1
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Jamal QMS, Ahmad V. Bacterial metabolomics: current applications for human welfare and future aspects. JOURNAL OF ASIAN NATURAL PRODUCTS RESEARCH 2024:1-24. [PMID: 39078342 DOI: 10.1080/10286020.2024.2385365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.
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Affiliation(s)
- Qazi Mohammad Sajid Jamal
- Department of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Varish Ahmad
- Health Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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2
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Guo Y, Zhang R, Wang J, Qin R, Feng J, Chen K, Wang X. Engineering yeasts to Co-utilize methanol or formate coupled with CO 2 fixation. Metab Eng 2024; 84:1-12. [PMID: 38759777 DOI: 10.1016/j.ymben.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/02/2024] [Accepted: 05/12/2024] [Indexed: 05/19/2024]
Abstract
The development of synthetic microorganisms that could use one-carbon compounds, such as carbon dioxide, methanol, or formate, has received considerable interest. In this study, we engineered Pichia pastoris and Saccharomyces cerevisiae to both synthetic methylotrophy and formatotrophy, enabling them to co-utilize methanol or formate with CO2 fixation through a synthetic C1-compound assimilation pathway (MFORG pathway). This pathway consisted of a methanol-formate oxidation module and the reductive glycine pathway. We first assembled the MFORG pathway in P. pastoris using endogenous enzymes, followed by blocking the native methanol assimilation pathway, modularly engineering genes of MFORG pathway, and compartmentalizing the methanol oxidation module. These modifications successfully enabled the methylotrophic yeast P. pastoris to utilize both methanol and formate. We then introduced the MFORG pathway from P. pastoris into the model yeast S. cerevisiae, establishing the synthetic methylotrophy and formatotrophy in this organism. The resulting strain could also successfully utilize both methanol and formate with consumption rates of 20 mg/L/h and 36.5 mg/L/h, respectively. The ability of the engineered P. pastoris and S. cerevisiae to co-assimilate CO2 with methanol or formate through the MFORG pathway was also confirmed by 13C-tracer analysis. Finally, production of 5-aminolevulinic acid and lactic acid by co-assimilating methanol and CO2 was demonstrated in the engineered P. pastoris and S. cerevisiae. This work indicates the potential of the MFORG pathway in developing different hosts to use various one-carbon compounds for chemical production.
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Affiliation(s)
- Yuanke Guo
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China
| | - Rui Zhang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China
| | - Jing Wang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China
| | - Ruirui Qin
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China
| | - Jiao Feng
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China
| | - Kequan Chen
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China
| | - Xin Wang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China.
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3
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Hilovsky D, Hartsell J, Young JD, Liu X. Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies. Metabolites 2024; 14:318. [PMID: 38921453 PMCID: PMC11205609 DOI: 10.3390/metabo14060318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 05/13/2024] [Accepted: 05/28/2024] [Indexed: 06/27/2024] Open
Abstract
Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.
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Affiliation(s)
- Dalton Hilovsky
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC 27695, USA; (D.H.); (J.H.)
| | - Joshua Hartsell
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC 27695, USA; (D.H.); (J.H.)
| | - Jamey D. Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37212, USA
| | - Xiaojing Liu
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC 27695, USA; (D.H.); (J.H.)
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4
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Liu S, Liu X, Locasale JW. Quantification of metabolic activity from isotope tracing data using automated methodology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581907. [PMID: 38464003 PMCID: PMC10925140 DOI: 10.1101/2024.02.25.581907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Isotope tracing is a widely used technique to study metabolic activities by introducing heavy labeled nutrients into living cells and organisms. However, interpreting isotope tracing data is often heuristic, and application of automated methods using artificial intelligence is limited due to the paucity of evaluative knowledge. Our study developed a new pipeline that efficiently predicts metabolic activity in expansive metabolic networks and systematically quantifies flux uncertainty of traditional computational methods. We further developed an algorithm adept at significantly reducing this uncertainty, enabling robust evaluations of metabolic activity with limited data. Using this technology, we discovered highly reprogrammed mitochondria-cytosol exchange cycles in tumor tissue of patients, and observed similar metabolic patterns influenced by nutritional conditions in cancer cells. Thus, our refined methodology provides robust automated quantification of metabolism allowing for new insight into metabolic network activity.
