1
|
Meijer NWF, Zwakenberg S, Gerrits J, Westland D, Ardisasmita AI, Fuchs SA, Verhoeven-Duif NM, Jans JJM, Zwartkruis FJT. Direct Infusion Mass Spectrometry to Rapidly Map Metabolic Flux of Substrates Labeled with Stable Isotopes. Metabolites 2024; 14:246. [PMID: 38786724 PMCID: PMC11122925 DOI: 10.3390/metabo14050246] [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: 03/11/2024] [Revised: 04/16/2024] [Accepted: 04/20/2024] [Indexed: 05/25/2024] Open
Abstract
Direct infusion-high-resolution mass spectrometry (DI-HRMS) allows for rapid profiling of complex mixtures of metabolites in blood, cerebrospinal fluid, tissue samples and cultured cells. Here, we present a DI-HRMS method suitable for the rapid determination of metabolic fluxes of isotopically labeled substrates in cultured cells and organoids. We adapted an automated annotation pipeline by selecting labeled adducts that best represent the majority of 13C and/or 15N-labeled glycolytic and tricarboxylic acid cycle intermediates as well as a number of their derivatives. Furthermore, valine, leucine and several of their degradation products were included. We show that DI-HRMS can determine anticipated and unanticipated alterations in metabolic fluxes along these pathways that result from the genetic alteration of single metabolic enzymes, including pyruvate dehydrogenase (PDHA1) and glutaminase (GLS). In addition, it can precisely pinpoint metabolic adaptations to the loss of methylmalonyl-CoA mutase in patient-derived liver organoids. Our results highlight the power of DI-HRMS in combination with stable isotopically labeled compounds as an efficient screening method for fluxomics.
Collapse
Affiliation(s)
- Nils W. F. Meijer
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Susan Zwakenberg
- Center for Molecular Medicine, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands (D.W.); (F.J.T.Z.)
| | - Johan Gerrits
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Denise Westland
- Center for Molecular Medicine, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands (D.W.); (F.J.T.Z.)
| | - Arif I. Ardisasmita
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Sabine A. Fuchs
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Nanda M. Verhoeven-Duif
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Judith J. M. Jans
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Fried J. T. Zwartkruis
- Center for Molecular Medicine, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands (D.W.); (F.J.T.Z.)
| |
Collapse
|
2
|
Fukuyama Y, Shimamura S, Sakai S, Michimori Y, Sumida T, Chikaraishi Y, Atomi H, Nunoura T. Development of a rapid and highly accurate method for 13C tracer-based metabolomics and its application on a hydrogenotrophic methanogen. ISME COMMUNICATIONS 2024; 4:ycad006. [PMID: 38282645 PMCID: PMC10809761 DOI: 10.1093/ismeco/ycad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 01/30/2024]
Abstract
Microfluidic capillary electrophoresis-mass spectrometry (CE-MS) is a rapid and highly accurate method to determine isotopomer patterns in isotopically labeled compounds. Here, we developed a novel method for tracer-based metabolomics using CE-MS for underivatized proteinogenic amino acids. The method consisting of a ZipChip CE system and a high-resolution Orbitrap Fusion Tribrid mass spectrometer allows us to obtain highly accurate data from 1 μl of 100 nmol/l amino acids comparable to a mere 1 [Formula: see text] 104-105 prokaryotic cells. To validate the capability of the CE-MS method, we analyzed 16 protein-derived amino acids from a methanogenic archaeon Methanothermobacter thermautotrophicus as a model organism, and the mass spectra showed sharp peaks with low mass errors and background noise. Tracer-based metabolome analysis was then performed to identify the central carbon metabolism in M. thermautotrophicus using 13C-labeled substrates. The mass isotopomer distributions of serine, aspartate, and glutamate revealed the occurrence of both the Wood-Ljungdahl pathway and an incomplete reductive tricarboxylic acid cycle for carbon fixation. In addition, biosynthesis pathways of 15 amino acids were constructed based on the mass isotopomer distributions of the detected protein-derived amino acids, genomic information, and public databases. Among them, the presence of alternative enzymes of alanine dehydrogenase, ornithine cyclodeaminase, and homoserine kinase was suggested in the biosynthesis pathways of alanine, proline, and threonine, respectively. To our knowledge, the novel 13C tracer-based metabolomics using CE-MS can be considered the most efficient method to identify central carbon metabolism and amino acid biosynthesis pathways and is applicable to any kind of isolated microbe.
Collapse
Affiliation(s)
- Yuto Fukuyama
- Research Center for Bioscience and Nanoscience (CeBN), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 2–15 Natsushima-cho, Yokosuka, Kanagawa 237–0061, Japan
| | - Shigeru Shimamura
- Institute for Extra-Cutting-Edge Science and Technology Avant-Garde Research (X-star), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 2–15 Natsushima-cho, Yokosuka, Kanagawa 237–0061, Japan
| | - Sanae Sakai
- Institute for Extra-Cutting-Edge Science and Technology Avant-Garde Research (X-star), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 2–15 Natsushima-cho, Yokosuka, Kanagawa 237–0061, Japan
| | - Yuta Michimori
- Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Katsura, Nishikyo-ku, Kyoto 615-8510, Japan
| | - Tomomi Sumida
- Research Center for Bioscience and Nanoscience (CeBN), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 2–15 Natsushima-cho, Yokosuka, Kanagawa 237–0061, Japan
| | - Yoshito Chikaraishi
- Institute of Low Temperature Science, Hokkaido University, Kita-19, Nishi-8, Kita-ku, Sapporo 060-0819, Japan
| | - Haruyuki Atomi
- Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Katsura, Nishikyo-ku, Kyoto 615-8510, Japan
| | - Takuro Nunoura
- Research Center for Bioscience and Nanoscience (CeBN), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 2–15 Natsushima-cho, Yokosuka, Kanagawa 237–0061, Japan
| |
Collapse
|
3
|
Law RC, O’Keeffe S, Nurwono G, Ki R, Lakhani A, Lai PK, Park JO. Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.06.565907. [PMID: 37986781 PMCID: PMC10659294 DOI: 10.1101/2023.11.06.565907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.
Collapse
Affiliation(s)
- Richard C. Law
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
| | - Samantha O’Keeffe
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
| | - Glenn Nurwono
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095
| | - Rachel Ki
- Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA 90095
| | - Aliya Lakhani
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
| | - Pin-Kuang Lai
- Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ 07030
| | - Junyoung O. Park
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
- California NanoSystems Institute and Jonsson Comprehensive Cancer Center at UCLA, Los Angeles, CA 90095
| |
Collapse
|
4
|
Mirveis Z, Howe O, Cahill P, Patil N, Byrne HJ. Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives. Metabolomics 2023; 19:67. [PMID: 37482587 PMCID: PMC10363518 DOI: 10.1007/s11306-023-02031-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge. AIM This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway. KEY SCIENTIFIC CONCEPTS Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.
Collapse
Affiliation(s)
- Zohreh Mirveis
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland.
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological, Health and Sport Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
| |
Collapse
|
5
|
Roell G, Schenk C, Anthony WE, Carr RR, Ponukumati A, Kim J, Akhmatskaya E, Foston M, Dantas G, Moon TS, Tang YJ, García Martín H. A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism. ACS Synth Biol 2023; 12:1632-1644. [PMID: 37186551 PMCID: PMC10278598 DOI: 10.1021/acssynbio.2c00618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Indexed: 05/17/2023]
Abstract
Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.
