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Moiz B, Li A, Padmanabhan S, Sriram G, Clyne AM. Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease. Metabolites 2022; 12:1066. [PMID: 36355149 PMCID: PMC9694183 DOI: 10.3390/metabo12111066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 04/28/2024] Open
Abstract
Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.
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Affiliation(s)
- Bilal Moiz
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Andrew Li
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Surya Padmanabhan
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Ganesh Sriram
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
| | - Alisa Morss Clyne
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
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2
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Tian B, Chen M, Liu L, Rui B, Deng Z, Zhang Z, Shen T. 13C metabolic flux analysis: Classification and characterization from the perspective of mathematical modeling and application in physiological research of neural cell. Front Mol Neurosci 2022; 15:883466. [PMID: 36157075 PMCID: PMC9493264 DOI: 10.3389/fnmol.2022.883466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/15/2022] [Indexed: 11/17/2022] Open
Abstract
13C metabolic flux analysis (13C-MFA) has emerged as a forceful tool for quantifying in vivo metabolic pathway activity of different biological systems. This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics. In this review, we classify and characterize the various branch of 13C-MFA from a unified perspective of mathematical modeling. By linking different parts in the model to each step of its workflow, the specific technologies of 13C-MFA are put into discussion, including the isotope labeling model (ILM), isotope pattern measuring technique, optimization algorithm and statistical method. Its application in physiological research in neural cell has also been reviewed.
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Affiliation(s)
- Birui Tian
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, China
| | - Meifeng Chen
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountainous Areas of Southwestern China, Key Laboratory of Plant Physiology and Development Regulation, School of Life Science, Guizhou Normal University, Guiyang, China
| | - Lunxian Liu
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountainous Areas of Southwestern China, Key Laboratory of Plant Physiology and Development Regulation, School of Life Science, Guizhou Normal University, Guiyang, China
| | - Bin Rui
- Eurofins Lancaster Laboratories Professional Scientific Services, Lancaster, PA, United States
| | - Zhouhui Deng
- China Guizhou Science Data Center Gui’an Supercomputing Center, Guiyang, China
| | - Zhengdong Zhang
- College of Mathematics and Information Science, Guiyang University, Guiyang, China
- *Correspondence: Zhengdong Zhang,
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, China
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountainous Areas of Southwestern China, Key Laboratory of Plant Physiology and Development Regulation, School of Life Science, Guizhou Normal University, Guiyang, China
- Tie Shen,
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3
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Effects of Acute Subdural Hematoma-Induced Brain Injury On Energy Metabolism in Peripheral Blood Mononuclear Cells. Shock 2020; 55:407-417. [PMID: 32826816 DOI: 10.1097/shk.0000000000001642] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
ABSTRACT In activated immune cells, differentiation and function are determined by cell type-specific modifications of metabolic patterns. After traumatic brain injury both immune cell activation and suppression were reported. Therefore, we sought to explore immune cell energy metabolism in a long-term, resuscitated porcine model of acute subdural hematoma (ASDH)-induced acute brain injury devoid of impaired systemic hemodynamics and oxygen transport.Before and up to 50 h after induction of ASDH, peripheral blood mononuclear cells (PBMCs) were separated by density gradient centrifugation, and cell metabolism was analyzed using high-resolution respirometry for mitochondrial respiration and electron spin resonance for reactive oxygen species production. After incubation with stable isotope-labeled 1,2-13C2-glucose or 13C5-glutamine, distinct labeling patterns of intermediates of glycolysis or tricarboxylic acid (TCA) cycle and 13CO2 production were measured by gas chromatography-mass spectroscopy. Principal component analysis was followed by a varimax rotation on the covariance across all measured variables and all measured time points.After ASDH induction, average PBMC metabolic activity remained unaffected, possibly because strict adherence to intensive care unit guidelines limited trauma to ASDH induction without any change in parameters of systemic hemodynamics, oxygen transport, and whole-body metabolism. Despite decreased glycolytic activity fueling the TCA cycle, the principal component analysis indicated a cell type-specific activation pattern with biosynthetic and proliferative characteristics.
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Bloem A, Rollero S, Seguinot P, Crépin L, Perez M, Picou C, Camarasa C. Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources. J Vis Exp 2018. [PMID: 29443074 DOI: 10.3791/56393] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations. We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples-facilitated by a robot-assisted system for online monitoring of fermentations-and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.