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Affiliation(s)
- Shiyu Liu
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham NC 27710, USA
- Computational Biology and Bioinformatics Graduate Program, Duke University, Durham NC 27710, USA
| | - Xiaojing Liu
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham NC 27710, USA
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh NC 27695, USA
| | - Jason W. Locasale
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham NC 27710, USA
- Computational Biology and Bioinformatics Graduate Program, Duke University, Durham NC 27710, USA
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh NC 27695, USA
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5
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Wu C, Guarnieri M, Xiong W. FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis. ACS Synth Biol 2023; 12:2707-2714. [PMID: 37561998 PMCID: PMC10510750 DOI: 10.1021/acssynbio.3c00265] [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: 04/26/2023] [Indexed: 08/12/2023]
Abstract
13C metabolic flux analysis is a powerful tool for metabolism characterization in metabolic engineering and synthetic biology. However, the widespread adoption of this tool is hindered by limited software availability and computational efficiency. Currently, the most widely accepted 13C-flux tools, such as INCA and 13CFLUX2, are developed in a closed-source environment. While several open-source packages or software are available, they are either computationally inefficient or only suitable for flux estimation at isotopic steady state. To address the need for a time-efficient computational tool for the more complicated flux analysis at an isotopically nonstationary state, especially for understanding the single-carbon substrate metabolism, we present FreeFlux. FreeFlux is an open-source Python package that performs labeling pattern simulation and flux analysis at both isotopic steady state and transient state, enabling a more comprehensive analysis of cellular metabolism. FreeFlux provides a set of interfaces to manipulate the objects abstracted from a labeling experiment and computational process, making it easy to integrate into other programs or pipelines. The flux estimation by FreeFlux is fast and reliable, and its validity has been confirmed by comparison with results from other computational tools using both synthetic and experimental data. FreeFlux is freely available at https://github.com/Chaowu88/freeflux with a detailed online tutorial and documentation provided at https://freeflux.readthedocs.io/en/latest/index.html.
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Affiliation(s)
- Chao Wu
- Biosciences Center, National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Michael Guarnieri
- Biosciences Center, National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Wei Xiong
- Biosciences Center, National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
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6
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Xiao Z, Connor AJ, Worland AM, Tang YJ, Zha RH, Koffas M. Silk fibroin production in Escherichia coli is limited by a positive feedback loop between metabolic burden and toxicity stress. Metab Eng 2023; 77:231-241. [PMID: 37024071 DOI: 10.1016/j.ymben.2023.03.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/11/2023] [Accepted: 03/25/2023] [Indexed: 04/08/2023]
Abstract
To investigate the metabolic elasticity and production bottlenecks for recombinant silk proteins in Escherichia coli, we performed a comprehensive characterization of one elastin-like peptide strain (ELP) and two silk protein strains (A5 4mer, A5 16mer). Our approach included 13C metabolic flux analysis, genome-scale modeling, transcription analysis, and 13C-assisted media optimization experiments. Three engineered strains maintained their central flux network during growth, while measurable metabolic flux redistributions (such as the Entner-Doudoroff pathway) were detected. Under metabolic burdens, the reduced TCA fluxes forced the engineered strain to rely more on substrate-level phosphorylation for ATP production, which increased acetate overflow. Acetate (as low as 10 mM) in the media was highly toxic to silk-producing strains, which reduced 4mer production by 43% and 16mer by 84%, respectively. Due to the high toxicity of large-size silk proteins, 16mer's productivity was limited, particularly in the minimal medium. Therefore, metabolic burden, overflow acetate, and toxicity of silk proteins may form a vicious positive feedback loop that fractures the metabolic network. Three solutions could be applied: 1) addition of building block supplements (i.e., eight key amino acids: His, Ile, Phe, Pro, Tyr, Lys, Met, Glu) to reduce metabolic burden; 2) disengagement of growth and production; and 3) use of non-glucose based substrate to reduce acetate overflow. Other reported strategies were also discussed in light of decoupling this positive feedback loop.
