 40536, USA
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, 40536, USA
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, 40536, USA
| | - Arnold J Stromberg
- Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, 40536, USA
| | - Teresa W M Fan
- Markey Cancer Center, University of Kentucky, Lexington, 40536, USA
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, 40536, USA
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, 40536, USA
| | - Chi Wang
- Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, 40536, USA.
- Markey Cancer Center, University of Kentucky, Lexington, 40536, USA.
- Division of Cancer Biostatistics, Department of Internal Medicine, University of Kentucky, Lexington, 40536, USA.
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2
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Isik OA, Cizmecioglu O. Rafting on the Plasma Membrane: Lipid Rafts in Signaling and Disease. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1436:87-108. [PMID: 36648750 DOI: 10.1007/5584_2022_759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The plasma membrane is not a uniform phospholipid bilayer; it has specialized membrane nano- or microdomains called lipid rafts. Lipid rafts are small cholesterol and sphingolipid-rich plasma membrane islands. Although their existence was long debated, their presence in the plasma membrane of living cells is now well accepted with the advent of super-resolution imaging techniques. It is interesting to note that lipid rafts function to compartmentalize receptors and their regulators and substantially modulate cellular signaling. In this review, we will examine the role of lipid rafts and caveolae-lipid raft-like microdomains with a distinct 3D morphology-in cellular signaling. Moreover, we will investigate how raft compartmentalized signaling regulates diverse physiological processes such as proliferation, apoptosis, immune signaling, and development. Also, the deregulation of lipid raft-mediated signaling during tumorigenesis and metastasis will be explored.
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Affiliation(s)
- Ozlem Aybuke Isik
- Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey
| | - Onur Cizmecioglu
- Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey.
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3
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Katzir R, Polat IH, Harel M, Katz S, Foguet C, Selivanov VA, Sabatier P, Cascante M, Geiger T, Ruppin E. The landscape of tiered regulation of breast cancer cell metabolism. Sci Rep 2019; 9:17760. [PMID: 31780802 PMCID: PMC6882817 DOI: 10.1038/s41598-019-54221-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Accepted: 10/21/2019] [Indexed: 01/10/2023] Open
Abstract
Altered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across three different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning, we systematically chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level. We distinguish between two types of reactions, directly and indirectly regulated. Directly-regulated reactions include those whose flux is regulated by transcriptomic alterations (~890) or via proteomic or phospho-proteomics alterations (~140) in the enzymes catalyzing them. We term the reactions that currently lack evidence for direct regulation as (putative) indirectly regulated (~930). Many metabolic pathways are predicted to be regulated at different levels, and those may change at different media conditions. Remarkably, we find that the flux of predicted indirectly regulated reactions is strongly coupled to the flux of the predicted directly regulated ones, uncovering a tiered hierarchical organization of breast cancer cell metabolism. Furthermore, the predicted indirectly regulated reactions are predominantly reversible. Taken together, this architecture may facilitate rapid and efficient metabolic reprogramming in response to the varying environmental conditions incurred by the tumor cells. The approach presented lays a conceptual and computational basis for mapping metabolic regulation in additional cancers.
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Affiliation(s)
- Rotem Katzir
- Center for BioInformatics and Computational Biology, Dept. of Computer Science and the University of Maryland Institute of Advanced Computer Studies (UMIACS), University of Maryland, College Park, MD, 20742, USA
| | - Ibrahim H Polat
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona and Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain.,Equipe environnement et prédiction de la santé des populations, Laboratoire TIMC (UMR 5525), CHU de Grenoble, Université Grenoble Alpes, La Tronche, France
| | - Michal Harel
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shir Katz
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of medicine, Tel Aviv University, Tel Aviv, Israel
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona and Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain
| | - Vitaly A Selivanov
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona and Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain
| | - Philippe Sabatier
- Equipe environnement et prédiction de la santé des populations, Laboratoire TIMC (UMR 5525), CHU de Grenoble, Université Grenoble Alpes, La Tronche, France
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona and Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Tamar Geiger
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Eytan Ruppin
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA.
