1
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Gopalakrishnan S, Johnson W, Valderrama-Gomez MA, Icten E, Tat J, Lay F, Diep J, Gomez N, Stevens J, Schlegel F, Rolandi P, Kontoravdi C, Lewis NE. Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks. Metab Eng 2024:S1096-7176(24)00099-5. [PMID: 39047894 DOI: 10.1016/j.ymben.2024.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 07/18/2024] [Accepted: 07/22/2024] [Indexed: 07/27/2024]
Abstract
Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producer clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.
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Affiliation(s)
| | | | | | | | - Jasmine Tat
- Process Development, Amgen; Department of Bioengineering, University of California San Diego
| | | | | | | | | | | | | | | | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego; Department of Bioengineering, University of California San Diego.
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2
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Gibbs TL, Gellner G, Levin SA, McCann KS, Hastings A, Levine JM. When can higher-order interactions produce stable coexistence? Ecol Lett 2024; 27:e14458. [PMID: 38877741 DOI: 10.1111/ele.14458] [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: 10/17/2023] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024]
Abstract
Most ecological models are based on the assumption that species interact in pairs. Diverse communities, however, can have higher-order interactions, in which two or more species jointly impact the growth of a third species. A pitfall of the common pairwise approach is that it misses the higher-order interactions potentially responsible for maintaining natural diversity. Here, we explore the stability properties of systems where higher-order interactions guarantee that a specified set of abundances is a feasible equilibrium of the dynamics. Even these higher-order interactions which lead to equilibria do not necessarily produce stable coexistence. Instead, these systems are more likely to be stable when the pairwise interactions are weak or facilitative. Correlations between the pairwise and higher-order interactions, however, do permit robust coexistence even in diverse systems. Our work not only reveals the challenges in generating stable coexistence through higher-order interactions but also uncovers interaction patterns that can enable diversity.
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Affiliation(s)
- Theo L Gibbs
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Gabriel Gellner
- Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | - Kevin S McCann
- Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada
| | - Alan Hastings
- Department of Environmental Science and Policy, University of California at Davis, Davis, California, USA
- Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Jonathan M Levine
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
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3
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Kugler A, Stensjö K. Machine learning predicts system-wide metabolic flux control in cyanobacteria. Metab Eng 2024; 82:171-182. [PMID: 38395194 DOI: 10.1016/j.ymben.2024.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/14/2024] [Accepted: 02/20/2024] [Indexed: 02/25/2024]
Abstract
Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights for the study of biological systems and biotechnological applications. However, quantitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited. In this work, we present ARCTICA, a computational framework that integrates constraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonstrate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by enzymes within the Calvin-Benson-Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the most, limiting step within the CBB cycle. Predicted metabolic reactions qualitatively align with prior experimental observations, validating our modelling approach. ARCTICA serves as a valuable pipeline for understanding cellular physiology and predicting rate-limiting steps in genome-scale metabolic networks, and thus provides guidance for bioengineering of cyanobacteria.
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Affiliation(s)
- Amit Kugler
- Microbial Chemistry, Department of Chemistry-Ångström Laboratory, Uppsala University, Box 523, SE-751 20, Uppsala, Sweden
| | - Karin Stensjö
- Microbial Chemistry, Department of Chemistry-Ångström Laboratory, Uppsala University, Box 523, SE-751 20, Uppsala, Sweden.
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4
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Galuzzi BG, Milazzo L, Damiani C. Adjusting for false discoveries in constraint-based differential metabolic flux analysis. J Biomed Inform 2024; 150:104597. [PMID: 38272432 DOI: 10.1016/j.jbi.2024.104597] [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: 02/28/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint-based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks. While many algorithms have been proposed and compared for efficiently and uniformly sampling the feasible region of metabolic networks, their impact on the risk of making false discoveries when comparing different samples has not been investigated yet, and no sampling strategy has been so far specifically designed to mitigate the problem. To be able to precisely assess the False Discovery Rate (FDR), in this work we compared different samples obtained from the very same metabolic model. We compared the FDR obtained for different model scales, sample sizes, parameters of the sampling algorithm, and strategies to filter out non-significant variations. To be able to compare the largely used hit-and-run strategy with the much less investigated corner-based strategy, we first assessed the intrinsic capability of current corner-based algorithms and of a newly proposed one to visit all vertices of a constraint-based region. We show that false discoveries can occur at high rates even for large samples of small-scale networks. However, we demonstrate that a statistical test based on the empirical null distribution of Kullback-Leibler divergence can effectively correct for false discoveries. We also show that our proposed corner-based algorithm is more efficient than state-of-the-art alternatives and much less prone to false discoveries than hit-and-run strategies. We report that the differences in the marginal distributions obtained with the two strategies are related to but not fully explained by differences in sample standard deviation, as previously thought. Overall, our study provides insights into the impact of sampling strategies on FDR in metabolic network analysis and offers new guidelines for more robust and reproducible analyses.
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Affiliation(s)
- Bruno G Galuzzi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo, 1, Milan, 20126, Italy; Institute of Molecular Bioimaging and Physiology (IBFM), Segrate, 20054, Italy; SYSBIO Centre of Systems Biology/ ISBE.IT, Milan, Italy.
| | - Luca Milazzo
- Department of Informatics, Systems, and Communications, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo, 1, Milan, 20126, Italy
| | - Chiara Damiani
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo, 1, Milan, 20126, Italy; SYSBIO Centre of Systems Biology/ ISBE.IT, Milan, Italy.
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5
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Jóhannsson F, Yurkovich JT, Guðmundsson S, Sigurjónsson ÓE, Rolfsson Ó. Temperature Dependence of Platelet Metabolism. Metabolites 2024; 14:91. [PMID: 38392983 PMCID: PMC10890334 DOI: 10.3390/metabo14020091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/25/2024] Open
Abstract
Temperature plays a fundamental role in biology, influencing cellular function, chemical reaction rates, molecular structures, and interactions. While the temperature dependence of many biochemical reactions is well defined in vitro, the effect of temperature on metabolic function at the network level is poorly understood, and it remains an important challenge in optimizing the storage of cells and tissues at lower temperatures. Here, we used time-course metabolomic data and systems biology approaches to characterize the effects of storage temperature on human platelets (PLTs) in a platelet additive solution. We observed that changes to the metabolome with storage time do not simply scale with temperature but instead display complex temperature dependence, with only a small subset of metabolites following an Arrhenius-type relationship. Investigation of PLT energy metabolism through integration with computational modeling revealed that oxidative metabolism is more sensitive to temperature changes than glycolysis. The increased contribution of glycolysis to ATP turnover at lower temperatures indicates a stronger glycolytic phenotype with decreasing storage temperature. More broadly, these results demonstrate that the temperature dependence of the PLT metabolic network is not uniform, suggesting that efforts to improve the health of stored PLTs could be targeted at specific pathways.
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Affiliation(s)
- Freyr Jóhannsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- School of Health Sciences, Medical Department, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
| | - James T Yurkovich
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Phenome Health, Seattle, WA 98109, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA 94945, USA
| | - Steinn Guðmundsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Dunhagi 3, 107 Reykjavik, Iceland
| | - Ólafur E Sigurjónsson
- The Blood Bank, Landspitali-University Hospital, Snorrabraut 60, 101 Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Menntavegur 1, 102 Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- School of Health Sciences, Medical Department, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
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6
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Kaste JA, Shachar-Hill Y. Model validation and selection in metabolic flux analysis and flux balance analysis. Biotechnol Prog 2024; 40:e3413. [PMID: 37997613 PMCID: PMC10922127 DOI: 10.1002/btpr.3413] [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: 09/21/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/25/2023]
Abstract
13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.
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Affiliation(s)
- Joshua A.M. Kaste
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd, East Lansing, MI 48823
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
| | - Yair Shachar-Hill
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824
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7
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Osinuga A, Solis AG, Cahoon RE, Al-Siyabi A, Cahoon EB, Saha R. Quantitative Dynamic Analysis of de novo Sphingolipid Biosynthesis in Arabidopsis thaliana. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.08.570827. [PMID: 38105963 PMCID: PMC10723408 DOI: 10.1101/2023.12.08.570827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Sphingolipids are pivotal for plant development and stress responses. Growing interest has been directed towards fully comprehending the regulatory mechanisms of the sphingolipid pathway. We explore its de novo biosynthesis and homeostasis in Arabidopsis thaliana cell cultures, shedding light on fundamental metabolic mechanisms. Employing 15N isotope labeling and quantitative dynamic modeling approach, we developed a regularized and constraint-based Dynamic Metabolic Flux Analysis (r-DMFA) framework to predict metabolic shifts due to enzymatic changes. Our analysis revealed key enzymes such as sphingoid-base hydroxylase (SBH) and long-chain-base kinase (LCBK) to be critical for maintaining sphingolipid homeostasis. Disruptions in these enzymes were found to affect cellular viability and increase the potential for programmed cell death (PCD). Thus, this work enhances our understanding of sphingolipid metabolism and demonstrates the utility of dynamic modeling in analyzing complex metabolic pathways.