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Affiliation(s)
- Zhengyang Xiao
- Department of Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Alexander J Connor
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Alyssa M Worland
- Department of Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Yinjie J Tang
- Department of Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
| | - R Helen Zha
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| | - Mattheos Koffas
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
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7
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Systems biology's role in leveraging microalgal biomass potential: Current status and future perspectives. ALGAL RES 2022. [DOI: 10.1016/j.algal.2022.102963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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8
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Czajka JJ, Banerjee D, Eng T, Menasalvas J, Yan C, Munoz NM, Poirier BC, Kim YM, Baker SE, Tang YJ, Mukhopadhyay A. Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production. Metab Eng Commun 2022; 15:e00206. [PMID: 36158112 PMCID: PMC9494242 DOI: 10.1016/j.mec.2022.e00206] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. 13C-metabolic flux analysis (13C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.
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Affiliation(s)
- Jeffrey J Czajka
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Deepanwita Banerjee
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Thomas Eng
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Javier Menasalvas
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Chunsheng Yan
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Nathalie Munoz Munoz
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.,Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Brenton C Poirier
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.,Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Young-Mo Kim
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.,Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Scott E Baker
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Yinjie J Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
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9
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Wu W, Dijkstra P, Hungate BA, Shi L, Dippold MA. In situ diversity of metabolism and carbon use efficiency among soil bacteria. SCIENCE ADVANCES 2022; 8:eabq3958. [PMID: 36332015 PMCID: PMC9635821 DOI: 10.1126/sciadv.abq3958] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
The central carbon (C) metabolic network harvests energy to power the cell and feed biosynthesis for growth. In pure cultures, bacteria use some but not all of the network's major pathways, such as glycolysis and pentose phosphate and Entner-Doudoroff pathways. However, how these pathways are used in microorganisms in intact soil communities is unknown. Here, we analyzed the incorporation of 13C from glucose isotopomers into phospholipid fatty acids. We showed that groups of Gram-positive and Gram-negative bacteria in an intact agricultural soil used different pathways to metabolize glucose. They also differed in C use efficiency (CUE), the efficiency with which a substrate is used for biosynthesis. Our results provide experimental evidence for diversity among microbes in the organization of their central carbon metabolic network and CUE under in situ conditions. These results have important implications for our understanding of how community composition affects soil C cycling and organic matter formation.
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Affiliation(s)
- Weichao Wu
- Biogeochemistry of Agroecosystem, University of Goettingen, Goettingen, Germany
- Department of Environmental Science, Stockholm University, Stockholm, Sweden
- Shanghai Engineering Research Center of Hadal Science and Technology, College of Marine Science, Shanghai Ocean University, Shanghai, China
| | - Paul Dijkstra
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Bruce A. Hungate
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Lingling Shi
- Key Laboratory of Economics Plants and Biotechnology, Institute of Botany, Chinese Academy of Sciences, Kunming, China
- World Agroforestry Centre, China and East-Asia Office, Kunming, China
- Geo-Biosphere Interactions, Department of Geosciences, University of Tuebingen, Tuebingen, Germany
| | - Michaela A. Dippold
- Biogeochemistry of Agroecosystem, University of Goettingen, Goettingen, Germany
- Geo-Biosphere Interactions, Department of Geosciences, University of Tuebingen, Tuebingen, Germany
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10
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de Falco B, Giannino F, Carteni F, Mazzoleni S, Kim DH. Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas. RSC Adv 2022; 12:25528-25548. [PMID: 36199351 PMCID: PMC9449821 DOI: 10.1039/d2ra03326g] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/26/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.
