1
|
Uy ALT, Yamamoto A, Matsuda M, Arae T, Hasunuma T, Demura T, Ohtani M. The Carbon Flow Shifts from Primary to Secondary Metabolism during Xylem Vessel Cell Differentiation in Arabidopsis thaliana. PLANT & CELL PHYSIOLOGY 2023; 64:1563-1575. [PMID: 37875012 PMCID: PMC10734892 DOI: 10.1093/pcp/pcad130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 10/12/2023] [Accepted: 10/23/2023] [Indexed: 10/26/2023]
Abstract
Xylem vessel cell differentiation is characterized by the deposition of a secondary cell wall (SCW) containing cellulose, hemicellulose and lignin. VASCULAR-RELATED NAC-DOMAIN7 (VND7), a plant-specific NAC (NAM, ATAF1/2, and CUC2) transcription factor, is a master regulator of xylem vessel cell differentiation in Arabidopsis (Arabidopsis thaliana). Previous metabolome analysis using the VND7-inducible system in tobacco BY-2 cells successfully revealed significant quantitative changes in primary metabolites during xylem vessel cell differentiation. However, the flow of primary metabolites is not yet well understood. Here, we performed a metabolomic analysis of VND7-inducible Arabidopsis T87 suspension cells. Capillary electrophoresis-time-of-flight mass spectrometry quantified 57 metabolites, and subsequent data analysis highlighted active changes in the levels of UDP-glucose and phenylalanine, which are building blocks of cellulose and lignin, respectively. In a metabolic flow analysis using stable carbon 13 (13C) isotope, the 13C-labeling ratio specifically increased in 3-phosphoglycerate after 12 h of VND7 induction, followed by an increase in shikimate after 24 h of induction, while the inflow of 13C into lactate from pyruvate was significantly inhibited, indicating an active shift of carbon flow from glycolysis to the shikimate pathway during xylem vessel cell differentiation. In support of this notion, most glycolytic genes involved in the downstream of glyceraldehyde 3-phosphate were downregulated following the induction of xylem vessel cell differentiation, whereas genes for the shikimate pathway and phenylalanine biosynthesis were upregulated. These findings provide evidence for the active shift of carbon flow from primary metabolic pathways to the SCW polymer biosynthetic pathway at specific points during xylem vessel cell differentiation.
Collapse
Affiliation(s)
| | - Atsushi Yamamoto
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8562 Japan
| | - Mami Matsuda
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo, 657-8501 Japan
| | - Toshihiro Arae
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8562 Japan
| | - Tomohisa Hasunuma
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo, 657-8501 Japan
- Engineering Biology Research Center, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo, 657-8501 Japan
| | - Taku Demura
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, 630-0192 Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-Cho, Tsurumi-Ku, Yokohama, Kanagawa, 230-0045 Japan
| | - Misato Ohtani
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, 630-0192 Japan
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8562 Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-Cho, Tsurumi-Ku, Yokohama, Kanagawa, 230-0045 Japan
| |
Collapse
|
2
|
Gonzales JN, Treece TR, Mayfield SP, Simkovsky R, Atsumi S. Utilization of lignocellulosic hydrolysates for photomixotrophic chemical production in Synechococcus elongatus PCC 7942. Commun Biol 2023; 6:1022. [PMID: 37813969 PMCID: PMC10562401 DOI: 10.1038/s42003-023-05394-w] [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: 07/06/2023] [Accepted: 09/27/2023] [Indexed: 10/11/2023] Open
Abstract
To meet the need for environmentally friendly commodity chemicals, feedstocks for biological chemical production must be diversified. Lignocellulosic biomass are an carbon source with the potential for effective use in a large scale and cost-effective production systems. Although the use of lignocellulosic biomass lysates for heterotrophic chemical production has been advancing, there are challenges to overcome. Here we aim to investigate the obligate photoautotroph cyanobacterium Synechococcus elongatus PCC 7942 as a chassis organism for lignocellulosic chemical production. When modified to import monosaccharides, this cyanobacterium is an excellent candidate for lysates-based chemical production as it grows well at high lysate concentrations and can fix CO2 to enhance carbon efficiency. This study is an important step forward in enabling the simultaneous use of two sugars as well as lignocellulosic lysate. Incremental genetic modifications enable catabolism of both sugars concurrently without experiencing carbon catabolite repression. Production of 2,3-butanediol is demonstrated to characterize chemical production from the sugars in lignocellulosic hydrolysates. The engineered strain achieves a titer of 13.5 g L-1 of 2,3-butanediol over 12 days under shake-flask conditions. This study can be used as a foundation for industrial scale production of commodity chemicals from a combination of sunlight, CO2, and lignocellulosic sugars.
Collapse
Affiliation(s)
- Jake N Gonzales
- Plant Biology Graduate Group, University of California, Davis, Davis, CA, 95616, USA
| | - Tanner R Treece
- Department of Chemistry, University of California, Davis, Davis, CA, 95616, USA
| | - Stephen P Mayfield
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- California Center for Algae Biotechnology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Ryan Simkovsky
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- California Center for Algae Biotechnology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Shota Atsumi
- Plant Biology Graduate Group, University of California, Davis, Davis, CA, 95616, USA.
- Department of Chemistry, University of California, Davis, Davis, CA, 95616, USA.
| |
Collapse
|
3
|
Mitosch K, Beyß M, Phapale P, Drotleff B, Nöh K, Alexandrov T, Patil KR, Typas A. A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella. PLoS Biol 2023; 21:e3002198. [PMID: 37594988 PMCID: PMC10468081 DOI: 10.1371/journal.pbio.3002198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/30/2023] [Accepted: 06/16/2023] [Indexed: 08/20/2023] Open
Abstract
Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells.
Collapse
Affiliation(s)
- Karin Mitosch
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Martin Beyß
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
- RWTH Aachen University, Computational Systems Biotechnology, Aachen, Germany
| | - Prasad Phapale
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Bernhard Drotleff
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Theodore Alexandrov
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- BioInnovation Institute, Copenhagen, Denmark
| | - Kiran R. Patil
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Athanasios Typas
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| |
Collapse
|
4
|
Thommen Q, Hurbain J, Pfeuty B. Stochastic simulation algorithm for isotope-based dynamic flux analysis. Metab Eng 2023; 75:100-109. [PMID: 36402409 DOI: 10.1016/j.ymben.2022.11.001] [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: 02/08/2022] [Revised: 10/05/2022] [Accepted: 11/03/2022] [Indexed: 11/19/2022]
Abstract
Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA), but lacks generalizability to non-stationary metabolic conditions. In this study, we present a stochastic simulation algorithm (SSA) derived from the chemical master equation of the isotope labeling system. This algorithm allows to compute the time evolution of isotopomer concentrations in non-stationary conditions, with the valuable property that computational time does not scale with the number of isotopomers. The efficiency and limitations of the algorithm is benchmarked for the forward and inverse problems of 13C-DMFA in the pentose phosphate pathways, and is compared with EMU-based methods for NMFA and MFA including the central carbon metabolism. Overall, SSA constitutes an alternative class to deterministic approaches for metabolic flux analysis that is well adapted to comprehensive dataset including parallel labeling experiments, and whose limitations associated to the sampling size can be overcome by using Monte Carlo sampling approaches.
Collapse
Affiliation(s)
- Quentin Thommen
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277 - CANTHER - Cancer Heterogeneity Plasticity and Resistance to Therapies, F-59000, Lille, France.
| | - Julien Hurbain
- Univ. Lille, CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, F-59000, Lille, France
| | - Benjamin Pfeuty
- Univ. Lille, CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, F-59000, Lille, France
| |
Collapse
|
5
|
Hou S, Chen P, He J, Chen J, Zhang J, Mammano F, Yang J. Dietary intake of deuterium oxide decreases cochlear metabolism and oxidative stress levels in a mouse model of age-related hearing loss. Redox Biol 2022; 57:102472. [PMID: 36162258 PMCID: PMC9513171 DOI: 10.1016/j.redox.2022.102472] [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: 06/15/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 11/15/2022] Open
Abstract
Age-related hearing loss (ARHL) is the most prevalent sensory disorder in the elderly. Currently, no treatment can effectively prevent or reverse ARHL. Aging auditory organs are often accompanied by exacerbated oxidative stress and metabolic deterioration. Here, we report the effect of deuterated oxygen (D2O), also known as "heavy water", mouse models of ARHL. Supplementing the normal mouse diet with 10% D2O from 4 to 9 weeks of age lowered hearing thresholds at selected frequencies in treated mice compared to untreated control group. Oxidative stress levels were significantly reduced and in the cochlear duct of treated vs. untreated mice. Through metabolic flux analysis, we found that D2O mainly slowed down catabolic reactions, and may delay metabolic deterioration related to aging to a certain extent. Experiments confirmed that the Nrf2/HO-1/glutathione axis was down-regulated in treated mice. Thus, D2O supplementation can hinder ARHL progression in mouse models by slowing the pace of metabolism and reducing endogenous oxidative stress production in the cochlea. These findings open new avenues for protecting the cochlea from oxidative stress and regulating metabolism to prevent ARHL.
