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Grankvist N, Jönsson C, Hedin K, Sundqvist N, Sandström P, Björnsson B, Begzati A, Mickols E, Artursson P, Jain M, Cedersund G, Nilsson R. Global 13C tracing and metabolic flux analysis of intact human liver tissue ex vivo. Nat Metab 2024:10.1038/s42255-024-01119-3. [PMID: 39210089 DOI: 10.1038/s42255-024-01119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/02/2024] [Indexed: 09/04/2024]
Abstract
Liver metabolism is central to human physiology and influences the pathogenesis of common metabolic diseases. Yet, our understanding of human liver metabolism remains incomplete, with much of current knowledge based on animal or cell culture models that do not fully recapitulate human physiology. Here, we perform in-depth measurement of metabolism in intact human liver tissue ex vivo using global 13C tracing, non-targeted mass spectrometry and model-based metabolic flux analysis. Isotope tracing allowed qualitative assessment of a wide range of metabolic pathways within a single experiment, confirming well-known features of liver metabolism but also revealing unexpected metabolic activities such as de novo creatine synthesis and branched-chain amino acid transamination, where human liver appears to differ from rodent models. Glucose production ex vivo correlated with donor plasma glucose, suggesting that cultured liver tissue retains individual metabolic phenotypes, and could be suppressed by postprandial levels of nutrients and insulin, and also by pharmacological inhibition of glycogen utilization. Isotope tracing ex vivo allows measuring human liver metabolism with great depth and resolution in an experimentally tractable system.
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Affiliation(s)
- Nina Grankvist
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Division of Cardiovascular Medicine, Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Cecilia Jönsson
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Karin Hedin
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Biomedical engineering, Linköping University, Linköping, Sweden
| | - Nicolas Sundqvist
- Department of Biomedical engineering, Linköping University, Linköping, Sweden
| | - Per Sandström
- Department of Surgery, Linköping University Hospital, Linköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Bergthor Björnsson
- Department of Surgery, Linköping University Hospital, Linköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Arjana Begzati
- Department of Medicine & Pharmacology, University of California, San Diego, La Jolla, CA, USA
| | | | - Per Artursson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Mohit Jain
- Department of Medicine & Pharmacology, University of California, San Diego, La Jolla, CA, USA
- Sapient Bioanalytics, San Diego, CA, USA
| | - Gunnar Cedersund
- Department of Biomedical engineering, Linköping University, Linköping, Sweden
| | - Roland Nilsson
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
- Division of Cardiovascular Medicine, Karolinska University Hospital, Stockholm, Sweden.
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
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2
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Kong Y, Chen H, Huang X, Chang L, Yang B, Chen W. Precise metabolic modeling in post-omics era: accomplishments and perspectives. Crit Rev Biotechnol 2024:1-19. [PMID: 39198033 DOI: 10.1080/07388551.2024.2390089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/18/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024]
Abstract
Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.
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Affiliation(s)
- Yawen Kong
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, P. R. China
- School of Food Science and Technology, Jiangnan University, Wuxi, P. R. China
| | - Haiqin Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, P. R. China
- School of Food Science and Technology, Jiangnan University, Wuxi, P. R. China
| | - Xinlei Huang
- The Key Laboratory of Industrial Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, P. R. China
| | - Lulu Chang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, P. R. China
- School of Food Science and Technology, Jiangnan University, Wuxi, P. R. China
| | - Bo Yang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, P. R. China
- School of Food Science and Technology, Jiangnan University, Wuxi, P. R. China
| | - Wei Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, P. R. China
- School of Food Science and Technology, Jiangnan University, Wuxi, P. R. China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, P. R. China
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3
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Mattanovich M, Hesselberg-Thomsen V, Lien A, Vaitkus D, Saad VS, McCloskey D. INCAWrapper: a Python wrapper for INCA for seamless data import, -export, and -processing. BIOINFORMATICS ADVANCES 2024; 4:vbae100. [PMID: 39006966 PMCID: PMC11245311 DOI: 10.1093/bioadv/vbae100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 06/24/2024] [Accepted: 07/03/2024] [Indexed: 07/16/2024]
Abstract
Motivation INCA is a powerful tool for metabolic flux analysis, however, import and export of data and results can be tedious and limit the use of INCA in automated workflows. Results The INCAWrapper enables the use of INCA purely through Python, which allows the use of INCA in common data science workflows. Availability and implementation The INCAWrapper is implemented in Python and can be found at https://github.com/biosustain/incawrapper. It is freely available under an MIT License. To run INCA, the user needs their own MATLAB and INCA licenses. INCA is freely available for noncommercial use at mfa.vueinnovations.com.
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Affiliation(s)
- Matthias Mattanovich
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, 2800, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Viktor Hesselberg-Thomsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, 2800, Denmark
| | - Annette Lien
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, 2800, Denmark
| | - Dovydas Vaitkus
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, 2800, Denmark
| | - Victoria Sara Saad
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, 2800, Denmark
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, 2800, Denmark
- BioMed X Institute, Artificial Intelligence, Heidelberg, Baden-Württemberg, 69120, Germany
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4
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Theorell A, Jadebeck JF, Wiechert W, McFadden J, Nöh K. Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference. Metab Eng 2024; 83:137-149. [PMID: 38582144 DOI: 10.1016/j.ymben.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/08/2024]
Abstract
Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.
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Affiliation(s)
- Axel Theorell
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Johann F Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Johnjoe McFadden
- Department of Microbial and Cellular Sciences, University of Surrey, GU2 7XH Guildford, United Kingdom
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
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5
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Parameter Identification in Metabolic Reaction Networks by Means of Multiple Steady-State Measurements. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
In this work, we investigate some theoretical aspects related to the estimation approach proposed by Liebermeister and Klipp, 2006, in which general rate laws, derived from standardized enzymatic mechanisms, are exploited to kinetically describe the fluxes of a metabolic reaction network, and multiple metabolic steady-state measurements are exploited to estimate the unknown kinetic parameters. Further mathematical details are deeply investigated, and necessary conditions on the amount of information required to solve the identification problem are given. Moreover, theoretical results for the parameter identifiability are provided, and symmetrical and modular properties of the proposed approach are highlighted when the global identification problem is decoupled into smaller and simpler identification problems related to the single reactions of the network. Among the advantages of the proposed innovative approach are (i) non-restrictive conditions to guarantee the solvability of the parameter estimation problem, (ii) the unburden of the usual computational complexity for such identification problems, and (iii) the ease of obtaining the required number of measurements, which are actually steady-state data, experimentally easier to obtain with respect to the time-dependent ones. A simple example concludes the paper, highlighting the mentioned advantages of the method and the implementation of the related theoretical result.
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6
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H +-Translocating Membrane-Bound Pyrophosphatase from Rhodospirillum rubrum Fuels Escherichia coli Cells via an Alternative Pathway for Energy Generation. Microorganisms 2023; 11:microorganisms11020294. [PMID: 36838259 PMCID: PMC9959109 DOI: 10.3390/microorganisms11020294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/11/2023] [Accepted: 01/14/2023] [Indexed: 01/24/2023] Open
Abstract
Inorganic pyrophosphatases (PPases) catalyze an essential reaction, namely, the hydrolysis of PPi, which is formed in large quantities as a side product of numerous cellular reactions. In the majority of living species, PPi hydrolysis is carried out by soluble cytoplasmic PPase (S-PPases) with the released energy dissipated in the form of heat. In Rhodospirillum rubrum, part of this energy can be conserved by proton-pumping pyrophosphatase (H+-PPaseRru) in the form of a proton electrochemical gradient for further ATP synthesis. Here, the codon-harmonized gene hppaRru encoding H+-PPaseRru was expressed in the Escherichia coli chromosome. We demonstrate, for the first time, that H+-PPaseRru complements the essential native S-PPase in E. coli cells. 13C-MFA confirmed that replacing native PPase to H+-PPaseRru leads to the re-distribution of carbon fluxes; a statistically significant 36% decrease in tricarboxylic acid (TCA) cycle fluxes was found compared with wild-type E. coli MG1655. Such a flux re-distribution can indicate the presence of an additional method for energy generation (e.g., ATP), which can be useful for the microbiological production of a number of compounds, the biosynthesis of which requires the consumption of ATP.
