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Hashemi S, Laitinen R, Nikoloski Z. Models and molecular mechanisms for trade-offs in the context of metabolism. Mol Ecol 2024; 33:e16879. [PMID: 36773330 DOI: 10.1111/mec.16879] [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: 08/10/2022] [Revised: 01/19/2023] [Accepted: 02/07/2023] [Indexed: 02/13/2023]
Abstract
Accumulating evidence for trade-offs involving metabolic traits has demonstrated their importance in the evolution of organisms. Metabolic models with different levels of complexity have already been considered when investigating mechanisms that explain various metabolic trade-offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade-offs between: (i) the kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) the rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into the mechanisms underlying these five types of metabolic trade-offs obtained from constraint-based metabolic modelling, we emphasize the relationship of metabolic trade-offs to the classical black box Y-model that provides a conceptual explanation for resource acquisition-allocation trade-offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade-offs by relying on the toolbox provided by constraint-based metabolic modelling for single organisms and microbial communities.
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Affiliation(s)
- Seirana Hashemi
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Roosa Laitinen
- Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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2
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Rao X, Barros J. Modeling lignin biosynthesis: a pathway to renewable chemicals. TRENDS IN PLANT SCIENCE 2024; 29:546-559. [PMID: 37802691 DOI: 10.1016/j.tplants.2023.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/01/2023] [Accepted: 09/18/2023] [Indexed: 10/08/2023]
Abstract
Plant biomass contains lignin that can be converted into high-value-added chemicals, fuels, and materials. The precise genetic manipulation of lignin content and composition in plant cells offers substantial environmental and economic benefits. However, the intricate regulatory mechanisms governing lignin formation challenge the development of crops with specific lignin profiles. Mathematical models and computational simulations have recently been employed to gain fundamental understanding of the metabolism of lignin and related phenolic compounds. This review article discusses the strategies used for modeling plant metabolic networks, focusing on the application of mathematical modeling for flux network analysis in monolignol biosynthesis. Furthermore, we highlight how current challenges might be overcome to optimize the use of metabolic modeling approaches for developing lignin-engineered plants.
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Affiliation(s)
- Xiaolan Rao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan 430062, China.
| | - Jaime Barros
- Division of Plant Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211, USA.
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3
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Xu Y, Koroma AA, Weise SE, Fu X, Sharkey TD, Shachar-Hill Y. Daylength variation affects growth, photosynthesis, leaf metabolism, partitioning, and metabolic fluxes. PLANT PHYSIOLOGY 2023; 194:475-490. [PMID: 37726946 PMCID: PMC10756764 DOI: 10.1093/plphys/kiad507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 09/21/2023]
Abstract
Daylength, a seasonal and latitudinal variable, exerts a substantial impact on plant growth. However, the relationship between daylength and growth is nonproportional, suggesting the existence of adaptive mechanisms. Thus, our study aimed to comprehensively investigate the adaptive strategies employed by plants in response to daylength variation. We grew false flax (Camelina sativa) plants, a model oilseed crop, under long-day (LD) and short-day (SD) conditions and used growth measurements, gas exchange measurements, and isotopic labeling techniques, including 13C, 14C, and 2H2O, to determine responses to different daylengths. Our findings revealed that daylength influences various growth parameters, photosynthetic physiology, carbon partitioning, metabolic fluxes, and metabolite levels. SD plants employed diverse mechanisms to compensate for reduced CO2 fixation in the shorter photoperiod. These mechanisms included enhanced photosynthetic rates and reduced respiration in the light (RL), leading to increased shoot investment. Additionally, SD plants exhibited reduced rates of the glucose 6-phosphate (G6P) shunt and greater partitioning of sugars into starch, thereby sustaining carbon availability during the longer night. Isotopic labeling results further demonstrated substantial alterations in the partitioning of amino acids and TCA cycle intermediates between rapidly and slowly turning over pools. Overall, the results point to multiple developmental, physiological, and metabolic ways in which plants adapt to different daylengths to maintain growth.