Collapse
Affiliation(s)
- Garrett
W. Roell
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Christina Schenk
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
| | - Winston E. Anthony
- The Edison
Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, United States
- Department
of Pathology and Immunology, Washington
University in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
| | - Rhiannon R. Carr
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Aditya Ponukumati
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Joonhoon Kim
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
- DOE
Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - Elena Akhmatskaya
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
- IKERBASQUE,
Basque Foundation for Science, Bilbao 48009, Spain
| | - Marcus Foston
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Gautam Dantas
- The Edison
Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, United States
- Department
of Pathology and Immunology, Washington
University in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
- Department
of Biomedical Engineering, Washington University
in St. Louis, St Louis, Missouri 63130, United States
- Department
of Molecular Microbiology, Washington University
in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
- Department
of Pediatrics, Washington University School
of Medicine in St Louis, St Louis, Missouri 63110, United States
| | - Tae Seok Moon
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Yinjie J. Tang
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Hector García Martín
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
- DOE
Joint BioEnergy Institute, Emeryville, California 94608, United States
| |
Collapse
|
6
|
Sheng Q, Yi L, Zhong B, Wu X, Liu L, Zhang B. Shikimic acid biosynthesis in microorganisms: Current status and future direction. Biotechnol Adv 2023; 62:108073. [PMID: 36464143 DOI: 10.1016/j.biotechadv.2022.108073] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/03/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022]
Abstract
Shikimic acid (SA), a hydroaromatic natural product, is used as a chiral precursor for organic synthesis of oseltamivir (Tamiflu®, an antiviral drug). The process of microbial production of SA has recently undergone vigorous development. Particularly, the sustainable construction of recombinant Corynebacterium glutamicum (141.2 g/L) and Escherichia coli (87 g/L) laid a solid foundation for the microbial fermentation production of SA. However, its industrial application is restricted by limitations such as the lack of fermentation tests for industrial-scale and the requirement of growth-limiting factors, antibiotics, and inducers. Therefore, the development of SA biosensors and dynamic molecular switches, as well as genetic modification strategies and optimization of the fermentation process based on omics technology could improve the performance of SA-producing strains. In this review, recent advances in the development of SA-producing strains, including genetic modification strategies, metabolic pathway construction, and biosensor-assisted evolution, are discussed and critically reviewed. Finally, future challenges and perspectives for further reinforcing the development of robust SA-producing strains are predicted, providing theoretical guidance for the industrial production of SA.
Collapse
Affiliation(s)
- Qi Sheng
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China
| | - Lingxin Yi
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China
| | - Bin Zhong
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China
| | - Xiaoyu Wu
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
| | - Bin Zhang
- College of Bioscience and Bioengineering, Jiangxi Agricultural University, Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Nanchang 330045, China; Jiangxi Engineering Laboratory for the Development and Utilization of Agricultural Microbial Resources, Jiangxi Agricultural University, Nanchang 330045, China.
| |
Collapse
|
7
|
Photobiological production of high-value pigments via compartmentalized co-cultures using Ca-alginate hydrogels. Sci Rep 2022; 12:22163. [PMID: 36550285 PMCID: PMC9780300 DOI: 10.1038/s41598-022-26437-y] [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: 09/23/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Engineered cyanobacterium Synechococcus elongatus can use light and CO2 to produce sucrose, making it a promising candidate for use in co-cultures with heterotrophic workhorses. However, this process is challenged by the mutual stresses generated from the multispecies microbial culture. Here we demonstrate an ecosystem where S. elongatus is freely grown in a photo-bioreactor (PBR) containing an engineered heterotrophic workhorse (either β-carotene-producing Yarrowia lipolytica or indigoidine-producing Pseudomonas putida) encapsulated in calcium-alginate hydrogel beads. The encapsulation prevents growth interference, allowing the cyanobacterial culture to produce high sucrose concentrations enabling the production of indigoidine and β-carotene in the heterotroph. Our experimental PBRs yielded an indigoidine titer of 7.5 g/L hydrogel and a β-carotene titer of 1.3 g/L hydrogel, amounts 15-22-fold higher than in a comparable co-culture without encapsulation. Moreover, 13C-metabolite analysis and protein overexpression tests indicated that the hydrogel beads provided a favorable microenvironment where the cell metabolism inside the hydrogel was comparable to that in a free culture. Finally, the heterotroph-containing hydrogels were easily harvested and dissolved by EDTA for product recovery, while the cyanobacterial culture itself could be reused for the next batch of immobilized heterotrophs. This co-cultivation and hydrogel encapsulation system is a successful demonstration of bioprocess optimization under photobioreactor conditions.
Collapse
|
8
|
Fast Sampling of the Cellular Metabolome. Methods Mol Biol 2021. [PMID: 34718989 DOI: 10.1007/978-1-0716-1585-0_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Obtaining meaningful snapshots of the metabolome of microorganisms requires rapid sampling and immediate quenching of all metabolic activity, to prevent any changes in metabolite levels after sampling. Furthermore, a suitable extraction method is required ensuring complete extraction of metabolites from the cells and inactivation of enzymatic activity, with minimal degradation of labile compounds. Finally, a sensitive, high-throughput analysis platform is needed to quantify a large number of metabolites in a small amount of sample. An issue which has often been overlooked in microbial metabolomics is the fact that many intracellular metabolites are also present in significant amounts outside the cells and may interfere with the quantification of the endo metabolome. Attempts to remove the extracellular metabolites with dedicated quenching methods often induce release of intracellular metabolites into the quenching solution. For eukaryotic microorganisms, this release can be minimized by adaptation of the quenching method. For prokaryotic cells, this has not yet been accomplished, so the application of a differential method whereby metabolites are measured in the culture supernatant as well as in total broth samples, to calculate the intracellular levels by subtraction, seems to be the most suitable approach. Here we present an overview of different sampling, quenching, and extraction methods developed for microbial metabolomics, described in the literature. Detailed protocols are provided for rapid sampling, quenching, and extraction, for measurement of metabolites in total broth samples, washed cell samples, and supernatant, to be applied for quantitative metabolomics of both eukaryotic and prokaryotic microorganisms.
Collapse
|
9
|
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]
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
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.
Collapse
|
12
|
Elucidation of photoautotrophic carbon flux topology in Synechocystis PCC 6803 using genome-scale carbon mapping models. Metab Eng 2018. [DOI: 10.1016/j.ymben.2018.03.008] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
13
|
Schwahn K, Nikoloski Z. Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism. FRONTIERS IN PLANT SCIENCE 2018; 9:538. [PMID: 29731765 PMCID: PMC5920133 DOI: 10.3389/fpls.2018.00538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 04/06/2018] [Indexed: 06/08/2023]
Abstract
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.
Collapse
Affiliation(s)
- Kevin Schwahn
- Systems Biology and Mathematical Modelling Group, Max Placnk Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modelling Group, Max Placnk Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| |
Collapse
|
14
|
Golubeva LI, Shupletsov MS, Mashko SV. Metabolic Flux Analysis Using 13C Isotopes (13C-MFA). 1. Experimental Basis of the Method and the Present State of Investigations. APPL BIOCHEM MICRO+ 2018. [DOI: 10.1134/s0003683817070031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
15
|
Backman TWH, Ando D, Singh J, Keasling JD, García Martín H. Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis. Metabolites 2018; 8:metabo8010003. [PMID: 29300340 PMCID: PMC5875993 DOI: 10.3390/metabo8010003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 12/23/2017] [Accepted: 01/02/2018] [Indexed: 12/19/2022] Open
Abstract
Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.
Collapse
Affiliation(s)
- Tyler W H Backman
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Agile BioFoundry, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
- QB3 Institute, University of California, Berkeley, CA 94720, USA.
| | - David Ando
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Agile BioFoundry, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
| | - Jahnavi Singh
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA.
- Department of Computer Science, University of California, Berkeley, CA 94720, USA.
| | - Jay D Keasling
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
- QB3 Institute, University of California, Berkeley, CA 94720, USA.