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7
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Nirati Y, Purushotham N, Alagesan S. Quantitative insight into the metabolism of isoprene-producing Synechocystis sp. PCC 6803 using steady state 13C-MFA. PHOTOSYNTHESIS RESEARCH 2022; 154:195-206. [PMID: 36070060 DOI: 10.1007/s11120-022-00957-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/24/2022] [Indexed: 06/15/2023]
Abstract
Cyanobacteria are photosynthetic bacteria, widely studied for the conversion of atmospheric carbon dioxide to useful platform chemicals. Isoprene is one such industrially important chemical, primarily used for production of synthetic rubber and biofuels. Synechocystis sp. PCC 6803, a genetically amenable cyanobacterium, produces isoprene on heterologous expression of isoprene synthase gene, albeit in very low quantities. Rationalized metabolic engineering to re-route the carbon flux for enhanced isoprene production requires in-dept knowledge of the metabolic flux distribution in the cell. Hence, in the present study, we undertook steady state 13C-metabolic flux analysis of glucose-tolerant wild-type (GTN) and isoprene-producing recombinant (ISP) Synechocystis sp. to understand and compare the carbon flux distribution in the two strains. The R-values for amino acids, flux analysis predictions and gene expression profiles emphasized predominance of Calvin cycle and glycogen metabolism in GTN. Alternatively, flux analysis predicted higher activity of the anaplerotic pathway through phosphoenolpyruvate carboxylase and malic enzyme in ISP. The striking difference in the Calvin cycle, glycogen metabolism and anaplerotic pathway activity in GTN and ISP suggested a possible role of energy molecules (ATP and NADPH) in regulating the carbon flux distribution in GTN and ISP. This claim was further supported by the transcript level of selected genes of the electron transport chain. This study provides the first quantitative insight into the carbon flux distribution of isoprene-producing cyanobacterium, information critical for developing Synechocystis sp. as a single cell factory for isoprenoid/terpenoid production.
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Affiliation(s)
- Yasha Nirati
- Institute of Bioinformatics and Applied Biotechnology (IBAB), Bengaluru, 560100, India
| | - Nidhish Purushotham
- Institute of Bioinformatics and Applied Biotechnology (IBAB), Bengaluru, 560100, India
- Dayananda Sagar University, Bengaluru, India
| | - Swathi Alagesan
- Institute of Bioinformatics and Applied Biotechnology (IBAB), Bengaluru, 560100, India.
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8
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de Falco B, Giannino F, Carteni F, Mazzoleni S, Kim DH. Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas. RSC Adv 2022; 12:25528-25548. [PMID: 36199351 PMCID: PMC9449821 DOI: 10.1039/d2ra03326g] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/26/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.
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Affiliation(s)
- Bruna de Falco
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
| | - Francesco Giannino
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Fabrizio Carteni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Stefano Mazzoleni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Dong-Hyun Kim
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
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9
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Liu Z, Zhang Z, Liang S, Chen Z, Xie X, Shen T. CeCaFLUX: the first web server for standardized and visual instationary 13C metabolic flux analysis. Bioinformatics 2022; 38:3481-3483. [PMID: 35595250 DOI: 10.1093/bioinformatics/btac341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 04/08/2022] [Accepted: 05/16/2022] [Indexed: 11/12/2022] Open
Abstract
SUMMARY The number of instationary 13C-metabolic flux (INST-MFA) studies grows every year, making it more important than ever to ensure the clarity, standardization and reproducibility of each study. We proposed CeCaFLUX, the first user-friendly web server that derives metabolic flux distribution from instationary 13C-labeled data. Flux optimization and statistical analysis are achieved through an evolutionary optimization in a parallel manner. It can visualize the flux optimizing process in real time and the ultimate flux outcome. It will also function as a database to enhance the consistency and to facilitate sharing of flux studies. AVAILABILITY AND IMPLEMENTATION CeCaFLUX is freely available at https://www.cecaflux.net, the source code can be downloaded at https://github.com/zhzhd82/CeCaFLUX. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhentao Liu
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou, China.,College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China
| | - Zhengdong Zhang
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou, China.,College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou, China
| | - Sheng Liang
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou, China
| | - Zhen Chen
- School of Mathematical Science, Guizhou Normal University, Guiyang, Guizhou, China
| | - Xiaoyao Xie
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou, China.,College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou, China
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10
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Kohlstedt M, Weimer A, Weiland F, Stolzenberger J, Selzer M, Sanz M, Kramps L, Wittmann C. Biobased PET from lignin using an engineered cis, cis-muconate-producing Pseudomonas putida strain with superior robustness, energy and redox properties. Metab Eng 2022; 72:337-352. [DOI: 10.1016/j.ymben.2022.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/18/2022] [Accepted: 05/04/2022] [Indexed: 11/26/2022]
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11
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Heistinger L, Dohm JC, Paes BG, Koizar D, Troyer C, Ata Ö, Steininger-Mairinger T, Mattanovich D. Genotypic and phenotypic diversity among Komagataella species reveals a hidden pathway for xylose utilization. Microb Cell Fact 2022; 21:70. [PMID: 35468837 PMCID: PMC9036795 DOI: 10.1186/s12934-022-01796-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 03/06/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The yeast genus Komagataella currently consists of seven methylotrophic species isolated from tree environments. Well-characterized strains of K. phaffii and K. pastoris are important hosts for biotechnological applications, but the potential of other species from the genus remains largely unexplored. In this study, we characterized 25 natural isolates from all seven described Komagataella species to identify interesting traits and provide a comprehensive overview of the genotypic and phenotypic diversity available within this genus. RESULTS Growth tests on different carbon sources and in the presence of stressors at two different temperatures allowed us to identify strains with differences in tolerance to high pH, high temperature, and growth on xylose. As Komagataella species are generally not considered xylose-utilizing yeasts, xylose assimilation was characterized in detail. Growth assays, enzyme activity measurements and 13C labeling confirmed the ability of K. phaffii to utilize D-xylose via the oxidoreductase pathway. In addition, we performed long-read whole-genome sequencing to generate genome assemblies of all Komagataella species type strains and additional K. phaffii and K. pastoris isolates for comparative analysis. All sequenced genomes have a similar size and share 83-99% average sequence identity. Genome structure analysis showed that K. pastoris and K. ulmi share the same rearrangements in difference to K. phaffii, while the genome structure of K. kurtzmanii is similar to K. phaffii. The genomes of the other, more distant species showed a larger number of structural differences. Moreover, we used the newly assembled genomes to identify putative orthologs of important xylose-related genes in the different Komagataella species. CONCLUSIONS By characterizing the phenotypes of 25 natural Komagataella isolates, we could identify strains with improved growth on different relevant carbon sources and stress conditions. Our data on the phenotypic and genotypic diversity will provide the basis for the use of so-far neglected Komagataella strains with interesting characteristics and the elucidation of the genetic determinants of improved growth and stress tolerance for targeted strain improvement.
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Affiliation(s)
- Lina Heistinger
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences Vienna (BOKU), 1190, Vienna, Austria.