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Affiliation(s)
- Yuan Xu
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
| | - Abubakarr A Koroma
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
- Department of Microbiology and Immunology, Emory University, Atlanta, GA 30329, USA
| | - Sean E Weise
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Xinyu Fu
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
| | - Thomas D Sharkey
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
- Plant Resilience Institute, Michigan State University, East Lansing, MI 48824, USA
| | - Yair Shachar-Hill
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
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4
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Hunt H, Leape S, Sidhu JS, Ajmera I, Lynch JP, Ratcliffe RG, Sweetlove LJ. A role for fermentation in aerobic conditions as revealed by computational analysis of maize root metabolism during growth by cell elongation. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:1553-1570. [PMID: 37831626 DOI: 10.1111/tpj.16478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 10/15/2023]
Abstract
The root is a well-studied example of cell specialisation, yet little is known about the metabolism that supports the transport functions and growth of different root cell types. To address this, we used computational modelling to study metabolism in the elongation zone of a maize lateral root. A functional-structural model captured the cell-anatomical features of the root and modelled how they changed as the root elongated. From these data, we derived constraints for a flux balance analysis model that predicted metabolic fluxes of the 11 concentric rings of cells in the root. We discovered a distinct metabolic flux pattern in the cortical cell rings, endodermis and pericycle (but absent in the epidermis) that involved a high rate of glycolysis and production of the fermentation end-products lactate and ethanol. This aerobic fermentation was confirmed experimentally by metabolite analysis. The use of fermentation in the model was not obligatory but was the most efficient way to meet the specific demands for energy, reducing power and carbon skeletons of expanding cells. Cytosolic acidification was avoided in the fermentative mode due to the substantial consumption of protons by lipid synthesis. These results expand our understanding of fermentative metabolism beyond that of hypoxic niches and suggest that fermentation could play an important role in the metabolism of aerobic tissues.
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Affiliation(s)
- Hilary Hunt
- Department of Biology, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Stefan Leape
- Department of Biology, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Jagdeep Singh Sidhu
- Department of Plant Science, The Pennsylvania State University, University Park, Pennsylvania, 16802, USA
| | - Ishan Ajmera
- Department of Plant Science, The Pennsylvania State University, University Park, Pennsylvania, 16802, USA
| | - Jonathan P Lynch
- Department of Plant Science, The Pennsylvania State University, University Park, Pennsylvania, 16802, USA
| | - R George Ratcliffe
- Department of Biology, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Lee J Sweetlove
- Department of Biology, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
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5
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Huß S, Nikoloski Z. Systematic comparison of local approaches for isotopically nonstationary metabolic flux analysis. FRONTIERS IN PLANT SCIENCE 2023; 14:1178239. [PMID: 37346134 PMCID: PMC10280729 DOI: 10.3389/fpls.2023.1178239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/04/2023] [Indexed: 06/23/2023]
Abstract
Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determine cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA) that relies on the patterns of isotope labeling of metabolites in the network. For metabolic systems, typical for plants, where all potentially labeled atoms effectively have only one source atom pool, only isotopically nonstationary MFA can provide information about intracellular fluxes. There are several global approaches that implement MFA for an entire metabolic network and estimate, at once, a steady-state flux distribution for all reactions with identifiable fluxes in the network. In contrast, local approaches deal with estimation of fluxes for a subset of reactions, with smaller data demand for flux estimation. Here we present a systematic comparative review and benchmarking of the existing local approaches for isotopically nonstationary MFA. The comparison is conducted with respect to the required data and underlying computational problems solved on a synthetic network example. Furthermore, we benchmark the performance of these approaches in estimating fluxes for a subset of reactions using data obtained from the simulation of nitrogen fluxes in the Arabidopsis thaliana core metabolism. The findings pinpoint practical aspects that need to be considered when applying local approaches for flux estimation in large-scale plant metabolic networks.
