1
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Taniguchi T, Okahashi N, Matsuda F. 13C-metabolic flux analysis reveals metabolic rewiring in HL-60 neutrophil-like cells through differentiation and immune stimulation. Metab Eng Commun 2024; 18:e00239. [PMID: 38883865 PMCID: PMC11176794 DOI: 10.1016/j.mec.2024.e00239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/18/2024] Open
Abstract
Neutrophils are innate immune cells and the first line of defense for the maintenance of homeostasis. However, our knowledge of the metabolic rewiring associated with their differentiation and immune stimulation is limited. Here, quantitative 13C-metabolic flux analysis was performed using HL-60 cells as the neutrophil model. A metabolic model for 13C-metabolic flux analysis of neutrophils was developed based on the accumulation of 13C in intracellular metabolites derived from 13C-labeled extracellular carbon sources and intracellular macromolecules. Aspartate and glutamate in the medium were identified as carbon sources that enter central carbon metabolism. Furthermore, the breakdown of macromolecules, estimated to be fatty acids and nucleic acids, was observed. Based on these results, a modified metabolic model was used for 13C-metabolic flux analysis of undifferentiated, differentiated, and lipopolysaccharide (LPS)-activated HL-60 cells. The glucose uptake rate and glycolytic flux decreased with differentiation, whereas the tricarboxylic acid (TCA) cycle flux remained constant. The addition of LPS to differentiated HL-60 cells activated the glucose uptake rate and pentose phosphate pathway (PPP) flux levels, resulting in an increased rate of total NADPH regeneration, which could be used to generate reactive oxygen species. The flux levels of fatty acid degradation and synthesis were also increased in LPS-activated HL-60 cells. Overall, this study highlights the quantitative metabolic alterations in multiple pathways via the differentiation and activation of HL-60 cells using 13C-metabolic flux analysis.
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Affiliation(s)
- Takeo Taniguchi
- 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
- Department of Biotechnology, Osaka University Shimadzu Analytical Innovation Research Laboratory, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
- Industrial Biotechnology Initiative Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
- Department of Biotechnology, Osaka University Shimadzu Analytical Innovation Research Laboratory, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
- Industrial Biotechnology Initiative Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
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2
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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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3
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Wada K, Uebayashi K, Toya Y, Putri SP, Matsuda F, Fukusaki E, C Liao J, Shimizu H. Effects of n-butanol production on metabolism and the photosystem in Synecococcus elongatus PCC 7942 based on metabolic flux and target proteome analyses. J GEN APPL MICROBIOL 2024; 69:185-195. [PMID: 36935115 DOI: 10.2323/jgam.2023.03.002] [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] [Indexed: 03/21/2023]
Abstract
Although n-butanol (BuOH) is an ideal fuel because of its superior physical properties, it has toxicity to microbes. Previously, a Synechococcus elongatus PCC 7942 derivative strain that produces BuOH from CO2 was developed by introducing six heterologous genes (BUOH-SE strain). To identify the bottleneck in BuOH production, the effects of BuOH production and its toxicity on central metabolism and the photosystem were investigated. Parental (WT) and BUOH-SE strains were cultured under autotrophic conditions. Consistent with the results of a previous study, BuOH production was observed only in the BUOH-SE strain. Isotopically non-stationary 13C-metabolic flux analysis revealed that the CO2 fixation rate was much larger than the BuOH production rate in the BUOH-SE strain (1.70 vs 0.03 mmol gDCW-1 h-1), implying that the carbon flow for BuOH biosynthesis was less affected by the entire flux distribution. No large difference was observed in the flux of metabolism between the WT and BUOH-SE strains. Contrastingly, in the photosystem, the chlorophyll content and maximum O2 evolution rate per dry cell weight of the BUOH-SE strain were decreased to 81% and 43% of the WT strain, respectively. Target proteome analysis revealed that the amounts of some proteins related to antennae (ApcA, ApcD, ApcE, and CpcC), photosystem II (PsbB, PsbU, and Psb28-2), and cytochrome b6f complex (PetB and PetC) in photosystems decreased in the BUOH-SE strain. The activation of photosynthesis would be a novel approach for further enhancing BuOH production in S. elongatus PCC 7942.