- Institute of Biochemistry, Department of Biology, ETH Zürich, 8093, Zürich, Switzerland.
| | - Juliane C Dohm
- Department of Biotechnology, Institute of Computational Biology, University of Natural Resources and Life Sciences Vienna (BOKU), 1190, Vienna, Austria
| | - Barbara G Paes
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences Vienna (BOKU), 1190, Vienna, Austria
- Department of Cell Biology, Institute of Biological Sciences, University of Brasilia (UnB), Brasilia, Brazil
| | - Daniel Koizar
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences Vienna (BOKU), 1190, Vienna, Austria
| | - Christina Troyer
- Department of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences Vienna (BOKU), 1190, Vienna, Austria
| | - Özge Ata
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences Vienna (BOKU), 1190, Vienna, Austria
- Austrian Centre of Industrial Biotechnology (Acib GmbH), 1190, Vienna, Austria
| | - Teresa Steininger-Mairinger
- Department of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences Vienna (BOKU), 1190, Vienna, Austria
| | - Diethard Mattanovich
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences Vienna (BOKU), 1190, Vienna, Austria
- Austrian Centre of Industrial Biotechnology (Acib GmbH), 1190, Vienna, Austria
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12
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Schulze D, Kohlstedt M, Becker J, Cahoreau E, Peyriga L, Makowka A, Hildebrandt S, Gutekunst K, Portais JC, Wittmann C. GC/MS-based 13C metabolic flux analysis resolves the parallel and cyclic photomixotrophic metabolism of Synechocystis sp. PCC 6803 and selected deletion mutants including the Entner-Doudoroff and phosphoketolase pathways. Microb Cell Fact 2022; 21:69. [PMID: 35459213 PMCID: PMC9034593 DOI: 10.1186/s12934-022-01790-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/05/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cyanobacteria receive huge interest as green catalysts. While exploiting energy from sunlight, they co-utilize sugar and CO2. This photomixotrophic mode enables fast growth and high cell densities, opening perspectives for sustainable biomanufacturing. The model cyanobacterium Synechocystis sp. PCC 6803 possesses a complex architecture of glycolytic routes for glucose breakdown that are intertwined with the CO2-fixing Calvin-Benson-Bassham (CBB) cycle. To date, the contribution of these pathways to photomixotrophic metabolism has remained unclear. RESULTS Here, we developed a comprehensive approach for 13C metabolic flux analysis of Synechocystis sp. PCC 6803 during steady state photomixotrophic growth. Under these conditions, the Entner-Doudoroff (ED) and phosphoketolase (PK) pathways were found inactive but the microbe used the phosphoglucoisomerase (PGI) (63.1%) and the oxidative pentose phosphate pathway (OPP) shunts (9.3%) to fuel the CBB cycle. Mutants that lacked the ED pathway, the PK pathway, or phosphofructokinases were not affected in growth under metabolic steady-state. An ED pathway-deficient mutant (Δeda) exhibited an enhanced CBB cycle flux and increased glycogen formation, while the OPP shunt was almost inactive (1.3%). Under fluctuating light, ∆eda showed a growth defect, different to wild type and the other deletion strains. CONCLUSIONS The developed approach, based on parallel 13C tracer studies with GC-MS analysis of amino acids, sugars, and sugar derivatives, optionally adding NMR data from amino acids, is valuable to study fluxes in photomixotrophic microbes to detail. In photomixotrophic cells, PGI and OPP form glycolytic shunts that merge at switch points and result in synergistic fueling of the CBB cycle for maximized CO2 fixation. However, redirected fluxes in an ED shunt-deficient mutant and the impossibility to delete this shunt in a GAPDH2 knockout mutant, indicate that either minor fluxes (below the resolution limit of 13C flux analysis) might exist that could provide catalytic amounts of regulatory intermediates or alternatively, that EDA possesses additional so far unknown functions. These ideas require further experiments.
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Affiliation(s)
- Dennis Schulze
- Institute of Systems Biotechnology, Saarland University, Saarbrücken, Germany
| | - Michael Kohlstedt
- Institute of Systems Biotechnology, Saarland University, Saarbrücken, Germany
| | - Judith Becker
- Institute of Systems Biotechnology, Saarland University, Saarbrücken, Germany
| | - Edern Cahoreau
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,MetaboHUB-MetaToul, National Infrastructure of Metabolomics & Fluxomics, Toulouse, France.,RESTORE, Université de Toulouse, Inserm U1031, CNRS 5070, UPS, EFS, Toulouse, France
| | - Lindsay Peyriga
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,MetaboHUB-MetaToul, National Infrastructure of Metabolomics & Fluxomics, Toulouse, France.,RESTORE, Université de Toulouse, Inserm U1031, CNRS 5070, UPS, EFS, Toulouse, France
| | | | | | - Kirstin Gutekunst
- Institute of Botany, Christian-Albrecht University, Kiel, Germany.,Molecular Plant Physiology, Bioenergetics in Photoautotrophs, University of Kassel, Kassel, Germany
| | - Jean-Charles Portais
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France.,MetaboHUB-MetaToul, National Infrastructure of Metabolomics & Fluxomics, Toulouse, France.,RESTORE, Université de Toulouse, Inserm U1031, CNRS 5070, UPS, EFS, Toulouse, France
| | - Christoph Wittmann
- Institute of Systems Biotechnology, Saarland University, Saarbrücken, Germany.
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13
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Narad P, Naresh G, Sengupta A. Metabolomics and flux balance analysis. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00008-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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14
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Karlstaedt A. Stable Isotopes for Tracing Cardiac Metabolism in Diseases. Front Cardiovasc Med 2021; 8:734364. [PMID: 34859064 PMCID: PMC8631909 DOI: 10.3389/fcvm.2021.734364] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/18/2021] [Indexed: 12/28/2022] Open
Abstract
Although metabolic remodeling during cardiovascular diseases has been well-recognized for decades, the recent development of analytical platforms and mathematical tools has driven the emergence of assessing cardiac metabolism using tracers. Metabolism is a critical component of cellular functions and adaptation to stress. The pathogenesis of cardiovascular disease involves metabolic adaptation to maintain cardiac contractile function even in advanced disease stages. Stable-isotope tracer measurements are a powerful tool for measuring flux distributions at the whole organism level and assessing metabolic changes at a systems level in vivo. The goal of this review is to summarize techniques and concepts for in vivo or ex vivo stable isotope labeling in cardiovascular research, to highlight mathematical concepts and their limitations, to describe analytical methods at the tissue and single-cell level, and to discuss opportunities to leverage metabolic models to address important mechanistic questions relevant to all patients with cardiovascular disease.
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Affiliation(s)
- Anja Karlstaedt
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.,Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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15
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Sacco SA, Young JD. 13C metabolic flux analysis in cell line and bioprocess development. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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16
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Shimizu H, Toya Y. Recent advances in metabolic engineering-integration of in silico design and experimental analysis of metabolic pathways. J Biosci Bioeng 2021; 132:429-436. [PMID: 34509367 DOI: 10.1016/j.jbiosc.2021.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/07/2021] [Indexed: 11/29/2022]
Abstract
Microorganisms are widely used to produce valuable compounds. Because thousands of metabolic reactions occur simultaneously and many metabolic reactions are related to target production and cell growth, the development of a rational design method for metabolic pathway modification to optimize target production is needed. In this paper, recent advances in metabolic engineering are reviewed, specifically considering computational pathway modification design and experimental evaluation of metabolic fluxes by 13C-metabolic flux analysis. Computational tools for seeking effective gene deletion targets and recruiting heterologous genes are described in flux balance analysis approaches. A kinetic model and adaptive laboratory evolution were applied to identify and eliminate the rate-limiting step in metabolic pathways. Data science-based approaches for process monitoring and control are described to maximize the performance of engineered cells in bioreactors.