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4
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Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Emami Khonsari P, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck C. PhenoMeNal: processing and analysis of metabolomics data in the cloud. Gigascience 2019; 8:giy149. [PMID: 30535405 PMCID: PMC6377398 DOI: 10.1093/gigascience/giy149] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 10/19/2018] [Accepted: 11/20/2018] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - James Bradbury
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Capuccini
- Division of Scientific Computing, Department of Information Technology, Uppsala University, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Timothy M D Ebbels
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Robert Glen
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB21EW, United Kingdom
| | - Alejandra Gonzalez-Beltran
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Ulrich L Günther
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Evangelos Handakas
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Thomas Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Stephanie Herman
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | | | - Massimiliano Izzo
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Daniel Jacob
- INRA, University of Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 33140 Villenave d'Ornon, France
| | - David Johnson
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
- Department of Informatics and Media, Uppsala University, Box 513, 751 20 Uppsala, Sweden
| | - Fabien Jourdan
- INRA - French National Institute for Agricultural Research, UMR1331, Toxalim, Research Centre in Food Toxicology, Toulouse, France
| | - Namrata Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, W2 1PG, London, United Kingdom
| | - Bita Khalili
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Payam Emami Khonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Anders Larsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Christian Ludwig
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
| | - Jon Ander Novella
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Jake T M Pearce
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Alina Peluso
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | | | | | - Michelle A C Reed
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Pierrick Roger
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Antonio Rosato
- Magnetic Resonance Center (CERM) and Department of Chemistry, University of Florence and CIRMMP, 50019 Sesto Fiorentino, Florence, Italy
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Noureddin Sadawi
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Daniel Schober
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Mattia Tomasoni
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Merlijn van Rijswijk
- Netherlands Metabolomics Center, Leiden, 2333 CC, Netherlands
- ELIXIR-NL, Dutch Techcentre for Life Sciences, Utrecht, 3503 RM, Netherlands
| | - Michael van Vliet
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Ralf J M Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | | | - Christoph Steinbeck
- Cheminformatics and Computational Metabolomics, Institute for Analytical Chemistry, Lessingstr. 8, 07743 Jena, Germany
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5
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Tambellini NP, Zaremberg V, Krishnaiah S, Turner RJ, Weljie AM. Primary Metabolism and Medium-Chain Fatty Acid Alterations Precede Long-Chain Fatty Acid Changes Impacting Neutral Lipid Metabolism in Response to an Anticancer Lysophosphatidylcholine Analogue in Yeast. J Proteome Res 2017; 16:3741-3752. [PMID: 28849941 DOI: 10.1021/acs.jproteome.7b00430] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The nonmetabolizable lysophosphatidylcholine (LysoPC) analogue edelfosine is the prototype of a class of compounds being investigated for their potential as selective chemotherapeutic agents. Edelfosine targets membranes, disturbing cellular homeostasis. Is not clear at this point how membrane alterations are communicated between intracellular compartments leading to growth inhibition and eventual cell death. In the present study, a combined metabolomics/lipidomics approach for the unbiased identification of metabolic pathways altered in yeast treated with sublethal concentrations of the LysoPC analogue was employed. Mass spectrometry of polar metabolites, fatty acids, and lipidomic profiling was used to study the effects of edelfosine on yeast metabolism. Amino acid and sugar metabolism, the Krebs cycle, and fatty acid profiles were most disrupted, with polar metabolites and short-medium chain fatty acid changes preceding long and very long-chain fatty acid variations. Initial increases in metabolites such as trehalose, proline, and γ-amino butyric acid with a concomitant decrease in metabolites of the Krebs cycle, citrate and fumarate, are interpreted as a cellular attempt to offset oxidative stress in response to mitochondrial dysfunction induced by the treatment. Notably, alanine, inositol, and myristoleic acid showed a steady increase during the period analyzed (2, 4, and 6 h after treatment). Of importance was the finding that edelfosine induced significant alterations in neutral glycerolipid metabolism resulting in a significant increase in the signaling lipid diacylglycerol.