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Affiliation(s)
- Sebastian Huß
- Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
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6
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Moiz B, Sriram G, Clyne AM. Interpreting metabolic complexity via isotope-assisted metabolic flux analysis. Trends Biochem Sci 2023; 48:553-567. [PMID: 36863894 PMCID: PMC10182253 DOI: 10.1016/j.tibs.2023.02.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/22/2023] [Accepted: 02/03/2023] [Indexed: 03/04/2023]
Abstract
Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.
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Affiliation(s)
- Bilal Moiz
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Ganesh Sriram
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
| | - Alisa Morss Clyne
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA.
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7
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Gainullina A, Mogilenko DA, Huang LH, Todorov H, Narang V, Kim KW, Yng LS, Kent A, Jia B, Seddu K, Krchma K, Wu J, Crozat K, Tomasello E, Dress R, See P, Scott C, Gibbings S, Bajpai G, Desai JV, Maier B, This S, Wang P, Aguilar SV, Poupel L, Dussaud S, Zhou TA, Angeli V, Blander JM, Choi K, Dalod M, Dzhagalov I, Gautier EL, Jakubzick C, Lavine K, Lionakis MS, Paidassi H, Sieweke MH, Ginhoux F, Guilliams M, Benoist C, Merad M, Randolph GJ, Sergushichev A, Artyomov MN. Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes. Cell Rep 2023; 42:112046. [PMID: 36708514 PMCID: PMC10372199 DOI: 10.1016/j.celrep.2023.112046] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 12/06/2022] [Accepted: 01/17/2023] [Indexed: 01/28/2023] Open
Abstract
The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages.
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Affiliation(s)
- Anastasiia Gainullina
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA; Computer Technologies Department, ITMO University, St. Petersburg 197101, Russia; Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, Moscow 119334, Russia
| | - Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Medicine, Department of Pathology, Microbiology, and Immunology, Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Li-Hao Huang
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Helena Todorov
- Laboratory of Immunoregulation, Inflammation Research Centre, VIB Ghent University, 9052 Ghent, Belgium
| | - Vipin Narang
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore 138648, Singapore
| | - Ki-Wook Kim
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lim Sheau Yng
- Immunology Translational Research Programme, Yong Loo Lin School of Medicine, Department of Microbiology and Immunology, National University of Singapore, Singapore 117545, Singapore; Immunology Programme, Life Sciences Institute, National University of Singapore, Singapore 117545, Singapore
| | - Andrew Kent
- The Jill Roberts Institute for Research in Inflammatory Bowel Disease, Joan and Sanford I. Weill Department of Medicine, Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Baosen Jia
- The Jill Roberts Institute for Research in Inflammatory Bowel Disease, Joan and Sanford I. Weill Department of Medicine, Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Kumba Seddu
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Karen Krchma
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jun Wu
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Karine Crozat
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, 13288 Marseille, France
| | - Elena Tomasello
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, 13288 Marseille, France
| | - Regine Dress
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore 138648, Singapore
| | - Peter See
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore 138648, Singapore
| | - Charlotte Scott
- Laboratory of Immunoregulation, Inflammation Research Centre, VIB Ghent University, 9052 Ghent, Belgium
| | - Sophie Gibbings
- Department of Pediatrics, National Jewish Health, Denver, CO 80206, USA
| | - Geetika Bajpai
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Jigar V Desai
- Fungal Pathogenesis Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Barbara Maier
- Immunology Institute and Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sébastien This
- Centre International de Recherche en Infectiologie (CIRI), University Lyon, Inserm, U1111, Université Claude Bernard Lyon ,1, CNRS, UMR5308, ENS de Lyon, 69007 Lyon, France
| | - Peter Wang