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Affiliation(s)
- Abraham Osinuga
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Ariadna Gonzalez Solis
- Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Rebecca E Cahoon
- Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Adil Al-Siyabi
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Edgar B Cahoon
- Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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8
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Rubinov M. Circular and unified analysis in network neuroscience. eLife 2023; 12:e79559. [PMID: 38014843 PMCID: PMC10684154 DOI: 10.7554/elife.79559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
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Affiliation(s)
- Mika Rubinov
- Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt UniversityNashvilleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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9
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Arora G, Banerjee M, Langthasa J, Bhat R, Chatterjee S. Targeting metabolic fluxes reverts metastatic transitions in ovarian cancer. iScience 2023; 26:108081. [PMID: 37876796 PMCID: PMC10590820 DOI: 10.1016/j.isci.2023.108081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/05/2023] [Accepted: 09/25/2023] [Indexed: 10/26/2023] Open
Abstract
The formation of spheroids during epithelial ovarian cancer progression is correlated with peritoneal metastasis, disease recurrence, and poor prognosis. Although metastasis has been demonstrated to be driven by metabolic changes in transformed cells, mechanistic associations between metabolism and phenotypic transitions remain ill-explored. We performed quantitative proteomics to identify protein signatures associated with three distinct phenotypic morphologies (2D monolayers and two geometrically distinct three-dimensional spheroidal states) of the high-grade serous ovarian cancer line OVCAR-3. We obtained disease-driving phenotype-specific metabolic reaction modules and elucidated gene knockout strategies to reduce metabolic alterations that could drive phenotypic transitions. Exploring the DrugBank database, we identified and evaluated drugs that could impair such transitions and, hence, cancer progression. Finally, we experimentally validated our predictions by confirming the ability of one of our predicted drugs, the neuraminidase inhibitor oseltamivir, to inhibit spheroidogenesis in three ovarian cancer cell lines without any cytotoxic effects on untransformed stromal mesothelia.
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Affiliation(s)
- Garhima Arora
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
| | - Mallar Banerjee
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
| | - Jimpi Langthasa
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
| | - Ramray Bhat
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
- BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
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10
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Backman TWH, Schenk C, Radivojevic T, Ando D, Singh J, Czajka JJ, Costello Z, Keasling JD, Tang Y, Akhmatskaya E, Garcia Martin H. BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale. PLoS Comput Biol 2023; 19:e1011111. [PMID: 37948450 PMCID: PMC10664898 DOI: 10.1371/journal.pcbi.1011111] [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: 04/18/2023] [Revised: 11/22/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023] Open
Abstract
Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.
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Affiliation(s)
- Tyler W. H. Backman
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Christina Schenk
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - Tijana Radivojevic
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - David Ando
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
| | - Jahnavi Singh
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, United States of America
| | - Jeffrey J. Czajka
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Zak Costello
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- DOE Agile BioFoundry, Emeryville, California, United States of America
| | - Jay D. Keasling
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California, United States of America
- Department of Bioengineering, University of California, Berkeley, California, United States of America
- QB3 Institute, University of California, Berkeley, California, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Yinjie Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Elena Akhmatskaya
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Hector Garcia Martin
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Biofuels and Bioproducts Division, Joint BioEnergy Institute, Emeryville, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
- DOE Agile BioFoundry, Emeryville, California, United States of America
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11
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Jadebeck JF, Wiechert W, Nöh K. Practical sampling of constraint-based models: Optimized thinning boosts CHRR performance. PLoS Comput Biol 2023; 19:e1011378. [PMID: 37566638 PMCID: PMC10446239 DOI: 10.1371/journal.pcbi.1011378] [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: 01/04/2023] [Revised: 08/23/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling constraint-based models, a highly relevant use-case in systems biology, we here demonstrate that thinning boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. By benchmarking CHRR with thinning with simplices and genome-scale metabolic networks of up to thousands of dimensions, we find a substantial increase in computational efficiency compared to unthinned CHRR, in our examples by orders of magnitude, as measured by the effective sample size per time (ESS/t), with performance gains growing with polytope (effective network) dimension. Using a set of benchmark models we derive a ready-to-apply guideline for tuning thinning to efficient and effective use of compute resources without requiring additional coding effort. Our guideline is validated using three (out-of-sample) large-scale networks and we show that it allows sampling convex polytopes uniformly to convergence in a fraction of time, thereby unlocking the rigorous investigation of hitherto intractable models. The derivation of our guideline is explained in detail, allowing future researchers to update it as needed as new model classes and more training data becomes available. CHRR with deliberate utilization of thinning thereby paves the way to keep pace with progressing model sizes derived with the constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.
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Affiliation(s)
- Johann F. Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
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12
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Molversmyr H, Øyås O, Rotnes F, Vik JO. Extracting functionally accurate context-specific models of Atlantic salmon metabolism. NPJ Syst Biol Appl 2023; 9:19. [PMID: 37244928 DOI: 10.1038/s41540-023-00280-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/05/2023] [Indexed: 05/29/2023] Open
Abstract
Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM's metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models' ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.
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Affiliation(s)
- Håvard Molversmyr
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Ove Øyås
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Filip Rotnes
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Jon Olav Vik
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway.
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
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13
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Santos-Merino M, Gargantilla-Becerra Á, de la Cruz F, Nogales J. Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling. Front Microbiol 2023; 14:1126030. [PMID: 36998399 PMCID: PMC10043229 DOI: 10.3389/fmicb.2023.1126030] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/22/2023] [Indexed: 03/15/2023] Open
Abstract
Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory.
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Affiliation(s)
- María Santos-Merino
- Instituto de Biomedicina y Biotecnología de Cantabria, Universidad de Cantabria—CSIC, Santander, Cantabria, Spain
- *Correspondence: María Santos-Merino,
| | - Álvaro Gargantilla-Becerra
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - Fernando de la Cruz
- Instituto de Biomedicina y Biotecnología de Cantabria, Universidad de Cantabria—CSIC, Santander, Cantabria, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
- Juan Nogales,
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14
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Park J, Lee SM, Ebrahim A, Scott-Nevros Z, Kim J, Yang L, Sastry A, Seo S, Palsson BO, Kim D. Model-driven experimental design workflow expands understanding of regulatory role of Nac in Escherichia coli. NAR Genom Bioinform 2023; 5:lqad006. [PMID: 36685725 PMCID: PMC9853098 DOI: 10.1093/nargab/lqad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/07/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023] Open
Abstract
The establishment of experimental conditions for transcriptional regulator network (TRN) reconstruction in bacteria continues to be impeded by the limited knowledge of activating conditions for transcription factors (TFs). Here, we present a novel genome-scale model-driven workflow for designing experimental conditions, which optimally activate specific TFs. Our model-driven workflow was applied to elucidate transcriptional regulation under nitrogen limitation by Nac and NtrC, in Escherichia coli. We comprehensively predict alternative nitrogen sources, including cytosine and cytidine, which trigger differential activation of Nac using a model-driven workflow. In accordance with the prediction, genome-wide measurements with ChIP-exo and RNA-seq were performed. Integrative data analysis reveals that the Nac and NtrC regulons consist of 97 and 43 genes under alternative nitrogen conditions, respectively. Functional analysis of Nac at the transcriptional level showed that Nac directly down-regulates amino acid biosynthesis and restores expression of tricarboxylic acid (TCA) cycle genes to alleviate nitrogen-limiting stress. We also demonstrate that both TFs coherently modulate α-ketoglutarate accumulation stress due to nitrogen limitation by co-activating amino acid and diamine degradation pathways. A systems-biology approach provided a detailed and quantitative understanding of both TF's roles and how nitrogen and carbon metabolic networks respond complementarily to nitrogen-limiting stress.