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Affiliation(s)
- Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
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17
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Dusny C, Schmid A. The Metabolic Flux Probe (MFP)-Secreted Protein as a Non-Disruptive Information Carrier for 13C-Based Metabolic Flux Analysis. Int J Mol Sci 2021; 22:ijms22179438. [PMID: 34502345 PMCID: PMC8430588 DOI: 10.3390/ijms22179438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 11/29/2022] Open
Abstract
Novel cultivation technologies demand the adaptation of existing analytical concepts. Metabolic flux analysis (MFA) requires stable-isotope labeling of biomass-bound protein as the primary information source. Obtaining the required protein in cultivation set-ups where biomass is inaccessible due to low cell densities and cell immobilization is difficult to date. We developed a non-disruptive analytical concept for 13C-based metabolic flux analysis based on secreted protein as an information carrier for isotope mapping in the protein-bound amino acids. This “metabolic flux probe” (MFP) concept was investigated in different cultivation set-ups with a recombinant, protein-secreting yeast strain. The obtained results grant insight into intracellular protein turnover dynamics. Experiments under metabolic but isotopically nonstationary conditions in continuous glucose-limited chemostats at high dilution rates demonstrated faster incorporation of isotope information from labeled glucose into the recombinant reporter protein than in biomass-bound protein. Our results suggest that the reporter protein was polymerized from intracellular amino acid pools with higher turnover rates than biomass-bound protein. The latter aspect might be vital for 13C-flux analyses under isotopically nonstationary conditions for analyzing fast metabolic dynamics.
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18
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Wiechert W, Nöh K. Quantitative Metabolic Flux Analysis Based on Isotope Labeling. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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19
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Becker J, Wittmann C. Metabolic Engineering of
Corynebacterium glutamicum. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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20
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Yuzawa T, Shirai T, Orishimo R, Kawai K, Kondo A, Hirasawa T. 13C-metabolic flux analysis in glycerol-assimilating strains of Saccharomyces cerevisiae. J GEN APPL MICROBIOL 2021; 67:142-149. [PMID: 33967166 DOI: 10.2323/jgam.2020.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Glycerol is an attractive raw material for the production of useful chemicals using microbial cells. We previously identified metabolic engineering targets for the improvement of glycerol assimilation ability in Saccharomyces cerevisiae based on adaptive laboratory evolution (ALE) and transcriptome analysis of the evolved cells. We also successfully improved glycerol assimilation ability by the disruption of the RIM15 gene encoding a Greatwall protein kinase together with overexpression of the STL1 gene encoding the glycerol/H+ symporter. To understand glycerol assimilation metabolism in the evolved glycerol-assimilating strains and STL1-overexpressing RIM15 disruptant, we performed metabolic flux analysis using 13C-labeled glycerol. Significant differences in metabolic flux distributions between the strains obtained from the culture after 35 and 85 generations in ALE were not found, indicating that metabolic flux changes might occur in the early phase of ALE (i.e., before 35 generations at least). Similarly, metabolic flux distribution was not significantly changed by RIM15 gene disruption. However, fluxes for the lower part of glycolysis and the TCA cycle were larger and, as a result, flux for the pentose phosphate pathway was smaller in the STL1-overexpressing RIM15 disruptant than in the strain obtained from the culture after 85 generations in ALE. It could be effective to increase flux for the pentose phosphate pathway to improve the glycerol assimilation ability in S. cerevisiae.
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Affiliation(s)
- Taiji Yuzawa
- School of Life Science and Technology, Tokyo Institute of Technology
| | | | | | - Kazuki Kawai
- School of Life Science and Technology, Tokyo Institute of Technology
| | - Akihiko Kondo
- Center for Sustainable Resource Science, RIKEN.,Graduate School of Science, Technology and Innovation, Kobe University
| | - Takashi Hirasawa
- School of Life Science and Technology, Tokyo Institute of Technology
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21
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Audano M, Pedretti S, Ligorio S, Giavarini F, Caruso D, Mitro N. Investigating metabolism by mass spectrometry: From steady state to dynamic view. JOURNAL OF MASS SPECTROMETRY : JMS 2021; 56:e4658. [PMID: 33084147 DOI: 10.1002/jms.4658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/10/2020] [Accepted: 09/14/2020] [Indexed: 06/11/2023]
Abstract
Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.
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Affiliation(s)
- Matteo Audano
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Silvia Pedretti
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Simona Ligorio
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Flavio Giavarini
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Donatella Caruso
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Nico Mitro
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
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22
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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23
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Wang Y, Wondisford FE, Song C, Zhang T, Su X. Metabolic Flux Analysis-Linking Isotope Labeling and Metabolic Fluxes. Metabolites 2020; 10:metabo10110447. [PMID: 33172051 PMCID: PMC7694648 DOI: 10.3390/metabo10110447] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 01/02/2023] Open
Abstract
Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.
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Affiliation(s)
- Yujue Wang
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA; (Y.W.); (F.E.W.)
- Metabolomics Shared Resource, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Fredric E. Wondisford
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA; (Y.W.); (F.E.W.)
| | - Chi Song
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, USA;
| | - Teng Zhang
- Department of Mathematics, University of Central Florida, Orlando, FL 32816, USA;
| | - Xiaoyang Su
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA; (Y.W.); (F.E.W.)
- Metabolomics Shared Resource, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
- Correspondence: ; Tel.: +1-732-235-5447
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24
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Tivendale ND, Hanson AD, Henry CS, Hegeman AD, Millar AH. Enzymes as Parts in Need of Replacement - and How to Extend Their Working Life. TRENDS IN PLANT SCIENCE 2020; 25:661-669. [PMID: 32526171 DOI: 10.1016/j.tplants.2020.02.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 02/11/2020] [Accepted: 02/14/2020] [Indexed: 06/11/2023]
Abstract
Enzymes catalyze reactions in vivo at different rates and each enzyme molecule has a lifetime limit before it is degraded and replaced to enable catalysis to continue. Considering these rates together as a unitless ratio of catalytic cycles until replacement (CCR) provides a new quantitative tool to assess the replacement schedule of and energy investment into enzymes as they relate to function. Here, we outline the challenges of determining CCRs and new approaches to overcome them and then assess the CCRs of selected enzymes in bacteria and plants to reveal a range of seven orders of magnitude for this ratio. Modifying CCRs in plants holds promise to lower cellular costs, to tailor enzymes for particular environments, and to breed enzyme improvements for crop productivity.
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Affiliation(s)
- Nathan D Tivendale
- ARC Centre for Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, M316, Perth, WA 6009, Australia
| | - Andrew D Hanson
- Horticultural Sciences Department, University of Florida, PO Box 110690, Gainesville, FL 32611-0690, USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA; Computation Institute, The University of Chicago, Chicago, IL 60637, USA
| | - Adrian D Hegeman
- Department of Horticultural Science, Department of Plant and Microbial Biology, and The Microbial and Plant Genomics Institute, University of Minnesota, 1970 Folwell Avenue, Saint Paul, MN 55108-6007, USA
| | - A Harvey Millar
- ARC Centre for Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, M316, Perth, WA 6009, Australia.
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25
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Zhang Z, Liu Z, Meng Y, Chen Z, Han J, Wei Y, Shen T, Yi Y, Xie X. Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale. BIOTECHNOLOGY FOR BIOFUELS 2020; 13:103. [PMID: 32523616 PMCID: PMC7278083 DOI: 10.1186/s13068-020-01737-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 05/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network. RESULT Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling. CONCLUSION This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.