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Stephanie Vargas Aguilar
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, 13288 Marseille, France; Center for Regenerative Therapies (CRTD), TU Dresden, 01307 Dresden, Germany; Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtzgemeinschaft (MDC), 13125 Berlin, Germany
| | - Lucie Poupel
- INSERM UMR-S 1166, Sorbonne Université, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Sébastien Dussaud
- INSERM UMR-S 1166, Sorbonne Université, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Tyng-An Zhou
- Institute of Microbiology and Immunology, National Yang-Ming University, Taipei 112, Taiwan
| | - Veronique Angeli
- Immunology Translational Research Programme, Yong Loo Lin School of Medicine, Department of Microbiology and Immunology, National University of Singapore, Singapore 117545, Singapore; Immunology Programme, Life Sciences Institute, National University of Singapore, Singapore 117545, Singapore
| | - J Magarian Blander
- The Jill Roberts Institute for Research in Inflammatory Bowel Disease, Joan and Sanford I. Weill Department of Medicine, Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Kyunghee Choi
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Marc Dalod
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, 13288 Marseille, France
| | - Ivan Dzhagalov
- Institute of Microbiology and Immunology, National Yang-Ming University, Taipei 112, Taiwan
| | - Emmanuel L Gautier
- INSERM UMR-S 1166, Sorbonne Université, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Claudia Jakubzick
- Department of Pediatrics, National Jewish Health, Denver, CO 80206, USA
| | - Kory Lavine
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Michail S Lionakis
- Fungal Pathogenesis Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Helena Paidassi
- Centre International de Recherche en Infectiologie (CIRI), University Lyon, Inserm, U1111, Université Claude Bernard Lyon ,1, CNRS, UMR5308, ENS de Lyon, 69007 Lyon, France
| | - Michael H Sieweke
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, 13288 Marseille, France; Center for Regenerative Therapies (CRTD), TU Dresden, 01307 Dresden, Germany; Max-Delbrück-Centrum für Molekulare Medizin in der Helmholtzgemeinschaft (MDC), 13125 Berlin, Germany
| | - Florent Ginhoux
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore 138648, Singapore
| | - Martin Guilliams
- Laboratory of Immunoregulation, Inflammation Research Centre, VIB Ghent University, 9052 Ghent, Belgium
| | | | - Miriam Merad
- Immunology Institute and Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gwendalyn J Randolph
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA; Computer Technologies Department, ITMO University, St. Petersburg 197101, Russia.
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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8
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Moiz B, Li A, Padmanabhan S, Sriram G, Clyne AM. Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease. Metabolites 2022; 12:1066. [PMID: 36355149 PMCID: PMC9694183 DOI: 10.3390/metabo12111066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 04/28/2024] Open
Abstract
Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.
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Affiliation(s)
- Bilal Moiz
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Andrew Li
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Surya Padmanabhan
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Ganesh Sriram
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
| | - Alisa Morss Clyne
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
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9
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Huß S, Judd RS, Koper K, Maeda HA, Nikoloski Z. An automated workflow that generates atom mappings for large-scale metabolic models and its application to Arabidopsis thaliana. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:1486-1500. [PMID: 35819300 DOI: 10.1111/tpj.15903] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large-scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large-scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana. We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating 15 N isotope enrichment and identifying nitrogen (N)-containing metabolites which show enrichment patterns that are informative for flux estimation in future 15 N-MFA studies of A. thaliana. The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph-theoretic structural analyses with large-scale metabolic networks.