Collapse
Affiliation(s)
- Shule Hou
- Department of Otorhinolaryngology-Head & Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Penghui Chen
- Department of Otorhinolaryngology-Head & Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China.
| | - Jingchun He
- Department of Otorhinolaryngology-Head & Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Junmin Chen
- Department of Otorhinolaryngology-Head & Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Jifang Zhang
- Department of Otorhinolaryngology-Head & Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Fabio Mammano
- Department of Physics and Astronomy "G. Galilei", University of Padua, Padova, Italy; Department of Biomedical Sciences, Institute of Cell Biology and Neurobiology, Italian National Research Council, Monterotondo, Italy.
| | - Jun Yang
- Department of Otorhinolaryngology-Head & Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China.
| |
Collapse
|
6
|
Trub AG, Wagner GR, Anderson KA, Crown SB, Zhang GF, Thompson JW, Ilkayeva OR, Stevens RD, Grimsrud PA, Kulkarni RA, Backos DS, Meier JL, Hirschey MD. Statin therapy inhibits fatty acid synthase via dynamic protein modifications. Nat Commun 2022; 13:2542. [PMID: 35538051 PMCID: PMC9090928 DOI: 10.1038/s41467-022-30060-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/12/2022] [Indexed: 12/15/2022] Open
Abstract
Statins are a class of drug widely prescribed for the prevention of cardiovascular disease, with pleiotropic cellular effects. Statins inhibit HMG-CoA reductase (HMGCR), which converts the metabolite HMG-CoA into mevalonate. Recent discoveries have shown HMG-CoA is a reactive metabolite that can non-enzymatically modify proteins and impact their activity. Therefore, we predicted that inhibition of HMGCR by statins might increase HMG-CoA levels and protein modifications. Upon statin treatment, we observe a strong increase in HMG-CoA levels and modification of only a single protein. Mass spectrometry identifies this protein as fatty acid synthase (FAS), which is modified on active site residues and, importantly, on non-lysine side-chains. The dynamic modifications occur only on a sub-pool of FAS that is located near HMGCR and alters cellular signaling around the ER and Golgi. These results uncover communication between cholesterol and lipid biosynthesis by the substrate of one pathway inhibiting another in a rapid and reversible manner.
Collapse
Affiliation(s)
- Alec G Trub
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA
- Department of Pharmacology & Cancer Biology, Durham, NC, USA
| | - Gregory R Wagner
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Durham, NC, USA
| | - Kristin A Anderson
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA
- Department of Pharmacology & Cancer Biology, Durham, NC, USA
| | - Scott B Crown
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA
| | - Guo-Fang Zhang
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Durham, NC, USA
| | - J Will Thompson
- Department of Pharmacology & Cancer Biology, Durham, NC, USA
- Duke Proteomics and Metabolomics Shared Resource, Duke University Medical Center, Durham, NC, 27710, USA
| | - Olga R Ilkayeva
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Durham, NC, USA
| | - Robert D Stevens
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA
| | - Paul A Grimsrud
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Durham, NC, USA
| | - Rhushikesh A Kulkarni
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Donald S Backos
- Computational Chemistry and Biology Core Facility, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan L Meier
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Matthew D Hirschey
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, NC, USA.
- Department of Pharmacology & Cancer Biology, Durham, NC, USA.
- Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Durham, NC, USA.
| |
Collapse
|
7
|
Sake CL, Metcalf AJ, Meagher M, Paola JD, Neeves KB, Boyle NR. Isotopically nonstationary 13C metabolic flux analysis in resting and activated human platelets. Metab Eng 2022; 69:313-322. [PMID: 34954086 PMCID: PMC8905147 DOI: 10.1016/j.ymben.2021.12.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 12/06/2021] [Accepted: 12/20/2021] [Indexed: 01/03/2023]
Abstract
Platelet metabolism is linked to platelet hyper- and hypoactivity in numerous human diseases. Developing a detailed understanding of the link between metabolic shifts and platelet activation state is integral to improving human health. Here, we show the first application of isotopically nonstationary 13C metabolic flux analysis to quantitatively measure carbon fluxes in both resting and thrombin activated platelets. Metabolic flux analysis results show that resting platelets primarily metabolize glucose to lactate via glycolysis, while acetate is oxidized to fuel the tricarboxylic acid cycle. Upon activation with thrombin, a potent platelet agonist, platelets increase their uptake of glucose 3-fold. This results in an absolute increase in flux throughout central metabolism, but when compared to resting platelets they redistribute carbon dramatically. Activated platelets decrease relative flux to the oxidative pentose phosphate pathway and TCA cycle from glucose and increase relative flux to lactate. These results provide the first report of reaction-level carbon fluxes in platelets and allow us to distinguish metabolic fluxes with much higher resolution than previous studies.
Collapse
Affiliation(s)
- Cara L. Sake
- Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, 80401 USA
| | - Alexander J. Metcalf
- Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, 80401 USA
| | - Michelle Meagher
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jorge Di Paola
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Keith B. Neeves
- Department of Bioengineering, University of Colorado, Aurora, CO, 80045 USA,Hemophilia and Thrombosis Center, University of Colorado, Aurora, CO, 80045 USA,Department of Pediatrics, Section of Hematology, Oncology, and Bone Marrow Transplant University of Colorado, Aurora, CO, 80045 USA
| | - Nanette R. Boyle
- Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, 80401 USA,Correspondence: , 423 Alderson Hall; 1613 Illinois St.; Golden, CO 80401
| |
Collapse
|
8
|
How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review. Processes (Basel) 2021. [DOI: 10.3390/pr9091577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.
Collapse
|
9
|
Wang Y, Hui S, Wondisford FE, Su X. Utilizing tandem mass spectrometry for metabolic flux analysis. J Transl Med 2021; 101:423-429. [PMID: 32994481 PMCID: PMC7987671 DOI: 10.1038/s41374-020-00488-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/01/2020] [Accepted: 09/01/2020] [Indexed: 12/23/2022] Open
Abstract
Metabolic flux analysis (MFA) aims at revealing the metabolic reaction rates in a complex biochemical network. To do so, MFA uses the input of stable isotope labeling patterns of the intracellular metabolites. Elementary metabolic unit (EMU) is the computational framework to simulate the metabolite labeling patterns in a network, which was originally designed for simulating mass isotopomer distributions (MIDs) at the MS1 level. Recently, the EMU framework is expanded to simulate tandem mass spectrometry data. Tandem mass spectrometry has emerged as a new experimental approach to provide information on the positional isotope labeling of metabolites and therefore greatly improves the precision of MFA. In this review, we will discuss the new EMU framework that can accommodate the tandem mass isotopomer distributions (TMIDs) data. We will also analyze the improvement on the MFA precision by using TMID. Our analysis shows that combining the MIDs of the parent and daughter ions and the TMID for the MFA is more powerful than using TMID alone.
Collapse
Affiliation(s)
- Yujue Wang
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Sheng Hui
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Fredric E Wondisford
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Xiaoyang Su
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
- Metabolomics Shared Resource, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
| |
Collapse
|
10
|
McNally LA, Altamimi TR, Fulghum K, Hill BG. Considerations for using isolated cell systems to understand cardiac metabolism and biology. J Mol Cell Cardiol 2020; 153:26-41. [PMID: 33359038 DOI: 10.1016/j.yjmcc.2020.12.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022]
Abstract
Changes in myocardial metabolic activity are fundamentally linked to cardiac health and remodeling. Primary cardiomyocytes, induced pluripotent stem cell-derived cardiomyocytes, and transformed cardiomyocyte cell lines are common models used to understand how (patho)physiological conditions or stimuli contribute to changes in cardiac metabolism. These cell models are helpful also for defining metabolic mechanisms of cardiac dysfunction and remodeling. Although technical advances have improved our capacity to measure cardiomyocyte metabolism, there is often heterogeneity in metabolic assay protocols and cell models, which could hinder data interpretation and discernment of the mechanisms of cardiac (patho)physiology. In this review, we discuss considerations for integrating cardiomyocyte cell models with techniques that have become relatively common in the field, such as respirometry and extracellular flux analysis. Furthermore, we provide overviews of metabolic assays that complement XF analyses and that provide information on not only catabolic pathway activity, but biosynthetic pathway activity and redox status as well. Cultivating a more widespread understanding of the advantages and limitations of metabolic measurements in cardiomyocyte cell models will continue to be essential for the development of coherent metabolic mechanisms of cardiac health and pathophysiology.
Collapse
Affiliation(s)
- Lindsey A McNally
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, KY, USA
| | - Tariq R Altamimi
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, KY, USA
| | - Kyle Fulghum
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, KY, USA
| | - Bradford G Hill
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, KY, USA.
| |
Collapse
|
11
|
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: 53] [Impact Index Per Article: 13.3] [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.
Collapse
Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Michigan, Ann Arbor, MI, 48109, USA.
| |
Collapse
|
12
|
Volkova S, Matos MRA, Mattanovich M, Marín de Mas I. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites 2020; 10:E303. [PMID: 32722118 PMCID: PMC7465778 DOI: 10.3390/metabo10080303] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/08/2020] [Accepted: 07/22/2020] [Indexed: 01/05/2023] Open
Abstract
Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.
Collapse
Affiliation(s)
| | | | | | - Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; (S.V.); (M.R.A.M.); (M.M.)
| |
Collapse
|
13
|
Clark TJ, Guo L, Morgan J, Schwender J. Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models. ANNUAL REVIEW OF PLANT BIOLOGY 2020; 71:303-326. [PMID: 32017600 DOI: 10.1146/annurev-arplant-050718-100221] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For 13C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.