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Affiliation(s)
- Joon Young Park
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Sang-Mok Lee
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Ali Ebrahim
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zoe K Scott-Nevros
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Jaehyung Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Laurence Yang
- Department of Chemical Engineering, Queen's University, Kingston, Canada
| | - Anand Sastry
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sang Woo Seo
- School of Chemical and Biological Engineering, and Interdisciplinary Program in Bioengineering, and Institute of Chemical Processes, and Bio-MAX Institute, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- The Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 6 Kogle Alle, Hørsholm, Denmark
| | - Donghyuk Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
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15
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Rios Garza D, Gonze D, Zafeiropoulos H, Liu B, Faust K. Metabolic models of human gut microbiota: Advances and challenges. Cell Syst 2023; 14:109-121. [PMID: 36796330 DOI: 10.1016/j.cels.2022.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/24/2022] [Accepted: 11/04/2022] [Indexed: 02/17/2023]
Abstract
The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.
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Affiliation(s)
- Daniel Rios Garza
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Didier Gonze
- Unité de Chronobiologie Théorique, Faculté des Sciences, CP 231, Université Libre de Bruxelles, Bvd du Triomphe, 1050 Bruxelles, Belgium
| | - Haris Zafeiropoulos
- Biology Department, University of Crete, Heraklion 700 13, Greece; Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
| | - Bin Liu
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium.
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16
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Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation. Commun Biol 2023; 6:165. [PMID: 36765199 PMCID: PMC9918512 DOI: 10.1038/s42003-023-04540-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
Pseudomonas aeruginosa is one of the leading causes of hospital-acquired infections. To decipher the metabolic mechanisms associated with virulence and antibiotic resistance, we have developed an updated genome-scale model (GEM) of P. aeruginosa. The model (iSD1509) is an extensively curated, three-compartment, and mass-and-charge balanced BiGG model containing 1509 genes, the largest gene content for any P. aeruginosa GEM to date. It is the most accurate with prediction accuracies as high as 92.4% (gene essentiality) and 93.5% (substrate utilization). In iSD1509, we newly added a recently discovered pathway for ubiquinone-9 biosynthesis which is required for anaerobic growth. We used a modified iSD1509 to demonstrate the role of virulence factor (phenazines) in the pathogen survival within biofilm/oxygen-limited condition. Further, the model can mechanistically explain the overproduction of a drug susceptibility biomarker in the P. aeruginosa mutants. Finally, we use iSD1509 to demonstrate the drug potentiation by metabolite supplementation, and elucidate the mechanisms behind the phenotype, which agree with experimental results.
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17
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Genome-scale reconstruction and metabolic modelling of the fast-growing thermophile Geobacillus sp. LC300. Metab Eng Commun 2022; 15:e00212. [DOI: 10.1016/j.mec.2022.e00212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 11/09/2022] Open
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18
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Wilken SE, Besançon M, Kratochvíl M, Foko Kuate CA, Trefois C, Gu W, Ebenhöh O. Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models. Metab Eng 2022; 74:72-82. [PMID: 36152931 DOI: 10.1016/j.ymben.2022.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/08/2022] [Accepted: 09/10/2022] [Indexed: 10/31/2022]
Abstract
Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.
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Affiliation(s)
- St Elmo Wilken
- Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany.
| | - Mathieu Besançon
- Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Takustraße 7, 14195, Berlin, Germany
| | - Miroslav Kratochvíl
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg
| | - Chilperic Armel Foko Kuate
- Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Christophe Trefois
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg
| | - Oliver Ebenhöh
- Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
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19
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Abstract
Polyhydroxyalkanoates (PHAs) are polyesters produced by numerous microorganisms for energy and carbon storage. Simultaneous synthesis and degradation of PHA drives a dynamic cycle linked to the central carbon metabolism, which modulates numerous and diverse bacterial processes, such as stress endurance, pathogenesis, and persistence. Here, we analyze the role of the PHA cycle in conferring robustness to the model bacterium P. putida KT2440. To assess the effect of this cycle in the cell, we began by constructing a PHA depolymerase (PhaZ) mutant strain that had its PHA cycle blocked. We then restored the flux through the cycle in the context of an engineered library of P. putida strains harboring differential levels of PhaZ. High-throughput phenotyping analyses of this collection of strains revealed significant changes in response to PHA cycle performance impacting cell number and size, PHA accumulation, and production of extracellular (R)-hydroxyalkanoic acids. To understand the metabolic changes at the system level due to PHA turnover, we contextualized these physiological data using the genome-scale metabolic model iJN1411. Model-based predictions suggest successive metabolic steady states during the growth curve and an important carbon flux rerouting driven by the activity of the PHA cycle. Overall, we demonstrate that modulating the activity of the PHA cycle gives us control over the carbon metabolism of P. putida, which in turn will give us the ability to tailor cellular mechanisms driving stress tolerance, e.g., defenses against oxidative stress, and any potential biotechnological applications. IMPORTANCE Despite large research efforts devoted to understanding the flexible metabolism of Pseudomonas beyond the role of key regulatory players, the metabolic basis powering the dynamic control of its biological fitness under disturbance conditions remains largely unknown. Among other metabolic hubs, the so-called PHA cycle, involving simultaneous synthesis and degradation of PHAs, is emerging as a pivotal metabolic trait powering metabolic robustness and resilience in this bacterial group. Here, we provide evidence suggesting that metabolic states in Pseudomonas can be anticipated, controlled, and engineered by tailoring the flux through the PHA cycle. Overall, our study suggests that the PHA cycle is a promising metabolic target toward achieving control over bacterial metabolic robustness. This is likely to open up a broad range of applications in areas as diverse as pathogenesis and biotechnology.
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20
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Sinha N, van Schothorst EM, Hooiveld GJEJ, Keijer J, Martins Dos Santos VAP, Suarez-Diez M. Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism. BMC Bioinformatics 2021; 22:574. [PMID: 34839828 PMCID: PMC8628452 DOI: 10.1186/s12859-021-04488-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 11/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. RESULTS Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. CONCLUSION We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.
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Affiliation(s)
- Neeraj Sinha
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.,Human and Animal Physiology, Wageningen University & Research, De Elst 1, 6708 WD, Wageningen, The Netherlands.,Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
| | - Evert M van Schothorst
- Human and Animal Physiology, Wageningen University & Research, De Elst 1, 6708 WD, Wageningen, The Netherlands
| | - Guido J E J Hooiveld
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
| | - Jaap Keijer
- Human and Animal Physiology, Wageningen University & Research, De Elst 1, 6708 WD, Wageningen, The Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.,LifeGlimmer GmbH., Markelstrasse 38, 12163, Berlin, Germany.,Bioprocess Engineering Group, Wageningen University & Research, PO Box 16, 6700 AA, Wageningen, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.
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21
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Comparison of metabolic states using genome-scale metabolic models. PLoS Comput Biol 2021; 17:e1009522. [PMID: 34748535 PMCID: PMC8601616 DOI: 10.1371/journal.pcbi.1009522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 11/18/2021] [Accepted: 10/04/2021] [Indexed: 11/25/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks. Cellular metabolism is a highly complex and interconnected system. As many lifestyle diseases in humans have a strong metabolic component, it is important to understand metabolic differences between healthy and diseased states. In systems biology, metabolic behaviours are investigated using genome-scale metabolic models. In addition to the sheer size and complexity of the genome-scale metabolic models of human systems, using existing analysis methods is challenging and the parameter selection is not straightforward. Therefore, novel methodological frameworks are necessary for analysing metabolic conditions despite the challenges posed by human models. Particularly, an ongoing challenge has been that of comparing several phenotypes for identifying condition- or disease-specific metabolic signatures. We address this significant challenge by developing a scalable and model-driven approach, ComMet (Comparison of Metabolic states). ComMet enables an in-depth investigation and comparison of metabolic phenotypes in large models while also identifying the underlying functional differences. Novel hypotheses can be generated using ComMet for not only understanding known metabolic phenotypes better but also for guiding the design of new experiments to validate the processes predicted by ComMet.