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Affiliation(s)
- Zhengdong Zhang
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou China
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| | - Zhentao Liu
- College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou China
| | - Yafei Meng
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou China
| | - Zhen Chen
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, Guizhou China
| | - Jiayu Han
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, Guizhou China
| | - Yimin Wei
- School of Mathematics Sciences and Key Laboratory of Mathematics for Nonlinear Sciences, Fudan University, Shanghai, China
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| | - Yin Yi
- College of Life Science, Guizhou Normal University, Guiyang, Guizhou China
| | - Xiaoyao Xie
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
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26
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Im D, Hong J, Gu B, Sung C, Oh M. 13
C Metabolic Flux Analysis of
Escherichia coli
Engineered for Gamma‐Aminobutyrate Production. Biotechnol J 2020; 15:e1900346. [DOI: 10.1002/biot.201900346] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/12/2020] [Indexed: 12/28/2022]
Affiliation(s)
- Dae‐Kyun Im
- Department of Chemical and Biological EngineeringKorea University 145 Anam‐ro, Seongbuk‐gu Seoul 02841 Korea
| | - Jaeseung Hong
- Department of Chemical and Biological EngineeringKorea University 145 Anam‐ro, Seongbuk‐gu Seoul 02841 Korea
| | - Boncheol Gu
- Department of Chemical and Biological EngineeringKorea University 145 Anam‐ro, Seongbuk‐gu Seoul 02841 Korea
| | - Changmin Sung
- Doping Control CenterKorea Institute of Science and Technology 5 Hwarang‐ro 14‐gil, Seongbuk‐gu Seoul 02792 Korea
| | - Min‐Kyu Oh
- Department of Chemical and Biological EngineeringKorea University 145 Anam‐ro, Seongbuk‐gu Seoul 02841 Korea
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27
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Quek LE, Krycer JR, Ohno S, Yugi K, Fazakerley DJ, Scalzo R, Elkington SD, Dai Z, Hirayama A, Ikeda S, Shoji F, Suzuki K, Locasale JW, Soga T, James DE, Kuroda S. Dynamic 13C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin. iScience 2020; 23:100855. [PMID: 32058966 PMCID: PMC7005519 DOI: 10.1016/j.isci.2020.100855] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 11/26/2019] [Accepted: 01/15/2020] [Indexed: 12/22/2022] Open
Abstract
Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.
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Affiliation(s)
- Lake-Ee Quek
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.
| | - James R Krycer
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Satoshi Ohno
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Katsuyuki Yugi
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan; YCI Laboratory for Trans-Omics, Young Chief Investigator Program, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan
| | - Daniel J Fazakerley
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Richard Scalzo
- Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia
| | - Sarah D Elkington
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Ziwei Dai
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Duke University, Durham, NC 27710, USA
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan; AMED-CREST, AMED, 1-7-1 Otemachi, Chiyoda-Ku, Tokyo 100-0004, Japan
| | - Satsuki Ikeda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Futaba Shoji
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Kumi Suzuki
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Jason W Locasale
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Duke University, Durham, NC 27710, USA
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan; AMED-CREST, AMED, 1-7-1 Otemachi, Chiyoda-Ku, Tokyo 100-0004, Japan
| | - David E James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia.
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan; CREST, Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan.
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Godard T, Zühlke D, Richter G, Wall M, Rohde M, Riedel K, Poblete-Castro I, Krull R, Biedendieck R. Metabolic Rearrangements Causing Elevated Proline and Polyhydroxybutyrate Accumulation During the Osmotic Adaptation Response of Bacillus megaterium. Front Bioeng Biotechnol 2020; 8:47. [PMID: 32161752 PMCID: PMC7053513 DOI: 10.3389/fbioe.2020.00047] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Accepted: 01/21/2020] [Indexed: 12/15/2022] Open
Abstract
For many years now, Bacillus megaterium serves as a microbial workhorse for the high-level production of recombinant proteins in the g/L-scale. However, efficient and stable production processes require the knowledge of the molecular adaptation strategies of the host organism to establish optimal environmental conditions. Here, we interrogated the osmotic stress response of B. megaterium using transcriptome, proteome, metabolome, and fluxome analyses. An initial transient adaptation consisted of potassium import and glutamate counterion synthesis. The massive synthesis of the compatible solute proline constituted the second longterm adaptation process. Several stress response enzymes involved in iron scavenging and reactive oxygen species (ROS) fighting proteins showed higher levels under prolonged osmotic stress induced by 1.8 M NaCl. At the same time, the downregulation of the expression of genes of the upper part of glycolysis resulted in the activation of the pentose phosphate pathway (PPP), generating an oversupply of NADPH. The increased production of lactate accompanied by the reduction of acetate secretion partially compensate for the unbalanced (NADH/NAD+) ratio. Besides, the tricarboxylic acid cycle (TCA) mainly supplies the produced NADH, as indicated by the higher mRNA and protein levels of involved enzymes, and further confirmed by 13C flux analyses. As a consequence of the metabolic flux toward acetyl-CoA and the generation of an excess of NADPH, B. megaterium redirected the produced acetyl-CoA toward the polyhydroxybutyrate (PHB) biosynthetic pathway accumulating around 30% of the cell dry weight (CDW) as PHB. This direct relation between osmotic stress and intracellular PHB content has been evidenced for the first time, thus opening new avenues for synthesizing this valuable biopolymer using varying salt concentrations under non-limiting nutrient conditions.
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Affiliation(s)
- Thibault Godard
- Institute of Biochemical Engineering, Technische Universität Braunschweig, Braunschweig, Germany
| | - Daniela Zühlke
- Institute of Microbiology, Universität Greifswald, Greifswald, Germany
| | - Georg Richter
- Institute of Biochemical Engineering, Technische Universität Braunschweig, Braunschweig, Germany
| | - Melanie Wall
- Institute of Biochemical Engineering, Technische Universität Braunschweig, Braunschweig, Germany
| | - Manfred Rohde
- Central Facility for Microscopy, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Katharina Riedel
- Institute of Microbiology, Universität Greifswald, Greifswald, Germany
| | - Ignacio Poblete-Castro
- Biosystems Engineering Laboratory, Center for Bioinformatics and Integrative Biology, Faculty of Life Sciences, Universidad Andres Bello, Santiago, Chile
| | - Rainer Krull
- Institute of Biochemical Engineering, Technische Universität Braunschweig, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.,Center of Pharmaceutical Engineering (PVZ), Technische Universität Braunschweig, Braunschweig, Germany
| | - Rebekka Biedendieck
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.,Institute of Microbiology, Technische Universität Braunschweig, Braunschweig, Germany
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Venayak N, Raj K, Mahadevan R. Impact framework: A python package for writing data analysis workflows to interpret microbial physiology. Metab Eng Commun 2019; 9:e00089. [PMID: 31011536 PMCID: PMC6462781 DOI: 10.1016/j.mec.2019.e00089] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 03/19/2019] [Accepted: 03/19/2019] [Indexed: 12/26/2022] Open
Abstract
Microorganisms can be genetically engineered to solve a range of challenges in diverse including health, environmental protection and sustainability. The natural complexity of biological systems makes this an iterative cycle, perturbing metabolism and making stepwise progress toward a desired phenotype through four major stages: design, build, test, and data interpretation. This cycle has been accelerated by advances in molecular biology (e.g. robust DNA synthesis and assembly techniques), liquid handling automation and scale-down characterization platforms, generating large heterogeneous data sets. Here, we present an extensible Python package for scientists and engineers working with large biological data sets to interpret, model, and visualize data: the IMPACT (Integrated Microbial Physiology: Analysis, Characterization and Translation) framework. Impact aims to ease the development of Python-based data analysis workflows for a range of stakeholders in the bioengineering process, offering open-source tools for data analysis, physiology characterization and translation to visualization. Using this framework, biologists and engineers can opt for reproducible and extensible programmatic data analysis workflows, mediating a bottleneck limiting the throughput of microbial engineering. The Impact framework is available at https://github.com/lmse/impact.