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Affiliation(s)
- Audrey Bloem
- UMR SPO, INRA, SupAgroM, Université de Montpellier
| | | | | | | | - Marc Perez
- UMR SPO, INRA, SupAgroM, Université de Montpellier
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Nilsson R, Roci I, Watrous J, Jain M. Estimation of flux ratios without uptake or release data: Application to serine and methionine metabolism. Metab Eng 2017; 43:137-146. [PMID: 28232235 DOI: 10.1016/j.ymben.2017.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 02/09/2017] [Accepted: 02/13/2017] [Indexed: 02/06/2023]
Abstract
Model-based metabolic flux analysis (MFA) using isotope-labeled substrates has provided great insight into intracellular metabolic activities across a host of organisms. One challenge with applying MFA in mammalian systems, however, is the need for absolute quantification of nutrient uptake, biomass composition, and byproduct release fluxes. Such measurements are often not feasible in complex culture systems or in vivo. One way to address this issue is to estimate flux ratios, the fractional contribution of a flux to a metabolite pool, which are independent of absolute measurements and yet informative for cellular metabolism. Prior work has focused on "local" estimation of a handful of flux ratios for specific metabolites and reactions. Here, we perform systematic, model-based estimation of all flux ratios in a metabolic network using isotope labeling data, in the absence of uptake/release data. In a series of examples, we investigate what flux ratios can be well estimated with reasonably tight confidence intervals, and contrast this with confidence intervals on normalized fluxes. We find that flux ratios can provide useful information on the metabolic state, and is complementary to normalized fluxes: for certain metabolic reactions, only flux ratios can be well estimated, while for others normalized fluxes can be obtained. Simulation studies of a large human metabolic network model suggest that estimation of flux ratios is technically feasible for complex networks, but additional studies on data from actual isotopomer labeling experiments are needed to validate these results. Finally, we experimentally study serine and methionine metabolism in cancer cells using flux ratios. We find that, in these cells, the methionine cycle is truncated with little remethylation from homocysteine, and polyamine synthesis in the absence of methionine salvage leads to loss of 5-methylthioadenosine, suggesting a new mode of overflow metabolism in cancer cells. This work highlights the potential for flux ratio analysis in the absence of absolute quantification, which we anticipate will be important for both in vitro and in vivo studies of cancer metabolism.
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Affiliation(s)
- Roland Nilsson
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Irena Roci
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Jeramie Watrous
- Departments of Medicine & Pharmacology, University of California, San Diego, USA
| | - Mohit Jain
- Departments of Medicine & Pharmacology, University of California, San Diego, USA
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6
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Recent advances in high-throughput 13C-fluxomics. Curr Opin Biotechnol 2016; 43:104-109. [PMID: 27838571 DOI: 10.1016/j.copbio.2016.10.010] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 10/21/2016] [Accepted: 10/25/2016] [Indexed: 12/11/2022]
Abstract
The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT 13C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT 13C-fluxomics workflows.
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Kogadeeva M, Zamboni N. SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis. PLoS Comput Biol 2016; 12:e1005109. [PMID: 27626798 PMCID: PMC5023139 DOI: 10.1371/journal.pcbi.1005109] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Accepted: 08/13/2016] [Indexed: 12/15/2022] Open
Abstract
Metabolic fluxes are a cornerstone of cellular physiology that emerge from a complex interplay of enzymes, carriers, and nutrients. The experimental assessment of in vivo intracellular fluxes using stable isotopic tracers is essential if we are to understand metabolic function and regulation. Flux estimation based on 13C or 2H labeling relies on complex simulation and iterative fitting; processes that necessitate a level of expertise that ordinarily preclude the non-expert user. To overcome this, we have developed SUMOFLUX, a methodology that is broadly applicable to the targeted analysis of 13C-metabolic fluxes. By combining surrogate modeling and machine learning, we trained a predictor to specialize in estimating flux ratios from measurable 13C-data. SUMOFLUX targets specific flux features individually, which makes it fast, user-friendly, applicable to experimental design and robust in terms of experimental noise and exchange flux magnitude. Collectively, we predict that SUMOFLUX's properties realistically pave the way to high-throughput flux analyses.
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Affiliation(s)
- Maria Kogadeeva
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- Life Science Zürich Graduate School, Zürich, Switzerland
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- * E-mail:
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8
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Niedenführ S, ten Pierick A, van Dam PTN, Suarez-Mendez CA, Nöh K, Wahl SA. Natural isotope correction of MS/MS measurements for metabolomics and (13)C fluxomics. Biotechnol Bioeng 2015; 113:1137-47. [PMID: 26479486 DOI: 10.1002/bit.25859] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 09/08/2015] [Accepted: 10/12/2015] [Indexed: 11/09/2022]
Abstract
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.