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Affiliation(s)
- Bruna de Falco
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
| | - Francesco Giannino
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Fabrizio Carteni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Stefano Mazzoleni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Dong-Hyun Kim
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
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11
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Liu Z, Zhang Z, Liang S, Chen Z, Xie X, Shen T. CeCaFLUX: the first web server for standardized and visual instationary 13C metabolic flux analysis. Bioinformatics 2022; 38:3481-3483. [PMID: 35595250 DOI: 10.1093/bioinformatics/btac341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 04/08/2022] [Accepted: 05/16/2022] [Indexed: 11/12/2022] Open
Abstract
SUMMARY The number of instationary 13C-metabolic flux (INST-MFA) studies grows every year, making it more important than ever to ensure the clarity, standardization and reproducibility of each study. We proposed CeCaFLUX, the first user-friendly web server that derives metabolic flux distribution from instationary 13C-labeled data. Flux optimization and statistical analysis are achieved through an evolutionary optimization in a parallel manner. It can visualize the flux optimizing process in real time and the ultimate flux outcome. It will also function as a database to enhance the consistency and to facilitate sharing of flux studies. AVAILABILITY AND IMPLEMENTATION CeCaFLUX is freely available at https://www.cecaflux.net, the source code can be downloaded at https://github.com/zhzhd82/CeCaFLUX. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhentao Liu
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou, China.,College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China
| | - Zhengdong Zhang
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou, China.,College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou, China
| | - Sheng Liang
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou, China
| | - Zhen Chen
- School of Mathematical Science, Guizhou Normal University, Guiyang, Guizhou, China
| | - Xiaoyao Xie
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou, China.,College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou, China
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12
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Rahim M, Ragavan M, Deja S, Merritt ME, Burgess SC, Young JD. INCA 2.0: A tool for integrated, dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis. Metab Eng 2022; 69:275-285. [PMID: 34965470 PMCID: PMC8789327 DOI: 10.1016/j.ymben.2021.12.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/17/2021] [Accepted: 12/22/2021] [Indexed: 01/03/2023]
Abstract
Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state 13C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different 13C acetate tracers, we show that MFA based on dynamic 13C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined 13C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.
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Affiliation(s)
- Mohsin Rahim
- Department of Chemical and Biomolecular, Nashville, TN, 37212, USA
| | - Mukundan Ragavan
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Stanislaw Deja
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matthew E Merritt
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Shawn C Burgess
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jamey D Young
- Department of Chemical and Biomolecular, Nashville, TN, 37212, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University, School of Engineering, Nashville, TN, 37212, USA.
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13
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Borah K, Xu Y, McFadden J. Dissecting Host-Pathogen Interactions in TB Using Systems-Based Omic Approaches. Front Immunol 2021; 12:762315. [PMID: 34795672 PMCID: PMC8593131 DOI: 10.3389/fimmu.2021.762315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 10/18/2021] [Indexed: 01/10/2023] Open
Abstract
Tuberculosis (TB) is a devastating infectious disease that kills over a million people every year. There is an increasing burden of multi drug resistance (MDR) and extensively drug resistance (XDR) TB. New and improved therapies are urgently needed to overcome the limitations of current treatment. The causative agent, Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens that can manipulate host cell environment for adaptation, evading immune defences, virulence, and pathogenesis of TB infection. Host-pathogen interaction is important to establish infection and it involves a complex set of processes. Metabolic cross talk between the host and pathogen is a facet of TB infection and has been an important topic of research where there is growing interest in developing therapies and drugs that target these interactions and metabolism of the pathogen in the host. Mtb scavenges multiple nutrient sources from the host and has adapted its metabolism to survive in the intracellular niche. Advancements in systems-based omic technologies have been successful to unravel host-pathogen interactions in TB. In this review we discuss the application and usefulness of omics in TB research that provides promising interventions for developing anti-TB therapies.