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA.
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2970 Horsholm, Denmark.
| | - Héctor García Martín
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Agile BioFoundry, 5885 Hollis Street, Emeryville, CA 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
| |
Collapse
|
16
|
Li H, Liang C, Chen W, Jin JM, Tang SY, Tao Y. Monitoring in vivo metabolic flux with a designed whole-cell metabolite biosensor of shikimic acid. Biosens Bioelectron 2017; 98:457-465. [DOI: 10.1016/j.bios.2017.07.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 06/01/2017] [Accepted: 07/08/2017] [Indexed: 01/24/2023]
|
17
|
Schatschneider S, Schneider J, Blom J, Létisse F, Niehaus K, Goesmann A, Vorhölter FJ. Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris. Microbiology (Reading) 2017; 163:1117-1144. [DOI: 10.1099/mic.0.000473] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Sarah Schatschneider
- Abteilung für Proteom und Metabolomforschung, Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Bielefeld, Germany
- Present address: Evonik Nutrition and Care GmbH, Kantstr. 2, 33790 Halle-Künsebeck, Germany
| | - Jessica Schneider
- Bioinformatics Resource Facility, Centrum für Biotechnologie, Universität Bielefeld, Germany
- Present address: Evonik Nutrition and Care GmbH, Kantstr. 2, 33790 Halle-Künsebeck, Germany
| | - Jochen Blom
- Bioinformatics and Systems Biology, Justus-Liebig-University Gießen, Germany
| | - Fabien Létisse
- LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France
| | - Karsten Niehaus
- Abteilung für Proteom und Metabolomforschung, Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Bielefeld, Germany
| | - Alexander Goesmann
- Bioinformatics and Systems Biology, Justus-Liebig-University Gießen, Germany
| | - Frank-Jörg Vorhölter
- Institut für Genomforschung und Systembiologie, Centrum für Biotechnology (CeBiTec), Universität Bielefeld, Bielefeld, Germany
- Present address: MVZ Dr. Eberhard & Partner Dortmund, Dortmund, Germany
| |
Collapse
|
18
|
Tang W, Deshmukh AT, Haringa C, Wang G, van Gulik W, van Winden W, Reuss M, Heijnen JJ, Xia J, Chu J, Noorman HJ. A 9-pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation byPenicillium chrysogenum. Biotechnol Bioeng 2017; 114:1733-1743. [DOI: 10.1002/bit.26294] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 02/26/2017] [Accepted: 03/14/2017] [Indexed: 12/30/2022]
Affiliation(s)
- Wenjun Tang
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | | | - Cees Haringa
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | - Walter van Gulik
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | | | - Matthias Reuss
- Institute of Biochemical Engineering; University of Stuttgart; Stuttgart Germany
| | - Joseph J. Heijnen
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | | |
Collapse
|
19
|
Nilsson R, Roci I, Watrous J, Jain M. Estimation of flux ratios without uptake or release data: Application to serine and methionine metabolism. Metab Eng 2017; 43:137-146. [PMID: 28232235 DOI: 10.1016/j.ymben.2017.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 02/09/2017] [Accepted: 02/13/2017] [Indexed: 02/06/2023]
Abstract
Model-based metabolic flux analysis (MFA) using isotope-labeled substrates has provided great insight into intracellular metabolic activities across a host of organisms. One challenge with applying MFA in mammalian systems, however, is the need for absolute quantification of nutrient uptake, biomass composition, and byproduct release fluxes. Such measurements are often not feasible in complex culture systems or in vivo. One way to address this issue is to estimate flux ratios, the fractional contribution of a flux to a metabolite pool, which are independent of absolute measurements and yet informative for cellular metabolism. Prior work has focused on "local" estimation of a handful of flux ratios for specific metabolites and reactions. Here, we perform systematic, model-based estimation of all flux ratios in a metabolic network using isotope labeling data, in the absence of uptake/release data. In a series of examples, we investigate what flux ratios can be well estimated with reasonably tight confidence intervals, and contrast this with confidence intervals on normalized fluxes. We find that flux ratios can provide useful information on the metabolic state, and is complementary to normalized fluxes: for certain metabolic reactions, only flux ratios can be well estimated, while for others normalized fluxes can be obtained. Simulation studies of a large human metabolic network model suggest that estimation of flux ratios is technically feasible for complex networks, but additional studies on data from actual isotopomer labeling experiments are needed to validate these results. Finally, we experimentally study serine and methionine metabolism in cancer cells using flux ratios. We find that, in these cells, the methionine cycle is truncated with little remethylation from homocysteine, and polyamine synthesis in the absence of methionine salvage leads to loss of 5-methylthioadenosine, suggesting a new mode of overflow metabolism in cancer cells. This work highlights the potential for flux ratio analysis in the absence of absolute quantification, which we anticipate will be important for both in vitro and in vivo studies of cancer metabolism.
Collapse
Affiliation(s)
- Roland Nilsson
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Irena Roci
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, SE-17176 Stockholm, Sweden
| | - Jeramie Watrous
- Departments of Medicine & Pharmacology, University of California, San Diego, USA
| | - Mohit Jain
- Departments of Medicine & Pharmacology, University of California, San Diego, USA
| |
Collapse
|
20
|
Guo W, Sheng J, Feng X. Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2017; 162:265-299. [PMID: 28424826 DOI: 10.1007/10_2017_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, 13C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.
Collapse
Affiliation(s)
- Weihua Guo
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Jiayuan Sheng
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Xueyang Feng
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA.
| |
Collapse
|
21
|
Martínez VS, Krömer JO. Quantification of Microbial Phenotypes. Metabolites 2016; 6:E45. [PMID: 27941694 PMCID: PMC5192451 DOI: 10.3390/metabo6040045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 12/05/2016] [Accepted: 12/06/2016] [Indexed: 11/16/2022] Open
Abstract
Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.
Collapse
Affiliation(s)
- Verónica S Martínez
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane 4072, Australia.
| | - Jens O Krömer
- Centre for Microbial Electrochemical Systems (CEMES), The University of Queensland, Brisbane 4072, Australia.
- Advanced Water Management Centre (AWMC), The University of Queensland, Brisbane 4072, Australia.
| |
Collapse
|
22
|
Recent advances in high-throughput 13C-fluxomics. Curr Opin Biotechnol 2016; 43:104-109. [PMID: 27838571 DOI: 10.1016/j.copbio.2016.10.010] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 10/21/2016] [Accepted: 10/25/2016] [Indexed: 12/11/2022]
Abstract
The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT 13C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT 13C-fluxomics workflows.
Collapse
|
23
|
Wu SG, Wang Y, Jiang W, Oyetunde T, Yao R, Zhang X, Shimizu K, Tang YJ, Bao FS. Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming. PLoS Comput Biol 2016; 12:e1004838. [PMID: 27092947 PMCID: PMC4836714 DOI: 10.1371/journal.pcbi.1004838] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Accepted: 03/01/2016] [Indexed: 12/17/2022] Open
Abstract
13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.