Collapse
Affiliation(s)
- Teresa J Clark
- Biology Department, Brookhaven National Laboratory, Upton, New York 11973, USA; ,
| | - Longyun Guo
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA; ,
| | - John Morgan
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA; ,
| | - Jorg Schwender
- Biology Department, Brookhaven National Laboratory, Upton, New York 11973, USA; ,
| |
Collapse
|
14
|
Deja S, Fu X, Fletcher JA, Kucejova B, Browning JD, Young JD, Burgess SC. Simultaneous tracers and a unified model of positional and mass isotopomers for quantification of metabolic flux in liver. Metab Eng 2019; 59:1-14. [PMID: 31891762 DOI: 10.1016/j.ymben.2019.12.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 12/12/2019] [Accepted: 12/21/2019] [Indexed: 12/23/2022]
Abstract
Computational models based on the metabolism of stable isotope tracers can yield valuable insight into the metabolic basis of disease. The complexity of these models is limited by the number of tracers and the ability to characterize tracer labeling in downstream metabolites. NMR spectroscopy is ideal for multiple tracer experiments since it precisely detects the position of tracer nuclei in molecules, but it lacks sensitivity for detecting low-concentration metabolites. GC-MS detects stable isotope mass enrichment in low-concentration metabolites, but lacks nuclei and positional specificity. We performed liver perfusions and in vivo infusions of 2H and 13C tracers, yielding complex glucose isotopomers that were assigned by NMR and fit to a newly developed metabolic model. Fluxes regressed from 2H and 13C NMR positional isotopomer enrichments served to validate GC-MS-based flux estimates obtained from the same experimental samples. NMR-derived fluxes were largely recapitulated by modeling the mass isotopomer distributions of six glucose fragment ions measured by GC-MS. Modest differences related to limited fragmentation coverage of glucose C1-C3 were identified, but fluxes such as gluconeogenesis, glycogenolysis, cataplerosis and TCA cycle flux were tightly correlated between the methods. Most importantly, modeling of GC-MS data could assign fluxes in primary mouse hepatocytes, an experiment that is impractical by 2H or 13C NMR.
Collapse
Affiliation(s)
- Stanislaw Deja
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Xiaorong Fu
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Justin A Fletcher
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Blanka Kucejova
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jeffrey D Browning
- Department of Clinical Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jamey D Young
- Department of Chemical and Biomolecular Engineering, Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37235, USA.
| | - Shawn C Burgess
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
| |
Collapse
|
15
|
Hill BG, Shiva S, Ballinger S, Zhang J, Darley-Usmar VM. Bioenergetics and translational metabolism: implications for genetics, physiology and precision medicine. Biol Chem 2019; 401:3-29. [PMID: 31815377 PMCID: PMC6944318 DOI: 10.1515/hsz-2019-0268] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 06/24/2019] [Indexed: 12/25/2022]
Abstract
It is now becoming clear that human metabolism is extremely plastic and varies substantially between healthy individuals. Understanding the biochemistry that underlies this physiology will enable personalized clinical interventions related to metabolism. Mitochondrial quality control and the detailed mechanisms of mitochondrial energy generation are central to understanding susceptibility to pathologies associated with aging including cancer, cardiac and neurodegenerative diseases. A precision medicine approach is also needed to evaluate the impact of exercise or caloric restriction on health. In this review, we discuss how technical advances in assessing mitochondrial genetics, cellular bioenergetics and metabolomics offer new insights into developing metabolism-based clinical tests and metabolotherapies. We discuss informatics approaches, which can define the bioenergetic-metabolite interactome and how this can help define healthy energetics. We propose that a personalized medicine approach that integrates metabolism and bioenergetics with physiologic parameters is central for understanding the pathophysiology of diseases with a metabolic etiology. New approaches that measure energetics and metabolomics from cells isolated from human blood or tissues can be of diagnostic and prognostic value to precision medicine. This is particularly significant with the development of new metabolotherapies, such as mitochondrial transplantation, which could help treat complex metabolic diseases.
Collapse
Affiliation(s)
- Bradford G. Hill
- Envirome Institute, Diabetes and Obesity Center, Department of Medicine, University of Louisville, Louisville, KY 40202
| | - Sruti Shiva
- Department of Pharmacology & Chemical Biology, Vascular Medicine Institute, Center for Metabolism & Mitochondrial Medicine, University of Pittsburgh, Pittsburgh, PA 15143
| | - Scott Ballinger
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294
- Mitochondrial Medicine Laboratory, University of Alabama at Birmingham, Birmingham, AL 35294
- Center for Free Radical Biology, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Jianhua Zhang
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294
- Mitochondrial Medicine Laboratory, University of Alabama at Birmingham, Birmingham, AL 35294
- Center for Free Radical Biology, University of Alabama at Birmingham, Birmingham, AL 35294
- Department of Veteran Affairs Medical Center, Birmingham, AL 35294
| | - Victor M. Darley-Usmar
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294
- Mitochondrial Medicine Laboratory, University of Alabama at Birmingham, Birmingham, AL 35294
- Center for Free Radical Biology, University of Alabama at Birmingham, Birmingham, AL 35294
| |
Collapse
|
16
|
Metabolic flux ratio analysis by parallel 13C labeling of isoprenoid biosynthesis in Rhodobacter sphaeroides. Metab Eng 2019; 57:228-238. [PMID: 31843486 DOI: 10.1016/j.ymben.2019.12.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 12/02/2019] [Accepted: 12/12/2019] [Indexed: 11/21/2022]
Abstract
Metabolic engineering for increased isoprenoid production often benefits from the simultaneous expression of the two naturally available isoprenoid metabolic routes, namely the 2-methyl-D-erythritol 4-phosphate (MEP) pathway and the mevalonate (MVA) pathway. Quantification of the contribution of these pathways to the overall isoprenoid production can help to obtain a better understanding of the metabolism within a microbial cell factory. Such type of investigation can benefit from 13C metabolic flux ratio studies. Here, we designed a method based on parallel labeling experiments (PLEs), using [1-13C]- and [4-13C]glucose as tracers to quantify the metabolic flux ratios in the glycolytic and isoprenoid pathways. By just analyzing a reporter isoprenoid molecule and employing only four equations, we could describe the metabolism involved from substrate catabolism to product formation. These equations infer 13C atom incorporation into the universal isoprenoid building blocks, isopentenyl-pyrophosphate (IPP) and dimethylallyl-pyrophosphate (DMAPP). Therefore, this renders the method applicable to the study of any of isoprenoid of interest. As proof of principle, we applied it to study amorpha-4,11-diene biosynthesis in the bacterium Rhodobacter sphaeroides. We confirmed that in this species the Entner-Doudoroff pathway is the major pathway for glucose catabolism, while the Embden-Meyerhof-Parnas pathway contributes to a lesser extent. Additionally, we demonstrated that co-expression of the MEP and MVA pathways caused a mutual enhancement of their metabolic flux capacity. Surprisingly, we also observed that the isoprenoid flux ratio remains constant under exponential growth conditions, independently from the expression level of the MVA pathway. Apart from proposing and applying a tool for studying isoprenoid biosynthesis within a microbial cell factory, our work reveals important insights from the co-expression of MEP and MVA pathways, including the existence of a yet unclear interaction between them.
Collapse
|
17
|
Lorkiewicz PK, Gibb AA, Rood BR, He L, Zheng Y, Clem BF, Zhang X, Hill BG. Integration of flux measurements and pharmacological controls to optimize stable isotope-resolved metabolomics workflows and interpretation. Sci Rep 2019; 9:13705. [PMID: 31548575 PMCID: PMC6757038 DOI: 10.1038/s41598-019-50183-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 09/02/2019] [Indexed: 11/29/2022] Open
Abstract
Stable isotope-resolved metabolomics (SIRM) provides information regarding the relative activity of numerous metabolic pathways and the contribution of nutrients to specific metabolite pools; however, SIRM experiments can be difficult to execute, and data interpretation is challenging. Furthermore, standardization of analytical procedures and workflows remain significant obstacles for widespread reproducibility. Here, we demonstrate the workflow of a typical SIRM experiment and suggest experimental controls and measures of cross-validation that improve data interpretation. Inhibitors of glycolysis and oxidative phosphorylation as well as mitochondrial uncouplers serve as pharmacological controls, which help define metabolic flux configurations that occur under well-controlled metabolic states. We demonstrate how such controls and time course labeling experiments improve confidence in metabolite assignments as well as delineate metabolic pathway relationships. Moreover, we demonstrate how radiolabeled tracers and extracellular flux analyses integrate with SIRM to improve data interpretation. Collectively, these results show how integration of flux methodologies and use of pharmacological controls increase confidence in SIRM data and provide new biological insights.
Collapse
Affiliation(s)
- Pawel K Lorkiewicz
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, USA
- Department of Chemistry, Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, USA
| | - Andrew A Gibb
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, USA
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Benjamin R Rood
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, USA
| | - Liqing He
- Department of Chemistry, Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, USA
| | - Yuting Zheng
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, USA
| | - Brian F Clem
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Xiang Zhang
- Department of Chemistry, Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, USA
| | - Bradford G Hill
- Department of Medicine, Division of Environmental Medicine, Christina Lee Brown Envirome Institute, Diabetes and Obesity Center, University of Louisville, Louisville, USA.