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Affiliation(s)
- Naveen Venayak
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada
| | - Kaushik Raj
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
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30
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Bhadra S, Blomberg P, Castillo S, Rousu J. Principal metabolic flux mode analysis. Bioinformatics 2019; 34:2409-2417. [PMID: 29420676 PMCID: PMC6041797 DOI: 10.1093/bioinformatics/bty049] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 02/06/2018] [Indexed: 01/01/2023] Open
Abstract
Motivation In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples. Results We propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches. Availability and implementation Matlab software for PMFA and SPMFA and dataset used for experiments are available in https://github.com/aalto-ics-kepaco/PMFA. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sahely Bhadra
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.,Computer Science and Engineering, Indian Institute of Technology, Palakkad, India
| | - Peter Blomberg
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Sandra Castillo
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Juho Rousu
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland
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31
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GC-MS-based 13C metabolic flux analysis resolves the parallel and cyclic glucose metabolism of Pseudomonas putida KT2440 and Pseudomonas aeruginosa PAO1. Metab Eng 2019; 54:35-53. [DOI: 10.1016/j.ymben.2019.01.008] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 01/16/2019] [Accepted: 01/16/2019] [Indexed: 01/05/2023]
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32
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High extracellular lactate causes reductive carboxylation in breast tissue cell lines grown under normoxic conditions. PLoS One 2019; 14:e0213419. [PMID: 31181081 PMCID: PMC6557470 DOI: 10.1371/journal.pone.0213419] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 05/27/2019] [Indexed: 11/19/2022] Open
Abstract
In cancer tumors, lactate accumulation was initially attributed to high glucose consumption associated with the Warburg Effect. Now it is evident that lactate can also serve as an energy source in cancer cell metabolism. Additionally, lactate has been shown to promote metastasis, generate gene expression patterns in cancer cells consistent with "cancer stem cell" phenotypes, and result in treatment resistant tumors. Therefore, the goal of this work was to quantify the impact of lactate on metabolism in three breast cell lines (one normal and two breast cancer cell lines-MCF 10A, MCF7, and MDA-MB-231), in order to better understand the role lactate may have in different disease cell types. Parallel labeling metabolic flux analysis (13C-MFA) was used to quantify the intracellular fluxes under normal and high extracellular lactate culture conditions. Additionally, high extracellular lactate cultures were labelled in parallel with [U-13C] lactate, which provided qualitative information regarding the lactate uptake and metabolism. The 13C-MFA model, which incorporated the measured extracellular fluxes and the parallel labeling mass isotopomer distributions (MIDs) for five glycolysis, four tricarboxylic acid cycle (TCA), and three intracellular amino acid metabolites, predicted lower glycolysis fluxes in the high lactate cultures. All three cell lines experienced reductive carboxylation of glutamine to citrate in the TCA cycle as a result of high extracellular lactate. Reductive carboxylation previously has been observed under hypoxia and other mitochondrial stresses, whereas these cultures were grown aerobically. In addition, this is the first study to investigate the intracellular metabolic responses of different stages of breast cancer progression to high lactate exposure. These results provide insight into the role lactate accumulation has on metabolic reaction distributions in the different disease cell types while the cells are still proliferating in lactate concentrations that do not significantly decrease exponential growth rates.
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33
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Snail-Overexpression Induces Epithelial-mesenchymal Transition and Metabolic Reprogramming in Human Pancreatic Ductal Adenocarcinoma and Non-tumorigenic Ductal Cells. J Clin Med 2019; 8:jcm8060822. [PMID: 31181802 PMCID: PMC6617272 DOI: 10.3390/jcm8060822] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 06/05/2019] [Accepted: 06/05/2019] [Indexed: 12/30/2022] Open
Abstract
The zinc finger transcription factor Snail is a known effector of epithelial-to-mesenchymal transition (EMT), a process that underlies the enhanced invasiveness and chemoresistance of common to cancerous cells. Induction of Snail-driven EMT has also been shown to drive a range of pro-survival metabolic adaptations in different cancers. In the present study, we sought to determine the specific role that Snail has in driving EMT and adaptive metabolic programming in pancreatic ductal adenocarcinoma (PDAC) by overexpressing Snail in a PDAC cell line, Panc1, and in immortalized, non-tumorigenic human pancreatic ductal epithelial (HPDE) cells. Snail overexpression was able to induce EMT in both pancreatic cell lines through suppression of epithelial markers and upregulation of mesenchymal markers alongside changes in cell morphology and enhanced migratory capacity. Snail-overexpressed pancreatic cells additionally displayed increased glucose uptake and lactate production with concomitant reduction in oxidative metabolism measurements. Snail overexpression reduced maximal respiration in both Panc1 and HPDE cells, with further reductions seen in ATP production, spare respiratory capacity and non-mitochondrial respiration in Snail overexpressing Panc1 cells. Accordingly, lower expression of mitochondrial electron transport chain proteins was observed with Snail overexpression, particularly within Panc1 cells. Modelling of 13C metabolite flux within both cell lines revealed decreased carbon flux from glucose in the TCA cycle in snai1-overexpressing Panc1 cells only. This work further highlights the role that Snail plays in EMT and demonstrates its specific effects on metabolic reprogramming of glucose metabolism in PDAC.
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34
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Beyß M, Azzouzi S, Weitzel M, Wiechert W, Nöh K. The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis. Front Microbiol 2019; 10:1022. [PMID: 31178829 PMCID: PMC6543931 DOI: 10.3389/fmicb.2019.01022] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 04/24/2019] [Indexed: 12/16/2022] Open
Abstract
13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.
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Affiliation(s)
- Martin Beyß
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Salah Azzouzi
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Michael Weitzel
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
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35
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Wu C, Chen CH, Lo J, Michener W, Maness P, Xiong W. EMUlator: An Elementary Metabolite Unit (EMU) Based Isotope Simulator Enabled by Adjacency Matrix. Front Microbiol 2019; 10:922. [PMID: 31114561 PMCID: PMC6503117 DOI: 10.3389/fmicb.2019.00922] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Accepted: 04/11/2019] [Indexed: 11/13/2022] Open
Abstract
Stable isotope based metabolic flux analysis is currently the unique methodology that allows the experimental study of the integrated responses of metabolic networks. This method primarily relies on isotope labeling and modeling, which could be a challenge in both experimental and computational biology. In particular, the algorithm implementation for isotope simulation is a critical step, limiting extensive usage of this powerful approach. Here, we introduce EMUlator a Python-based isotope simulator which is developed on Elementary Metabolite Unit (EMU) algorithm, an efficient and powerful algorithm for isotope modeling. We propose a novel adjacency matrix method to implement EMU modeling and exemplify it stepwise. This method is intuitively straightforward and can be conveniently mastered for various customized purposes. We apply this arithmetic pipeline to understand the phosphoketolase flux in the metabolic network of an industrial microbe Clostridium acetobutylicum. The resulting design enables a high-throughput and non-invasive approach for estimating phosphoketolase flux in vivo. Our computational insights allow the systematic design and prediction of isotope-based metabolic models and yield a comprehensive understanding of their limitations and potentials.
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Affiliation(s)
- Chao Wu
- National Renewable Energy Laboratory, Golden, CO, United States
| | | | - Jonathan Lo
- National Renewable Energy Laboratory, Golden, CO, United States
| | | | - PinChing Maness
- National Renewable Energy Laboratory, Golden, CO, United States
| | - Wei Xiong
- National Renewable Energy Laboratory, Golden, CO, United States
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36
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Lehnen M, Ebert BE, Blank LM. Elevated temperatures do not trigger a conserved metabolic network response among thermotolerant yeasts. BMC Microbiol 2019; 19:100. [PMID: 31101012 PMCID: PMC6525440 DOI: 10.1186/s12866-019-1453-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 04/09/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Thermotolerance is a highly desirable trait of microbial cell factories and has been the focus of extensive research. Yeast usually tolerate only a narrow temperature range and just two species, Kluyveromyces marxianus and Ogataea polymorpha have been described to grow at reasonable rates above 40 °C. However, the complex mechanisms of thermotolerance in yeast impede its full comprehension and the rare physiological data at elevated temperatures has so far not been matched with corresponding metabolic analyses. RESULTS To elaborate on the metabolic network response to increased fermentation temperatures of up to 49 °C, comprehensive physiological datasets of several Kluyveromyces and Ogataea strains were generated and used for 13C-metabolic flux analyses. While the maximum growth temperature was very similar in all investigated strains, the metabolic network response to elevated temperatures was not conserved among the different species. In fact, metabolic flux distributions were remarkably irresponsive to increasing temperatures in O. polymorpha, while the K. marxianus strains exhibited extensive flux rerouting at elevated temperatures. CONCLUSIONS While a clear mechanism of thermotolerance is not deducible from the fluxome level alone, the generated data can be valued as a knowledge repository for using temperature to modulate the metabolic activity towards engineering goals.