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Affiliation(s)
- Sebastian Niedenführ
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Angela ten Pierick
- Department of Biotechnology, Delft University of Technology, 2628BC Delft, The Netherlands
| | - Patricia T N van Dam
- Department of Biotechnology, Delft University of Technology, 2628BC Delft, The Netherlands
| | - Camilo A Suarez-Mendez
- Department of Biotechnology, Delft University of Technology, 2628BC Delft, The Netherlands. .,Departamento de Procesos y Energia, Universidad Nacional de Colombia, Carrera 80 No. 65-223, Blq. M3, Medellin, Colombia.
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
| | - S Aljoscha Wahl
- Department of Biotechnology, Delft University of Technology, 2628BC Delft, The Netherlands.
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9
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Efficient Modeling of MS/MS Data for Metabolic Flux Analysis. PLoS One 2015; 10:e0130213. [PMID: 26230524 PMCID: PMC4521746 DOI: 10.1371/journal.pone.0130213] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 05/16/2015] [Indexed: 01/25/2023] Open
Abstract
Metabolic flux analysis (MFA) is a widely used method for quantifying intracellular metabolic fluxes. It works by feeding cells with isotopic labeled nutrients, measuring metabolite isotopic labeling, and computationally interpreting the measured labeling data to estimate flux. Tandem mass-spectrometry (MS/MS) has been shown to be useful for MFA, providing positional isotopic labeling data. Specifically, MS/MS enables the measurement of a metabolite tandem mass-isotopomer distribution, representing the abundance in which certain parent and product fragments of a metabolite have different number of labeled atoms. However, a major limitation in using MFA with MS/MS data is the lack of a computationally efficient method for simulating such isotopic labeling data. Here, we describe the tandemer approach for efficiently computing metabolite tandem mass-isotopomer distributions in a metabolic network, given an estimation of metabolic fluxes. This approach can be used by MFA to find optimal metabolic fluxes, whose induced metabolite labeling patterns match tandem mass-isotopomer distributions measured by MS/MS. The tandemer approach is applied to simulate MS/MS data in a small-scale metabolic network model of mammalian methionine metabolism and in a large-scale metabolic network model of E. coli. It is shown to significantly improve the running time by between two to three orders of magnitude compared to the state-of-the-art, cumomers approach. We expect the tandemer approach to promote broader usage of MS/MS technology in metabolic flux analysis. Implementation is freely available at www.cs.technion.ac.il/~tomersh/methods.html
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10
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Saunders EC, de Souza DP, Chambers JM, Ng M, Pyke J, McConville MJ. Use of (13)C stable isotope labelling for pathway and metabolic flux analysis in Leishmania parasites. Methods Mol Biol 2015; 1201:281-296. [PMID: 25388122 DOI: 10.1007/978-1-4939-1438-8_18] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This protocol describes the combined use of metabolite profiling and stable isotope labelling to define pathways of central carbon metabolism in the protozoa parasite, Leishmania mexicana. Parasite stages are cultivated in standard or completely defined media and then rapidly transferred to chemically equivalent media containing a single (13)C-labelled nutrient. The incorporation of label can be followed over time or after establishment of isotopic equilibrium by harvesting parasites with rapid metabolic quenching. (13)C enrichment of multiple intracellular polar and apolar (lipidic) metabolites can be quantified using gas chromatography-mass spectrometry (GC-MS), while the uptake and secretion of (13)C-labelled metabolites can be measured by (13)C-NMR. Analysis of the mass isotopomer distribution of key metabolites provides information on pathway structure, while analysis of labelling kinetics can be used to infer metabolic fluxes. This protocol is exemplified using L. mexicana labelled with (13)C-U-glucose. The method can be used to measure perturbations in parasite metabolism induced by drug inhibition or genetic manipulation of enzyme levels and is broadly applicable to any cultured parasite stages.