| |
Collapse
|
18
|
GC-MS-based 13C metabolic flux analysis resolves the parallel and cyclic glucose metabolism of Pseudomonas putida KT2440 and Pseudomonas aeruginosa PAO1. Metab Eng 2019; 54:35-53. [DOI: 10.1016/j.ymben.2019.01.008] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 01/16/2019] [Accepted: 01/16/2019] [Indexed: 01/05/2023]
|
19
|
Nöh K, Niedenführ S, Beyß M, Wiechert W. A Pareto approach to resolve the conflict between information gain and experimental costs: Multiple-criteria design of carbon labeling experiments. PLoS Comput Biol 2018; 14:e1006533. [PMID: 30379837 PMCID: PMC6209137 DOI: 10.1371/journal.pcbi.1006533] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 09/27/2018] [Indexed: 01/23/2023] Open
Abstract
Science revolves around the best way of conducting an experiment to obtain insightful results. Experiments with maximal information content can be found by computational experimental design (ED) strategies that identify optimal conditions under which to perform the experiment. Several criteria have been proposed to measure the information content, each emphasizing different aspects of the design goal, i.e., reduction of uncertainty. Where experiments are complex or expensive, second sight is at the budget governing the achievable amount of information. In this context, the design objectives cost and information gain are often incommensurable, though dependent. By casting the ED task into a multiple-criteria optimization problem, a set of trade-off designs is derived that approximates the Pareto-frontier which is instrumental for exploring preferable designs. In this work, we present a computational methodology for multiple-criteria ED of information-rich experiments that accounts for virtually any set of design criteria. The methodology is implemented for the case of 13C metabolic flux analysis (MFA), which is arguably the most expensive type among the ‘omics’ technologies, featuring dozens of design parameters (tracer composition, analytical platform, measurement selection etc.). Supported by an innovative visualization scheme, we demonstrate with two realistic showcases that the use of multiple criteria reveals deep insights into the conflicting interplay between information carriers and cost factors that are not amendable to single-objective ED. For instance, tandem mass spectrometry turns out as best-in-class with respect to information gain, while it delivers this information quality cheaper than the other, routinely applied analytical technologies. Therewith, our Pareto approach to ED offers the investigator great flexibilities in the conception phase of a study to balance costs and benefits. Designing experiments is obligatory in the biosciences to valorize their scientific outcome. When the experiments are expensive, unfortunately, in practice often the costs emerge to be showstoppers. In this situation the question arises: How to get the most out of the experiment for your invest in terms of time and money? We approach this question by formulating the design task as a multiple-criteria optimization problem. Its solution produces a set of Pareto-optimal design proposals that feature the trade-off between information gain, as measured by different metrics, and the costs. Then, exploration of the design proposals allows us to make the best decision on information-economic experiments under given circumstances. Implemented in the field of isotope-based metabolic flux analysis, practical application of the Pareto approach provides detailed insight into the tight interplay of plenty of information carriers and cost factors. Supported by an innovative tailored visual representation scheme, the investigator is enabled to explore the options before conducting the experiment. With a practical showcase at hand, our computational study highlights the benefits of incorporating multiple information criteria apart from the costs, balancing the shortcomings of conventional single-objective experimental design strategies.
Collapse
Affiliation(s)
- Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
- * E-mail:
| | - Sebastian Niedenführ
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Martin Beyß
- 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, RWTH Aachen University, Aachen, Germany
| |
Collapse
|
20
|
Ahn WS, Dong W, Zhang Z, Cantor JR, Sabatini DM, Iliopoulos O, Stephanopoulos G. Glyceraldehyde 3-phosphate dehydrogenase modulates nonoxidative pentose phosphate pathway to provide anabolic precursors in hypoxic tumor cells. AIChE J 2018. [DOI: 10.1002/aic.16423] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Woo S. Ahn
- Dept. of Chemical Engineering; Massachusetts Institute of Technology; Cambridge MA 02139
| | - Wentao Dong
- Dept. of Chemical Engineering; Massachusetts Institute of Technology; Cambridge MA 02139
| | - Zhe Zhang
- Dept. of Chemical Engineering; Massachusetts Institute of Technology; Cambridge MA 02139
| | - Jason R. Cantor
- Dept. of Biology; Whitehead Institute for Biomedical Research and Massachusetts Institute of Technology; Cambridge MA 02142
- Dept. of Biology; Howard Hughes Medical Institute, Massachusetts Institute of Technology; Cambridge MA 02139
- Koch Institute for Integrative Cancer Research; Cambridge MA 02139
- Broad Institute of Harvard and Massachusetts Institute of Technology; Cambridge MA 02142
| | - David M. Sabatini
- Dept. of Biology; Whitehead Institute for Biomedical Research and Massachusetts Institute of Technology; Cambridge MA 02142
- Dept. of Biology; Howard Hughes Medical Institute, Massachusetts Institute of Technology; Cambridge MA 02139
- Koch Institute for Integrative Cancer Research; Cambridge MA 02139
- Broad Institute of Harvard and Massachusetts Institute of Technology; Cambridge MA 02142
| | - Othon Iliopoulos
- Center for Cancer Research; Massachusetts General Hospital Cancer Center; Boston MA 02114
- Dept. of Medicine; Harvard Medical School; Boston MA 02115
- Division of Hematology-Oncology, Dept. of Medicine; Massachusetts General Hospital; Boston MA 02114
| | - Gregory Stephanopoulos
- Dept. of Chemical Engineering; Massachusetts Institute of Technology; Cambridge MA 02139
| |
Collapse
|
21
|
Jaiswal D, Prasannan CB, Hendry JI, Wangikar PP. SWATH Tandem Mass Spectrometry Workflow for Quantification of Mass Isotopologue Distribution of Intracellular Metabolites and Fragments Labeled with Isotopic 13C Carbon. Anal Chem 2018; 90:6486-6493. [PMID: 29712418 DOI: 10.1021/acs.analchem.7b05329] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Accurate quantification of mass isotopologue distribution (MID) of metabolites is a prerequisite for 13C-metabolic flux analysis. Currently used mass spectrometric (MS) techniques based on multiple reaction monitoring (MRM) place limitations on the number of MIDs that can be analyzed in a single run. Moreover, the deconvolution step results in amplification of error. Here, we demonstrate that SWATH MS/MS, a data independent acquisition (DIA) technique allows quantification of a large number of precursor and product MIDs in a single run. SWATH sequentially fragments all precursor ions in stacked mass isolation windows. Co-fragmentation of all precursor isotopologues in a single SWATH window yields higher sensitivity enabling quantification of MIDs of fragments with low abundance and lower systematic and random errors. We quantify the MIDs of 53 precursor and product ions corresponding to 19 intracellular metabolites from a dynamic 13C-labeling of a model cyanobacterium, Synechococcus sp. PCC 7002. The use of product MIDs resulted in an improved precision of many measured fluxes compared to when only precursor MIDs were used for flux analysis. The approach is truly untargeted and allows additional metabolites to be quantified from the same data.
Collapse
Affiliation(s)
- Damini Jaiswal
- Department of Chemical Engineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India
| | - Charulata B Prasannan
- Department of Chemical Engineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India.,DBT-Pan IIT Center for Bioenergy , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India
| | - John I Hendry
- Department of Chemical Engineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India
| | - Pramod P Wangikar
- Department of Chemical Engineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India.,DBT-Pan IIT Center for Bioenergy , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India.,Wadhwani Research Center for Bioengineering , Indian Institute of Technology Bombay , Powai , Mumbai 400076 , India
| |
Collapse
|
22
|
A guide to 13C metabolic flux analysis for the cancer biologist. Exp Mol Med 2018; 50:1-13. [PMID: 29657327 PMCID: PMC5938039 DOI: 10.1038/s12276-018-0060-y] [Citation(s) in RCA: 153] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 12/21/2017] [Indexed: 01/15/2023] Open
Abstract
Cancer metabolism is significantly altered from normal cellular metabolism allowing cancer cells to adapt to changing microenvironments and maintain high rates of proliferation. In the past decade, stable-isotope tracing and network analysis have become powerful tools for uncovering metabolic pathways that are differentially activated in cancer cells. In particular, 13C metabolic flux analysis (13C-MFA) has emerged as the primary technique for quantifying intracellular fluxes in cancer cells. In this review, we provide a practical guide for investigators interested in getting started with 13C-MFA. We describe best practices in 13C-MFA, highlight potential pitfalls and alternative approaches, and conclude with new developments that can further enhance our understanding of cancer metabolism. Tracing tagged molecules can help researchers understand the altered metabolism of cancer cells. The abilities of cancer cells to multiply rapidly and invade new tissues are supported by metabolic alterations, which can be investigated by feeding tagged molecules to cells and tracing how they are metabolized. These techniques, such as 13C metabolic flux analysis (13C-MFA), have been perceived as difficult to use, but recent advances are making them more accessible. Maciek Antoniewicz, University of Delaware, Newark, USA, has published a practical guide for researchers wanting to use 13C-MFA. The review includes best practices, pitfalls, alternative approaches, and new developments, especially new user-friendly software that allows researchers without extensive training in mathematics, statistics, or coding to perform 13C-MFA. Broadening access to tools for investigating altered metabolic pathways may spur development of new cancer therapies targeting these pathways.
Collapse
|
23
|
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: 120] [Impact Index Per Article: 20.0] [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.
Collapse
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.
| |
Collapse
|
24
|
Armitage EG, Ciborowski M. Applications of Metabolomics in Cancer Studies. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 965:209-234. [PMID: 28132182 DOI: 10.1007/978-3-319-47656-8_9] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.
