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Sun Q, An P, Li P, Wang H, Tao S, Liu Y. Unraveling Time-Resolved Transcriptional and Metabolic Shifts in the Mixed Fermentation of Saccharomyces cerevisiae and Hanseniaspora uvarum. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2025; 73:12418-12432. [PMID: 40310988 DOI: 10.1021/acs.jafc.5c00722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2025]
Abstract
Wine fermentation and flavor formation are shaped by complex biochemical reactions driven by a variety of microorganisms. Non-Saccharomyces yeasts, such as Hanseniaspora uvarum (HU), are often used in mixed fermentation with Saccharomyces cerevisiae (SC) to enhance wine aroma. However, the lack of systematic knowledge regarding transcriptional responses and metabolic behaviors during fermentation has hindered the rational control of the mixed fermentation processes. To address this, we investigated transcriptional dynamics and metabolic behavior throughout the entire fermentation process, with a particular focus on the roles of microbial metabolism in flavor formation during mixed fermentation with HU. At the transcriptional level, the addition of HU led to significant changes in SC's gene expression, particularly in pathways related to glyoxylate and dicarboxylate metabolism, pyruvate metabolism, and amino sugar and nucleotide sugar metabolism. Furthermore, using genome-scale metabolic modeling, we uncovered key metabolic strategies employed by the two strains in mixed fermentation. These include distinct sugar utilization patterns, ethanol production, fatty acid metabolism, and central carbon allocation strategies. Notably, we identified two metabolic bypasses, from dihydroxyacetone phosphate to glycerol and from glucose-6-phosphate to the pentose phosphate pathway, which were found to reduce ethanol production and maintain the metabolic balance. Flux distribution analysis also revealed connections among organic acids, amino acids, and fermentation products, highlighting the role of a partial TCA cycle during fermentation. Additionally, metabolic interactions between SC and HU were identified, contributing to the enhanced production of volatile compounds, such as 2-phenylethanol and indole-3-ethanol in mixed fermentation. These findings provide a more comprehensive understanding of transcriptional regulation and metabolic strategies under fermentation conditions. They also offer practical targets for future bioengineering efforts aimed at controlling and optimizing the wine flavor.
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Affiliation(s)
- Qing Sun
- College of Enology, Northwest A&F University, Yangling, Shaanxi 712100, China
- Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Peng An
- College of Enology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Peiyang Li
- Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Hao Wang
- Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Shiheng Tao
- Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yanlin Liu
- College of Enology, Northwest A&F University, Yangling, Shaanxi 712100, China
- Vocational & Technical College of Inner Mongolia Agriculture University, Tumed Youqi 110 National Road, Baotou, Inner Mongolia 014109, China
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2
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Cheng Y, Yu W, Bi X, Liu Y, Li J, Du G, Chen J, Lv X, Liu L. CarveAdornCurate: a versatile cloud-based platform for constructing multiscale metabolic models. Trends Biotechnol 2025; 43:1234-1259. [PMID: 40044549 DOI: 10.1016/j.tibtech.2025.01.011] [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: 08/04/2024] [Revised: 01/27/2025] [Accepted: 01/29/2025] [Indexed: 05/10/2025]
Abstract
Multiscale modeling is a promising approach for understanding cellular behaviors. However, existing multiscale modeling tools require meticulously curated genome-scale metabolic models (GEMs) as inputs, limiting the broad applications of multiscale models due to complex and time-consuming construction processes. To this end, we developed a novel workflow named CarveAdornCurate (CAC) for de novo multiscale modeling. The Carve module generates an ensemble of GEMs with strong genetic evidence, which is then upgraded to multiscale models using Adorn module. The Curate module was designed to find features important to the generated models. These three modules are integrated into a cloud-based platform to promote broad accessibility. As proof of concept, we constructed CAC-based multiscale models for Corynebacterium glutamicum and Yarrowia lipolytica, demonstrating their potential in guiding metabolic engineering. Overall, CAC is demonstrated to be an efficient and user-friendly tool for constructing multiscale models. It is available online at www.carveadorncurate.com/.
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Affiliation(s)
- Yang Cheng
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Wenwen Yu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Jian Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
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3
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Li X, Walhout AJM, Yilmaz LS. Enhanced flux potential analysis links changes in enzyme expression to metabolic flux. Mol Syst Biol 2025; 21:413-445. [PMID: 39962320 PMCID: PMC11965317 DOI: 10.1038/s44320-025-00090-9] [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: 08/28/2023] [Revised: 01/29/2025] [Accepted: 02/07/2025] [Indexed: 03/28/2025] Open
Abstract
Algorithms that constrain metabolic network models with enzyme levels to predict metabolic activity assume that changes in enzyme levels are indicative of flux variations. However, metabolic flux can also be regulated by other mechanisms such as allostery and mass action. To systematically explore the relationship between fluctuations in enzyme expression and flux, we combine available yeast proteomic and fluxomic data to reveal that flux changes can be best predicted from changes in enzyme levels of pathways, rather than the whole network or only cognate reactions. We implement this principle in an 'enhanced flux potential analysis' (eFPA) algorithm that integrates enzyme expression data with metabolic network architecture to predict relative flux levels of reactions including those regulated by other mechanisms. Applied to human data, eFPA consistently predicts tissue metabolic function using either proteomic or transcriptomic data. Additionally, eFPA efficiently handles data sparsity and noisiness, generating robust flux predictions with single-cell gene expression data. Our approach outperforms alternatives by striking an optimal balance, evaluating enzyme expression at pathway level, rather than either single-reaction or whole-network levels.
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Affiliation(s)
- Xuhang Li
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Albertha J M Walhout
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA.
| | - L Safak Yilmaz
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA.
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4
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Pandey V. MiNEApy: enhancing enrichment network analysis in metabolic networks. Bioinformatics 2025; 41:btaf077. [PMID: 39985451 PMCID: PMC11889450 DOI: 10.1093/bioinformatics/btaf077] [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: 06/25/2024] [Revised: 02/01/2025] [Accepted: 02/20/2025] [Indexed: 02/24/2025] Open
Abstract
MOTIVATION Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.
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Affiliation(s)
- Vikash Pandey
- Department of Molecular Biology, Umeå University, Umeå, 90187, Sweden
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Scott H, Segrè D. Metabolic Flux Modeling in Marine Ecosystems. ANNUAL REVIEW OF MARINE SCIENCE 2025; 17:593-620. [PMID: 39259978 DOI: 10.1146/annurev-marine-032123-033718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Ocean metabolism constitutes a complex, multiscale ensemble of biochemical reaction networks harbored within and between the boundaries of a myriad of organisms. Gaining a quantitative understanding of how these networks operate requires mathematical tools capable of solving in silico the resource allocation problem each cell faces in real life. Toward this goal, stoichiometric modeling of metabolism, such as flux balance analysis, has emerged as a powerful computational tool for unraveling the intricacies of metabolic processes in microbes, microbial communities, and multicellular organisms. Here, we provide an overview of this approach and its applications, future prospects, and practical considerations in the context of marine sciences. We explore how flux balance analysis has been employed to study marine organisms, help elucidate nutrient cycling, and predict metabolic capabilities within diverse marine environments, and highlight future prospects for this field in advancing our knowledge of marine ecosystems and their sustainability.