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Affiliation(s)
- Eleanor C Saunders
- Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, 30 Flemington Rd, Parkville, VIC, 3010, Australia
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Pitkänen E, Jouhten P, Hou J, Syed MF, Blomberg P, Kludas J, Oja M, Holm L, Penttilä M, Rousu J, Arvas M. Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species. PLoS Comput Biol 2014; 10:e1003465. [PMID: 24516375 PMCID: PMC3916221 DOI: 10.1371/journal.pcbi.1003465] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2013] [Accepted: 12/18/2013] [Indexed: 12/12/2022] Open
Abstract
We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. Advances in next-generation sequencing technologies are revolutionizing molecular biology. Sequencing-enabled cost-effective characterization of microbial genomes is a particularly exciting development in metabolic engineering. There, considerable effort has been put to reconstructing genome-scale metabolic networks that describe the collection of hundreds to thousands of biochemical reactions available for a microbial cell. These network models are instrumental in understanding microbial metabolism and guiding metabolic engineering efforts to improve biochemical yields. We have developed a novel computational method, CoReCo, which bridges the growing gap between the availability of sequenced genomes and respective reconstructed metabolic networks. The method reconstructs genome-scale metabolic networks simultaneously for related microbial species. It utilizes the available sequencing data from these species to correct for incomplete and missing data. We used the method to reconstruct metabolic networks for a set of 49 fungal species providing the method protein sequence data and a phylogenetic tree describing the evolutionary relationships between the species. We demonstrate the applicability of the method by comparing a metabolic reconstruction of Saccharomyces cerevisiae to the manually curated, high-quality consensus network. We also provide an easy-to-use implementation of the method, usable both in single computer and distributed computing environments.
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Affiliation(s)
- Esa Pitkänen
- Department of Computer Science, University of Helsinki, Helsinki, Finland
- Department of Medical Genetics, Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
- * E-mail:
| | - Paula Jouhten
- VTT Technical Research Centre of Finland, Espoo, Finland
| | - Jian Hou
- Department of Computer Science, University of Helsinki, Helsinki, Finland
- Department of Information and Computer Science, Aalto University, Espoo, Finland
| | | | - Peter Blomberg
- VTT Technical Research Centre of Finland, Espoo, Finland
| | - Jana Kludas
- Department of Information and Computer Science, Aalto University, Espoo, Finland
| | - Merja Oja
- VTT Technical Research Centre of Finland, Espoo, Finland
| | - Liisa Holm
- Institute of Biotechnology & Department of Biosciences, University of Helsinki, Helsinki, Finland
| | - Merja Penttilä
- VTT Technical Research Centre of Finland, Espoo, Finland
| | - Juho Rousu
- Department of Information and Computer Science, Aalto University, Espoo, Finland
| | - Mikko Arvas
- VTT Technical Research Centre of Finland, Espoo, Finland
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12
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Abstract
NMR spectroscopy is an efficient method for analyzing (13)C labelling of cellular metabolites. The strength of it is especially the ability to provide direct quantitative positional information on the (13)C labelling status of carbon atoms in metabolites. NMR spectroscopic methods allow also for detection of contiguously (13)C-labelled fragments in the carbon backbones of the metabolites. Furthermore, the recent developments of NMR spectroscopy hardware have substantially improved the sensitivity of the methods. In this chapter we describe a method for analyzing the (13)C labelling of the biomass amino acids for metabolic flux analysis, sample preparation for NMR spectroscopy, acquiring and processing the NMR spectra, and extracting the (13)C labelling information from the NMR data. Different NMR methods are applied depending on the (13)C labelling strategy chosen. These strategies include uniform (13)C labelling, positional (13)C labelling, or a combination of both. Not only the preparation of sample for analysis of (13)C labelling in proteinogenic amino acids in biomass is described, but also the necessary modifications to the method when analysis of (13)C labelling in free metabolic intermediates is of interest. Finally the strategies for using the different NMR-detected (13)C labelling data in (13)C MFA are discussed.
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13
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Oberhardt MA, Yizhak K, Ruppin E. Metabolically re-modeling the drug pipeline. Curr Opin Pharmacol 2013; 13:778-85. [DOI: 10.1016/j.coph.2013.05.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Revised: 05/04/2013] [Accepted: 05/06/2013] [Indexed: 02/07/2023]
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Ahmed Z, Zeeshan S, Huber C, Hensel M, Schomburg D, Münch R, Eisenreich W, Dandekar T. Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling. BMC Bioinformatics 2013; 14:218. [PMID: 23837681 PMCID: PMC3720290 DOI: 10.1186/1471-2105-14-218] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2012] [Accepted: 06/23/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution. RESULTS The open-source software "Least Square Mass Isotopomer Analyzer" (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman's least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed. CONCLUSIONS The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.