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Affiliation(s)
- Khushboo Borah
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | | | - Johnjoe McFadden
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
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14
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Matsuda F, Maeda K, Taniguchi T, Kondo Y, Yatabe F, Okahashi N, Shimizu H. mfapy: An open-source Python package for 13C-based metabolic flux analysis. Metab Eng Commun 2021; 13:e00177. [PMID: 34354925 PMCID: PMC8322459 DOI: 10.1016/j.mec.2021.e00177] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/01/2021] [Accepted: 07/05/2021] [Indexed: 11/28/2022] Open
Abstract
13C-based metabolic flux analysis (13C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In 13C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for 13C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of 13C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy). An open-source Python package, mfapy, is developed for 13C-MFA. mfapy enables users to write Python codes for data analysis procedures of 13C-MFA. mfapy has a flexibility and extensibility to support various data analysis procedures. Computer simulations of 13C-MFA experiments is supported for experimental design.
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Affiliation(s)
- Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kousuke Maeda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Takeo Taniguchi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yuya Kondo
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Futa Yatabe
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Nobuyuki Okahashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
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15
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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16
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Antoniewicz MR. A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications. Metab Eng 2020; 63:2-12. [PMID: 33157225 DOI: 10.1016/j.ymben.2020.11.002] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 10/28/2020] [Accepted: 11/01/2020] [Indexed: 12/22/2022]
Abstract
The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. 13C-metabolic flux analysis (13C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.
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Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Michigan, Ann Arbor, MI, 48109, USA.
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17
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Grim CM, Luu GT, Sanchez LM. Staring into the void: demystifying microbial metabolomics. FEMS Microbiol Lett 2020; 366:5519856. [PMID: 31210257 DOI: 10.1093/femsle/fnz135] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 06/14/2019] [Indexed: 12/18/2022] Open
Abstract
Metabolites give us a window into the chemistry of microbes and are split into two subclasses: primary and secondary. Primary metabolites are required for life whereas secondary metabolites have historically been classified as those appearing after exponential growth and are not necessarily needed for survival. Many microbial species are estimated to produce hundreds of metabolites and can be affected by differing nutrients. Using various analytical techniques, metabolites can be directly detected in order to elucidate their biological significance. Currently, a single experiment can produce anywhere from megabytes to terabytes of data. This big data has motivated scientists to develop informatics tools to help target specific metabolites or sets of metabolites. Broadly, it is imperative to identify clear biological questions before embarking on a study of metabolites (metabolomics). For instance, studying the effect of a transposon insertion on phenazine biosynthesis in Pseudomonas is a very different from asking what molecules are present in a specific banana-derived strain of Pseudomonas. This review is meant to serve as a primer for a 'choose your own adventure' approach for microbiologists with limited mass spectrometry expertise, with a strong focus on liquid chromatography mass spectrometry based workflows developed or optimized within the past five years.
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Affiliation(s)
- Cynthia M Grim
- Department of Pharmaceutical Sciences, University of Ilinois at Chicago, 833 S Wood St, Chicago, IL 60612, USA
| | - Gordon T Luu
- Department of Pharmaceutical Sciences, University of Ilinois at Chicago, 833 S Wood St, Chicago, IL 60612, USA
| | - Laura M Sanchez
- Department of Pharmaceutical Sciences, University of Ilinois at Chicago, 833 S Wood St, Chicago, IL 60612, USA
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18
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Zhang Z, Liu Z, Meng Y, Chen Z, Han J, Wei Y, Shen T, Yi Y, Xie X. Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale. BIOTECHNOLOGY FOR BIOFUELS 2020; 13:103. [PMID: 32523616 PMCID: PMC7278083 DOI: 10.1186/s13068-020-01737-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 05/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network. RESULT Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling. CONCLUSION This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.