Collapse
Affiliation(s)
- Stephen Gang Wu
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Yuxuan Wang
- Department of Computer Science and Engineering, Ohio State University, Columbus, Ohio, United States of America
| | - Wu Jiang
- Boxed Wholesale, Edison, New Jersey, United States of America
| | - Tolutola Oyetunde
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Ruilian Yao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, People’s Republic of China
| | - Xuehong Zhang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, People’s Republic of China
| | - Kazuyuki Shimizu
- Institute of Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Yinjie J. Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
- * E-mail: (YJT); (FSB)
| | - Forrest Sheng Bao
- Department of Electrical and Computer Engineering, University of Akron, Akron, Ohio, United States of America
- * E-mail: (YJT); (FSB)
| |
Collapse
|
24
|
Wu SG, Shimizu K, Tang JKH, Tang YJ. Facilitate Collaborations among Synthetic Biology, Metabolic Engineering and Machine Learning. CHEMBIOENG REVIEWS 2016. [DOI: 10.1002/cben.201500024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
25
|
Metabolic Engineering for Production of Small Molecule Drugs: Challenges and Solutions. FERMENTATION-BASEL 2016. [DOI: 10.3390/fermentation2010004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
26
|
Wang J, Wu G, Chen L, Zhang W. Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressivity and Factors Affecting Translational Efficiency. Methods Mol Biol 2016; 1375:123-136. [PMID: 25762301 DOI: 10.1007/7651_2015_242] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Integrated analysis of large-scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. In this chapter, we present methods to address two aspects of issues related to integrated transcriptomic and proteomic analysis. First, due to the fact that proteomic datasets are often incomplete, and integrated analysis of partial proteomic data may introduce significant bias. To address these issues, we describe a zero-inflated Poisson (ZIP)-based model to uncover the complicated relationships between protein abundances and mRNA expression levels, and then apply them to predict protein abundance for the proteins not experimentally detected. The ZIP model takes into consideration the undetected proteins by assuming that there is a probability mass at zero representing expressed proteins that were undetected owing to technical limitations. The model validity is demonstrated using biological information of operons, regulons, and pathways. Second, weak correlation between transcriptomic and proteomic datasets is often due to biological factors affecting translational processes. To quantify the effects of these factors, we describe a multiple regression-based statistical framework to quantitatively examine the effects of various translational efficiency-related sequence features on mRNA-protein correlation. Using the datasets from sulfate-reducing bacteria Desulfovibrio vulgaris, the analysis shows that translation-related sequence features can contribute up to 15.2-26.2% of the total variation of the correlation between transcriptomic and proteomic datasets, and also reveals the relative importance of various features in translation process.
Collapse
Affiliation(s)
- Jiangxin Wang
- Laboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China
- Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, 300072, People's Republic of China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, People's Republic of China
| | - Gang Wu
- University of Maryland at Baltimore Country, Baltimore County, MD, USA
| | - Lei Chen
- Laboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China
- Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, 300072, People's Republic of China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, People's Republic of China
| | - Weiwen Zhang
- Laboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China.
- Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, 300072, People's Republic of China.
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, People's Republic of China.
| |
Collapse
|
27
|
13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production. Bioengineering (Basel) 2015; 3:bioengineering3010003. [PMID: 28952565 PMCID: PMC5597161 DOI: 10.3390/bioengineering3010003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 12/10/2015] [Accepted: 12/18/2015] [Indexed: 12/15/2022] Open
Abstract
Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many 13C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms
Collapse
|
28
|
Sokol S, Portais JC. Theoretical Basis for Dynamic Label Propagation in Stationary Metabolic Networks under Step and Periodic Inputs. PLoS One 2015; 10:e0144652. [PMID: 26641860 PMCID: PMC4671543 DOI: 10.1371/journal.pone.0144652] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Accepted: 11/20/2015] [Indexed: 11/19/2022] Open
Abstract
The dynamics of label propagation in a stationary metabolic network during an isotope labeling experiment can provide highly valuable information on the network topology, metabolic fluxes, and on the size of metabolite pools. However, major issues, both in the experimental set-up and in the accompanying numerical methods currently limit the application of this approach. Here, we propose a method to apply novel types of label inputs, sinusoidal or more generally periodic label inputs, to address both the practical and numerical challenges of dynamic labeling experiments. By considering a simple metabolic system, i.e. a linear, non-reversible pathway of arbitrary length, we develop mathematical descriptions of label propagation for both classical and novel label inputs. Theoretical developments and computer simulations show that the application of rectangular periodic pulses has both numerical and practical advantages over other approaches. We applied the strategy to estimate fluxes in a simulated experiment performed on a complex metabolic network (the central carbon metabolism of Escherichia coli), to further demonstrate its value in conditions which are close to those in real experiments. This study provides a theoretical basis for the rational interpretation of label propagation curves in real experiments, and will help identify the strengths, pitfalls and limitations of such experiments. The cases described here can also be used as test cases for more general numerical methods aimed at identifying network topology, analyzing metabolic fluxes or measuring concentrations of metabolites.
Collapse
Affiliation(s)
- Serguei Sokol
- Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, INSA, UPS, INP, Toulouse, France
- Laboratoire Ingénierie des Systèmes Biologiques et des Procédés, INRA UMR792, Toulouse, France
- UMR5504, CNRS, Toulouse, France
- * E-mail:
| | - Jean-Charles Portais
- Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, INSA, UPS, INP, Toulouse, France
- Laboratoire Ingénierie des Systèmes Biologiques et des Procédés, INRA UMR792, Toulouse, France
- UMR5504, CNRS, Toulouse, France
| |
Collapse
|
29
|
Gorietti D, Zanni E, Palleschi C, Delfini M, Uccelletti D, Saliola M, Puccetti C, Sobolev A, Mannina L, Miccheli A. 13C NMR based profiling unveils different α-ketoglutarate pools involved into glutamate and lysine synthesis in the milk yeast Kluyveromyces lactis. Biochim Biophys Acta Gen Subj 2015; 1850:2222-7. [DOI: 10.1016/j.bbagen.2015.07.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 07/01/2015] [Accepted: 07/22/2015] [Indexed: 12/26/2022]
|
30
|
Zhang W, Kolte R, Dill DL. Towards in vivo estimation of reaction kinetics using high-throughput metabolomics data: a maximum likelihood approach. BMC SYSTEMS BIOLOGY 2015; 9:66. [PMID: 26437964 PMCID: PMC4595320 DOI: 10.1186/s12918-015-0214-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 09/15/2015] [Indexed: 11/25/2022]
Abstract
Background High-throughput assays such as mass spectrometry have opened up the possibility for large-scale in vivo measurements of the metabolome. This data could potentially be used to estimate kinetic parameters for many metabolic reactions. However, high-throughput in vivo measurements have special properties that are not taken into account in existing methods for estimating kinetic parameters, including significant relative errors in measurements of metabolite concentrations and reaction rates, and reactions with multiple substrates and products, which are sometimes reversible. A new method is needed to estimate kinetic parameters taking into account these factors. Results A new method, InVEst (In Vivo Estimation), is described for estimating reaction kinetic parameters, which addresses the specific challenges of in vivo data. InVEst uses maximum likelihood estimation based on a model where all measurements have relative errors. Simulations show that InVEst produces accurate estimates for a reversible enzymatic reaction with multiple reactants and products, that estimated parameters can be used to predict the effects of genetic variants, and that InVEst is more accurate than general least squares and graphic methods on data with relative errors. InVEst uses the bootstrap method to evaluate the accuracy of its estimates. Conclusions InVEst addresses several challenges of in vivo data, which are not taken into account by existing methods. When data have relative errors, InVEst produces more accurate and robust estimates. InVEst also provides useful information about estimation accuracy using bootstrapping. It has potential applications of quantifying the effects of genetic variants, inference of the target of a mutation or drug treatment and improving flux estimation. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0214-7) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Weiruo Zhang
- Department of Electrical Engineering, Stanford University, 450 Serra Mall, Stanford, CA94305, USA.
| | - Ritesh Kolte
- Department of Electrical Engineering, Stanford University, 450 Serra Mall, Stanford, CA94305, USA.
| | - David L Dill
- Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA94305, USA.