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Affiliation(s)
- Zeeshan Ahmed
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Saman Zeeshan
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- Institute of Molecular and Translational Therapeutic Strategies, Hannover Medical School, Hanover, Germany
| | - Claudia Huber
- Lehrstuhl für Biochemie, Technische Universität München, München, Germany
| | - Michael Hensel
- Department of Microbiology, University of Osnabrück, Osnabrück, Germany
| | - Dietmar Schomburg
- Department of Bioinformatics and Biochemistry, Technical University Braunschweig, Braunschweig, Germany
| | - Richard Münch
- Institute for Microbiology, Technical University Braunschweig, Braunschweig, Germany
| | | | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- EMBL, Structural and Computational Biology Unit, Heidelberg, Germany
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15
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Ma X, Dagan S, Somogyi Á, Wysocki VH, Scaraffia PY. Low mass MS/MS fragments of protonated amino acids used for distinction of their 13C-isotopomers in metabolic studies. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2013; 24:622-31. [PMID: 23444051 PMCID: PMC3624025 DOI: 10.1007/s13361-012-0574-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Revised: 12/10/2012] [Accepted: 12/13/2012] [Indexed: 05/14/2023]
Abstract
Glu, Gln, Pro, and Ala are the main amino acids involved in ammonia detoxification in mosquitoes. In order to develop a tandem mass spectrometry method (MS(2)) to monitor each carbon of the above isotopically-labeled (13)C-amino acids for metabolic studies, the compositions and origins of atoms in fragments of the protonated amino acid should be first elucidated. Thus, various electrospray (ESI)-based MS(2) tools were employed to study the fragmentation of these unlabeled and isotopically-labeled amino acids and better understand their dissociation pathways. A broad range of fragments, including previously-undescribed low m/z fragments was revealed. The formulae of the fragments (from m/z 130 down to m/z 27) were confirmed by their accurate masses. The structures and conformations of the larger fragments of Glu were also explored by ion mobility mass spectrometry (IM-MS) and gas-phase hydrogen/deuterium exchange (HDX) experiments. It was found that some low m/z fragments (m/z 27-30) are common to Glu, Gln, Pro, and Ala. The origins of carbons in these small fragments are discussed and additional collision induced dissociation (CID) MS(2) fragmentation pathways are proposed for them. It was also found that small fragments (≤m/z 84) of protonated, methylated Glu, and methylated Gln are the same as those of the underivatized Glu and Gln. Taken together, the new approach of utilizing low m/z fragments can be applied to distinguish, identify, and quantify (13)C-amino acids labeled at various positions, either in the backbone or side chain.
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Affiliation(s)
- Xin Ma
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
| | - Shai Dagan
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
| | - Árpád Somogyi
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
| | - Vicki H. Wysocki
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
| | - Patricia Y. Scaraffia
- Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721, United States
- Corresponding author. Address reprint requests to Dr. Patricia Y. Scaraffia, Department of Chemistry and Biochemistry, The University of Arizona, Tucson, Arizona 85721-0088, United States. . Phone: (520) 626-5052 Fax : (520) 626-9204
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16
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Chen WL, Chen DZ, Taylor KT. Automatic reaction mapping and reaction center detection. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2013. [DOI: 10.1002/wcms.1140] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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17
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Abstract
Isotope-based metabolic flux analysis is one of the emerging technologies applied to system level metabolic phenotype characterization in metabolic engineering. Among the developed approaches, (13)C-based metabolic flux analysis has been established as a standard tool and has been widely applied to quantitative pathway characterization of diverse biological systems. To implement (13)C-based metabolic flux analysis in practice, comprehending the underlying mathematical and computational modeling fundamentals is of importance along with carefully conducted experiments and analytical measurements. Such knowledge is also crucial when designing (13)C-labeling experiments and properly acquiring key data sets essential for in vivo flux analysis implementation. In this regard, the modeling fundamentals of (13)C-labeling systems and analytical data processing are the main topics we will deal with in this chapter. Along with this, the relevant numerical optimization techniques are addressed to help implementation of the entire computational procedures aiming at (13)C-based metabolic flux analysis in vivo.
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Ebert BE, Lamprecht AL, Steffen B, Blank LM. Flux-p: automating metabolic flux analysis. Metabolites 2012; 2:872-90. [PMID: 24957766 PMCID: PMC3901227 DOI: 10.3390/metabo2040872] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Revised: 10/29/2012] [Accepted: 11/01/2012] [Indexed: 11/18/2022] Open
Abstract
Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize 13C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses.