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Affiliation(s)
- Nicolas P Tambellini
- Department of Biological Sciences, University of Calgary , Calgary, Alberta T2N 1N4, Canada.,Metabolomics Research Centre, University of Calgary , Calgary, Alberta T2N 1N4, Canada
| | - Vanina Zaremberg
- Department of Biological Sciences, University of Calgary , Calgary, Alberta T2N 1N4, Canada
| | - Saikumari Krishnaiah
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, Pennsylvania 19104-5158, United States of America
| | - Raymond J Turner
- Department of Biological Sciences, University of Calgary , Calgary, Alberta T2N 1N4, Canada
| | - Aalim M Weljie
- Department of Biological Sciences, University of Calgary , Calgary, Alberta T2N 1N4, Canada.,Metabolomics Research Centre, University of Calgary , Calgary, Alberta T2N 1N4, Canada.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine , Philadelphia, Pennsylvania 19104-5158, United States of America
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6
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Selivanov VA, Benito A, Miranda A, Aguilar E, Polat IH, Centelles JJ, Jayaraman A, Lee PWN, Marin S, Cascante M. MIDcor, an R-program for deciphering mass interferences in mass spectra of metabolites enriched in stable isotopes. BMC Bioinformatics 2017; 18:88. [PMID: 28158972 PMCID: PMC5291980 DOI: 10.1186/s12859-017-1513-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 01/31/2017] [Indexed: 12/26/2022] Open
Abstract
Background Tracing stable isotopes, such as 13C using various mass spectrometry (MS) methods provides a valuable information necessary for the study of biochemical processes in cells. However, extracting such information requires special care, such as a correction for naturally occurring isotopes, or overlapping mass spectra of various components of the cell culture medium. Developing a method for a correction of overlapping peaks is the primary objective of this study. Results Our computer program-MIDcor (free at https://github.com/seliv55/mid_correct) written in the R programming language, corrects the raw MS spectra both for the naturally occurring isotopes and for the overlapping of peaks corresponding to various substances. To this end, the mass spectra of unlabeled metabolites measured in two media are necessary: in a minimal medium containing only derivatized metabolites and chemicals for derivatization, and in a complete cell incubated medium. The MIDcor program calculates the difference (D) between the theoretical and experimentally measured spectra of metabolites containing only the naturally occurring isotopes. The result of comparison of D in the two media determines a way of deciphering the true spectra. (1) If D in the complete medium is greater than that in the minimal medium in at least one peak, then unchanged D is subtracted from the raw spectra of the labeled metabolite. (2) If D does not depend on the medium, then the spectrum probably overlaps with a derivatized fragment of the same metabolite, and D is modified proportionally to the metabolite labeling. The program automatically reaches a decision regarding the way of correction. For some metabolites/fragments in the case (2) D was found to decrease when the tested substance was 13C labeled, and this isotopic effect also can be corrected automatically, if the user provides a measured spectrum of the substance in which the 13C labeling is known a priori. Conclusion Using the developed program improves the reliability of stable isotope tracer data analysis. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1513-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Vitaly A Selivanov
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain. .,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain.
| | - Adrián Benito
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Anibal Miranda
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Esther Aguilar
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Ibrahim Halil Polat
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Josep J Centelles
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Anusha Jayaraman
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Paul W N Lee
- Department of Pediatrics, Harbor-UCLA Medical Center, Research and Education Institute, Torrance, CA, 90502, USA
| | - Silvia Marin
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain.,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain. .,Institute of Biomedicine of the Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain.
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7
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Foguet C, Marin S, Selivanov VA, Fanchon E, Lee WNP, Guinovart JJ, de Atauri P, Cascante M. HepatoDyn: A Dynamic Model of Hepatocyte Metabolism That Integrates 13C Isotopomer Data. PLoS Comput Biol 2016; 12:e1004899. [PMID: 27124774 PMCID: PMC4849781 DOI: 10.1371/journal.pcbi.1004899] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 04/05/2016] [Indexed: 11/19/2022] Open
Abstract
The liver performs many essential metabolic functions, which can be studied using computational models of hepatocytes. Here we present HepatoDyn, a highly detailed dynamic model of hepatocyte metabolism. HepatoDyn includes a large metabolic network, highly detailed kinetic laws, and is capable of dynamically simulating the redox and energy metabolism of hepatocytes. Furthermore, the model was coupled to the module for isotopic label propagation of the software package IsoDyn, allowing HepatoDyn to integrate data derived from 13C based experiments. As an example of dynamical simulations applied to hepatocytes, we studied the effects of high fructose concentrations on hepatocyte metabolism by integrating data from experiments in which rat hepatocytes were incubated with 20 mM glucose supplemented with either 3 mM or 20 mM fructose. These experiments showed that glycogen accumulation was significantly lower in hepatocytes incubated with medium supplemented with 20 mM fructose than in hepatocytes incubated with medium supplemented with 3 mM fructose. Through the integration of extracellular fluxes and 13C enrichment measurements, HepatoDyn predicted that this phenomenon can be attributed to a depletion of cytosolic ATP and phosphate induced by high fructose concentrations in the medium.