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Affiliation(s)
- Helen Scott
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Faculty of Computing and Data Science, Boston University, Boston, Massachusetts, USA; ,
| | - Daniel Segrè
- Department of Biology, Department of Physics, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Faculty of Computing and Data Science, Boston University, Boston, Massachusetts, USA; ,
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6
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Carter EL, Waterfield NR, Constantinidou C, Alam MT. A temperature-induced metabolic shift in the emerging human pathogen Photorhabdus asymbiotica. mSystems 2024; 9:e0097023. [PMID: 39445821 PMCID: PMC11575385 DOI: 10.1128/msystems.00970-23] [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: 09/19/2023] [Accepted: 11/29/2023] [Indexed: 10/25/2024] Open
Abstract
Photorhabdus is a bacterial genus containing both insect and emerging human pathogens. Most insect-restricted species display temperature restriction, unable to grow above 34°C, while Photorhabdus asymbiotica can grow at 37°C to infect mammalian hosts and cause Photorhabdosis. Metabolic adaptations have been proposed to facilitate the survival of this pathogen at higher temperatures, yet the biological mechanisms underlying these are poorly understood. We have reconstructed an extensively manually curated genome-scale metabolic model of P. asymbiotica (iEC1073, BioModels ID MODEL2309110001), validated through in silico gene knockout and nutrient utilization experiments with an excellent agreement between experimental data and model predictions. Integration of iEC1073 with transcriptomics data obtained for P. asymbiotica at temperatures of 28°C and 37°C allowed the development of temperature-specific reconstructions representing metabolic adaptations the pathogen undergoes when shifting to a higher temperature in a mammalian compared to insect host. Analysis of these temperature-specific reconstructions reveals that nucleotide metabolism is enriched with predicted upregulated and downregulated reactions. iEC1073 could be used as a powerful tool to study the metabolism of P. asymbiotica, in different genetic or environmental conditions. IMPORTANCE Photorhabdus bacterial species contain both human and insect pathogens, and most of these species cannot grow in higher temperatures. However, Photorhabdus asymbiotica, which infects both humans and insects, can grow in higher temperatures and undergoes metabolic adaptations at a temperature of 37°C compared to that of insect body temperature. Therefore, it is important to examine how this bacterial species can metabolically adapt to survive in higher temperatures. In this work, using a mathematical model, we have examined the metabolic shift that takes place when the bacteria switch from growth conditions in 28°C to 37°C. We show that P. asymbiotica potentially experiences predicted temperature-induced metabolic adaptations at 37°C predominantly clustered within the nucleotide metabolism pathway.
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Affiliation(s)
- Elena Lucy Carter
- Warwick Medical School, University of Warwick, Gibbet Hill Campus, Coventry, United Kingdom
| | - Nicholas R Waterfield
- Warwick Medical School, University of Warwick, Gibbet Hill Campus, Coventry, United Kingdom
| | - Chrystala Constantinidou
- Warwick Medical School, University of Warwick, Gibbet Hill Campus, Coventry, United Kingdom
- Bioinformatics Research Technology Platform, University of Warwick, Warwick, United Kingdom
| | - Mohammad Tauqeer Alam
- Department of Biology, College of Science, United Arab Emirates University, Al-Ain, United Arab Emirates
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7
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Narasimha SM, Malpani T, Mohite OS, Nath JS, Raman K. Understanding flux switching in metabolic networks through an analysis of synthetic lethals. NPJ Syst Biol Appl 2024; 10:104. [PMID: 39289347 PMCID: PMC11408705 DOI: 10.1038/s41540-024-00426-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 08/17/2024] [Indexed: 09/19/2024] Open
Abstract
Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.
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Affiliation(s)
- Sowmya Manojna Narasimha
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Neuroscience Graduate Program, University of California San Diego, San Diego, CA, 92092, USA
| | - Tanisha Malpani
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
| | - Omkar S Mohite
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark
| | - J Saketha Nath
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Hyderabad, Hyderabad, 502 284, India
| | - Karthik Raman
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
- Department of Data Science and AI, Wadhwani School of Data Science and AI (WSAI), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
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8
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Choudhury S, Narayanan B, Moret M, Hatzimanikatis V, Miskovic L. Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states. Nat Catal 2024; 7:1086-1098. [PMID: 39463726 PMCID: PMC11499278 DOI: 10.1038/s41929-024-01220-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 08/06/2024] [Indexed: 10/29/2024]
Abstract
Generating large omics datasets has become routine for gaining insights into cellular processes, yet deciphering these datasets to determine metabolic states remains challenging. Kinetic models can help integrate omics data by explicitly linking metabolite concentrations, metabolic fluxes and enzyme levels. Nevertheless, determining the kinetic parameters that underlie cellular physiology poses notable obstacles to the widespread use of these mathematical representations of metabolism. Here we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration of diverse omics data and other relevant information, including extracellular medium composition, physicochemical data and expertise of domain specialists, RENAISSANCE accurately characterizes intracellular metabolic states in Escherichia coli. It also estimates missing kinetic parameters and reconciles them with sparse experimental data, substantially reducing parameter uncertainty and improving accuracy. This framework will be valuable for researchers studying metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnology.
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Affiliation(s)
- Subham Choudhury
- Laboratory of Computational Systems Biology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Bharath Narayanan
- Laboratory of Computational Systems Biology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Present Address: Department of Oncology, University of Cambridge, Cambridge, UK
| | - Michael Moret
- Laboratory of Computational Systems Biology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Present Address: Department of Genetics, Harvard Medical School, Boston, MA USA
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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9
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Curvello R, Berndt N, Hauser S, Loessner D. Recreating metabolic interactions of the tumour microenvironment. Trends Endocrinol Metab 2024; 35:518-532. [PMID: 38212233 DOI: 10.1016/j.tem.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/05/2023] [Accepted: 12/12/2023] [Indexed: 01/13/2024]
Abstract
Tumours are heterogeneous tissues containing diverse populations of cells and an abundant extracellular matrix (ECM). This tumour microenvironment prompts cancer cells to adapt their metabolism to survive and grow. Besides epigenetic factors, the metabolism of cancer cells is shaped by crosstalk with stromal cells and extracellular components. To date, most experimental models neglect the complexity of the tumour microenvironment and its relevance in regulating the dynamics of the metabolism in cancer. We discuss emerging strategies to model cellular and extracellular aspects of cancer metabolism. We highlight cancer models based on bioengineering, animal, and mathematical approaches to recreate cell-cell and cell-matrix interactions and patient-specific metabolism. Combining these approaches will improve our understanding of cancer metabolism and support the development of metabolism-targeting therapies.