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Affiliation(s)
- Birgitta E Ebert
- Institute of Applied Microbiology (iAMB), RWTH Aachen University, Worringer Weg 1 52074 Aachen, Germany.
| | - Anna-Lena Lamprecht
- Service and Software Engineering, University of Potsdam, August-Bebel-Straße 89, 14482 Potsdam, Germany.
| | - Bernhard Steffen
- Programming Systems, TU Dortmund University, Otto-Hahn-Str. 14, 44227 Dortmund, Germany.
| | - Lars M Blank
- Institute of Applied Microbiology (iAMB), RWTH Aachen University, Worringer Weg 1 52074 Aachen, Germany.
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19
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Jouhten P. Metabolic modelling in the development of cell factories by synthetic biology. Comput Struct Biotechnol J 2012; 3:e201210009. [PMID: 24688669 PMCID: PMC3962133 DOI: 10.5936/csbj.201210009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 11/05/2012] [Accepted: 11/07/2012] [Indexed: 11/22/2022] Open
Abstract
Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.
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Affiliation(s)
- Paula Jouhten
- VTT Technical Research Centre of Finland, Tietotie 2, 02044 VTT, Espoo, Finland
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20
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Sonkar K, Purusottam RN, Sinha N. Metabonomic Study of Host–Phage Interaction by Nuclear Magnetic Resonance- and Statistical Total Correlation Spectroscopy-Based Analysis. Anal Chem 2012; 84:4063-70. [DOI: 10.1021/ac300096j] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Kanchan Sonkar
- Centre of Biomedical Magnetic Resonance, SGPGIMS Campus,
Raebareli Road, Lucknow 226014, India
| | - Rudra N. Purusottam
- Centre of Biomedical Magnetic Resonance, SGPGIMS Campus,
Raebareli Road, Lucknow 226014, India
| | - Neeraj Sinha
- Centre of Biomedical Magnetic Resonance, SGPGIMS Campus,
Raebareli Road, Lucknow 226014, India
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21
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Heinonen M, Lappalainen S, Mielikäinen T, Rousu J. Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism. J Comput Biol 2011; 18:43-58. [DOI: 10.1089/cmb.2009.0216] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Markus Heinonen
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Sampsa Lappalainen
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | | | - Juho Rousu
- Department of Computer Science, University of Helsinki, Helsinki, Finland
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22
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Yizhak K, Benyamini T, Liebermeister W, Ruppin E, Shlomi T. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 2010; 26:i255-60. [PMID: 20529914 PMCID: PMC2881368 DOI: 10.1093/bioinformatics/btq183] [Citation(s) in RCA: 170] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Motivation: The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations. Results: IOMA is shown to successfully predict the metabolic state of human erythrocytes (compared to kinetic model simulations), showing a significant advantage over the commonly used methods flux balance analysis and minimization of metabolic adjustment. Thereafter, IOMA is shown to correctly predict metabolic fluxes in Escherichia coli under different gene knockouts for which both metabolomic and proteomic data is available, achieving higher prediction accuracy over the extant methods. Considering the lack of high-throughput flux measurements, while high-throughput metabolomic and proteomic data are becoming readily available, we expect IOMA to significantly contribute to future research of cellular metabolism. Contacts:kerenyiz@post.tau.ac.il; tomersh@cs.technion.ac.il
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Affiliation(s)
- Keren Yizhak
- The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel.
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23
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Zamboni N. 13C metabolic flux analysis in complex systems. Curr Opin Biotechnol 2010; 22:103-8. [PMID: 20833526 DOI: 10.1016/j.copbio.2010.08.009] [Citation(s) in RCA: 132] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2010] [Revised: 08/17/2010] [Accepted: 08/18/2010] [Indexed: 12/13/2022]
Abstract
Experimental determination of in vivo metabolic rates by methods of (13)C metabolic flux analysis is a pivotal approach to unravel structure and regulation of metabolic networks, in particular with microorganisms grown in minimal media. However, the study of real-life and eukaryotic systems calls for the quantification of fluxes also in cellular compartments, rich media, cell-wide metabolic networks, dynamic systems or single cells. These scenarios drastically increase the complexity of the task, which is only partly dealt by existing approaches that rely on rigorous simulations of label propagation through metabolic networks and require multiple labeling experiments or a priori information on pathway inactivity to simplify the problem. Albeit qualitative and largely driven by human interpretation, statistical analysis of measured (13)C-patterns remains the exclusive alternative to comprehensively handle such complex systems. In the future, this practice will be complemented by novel modeling frameworks to assay particular fluxes within a network by stable isotopic tracer for targeted validation of well-defined hypotheses.