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22
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Antonakoudis A, Strain B, Barbosa R, Jimenez del Val I, Kontoravdi C. Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107471] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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23
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Scott WT, Smid EJ, Block DE, Notebaart RA. Metabolic flux sampling predicts strain-dependent differences related to aroma production among commercial wine yeasts. Microb Cell Fact 2021; 20:204. [PMID: 34674718 PMCID: PMC8532357 DOI: 10.1186/s12934-021-01694-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Metabolomics coupled with genome-scale metabolic modeling approaches have been employed recently to quantitatively analyze the physiological states of various organisms, including Saccharomyces cerevisiae. Although yeast physiology in laboratory strains is well-studied, the metabolic states under industrially relevant scenarios such as winemaking are still not sufficiently understood, especially as there is considerable variation in metabolism between commercial strains. To study the potential causes of strain-dependent variation in the production of volatile compounds during enological conditions, random flux sampling and statistical methods were used, along with experimental extracellular metabolite flux data to characterize the differences in predicted intracellular metabolic states between strains. RESULTS It was observed that four selected commercial wine yeast strains (Elixir, Opale, R2, and Uvaferm) produced variable amounts of key volatile organic compounds (VOCs). Principal component analysis was performed on extracellular metabolite data from the strains at three time points of cell cultivation (24, 58, and 144 h). Separation of the strains was observed at all three time points. Furthermore, Uvaferm at 24 h, for instance, was most associated with propanol and ethyl hexanoate. R2 was found to be associated with ethyl acetate and Opale could be associated with isobutanol while Elixir was most associated with phenylethanol and phenylethyl acetate. Constraint-based modeling (CBM) was employed using the latest genome-scale metabolic model of yeast (Yeast8) and random flux sampling was performed with experimentally derived fluxes at various stages of growth as constraints for the model. The flux sampling simulations allowed us to characterize intracellular metabolic flux states and illustrate the key parts of metabolism that likely determine the observed strain differences. Flux sampling determined that Uvaferm and Elixir are similar while R2 and Opale exhibited the highest degree of differences in the Ehrlich pathway and carbon metabolism, thereby causing strain-specific variation in VOC production. The model predictions also established the top 20 fluxes that relate to phenotypic strain variation (e.g. at 24 h). These fluxes indicated that Opale had a higher median flux for pyruvate decarboxylase reactions compared with the other strains. Conversely, R2 which was lower in all VOCs, had higher median fluxes going toward central metabolism. For Elixir and Uvaferm, the differences in metabolism were most evident in fluxes pertaining to transaminase and hexokinase associated reactions. The applied analysis of metabolic divergence unveiled strain-specific differences in yeast metabolism linked to fusel alcohol and ester production. CONCLUSIONS Overall, this approach proved useful in elucidating key reactions in amino acid, carbon, and glycerophospholipid metabolism which suggest genetic divergence in activity in metabolic subsystems among these wine strains related to the observed differences in VOC formation. The findings in this study could steer more focused research endeavors in developing or selecting optimal aroma-producing yeast stains for winemaking and other types of alcoholic fermentations.
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Affiliation(s)
- William T Scott
- Department of Chemical Engineering, University of California, Davis, CA, USA.,Food Microbiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Eddy J Smid
- Food Microbiology, Wageningen University & Research, Wageningen, The Netherlands
| | - David E Block
- Department of Chemical Engineering, University of California, Davis, CA, USA.,Department of Viticulture and Enology, University of California, Davis, CA, USA
| | - Richard A Notebaart
- Food Microbiology, Wageningen University & Research, Wageningen, The Netherlands.
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24
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Argininosuccinate lyase is a metabolic vulnerability in breast development and cancer. NPJ Syst Biol Appl 2021; 7:36. [PMID: 34535676 PMCID: PMC8448827 DOI: 10.1038/s41540-021-00195-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 08/09/2021] [Indexed: 02/07/2023] Open
Abstract
Epithelial-to-mesenchymal transition (EMT) is fundamental to both normal tissue development and cancer progression. We hypothesized that EMT plasticity defines a range of metabolic phenotypes and that individual breast epithelial metabolic phenotypes are likely to fall within this phenotypic landscape. To determine EMT metabolic phenotypes, the metabolism of EMT was described within genome-scale metabolic models (GSMMs) using either transcriptomic or proteomic data from the breast epithelial EMT cell culture model D492. The ability of the different data types to describe breast epithelial metabolism was assessed using constraint-based modeling which was subsequently verified using 13C isotope tracer analysis. The application of proteomic data to GSMMs provided relatively higher accuracy in flux predictions compared to the transcriptomic data. Furthermore, the proteomic GSMMs predicted altered cholesterol metabolism and increased dependency on argininosuccinate lyase (ASL) following EMT which were confirmed in vitro using drug assays and siRNA knockdown experiments. The successful verification of the proteomic GSMMs afforded iBreast2886, a breast GSMM that encompasses the metabolic plasticity of EMT as defined by the D492 EMT cell culture model. Analysis of breast tumor proteomic data using iBreast2886 identified vulnerabilities within arginine metabolism that allowed prognostic discrimination of breast cancer patients on a subtype-specific level. Taken together, we demonstrate that the metabolic reconstruction iBreast2886 formalizes the metabolism of breast epithelial cell development and can be utilized as a tool for the functional interpretation of high throughput clinical data.
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25
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Singhania RR, Patel AK, Tsai ML, Chen CW, Di Dong C. Genetic modification for enhancing bacterial cellulose production and its applications. Bioengineered 2021; 12:6793-6807. [PMID: 34519629 PMCID: PMC8806912 DOI: 10.1080/21655979.2021.1968989] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Bacterial cellulose (BC) is higher in demand due to its excellent properties which is attributed to its purity and nano size. Komagataeibacter xylinum is a model organism where BC production has been studied in detail because of its higher cellulose production capacity. BC production mechanism shows involvement of a series of sequential reactions with enzymes for biosynthesis of cellulose. It is necessary to know the mechanism to understand the involvement of regulatory proteins which could be the probable targets for genetic modification to enhance or regulate yield of BC and to alter BC properties as well. For the industrial production of BC, controlled synthesis is desired so as to save energy, hence genetic manipulation opens up avenues for upregulating or controlling the cellulose synthesis in the bacterium by targeting genes involved in cellulose biosynthesis. In this review article genetic modification has been presented as a tool to introduce desired changes at genetic level resulting in improved yield or properties. There has been a lack of studies on genetic modification for BC production due to limited availability of information on whole genome and genetic toolkits; however, in last few years, the number of studies has been increased on this aspect as whole genome sequencing of several Komagataeibacter strains are being done. In this review article, we have presented the mechanisms and the targets for genetic modifications in order to achieve desired changes in the BC production titer as well as its characteristics.
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Affiliation(s)
- Reeta Rani Singhania
- Department of Marine Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan
| | - Anil Kumar Patel
- Department of Marine Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan
| | - Mei-Ling Tsai
- Department of Seafood Science, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan
| | - Chiu-Wen Chen
- Department of Marine Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan
| | - Cheng Di Dong
- Department of Marine Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan
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Jadebeck JF, Theorell A, Leweke S, Nöh K. HOPS: high-performance library for (non-)uniform sampling of convex-constrained models. Bioinformatics 2021; 37:1776-1777. [PMID: 33045081 DOI: 10.1093/bioinformatics/btaa872] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/30/2022] Open
Abstract
SUMMARY The C++ library Highly Optimized Polytope Sampling (HOPS) provides implementations of efficient and scalable algorithms for sampling convex-constrained models that are equipped with arbitrary target functions. For uniform sampling, substantial performance gains were achieved compared to the state-of-the-art. The ease of integration and utility of non-uniform sampling is showcased in a Bayesian inference setting, demonstrating how HOPS interoperates with third-party software. AVAILABILITY AND IMPLEMENTATION Source code is available at https://github.com/modsim/hops/, tested on Linux and MS Windows, includes unit tests, detailed documentation, example applications and a Dockerfile. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Johann F Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.,Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Axel Theorell
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Samuel Leweke
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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27
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Theorell A, Jadebeck JF, Nöh K, Stelling J. PolyRound: polytope rounding for random sampling in metabolic networks. Bioinformatics 2021; 38:566-567. [PMID: 34329395 PMCID: PMC8723145 DOI: 10.1093/bioinformatics/btab552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/25/2021] [Accepted: 07/29/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY Random flux sampling is a powerful tool for the constraint-based analysis of metabolic networks. The most efficient sampling method relies on a rounding transform of the constraint polytope, but no available rounding implementation can round all relevant models. By removing redundant polytope constraints on the go, PolyRound simplifies the numerical problem and rounds all the 108 models in the BiGG database without parameter tuning, compared to ∼50% for the state-of-the-art implementation. AVAILABILITY AND IMPLEMENTATION The implementation is available on gitlab: https://gitlab.com/csb.ethz/PolyRound. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Johann F Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany,Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany
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Nanda P, Ghosh A. Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS-CoV-2 infection. PLoS Comput Biol 2021; 17:e1008860. [PMID: 33835998 PMCID: PMC8034727 DOI: 10.1371/journal.pcbi.1008860] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/09/2021] [Indexed: 12/27/2022] Open
Abstract
The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux. Metabolic flux analysis in disease biology is opening up new avenues for therapeutic interventions. Numerous diseases lead to disturbance in the metabolic homeostasis and it is becoming increasingly important to be able to quantify the difference in interaction under normal and diseased condition. While genome-scale metabolic models have been used to study those differences, there are limited methods to probe into the differences in flux between these two conditions. Our method of conducting a differential flux analysis can be leveraged to find which reactions are altered between the diseased and normal state. We applied this to study the altered reactions in the case of SARS-CoV-2 infection. We further corroborated our results with other multi-omics studies and found significant agreement.