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Affiliation(s)
- Rodrigo Curvello
- Department of Chemical and Biological Engineering, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia
| | - Nikolaus Berndt
- Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany; Institute of Computer-assisted Cardiovascular Medicine, Deutsches Herzzentrum der Charité, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sandra Hauser
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Department of Radiopharmaceutical and Chemical Biology, Dresden, Germany
| | - Daniela Loessner
- Department of Chemical and Biological Engineering, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia; Leibniz Institute of Polymer Research Dresden e.V., Max Bergmann Center of Biomaterials, Dresden, Germany; Department of Materials Science and Engineering, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia; Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Science, Monash University, Melbourne, Victoria, Australia.
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10
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Morotti M, Grimm AJ, Hope HC, Arnaud M, Desbuisson M, Rayroux N, Barras D, Masid M, Murgues B, Chap BS, Ongaro M, Rota IA, Ronet C, Minasyan A, Chiffelle J, Lacher SB, Bobisse S, Murgues C, Ghisoni E, Ouchen K, Bou Mjahed R, Benedetti F, Abdellaoui N, Turrini R, Gannon PO, Zaman K, Mathevet P, Lelievre L, Crespo I, Conrad M, Verdeil G, Kandalaft LE, Dagher J, Corria-Osorio J, Doucey MA, Ho PC, Harari A, Vannini N, Böttcher JP, Dangaj Laniti D, Coukos G. PGE 2 inhibits TIL expansion by disrupting IL-2 signalling and mitochondrial function. Nature 2024; 629:426-434. [PMID: 38658764 PMCID: PMC11078736 DOI: 10.1038/s41586-024-07352-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 03/26/2024] [Indexed: 04/26/2024]
Abstract
Expansion of antigen-experienced CD8+ T cells is critical for the success of tumour-infiltrating lymphocyte (TIL)-adoptive cell therapy (ACT) in patients with cancer1. Interleukin-2 (IL-2) acts as a key regulator of CD8+ cytotoxic T lymphocyte functions by promoting expansion and cytotoxic capability2,3. Therefore, it is essential to comprehend mechanistic barriers to IL-2 sensing in the tumour microenvironment to implement strategies to reinvigorate IL-2 responsiveness and T cell antitumour responses. Here we report that prostaglandin E2 (PGE2), a known negative regulator of immune response in the tumour microenvironment4,5, is present at high concentrations in tumour tissue from patients and leads to impaired IL-2 sensing in human CD8+ TILs via the PGE2 receptors EP2 and EP4. Mechanistically, PGE2 inhibits IL-2 sensing in TILs by downregulating the IL-2Rγc chain, resulting in defective assembly of IL-2Rβ-IL2Rγc membrane dimers. This results in impaired IL-2-mTOR adaptation and PGC1α transcriptional repression, causing oxidative stress and ferroptotic cell death in tumour-reactive TILs. Inhibition of PGE2 signalling to EP2 and EP4 during TIL expansion for ACT resulted in increased IL-2 sensing, leading to enhanced proliferation of tumour-reactive TILs and enhanced tumour control once the cells were transferred in vivo. Our study reveals fundamental features that underlie impairment of human TILs mediated by PGE2 in the tumour microenvironment. These findings have therapeutic implications for cancer immunotherapy and cell therapy, and enable the development of targeted strategies to enhance IL-2 sensing and amplify the IL-2 response in TILs, thereby promoting the expansion of effector T cells with enhanced therapeutic potential.
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MESH Headings
- Animals
- Humans
- Mice
- CD8-Positive T-Lymphocytes/cytology
- CD8-Positive T-Lymphocytes/immunology
- CD8-Positive T-Lymphocytes/metabolism
- Cell Proliferation
- Dinoprostone/metabolism
- Down-Regulation
- Ferroptosis
- Interleukin Receptor Common gamma Subunit/biosynthesis
- Interleukin Receptor Common gamma Subunit/deficiency
- Interleukin Receptor Common gamma Subunit/metabolism
- Interleukin-2/antagonists & inhibitors
- Interleukin-2/immunology
- Interleukin-2/metabolism
- Interleukin-2 Receptor beta Subunit/metabolism
- Lymphocytes, Tumor-Infiltrating/cytology
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
- Mitochondria/metabolism
- Oxidative Stress
- Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha/metabolism
- Receptors, Prostaglandin E, EP2 Subtype/metabolism
- Receptors, Prostaglandin E, EP2 Subtype/antagonists & inhibitors
- Receptors, Prostaglandin E, EP4 Subtype/metabolism
- Receptors, Prostaglandin E, EP4 Subtype/antagonists & inhibitors
- Signal Transduction
- TOR Serine-Threonine Kinases/metabolism
- Tumor Microenvironment/immunology
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Affiliation(s)
- Matteo Morotti
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Alizee J Grimm
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Helen Carrasco Hope
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Mathieu Desbuisson
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Nicolas Rayroux
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - David Barras
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Maria Masid
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Baptiste Murgues
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Bovannak S Chap
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Marco Ongaro
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Ioanna A Rota
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Catherine Ronet
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Aspram Minasyan
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Sebastian B Lacher
- Institute of Molecular Immunology, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
| | - Sara Bobisse
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Clément Murgues
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Eleonora Ghisoni
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Khaoula Ouchen
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Ribal Bou Mjahed
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Fabrizio Benedetti
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Naoill Abdellaoui
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Riccardo Turrini
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippe O Gannon
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Khalil Zaman
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Patrice Mathevet
- Department of Gynaecology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Loic Lelievre
- Department of Gynaecology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Isaac Crespo
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Marcus Conrad
- Institute of Metabolism and Cell Death, Molecular Target and Therapeutics Centre, Helmholtz Munich, Neuherberg, Germany
| | - Gregory Verdeil
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Lana E Kandalaft
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Julien Dagher
- Unit of Translational Oncopathology, Institute of Pathology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Jesus Corria-Osorio
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Marie-Agnes Doucey
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
| | - Ping-Chih Ho
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Nicola Vannini
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Jan P Böttcher
- Institute of Molecular Immunology, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
| | - Denarda Dangaj Laniti
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland.
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
- Agora Cancer Research Center, Lausanne, Switzerland.
| | - George Coukos
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland.
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
- Agora Cancer Research Center, Lausanne, Switzerland.