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Affiliation(s)
- Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
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24
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Dauner M. From fluxes and isotope labeling patterns towards in silico cells. Curr Opin Biotechnol 2010; 21:55-62. [DOI: 10.1016/j.copbio.2010.01.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2009] [Revised: 01/23/2010] [Accepted: 01/31/2010] [Indexed: 10/19/2022]
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25
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Pitkänen E, Rousu J, Ukkonen E. Computational methods for metabolic reconstruction. Curr Opin Biotechnol 2010; 21:70-7. [DOI: 10.1016/j.copbio.2010.01.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2009] [Revised: 01/17/2010] [Accepted: 01/20/2010] [Indexed: 12/19/2022]
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26
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Biedendieck R, Bunk B, Fürch T, Franco-Lara E, Jahn M, Jahn D. Systems biology of recombinant protein production in Bacillus megaterium. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2010; 120:133-161. [PMID: 20140656 DOI: 10.1007/10_2009_62] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Over the last two decades the Gram-positive bacterium Bacillus megaterium was systematically developed to a useful alternative protein production host. Multiple vector systems for high yield intra- and extracellular protein production were constructed. Strong inducible promoters were combined with DNA sequences for optimised ribosome binding sites, various leader peptides for protein export and N- as well as C-terminal affinity tags for affinity chromatographic purification of the desired protein. High cell density cultivation and recombinant protein production were successfully tested. For further system biology based control and optimisation of the production process the genomes of two B. megaterium strains were completely elucidated, DNA arrays designed, proteome, fluxome and metabolome analyses performed and all data integrated using the bioinformatics platform MEGABAC. Now, solid theoretical and experimental bases for primary modeling attempts of the production process are available.
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Affiliation(s)
- Rebekka Biedendieck
- Protein Science Group, Department of Biosciences, University of Kent, Canterbury, Kent, CT27NJ, UK
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27
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Pitkänen E, Jouhten P, Rousu J. Inferring branching pathways in genome-scale metabolic networks. BMC SYSTEMS BIOLOGY 2009; 3:103. [PMID: 19874610 PMCID: PMC2791103 DOI: 10.1186/1752-0509-3-103] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2009] [Accepted: 10/29/2009] [Indexed: 11/17/2022]
Abstract
Background A central problem in computational metabolic modelling is how to find biochemically plausible pathways between metabolites in a metabolic network. Two general, complementary frameworks have been utilized to find metabolic pathways: constraint-based modelling and graph-theoretical path finding approaches. In constraint-based modelling, one aims to find pathways where metabolites are balanced in a pseudo steady-state. Constraint-based methods, such as elementary flux mode analysis, have typically a high computational cost stemming from a large number of steady-state pathways in a typical metabolic network. On the other hand, graph-theoretical approaches avoid the computational complexity of constraint-based methods by solving a simpler problem of finding shortest paths. However, while scaling well with network size, graph-theoretic methods generally tend to return more false positive pathways than constraint-based methods. Results In this paper, we introduce a computational method, ReTrace, for finding biochemically relevant, branching metabolic pathways in an atom-level representation of metabolic networks. The method finds compact pathways which transfer a high fraction of atoms from source to target metabolites by considering combinations of linear shortest paths. In contrast to current steady-state pathway analysis methods, our method scales up well and is able to operate on genome-scale models. Further, we show that the pathways produced are biochemically meaningful by an example involving the biosynthesis of inosine 5'-monophosphate (IMP). In particular, the method is able to avoid typical problems associated with graph-theoretic approaches such as the need to define side metabolites or pathways not carrying any net carbon flux appearing in results. Finally, we discuss an application involving reconstruction of amino acid pathways of a recently sequenced organism demonstrating how measurement data can be easily incorporated into ReTrace analysis. ReTrace is licensed under GPL and is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/retrace/. Conclusion ReTrace is a useful method in metabolic path finding tasks, combining some of the best aspects in constraint-based and graph-theoretic methods. It finds use in a multitude of tasks ranging from metabolic engineering to metabolic reconstruction of recently sequenced organisms.
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Affiliation(s)
- Esa Pitkänen
- Department of Computer Science, University of Helsinki, Finland.