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Affiliation(s)
- Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Silvia Marin
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Vitaly A. Selivanov
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
| | - Eric Fanchon
- UGA – CNRS, TIMC-IMAG UMR 5525, Grenoble, France
| | - Wai-Nang Paul Lee
- Department of Pediatrics, Los Angeles Biomedical Research Institute, Torrance, California, United States of America
| | - Joan J. Guinovart
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
- * E-mail: (PdA); (MC)
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and Associated Unit to CSIC, Barcelona, Spain
- * E-mail: (PdA); (MC)
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8
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Fan TWM, Lane AN. Applications of NMR spectroscopy to systems biochemistry. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2016; 92-93:18-53. [PMID: 26952191 PMCID: PMC4850081 DOI: 10.1016/j.pnmrs.2016.01.005] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Revised: 01/26/2016] [Accepted: 01/28/2016] [Indexed: 05/05/2023]
Abstract
The past decades of advancements in NMR have made it a very powerful tool for metabolic research. Despite its limitations in sensitivity relative to mass spectrometric techniques, NMR has a number of unparalleled advantages for metabolic studies, most notably the rigor and versatility in structure elucidation, isotope-filtered selection of molecules, and analysis of positional isotopomer distributions in complex mixtures afforded by multinuclear and multidimensional experiments. In addition, NMR has the capacity for spatially selective in vivo imaging and dynamical analysis of metabolism in tissues of living organisms. In conjunction with the use of stable isotope tracers, NMR is a method of choice for exploring the dynamics and compartmentation of metabolic pathways and networks, for which our current understanding is grossly insufficient. In this review, we describe how various direct and isotope-edited 1D and 2D NMR methods can be employed to profile metabolites and their isotopomer distributions by stable isotope-resolved metabolomic (SIRM) analysis. We also highlight the importance of sample preparation methods including rapid cryoquenching, efficient extraction, and chemoselective derivatization to facilitate robust and reproducible NMR-based metabolomic analysis. We further illustrate how NMR has been applied in vitro, ex vivo, or in vivo in various stable isotope tracer-based metabolic studies, to gain systematic and novel metabolic insights in different biological systems, including human subjects. The pathway and network knowledge generated from NMR- and MS-based tracing of isotopically enriched substrates will be invaluable for directing functional analysis of other 'omics data to achieve understanding of regulation of biochemical systems, as demonstrated in a case study. Future developments in NMR technologies and reagents to enhance both detection sensitivity and resolution should further empower NMR in systems biochemical research.
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Affiliation(s)
- Teresa W-M Fan
- Department of Toxicology and Cancer Biology, University of Kentucky, 789 S. Limestone St., Lexington, KY 40536, United States.
| | - Andrew N Lane
- Department of Toxicology and Cancer Biology, University of Kentucky, 789 S. Limestone St., Lexington, KY 40536, United States.
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9
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Niedenführ S, Wiechert W, Nöh K. How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics. Curr Opin Biotechnol 2014; 34:82-90. [PMID: 25531408 DOI: 10.1016/j.copbio.2014.12.003] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Revised: 11/28/2014] [Accepted: 12/01/2014] [Indexed: 12/24/2022]
Abstract
Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to 'measure' the in vivo fluxes within an organism that are not directly observable. In recent years, (13)C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific 'travelers' who are striving to resolve research questions with the currently available (13)C fluxomics toolset.