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11
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Hou C, Song X, Xiong Z, Wang G, Xia Y, Ai L. Investigating the Role of β-Disodium Glycerophosphate and Urea in Promoting Growth of Streptococcus thermophilus from Omics-Integrated Genome-Scale Models. Foods 2024; 13:1006. [PMID: 38611312 PMCID: PMC11011449 DOI: 10.3390/foods13071006] [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: 02/29/2024] [Revised: 03/20/2024] [Accepted: 03/23/2024] [Indexed: 04/14/2024] Open
Abstract
This study investigates the impact of urea and β-GP on the growth of Streptococcus thermophilus S-3, a bacterium commonly used in industrial fermentation processes. Through a series of growth experiments, transcriptome, metabolome, and omics-based analyses, the research demonstrates that both urea and β-GP can enhance the biomass of S. thermophilus, with urea showing a more significant effect. The optimal urea concentration for growth was determined to be 3 g/L in M17 medium. The study also highlights the metabolic pathways influenced by urea and β-GP, particularly the galactose metabolism pathway, which is crucial for cell growth when lactose is the substrate. The integration of omics data into the genome-scale metabolic model of S. thermophilus, iCH502, allowed for a more accurate prediction of metabolic fluxes and growth rates. The study concludes that urea can serve as a viable substitute for β-GP in the cultivation of S. thermophilus, offering potential cost and efficiency benefits in industrial fermentation processes. The findings are supported by validation experiments with 11 additional strains of S. thermophilus, which showed increased biomass in UM17 medium.
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Affiliation(s)
| | | | | | | | | | - Lianzhong Ai
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; (C.H.); (X.S.); (Z.X.); (G.W.); (Y.X.)
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12
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Tsouka S, Kumar P, Seubnooch P, Freiburghaus K, St-Pierre M, Dufour JF, Masoodi M. Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human. COMMUNICATIONS MEDICINE 2024; 4:39. [PMID: 38443644 PMCID: PMC10914730 DOI: 10.1038/s43856-024-00465-3] [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: 09/18/2023] [Accepted: 02/22/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic liver disease worldwide, and can rapidly progress to metabolic dysfunction-associated steatohepatitis (MASH). Accurate preclinical models and methodologies are needed to understand underlying metabolic mechanisms and develop treatment strategies. Through meta-analysis of currently proposed mouse models, we hypothesized that a diet- and chemical-induced MASH model closely resembles the observed lipid metabolism alterations in humans. METHODS We developed transcriptomics-driven metabolic pathway analysis (TDMPA), a method to aid in the evaluation of metabolic resemblance. TDMPA uses genome-scale metabolic models to calculate enzymatic reaction perturbations from gene expression data. We performed TDMPA to score and compare metabolic pathway alterations in MASH mouse models to human MASH signatures. We used an already-established WD+CCl4-induced MASH model and performed functional assays and lipidomics to confirm TDMPA findings. RESULTS Both human MASH and mouse models exhibit numerous altered metabolic pathways, including triglyceride biosynthesis, fatty acid beta-oxidation, bile acid biosynthesis, cholesterol metabolism, and oxidative phosphorylation. We confirm a significant reduction in mitochondrial functions and bioenergetics, as well as in acylcarnitines for the mouse model. We identify a wide range of lipid species within the most perturbed pathways predicted by TDMPA. Triglycerides, phospholipids, and bile acids are increased significantly in mouse MASH liver, confirming our initial observations. CONCLUSIONS We introduce TDMPA, a methodology for evaluating metabolic pathway alterations in metabolic disorders. By comparing metabolic signatures that typify human MASH, we show a good metabolic resemblance of the WD+CCl4 mouse model. Our presented approach provides a valuable tool for defining metabolic space to aid experimental design for assessing metabolism.
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Affiliation(s)
- Sofia Tsouka
- Institute of Clinical Chemistry, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Pavitra Kumar
- Department for BioMedical Research, Visceral Surgery and Medicine, University of Bern, Bern, Switzerland
| | - Patcharamon Seubnooch
- Institute of Clinical Chemistry, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Katrin Freiburghaus
- Institute of Clinical Chemistry, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Marie St-Pierre
- Department for BioMedical Research, Visceral Surgery and Medicine, University of Bern, Bern, Switzerland
| | - Jean-François Dufour
- Department for BioMedical Research, Visceral Surgery and Medicine, University of Bern, Bern, Switzerland
- Centre des Maladie Digestives, Lausanne, Switzerland
| | - Mojgan Masoodi
- Institute of Clinical Chemistry, Inselspital, Bern University Hospital, Bern, Switzerland.
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13
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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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14
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Paklao T, Suratanee A, Plaimas K. ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology. BMC Bioinformatics 2023; 24:492. [PMID: 38129786 PMCID: PMC10740312 DOI: 10.1186/s12859-023-05599-0] [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: 08/28/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Flux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable insights. RESULTS To fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein-protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic engineering. CONCLUSION ICON-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://github.com/ThummaratPaklao/ICOM-GEMs.git .
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Affiliation(s)
- Thummarat Paklao
- Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, 10800, Thailand
| | - Kitiporn Plaimas
- Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
- Omics Sciences and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
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Carter EL, Constantinidou C, Alam MT. Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations. Brief Bioinform 2023; 25:bbad439. [PMID: 38048080 PMCID: PMC10694557 DOI: 10.1093/bib/bbad439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/21/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.
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Affiliation(s)
- Elena Lucy Carter
- Warwick Medical School, University of Warwick, Coventry, CV4 7HL, UK
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16
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Wendering P, Nikoloski Z. Model-driven insights into the effects of temperature on metabolism. Biotechnol Adv 2023; 67:108203. [PMID: 37348662 DOI: 10.1016/j.biotechadv.2023.108203] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/22/2023] [Accepted: 06/18/2023] [Indexed: 06/24/2023]
Abstract
Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.
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Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.
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17
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Bessell B, Loecker J, Zhao Z, Aghamiri SS, Mohanty S, Amin R, Helikar T, Puniya BL. COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery. Brief Bioinform 2023; 24:bbad387. [PMID: 37930022 PMCID: PMC10627799 DOI: 10.1093/bib/bbad387] [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: 06/21/2023] [Revised: 09/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (COMO), a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases and disease data to aid drug discovery. COMO can be installed as a Docker Image or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for model construction and a streamlined approach for predicting repurposable drugs, COMO enables researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease where metabolic inhibition is relevant. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies. The source code of the COMO pipeline is available at https://github.com/HelikarLab/COMO. The Docker image can be pulled at https://github.com/HelikarLab/COMO/pkgs/container/como.
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Affiliation(s)
- Brandt Bessell
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Josh Loecker
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Zhongyuan Zhao
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | | | | | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
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18
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Li G, Liu L, Du W, Cao H. Local flux coordination and global gene expression regulation in metabolic modeling. Nat Commun 2023; 14:5700. [PMID: 37709734 PMCID: PMC10502109 DOI: 10.1038/s41467-023-41392-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.