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Abstract
Stable isotope, and in particular (13)C-based flux analysis, is the exclusive approach to experimentally quantify the integrated responses of metabolic networks. Here we describe a protocol that is based on growing microbes on (13)C-labeled glucose and subsequent gas chromatography mass spectrometric detection of (13)C-patterns in protein-bound amino acids. Relying on publicly available software packages, we then describe two complementary mathematical approaches to estimate either local ratios of converging fluxes or absolute fluxes through different pathways. As amino acids in cell protein are abundant and stable, this protocol requires a minimum of equipment and analytical expertise. Most other flux methods are variants of the principles presented here. A true alternative is the analytically more demanding dynamic flux analysis that relies on (13)C-pattern in free intracellular metabolites. The presented protocols take 5-10 d, have been used extensively in the past decade and are exemplified here for the central metabolism of Escherichia coli.
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Affiliation(s)
- Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
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Quek LE, Wittmann C, Nielsen LK, Krömer JO. OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis. Microb Cell Fact 2009; 8:25. [PMID: 19409084 PMCID: PMC2689189 DOI: 10.1186/1475-2859-8-25] [Citation(s) in RCA: 192] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2009] [Accepted: 05/01/2009] [Indexed: 11/30/2022] Open
Abstract
Background The quantitative analysis of metabolic fluxes, i.e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and metabolic engineering. It is based on a comprehensive approach combining (i) tracer cultivation on 13C substrates, (ii) 13C labelling analysis by mass spectrometry and (iii) mathematical modelling for experimental design, data processing, flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in flux studies is limiting the application of metabolic flux analysis. Results We have developed OpenFLUX as a user friendly, yet flexible software application for small and large scale 13C metabolic flux analysis. The application is based on the new Elementary Metabolite Unit (EMU) framework, significantly enhancing computation speed for flux calculation. From simple notation of metabolic reaction networks defined in a spreadsheet, the OpenFLUX parser automatically generates MATLAB-readable metabolite and isotopomer balances, thus strongly facilitating model creation. The model can be used to perform experimental design, parameter estimation and sensitivity analysis either using the built-in gradient-based search or Monte Carlo algorithms or in user-defined algorithms. Exemplified for a microbial flux study with 71 reactions, 8 free flux parameters and mass isotopomer distribution of 10 metabolites, OpenFLUX allowed to automatically compile the EMU-based model from an Excel file containing metabolic reactions and carbon transfer mechanisms, showing it's user-friendliness. It reliably reproduced the published data and optimum flux distributions for the network under study were found quickly (<20 sec). Conclusion We have developed a fast, accurate application to perform steady-state 13C metabolic flux analysis. OpenFLUX will strongly facilitate and enhance the design, calculation and interpretation of metabolic flux studies. By providing the software open source, we hope it will evolve with the rapidly growing field of fluxomics.
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Affiliation(s)
- Lake-Ee Quek
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, QLD 4072, Australia.
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Heinonen M, Rantanen A, Mielikäinen T, Kokkonen J, Kiuru J, Ketola RA, Rousu J. FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2008; 22:3043-3052. [PMID: 18763276 DOI: 10.1002/rcm.3701] [Citation(s) in RCA: 72] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We present FiD (Fragment iDentificator), a software tool for the structural identification of product ions produced with tandem mass spectrometric measurement of low molecular weight organic compounds. Tandem mass spectrometry (MS/MS) has proven to be an indispensable tool in modern, cell-wide metabolomics and fluxomics studies. In such studies, the structural information of the MS(n) product ions is usually needed in the downstream analysis of the measurement data. The manual identification of the structures of MS(n) product ions is, however, a nontrivial task requiring expertise, and calls for computer assistance. Commercial software tools, such as Mass Frontier and ACD/MS Fragmenter, rely on fragmentation rule databases for the identification of MS(n) product ions. FiD, on the other hand, conducts a combinatorial search over all possible fragmentation paths and outputs a ranked list of alternative structures. This gives the user an advantage in situations where the MS/MS data of compounds with less well-known fragmentation mechanisms are processed. FiD software implements two fragmentation models, the single-step model that ignores intermediate fragmentation states and the multi-step model, which allows for complex fragmentation pathways. The software works for MS/MS data produced both in positive- and negative-ion modes. The software has an easy-to-use graphical interface with built-in visualization capabilities for structures of product ions and fragmentation pathways. In our experiments involving amino acids and sugar-phosphates, often found, e.g., in the central carbon metabolism of yeasts, FiD software correctly predicted the structures of product ions on average in 85% of the cases. The FiD software is free for academic use and is available for download from www.cs.helsinki.fi/group/sysfys/software/fragid.
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Affiliation(s)
- Markus Heinonen
- Department of Computer Science, University of Helsinki, Helsinki, Finland.
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