| |
Collapse
|
31
|
Adebiyi AO, Jazmin LJ, Young JD. 13 C flux analysis of cyanobacterial metabolism. PHOTOSYNTHESIS RESEARCH 2015; 126:19-32. [PMID: 25280933 DOI: 10.1007/s11120-014-0045-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Accepted: 09/22/2014] [Indexed: 06/03/2023]
Abstract
(13)C metabolic flux analysis (MFA) has made important contributions to our understanding of the physiology of model strains of E. coli and yeast, and it has been widely used to guide metabolic engineering efforts in these microorganisms. Recent advancements in (13)C MFA methodology combined with publicly available software tools are creating new opportunities to extend this approach to examine less characterized microbes. In particular, growing interest in the use of cyanobacteria as industrial hosts for photosynthetic production of biofuels and biochemicals has led to a critical need to better understand how cyanobacterial metabolic fluxes are regulated in response to changes in growth conditions or introduction of heterologous pathways. In this contribution, we review several prior studies that have applied isotopic steady-state (13)C MFA to examine heterotrophic or mixotrophic growth of cyanobacteria, as well as recent studies that have pioneered the use of isotopically nonstationary MFA (INST-MFA) to study autotrophic cultures. We also provide recommendations for the design and analysis of INST-MFA experiments in cyanobacteria, based on our previous experience and a series of simulation studies used to assess the selection of measurements and sample time points. We anticipate that this emerging knowledgebase of prior (13)C MFA studies, optimized experimental protocols, and public software tools will catalyze increasing use of (13)C MFA techniques by the cyanobacteria research community.
Collapse
Affiliation(s)
- Adeola O Adebiyi
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Lara J Jazmin
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, 37235, USA.
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37235, USA.
| |
Collapse
|
32
|
García Martín H, Kumar VS, Weaver D, Ghosh A, Chubukov V, Mukhopadhyay A, Arkin A, Keasling JD. A Method to Constrain Genome-Scale Models with 13C Labeling Data. PLoS Comput Biol 2015; 11:e1004363. [PMID: 26379153 PMCID: PMC4574858 DOI: 10.1371/journal.pcbi.1004363] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 05/29/2015] [Indexed: 01/31/2023] Open
Abstract
Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems. While metabolic fluxes constitute the most direct window into a cell’s metabolism, their accurate measurement is non trivial. The gold standard for flux measurement involves providing a labeled feed where some of the carbon atoms have been substituted by isotopes with higher atomic mass (13C instead of 12C). The ensuing labeling found in intracellular metabolites is then used to computationally infer the metabolic fluxes that produced the observed pattern. However, this procedure is typically performed with small metabolic models encompassing only central carbon metabolism. The genomic revolution has afforded us easily available genomes and, with them, comprehensive genome-scale models of cellular metabolism. It would be desirable to use the 13C labeling experimental data to constrain genome-scale models: these data constrain fluxes very effectively and provide in the labeling data fit an obvious proof that the underlying model correctly explains measured quantities. Here, we introduce a rigorous, self-consistent method that uses the full amount of information contained in 13C labeling data to constrain fluxes for a genome-scale model where underlying assumptions are explicitly stated.
Collapse
Affiliation(s)
- Héctor García Martín
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
- * E-mail:
| | - Vinay Satish Kumar
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Daniel Weaver
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Amit Ghosh
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Victor Chubukov
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Aindrila Mukhopadhyay
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
| | - Adam Arkin
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Department of Bioengineering, University of California, Berkeley, Berkely, United States of America
| | - Jay D. Keasling
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America
- Joint BioEnergy Institute, Emeryville, United States of America
- Department of Bioengineering, University of California, Berkeley, Berkely, United States of America
- Department of Chemical Engineering, University of California, Berkeley, Berkeley, United States of America
| |
Collapse
|
33
|
White biotechnology: State of the art strategies for the development of biocatalysts for biorefining. Biotechnol Adv 2015; 33:1653-70. [PMID: 26303096 DOI: 10.1016/j.biotechadv.2015.08.004] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 07/31/2015] [Accepted: 08/17/2015] [Indexed: 12/31/2022]
Abstract
White biotechnology is a term that is now often used to describe the implementation of biotechnology in the industrial sphere. Biocatalysts (enzymes and microorganisms) are the key tools of white biotechnology, which is considered to be one of the key technological drivers for the growing bioeconomy. Biocatalysts are already present in sectors such as the chemical and agro-food industries, and are used to manufacture products as diverse as antibiotics, paper pulp, bread or advanced polymers. This review proposes an original and global overview of highly complementary fields of biotechnology at both enzyme and microorganism level. A certain number of state of the art approaches that are now being used to improve the industrial fitness of biocatalysts particularly focused on the biorefinery sector are presented. The first part deals with the technologies that underpin the development of industrial biocatalysts, notably the discovery of new enzymes and enzyme improvement using directed evolution techniques. The second part describes the toolbox available by the cell engineer to shape the metabolism of microorganisms. And finally the last part focuses on the 'omic' technologies that are vital for understanding and guide microbial engineering toward more efficient microbial biocatalysts. Altogether, these techniques and strategies will undoubtedly help to achieve the challenging task of developing consolidated bioprocessing (i.e. CBP) readily available for industrial purpose.
Collapse
|
34
|
Chen MM, Li AL, Sun MC, Feng Z, Meng XC, Wang Y. Optimization of the quenching method for metabolomics analysis of Lactobacillus bulgaricus. J Zhejiang Univ Sci B 2015; 15:333-42. [PMID: 24711354 DOI: 10.1631/jzus.b1300149] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
This study proposed a quenching protocol for metabolite analysis of Lactobacillus delbrueckii subsp. bulgaricus. Microbial cells were quenched with 60% methanol/water, 80% methanol/glycerol, or 80% methanol/water. The effect of the quenching process was assessed by the optical density (OD)-based method, flow cytometry, and gas chromatography-mass spectrometry (GC-MS). The principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were employed for metabolite identification. The results indicated that quenching with 80% methanol/water solution led to less damage to the L. bulgaricus cells, characterized by the lower relative fraction of prodium iodide (PI)-labeled cells and the higher OD recovery ratio. Through GC-MS analysis, higher levels of intracellular metabolites (including focal glutamic acid, aspartic acid, alanine, and AMP) and a lower leakage rate were detected in the sample quenched with 80% methanol/water compared with the others. In conclusion, we suggested a higher concentration of cold methanol quenching for L. bulgaricus metabolomics due to its decreasing metabolite leakage.
Collapse
Affiliation(s)
- Ming-ming Chen
- MOE Key Laboratory of Dairy Science, College of Food Science, Northeast Agricultural University, Harbin 150030, China; School of Public Health, Jilin Medical College, Jilin 132013, China
| | | | | | | | | | | |
Collapse
|
35
|
Schultz A, Qutub AA. Predicting internal cell fluxes at sub-optimal growth. BMC SYSTEMS BIOLOGY 2015; 9:18. [PMID: 25890056 PMCID: PMC4397736 DOI: 10.1186/s12918-015-0153-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Accepted: 02/20/2015] [Indexed: 11/17/2022]
Abstract
Background Flux Balance Analysis (FBA) is a widely used tool to model metabolic behavior and cellular function. Applications of FBA span a breadth of research from synthetic engineering of biofuels to understanding evolutionary adaptations. FBA predicts metabolic reaction fluxes that optimize a given objective. This objective is generally defined for unicellular organisms by a theoretical reaction which simulates biomass production. FBA has been extremely successful at predicting in E. coli growth rates under different media and gene essentiality, amongst other things. In order to improve predictions, additional constraints are coupled with optimization of the biomass function. Studies have suggested, however, that unicellular organisms - like multicellular organisms - do not grow at optimal rates. To further improve FBA predictions, particularly of internal cell fluxes, new techniques to explore the sub-optimal solution space need to be developed. Results We present an innovative FBA method called corsoFBA based on the optimization of protein cost at sub-optimal objective levels. Our method shows good agreement with experimental data of E. coli grown at different dilution rates. Maintaining the objective function close to its maximum value predicts metabolic states that closely resemble low dilution rates; while higher dilution rates can be mirrored by lowering the biomass production value. By using a modified version of Extreme Pathways, we are also able to quantify the energy production and overall protein cost for all possible pathways in the central carbon metabolism. Conclusion Metabolic flux distributions at the optimal objective can be substantially different from the near-optimal distributions. Importantly, the behavior of E. coli central carbon metabolism can be better predicted by exploring the sub-optimal FBA solution space. The corsoFBA method presented here is able to predict the behavior of PEP Carboxylase, the glyoxylate shunt and the Entner-Doudoroff pathway at different glucose levels, a behavior not predicted by the minimization of metabolic steps and FBA alone. This technique can be used to better predict internal cell fluxes under different conditions, and corsoFBA will be of great help for the study of cells from multicellular organisms using Flux Balance Analysis. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0153-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- André Schultz
- Department of Bioengineering, Rice University, Main Street, Houston, 6500, USA.