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Affiliation(s)
- Keisuke Wada
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
| | - Kiyoka Uebayashi
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
| | - Sastia Prama Putri
- Department of Biotechnology, Graduate School of Engineering, Osaka University
| | - Fumio Matsuda
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
| | - Eiichiro Fukusaki
- Department of Biotechnology, Graduate School of Engineering, Osaka University
| | - James C Liao
- Department of Chemical and Biomolocular Engineering, University of California
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University
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4
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Lakhani A, Kang DH, Kang YE, Park JO. Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer. Endocrinol Metab (Seoul) 2023; 38:619-630. [PMID: 37989266 PMCID: PMC10764991 DOI: 10.3803/enm.2023.1814] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/27/2023] [Accepted: 11/01/2023] [Indexed: 11/23/2023] Open
Abstract
Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing's syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing's syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.
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Affiliation(s)
- Aliya Lakhani
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Da Hyun Kang
- Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Korea
| | - Yea Eun Kang
- Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Korea
| | - Junyoung O. Park
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, USA
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5
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Kaste JAM, Green A, Shachar-Hill Y. Integrative teaching of metabolic modeling and flux analysis with interactive python modules. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2023; 51:653-661. [PMID: 37584426 DOI: 10.1002/bmb.21777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 07/06/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The modeling of rates of biochemical reactions-fluxes-in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint-based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to-date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C-metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands-on computer laboratory component of a four-day metabolic modeling workshop and participant survey results showed improvements in learners' self-assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.
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Affiliation(s)
- Joshua A M Kaste
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, USA
| | - Antwan Green
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, USA
| | - Yair Shachar-Hill
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, USA
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6
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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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7
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Chen Q, Li H, Tian H, Lam SM, Liao Y, Zhang Z, Dong M, Chen S, Yao Y, Meng J, Zhang Y, Zheng L, Meng ZX, Han W, Shui G, Zhu D, Fu S. Global determination of reaction rates and lipid turnover kinetics in Mus musculus. Cell Metab 2023; 35:711-721.e4. [PMID: 37019081 DOI: 10.1016/j.cmet.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/01/2022] [Accepted: 03/07/2023] [Indexed: 04/07/2023]
Abstract
Metabolism is fundamental to life, but measuring metabolic reaction rates remains challenging. Here, we applied C13 fluxomics to monitor the metabolism of dietary glucose carbon in 12 tissues, 9 brain compartments, and over 1,000 metabolite isotopologues over a 4-day period. The rates of 85 reactions surrounding central carbon metabolism are determined with elementary metabolite unit (EMU) modeling. Lactate oxidation, not glycolysis, occurs at a comparable pace with the tricarboxylic acid cycle (TCA), supporting lactate as the primary fuel. We expand the EMU framework to track and quantify metabolite flows across tissues. Specifically, multi-organ EMU simulation of uridine metabolism shows that tissue-blood exchange, not synthesis, controls nucleotide homeostasis. In contrast, isotopologue fingerprinting and kinetic analyses reveal the brown adipose tissue (BAT) having the highest palmitate synthesis activity but no apparent contribution to circulation, suggesting a tissue-autonomous synthesis-to-burn mechanism. Together, this study demonstrates the utility of dietary fluxomics for kinetic mapping in vivo and provides a rich resource for elucidating inter-organ metabolic cross talk.