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Affiliation(s)
| | - Wolfgang Wiechert
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Katharina Nöh
- IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
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10
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Guzmán S, Marin S, Miranda A, Selivanov VA, Centelles JJ, Harmancey R, Smih F, Turkieh A, Durocher Y, Zorzano A, Rouet P, Cascante M. (13)C metabolic flux analysis shows that resistin impairs the metabolic response to insulin in L6E9 myotubes. BMC SYSTEMS BIOLOGY 2014; 8:109. [PMID: 25217974 PMCID: PMC4363945 DOI: 10.1186/s12918-014-0109-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2014] [Accepted: 08/29/2014] [Indexed: 12/11/2022]
Abstract
Background It has been suggested that the adipokine resistin links obesity and insulin resistance, although how resistin acts on muscle metabolism is controversial. We aimed to quantitatively analyse the effects of resistin on the glucose metabolic flux profile and on insulin response in L6E9 myotubes at the metabolic level using a tracer-based metabolomic approach and our in-house developed software, Isodyn. Results Resistin significantly increased glucose uptake and glycolysis, altering pyruvate utilisation by the cell. In the presence of resistin, insulin only slightly increased glucose uptake and glycolysis, and did not alter the flux profile around pyruvate induced by resistin. Resistin prevented the increase in gene expression in pyruvate dehydrogenase-E1 and the sharp decrease in gene expression in cytosolic phosphoenolpyruvate carboxykinase-1 induced by insulin. Conclusions These data suggest that resistin impairs the metabolic activation of insulin. This impairment cannot be explained by the activity of a single enzyme, but instead due to reorganisation of the whole metabolic flux distribution.
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Affiliation(s)
- Shirley Guzmán
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028, Barcelona, Spain. .,Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
| | - Silvia Marin
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028, Barcelona, Spain. .,Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
| | - Anibal Miranda
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028, Barcelona, Spain. .,Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
| | - Vitaly A Selivanov
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028, Barcelona, Spain. .,Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
| | - Josep J Centelles
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028, Barcelona, Spain. .,Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
| | - Romain Harmancey
- Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1048, Toulouse, France. .,Université Toulouse III Paul-Sabatier, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Equipe n°7, Toulouse, France.
| | - Fatima Smih
- Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1048, Toulouse, France. .,Université Toulouse III Paul-Sabatier, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Equipe n°7, Toulouse, France.
| | - Annie Turkieh
- Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1048, Toulouse, France. .,Université Toulouse III Paul-Sabatier, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Equipe n°7, Toulouse, France.
| | - Yves Durocher
- Animal Cell Technology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada.
| | - Antonio Zorzano
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028, Barcelona, Spain. .,Institute for Research in Biomedicine (IRB Barcelona) and CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Barcelona, Spain.
| | - Philippe Rouet
- Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1048, Toulouse, France. .,Université Toulouse III Paul-Sabatier, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Equipe n°7, Toulouse, France.
| | - Marta Cascante
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028, Barcelona, Spain. .,Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
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11
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Lasa-Saracíbar B, Estella-Hermoso de Mendoza A, Mollinedo F, Odero MD, Blanco-Príeto MJ. Edelfosine lipid nanosystems overcome drug resistance in leukemic cell lines. Cancer Lett 2013; 334:302-10. [PMID: 23353057 DOI: 10.1016/j.canlet.2013.01.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2012] [Revised: 01/10/2013] [Accepted: 01/11/2013] [Indexed: 11/25/2022]
Abstract
Although current therapies have improved leukemia survival rates, adverse drug effects and relapse are frequent. Encapsulation of edelfosine (ET) in lipid nanoparticles (LNs) improves its oral bioavailability and decreases its toxicity. Here we evaluated the efficacy of ET-LN in myeloid leukemia cell lines. Drug-loaded LN were as effective as free ET in sensitive leukemia cell lines. Moreover, the encapsulated drug overcame the resistance of the K562 cell line to the drug. LN containing ET might be used as a promising drug delivery system in leukemia due to their capacity to overcome the in vivo pitfalls of the free drug and their efficacy in vitro in leukemia cell lines.
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Affiliation(s)
- Beatriz Lasa-Saracíbar
- Department of Pharmaceutics and Pharmaceutical Technology, School of Pharmacy, University of Navarra, E-31080 Pamplona, Spain
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12
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Mueller D, Heinzle E. Stable isotope-assisted metabolomics to detect metabolic flux changes in mammalian cell cultures. Curr Opin Biotechnol 2012; 24:54-9. [PMID: 23142545 DOI: 10.1016/j.copbio.2012.10.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2012] [Revised: 10/08/2012] [Accepted: 10/18/2012] [Indexed: 12/28/2022]
Abstract
The determination of metabolic fluxes provides detailed information of cellular physiology, and the assessment of metabolic flux changes upon a certain perturbation can help to improve biotechnological and pharmaceutical processes. Stable isotope-assisted metabolomics using tracer-labeled substrates is the method of choice to determine the fluxes. Though well-established for microbial cultures, the application to mammalian cells is generally complex and still limited. However, there have been great achievements in recent years and it is now emerging that stable isotope-assisted metabolic flux analysis in mammalian cell cultures will help improving biotechnological production and will also support drug development and discovery.