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Affiliation(s)
- Mathias Lehnen
- iAMB – Institute of Applied Microbiology, ABBt – Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, D-52074 Aachen, Germany
| | - Birgitta E. Ebert
- iAMB – Institute of Applied Microbiology, ABBt – Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, D-52074 Aachen, Germany
| | - Lars M. Blank
- iAMB – Institute of Applied Microbiology, ABBt – Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, D-52074 Aachen, Germany
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37
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Tomàs-Gamisans M, Ødum ASR, Workman M, Ferrer P, Albiol J. Glycerol metabolism of Pichia pastoris (Komagataella spp.) characterised by 13C-based metabolic flux analysis. N Biotechnol 2019; 50:52-59. [DOI: 10.1016/j.nbt.2019.01.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 01/11/2019] [Accepted: 01/13/2019] [Indexed: 12/12/2022]
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38
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Correia DM, Sargo CR, Silva AJ, Santos ST, Giordano RC, Ferreira EC, Zangirolami TC, Ribeiro MPA, Rocha I. Mapping Salmonella typhimurium pathways using 13C metabolic flux analysis. Metab Eng 2019; 52:303-314. [PMID: 30529284 DOI: 10.1016/j.ymben.2018.11.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 11/26/2018] [Accepted: 11/28/2018] [Indexed: 12/20/2022]
Abstract
In the last years, Salmonella has been extensively studied not only due to its importance as a pathogen, but also as a host to produce pharmaceutical compounds. However, the full exploitation of Salmonella as a platform for bioproduct delivery has been hampered by the lack of information about its metabolism. Genome-scale metabolic models can be valuable tools to delineate metabolic engineering strategies as long as they closely represent the actual metabolism of the target organism. In the present study, a 13C-MFA approach was applied to map the fluxes at the central carbon pathways of S. typhimurium LT2 growing at glucose-limited chemostat cultures. The experiments were carried out in a 2L bioreactor, using defined medium enriched with 20% 13C-labeled glucose. Metabolic flux distributions in central carbon pathways of S. typhimurium LT2 were estimated using OpenFLUX2 based on the labeling pattern of biomass protein hydrolysates together with biomass composition. The results suggested that pentose phosphate is used to catabolize glucose, with minor fluxes through glycolysis. In silico simulations, using Optflux and pFBA as simulation method, allowed to study the performance of the genome-scale metabolic model. In general, the accuracy of in silico simulations was improved by the superimposition of estimated intracellular fluxes to the existing genome-scale metabolic model, showing a better fitting to the experimental extracellular fluxes, whereas the intracellular fluxes of pentose phosphate and anaplerotic reactions were poorly described.
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Affiliation(s)
- Daniela M Correia
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Cintia R Sargo
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Adilson J Silva
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Sophia T Santos
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal
| | - Roberto C Giordano
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Eugénio C Ferreira
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal
| | - Teresa C Zangirolami
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Marcelo P A Ribeiro
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, SP 13565-905, Brazil
| | - Isabel Rocha
- CEB-Centre of Biological Engineering, University of Minho, Campus De Gualtar, Braga 4710-057, Portugal; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal.
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39
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Enhanced synthesis of medium-chain-length poly(3-hydroxyalkanoates) by inactivating the tricarboxylate transport system of Pseudomonas putida KT2440 and process development using waste vegetable oil. Process Biochem 2019. [DOI: 10.1016/j.procbio.2018.10.012] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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40
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Lin W, Huang M, Wang Z, Zhuang Y, Zhang S. Modelling steady state intercellular isotopic distributions with isotopomer decomposition units. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2018.09.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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41
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Choi J, Antoniewicz MR. Tandem Mass Spectrometry for 13C Metabolic Flux Analysis: Methods and Algorithms Based on EMU Framework. Front Microbiol 2019; 10:31. [PMID: 30733712 PMCID: PMC6353858 DOI: 10.3389/fmicb.2019.00031] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 01/09/2019] [Indexed: 02/01/2023] Open
Abstract
In the past two decades, 13C metabolic flux analysis (13C-MFA) has matured into a powerful and widely used scientific tool in metabolic engineering and systems biology. Traditionally, metabolic fluxes have been determined from measurements of isotopic labeling by means of mass spectrometry (MS) or nuclear magnetic resonance (NMR). In recent years, tandem MS has emerged as a new analytical technique that can provide additional information for high-resolution quantification of metabolic fluxes in complex biological systems. In this paper, we present recent advances in methods and algorithms for incorporating tandem MS measurements into existing 13C-MFA approaches that are based on the elementary metabolite units (EMU) framework. Specifically, efficient EMU-based algorithms are presented for simulating tandem MS data, tracing isotopic labeling in biochemical network models and for correcting tandem MS data for natural isotope abundances.
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Affiliation(s)
- Jungik Choi
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, United States
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, United States
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42
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Hollinshead W, He L, Tang YJ. 13C-Fingerprinting and Metabolic Flux Analysis of Bacterial Metabolisms. Methods Mol Biol 2019; 1927:215-230. [PMID: 30788795 DOI: 10.1007/978-1-4939-9142-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
13C-assisted metabolism analysis provides rigorous calculations of the intracellular reaction rates (i.e., fluxes) within the central metabolism of microbial hosts. This mapping of the intracellular network within microbes has proven to be essential for understanding the cell physiology. The approach is also a key to identifying central metabolic nodes, probing the rigidity of a metabolic network, revealing cofactor balances, and delineating hidden pathways. Here we present the methodology of using stable isotopic carbon substrates for both qualitative (13C-fingerprinting of functional pathways) and quantitative (Metabolic Flux Analysis) metabolism studies on bacterial species. In this methodology, we include step-by-step instructions to use the open source WUflux software for the steady-state flux calculations based on labeling information of amino acids or free metabolites.
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Affiliation(s)
- Whitney Hollinshead
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA
| | - Lian He
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA
| | - Yinjie J Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA.