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Affiliation(s)
- Gaoyang Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Li Liu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China
| | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Huansheng Cao
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China.
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19
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Kaste JAM, Shachar-Hill Y. Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis. ARXIV 2023:arXiv:2303.12651v1. [PMID: 36994165 PMCID: PMC10055486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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 of these 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. A number of approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to decide on and/or discriminate between alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, 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 the adoption of 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 in particular.
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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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Abstract
Microbial communities experience continuous environmental changes, with temperature fluctuations being the most impacting. This is particularly important considering the ongoing global warming but also in the "simpler" context of seasonal variability of sea-surface temperature. Understanding how microorganisms react at the cellular level can improve our understanding of their possible adaptations to a changing environment. In this work, we investigated the mechanisms through which metabolic homeostasis is maintained in a cold-adapted marine bacterium during growth at temperatures that differ widely (15 and 0°C). We have quantified its intracellular and extracellular central metabolomes together with changes occurring at the transcriptomic level in the same growth conditions. This information was then used to contextualize a genome-scale metabolic reconstruction, and to provide a systemic understanding of cellular adaptation to growth at 2 different temperatures. Our findings indicate a strong metabolic robustness at the level of the main central metabolites, counteracted by a relatively deep transcriptomic reprogramming that includes changes in gene expression of hundreds of metabolic genes. We interpret this as a transcriptomic buffering of cellular metabolism, able to produce overlapping metabolic phenotypes, despite the wide temperature gap. Moreover, we show that metabolic adaptation seems to be mostly played at the level of few key intermediates (e.g., phosphoenolpyruvate) and in the cross talk between the main central metabolic pathways. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the leveraging of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations. IMPORTANCE This manuscript addresses a central and broad interest topic in environmental microbiology, i.e. the effect of growth temperature on microbial cell physiology. We investigated if and how metabolic homeostasis is maintained in a cold-adapted bacterium during growth at temperatures that differ widely and that match measured changes on the field. Our integrative approach revealed an extraordinary robustness of the central metabolome to growth temperature. However, this was counteracted by deep changes at the transcriptional level, and especially in the metabolic part of the transcriptome. This conflictual scenario was interpreted as a transcriptomic buffering of cellular metabolism, and was investigated using genome-scale metabolic modeling. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the use of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations.
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Metabolomic and transcriptomic response to imatinib treatment of gastrointestinal stromal tumour in xenograft-bearing mice. Transl Oncol 2023; 30:101632. [PMID: 36774883 PMCID: PMC9945753 DOI: 10.1016/j.tranon.2023.101632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/09/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Although imatinib is a well-established first-line drug for treating a vast majority of gastrointestinal stromal tumours (GIST), GISTs acquire secondary resistance during therapy. Multi-omics approaches provide an integrated perspective to empower the development of personalised therapies through a better understanding of functional biology underlying the disease and molecular-driven selection of the best-targeted individualised therapy. In this study, we applied integrative metabolomic and transcriptomic analyses to elucidate tumour biochemical processes affected by imatinib treatment. MATERIALS AND METHODS A GIST xenograft mouse model was used in the study, including 10 mice treated with imatinib and 10 non-treated controls. Metabolites in tumour extracts were analysed using gas chromatography coupled with mass spectrometry (GC-MS). RNA sequencing was also performed on the samples subset (n=6). RESULTS Metabolomic analysis revealed 21 differentiating metabolites, whereas next-generation RNA sequencing data analysis resulted in 531 differentially expressed genes. Imatinib significantly changed the profile of metabolites associated mainly with purine and pyrimidine metabolism, butanoate metabolism, as well as alanine, aspartate, and glutamate metabolism. The related changes in transcriptomic profiles included genes involved in kinase activity and immune responses, as well as supported its impact on the purine biosynthesis pathway. CONCLUSIONS Our multi-omics study confirmed previously known pathways involved in imatinib anticancer activity as well as correlated imatinib-relevant downregulation of expression of purine biosynthesis pathway genes with the reduction of respectful metabolites. Furthermore, considering the importance of the purine biosynthesis pathway for cancer proliferation, we identified a potentially novel mechanism for the anti-tumour activity of imatinib. Based on the results, we hypothesise metabolic modulations aiming at the reduction in purine and pyrimidine pool may ensure higher imatinib efficacy or re-sensitise imatinib-resistant tumours.
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22
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Aminian-Dehkordi J, Valiei A, Mofrad MRK. Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases. Front Cardiovasc Med 2022; 9:987104. [PMID: 36299869 PMCID: PMC9589059 DOI: 10.3389/fcvm.2022.987104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.
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Affiliation(s)
| | | | - Mohammad R. K. Mofrad
- Department of Bioengineering and Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States
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23
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Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data. mSystems 2022; 7:e0134721. [PMID: 35695574 PMCID: PMC9239220 DOI: 10.1128/msystems.01347-21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations. IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.
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24
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Zhu Y, Zhao J, Li J. Genome-scale metabolic modeling in antimicrobial pharmacology. ENGINEERING MICROBIOLOGY 2022; 2:100021. [PMID: 39628842 PMCID: PMC11610950 DOI: 10.1016/j.engmic.2022.100021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 04/12/2022] [Accepted: 04/13/2022] [Indexed: 12/06/2024]
Abstract
The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades. This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant microbial pathogens. However, the detailed mechanisms of action, resistance, and toxicity of many antimicrobials remain uncertain, significantly hampering the development of novel antimicrobials. Genome-scale metabolic model (GSMM) has been increasingly employed to investigate microbial metabolism. In this review, we discuss the latest progress of GSMM in antimicrobial pharmacology, particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity, resistance, and toxicity. We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities (e.g., gene expression), identification of potential drug targets, and integration with machine learning and pharmacokinetic/pharmacodynamic modeling. Overall, GSMM has significant potential in elucidating the critical role of metabolic changes in antimicrobial pharmacology, providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.
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Affiliation(s)
- Yan Zhu
- Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University, 19 Innovation Walk, Melbourne, Victoria 3800, Australia
| | - Jinxin Zhao
- Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University, 19 Innovation Walk, Melbourne, Victoria 3800, Australia
| | - Jian Li
- Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University, 19 Innovation Walk, Melbourne, Victoria 3800, Australia
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25
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Wang P, Schumacher AM, Shiu SH. Computational prediction of plant metabolic pathways. CURRENT OPINION IN PLANT BIOLOGY 2022; 66:102171. [PMID: 35078130 DOI: 10.1016/j.pbi.2021.102171] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/07/2021] [Accepted: 12/18/2021] [Indexed: 06/14/2023]
Abstract
Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.