Collapse
Affiliation(s)
- Emily Grace Armitage
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad CEU San Pablo, Campus Monteprincipe, Madrid, Spain. .,Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, Sir Graeme Davies Building, University of Glasgow, Glasgow, UK. .,Glasgow Polyomics, Wolfson Wohl Cancer Research Centre, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
| | - Michal Ciborowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| |
Collapse
|
25
|
MOU H, HONG M, LIU XY, LI MC, HUANG MZ, CHU J, ZHUANG YP, ZHANG SL. Accurate Determination of Isotopic Abundance of Intracellular Metabolites of Saccharopolysporaerythraea Based on Ultra Performance Liquid Chromatography-Triple Quadrupole Mass Spectrometry. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2017. [DOI: 10.1016/s1872-2040(17)61036-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
26
|
Haribal M, Jander G. Stable isotope studies reveal pathways for the incorporation of non-essential amino acids in Acyrthosiphon pisum (pea aphids). ACTA ACUST UNITED AC 2017; 218:3797-806. [PMID: 26632455 DOI: 10.1242/jeb.129189] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Plant roots incorporate inorganic nitrogen into the amino acids glutamine, glutamic acid, asparagine and aspartic acid, which together serve as the primary metabolites of nitrogen transport to other tissues. Given the preponderance of these four amino acids, phloem sap is a nutritionally unbalanced diet for phloem-feeding insects. Therefore, aphids and other phloem feeders typically rely on microbial symbionts for the synthesis of essential amino acids. To investigate the metabolism of the four main transport amino acids by the pea aphid (Acyrthosiphon pisum), and its Buchnera aphidicola endosymbionts, aphids were fed defined diets with stable isotope-labeled glutamine, glutamic acid, asparagine or aspartic acid (U-(13)C, U-(15)N; U-(15)N; α-(15)N; or γ-(15)N). The metabolic fate of the dietary (15)N and (13)C was traced using gas chromatography-mass spectrometry (GC-MS). Nitrogen was the major contributor to the observed amino acid isotopomers with one additional unit mass (M+1). However, there was differential incorporation, with the amine nitrogen of asparagine being incorporated into other amino acids more efficiently than the amide nitrogen. Higher isotopomers (M+2, M+3 and M+4) indicated the incorporation of varying numbers of (13)C atoms into essential amino acids. GC-MS assays also showed that, even with an excess of dietary labeled glutamine, glutamic acid, asparagine or aspartic acid, the overall content of these amino acids in aphid bodies was mostly the product of catabolism of dietary amino acids and subsequent re-synthesis within the aphids. Thus, these predominant dietary amino acids are not passed directly to Buchnera endosymbionts for synthesis of essential amino acids, but are rather are produced de novo, most likely by endogenous aphid enzymes.
Collapse
Affiliation(s)
- Meena Haribal
- Boyce Thompson Institute, 533 Tower Road, Ithaca, NY 14853, USA
| | - Georg Jander
- Boyce Thompson Institute, 533 Tower Road, Ithaca, NY 14853, USA
| |
Collapse
|
27
|
Guo W, Sheng J, Feng X. Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2017; 162:265-299. [PMID: 28424826 DOI: 10.1007/10_2017_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, 13C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.
Collapse
Affiliation(s)
- Weihua Guo
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Jiayuan Sheng
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Xueyang Feng
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA.
| |
Collapse
|
28
|
Adrian L, Marco-Urrea E. Isotopes in geobiochemistry: tracing metabolic pathways in microorganisms of environmental relevance with stable isotopes. Curr Opin Biotechnol 2016; 41:19-25. [DOI: 10.1016/j.copbio.2016.03.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Revised: 03/02/2016] [Accepted: 03/18/2016] [Indexed: 11/25/2022]
|
29
|
Crown SB, Kelleher JK, Rouf R, Muoio DM, Antoniewicz MR. Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts. Am J Physiol Heart Circ Physiol 2016; 311:H881-H891. [PMID: 27496880 DOI: 10.1152/ajpheart.00428.2016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 07/25/2016] [Indexed: 11/22/2022]
Abstract
In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using 13C-metabolic flux analysis (13C-MFA) aided by multiple tracer experiments and integrated data analysis. Using 13C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different 13C-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of 13C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems.
Collapse
Affiliation(s)
- Scott B Crown
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Duke University, Durham, North Carolina;
| | - Joanne K Kelleher
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Rosanne Rouf
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Deborah M Muoio
- Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Duke University, Durham, North Carolina; Department of Medicine, Duke University, Durham, North Carolina; Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina; and
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delware
| |
Collapse
|
30
|
Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system. Metab Eng 2016; 38:10-18. [PMID: 27267409 DOI: 10.1016/j.ymben.2016.06.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 06/01/2016] [Accepted: 06/03/2016] [Indexed: 12/11/2022]
Abstract
13C-Metabolic flux analysis (13C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by 13C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for 13C-MFA. The best single tracers were doubly 13C-labeled glucose tracers, including [1,6-13C]glucose, [5,6-13C]glucose and [1,2-13C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6-13C]glucose and [1,2-13C]glucose. Combined analysis of [1,6-13C]glucose and [1,2-13C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1-13C]glucose +20% [U-13C]glucose.
Collapse
|
31
|
Long CP, Gonzalez JE, Sandoval NR, Antoniewicz MR. Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism. Metab Eng 2016; 37:102-113. [PMID: 27212692 DOI: 10.1016/j.ymben.2016.05.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2016] [Revised: 05/09/2016] [Accepted: 05/16/2016] [Indexed: 11/17/2022]
Abstract
Understanding the impact of gene knockouts on cellular physiology, and metabolism in particular, is centrally important to quantitative systems biology and metabolic engineering. Here, we present a comprehensive physiological characterization of wild-type Escherichia coli and 22 knockouts of enzymes in the upper part of central carbon metabolism, including the PTS system, glycolysis, pentose phosphate pathway and Entner-Doudoroff pathway. Our results reveal significant metabolic changes that are affected by specific gene knockouts. Analysis of collective trends and correlations in the data using principal component analysis (PCA) provide new, and sometimes surprising, insights into E. coli physiology. Additionally, by comparing the data-to-model predictions from constraint-based approaches such as FBA, MOMA and RELATCH we demonstrate the important role of less well-understood kinetic and regulatory effects in central carbon metabolism.
Collapse
Affiliation(s)
- Christopher P Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Jacqueline E Gonzalez
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Nicholas R Sandoval
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA.
| |
Collapse
|
32
|
Ahn WS, Crown SB, Antoniewicz MR. Evidence for transketolase-like TKTL1 flux in CHO cells based on parallel labeling experiments and (13)C-metabolic flux analysis. Metab Eng 2016; 37:72-78. [PMID: 27174718 DOI: 10.1016/j.ymben.2016.05.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 04/07/2016] [Accepted: 05/05/2016] [Indexed: 01/09/2023]
Abstract
The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. It provides precursors for the biosynthesis of nucleotides and contributes to the production of reducing power in the form of NADPH. It has been hypothesized that mammalian cells may contain a hidden reaction in PPP catalyzed by transketolase-like protein 1 (TKTL1) that is closely related to the classical transketolase enzyme; however, until now there has been no direct experimental evidence for this reaction. In this work, we have applied state-of-the-art techniques in (13)C metabolic flux analysis ((13)C-MFA) based on parallel labeling experiments and integrated flux fitting to estimate the TKTL1 flux in CHO cells. We identified a set of three parallel labeling experiments with [1-(13)C]glucose+[4,5,6-(13)C]glucose, [2-(13)C]glucose+[4,5,6-(13)C]glucose, and [3-(13)C]glucose+[4,5,6-(13)C]glucose and developed a new method to measure (13)C-labeling of fructose 6-phosphate by GC-MS that allows intuitive interpretation of mass isotopomer distributions to determine key fluxes in the model, including glycolysis, oxidative PPP, non-oxidative PPP, and the TKTL1 flux. Using these tracers we detected a significant TKTL1 flux in CHO cells at the stationary phase. The flux results suggest that the main function of oxidative PPP in CHO cells at the stationary phase is to fuel the TKTL1 reaction. Overall, this study demonstrates for the first time that carbon atoms can be lost in the PPP, by means other than the oxidative PPP, and that this loss of carbon atoms is consistent with the hypothesized TKTL1 reaction in mammalian cells.
Collapse
Affiliation(s)
- Woo Suk Ahn
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Scott B Crown
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
| |
Collapse
|
33
|
Cordova LT, Lu J, Cipolla RM, Sandoval NR, Long CP, Antoniewicz MR. Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by (13)C metabolic flux analysis and whole genome sequencing. Metab Eng 2016; 37:63-71. [PMID: 27164561 DOI: 10.1016/j.ymben.2016.05.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 04/04/2016] [Accepted: 05/05/2016] [Indexed: 01/20/2023]
Abstract
We evolved Thermus thermophilus to efficiently co-utilize glucose and xylose, the two most abundant sugars in lignocellulosic biomass, at high temperatures without carbon catabolite repression. To generate the strain, T. thermophilus HB8 was first evolved on glucose to improve its growth characteristics, followed by evolution on xylose. The resulting strain, T. thermophilus LC113, was characterized in growth studies, by whole genome sequencing, and (13)C-metabolic flux analysis ((13)C-MFA) with [1,6-(13)C]glucose, [5-(13)C]xylose, and [1,6-(13)C]glucose+[5-(13)C]xylose as isotopic tracers. Compared to the starting strain, the evolved strain had an increased growth rate (~2-fold), increased biomass yield, increased tolerance to high temperatures up to 90°C, and gained the ability to grow on xylose in minimal medium. At the optimal growth temperature of 81°C, the maximum growth rate on glucose and xylose was 0.44 and 0.46h(-1), respectively. In medium containing glucose and xylose the strain efficiently co-utilized the two sugars. (13)C-MFA results provided insights into the metabolism of T. thermophilus LC113 that allows efficient co-utilization of glucose and xylose. Specifically, (13)C-MFA revealed that metabolic fluxes in the upper part of metabolism adjust flexibly to sugar availability, while fluxes in the lower part of metabolism remain relatively constant. Whole genome sequence analysis revealed two large structural changes that can help explain the physiology of the evolved strain: a duplication of a chromosome region that contains many sugar transporters, and a 5x multiplication of a region on the pVV8 plasmid that contains xylose isomerase and xylulokinase genes, the first two enzymes of xylose catabolism. Taken together, (13)C-MFA and genome sequence analysis provided complementary insights into the physiology of the evolved strain.