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Affiliation(s)
- Zhengdong Zhang
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou China
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| | - Zhentao Liu
- College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou China
| | - Yafei Meng
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou China
| | - Zhen Chen
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, Guizhou China
| | - Jiayu Han
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, Guizhou China
| | - Yimin Wei
- School of Mathematics Sciences and Key Laboratory of Mathematics for Nonlinear Sciences, Fudan University, Shanghai, China
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| | - Yin Yi
- College of Life Science, Guizhou Normal University, Guiyang, Guizhou China
| | - Xiaoyao Xie
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
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19
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Wang B, Guo Y, Xu Z, Tu R, Wang Q. Genomic, transcriptomic, and metabolic characterizations of Escherichia coli adapted to branched-chain higher alcohol tolerance. Appl Microbiol Biotechnol 2020; 104:4171-4184. [PMID: 32189046 DOI: 10.1007/s00253-020-10507-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 02/15/2020] [Accepted: 02/28/2020] [Indexed: 02/07/2023]
Abstract
Microbial-produced branched-chain higher alcohols (BCHAs), such as isopropanol, isobutanol, and isopentanol in Escherichia coli, have emerged as promising alternative biofuels under development. Elucidating and improving the tolerance of E. coli to BCHAs are important issues for microbial production of BCHAs due to their physiological inhibitory effect. Previous works aimed at understanding the genetic basis of E. coli tolerance to BCHAs with a comparative genome, reverse engineering, or transcriptome approach have gained some important insights into the mechanism of tolerance. However, investigation on BCHA tolerance from the whole-genomic, transcriptomic, and metabolic levels via a systematic approach has not yet been completely elucidated. Here, in this study, genomic, transcriptomic, and 13C-metabolic flux analyses (13C-MFA) of an evolved E. coli strain adapted to BCHA tolerance were conducted. Genome mutation of negative regulation factor (rssB, acrB, and clpX) of RpoS level suggested upregulation of RpoS activity in BCHA tolerance of E. coli. From a more detailed perspective, enhanced energy metabolism was observed to be the main characteristic of E. coli strain tolerant to BCHAs. Enhanced energy metabolism has been achieved through several routes, which included redistribution of the central carbon metabolism, upregulation of the energy generation machinery, and facilitating the operation of electron transferring chain. Evidence of multiple solutions of genotype modification toward BCHA tolerance was also revealed through comparative analysis of previous works from different groups.
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Affiliation(s)
- Baowei Wang
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.,SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, People's Republic of China
| | - Yufeng Guo
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China
| | - Zixiang Xu
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China
| | - Ran Tu
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China
| | - Qinhong Wang
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.
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20
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Beyß M, Azzouzi S, Weitzel M, Wiechert W, Nöh K. The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis. Front Microbiol 2019; 10:1022. [PMID: 31178829 PMCID: PMC6543931 DOI: 10.3389/fmicb.2019.01022] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 04/24/2019] [Indexed: 12/16/2022] Open
Abstract
13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.
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Affiliation(s)
- Martin Beyß
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Salah Azzouzi
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Michael Weitzel
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
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21
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Hollinshead W, He L, Tang YJ. 13C-Fingerprinting and Metabolic Flux Analysis of Bacterial Metabolisms. Methods Mol Biol 2019; 1927:215-230. [PMID: 30788795 DOI: 10.1007/978-1-4939-9142-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
13C-assisted metabolism analysis provides rigorous calculations of the intracellular reaction rates (i.e., fluxes) within the central metabolism of microbial hosts. This mapping of the intracellular network within microbes has proven to be essential for understanding the cell physiology. The approach is also a key to identifying central metabolic nodes, probing the rigidity of a metabolic network, revealing cofactor balances, and delineating hidden pathways. Here we present the methodology of using stable isotopic carbon substrates for both qualitative (13C-fingerprinting of functional pathways) and quantitative (Metabolic Flux Analysis) metabolism studies on bacterial species. In this methodology, we include step-by-step instructions to use the open source WUflux software for the steady-state flux calculations based on labeling information of amino acids or free metabolites.
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Affiliation(s)
- Whitney Hollinshead
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA
| | - Lian He
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA
| | - Yinjie J Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA.