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43
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David C, Schmid A, Bühler K. Cellular physiology controls photoautotrophic production of 1,2-propanediol from pools of CO2and glycogen. Biotechnol Bioeng 2018; 116:882-892. [DOI: 10.1002/bit.26883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 11/19/2018] [Accepted: 11/22/2018] [Indexed: 12/28/2022]
Affiliation(s)
- Christian David
- Department Solar Materials; Helmholtz Centre for Environmental Research-UFZ; Leipzig Germany
| | - Andreas Schmid
- Department Solar Materials; Helmholtz Centre for Environmental Research-UFZ; Leipzig Germany
| | - Katja Bühler
- Department Solar Materials; Helmholtz Centre for Environmental Research-UFZ; Leipzig Germany
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44
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Schwechheimer SK, Becker J, Wittmann C. Towards better understanding of industrial cell factories: novel approaches for 13C metabolic flux analysis in complex nutrient environments. Curr Opin Biotechnol 2018; 54:128-137. [DOI: 10.1016/j.copbio.2018.07.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/10/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022]
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45
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Ata Ö, Rebnegger C, Tatto NE, Valli M, Mairinger T, Hann S, Steiger MG, Çalık P, Mattanovich D. A single Gal4-like transcription factor activates the Crabtree effect in Komagataella phaffii. Nat Commun 2018; 9:4911. [PMID: 30464212 PMCID: PMC6249229 DOI: 10.1038/s41467-018-07430-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 10/31/2018] [Indexed: 12/13/2022] Open
Abstract
The Crabtree phenotype defines whether a yeast can perform simultaneous respiration and fermentation under aerobic conditions at high growth rates. It provides Crabtree positive yeasts an evolutionary advantage of consuming glucose faster and producing ethanol to outcompete other microorganisms in sugar rich environments. While a number of genetic events are associated with the emergence of the Crabtree effect, its evolution remains unresolved. Here we show that overexpression of a single Gal4-like transcription factor is sufficient to convert Crabtree-negative Komagataella phaffii (Pichia pastoris) into a Crabtree positive yeast. Upregulation of the glycolytic genes and a significant increase in glucose uptake rate due to the overexpression of the Gal4-like transcription factor leads to an overflow metabolism, triggering both short-term and long-term Crabtree phenotypes. This indicates that a single genetic perturbation leading to overexpression of one gene may have been sufficient as the first molecular event towards respiro-fermentative metabolism in the course of yeast evolution. Aerobic ethanol production, a phenomenon referred as Crabtree effect, allows yeast to outcompete other microorganisms in sugar rich environments. Here, the authors show that overexpression of a Gal4-like transcription factor can transform Komagataella phaffii from Crabtree effect negative to positive.
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Affiliation(s)
- Özge Ata
- Department of Biotechnology, University of Natural Resources and Life Sciences, 1190, Vienna, Austria.,Department of Biotechnology, Graduate School of Natural and Applied Sciences, Middle East Technical University, 06800, Ankara, Turkey
| | - Corinna Rebnegger
- Department of Biotechnology, University of Natural Resources and Life Sciences, 1190, Vienna, Austria.,CD-Laboratory for Growth-Decoupled Protein Production in Yeast, Department of Biotechnology, University of Natural Resources and Life Sciences, 1190, Vienna, Austria
| | - Nadine E Tatto
- Department of Biotechnology, University of Natural Resources and Life Sciences, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria.,School of Bioengineering, University of Applied Sciences FH-Campus, 1190, Vienna, Austria
| | - Minoska Valli
- Department of Biotechnology, University of Natural Resources and Life Sciences, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria
| | - Teresa Mairinger
- Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria.,Department of Chemistry, University of Natural Resources and Life Sciences, 1190, Vienna, Austria.,Swiss Federal Institute of Aquatic Science and Technology (EAWAG), 8600, Dübendorf, Switzerland
| | - Stephan Hann
- Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria.,Department of Chemistry, University of Natural Resources and Life Sciences, 1190, Vienna, Austria
| | - Matthias G Steiger
- Department of Biotechnology, University of Natural Resources and Life Sciences, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria
| | - Pınar Çalık
- Department of Biotechnology, Graduate School of Natural and Applied Sciences, Middle East Technical University, 06800, Ankara, Turkey.,Department of Chemical Engineering, Industrial Biotechnology and Metabolic Engineering Laboratory, Middle East Technical University, 06800, Ankara, Turkey
| | - Diethard Mattanovich
- Department of Biotechnology, University of Natural Resources and Life Sciences, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria.
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46
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Triebl A, Wenk MR. Analytical Considerations of Stable Isotope Labelling in Lipidomics. Biomolecules 2018; 8:biom8040151. [PMID: 30453585 PMCID: PMC6315579 DOI: 10.3390/biom8040151] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 11/12/2018] [Accepted: 11/13/2018] [Indexed: 12/26/2022] Open
Abstract
Over the last two decades, lipids have come to be understood as far more than merely components of cellular membranes and forms of energy storage, and are now also being implicated to play important roles in a variety of diseases, with lipid biomarker research one of the most widespread applications of lipidomic techniques both in research and in clinical settings. Stable isotope labelling has become a staple technique in the analysis of small molecule metabolism and dynamics, as it is the only experimental setup by which biosynthesis, remodelling and degradation of biomolecules can be directly measured. Using state-of-the-art analytical technologies such as chromatography-coupled high resolution tandem mass spectrometry, the stable isotope label can be precisely localized and quantified within the biomolecules. The application of stable isotope labelling to lipidomics is however complicated by the diversity of lipids and the complexity of the necessary data analysis. This article discusses key experimental aspects of stable isotope labelling in the field of mass spectrometry-based lipidomics, summarizes current applications and provides an outlook on future developments and potential.
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Affiliation(s)
- Alexander Triebl
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore; Singapore 117596, Singapore.
| | - Markus R Wenk
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore; Singapore 117596, Singapore.
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47
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Metabolically engineered Corynebacterium glutamicum for bio-based production of chemicals, fuels, materials, and healthcare products. Metab Eng 2018; 50:122-141. [DOI: 10.1016/j.ymben.2018.07.008] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 07/17/2018] [Accepted: 07/18/2018] [Indexed: 01/15/2023]
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48
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Cui J, Diao J, Sun T, Shi M, Liu L, Wang F, Chen L, Zhang W. 13C Metabolic Flux Analysis of Enhanced Lipid Accumulation Modulated by Ethanolamine in Crypthecodinium cohnii. Front Microbiol 2018; 9:956. [PMID: 29867861 PMCID: PMC5963191 DOI: 10.3389/fmicb.2018.00956] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 04/24/2018] [Indexed: 11/13/2022] Open
Abstract
The heterotrophic microalga Crypthecodinium cohnii has attracted considerable attention due to its capability of accumulating lipids with a high fraction of docosahexaenoic acid (DHA). In our previous study, ethanolamine (ETA) was identified as an effective chemical modulator for lipid accumulation in C. cohnii. In this study, to gain a better understanding of the lipid metabolism and mechanism for the positive effects of modulator ETA, metabolic flux analysis was performed using 13C-labeled glucose with and without 1 mM ETA modulator. The analysis of flux distribution showed that with the addition of ETA, flux in glycolysis pathway and citrate pyruvate cycle was strengthened while flux in pentose phosphate pathway was decreased. In addition, flux in TCA cycle was slightly decreased compared with the control without ETA. The enzyme activity of malic enzyme (ME) was significantly increased, suggesting that NADP+-dependent ME might be the major source of NADPH for lipid accumulation. The flux information obtained by this study could be valuable for the further efforts in improving lipid accumulation and DHA production in C. cohnii.
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Affiliation(s)
- Jinyu Cui
- Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, China.,Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
| | - Jinjin Diao
- Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, China.,Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
| | - Tao Sun
- Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, China.,Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
| | - Mengliang Shi
- Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, China.,Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
| | - Liangsen Liu
- Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, China.,Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
| | - Fangzhong Wang
- Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, China.,Center for Biosafety Research and Strategy, Tianjin University, Tianjin, China
| | - Lei Chen
- Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, China.,Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
| | - Weiwen Zhang
- Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, China.,Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China.,Center for Biosafety Research and Strategy, Tianjin University, Tianjin, China
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49
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Lysine production from the sugar alcohol mannitol: Design of the cell factory Corynebacterium glutamicum SEA-3 through integrated analysis and engineering of metabolic pathway fluxes. Metab Eng 2018; 47:475-487. [DOI: 10.1016/j.ymben.2018.04.019] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 04/09/2018] [Accepted: 04/24/2018] [Indexed: 11/30/2022]
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50
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Desai TS, Srivastava S. FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses. PeerJ 2018; 6:e4716. [PMID: 29736347 PMCID: PMC5933345 DOI: 10.7717/peerj.4716] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 04/13/2018] [Indexed: 02/02/2023] Open
Abstract
13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package.
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Affiliation(s)
- Trunil S Desai
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India.,DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India
| | - Shireesh Srivastava
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India.,DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India
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