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Affiliation(s)
- Sebastian Huß
- Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24- 25, 14476, Potsdam, Germany
| | - Rika Siedah Judd
- Department of Botany, University of Wisconsin-Madison, 430, Lincoln, Dr. Madison, Wisconsin, 53706, USA
| | - Kaan Koper
- Department of Botany, University of Wisconsin-Madison, 430, Lincoln, Dr. Madison, Wisconsin, 53706, USA
| | - Hiroshi A Maeda
- Department of Botany, University of Wisconsin-Madison, 430, Lincoln, Dr. Madison, Wisconsin, 53706, USA
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24- 25, 14476, Potsdam, Germany
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10
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Srivastava A, Kalwani M, Chakdar H, Pabbi S, Shukla P. Biosynthesis and biotechnological interventions for commercial production of microalgal pigments: A review. BIORESOURCE TECHNOLOGY 2022; 352:127071. [PMID: 35351568 DOI: 10.1016/j.biortech.2022.127071] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/20/2022] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
Microalgae are photosynthetic eukaryotes that serve as microbial cell factories for the production of useful biochemicals, including pigments. These pigments are eco-friendly alternatives to synthetic dyes and reduce environmental and health risks. They also exhibit excellent anti-oxidative properties, making them a useful commodity in the nutrition and pharmaceutical industries. Light-harvesting pigments such as chlorophylls and phycobilins, and photoprotective carotenoids are some of the most common microalgal pigments. The increasing demand for these pigments in industrial applications has prompted a need to improve their metabolic yield in microalgal cells. So far, expensive cultivation methods and sensitivity to microbial contamination remain the main obstacles to the large-scale production of these pigments. This review highlights current issues and future prospects related to the production of microalgal pigments. The review also emphasizes the use of engineering approaches such as genetic engineering, and optimization of media components and physical parameters to increase their commercial-scale production.
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Affiliation(s)
- Amit Srivastava
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | - Mohneesh Kalwani
- School of Biotechnology, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India; Centre for Conservation and Utilisation of Blue Green Algae (CCUBGA), Division of Microbiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Hillol Chakdar
- ICAR-National Bureau of Agriculturally Important Microorganisms (NBAIM), Mau, Uttar Pradesh 275103, India
| | - Sunil Pabbi
- Centre for Conservation and Utilisation of Blue Green Algae (CCUBGA), Division of Microbiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Pratyoosh Shukla
- School of Biotechnology, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India.
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11
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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: 0.5] [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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12
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Seydel C, Biener J, Brodsky V, Eberlein S, Nägele T. Predicting plant growth response under fluctuating temperature by carbon balance modelling. Commun Biol 2022; 5:164. [PMID: 35210545 PMCID: PMC8873469 DOI: 10.1038/s42003-022-03100-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 02/02/2022] [Indexed: 11/09/2022] Open
Abstract
Quantification of system dynamics is a central aim of mathematical modelling in biology. Defining experimentally supported functional relationships between molecular entities by mathematical terms enables the application of computational routines to simulate and analyse the underlying molecular system. In many fields of natural sciences and engineering, trigonometric functions are applied to describe oscillatory processes. As biochemical oscillations occur in many aspects of biochemistry and biophysics, Fourier analysis of metabolic functions promises to quantify, describe and analyse metabolism and its reaction towards environmental fluctuations. Here, Fourier polynomials were developed from experimental time-series data and combined with block diagram simulation of plant metabolism to study heat shock response of photosynthetic CO2 assimilation and carbohydrate metabolism in Arabidopsis thaliana. Simulations predicted a stabilising effect of reduced sucrose biosynthesis capacity and increased capacity of starch biosynthesis on carbon assimilation under transient heat stress. Model predictions were experimentally validated by quantifying plant growth under such stress conditions. In conclusion, this suggests that Fourier polynomials represent a predictive mathematical approach to study dynamic plant-environment interactions.
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Affiliation(s)
- Charlotte Seydel
- Ludwig-Maximilians-Universität München, Faculty of Biology, Plant Development, 82152, Planegg-Martinsried, Germany.,Ludwig-Maximilians-Universität München, Faculty of Biology, Plant Evolutionary Cell Biology, 82152, Planegg-Martinsried, Germany
| | - Julia Biener
- Ludwig-Maximilians-Universität München, Faculty of Biology, Plant Evolutionary Cell Biology, 82152, Planegg-Martinsried, Germany
| | - Vladimir Brodsky
- Ludwig-Maximilians-Universität München, Faculty of Biology, Plant Evolutionary Cell Biology, 82152, Planegg-Martinsried, Germany
| | - Svenja Eberlein
- Ludwig-Maximilians-Universität München, Faculty of Biology, Plant Evolutionary Cell Biology, 82152, Planegg-Martinsried, Germany
| | - Thomas Nägele
- Ludwig-Maximilians-Universität München, Faculty of Biology, Plant Evolutionary Cell Biology, 82152, Planegg-Martinsried, Germany.