| | - Amina A Qutub
- Department of Bioengineering, Rice University, Main Street, Houston, 6500, USA.
| |
Collapse
|
36
|
Antoniewicz MR. Methods and advances in metabolic flux analysis: a mini-review. J Ind Microbiol Biotechnol 2015; 42:317-25. [PMID: 25613286 DOI: 10.1007/s10295-015-1585-x] [Citation(s) in RCA: 145] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 01/09/2015] [Indexed: 01/12/2023]
Abstract
Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, (13)C metabolic flux analysis, isotopic non-stationary (13)C metabolic flux analysis, dynamic metabolic flux analysis, and (13)C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.
Collapse
Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE, 19716, USA,
| |
Collapse
|
37
|
Guo W, Luo S, He Z, Feng X. 13C pathway analysis of biofilm metabolism of Shewanella oneidensis MR-1. RSC Adv 2015. [DOI: 10.1039/c5ra05573c] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Biofilm metabolism ofShewanellawas analyzedvia13C tracing experiments for the first time.
Collapse
Affiliation(s)
- Weihua Guo
- Department of Biological Systems Engineering
- Virginia Polytechnic Institute and State University
- Blacksburg
- USA
| | - Shuai Luo
- Department of Civil and Environmental Engineering
- Virginia Polytechnic Institute and State University
- Blacksburg
- USA
| | - Zhen He
- Department of Civil and Environmental Engineering
- Virginia Polytechnic Institute and State University
- Blacksburg
- USA
| | - Xueyang Feng
- Department of Biological Systems Engineering
- Virginia Polytechnic Institute and State University
- Blacksburg
- USA
| |
Collapse
|
38
|
Saunders EC, de Souza DP, Chambers JM, Ng M, Pyke J, McConville MJ. Use of (13)C stable isotope labelling for pathway and metabolic flux analysis in Leishmania parasites. Methods Mol Biol 2015; 1201:281-296. [PMID: 25388122 DOI: 10.1007/978-1-4939-1438-8_18] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This protocol describes the combined use of metabolite profiling and stable isotope labelling to define pathways of central carbon metabolism in the protozoa parasite, Leishmania mexicana. Parasite stages are cultivated in standard or completely defined media and then rapidly transferred to chemically equivalent media containing a single (13)C-labelled nutrient. The incorporation of label can be followed over time or after establishment of isotopic equilibrium by harvesting parasites with rapid metabolic quenching. (13)C enrichment of multiple intracellular polar and apolar (lipidic) metabolites can be quantified using gas chromatography-mass spectrometry (GC-MS), while the uptake and secretion of (13)C-labelled metabolites can be measured by (13)C-NMR. Analysis of the mass isotopomer distribution of key metabolites provides information on pathway structure, while analysis of labelling kinetics can be used to infer metabolic fluxes. This protocol is exemplified using L. mexicana labelled with (13)C-U-glucose. The method can be used to measure perturbations in parasite metabolism induced by drug inhibition or genetic manipulation of enzyme levels and is broadly applicable to any cultured parasite stages.
Collapse
Affiliation(s)
- Eleanor C Saunders
- Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, 30 Flemington Rd, Parkville, VIC, 3010, Australia
| | | | | | | | | | | |
Collapse
|
39
|
Jung JY, Oh MK. Isotope labeling pattern study of central carbon metabolites using GC/MS. J Chromatogr B Analyt Technol Biomed Life Sci 2014; 974:101-8. [PMID: 25463204 DOI: 10.1016/j.jchromb.2014.10.033] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Revised: 10/17/2014] [Accepted: 10/26/2014] [Indexed: 12/31/2022]
Abstract
Determination of fluxes by (13)C tracer experiments depends on monitoring the (13)C labeling pattern of metabolites during isotope experiments. In metabolome-based (13)C metabolic flux analysis, liquid chromatography combined with mass spectrometry or tandem mass spectrometry (LC/MS or LC/MS/MS, respectively) has been mainly used as an analytical platform for isotope pattern studies of central carbon metabolites. However, gas chromatography with mass spectrometry (GC/MS) has several advantages over LC/MS, such as high sensitivity, low cost, ease of operation, and availability of mass spectra databases for comparison. In this study, analysis of isotope pattern for central carbon metabolites using GC/MS was demonstrated. First, a proper set of mass ions for central carbon metabolites was selected based on carbon backbone information and structural isomers of mass fragment ions. A total of 34 mass fragment ions was selected and used for the quantification of 25 central carbon metabolites. Then, to quantify isotope fractions, a natural mass isotopomer library for selected mass fragment ions was constructed and subtracted from isotopomer mass spectra data. The results revealed a surprisingly high abundance of partially labeled (13)C intermediates, such as 56.4% of fructose 6-phosphate and 47.6% of dihydroxyacetone phosphate at isotopic steady state, which were generated in the pentose phosphate pathway. Finally, dynamic changes of isotope fragments of central metabolites were monitored with a U-(13)C glucose stimulus response experiment in Kluyveromyces marxianus. With a comprehensive study of isotope patterns of central carbon metabolites using GC/MS, 25 central carbon metabolites and their isotopic fractions were successfully quantified. Dynamic and precise acquisition of isotope pattern can then be used in combination with proper kinetic models to calculate metabolic fluxes.
Collapse
Affiliation(s)
- Joon-Young Jung
- Systems Bioengineering Laboratory, Department of Chemical and Biological Engineering, Korea University, Seoul 136-701, Republic of Korea
| | - Min-Kyu Oh
- Systems Bioengineering Laboratory, Department of Chemical and Biological Engineering, Korea University, Seoul 136-701, Republic of Korea.
| |
Collapse
|
40
|
Nöh K, Droste P, Wiechert W. Visual workflows for 13 C-metabolic flux analysis. Bioinformatics 2014; 31:346-54. [DOI: 10.1093/bioinformatics/btu585] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
|
41
|
Ghosh A, Nilmeier J, Weaver D, Adams PD, Keasling JD, Mukhopadhyay A, Petzold CJ, Martín HG. A peptide-based method for 13C Metabolic Flux Analysis in microbial communities. PLoS Comput Biol 2014; 10:e1003827. [PMID: 25188426 PMCID: PMC4154649 DOI: 10.1371/journal.pcbi.1003827] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Accepted: 07/23/2014] [Indexed: 01/08/2023] Open
Abstract
The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species. Microbial communities underlie a variety of important biochemical processes ranging from underground cave formation to gold mining or the onset of obesity. Metabolic fluxes describe how carbon and energy flow through the microbial community and therefore provide insights that are rarely captured by other techniques, such as metatranscriptomics or metaproteomics. The most authoritative method to measure fluxes for pure cultures consists of feeding the cells a labeled carbon source and deriving the fluxes from the ensuing metabolite labeling pattern (typically amino acids). Since we cannot easily separate cells of metabolite for each species in a community, this approach is not generally applicable to microbial communities. Here we present a method to derive fluxes from the labeling of peptides, instead of amino acids. This approach has the advantage that peptides can be assigned to each species in a community in a high-throughput fashion through modern proteomic methods. We show that, by using this method, it is theoretically possible to recover the same amount of information as through the standard approach, if enough peptides are used. We computationally tested this method with a well-characterized simple microbial community consisting of two species.