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Affiliation(s)
- Piyush Nanda
- Department of Biotechnology, Indian Institute of Technology Kharagpur, West Bengal, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal, India
- * E-mail:
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29
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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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Rodríguez-Mier P, Poupin N, de Blasio C, Le Cam L, Jourdan F. DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks. PLoS Comput Biol 2021; 17:e1008730. [PMID: 33571201 PMCID: PMC7904180 DOI: 10.1371/journal.pcbi.1008730] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 02/24/2021] [Accepted: 01/21/2021] [Indexed: 11/18/2022] Open
Abstract
The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.
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Affiliation(s)
- Pablo Rodríguez-Mier
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Carlo de Blasio
- IRCM, Institut de Recherche en Cancérologie de Montpellier, INSERM U1194, Université de Montpellier, Institut régional du Cancer de Montpellier, Montpellier, France
- Equipe Labellisée par la Ligue contre le Cancer, Paris, France
| | - Laurent Le Cam
- IRCM, Institut de Recherche en Cancérologie de Montpellier, INSERM U1194, Université de Montpellier, Institut régional du Cancer de Montpellier, Montpellier, France
- Equipe Labellisée par la Ligue contre le Cancer, Paris, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- * E-mail:
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31
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Pham N, Reijnders M, Suarez-Diez M, Nijsse B, Springer J, Eggink G, Schaap PJ. Genome-scale metabolic modeling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:2. [PMID: 33407779 PMCID: PMC7788717 DOI: 10.1186/s13068-020-01838-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 11/23/2020] [Indexed: 05/03/2023]
Abstract
BACKGROUND Cutaneotrichosporon oleaginosus ATCC 20509 is a fast-growing oleaginous basidiomycete yeast that is able to grow in a wide range of low-cost carbon sources including crude glycerol, a byproduct of biodiesel production. When glycerol is used as a carbon source, this yeast can accumulate more than 50% lipids (w/w) with high concentrations of mono-unsaturated fatty acids. RESULTS To increase our understanding of this yeast and to provide a knowledge base for further industrial use, a FAIR re-annotated genome was used to build a genome-scale, constraint-based metabolic model containing 1553 reactions involving 1373 metabolites in 11 compartments. A new description of the biomass synthesis reaction was introduced to account for massive lipid accumulation in conditions with high carbon-to-nitrogen (C/N) ratio in the media. This condition-specific biomass objective function is shown to better predict conditions with high lipid accumulation using glucose, fructose, sucrose, xylose, and glycerol as sole carbon source. CONCLUSION Contributing to the economic viability of biodiesel as renewable fuel, C. oleaginosus ATCC 20509 can effectively convert crude glycerol waste streams in lipids as a potential bioenergy source. Performance simulations are essential to identify optimal production conditions and to develop and fine tune a cost-effective production process. Our model suggests ATP-citrate lyase as a possible target to further improve lipid production.
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Affiliation(s)
- Nhung Pham
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Maarten Reijnders
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- Department of Ecology and Evolution, University of Lausanne, Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Bart Nijsse
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Jan Springer
- Food and Biobased Research and AlgaePARC, Wageningen University and Research, Wageningen, the Netherlands
| | - Gerrit Eggink
- Food and Biobased Research and AlgaePARC, Wageningen University and Research, Wageningen, the Netherlands
- Bioprocess Engineering and AlgaePARC, Wageningen University and Research, Wageningen, the Netherlands
| | - Peter J Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
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32
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Haiman ZB, Zielinski DC, Koike Y, Yurkovich JT, Palsson BO. MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics. PLoS Comput Biol 2021; 17:e1008208. [PMID: 33507922 PMCID: PMC7872247 DOI: 10.1371/journal.pcbi.1008208] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 02/09/2021] [Accepted: 12/21/2020] [Indexed: 01/01/2023] Open
Abstract
Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.
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Affiliation(s)
- Zachary B. Haiman
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Daniel C. Zielinski
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Yuko Koike
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - James T. Yurkovich
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
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33
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Wanichthanarak K, Boonchai C, Kojonna T, Chadchawan S, Sangwongchai W, Thitisaksakul M. Deciphering rice metabolic flux reprograming under salinity stress via in silico metabolic modeling. Comput Struct Biotechnol J 2020; 18:3555-3566. [PMID: 33304454 PMCID: PMC7708941 DOI: 10.1016/j.csbj.2020.11.023] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 11/30/2022] Open
Abstract
Rice is one of the most economically important commodities globally. However, rice plants are salt susceptible species in which high salinity can significantly constrain its productivity. Several physiological parameters in adaptation to salt stress have been observed, though changes in metabolic aspects remain to be elucidated. In this study, rice metabolic activities of salt-stressed flag leaf were systematically characterized. Transcriptomics and metabolomics data were combined to identify disturbed pathways, altered metabolites and metabolic hotspots within the rice metabolic network under salt stress condition. Besides, the feasible flux solutions in different context-specific metabolic networks were estimated and compared. Our findings highlighted metabolic reprogramming in primary metabolic pathways, cellular respiration, antioxidant biosynthetic pathways, and phytohormone biosynthetic pathways. Photosynthesis and hexose utilization were among the major disturbed pathways in the stressed flag leaf. Notably, the increased flux distribution of the photorespiratory pathway could contribute to cellular redox control. Predicted flux statuses in several pathways were consistent with the results from transcriptomics, end-point metabolomics, and physiological studies. Our study illustrated that the contextualized genome-scale model together with multi-omics analysis is a powerful approach to unravel the metabolic responses of rice to salinity stress.
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Key Words
- 3-PGA, 3-Phosphoglycerate
- ADH, Arogenate dehydrogenase
- ASA, Ascorbate
- CGS, Cystathionine γ-synthase
- CINV, Cytosolic invertase
- Ci, Intercellular CO2 concentration
- E, Transpiration rate
- GAPDH, Glyceraldehyde-3-phosphate dehydrogenase
- GC-TOF-MS, Gas chromatography time-of-flight mass spectrometry
- GEM, Genome-scale metabolic model
- GLYK, 3-Phosphoglycerate kinase
- GMD, Golm Metabolome Database
- GSH, Glutathione
- GSSG, Glutathione disulfide
- IAA, Indole-3-acetic acid
- IPA, Indolepyruvate
- MAPK, Mitogen-activated protein kinase
- MDH, Malate dehydrogenase
- Metabolic flux analysis
- Metabolic modeling
- Metabolomics
- Multi-omics analysis
- PFK, Phosphofructokinase
- PGK, Phosphoglycerate kinase
- PLS-DA, Partial-Least Squares Discriminant Analysis
- Pn, Net photosynthesis rate
- Rice (Oryza sativa L.)
- SOD, Superoxide dismutase
- Salinity stress
- Systems biology
- TAT, Tyrosine aminotransferase
- Transcriptomics
- gs, Stomatal conductance
- iMAT, Integrative Metabolic Analysis Tool
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Affiliation(s)
- Kwanjeera Wanichthanarak
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Chuthamas Boonchai
- Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Future Innovation and Research in Science and Technology, Chulalongkorn University, Bangkok 10330, Thailand
| | - Thammaporn Kojonna
- Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Supachitra Chadchawan
- Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Wichian Sangwongchai
- Department of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Maysaya Thitisaksakul
- Department of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
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Sertbas M, Ulgen KO. Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens. Front Cell Dev Biol 2020; 8:566702. [PMID: 33251208 PMCID: PMC7673413 DOI: 10.3389/fcell.2020.566702] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022] Open
Abstract
Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.