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Affiliation(s)
- Qishan Chen
- Guangzhou Laboratory, Guangzhou, Guangdong 510005, China
| | - Hu Li
- Bioland Laboratory, Guangzhou, Guangdong 510320, China
| | - He Tian
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Sin Man Lam
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; LipidALL Technologies Company Limited, Changzhou, Jiangsu 213022, China
| | - Yilie Liao
- Bioland Laboratory, Guangzhou, Guangdong 510320, China; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A(∗)STAR), Singapore 138673, Singapore
| | - Ziyin Zhang
- Department of Pathology and Pathophysiology and Department of Cardiology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Manyuan Dong
- The Institute of Cardiovascular Sciences and Institute of Systems Biomedicine, School of Basic Medical Sciences, Key Laboratory of Molecular Cardiovascular Science of Ministry of Education, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University, Beijing 100191, China
| | - Shaoru Chen
- Bioland Laboratory, Guangzhou, Guangdong 510320, China
| | - Yuxiao Yao
- Bioland Laboratory, Guangzhou, Guangdong 510320, China
| | - Jiemiao Meng
- Bioland Laboratory, Guangzhou, Guangdong 510320, China
| | - Yong Zhang
- Bioland Laboratory, Guangzhou, Guangdong 510320, China; The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
| | - Lemin Zheng
- The Institute of Cardiovascular Sciences and Institute of Systems Biomedicine, School of Basic Medical Sciences, Key Laboratory of Molecular Cardiovascular Science of Ministry of Education, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University, Beijing 100191, China
| | - Zhuo-Xian Meng
- Department of Pathology and Pathophysiology and Department of Cardiology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Weiping Han
- Bioland Laboratory, Guangzhou, Guangdong 510320, China; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A(∗)STAR), Singapore 138673, Singapore
| | - Guanghou Shui
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Dahai Zhu
- Bioland Laboratory, Guangzhou, Guangdong 510320, China; The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
| | - Suneng Fu
- Guangzhou Laboratory, Guangzhou, Guangdong 510005, China.
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8
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Isotope Calculation Gadgets: A Series of Software for Isotope-Tracing Experiments in Garuda Platform. Metabolites 2022; 12:metabo12070646. [PMID: 35888770 PMCID: PMC9318330 DOI: 10.3390/metabo12070646] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/12/2022] [Indexed: 02/06/2023] Open
Abstract
Isotope tracing is a powerful technique for elucidating intracellular metabolism. Experiments utilizing this technique involve various processes, such as the correction of natural isotopes. Although some previously developed software are available for these procedures, there are still time-consuming steps in isotope tracing including the creation of an isotope measurement method in mass spectrometry (MS) and the interpretation of obtained labeling data. Additionally, these multi-step tasks often require data format conversion, which is also time-consuming. In this study, the Isotope Calculation Gadgets, a series of software that supports an entire workflow of isotope-tracing experiments, was developed in the Garuda platform, an open community. Garuda is a graphical user interface-based platform that allows individual operations to be sequentially performed, without data format conversion, which significantly reduces the required time and effort. The developed software includes new features that construct channels for isotopomer measurements, as well as conventional functions such as natural isotope correction, the calculation of fractional labeling and split ratio, and data mapping, thus facilitating an overall workflow of isotope-tracing experiments through smooth functional integration.
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9
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Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y. Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Front Oncol 2022; 12:914594. [PMID: 35875150 PMCID: PMC9303011 DOI: 10.3389/fonc.2022.914594] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.
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Affiliation(s)
- Rachel H. Ng
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Jihoon W. Lee
- Medical Scientist Training Program, University of Washington, Seattle, WA, United States
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | | | - James R. Heath
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- *Correspondence: James R. Heath, ; Yapeng Su,
| | - Yapeng Su
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- Herbold Computational Biology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- *Correspondence: James R. Heath, ; Yapeng Su,
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Noguchi S, Wakita K, Matsuda F, Shimizu H. 13C metabolic flux analysis clarifies distinct metabolic phenotypes of cancer cell spheroid mimicking tumor hypoxia. Metab Eng 2022; 73:192-200. [DOI: 10.1016/j.ymben.2022.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/09/2022] [Accepted: 07/25/2022] [Indexed: 11/30/2022]
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