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Affiliation(s)
- Peipei Wang
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA.
| | - Ally M Schumacher
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Shin-Han Shiu
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA; Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
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26
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Galvão Ferrarini M, Ziska I, Andrade R, Julien-Laferrière A, Duchemin L, César RM, Mary A, Vinga S, Sagot MF. Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations. Front Genet 2022; 13:815476. [PMID: 35281848 PMCID: PMC8905348 DOI: 10.3389/fgene.2022.815476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/24/2022] [Indexed: 11/16/2022] Open
Abstract
Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability:Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.
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Affiliation(s)
- Mariana Galvão Ferrarini
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,Univ Lyon, INRAE, INSA-Lyon, BF2I, UMR 203, Villeurbanne, France
| | - Irene Ziska
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
| | - Ricardo Andrade
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,Institute of Mathematics and Statistics (IME), University of São Paulo, São Paulo, Brazil
| | | | - Louis Duchemin
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France
| | | | - Arnaud Mary
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Marie-France Sagot
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
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27
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Li X, Yilmaz LS, Walhout AJ. Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective. CURRENT OPINION IN SYSTEMS BIOLOGY 2022; 29:100407. [PMID: 35224313 PMCID: PMC8865431 DOI: 10.1016/j.coisb.2021.100407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In multicellular organisms, metabolism is compartmentalized at many levels, including tissues and organs, different cell types, and subcellular compartments. Compartmentalization creates a coordinated homeostatic system where each compartment contributes to the production of energy and biomolecules the organism needs to carrying out specific metabolic tasks. Experimentally studying metabolic compartmentalization and metabolic interactions between cells and tissues in multicellular organisms is challenging at a systems level. However, recent progress in computational modeling provides an alternative approach to this problem. Here we discuss how integrating metabolic network modeling with omics data offers an opportunity to reveal metabolic states at the level of organs, tissues and, ultimately, individual cells. We review the current status of genome-scale metabolic network models in multicellular organisms, methods to study metabolic compartmentalization in silico, and insights gained from computational analyses. We also discuss outstanding challenges and provide perspectives for the future directions of the field.
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28
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Di Filippo M, Pescini D, Galuzzi BG, Bonanomi M, Gaglio D, Mangano E, Consolandi C, Alberghina L, Vanoni M, Damiani C. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLoS Comput Biol 2022; 18:e1009337. [PMID: 35130273 PMCID: PMC8853556 DOI: 10.1371/journal.pcbi.1009337] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 02/17/2022] [Accepted: 01/13/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
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Affiliation(s)
- Marzia Di Filippo
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Dario Pescini
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Bruno Giovanni Galuzzi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marcella Bonanomi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Daniela Gaglio
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Italy
| | - Eleonora Mangano
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Clarissa Consolandi
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Lilia Alberghina
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marco Vanoni
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Chiara Damiani
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
- * E-mail:
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29
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Narad P, Naresh G, Sengupta A. Metabolomics and flux balance analysis. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00008-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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30
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Pathania R, Srivastava A, Srivastava S, Shukla P. Metabolic systems biology and multi-omics of cyanobacteria: Perspectives and future directions. BIORESOURCE TECHNOLOGY 2022; 343:126007. [PMID: 34634665 DOI: 10.1016/j.biortech.2021.126007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/17/2021] [Accepted: 09/19/2021] [Indexed: 06/13/2023]
Abstract
Cyanobacteria are oxygenic photoautotrophs whose metabolism contains key biochemical pathways to fix atmospheric CO2 and synthesize various metabolites. The development of bioengineering tools has enabled the manipulation of cyanobacterial chassis to produce various valuable bioproducts photosynthetically. However, effective utilization of cyanobacteria as photosynthetic cell factories needs a detailed understanding of their metabolism and its interaction with other cellular processes. Implementing systems and synthetic biology tools has generated a wealth of information on various metabolic pathways. However, to design effective engineering strategies for further improvement in growth, photosynthetic efficiency, and enhanced production of target biochemicals, in-depth knowledge of their carbon/nitrogen metabolism, pathway fluxe distribution, genetic regulation and integrative analyses are necessary. In this review, we discuss the recent advances in the development of genome-scale metabolic models (GSMMs), omics analyses (metabolomics, transcriptomics, proteomics, fluxomics), and integrative modeling approaches to showcase the current understanding of cyanobacterial metabolism.
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Affiliation(s)
- Ruchi Pathania
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Amit Srivastava
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, United States
| | - Shireesh Srivastava
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi 110067, India; DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Pratyoosh Shukla
- School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India; Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak 124001, Haryana, India.
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31
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Shichkova P, Coggan JS, Markram H, Keller D. A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation. Front Mol Neurosci 2021; 14:604559. [PMID: 34858137 PMCID: PMC8631404 DOI: 10.3389/fnmol.2021.604559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.
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Affiliation(s)
- Polina Shichkova
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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32
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Ravi S, Gunawan R. ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data. PLoS Comput Biol 2021; 17:e1009589. [PMID: 34758020 PMCID: PMC8608322 DOI: 10.1371/journal.pcbi.1009589] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 11/22/2021] [Accepted: 10/25/2021] [Indexed: 12/04/2022] Open
Abstract
Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences. Metabolic alterations are often used as hallmarks of observable phenotypes. In this regard, reconstructed genome-scale metabolic models (GEMs) provide a rich and computable representation of the entire set of biochemical reactions in a cell. However, the performance of analytical tools for predicting metabolic reaction rates or fluxes using GEMs is sensitive to the assumed metabolic objective that is often unknown and likely context-specific. Here, we propose a novel method called ΔFBA that combines differential gene expression data and GEMs to evaluate differences in the metabolic fluxes between two conditions (perturbation vs. control) without the need for specifying a metabolic objective. In our demonstration, ΔFBA outperformed other existing methods in predicting metabolic flux alterations.
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Affiliation(s)
- Sudharshan Ravi
- Department of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo, New York, United States of America
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo, New York, United States of America
- * E-mail:
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33
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Frades I, Foguet C, Cascante M, Araúzo-Bravo MJ. Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment. Cancers (Basel) 2021; 13:4609. [PMID: 34572839 PMCID: PMC8470216 DOI: 10.3390/cancers13184609] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/06/2021] [Accepted: 09/09/2021] [Indexed: 12/31/2022] Open
Abstract
The tumor's physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.
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Affiliation(s)
- Itziar Frades
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marcos J. Araúzo-Bravo
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
- Max Planck Institute of Molecular Biomedicine, 48167 Münster, Germany
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERfes), 28015 Madrid, Spain
- Translational Bioinformatics Network (TransBioNet), 8001 Barcelona, Spain
- Ikerbasque, Basque Foundation for Science, 48012 Bilbao, Spain
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34
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Herrmann HA, Rusz M, Baier D, Jakupec MA, Keppler BK, Berger W, Koellensperger G, Zanghellini J. Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer. Cancers (Basel) 2021; 13:4130. [PMID: 34439283 PMCID: PMC8391396 DOI: 10.3390/cancers13164130] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/23/2021] [Accepted: 08/03/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. METHODS Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. RESULTS Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. CONCLUSIONS Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.