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Affiliation(s)
- Mustafa Sertbas
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey.,Department of Chemical Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Kutlu O Ulgen
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
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35
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Keaty TC, Jensen PA. Gapsplit: efficient random sampling for non-convex constraint-based models. Bioinformatics 2020; 36:2623-2625. [PMID: 31913465 DOI: 10.1093/bioinformatics/btz971] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 12/20/2019] [Accepted: 01/02/2020] [Indexed: 12/24/2022] Open
Abstract
SUMMARY Gapsplit generates random samples from convex and non-convex constraint-based models by targeting under-sampled regions of the solution space. Gapsplit provides uniform coverage of linear, mixed-integer and general non-linear models. AVAILABILITY AND IMPLEMENTATION Python and Matlab source code are freely available at http://jensenlab.net/tools. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thomas C Keaty
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Paul A Jensen
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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36
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Valcárcel LV, Torrano V, Tobalina L, Carracedo A, Planes FJ. rMTA: robust metabolic transformation analysis. Bioinformatics 2020; 35:4350-4355. [PMID: 30923806 DOI: 10.1093/bioinformatics/btz231] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 03/15/2019] [Accepted: 03/27/2019] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION The development of computational tools exploiting -omics data and high-quality genome-scale metabolic networks for the identification of novel drug targets is a relevant topic in Systems Medicine. Metabolic Transformation Algorithm (MTA) is one of these tools, which aims to identify targets that transform a disease metabolic state back into a healthy state, with potential application in any disease where a clear metabolic alteration is observed. RESULTS Here, we present a robust extension to MTA (rMTA), which additionally incorporates a worst-case scenario analysis and minimization of metabolic adjustment to evaluate the beneficial effect of gene knockouts. We show that rMTA complements MTA in the different datasets analyzed (gene knockout perturbations in different organisms, Alzheimer's disease and prostate cancer), bringing a more accurate tool for predicting therapeutic targets. AVAILABILITY AND IMPLEMENTATION rMTA is freely available on The Cobra Toolbox: https://opencobra.github.io/cobratoolbox/latest/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Luis V Valcárcel
- Tecnun, University of Navarra, San Sebastián 20018, Spain
- Area de Hemato-Oncología, IDISNA, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany
| | - Verónica Torrano
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany
- CIC bioGUNE, Bizkaia Technology Park, Derio, Spain
| | - Luis Tobalina
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany
| | - Arkaitz Carracedo
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany
- CIC bioGUNE, Bizkaia Technology Park, Derio, Spain
- Ikerbasque, Basque foundation for science, Bilbao, Spain
- Biochemistry and Molecular Biology Department, University of the Basque Country (UPV/EHU), Bilbao E-48080, Spain
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Nguyen AD, Nam G, Kim D, Lee EY. Metabolic role of pyrophosphate-linked phosphofructokinase pfk for C1 assimilation in Methylotuvimicrobium alcaliphilum 20Z. Microb Cell Fact 2020; 19:131. [PMID: 32546161 PMCID: PMC7298851 DOI: 10.1186/s12934-020-01382-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 05/30/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Methanotrophs is a promising biocatalyst in biotechnological applications with their ability to utilize single carbon (C1) feedstock to produce high-value compounds. Understanding the behavior of biological networks of methanotrophic bacteria in different parameters is vital to systems biology and metabolic engineering. Interestingly, methanotrophic bacteria possess the pyrophosphate-dependent 6-phosphofructokinase (PPi-PFK) instead of the ATP-dependent 6-phosphofructokinase, indicating their potentials to serve as promising model for investigation the role of inorganic pyrophosphate (PPi) and PPi-dependent glycolysis in bacteria. Gene knockout experiments along with global-omics approaches can be used for studying gene functions as well as unraveling regulatory networks that rely on the gene product. RESULTS In this study, we performed gene knockout and RNA-seq experiments in Methylotuvimicrobium alcaliphilum 20Z to investigate the functional roles of PPi-PFK in C1 metabolism when cells were grown on methane and methanol, highlighting its metabolic importance in C1 assimilation in M. alcaliphilum 20Z. We further conducted adaptive laboratory evolution (ALE) to investigate regulatory architecture in pfk knockout strain. Whole-genome resequencing and RNA-seq approaches were performed to characterize the genetic and metabolic responses of adaptation to pfk knockout. A number of mutations, as well as gene expression profiles, were identified in pfk ALE strain to overcome insufficient C1 assimilation pathway which limits the growth in the unevolved strain. CONCLUSIONS This study first revealed the regulatory roles of PPi-PFK on C1 metabolism and then provided novel insights into mechanism of adaptation to the loss of this major metabolic enzyme as well as an improved basis for future strain design in type I methanotrophs.
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Affiliation(s)
- Anh Duc Nguyen
- Department of Chemical Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea
| | - Gayoung Nam
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea
| | - Donghyuk Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea.
| | - Eun Yeol Lee
- Department of Chemical Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea.
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38
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García-Romero I, Nogales J, Díaz E, Santero E, Floriano B. Understanding the metabolism of the tetralin degrader Sphingopyxis granuli strain TFA through genome-scale metabolic modelling. Sci Rep 2020; 10:8651. [PMID: 32457330 PMCID: PMC7250832 DOI: 10.1038/s41598-020-65258-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 04/30/2020] [Indexed: 11/23/2022] Open
Abstract
Sphingopyxis granuli strain TFA is an α-proteobacterium that belongs to the sphingomonads, a group of bacteria well-known for its degradative capabilities and oligotrophic metabolism. Strain TFA is the only bacterium in which the mineralisation of the aromatic pollutant tetralin has been completely characterized at biochemical, genetic, and regulatory levels and the first Sphingopyxis characterised as facultative anaerobe. Here we report additional metabolic features of this α-proteobacterium using metabolic modelling and the functional integration of genomic and transcriptomic data. The genome-scale metabolic model (GEM) of strain TFA, which has been manually curated, includes information on 743 genes, 1114 metabolites and 1397 reactions. This represents the largest metabolic model for a member of the Sphingomonadales order thus far. The predictive potential of this model was validated against experimentally calculated growth rates on different carbon sources and under different growth conditions, including both aerobic and anaerobic metabolisms. Moreover, new carbon and nitrogen sources were predicted and experimentally validated. The constructed metabolic model was used as a platform for the incorporation of transcriptomic data, generating a more robust and accurate model. In silico flux analysis under different metabolic scenarios highlighted the key role of the glyoxylate cycle in the central metabolism of strain TFA.
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Affiliation(s)
- Inmaculada García-Romero
- Centro Andaluz de Biología del Desarrollo, CSIC-Universidad Pablo de Olavide, ES-41013, Seville, Spain
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, BT9 7BL, United Kingdom
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), 28049, Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - Eduardo Díaz
- Department of Microbial and Plant Biotechnology. Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Científicas (CIB-CSIC), 28040, Madrid, Spain
| | - Eduardo Santero
- Centro Andaluz de Biología del Desarrollo, CSIC-Universidad Pablo de Olavide, ES-41013, Seville, Spain
| | - Belén Floriano
- Department of Molecular Biology and Biochemical Engineering. Universidad Pablo de Olavide, ES-41013, Seville, Spain.
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A biochemically-interpretable machine learning classifier for microbial GWAS. Nat Commun 2020; 11:2580. [PMID: 32444610 PMCID: PMC7244534 DOI: 10.1038/s41467-020-16310-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 04/16/2020] [Indexed: 12/28/2022] Open
Abstract
Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC = 0.93) while enabling a biochemical interpretation of the genotype-phenotype map. Interpretation of MACs for three antibiotics (pyrazinamide, para-aminosalicylic acid, and isoniazid) recapitulates known AMR mechanisms and suggest a biochemical basis for how the identified alleles cause AMR. Extending flux balance analysis to identify accurate sequence classifiers thus contributes mechanistic insights to GWAS, a field thus far dominated by mechanism-agnostic results. Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles.
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40
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Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions. PLoS Comput Biol 2020; 16:e1007847. [PMID: 32348298 PMCID: PMC7213742 DOI: 10.1371/journal.pcbi.1007847] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 05/11/2020] [Accepted: 04/03/2020] [Indexed: 11/19/2022] Open
Abstract
Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org) and from github (https://github.com/opencobra/Medusa), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest.
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41
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Jenior ML, Moutinho TJ, Dougherty BV, Papin JA. Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. PLoS Comput Biol 2020; 16:e1007099. [PMID: 32298268 PMCID: PMC7188308 DOI: 10.1371/journal.pcbi.1007099] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 04/28/2020] [Accepted: 02/24/2020] [Indexed: 11/18/2022] Open
Abstract
The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities. Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.