Collapse
Affiliation(s)
- Amit Ghosh
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Jerome Nilmeier
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Daniel Weaver
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Paul D. Adams
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
- Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America
| | - Jay D. Keasling
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
- Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America
- Department of Chemical Engineering, University of California, Berkeley, Berkeley, United States of America
| | - Aindrila Mukhopadhyay
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Christopher J. Petzold
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Héctor García Martín
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Joint BioEnergy Institute, Emeryville, California, United States of America
- * E-mail:
| |
Collapse
|
42
|
Nakajima T, Kajihata S, Yoshikawa K, Matsuda F, Furusawa C, Hirasawa T, Shimizu H. Integrated metabolic flux and omics analysis of Synechocystis sp. PCC 6803 under mixotrophic and photoheterotrophic conditions. PLANT & CELL PHYSIOLOGY 2014; 55:1605-12. [PMID: 24969233 DOI: 10.1093/pcp/pcu091] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Cyanobacteria have flexible metabolic capability that enables them to adapt to various environments. To investigate their underlying metabolic regulation mechanisms, we performed an integrated analysis of metabolic flux using transcriptomic and metabolomic data of a cyanobacterium Synechocystis sp. PCC 6803, under mixotrophic and photoheterotrophic conditions. The integrated analysis indicated drastic metabolic flux changes, with much smaller changes in gene expression levels and metabolite concentrations between the conditions, suggesting that the flux change was not caused mainly by the expression levels of the corresponding genes. Under photoheterotrophic conditions, created by the addition of the photosynthesis inhibitor atrazine in mixotrophic conditions, the result of metabolic flux analysis indicated the significant repression of carbon fixation and the activation of the oxidative pentose phosphate pathway (PPP). Moreover, we observed gluconeogenic activity of upstream of glycolysis, which enhanced the flux of the oxidative PPP to compensate for NADPH depletion due to the inhibition of the light reaction of photosynthesis. 'Omics' data suggested that these changes were probably caused by the repression of the gap1 gene, which functions as a control valve in the metabolic network. Since metabolic flux is the outcome of a complicated interplay of cellular components, integrating metabolic flux with other 'omics' layers can identify metabolic changes and narrow down these regulatory mechanisms more effectively.
Collapse
Affiliation(s)
- Tsubasa Nakajima
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (JST, CREST)
| | - Shuichi Kajihata
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (JST, CREST)
| | - Katsunori Yoshikawa
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (JST, CREST)
| | - Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (JST, CREST)
| | - Chikara Furusawa
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (JST, CREST) Quantitative Biology Center (QBiC), RIKEN, 6-2-3 Furuedai, Suita, Osaka, 565-0874 Japan
| | - Takashi Hirasawa
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (JST, CREST) Department of Bioengineering, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501 Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (JST, CREST)
| |
Collapse
|
43
|
Hollinshead W, He L, Tang YJ. Biofuel production: an odyssey from metabolic engineering to fermentation scale-up. Front Microbiol 2014; 5:344. [PMID: 25071754 PMCID: PMC4088188 DOI: 10.3389/fmicb.2014.00344] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 06/20/2014] [Indexed: 12/21/2022] Open
Abstract
Metabolic engineering has developed microbial cell factories that can convert renewable carbon sources into biofuels. Current molecular biology tools can efficiently alter enzyme levels to redirect carbon fluxes toward biofuel production, but low product yield and titer in large bioreactors prevent the fulfillment of cheap biofuels. There are three major roadblocks preventing economical biofuel production. First, carbon fluxes from the substrate dissipate into a complex metabolic network. Besides the desired product, microbial hosts direct carbon flux to synthesize biomass, overflow metabolites, and heterologous enzymes. Second, microbial hosts need to oxidize a large portion of the substrate to generate both ATP and NAD(P)H to power biofuel synthesis. High cell maintenance, triggered by the metabolic burdens from genetic modifications, can significantly affect the ATP supply. Thereby, fermentation of advanced biofuels (such as biodiesel and hydrocarbons) often requires aerobic respiration to resolve the ATP shortage. Third, mass transfer limitations in large bioreactors create heterogeneous growth conditions and micro-environmental fluctuations (such as suboptimal O2 level and pH) that induce metabolic stresses and genetic instability. To overcome these limitations, fermentation engineering should merge with systems metabolic engineering. Modern fermentation engineers need to adopt new metabolic flux analysis tools that integrate kinetics, hydrodynamics, and 13C-proteomics, to reveal the dynamic physiologies of the microbial host under large bioreactor conditions. Based on metabolic analyses, fermentation engineers may employ rational pathway modifications, synthetic biology circuits, and bioreactor control algorithms to optimize large-scale biofuel production.
Collapse
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
| |
Collapse
|
44
|
Heux S, Poinot J, Massou S, Sokol S, Portais JC. A novel platform for automated high-throughput fluxome profiling of metabolic variants. Metab Eng 2014; 25:8-19. [PMID: 24930895 DOI: 10.1016/j.ymben.2014.06.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 04/07/2014] [Accepted: 06/04/2014] [Indexed: 11/15/2022]
Abstract
Advances in metabolic engineering are enabling the creation of a large number of cell factories. However, high-throughput platforms do not yet exist for rapidly analyzing the metabolic network of the engineered cells. To fill the gap, we developed an integrated solution for fluxome profiling of large sets of biological systems and conditions. This platform combines a robotic system for (13)C-labelling experiments and sampling of labelled material with NMR-based isotopic fingerprinting and automated data interpretation. As a proof-of-concept, this workflow was applied to discriminate between Escherichia coli mutants with gradual expression of the glucose-6-phosphate dehydrogenase. Metabolic variants were clearly discriminated while pathways that support metabolic flexibility towards modulation of a single enzyme were elucidating. By directly connecting the data flow between cell cultivation and flux quantification, considerable advances in throughput, robustness, release of resources and screening capacity were achieved. This will undoubtedly facilitate the development of efficient cell factories.
Collapse
Affiliation(s)
- Stéphanie Heux
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France; INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France; CNRS, UMR5504, F-31400 Toulouse, France.
| | - Juliette Poinot
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France; INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France; CNRS, UMR5504, F-31400 Toulouse, France
| | - Stéphane Massou
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France; INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France; CNRS, UMR5504, F-31400 Toulouse, France
| | - Serguei Sokol
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France; INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France; CNRS, UMR5504, F-31400 Toulouse, France
| | - Jean-Charles Portais
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France; INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France; CNRS, UMR5504, F-31400 Toulouse, France
| |
Collapse
|
45
|
Fernández-Castané A, Fehér T, Carbonell P, Pauthenier C, Faulon JL. Computer-aided design for metabolic engineering. J Biotechnol 2014; 192 Pt B:302-13. [PMID: 24704607 DOI: 10.1016/j.jbiotec.2014.03.029] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 03/18/2014] [Accepted: 03/24/2014] [Indexed: 12/20/2022]
Abstract
The development and application of biotechnology-based strategies has had a great socio-economical impact and is likely to play a crucial role in the foundation of more sustainable and efficient industrial processes. Within biotechnology, metabolic engineering aims at the directed improvement of cellular properties, often with the goal of synthesizing a target chemical compound. The use of computer-aided design (CAD) tools, along with the continuously emerging advanced genetic engineering techniques have allowed metabolic engineering to broaden and streamline the process of heterologous compound-production. In this work, we review the CAD tools available for metabolic engineering with an emphasis, on retrosynthesis methodologies. Recent advances in genetic engineering strategies for pathway implementation and optimization are also reviewed as well as a range of bionalytical tools to validate in silico predictions. A case study applying retrosynthesis is presented as an experimental verification of the output from Retropath, the first complete automated computational pipeline applicable to metabolic engineering. Applying this CAD pipeline, together with genetic reassembly and optimization of culture conditions led to improved production of the plant flavonoid pinocembrin. Coupling CAD tools with advanced genetic engineering strategies and bioprocess optimization is crucial for enhanced product yields and will be of great value for the development of non-natural products through sustainable biotechnological processes.