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22
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Schwechheimer SK, Becker J, Wittmann C. Towards better understanding of industrial cell factories: novel approaches for 13C metabolic flux analysis in complex nutrient environments. Curr Opin Biotechnol 2018; 54:128-137. [DOI: 10.1016/j.copbio.2018.07.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/10/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022]
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23
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Advances in metabolic flux analysis toward genome-scale profiling of higher organisms. Biosci Rep 2018; 38:BSR20170224. [PMID: 30341247 PMCID: PMC6250807 DOI: 10.1042/bsr20170224] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 10/06/2018] [Accepted: 10/14/2018] [Indexed: 11/25/2022] Open
Abstract
Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.
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24
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Wan N, Wang H, Ng CK, Mukherjee M, Ren D, Cao B, Tang YJ. Bacterial Metabolism During Biofilm Growth Investigated by 13C Tracing. Front Microbiol 2018; 9:2657. [PMID: 30515135 PMCID: PMC6255981 DOI: 10.3389/fmicb.2018.02657] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 10/17/2018] [Indexed: 01/27/2023] Open
Abstract
This study investigated the metabolism of Pseudomonas aeruginosa PAO1 during its biofilm development via microscopy imaging, gene expression analysis, and 13C-labeling. First, dynamic labeling was employed to investigate glucose utilization rate in fresh biofilms (thickness 40∼60 micrometer). The labeling turnover time of glucose-6-P indicated biofilm metabolism was substantially slower than planktonic cells. Second, PAO1 was cultured in continuous tubular biofilm reactors or shake flasks. Then 13C-metabolic flux analysis of PAO1 was performed based on the isotopomer patterns of proteinogenic amino acids. The results showed that PAO1 biofilm cells during growth conserved the flux features as their planktonic mode. (1) Glucose could be degraded by two cyclic routes (the TCA cycle and the Entner-Doudoroff-Embden-Meyerhof-Parnas loop) that facilitated NAD(P)H supplies. (2) Anaplerotic pathways (including pyruvate shunt) increased flux plasticity. (3) Biofilm growth phenotype did not require significant intracellular flux rewiring (variations between biofilm and planktonic flux network, normalized by glucose uptake rate as 100%, were less than 20%). (4) Transcription analysis indicated that key catabolic genes in fresh biofilm cells had expression levels comparable to planktonic cells. Finally, PAO1, Shewanella oneidensis (as the comparing group), and their c-di-GMP transconjugants (with different biofilm formation capabilities) were 13C-labeled under biofilm reactors or planktonic conditions. Analysis of amino acid labeling variances from different cultures indicated Shewanella flux network was more flexibly changed than PAO1 during its biofilm formation.
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Affiliation(s)
- Ni Wan
- Mechanical Engineering and Materials Science, Washington University, St. Louis, MO, United States
| | - Hao Wang
- Department of Biomedical and Chemical Engineering, Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY, United States
| | - Chun Kiat Ng
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore.,Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore
| | - Manisha Mukherjee
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore.,Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore
| | - Dacheng Ren
- Department of Biomedical and Chemical Engineering, Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY, United States.,Department of Civil and Environmental Engineering, and Biology, Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY, United States
| | - Bin Cao
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore.,Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yinjie J Tang
- Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, United States
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25
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Model metabolic strategy for heterotrophic bacteria in the cold ocean based on Colwellia psychrerythraea 34H. Proc Natl Acad Sci U S A 2018; 115:12507-12512. [PMID: 30446608 DOI: 10.1073/pnas.1807804115] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Colwellia psychrerythraea 34H is a model psychrophilic bacterium found in the cold ocean-polar sediments, sea ice, and the deep sea. Although the genomes of such psychrophiles have been sequenced, their metabolic strategies at low temperature have not been quantified. We measured the metabolic fluxes and gene expression of 34H at 4 °C (the mean global-ocean temperature and a normal-growth temperature for 34H), making comparative analyses at room temperature (above its upper-growth temperature of 18 °C) and with mesophilic Escherichia coli When grown at 4 °C, 34H utilized multiple carbon substrates without catabolite repression or overflow byproducts; its anaplerotic pathways increased flux network flexibility and enabled CO2 fixation. In glucose-only medium, the Entner-Doudoroff (ED) pathway was the primary glycolytic route; in lactate-only medium, gluconeogenesis and the glyoxylate shunt became active. In comparison, E. coli, cold stressed at 4 °C, had rapid glycolytic fluxes but no biomass synthesis. At their respective normal-growth temperatures, intracellular concentrations of TCA cycle metabolites (α-ketoglutarate, succinate, malate) were 4-17 times higher in 34H than in E. coli, while levels of energy molecules (ATP, NADH, NADPH) were 10- to 100-fold lower. Experiments with E. coli mutants supported the thermodynamic advantage of the ED pathway at cold temperature. Heat-stressed 34H at room temperature (2 hours) revealed significant down-regulation of genes associated with glycolytic enzymes and flagella, while 24 hours at room temperature caused irreversible cellular damage. We suggest that marine heterotrophic bacteria in general may rely upon simplified metabolic strategies to overcome thermodynamic constraints and thrive in the cold ocean.