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Affiliation(s)
- Matthew L. Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Thomas J. Moutinho
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Bonnie V. Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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Functional cooperation of the glycine synthase-reductase and Wood-Ljungdahl pathways for autotrophic growth of Clostridium drakei. Proc Natl Acad Sci U S A 2020; 117:7516-7523. [PMID: 32170009 PMCID: PMC7132306 DOI: 10.1073/pnas.1912289117] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Despite sharing the first four reactions, coutilization of the Wood–Ljungdahl pathway (WLP) with the glycine synthase-reductase pathway (GSRP) and reductive glycine pathway (RGP) to fix C1 compounds has remained unknown. In this study, using Clostridium drakei, we elucidated the role of the GSRP and RGP in the presence of the WLP, via a genome-scale metabolic model, RNA-seq, 13C isotope-based metabolite-tracing experiments, biochemical assays, and heterologous expression. Overall, the data suggested the pathways are functional under autotrophic conditions. Along with the WLP, GSRP and RGP convert CO2 to glycine and then to acetyl-phosphate and serine, which then obtain ATP by producing acetate and operate with limited reducing power. This is a unique coutilization of the pathways under autotrophic conditions in acetogens. Among CO2-fixing metabolic pathways in nature, the linear Wood–Ljungdahl pathway (WLP) in phylogenetically diverse acetate-forming acetogens comprises the most energetically efficient pathway, requires the least number of reactions, and converts CO2 to formate and then into acetyl-CoA. Despite two genes encoding glycine synthase being well-conserved in WLP gene clusters, the functional role of glycine synthase under autotrophic growth conditions has remained uncertain. Here, using the reconstructed genome-scale metabolic model iSL771 based on the completed genome sequence, transcriptomics, 13C isotope-based metabolite-tracing experiments, biochemical assays, and heterologous expression of the pathway in another acetogen, we discovered that the WLP and the glycine synthase pathway are functionally interconnected to fix CO2, subsequently converting CO2 into acetyl-CoA, acetyl-phosphate, and serine. Moreover, the functional cooperation of the pathways enhances CO2 consumption and cellular growth rates via bypassing reducing power required reactions for cellular metabolism during autotrophic growth of acetogens.
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The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models. Nat Commun 2020; 11:30. [PMID: 31937763 PMCID: PMC6959363 DOI: 10.1038/s41467-019-13818-7] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/28/2019] [Indexed: 11/09/2022] Open
Abstract
Systems biology has long been interested in models capturing both metabolism and expression in a cell. We propose here an implementation of the metabolism and expression model formalism (ME-models), which we call ETFL, for Expression and Thermodynamics Flux models. ETFL is a hierarchical model formulation, from metabolism to RNA synthesis, that allows simulating thermodynamics-compliant intracellular fluxes as well as enzyme and mRNA concentration levels. ETFL formulates a mixed-integer linear problem (MILP) that enables both relative and absolute metabolite, protein, and mRNA concentration integration. ETFL is compatible with standard MILP solvers and does not require a non-linear solver, unlike the previous state of the art. It also accounts for growth-dependent parameters, such as relative protein or mRNA content. We present ETFL along with its validation using results obtained from a well-characterized E. coli model. We show that ETFL is able to reproduce proteome-limited growth. We also subject it to several analyses, including the prediction of feasible mRNA and enzyme concentrations and gene essentiality. Accounting for the effects of genetic expression in genome-scale metabolic models is challenging. Here, the authors introduce a model formulation that efficiently simulates thermodynamic-compliant fluxes, enzyme and mRNA concentration levels, allowing omics integration and broad analysis of in silico cellular physiology.
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44
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McGarrity S, Karvelsson ST, Sigurjónsson ÓE, Rolfsson Ó. Comparative Metabolic Network Flux Analysis to Identify Differences in Cellular Metabolism. Methods Mol Biol 2020; 2088:223-269. [PMID: 31893377 DOI: 10.1007/978-1-0716-0159-4_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Metabolic network flux analysis uses genome-scale metabolic reconstructions to integrate transcriptomics, proteomics, and/or metabolomics data to allow for comprehensive interpretation of genotype to metabolic phenotype relationships. The compilation of many Constraint-based model analysis methods into one MATLAB package, the COBRAtoolbox, has opened the possibility of using these methods to the many biologists with some knowledge of the commonly used statistical program, MATLAB. Here we outline the steps required to take a published genome-scale metabolic reconstruction and interrogate its consistency and biological feasibility. Subsequently, we demonstrate how mRNA expression data and metabolomics data, relating to one or more cell types or biological contexts, can be applied to constrain and generate metabolic models descriptive of metabolic flux phenotypes. Finally, we describe the comparison of the resulting models and model outputs with the aim of identifying metabolic biomarkers and changes in cellular metabolism.
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Affiliation(s)
- Sarah McGarrity
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Sigurður T Karvelsson
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Ólafur E Sigurjónsson
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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45
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Jóhannsson F, Árnason NÁ, Landrö R, Guðmundsson S, Sigurjonsson ÓE, Rolfsson Ó. Metabolomics study of platelet concentrates photochemically treated with amotosalen and UVA light for pathogen inactivation. Transfusion 2019; 60:367-377. [PMID: 31802514 DOI: 10.1111/trf.15610] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/18/2019] [Accepted: 10/19/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND The risk of bacterial contamination and the deterioration of platelet (PLT) quality limit the shelf-life of platelet concentrates (PCs). The INTERCEPT pathogen inactivation system reduces the risk of pathogen transmission by inhibiting nucleic acid replication using a combination of a photo-reactive compound and UVA illumination. The goal of this study was to investigate the effects the INTERCEPT system has on the PLT metabolome and metabolic activity. STUDY DESIGN AND METHODS Paired units of buffy coat-derived PCs were generated using a pool and split strategy (n = 8). The paired PCs were either treated with the INTERCEPT system or left untreated. Samples were collected on Days 1, 2, 4, and 7 of storage. Ultra-performance chromatography coupled with time-of-flight mass spectrometry was used to analyze the extra- and intracellular metabolomes. Constraint-based metabolic modeling was then used to predict the metabolic activity of the stored PLTs. RESULTS A relatively large number of metabolites in the extracellular environment were depleted during the processing steps of the INTERCEPT system, in particular, metabolites with hydrophobic functional groups, including acylcarnitines and lysophosphatidylcholines. In the intracellular environment, alterations in glucose and glycerophospholipid metabolism and decreased levels of 2-hydroxyglutarate were observed following the INTERCEPT treatment. Untargeted metabolomics analysis revealed residual amotosalen dimers present in the treated PCs. Systems-level analysis of PLT metabolism indicated that the INTERCEPT system does not have a significant impact on the PLT energy metabolism and nutrient utilization. CONCLUSIONS The INTERCEPT system significantly alters the metabolome of the stored PCs without significantly influencing PLT energy metabolism.
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Affiliation(s)
- Freyr Jóhannsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, Reykjavik, Iceland.,Medical Department, University of Iceland, Sturlugata 8, Reykjavik, Iceland
| | - Níels Á Árnason
- The Blood Bank, Landspitali-University Hospital, Snorrabraut 60, Reykjavik, Iceland
| | - Ragna Landrö
- The Blood Bank, Landspitali-University Hospital, Snorrabraut 60, Reykjavik, Iceland
| | - Sveinn Guðmundsson
- The Blood Bank, Landspitali-University Hospital, Snorrabraut 60, Reykjavik, Iceland
| | - Ólafur E Sigurjonsson
- The Blood Bank, Landspitali-University Hospital, Snorrabraut 60, Reykjavik, Iceland.,School of Science and Engineering, Reykjavik University, Menntavegur 1, Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, Reykjavik, Iceland.,Medical Department, University of Iceland, Sturlugata 8, Reykjavik, Iceland
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46
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Nguyen AD, Park JY, Hwang IY, Hamilton R, Kalyuzhnaya MG, Kim D, Lee EY. Genome-scale evaluation of core one-carbon metabolism in gammaproteobacterial methanotrophs grown on methane and methanol. Metab Eng 2019; 57:1-12. [PMID: 31626985 DOI: 10.1016/j.ymben.2019.10.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/18/2019] [Accepted: 10/14/2019] [Indexed: 11/29/2022]
Abstract
Methylotuvimicrobium alcaliphilum 20Z is a promising platform strain for bioconversion of one-carbon (C1) substrates into value-added products. To carry out robust metabolic engineering with methylotrophic bacteria and to implement C1 conversion machinery in non-native hosts, systems-level evaluation and understanding of central C1 metabolism in methanotrophs under various conditions is pivotal but yet elusive. In this study, a genome-scale integrated approach was used to provide in-depth knowledge on the metabolic pathways of M. alcaliphilum 20Z grown on methane and methanol. Systems assessment of core carbon metabolism indicated the methanol assimilation pathway is mostly coupled with the efficient Embden-Meyerhof-Parnas (EMP) pathway along with the serine cycle. In addition, an incomplete TCA cycle operated in M. alcaliphilum 20Z on methanol, which might only supply precursors for de novo synthesis but not reducing powers. Instead, it appears that the direct formaldehyde oxidation pathway supply energy for the whole metabolic system. Additionally, a comparative transcriptomic analysis in multiple gammaproteobacterial methanotrophs also revealed the transcriptional responses of central metabolism on carbon substrate change. These findings provided a systems-level understanding of carbon metabolism and new opportunities for strain design to produce relevant products from different C1-feedstocks.