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Affiliation(s)
- Daniel Mueller
- Biochemical Engineering, Campus A1 5, Saarland University, D-66123 Saarbruecken, Germany.
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13
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Kim KB, Yang JY, Kwack SJ, Kim HS, Ryu DH, Kim YJ, Bae JY, Lim DS, Choi SM, Kwon MJ, Bang DY, Lim SK, Kim YW, Hwang GS, Lee BM. Potential metabolomic biomarkers for evaluation of adriamycin efficacy using a urinary 1H-NMR spectroscopy. J Appl Toxicol 2012; 33:1251-9. [PMID: 22782856 DOI: 10.1002/jat.2778] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 04/19/2012] [Accepted: 04/19/2012] [Indexed: 12/16/2022]
Abstract
A metabolomics approach using proton nuclear magnetic resonance (NMR) was applied to investigate metabolic alterations following adriamycin (ADR) treatment for gastric adenocarcinoma. After BALB/c-nu/nu mice were implanted with human gastric adenocarcinoma, ADR (1 or 3 mg kg(-1) per day) was intraperitoneally administered for 5 days. Urine was collected on days 2 and 5 and analyzed by NMR. The levels of trimethylamine oxide (TMAO, ×0.3), hippurate (×0.3) and taurine (×0.6) decreased significantly (P < 0.05), whereas the levels of 3-indoxylsulfate (×12.6), trigonelline (×1.5), citrate (×2.5), trimethylamine (TMA, ×2.0) and 2-oxoglutarate (×2.3) increased significantly (P < 0.05) in the tumor model. After ADR treatment, TMAO, hippuarte and taurine were increased significantly on day 5 compared with those of the tumor model. The levels of 2-oxoglutarate, 3-indoxylsulfate, trigonelline, TMA and citrate, which increased in the tumor model, significantly decreased to those of normal control by ADR treatment. Furthermore, the ratio between TMA and TMAO was dramatically altered in both tumor and ADR-treated groups. Overall, metabolites such as TMAO, TMA, 3-indoxylsulfate, hippurate, trigonelline, citrate and 2-oxoglutarate related to the tricarboxylic acid (TCA) cycle might be considered as therapeutic targets to potentiate the efficacy of ADR. Thus, these results suggest that the metabolomics analysis of tumor response to ADR treatment may be applicable for demonstrating the efficacy of anticancer agent, ADR and treatment adaptation.
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Affiliation(s)
- Kyu-Bong Kim
- College of Pharmacy, Dankook University, Dandae-ro, Cheonan, Chungnam, 330-714, Korea
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14
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Pey J, Rubio A, Theodoropoulos C, Cascante M, Planes FJ. Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes. Metab Eng 2012; 14:344-53. [DOI: 10.1016/j.ymben.2012.03.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2011] [Revised: 03/01/2012] [Accepted: 03/26/2012] [Indexed: 01/10/2023]
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15
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Fan TWM, Lorkiewicz PK, Sellers K, Moseley HNB, Higashi RM, Lane AN. Stable isotope-resolved metabolomics and applications for drug development. Pharmacol Ther 2012; 133:366-91. [PMID: 22212615 PMCID: PMC3471671 DOI: 10.1016/j.pharmthera.2011.12.007] [Citation(s) in RCA: 151] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2011] [Accepted: 12/06/2011] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Teresa W-M Fan
- Department of Chemistry, University of Louisville, KY 40292, USA.
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16
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de Mas IM, Selivanov VA, Marin S, Roca J, Orešič M, Agius L, Cascante M. Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions. BMC SYSTEMS BIOLOGY 2011; 5:175. [PMID: 22034837 PMCID: PMC3292525 DOI: 10.1186/1752-0509-5-175] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2011] [Accepted: 10/28/2011] [Indexed: 11/24/2022]
Abstract
Background Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution. Results The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated 13C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells. Conclusions The analysis of compartmentation of metabolic networks based on the measured 13C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured 13C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution indicates the presence of a separate pool of hexose phosphates that is channeled towards glycogen synthesis.
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Affiliation(s)
- Igor Marin de Mas
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain
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