Collapse
Affiliation(s)
- Lauren T Cordova
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Jing Lu
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Robert M Cipolla
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Nicholas R Sandoval
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Christopher P Long
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical & Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
| |
Collapse
|
34
|
Croyal M, Bourgeois R, Ouguerram K, Billon-Crossouard S, Aguesse A, Nguyen P, Krempf M, Ferchaud-Roucher V, Nobécourt E. Comparison of gas chromatography-mass spectrometry and gas chromatography-combustion-isotope ratio mass spectrometry analysis for in vivo estimates of metabolic fluxes. Anal Biochem 2016; 500:63-5. [PMID: 26898306 DOI: 10.1016/j.ab.2016.02.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 12/31/2015] [Accepted: 02/04/2016] [Indexed: 12/11/2022]
Abstract
Gas chromatography-mass spectrometry (GC-MS) was compared with gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) for measurements of cholesterol (13)C enrichment after infusion of labeled precursor ([(13)C1,2]acetate). Paired results were significantly correlated, although GC-MS was less accurate than GC-C-IRMS for higher enrichments. Nevertheless, only GC-MS was able to provide information on isotopologue distribution, bringing new insights to lipid metabolism. Therefore, we assessed the isotopologue distribution of cholesterol in humans and dogs known to present contrasted cholesterol metabolic pathways. The labeled tracer incorporation was different in both species, highlighting the subsidiarity of GC-MS and GC-C-IRMS to analyze in vivo stable isotope studies.
Collapse
Affiliation(s)
- Mikaël Croyal
- INRA, UMR 1280, Physiologie des Adaptations Nutritionnelles, CHU Hôtel-Dieu, F-44000 Nantes, France; CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France
| | - Raphaëlle Bourgeois
- INRA, UMR 1280, Physiologie des Adaptations Nutritionnelles, CHU Hôtel-Dieu, F-44000 Nantes, France; CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France
| | - Khadija Ouguerram
- INRA, UMR 1280, Physiologie des Adaptations Nutritionnelles, CHU Hôtel-Dieu, F-44000 Nantes, France; CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France
| | - Stéphanie Billon-Crossouard
- INRA, UMR 1280, Physiologie des Adaptations Nutritionnelles, CHU Hôtel-Dieu, F-44000 Nantes, France; CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France
| | - Audrey Aguesse
- INRA, UMR 1280, Physiologie des Adaptations Nutritionnelles, CHU Hôtel-Dieu, F-44000 Nantes, France; CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France
| | - Patrick Nguyen
- CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France; Oniris, National College of Veterinary Medicine, Food Science, and Engineering, Nutrition and Endocrinology Unit, F-44307 Nantes, France
| | - Michel Krempf
- INRA, UMR 1280, Physiologie des Adaptations Nutritionnelles, CHU Hôtel-Dieu, F-44000 Nantes, France; CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France; Endocrinology, G and R Laennec Hospital, F-44093 Nantes, France.
| | - Véronique Ferchaud-Roucher
- INRA, UMR 1280, Physiologie des Adaptations Nutritionnelles, CHU Hôtel-Dieu, F-44000 Nantes, France; CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France
| | - Estelle Nobécourt
- INRA, UMR 1280, Physiologie des Adaptations Nutritionnelles, CHU Hôtel-Dieu, F-44000 Nantes, France; CRNH, Human Nutrition Research Center, CHU Hôtel-Dieu, F-44093 Nantes, France; Endocrinology, G and R Laennec Hospital, F-44093 Nantes, France
| |
Collapse
|
35
|
Catabolism of Branched Chain Amino Acids Contributes Significantly to Synthesis of Odd-Chain and Even-Chain Fatty Acids in 3T3-L1 Adipocytes. PLoS One 2015; 10:e0145850. [PMID: 26710334 PMCID: PMC4692509 DOI: 10.1371/journal.pone.0145850] [Citation(s) in RCA: 128] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 12/09/2015] [Indexed: 12/21/2022] Open
Abstract
The branched chain amino acids (BCAA) valine, leucine and isoleucine have been implicated in a number of diseases including obesity, insulin resistance, and type 2 diabetes mellitus, although the mechanisms are still poorly understood. Adipose tissue plays an important role in BCAA homeostasis by actively metabolizing circulating BCAA. In this work, we have investigated the link between BCAA catabolism and fatty acid synthesis in 3T3-L1 adipocytes using parallel 13C-labeling experiments, mass spectrometry and model-based isotopomer data analysis. Specifically, we performed parallel labeling experiments with four fully 13C-labeled tracers, [U-13C]valine, [U-13C]leucine, [U-13C]isoleucine and [U-13C]glutamine. We measured mass isotopomer distributions of fatty acids and intracellular metabolites by GC-MS and analyzed the data using the isotopomer spectral analysis (ISA) framework. We demonstrate that 3T3-L1 adipocytes accumulate significant amounts of even chain length (C14:0, C16:0 and C18:0) and odd chain length (C15:0 and C17:0) fatty acids under standard cell culture conditions. Using a novel GC-MS method, we demonstrate that propionyl-CoA acts as the primer on fatty acid synthase for the production of odd chain fatty acids. BCAA contributed significantly to the production of all fatty acids. Leucine and isoleucine contributed at least 25% to lipogenic acetyl-CoA pool, and valine and isoleucine contributed 100% to lipogenic propionyl-CoA pool. Our results further suggest that low activity of methylmalonyl-CoA mutase and mass action kinetics of propionyl-CoA on fatty acid synthase result in high rates of odd chain fatty acid synthesis in 3T3-L1 cells. Overall, this work provides important new insights into the connection between BCAA catabolism and fatty acid synthesis in adipocytes and underscores the high capacity of adipocytes for metabolizing BCAA.
Collapse
|
36
|
13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production. Bioengineering (Basel) 2015; 3:bioengineering3010003. [PMID: 28952565 PMCID: PMC5597161 DOI: 10.3390/bioengineering3010003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 12/10/2015] [Accepted: 12/18/2015] [Indexed: 12/15/2022] Open
Abstract
Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many 13C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms
Collapse
|
37
|
Cordova LT, Long CP, Venkataramanan KP, Antoniewicz MR. Complete genome sequence, metabolic model construction and phenotypic characterization of Geobacillus LC300, an extremely thermophilic, fast growing, xylose-utilizing bacterium. Metab Eng 2015; 32:74-81. [PMID: 26391740 DOI: 10.1016/j.ymben.2015.09.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 08/26/2015] [Accepted: 09/11/2015] [Indexed: 02/07/2023]
Abstract
We have isolated a new extremely thermophilic fast-growing Geobacillus strain that can efficiently utilize xylose, glucose, mannose and galactose for cell growth. When grown aerobically at 72 °C, Geobacillus LC300 has a growth rate of 2.15 h(-1) on glucose and 1.52 h(-1) on xylose (doubling time less than 30 min). The corresponding specific glucose and xylose utilization rates are 5.55 g/g/h and 5.24 g/g/h, respectively. As such, Geobacillus LC300 grows 3-times faster than E. coli on glucose and xylose, and has a specific xylose utilization rate that is 3-times higher than the best metabolically engineered organism to date. To gain more insight into the metabolism of Geobacillus LC300 its genome was sequenced using PacBio's RS II single-molecule real-time (SMRT) sequencing platform and annotated using the RAST server. Based on the genome annotation and the measured biomass composition a core metabolic network model was constructed. To further demonstrate the biotechnological potential of this organism, Geobacillus LC300 was grown to high cell-densities in a fed-batch culture, where cells maintained a high xylose utilization rate under low dissolved oxygen concentrations. All of these characteristics make Geobacillus LC300 an attractive host for future metabolic engineering and biotechnology applications.
Collapse
Affiliation(s)
- Lauren T Cordova
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Christopher P Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Keerthi P Venkataramanan
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE 19716, USA.
| |
Collapse
|
38
|
Hollinshead WD, Henson WR, Abernathy M, Moon TS, Tang YJ. Rapid metabolic analysis of
Rhodococcus opacus
PD630 via parallel
13
C‐metabolite fingerprinting. Biotechnol Bioeng 2015; 113:91-100. [DOI: 10.1002/bit.25702] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2015] [Revised: 07/04/2015] [Accepted: 07/07/2015] [Indexed: 12/17/2022]
Affiliation(s)
- Whitney D. Hollinshead
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisSt. LouisMissouri63130
| | - William R. Henson
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisSt. LouisMissouri63130
| | - Mary Abernathy
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisSt. LouisMissouri63130
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisSt. LouisMissouri63130
| | - Yinjie J. Tang
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisSt. LouisMissouri63130
| |
Collapse
|
39
|
Antoniewicz MR. Parallel labeling experiments for pathway elucidation and (13)C metabolic flux analysis. Curr Opin Biotechnol 2015; 36:91-7. [PMID: 26322734 DOI: 10.1016/j.copbio.2015.08.014] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 08/07/2015] [Accepted: 08/09/2015] [Indexed: 12/21/2022]
Abstract
Metabolic pathway models provide the foundation for quantitative studies of cellular physiology through the measurement of intracellular metabolic fluxes. For model organisms metabolic models are well established, with many manually curated genome-scale model reconstructions, gene knockout studies and stable-isotope tracing studies. However, for non-model organisms a similar level of knowledge is often lacking. Compartmentation of cellular metabolism in eukaryotic systems also presents significant challenges for quantitative (13)C-metabolic flux analysis ((13)C-MFA). Recently, innovative (13)C-MFA approaches have been developed based on parallel labeling experiments, the use of multiple isotopic tracers and integrated data analysis, that allow more rigorous validation of pathway models and improved quantification of metabolic fluxes. Applications of these approaches open new research directions in metabolic engineering, biotechnology and medicine.