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26
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Abernathy MH, Zhang Y, Hollinshead WD, Wang G, Baidoo EEK, Liu T, Tang YJ. Comparative studies of glycolytic pathways and channeling under
in vitro
and
in vivo
modes. AIChE J 2018. [DOI: 10.1002/aic.16367] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Mary H. Abernathy
- Dept. of Energy, Environmental and Chemical Engineering Washington University St. Louis MO 63130
| | - Yuchen Zhang
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery Ministry of Education and Wuhan University School of Pharmaceutical Sciences Wuhan 430071 China
| | - Whitney D. Hollinshead
- Dept. of Energy, Environmental and Chemical Engineering Washington University St. Louis MO 63130
| | - George Wang
- Lawrence Berkeley National Laboratory Emeryville CA 64608
| | | | - Tiangang Liu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery Ministry of Education and Wuhan University School of Pharmaceutical Sciences Wuhan 430071 China
| | - Yinjie J. Tang
- Dept. of Energy, Environmental and Chemical Engineering Washington University St. Louis MO 63130
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27
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Desai TS, Srivastava S. FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses. PeerJ 2018; 6:e4716. [PMID: 29736347 PMCID: PMC5933345 DOI: 10.7717/peerj.4716] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 04/13/2018] [Indexed: 02/02/2023] Open
Abstract
13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package.
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Affiliation(s)
- Trunil S Desai
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India.,DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India
| | - Shireesh Srivastava
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India.,DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, Delhi, India
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Rosato A, Tenori L, Cascante M, De Atauri Carulla PR, Martins Dos Santos VAP, Saccenti E. From correlation to causation: analysis of metabolomics data using systems biology approaches. Metabolomics 2018; 14:37. [PMID: 29503602 PMCID: PMC5829120 DOI: 10.1007/s11306-018-1335-y] [Citation(s) in RCA: 123] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 01/31/2018] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches. OBJECTIVES This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods. METHODS We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis. RESULTS We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner. CONCLUSIONS Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.
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Affiliation(s)
- Antonio Rosato
- Magnetic Resonance Center and Department of Chemistry "Ugo Schiff", University of Florence, Florence, Italy.
| | - Leonardo Tenori
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Marta Cascante
- CIBER de Enfermedades hepáticas y digestivas (CIBERHD, Madrid) and Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona, Barcelona, Spain
| | - Pedro Ramon De Atauri Carulla
- CIBER de Enfermedades hepáticas y digestivas (CIBERHD, Madrid) and Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona, Barcelona, Spain
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands
- LifeGlimmer GmbH, Berlin, Germany
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands.
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Matsuda F, Toya Y, Shimizu H. Learning from quantitative data to understand central carbon metabolism. Biotechnol Adv 2017; 35:971-980. [DOI: 10.1016/j.biotechadv.2017.09.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 09/01/2017] [Accepted: 09/14/2017] [Indexed: 12/23/2022]
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