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13
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Yadav M, Joshi C, Paritosh K, Thakur J, Pareek N, Masakapalli SK, Vivekanand V. Reprint of:Organic waste conversion through anaerobic digestion: A critical insight into the metabolic pathways and microbial interactions. Metab Eng 2022; 71:62-76. [DOI: 10.1016/j.ymben.2022.02.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/30/2021] [Indexed: 12/25/2022]
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14
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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: 4.5] [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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15
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Organic waste conversion through anaerobic digestion: A critical insight into the metabolic pathways and microbial interactions. Metab Eng 2021; 69:323-337. [PMID: 34864213 DOI: 10.1016/j.ymben.2021.11.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/30/2021] [Indexed: 11/23/2022]
Abstract
Anaerobic digestion is a promising method for energy recovery through conversion of organic waste to biogas and other industrial valuables. However, to tap the full potential of anaerobic digestion, deciphering the microbial metabolic pathway activities and their underlying bioenergetics is required. In addition, the behavior of organisms in consortia along with the analytical abilities to kinetically measure their metabolic interactions will allow rational optimization of the process. This review aims to explore the metabolic bottlenecks of the microbial communities adopting latest advances of profiling and 13C tracer-based analysis using state of the art analytical platforms (GC, GC-MS, LC-MS, NMR). The review summarizes the phases of anaerobic digestion, the role of microbial communities, key process parameters of significance, syntrophic microbial interactions and the bottlenecks that are critical for optimal bioenergetics and enhanced production of valuables. Considerations into the designing of efficient synthetic microbial communities as well as the latest advances in capturing their metabolic cross talk will be highlighted. The review further explores how the presence of additives and inhibiting factors affect the metabolic pathways. The critical insight into the reaction mechanism covered in this review may be helpful to optimize and upgrade the anaerobic digestion system.
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16
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Metabolomics for Crop Breeding: General Considerations. Genes (Basel) 2021; 12:genes12101602. [PMID: 34680996 PMCID: PMC8535592 DOI: 10.3390/genes12101602] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/05/2021] [Accepted: 10/08/2021] [Indexed: 12/16/2022] Open
Abstract
The development of new, more productive varieties of agricultural crops is becoming an increasingly difficult task. Modern approaches for the identification of beneficial alleles and their use in elite cultivars, such as quantitative trait loci (QTL) mapping and marker-assisted selection (MAS), are effective but insufficient for keeping pace with the improvement of wheat or other crops. Metabolomics is a powerful but underutilized approach that can assist crop breeding. In this review, basic methodological information is summarized, and the current strategies of applications of metabolomics related to crop breeding are explored using recent examples. We briefly describe classes of plant metabolites, cellular localization of metabolic pathways, and the strengths and weaknesses of the main metabolomics technique. Among the commercialized genetically modified crops, about 50 with altered metabolic enzyme activities have been identified in the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. These plants are reviewed as encouraging examples of the application of knowledge of biochemical pathways. Based on the recent examples of metabolomic studies, we discuss the performance of metabolic markers, the integration of metabolic and genomic data in metabolic QTLs (mQTLs) and metabolic genome-wide association studies (mGWAS). The elucidation of metabolic pathways and involved genes will help in crop breeding and the introgression of alleles of wild relatives in a more targeted manner.
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17
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Furlani IL, da Cruz Nunes E, Canuto GAB, Macedo AN, Oliveira RV. Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1336:179-213. [PMID: 34628633 DOI: 10.1007/978-3-030-77252-9_10] [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: 04/28/2023]
Abstract
Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.