Collapse
Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
| |
Collapse
|
40
|
Buescher JM, Antoniewicz MR, Boros LG, Burgess SC, Brunengraber H, Clish CB, DeBerardinis RJ, Feron O, Frezza C, Ghesquiere B, Gottlieb E, Hiller K, Jones RG, Kamphorst JJ, Kibbey RG, Kimmelman AC, Locasale JW, Lunt SY, Maddocks ODK, Malloy C, Metallo CM, Meuillet EJ, Munger J, Nöh K, Rabinowitz JD, Ralser M, Sauer U, Stephanopoulos G, St-Pierre J, Tennant DA, Wittmann C, Vander Heiden MG, Vazquez A, Vousden K, Young JD, Zamboni N, Fendt SM. A roadmap for interpreting (13)C metabolite labeling patterns from cells. Curr Opin Biotechnol 2015; 34:189-201. [PMID: 25731751 PMCID: PMC4552607 DOI: 10.1016/j.copbio.2015.02.003] [Citation(s) in RCA: 442] [Impact Index Per Article: 49.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Revised: 02/10/2015] [Accepted: 02/10/2015] [Indexed: 12/12/2022]
Abstract
Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.
Collapse
Affiliation(s)
- Joerg M Buescher
- Vesalius Research Center, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA
| | - Laszlo G Boros
- Department of Pediatrics, UCLA School of Medicine, Los Angeles Biomedical Research Institute at the Harbor-UCLA Medical Center and Sidmap, LLC, Los Angeles, CA, USA
| | - Shawn C Burgess
- Advanced Imaging Research Center-Division of Metabolic Mechanisms of Disease and Department of Pharmacology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Henri Brunengraber
- Department of Nutrition, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ralph J DeBerardinis
- Children's Medical Center Research Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Olivier Feron
- Pole of Pharmacology and Therapeutics (FATH), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Brussels, Belgium
| | - Christian Frezza
- MRC Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Bart Ghesquiere
- Vesalius Research Center, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | | | - Karsten Hiller
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Russell G Jones
- Goodman Cancer Research Centre, Department of Physiology, McGill University, Montreal, QC, Canada
| | | | - Richard G Kibbey
- Internal Medicine, Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT, USA
| | - Alec C Kimmelman
- Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jason W Locasale
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA
| | - Sophia Y Lunt
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | | | - Craig Malloy
- Advanced Imaging Research Center-Division of Metabolic Mechanisms of Disease and Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Christian M Metallo
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Emmanuelle J Meuillet
- L'Institut des Technologies Avancées en Sciences du Vivant (ITAV), Toulouse Cedex 1, France; The University of Arizona Cancer Center, and Department of Nutritional Sciences, The University of Arizona, Tucson, AZ, USA
| | - Joshua Munger
- Department of Biochemistry, University of Rochester Medical Center, Rochester, NY, USA; Department of Biophysics, University of Rochester Medical Center, Rochester, NY, USA
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Joshua D Rabinowitz
- Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Markus Ralser
- Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, UK; Division of Physiology and Metabolism, MRC National Institute for Medical Research, London, UK
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Gregory Stephanopoulos
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Julie St-Pierre
- Goodman Cancer Research Centre, and Department of Biochemistry, McGill University, Montreal, Quebec, Canada
| | - Daniel A Tennant
- School of Cancer Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Christoph Wittmann
- Institute of Systems Biotechnology, Saarland University, Saarbrücken, Germany
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research at Massachusetts Institute of Technology, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Sarah-Maria Fendt
- Vesalius Research Center, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium.
| |
Collapse
|
41
|
Abstract
In this work, we present a novel approach for performing (13)C metabolic flux analysis ((13)C-MFA) of co-culture systems. We demonstrate for the first time that it is possible to determine metabolic flux distributions in multiple species simultaneously without the need for physical separation of cells or proteins, or overexpression of species-specific products. Instead, metabolic fluxes for each species in a co-culture are estimated directly from isotopic labeling of total biomass obtained using conventional mass spectrometry approaches such as GC-MS. In addition to determining metabolic fluxes, this approach estimates the relative population size of each species in a mixed culture and inter-species metabolite exchange. As such, it enables detailed studies of microbial communities including species dynamics and interactions between community members. The methodology is experimentally validated here using a co-culture of two E. coli knockout strains. Taken together, this work greatly extends the scope of (13)C-MFA to a large number of multi-cellular systems that are of significant importance in biotechnology and medicine.
Collapse
Affiliation(s)
- Nikodimos A Gebreselassie
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
| |
Collapse
|
42
|
Cordova LT, Antoniewicz MR. (13)C metabolic flux analysis of the extremely thermophilic, fast growing, xylose-utilizing Geobacillus strain LC300. Metab Eng 2015; 33:148-157. [PMID: 26100076 DOI: 10.1016/j.ymben.2015.06.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 05/24/2015] [Accepted: 06/12/2015] [Indexed: 01/24/2023]
Abstract
Thermophiles are increasingly used as versatile hosts in the biotechnology industry. One of the key advantages of thermophiles is the potential to achieve high rates of feedstock conversion at elevated temperatures. The recently isolated Geobacillus strain LC300 grows extremely fast on xylose, with a doubling time of less than 30 min. In the accompanying paper, the genome of Geobacillus LC300 was sequenced and annotated. In this work, we have experimentally validated the metabolic network model using parallel (13)C-labeling experiments and applied (13)C-metabolic flux analysis to quantify precise metabolic fluxes. Specifically, the complete set of singly labeled xylose tracers, [1-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], and [5-(13)C]xylose, was used for the first time. Isotopic labeling of biomass amino acids was measured by gas chromatography mass spectrometry (GC-MS). Isotopic labeling of carbon dioxide in the off-gas was also measured by an on-line mass spectrometer. The (13)C-labeling data was then rigorously integrated for flux elucidation using the COMPLETE-MFA approach. The results provided important new insights into the metabolism of Geobacillus LC300, its efficient xylose utilization pathways, and the balance between carbon, redox and energy fluxes. The pentose phosphate pathway, glycolysis and TCA cycle were found to be highly active in Geobacillus LC300. The oxidative pentose phosphate pathway was also active and contributed significantly to NADPH production. No transhydrogenase activity was detected. Results from this work provide a solid foundation for future studies of this strain and its metabolic engineering and biotechnological applications.
Collapse
Affiliation(s)
- Lauren T Cordova
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
| |
Collapse
|
43
|
Wasylenko TM, Ahn WS, Stephanopoulos G. The oxidative pentose phosphate pathway is the primary source of NADPH for lipid overproduction from glucose in Yarrowia lipolytica. Metab Eng 2015; 30:27-39. [PMID: 25747307 DOI: 10.1016/j.ymben.2015.02.007] [Citation(s) in RCA: 197] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 02/21/2015] [Indexed: 12/26/2022]
Abstract
Oleaginous microbes represent an attractive means of converting a diverse range of feedstocks into oils that can be transesterified to biodiesel. However, the mechanism of lipid overproduction in these organisms is incompletely understood, hindering the development of strategies for engineering superior biocatalysts for "single-cell oil" production. In particular, it is unclear which pathways are used to generate the large quantities of NADPH required for overproduction of the highly reduced fatty acid species. While early studies implicated malic enzyme as having a key role in production of lipogenic NADPH in oleaginous fungi, several recent reports have cast doubts as to whether malic enzyme may contribute to production of lipogenic NADPH in the model oleaginous yeast Yarrowia lipolytica. To address this problem we have used (13)C-Metabolic Flux Analysis to estimate the metabolic flux distributions during lipid accumulation in two Y. lipolytica strains; a control strain and a previously published engineered strain capable of producing lipids at roughly twice the yield. We observe a dramatic rearrangement of the metabolic flux distribution in the engineered strain which supports lipid overproduction. The NADPH-producing flux through the oxidative Pentose Phosphate Pathway is approximately doubled in the engineered strain in response to the roughly two-fold increase in fatty acid biosynthesis, while the flux through malic enzyme does not differ significantly between the two strains. Moreover, the estimated rate of NADPH production in the oxidative Pentose Phosphate Pathway is in good agreement with the estimated rate of NADPH consumption in fatty acid biosynthesis in both strains. These results suggest the oxidative Pentose Phosphate Pathway is the primary source of lipogenic NADPH in Y. lipolytica.
Collapse
Affiliation(s)
- Thomas M Wasylenko
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Woo Suk Ahn
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Gregory Stephanopoulos
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| |
Collapse
|
44
|
Antoniewicz MR. Methods and advances in metabolic flux analysis: a mini-review. J Ind Microbiol Biotechnol 2015; 42:317-25. [PMID: 25613286 DOI: 10.1007/s10295-015-1585-x] [Citation(s) in RCA: 145] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 01/09/2015] [Indexed: 01/12/2023]
Abstract
Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, (13)C metabolic flux analysis, isotopic non-stationary (13)C metabolic flux analysis, dynamic metabolic flux analysis, and (13)C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.