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Affiliation(s)
- Anh Duc Nguyen
- Department of Chemical Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea
| | - Joon Young Park
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea
| | - In Yeub Hwang
- Department of Chemical Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea
| | - Richard Hamilton
- Biology Department, San Diego State University, San Diego, CA, 92182-4614, United States
| | - Marina G Kalyuzhnaya
- Biology Department, San Diego State University, San Diego, CA, 92182-4614, United States
| | - Donghyuk Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea.
| | - Eun Yeol Lee
- Department of Chemical Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea.
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47
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Kabimoldayev I, Nguyen AD, Yang L, Park S, Lee EY, Kim D. Basics of genome-scale metabolic modeling and applications on C1-utilization. FEMS Microbiol Lett 2019; 365:5106816. [PMID: 30256945 DOI: 10.1093/femsle/fny241] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 09/23/2018] [Indexed: 12/11/2022] Open
Abstract
It is fundamental to understand the relationship between genotype and phenotype in biology. This requires comprehensive knowledge of metabolic pathways, genetic information and well-defined mathematic modeling. Integration of knowledge on metabolism with mathematical modeling results in genome-scale metabolic models which have proven useful to investigate bacterial metabolism and to engineer bacterial strains capable of producing value-added biochemical. Single carbon substrates such as methane and carbon monoxide have drawn interests and they assumed one of next-generation feedstocks because of their high abundance and low price. The methylotroph and acetogen-based biorefineries hold promises for bioconversion of C1 substrates into biofuels and high value compounds. As an effort on expanding our knowledge on C1 utilization approaches, in silico computational framework of C1-metabolism in methylotrophic and acetogenic bacteria has been developed. In this review, genome-scale metabolic models for C1-utilizing bacteria and well-established analysis tools are presented for potential uses for study of C1 metabolism at the genome scale and its application in metabolic engineering.
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Affiliation(s)
- Ilyas Kabimoldayev
- Department of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 17104, South Korea
| | - Anh Duc Nguyen
- Department of Chemical Engineering, Kyung Hee University, Yongin 17104, South Korea
| | - Laurence Yang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.,The Novo Nordisk Foundation Center for Biosustainabiliy, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Sunghoon Park
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Eun Yeol Lee
- Department of Chemical Engineering, Kyung Hee University, Yongin 17104, South Korea
| | - Donghyuk Kim
- Department of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 17104, South Korea.,School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.,School of Biological Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
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Jang WD, Kim TY, Kim HU, Shim WY, Ryu JY, Park JH, Lee SY. Genomic and metabolic analysis of Komagataeibacter xylinus DSM 2325 producing bacterial cellulose nanofiber. Biotechnol Bioeng 2019; 116:3372-3381. [PMID: 31433066 DOI: 10.1002/bit.27150] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/08/2019] [Accepted: 08/04/2019] [Indexed: 12/12/2022]
Abstract
Bacterial cellulose nanofiber (CNF) is a polymer with a wide range of potential industrial applications. Several Komagataeibacter species, including Komagataeibacter xylinus as a model organism, produce CNF. However, the industrial application of CNF has been hampered by inefficient CNF production, necessitating metabolic engineering for the enhanced CNF production. Here, we present complete genome sequence and a genome-scale metabolic model KxyMBEL1810 of K. xylinus DSM 2325 for metabolic engineering applications. Genome analysis of this bacterium revealed that a set of genes associated with CNF biosynthesis and regulation were present in this bacterium, which were also conserved in another six representative Komagataeibacter species having complete genome information. To better understand the metabolic characteristics of K. xylinus DSM 2325, KxyMBEL1810 was reconstructed using genome annotation data, relevant computational resources and experimental growth data generated in this study. Random sampling and correlation analysis of the KxyMBEL1810 predicted pgi and gnd genes as novel overexpression targets for the enhanced CNF production. Among engineered K. xylinus strains individually overexpressing heterologous pgi and gnd genes, either from Escherichia coli or Corynebacterium glutamicum, batch fermentation of a strain overexpressing the E. coli pgi gene produced 3.15 g/L of CNF in a complex medium containing glucose, which was the best CNF concentration achieved in this study, and 115.8% higher than that (1.46 g/L) obtained from the control strain. Genome sequence data and KxyMBEL1810 generated in this study should be useful resources for metabolic engineering of K. xylinus for the enhanced CNF production.
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Affiliation(s)
- Woo Dae Jang
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 plus program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Tae Yong Kim
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Hyun Uk Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 plus program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, Republic of Korea
| | - Woo Yong Shim
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Jae Yong Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 plus program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Jin Hwan Park
- Biomaterials Lab, Material Research Center, Samsung Advanced Institute of Technology, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 plus program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, Republic of Korea
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49
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Herrmann HA, Dyson BC, Vass L, Johnson GN, Schwartz JM. Flux sampling is a powerful tool to study metabolism under changing environmental conditions. NPJ Syst Biol Appl 2019; 5:32. [PMID: 31482008 PMCID: PMC6718391 DOI: 10.1038/s41540-019-0109-0] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 08/06/2019] [Indexed: 12/26/2022] Open
Abstract
The development of high-throughput 'omic techniques has sparked a rising interest in genome-scale metabolic models, with applications ranging from disease diagnostics to crop adaptation. Efficient and accurate methods are required to analyze large metabolic networks. Flux sampling can be used to explore the feasible flux solutions in metabolic networks by generating probability distributions of steady-state reaction fluxes. Unlike other methods, flux sampling can be used without assuming a particular cellular objective. We have undertaken a rigorous comparison of several sampling algorithms and concluded that the coordinate hit-and-run with rounding (CHRR) algorithm is the most efficient based on both run-time and multiple convergence diagnostics. We demonstrate the power of CHRR by using it to study the metabolic changes that underlie photosynthetic acclimation to cold of Arabidopsis thaliana plant leaves. In combination with experimental measurements, we show how the regulated interplay between diurnal starch and organic acid accumulation defines the plant acclimation process. We confirm fumarate accumulation as a requirement for cold acclimation and further predict γ-aminobutyric acid to have a key role in metabolic signaling under cold conditions. These results demonstrate how flux sampling can be used to analyze the feasible flux solutions across changing environmental conditions, whereas eliminating the need to make assumptions which introduce observer bias.
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Affiliation(s)
- Helena A. Herrmann
- Department of Earth and Environmental Sciences, University of Manchester, Manchester, UK
| | - Beth C. Dyson
- Department of Earth and Environmental Sciences, University of Manchester, Manchester, UK
- Present Address: Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK
| | - Lucy Vass
- School of Biological Sciences, University of Manchester, Manchester, UK
- Present Address: Bristol Veterinary School and Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Giles N. Johnson
- Department of Earth and Environmental Sciences, University of Manchester, Manchester, UK
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50
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Landon S, Rees-Garbutt J, Marucci L, Grierson C. Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering. Essays Biochem 2019; 63:267-284. [PMID: 31243142 PMCID: PMC6610458 DOI: 10.1042/ebc20180045] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 05/19/2019] [Accepted: 05/23/2019] [Indexed: 01/04/2023]
Abstract
Producing 'designer cells' with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.
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Affiliation(s)
- Sophie Landon
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, U.K
| | - Joshua Rees-Garbutt
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K
- School of Biological Sciences, University of Bristol, Life Sciences Building, Bristol BS8 1TQ, U.K
| | - Lucia Marucci
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K.
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, U.K
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1UB, U.K
| | - Claire Grierson
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K.
- School of Biological Sciences, University of Bristol, Life Sciences Building, Bristol BS8 1TQ, U.K
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