Collapse
Affiliation(s)
- Alfred Fernández-Castané
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Tamás Fehér
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Pablo Carbonell
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Cyrille Pauthenier
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| | - Jean-Loup Faulon
- Institute of Systems and Synthetic Biology, University of Evry-Val-d'Essonne, CNRS FRE3561, Genopole(®) Campus 1, Genavenir 6, 5 rue Henri Desbruères, F-91030 Evry Cedex, France.
| |
Collapse
|
46
|
Quek LE, Nielsen LK. Customization of ¹³C-MFA strategy according to cell culture system. Methods Mol Biol 2014; 1191:81-90. [PMID: 25178785 DOI: 10.1007/978-1-4939-1170-7_5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
(13)C-MFA is far from being a simple assay for quantifying metabolic activity. It requires considerable up-front experimental planning and familiarity with the cell culture system in question, as well as optimized analytics and adequate computation frameworks. The success of a (13)C-MFA experiment is ultimately rated by the ability to accurately quantify the flux of one or more reactions of interest. In this chapter, we describe the different (13)C-MFA strategies that have been developed for the various fermentation or cell culture systems, as well as the limitations of the respective strategies. The strategies are affected by many factors and the (13)C-MFA modeling and experimental strategy must be tailored to conditions. The prevailing philosophy in the computation process is that any metabolic processes that produce significant systematic bias in the labeling pattern of the metabolites being measured must be described in the model. It is equally important to plan a labeling strategy by analytical screening or by heuristics.
Collapse
Affiliation(s)
- Lake-Ee Quek
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Building 75, Corner of College and Cooper Road, Brisbane, QLD, 4072, Australia
| | | |
Collapse
|
47
|
Liu D, Hoynes-O'Connor A, Zhang F. Bridging the gap between systems biology and synthetic biology. Front Microbiol 2013; 4:211. [PMID: 23898328 PMCID: PMC3722476 DOI: 10.3389/fmicb.2013.00211] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2013] [Accepted: 07/07/2013] [Indexed: 12/24/2022] Open
Abstract
Systems biology is an inter-disciplinary science that studies the complex interactions and the collective behavior of a cell or an organism. Synthetic biology, as a technological subject, combines biological science and engineering, allowing the design and manipulation of a system for certain applications. Both systems and synthetic biology have played important roles in the recent development of microbial platforms for energy, materials, and environmental applications. More importantly, systems biology provides the knowledge necessary for the development of synthetic biology tools, which in turn facilitates the manipulation and understanding of complex biological systems. Thus, the combination of systems and synthetic biology has huge potential for studying and engineering microbes, especially to perform advanced tasks, such as producing biofuels. Although there have been very few studies in integrating systems and synthetic biology, existing examples have demonstrated great power in extending microbiological capabilities. This review focuses on recent efforts in microbiological genomics, transcriptomics, proteomics, and metabolomics, aiming to fill the gap between systems and synthetic biology.
Collapse
Affiliation(s)
- Di Liu
- Department of Energy, Environmental and Chemical Engineering, Washington University St. Louis, MO, USA
| | | | | |
Collapse
|
48
|
Tymoshenko S, Oppenheim RD, Soldati-Favre D, Hatzimanikatis V. Functional genomics of Plasmodium falciparum using metabolic modelling and analysis. Brief Funct Genomics 2013; 12:316-27. [PMID: 23793264 PMCID: PMC3743259 DOI: 10.1093/bfgp/elt017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Plasmodium falciparum is an obligate intracellular parasite and the leading cause of severe malaria responsible for tremendous morbidity and mortality particularly in sub-Saharan Africa. Successful completion of the P. falciparum genome sequencing project in 2002 provided a comprehensive foundation for functional genomic studies on this pathogen in the following decade. Over this period, a large spectrum of experimental approaches has been deployed to improve and expand the scope of functionally annotated genes. Meanwhile, rapidly evolving methods of systems biology have also begun to contribute to a more global understanding of various aspects of the biology and pathogenesis of malaria. Herein we provide an overview on metabolic modelling, which has the capability to integrate information from functional genomics studies in P. falciparum and guide future malaria research efforts towards the identification of novel candidate drug targets.
Collapse
Affiliation(s)
- Stepan Tymoshenko
- Institute of Chemical Engineering, Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH-1015, Switzerland.
| | | | | | | |
Collapse
|
49
|
Zhang X, Luo Q, Apostol I, Luo S, Jerums M, Huang G, Jiang XG, Gastwirt J, Savjani N, Lewis J, Keener R, Wypych J. Assessment of stable isotope incorporation into recombinant proteins. Anal Biochem 2013; 433:137-49. [DOI: 10.1016/j.ab.2012.10.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2012] [Revised: 10/03/2012] [Accepted: 10/08/2012] [Indexed: 11/16/2022]
|
50
|
Goyal Y, Kumar M, Gayen K. Metabolic engineering for enhanced hydrogen production: a review. Can J Microbiol 2013; 59:59-78. [DOI: 10.1139/cjm-2012-0494] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Hydrogen gas exhibits potential as a sustainable fuel for the future. Therefore, many attempts have been made with the aim of producing high yields of hydrogen gas through renewable biological routes. Engineering of strains to enhance the production of hydrogen gas has been an active area of research for the past 2 decades. This includes overexpression of hydrogen-producing genes (native and heterologous), knockout of competitive pathways, creation of a new productive pathway, and creation of dual systems. Interestingly, genetic mutations in 2 different strains of the same species may not yield similar results. Similarly, 2 different studies on hydrogen productivities may differ largely for the same mutation and on the same species. Consequently, here we analyzed the effect of various genetic modifications on several species, considering a wide range of published data on hydrogen biosynthesis. This article includes a comprehensive metabolic engineering analysis of hydrogen-producing organisms, namely Escherichia coli, Clostridium, and Enterobacter species, and in addition, a short discussion on thermophilic and halophilic organisms. Also, apart from single-culture utilization, dual systems of various organisms and associated developments have been discussed, which are considered potential future targets for economical hydrogen production. Additionally, an indirect contribution towards hydrogen production has been reviewed for associated species.
Collapse
Affiliation(s)
- Yogesh Goyal
- Department of Chemical Engineering, Indian Institute of Technology, Gandhinagar, VGEC Complex, Chandkheda, Ahmedabad 382424 (Gujarat), India
| | - Manish Kumar
- Department of Chemical Engineering, Indian Institute of Technology, Gandhinagar, VGEC Complex, Chandkheda, Ahmedabad 382424 (Gujarat), India
| | - Kalyan Gayen
- Department of Chemical Engineering, National Institute of Technology Agartala, Barjala, Jirania, West Tripura-799055, Tripura, India
| |
Collapse
|