Collapse
Affiliation(s)
- Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St, Newark, DE, 19716, USA,
| |
Collapse
|
45
|
Crown SB, Long CP, Antoniewicz MR. Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli. Metab Eng 2015; 28:151-158. [PMID: 25596508 DOI: 10.1016/j.ymben.2015.01.001] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 12/29/2014] [Accepted: 01/05/2015] [Indexed: 01/19/2023]
Abstract
The use of parallel labeling experiments for (13)C metabolic flux analysis ((13)C-MFA) has emerged in recent years as the new gold standard in fluxomics. The methodology has been termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. In this contribution, we have tested the limits of COMPLETE-MFA by demonstrating integrated analysis of 14 parallel labeling experiments with Escherichia coli. An effort on such a massive scale has never been attempted before. In addition to several widely used isotopic tracers such as [1,2-(13)C]glucose and mixtures of [1-(13)C]glucose and [U-(13)C]glucose, four novel tracers were applied in this study: [2,3-(13)C]glucose, [4,5,6-(13)C]glucose, [2,3,4,5,6-(13)C]glucose and a mixture of [1-(13)C]glucose and [4,5,6-(13)C]glucose. This allowed us for the first time to compare the performance of a large number of isotopic tracers. Overall, there was no single best tracer for the entire E. coli metabolic network model. Tracers that produced well-resolved fluxes in the upper part of metabolism (glycolysis and pentose phosphate pathways) showed poor performance for fluxes in the lower part of metabolism (TCA cycle and anaplerotic reactions), and vice versa. The best tracer for upper metabolism was 80% [1-(13)C]glucose+20% [U-(13)C]glucose, while [4,5,6-(13)C]glucose and [5-(13)C]glucose both produced optimal flux resolution in the lower part of metabolism. COMPLETE-MFA improved both flux precision and flux observability, i.e. more independent fluxes were resolved with smaller confidence intervals, especially exchange fluxes. Overall, this study demonstrates that COMPLETE-MFA is a powerful approach for improving flux measurements and that this methodology should be considered in future studies that require very high flux resolution.
Collapse
Affiliation(s)
- Scott B Crown
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Christopher P Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
| |
Collapse
|
46
|
Sá JV, Duarte TM, Carrondo MJT, Alves PM, Teixeira AP. Metabolic Flux Analysis: A Powerful Tool in Animal Cell Culture. CELL ENGINEERING 2015. [DOI: 10.1007/978-3-319-10320-4_16] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
|
47
|
Shupletsov MS, Golubeva LI, Rubina SS, Podvyaznikov DA, Iwatani S, Mashko SV. OpenFLUX2: (13)C-MFA modeling software package adjusted for the comprehensive analysis of single and parallel labeling experiments. Microb Cell Fact 2014; 13:152. [PMID: 25408234 PMCID: PMC4263107 DOI: 10.1186/s12934-014-0152-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Accepted: 10/18/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Steady-state (13)C-based metabolic flux analysis ((13)C-MFA) is the most powerful method available for the quantification of intracellular fluxes. These analyses include concertedly linked experimental and computational stages: (i) assuming the metabolic model and optimizing the experimental design; (ii) feeding the investigated organism using a chosen (13)C-labeled substrate (tracer); (iii) measuring the extracellular effluxes and detecting the (13)C-patterns of intracellular metabolites; and (iv) computing flux parameters that minimize the differences between observed and simulated measurements, followed by evaluating flux statistics. In its early stages, (13)C-MFA was performed on the basis of data obtained in a single labeling experiment (SLE) followed by exploiting the developed high-performance computational software. Recently, the advantages of parallel labeling experiments (PLEs), where several LEs are conducted under the conditions differing only by the tracer(s) choice, were demonstrated, particularly with regard to improving flux precision due to the synergy of complementary information. The availability of an open-source software adjusted for PLE-based (13)C-MFA is an important factor for PLE implementation. RESULTS The open-source software OpenFLUX, initially developed for the analysis of SLEs, was extended for the computation of PLE data. Using the OpenFLUX2, in silico simulation confirmed that flux precision is improved when (13)C-MFA is implemented by fitting PLE data to the common model compared with SLE-based analysis. Efficient flux resolution could be achieved in the PLE-mediated analysis when the choice of tracer was based on an experimental design computed to minimize the flux variances from different parts of the metabolic network. The analysis provided by OpenFLUX2 mainly includes (i) the optimization of the experimental design, (ii) the computation of the flux parameters from LEs data, (iii) goodness-of-fit testing of the model's adequacy, (iv) drawing conclusions concerning the identifiability of fluxes and construction of a contribution matrix reflecting the relative contribution of the measurement variances to the flux variances, and (v) precise determination of flux confidence intervals using a fine-tunable and convergence-controlled Monte Carlo-based method. CONCLUSIONS The developed open-source OpenFLUX2 provides a friendly software environment that facilitates beginners and existing OpenFLUX users to implement LEs for steady-state (13)C-MFA including experimental design, quantitative evaluation of flux parameters and statistics.
Collapse
Affiliation(s)
- Mikhail S Shupletsov
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation. .,Computational Mathematics and Cybernetics Department, Lomonosov Moscow State University, 119991, Moscow, Russian Federation.
| | - Lyubov I Golubeva
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation.
| | - Svetlana S Rubina
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation.
| | - Dmitry A Podvyaznikov
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation. .,Department of Theoretical and Experimental Physics, Moscow Physical Engineering Institute (Technical University), 115409, Moscow, Russian Federation.
| | - Shintaro Iwatani
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation. .,Present address: Fermentation Group, Process Industrialization Section, Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co., Inc., 840-2193, SAGA, Saga-shi, Morodomi-cho, 450 Morodomitsu, Japan.
| | - Sergey V Mashko
- Ajinomoto-Genetika Research Institute, 117545, Moscow, Russian Federation. .,Department of Theoretical and Experimental Physics, Moscow Physical Engineering Institute (Technical University), 115409, Moscow, Russian Federation. .,Biological Department, Lomonosov Moscow State University, 119991, Moscow, Russian Federation.
| |
Collapse
|
48
|
Young JD. (13)C metabolic flux analysis of recombinant expression hosts. Curr Opin Biotechnol 2014; 30:238-45. [PMID: 25456032 DOI: 10.1016/j.copbio.2014.10.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 10/10/2014] [Accepted: 10/11/2014] [Indexed: 12/11/2022]
Abstract
Identifying host cell metabolic phenotypes that promote high recombinant protein titer is a major goal of the biotech industry. (13)C metabolic flux analysis (MFA) provides a rigorous approach to quantify these metabolic phenotypes by applying isotope tracers to map the flow of carbon through intracellular metabolic pathways. Recent advances in tracer theory and measurements are enabling more information to be extracted from (13)C labeling experiments. Sustained development of publicly available software tools and standardization of experimental workflows is simultaneously encouraging increased adoption of (13)C MFA within the biotech research community. A number of recent (13)C MFA studies have identified increased citric acid cycle and pentose phosphate pathway fluxes as consistent markers of high recombinant protein expression, both in mammalian and microbial hosts. Further work is needed to determine whether redirecting flux into these pathways can effectively enhance protein titers while maintaining acceptable glycan profiles.
Collapse
Affiliation(s)
- Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, PMB 351604, Nashville, TN 37235-1604, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University, PMB 351604, Nashville, TN 37235-1604, USA.
| |
Collapse
|
49
|
Abstract
We developed a set of methods for the quantification of four major components of microbial biomass using gas chromatography/mass spectrometry (GC/MS). Specifically, methods are described to quantify amino acids, RNA, fatty acids, and glycogen, which comprise an estimated 88% of the dry weight of Escherichia coli. Quantification is performed by isotope ratio analysis with fully (13)C-labeled biomass as internal standard, which is generated by growing E. coli on [U-(13)C]glucose. This convenient, reliable, and accurate single-platform (GC/MS) workflow for measuring biomass composition offers significant advantages over existing methods. We demonstrate the consistency, accuracy, precision, and utility of this procedure by applying it to three metabolically unique E. coli strains. The presented methods will have widespread applicability in systems microbiology and bioengineering.
Collapse
Affiliation(s)
- Christopher P. Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R. Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| |
Collapse
|
50
|
He L, Xiao Y, Gebreselassie N, Zhang F, Antoniewiez MR, Tang YJ, Peng L. Central metabolic responses to the overproduction of fatty acids in Escherichia coli based on 13C-metabolic flux analysis. Biotechnol Bioeng 2014; 111:575-85. [PMID: 24122357 DOI: 10.1002/bit.25124] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 09/25/2013] [Accepted: 09/25/2013] [Indexed: 01/12/2023]
Abstract
We engineered a fatty acid overproducing Escherichia coli strain through overexpressing tesA (“pull”) and fadR (“push”) and knocking out fadE (“block”). This “pull-push-block” strategy yielded 0.17 g of fatty acids (C12–C18) per gram of glucose (equivalent to 48% of the maximum theoretical yield) in batch cultures during the exponential growth phase under aerobic conditions. Metabolic fluxes were determined for the engineered E. coli and its control strain using tracer ([1,2-13C]glucose) experiments and 13C-metabolic flux analysis. Cofactor (NADPH) and energy (ATP) balances were also investigated for both strains based on estimated fluxes. Compared to the control strain, fatty acid overproduction led to significant metabolic responses in the central metabolism: (1) Acetic acid secretion flux decreased 10-fold; (2) Pentose phosphate pathway and Entner–Doudoroff pathway fluxes increased 1.5- and 2.0-fold, respectively; (3) Biomass synthesis flux was reduced 1.9-fold; (4) Anaplerotic phosphoenolpyruvate carboxylation flux decreased 1.7-fold; (5) Transhydrogenation flux converting NADH to NADPH increased by 1.7-fold. Real-time quantitative RT-PCR analysis revealed the engineered strain increased the transcription levels of pntA (encoding the membrane-bound transhydrogenase) by 2.1-fold and udhA (encoding the soluble transhydrogenase) by 1.4-fold, which is in agreement with the increased transhydrogenation flux. Cofactor and energy balances analyses showed that the fatty acid overproducing E. coli consumed significantly higher cellular maintenance energy than the control strain. We discussed the strategies to future strain development and process improvements for fatty acid production in E. coli.
Collapse
|