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Affiliation(s)
- Helena A. Herrmann
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
| | - Mate Rusz
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
| | - Dina Baier
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
| | - Michael A. Jakupec
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
| | - Bernhard K. Keppler
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
| | - Walter Berger
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
- Institute of Cancer Research and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Vienna Metabolomics Center (VIME), University of Vienna, 1090 Vienna, Austria
- Research Network Chemistry Meets Microbiology, University of Vienna, 1090 Vienna, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
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Leggieri PA, Liu Y, Hayes M, Connors B, Seppälä S, O'Malley MA, Venturelli OS. Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes. Annu Rev Biomed Eng 2021; 23:169-201. [PMID: 33781078 PMCID: PMC8277735 DOI: 10.1146/annurev-bioeng-082120-022836] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.
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Affiliation(s)
- Patrick A Leggieri
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Yiyi Liu
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Madeline Hayes
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
| | - Bryce Connors
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Susanna Seppälä
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Michelle A O'Malley
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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Seep L, Razaghi-Moghadam Z, Nikoloski Z. Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis. Sci Rep 2021; 11:8544. [PMID: 33879809 PMCID: PMC8058346 DOI: 10.1038/s41598-021-87643-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/26/2021] [Indexed: 11/13/2022] Open
Abstract
Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation [Formula: see text], of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown [Formula: see text]. However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown [Formula: see text] whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown [Formula: see text] are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.
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Affiliation(s)
- Lea Seep
- Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, 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: 25] [Impact Index Per Article: 6.3] [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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Qi Y, Wang H, Chen X, Wei G, Tao S, Fan M. Altered Metabolic Strategies: Elaborate Mechanisms Adopted by Oenococcus oeni in Response to Acid Stress. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:2906-2918. [PMID: 33587641 DOI: 10.1021/acs.jafc.0c07599] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Oenococcus oeni plays a key role in inducing malolactic fermentation in wine. Acid stress is often encountered under wine conditions. However, the lack of systematic studies of acid resistance mechanisms limits the downstream fermentation applications. In this study, the acid responses of O. oeni were investigated by combining transcriptome, metabolome, and genome-scale metabolic modeling approaches. Metabolite profiling highlighted the decreased abundance of nucleotides under acid stress. The gene-metabolite bipartite network showed negative correlations between nucleotides and genes involved in ribosome assembly, translation, and post-translational processes, suggesting that stringent response could be activated under acid stress. Genome-scale metabolic modeling revealed marked flux rerouting, including reallocation of pyruvate, attenuation of glycolysis, utilization of carbon sources other than glucose, and enhancement of nucleotide salvage and the arginine deiminase pathway. This study provided novel insights into the acid responses of O. oeni, which will be useful for designing strategies to address acid stress in wine malolactic fermentation.
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Affiliation(s)
- Yiman Qi
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Hao Wang
- College of Life Sciences and State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A&F University, Yangling, Shaanxi 712100, China
- Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiangdan Chen
- College of Life Sciences and State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A&F University, Yangling, Shaanxi 712100, China
- Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Gehong Wei
- College of Life Sciences and State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Shiheng Tao
- College of Life Sciences and State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A&F University, Yangling, Shaanxi 712100, China
- Bioinformatics Center, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Mingtao Fan
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
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Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism. Sci Rep 2021; 11:4787. [PMID: 33637852 PMCID: PMC7910480 DOI: 10.1038/s41598-021-84114-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023] Open
Abstract
Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 \documentclass[12pt]{minimal}
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\begin{document}$$^{\circ }\hbox {C}$$\end{document}∘C chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.
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Weglarz-Tomczak E, Rijlaarsdam DJ, Tomczak JM, Brul S. GEM-Based Metabolic Profiling for Human Bone Osteosarcoma under Different Glucose and Glutamine Availability. Int J Mol Sci 2021; 22:1470. [PMID: 33540580 PMCID: PMC7867237 DOI: 10.3390/ijms22031470] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/24/2021] [Accepted: 01/27/2021] [Indexed: 12/31/2022] Open
Abstract
Cancer cell metabolism is dependent on cell-intrinsic factors, such as genetics, and cell-extrinsic factors, such nutrient availability. In this context, understanding how these two aspects interact and how diet influences cellular metabolism is important for developing personalized treatment. In order to achieve this goal, genome-scale metabolic models (GEMs) are used; however, genetics and nutrient availability are rarely considered together. Here, we propose integrated metabolic profiling, a framework that allows enriching GEMs with metabolic gene expression data and information about nutrients. First, the RNA-seq is converted into Reaction Activity Score (RAS) to further scale reaction bounds. Second, nutrient availability is converted to Maximal Uptake Rate (MUR) to modify exchange reactions in a GEM. We applied our framework to the human osteosarcoma cell line (U2OS). Osteosarcoma is a common and primary malignant form of bone cancer with poor prognosis, and, as indicated in our study, a glutamine-dependent type of cancer.
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Affiliation(s)
- Ewelina Weglarz-Tomczak
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands; (D.J.R.); (S.B.)
| | - Demi J. Rijlaarsdam
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands; (D.J.R.); (S.B.)
| | - Jakub M. Tomczak
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands;
| | - Stanley Brul
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH Amsterdam, The Netherlands; (D.J.R.); (S.B.)
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Weglarz-Tomczak E, Tomczak JM, Brul S. M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models. Bioinformatics 2021; 37:2785-2786. [PMID: 33523116 PMCID: PMC8428599 DOI: 10.1093/bioinformatics/btab060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 12/31/2020] [Accepted: 01/26/2021] [Indexed: 11/16/2022] Open
Abstract
Motivation The gut microbiota is the human body’s largest population of microorganisms that interact with human intestinal cells. They use ingested nutrients for fundamental biological processes and have important impacts on human physiology, immunity and metabolome in the gastrointestinal tract. Results Here, we present M2R, a Python add-on to cobrapy that allows incorporating information about the gut microbiota metabolism models to human genome-scale metabolic models (GEMs) like RECON3D. The idea behind the software is to modify the lower bounds of the exchange reactions in the model using aggregated in- and out-fluxes from selected microbes. M2R enables users to quickly and easily modify the pool of the metabolites that enter and leave the GEM, which is particularly important for those looking into an analysis of the metabolic interaction between the gut microbiota and human cells and its dysregulation. Availability and implementation M2R is freely available under an MIT License at https://github.com/e-weglarz-tomczak/m2r. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ewelina Weglarz-Tomczak
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, the Netherlands
| | - Jakub M Tomczak
- Department of Computer Science, Faculty of Science, Vrije Universiteit Amsterdam, the Netherlands
| | - Stanley Brul
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, the Netherlands
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Tomi-Andrino C, Norman R, Millat T, Soucaille P, Winzer K, Barrett DA, King J, Kim DH. Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions. PLoS Comput Biol 2021; 17:e1007694. [PMID: 33493151 PMCID: PMC7861524 DOI: 10.1371/journal.pcbi.1007694] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 02/04/2021] [Accepted: 12/28/2020] [Indexed: 12/11/2022] Open
Abstract
Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed. Biotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.
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Affiliation(s)
- Claudio Tomi-Andrino
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, United Kingdom
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Rupert Norman
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Thomas Millat
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Philippe Soucaille
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
- INSA, UPS, INP, Toulouse Biotechnology Institute, (TBI), Université de Toulouse, Toulouse, France
- INRA, UMR792, Toulouse, France
- CNRS, UMR5504, Toulouse, France
| | - Klaus Winzer
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, BioDiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - David A. Barrett
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, United Kingdom
| | - John King
- Nottingham BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Dong-Hyun Kim
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, United Kingdom
- * E-mail:
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van Rosmalen RP, Smith RW, Martins Dos Santos VAP, Fleck C, Suarez-Diez M. Model reduction of genome-scale metabolic models as a basis for targeted kinetic models. Metab Eng 2021; 64:74-84. [PMID: 33486094 DOI: 10.1016/j.ymben.2021.01.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 01/05/2021] [Accepted: 01/15/2021] [Indexed: 11/26/2022]
Abstract
Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.
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Affiliation(s)
- R P van Rosmalen
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, Wageningen, the Netherlands
| | - R W Smith
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, Wageningen, the Netherlands
| | - V A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, Wageningen, the Netherlands; Lifeglimmer GmbH, Berlin, Germany
| | - C Fleck
- Freiburg Center for Data Analysis and Modelling University of Freiburg Freiburg Germany; Control Theory and Systems Biology Laboratory, Department of Biosystems Science and En- gineering, ETH Zürich, Basel, Switzerland
| | - M Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, Wageningen, the Netherlands.
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Zielinski DC, Patel A, Palsson BO. The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale. Microorganisms 2020; 8:E2050. [PMID: 33371386 PMCID: PMC7767376 DOI: 10.3390/microorganisms8122050] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/07/2020] [Accepted: 12/16/2020] [Indexed: 02/06/2023] Open
Abstract
Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.
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Affiliation(s)
- Daniel Craig Zielinski
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
| | - Arjun Patel
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
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Desmet S, Brouckaert M, Boerjan W, Morreel K. Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks. Comput Struct Biotechnol J 2020; 19:72-85. [PMID: 33384856 PMCID: PMC7753198 DOI: 10.1016/j.csbj.2020.11.050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/26/2020] [Accepted: 11/28/2020] [Indexed: 02/06/2023] Open
Abstract
Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.
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Affiliation(s)
- Sandrien Desmet
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Marlies Brouckaert
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Wout Boerjan
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Kris Morreel
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
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Kessell AK, McCullough HC, Auchtung JM, Bernstein HC, Song HS. Predictive interactome modeling for precision microbiome engineering. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2020.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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Yilmaz LS, Li X, Nanda S, Fox B, Schroeder F, Walhout AJ. Modeling tissue-relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels. Mol Syst Biol 2020; 16:e9649. [PMID: 33022146 PMCID: PMC7537831 DOI: 10.15252/msb.20209649] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 09/10/2020] [Accepted: 09/11/2020] [Indexed: 01/04/2023] Open
Abstract
Metabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of metabolism. However, studying the compartmentalization of metabolism in different cell types in a whole animal and for a particular stage of life is difficult. Here, we present MEtabolic models Reconciled with Gene Expression (MERGE), a computational pipeline that we used to predict tissue-relevant metabolic function at the network, pathway, reaction, and metabolite levels based on single-cell RNA-sequencing (scRNA-seq) data from the nematode Caenorhabditis elegans. Our analysis recapitulated known tissue functions in C. elegans, captured metabolic properties that are shared with similar tissues in human, and provided predictions for novel metabolic functions. MERGE is versatile and applicable to other systems. We envision this work as a starting point for the development of metabolic network models for individual cells as scRNA-seq continues to provide higher-resolution gene expression data.
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Affiliation(s)
- Lutfu Safak Yilmaz
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Xuhang Li
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Shivani Nanda
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Bennett Fox
- Boyce Thompson Institute, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Frank Schroeder
- Boyce Thompson Institute, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Albertha Jm Walhout
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
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Dahal S, Yurkovich JT, Xu H, Palsson BO, Yang L. Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models. Proteomics 2020; 20:e1900282. [PMID: 32579720 PMCID: PMC7501203 DOI: 10.1002/pmic.201900282] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 06/13/2020] [Indexed: 12/18/2022]
Abstract
Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.
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Affiliation(s)
- Sanjeev Dahal
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | | | - Hao Xu
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Laurence Yang
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
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Obata T. Toward an evaluation of metabolite channeling in vivo. Curr Opin Biotechnol 2020; 64:55-61. [DOI: 10.1016/j.copbio.2019.09.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 09/11/2019] [Accepted: 09/12/2019] [Indexed: 12/25/2022]
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50
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Halling PJ. Thermodynamic Favorability of End Products of Anaerobic Glucose Metabolism. ACS OMEGA 2020; 5:15843-15849. [PMID: 32656405 PMCID: PMC7345408 DOI: 10.1021/acsomega.0c00790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 06/11/2020] [Indexed: 06/11/2023]
Abstract
The eQuilibrator component contribution method allows calculation of the overall Gibbs energy changes for conversion of glucose to a wide range of final products in the absence of other oxidants. Values are presented for all possible combinations of products with up to three carbons and selected others. The most negative Gibbs energy change is for the formation of graphite and water (-499 kJ mol-1) followed by CH4 and CO2 (-430 kJ mol-1), the observed final products of anaerobic digestion. Other favored products (with various combinations having Gibbs energy changes between -300 and -367 kJ mol-1) are short-chain alkanes, fatty acids, dicarboxylic acids, and even hexane and benzene. The most familiar products, lactate and ethanol + CO2, are less favored (Gibbs energy changes of -206 and -265 kJ mol-1 respectively). The values presented offer an interesting perspective on observed metabolism and its evolutionary origins as well as on cells engineered for biotechnological purposes.
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Affiliation(s)
- Peter J. Halling
- WestCHEM, Department of Pure
& Applied Chemistry, University of Strathclyde, Glasgow G1 1XL, U.K.
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