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Affiliation(s)
- Izadora L Furlani
- Núcleo de Pesquisa em Cromatografia (Separare), Department of Chemistry, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Estéfane da Cruz Nunes
- Department of Analytical Chemistry, Institute of Chemistry, Federal University of Bahia, Salvador, BA, Brazil
| | - Gisele A B Canuto
- Department of Analytical Chemistry, Institute of Chemistry, Federal University of Bahia, Salvador, BA, Brazil
| | - Adriana N Macedo
- Department of Chemistry, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Regina V Oliveira
- Núcleo de Pesquisa em Cromatografia (Separare), Department of Chemistry, Federal University of São Carlos, São Carlos, SP, Brazil.
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18
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Natoli G, Pileri F, Gualdrini F, Ghisletti S. Integration of transcriptional and metabolic control in macrophage activation. EMBO Rep 2021; 22:e53251. [PMID: 34328708 DOI: 10.15252/embr.202153251] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 11/09/2022] Open
Abstract
Macrophages react to microbial and endogenous danger signals by activating a broad panel of effector and homeostatic responses. Such responses entail rapid and stimulus-specific changes in gene expression programs accompanied by extensive rewiring of metabolism, with alterations in chromatin modifications providing one layer of integration of transcriptional and metabolic regulation. A systematic and mechanistic understanding of the mutual influences between signal-induced metabolic changes and gene expression is still lacking. Here, we discuss current evidence, controversies, knowledge gaps, and future areas of investigation on how metabolic and transcriptional changes are dynamically integrated during macrophage activation. The cross-talk between metabolism and inflammatory gene expression is in part accounted for by alterations in the production, usage, and availability of metabolic intermediates that impact the macrophage epigenome. In addition, stimulus-inducible gene expression changes alter the production of inflammatory mediators, such as nitric oxide, that in turn modulate the activity of metabolic enzymes thus determining complex regulatory loops. Critical issues remain to be understood, notably whether and how metabolic rewiring can bring about gene-specific (as opposed to global) expression changes.
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Affiliation(s)
- Gioacchino Natoli
- Department of Experimental Oncology, European Institute of Oncology (IEO) IRCCS, Milan, Italy.,Humanitas University, Milan, Italy
| | - Francesco Pileri
- Department of Experimental Oncology, European Institute of Oncology (IEO) IRCCS, Milan, Italy
| | - Francesco Gualdrini
- Department of Experimental Oncology, European Institute of Oncology (IEO) IRCCS, Milan, Italy
| | - Serena Ghisletti
- Department of Experimental Oncology, European Institute of Oncology (IEO) IRCCS, Milan, Italy
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19
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Characterization of effects of genetic variants via genome-scale metabolic modelling. Cell Mol Life Sci 2021; 78:5123-5138. [PMID: 33950314 PMCID: PMC8254712 DOI: 10.1007/s00018-021-03844-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 04/23/2021] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.
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20
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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: 1.3] [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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21
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Sangha GS, Goergen CJ, Prior SJ, Ranadive SM, Clyne AM. Preclinical techniques to investigate exercise training in vascular pathophysiology. Am J Physiol Heart Circ Physiol 2021; 320:H1566-H1600. [PMID: 33385323 PMCID: PMC8260379 DOI: 10.1152/ajpheart.00719.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Atherosclerosis is a dynamic process starting with endothelial dysfunction and inflammation and eventually leading to life-threatening arterial plaques. Exercise generally improves endothelial function in a dose-dependent manner by altering hemodynamics, specifically by increased arterial pressure, pulsatility, and shear stress. However, athletes who regularly participate in high-intensity training can develop arterial plaques, suggesting alternative mechanisms through which excessive exercise promotes vascular disease. Understanding the mechanisms that drive atherosclerosis in sedentary versus exercise states may lead to novel rehabilitative methods aimed at improving exercise compliance and physical activity. Preclinical tools, including in vitro cell assays, in vivo animal models, and in silico computational methods, broaden our capabilities to study the mechanisms through which exercise impacts atherogenesis, from molecular maladaptation to vascular remodeling. Here, we describe how preclinical research tools have and can be used to study exercise effects on atherosclerosis. We then propose how advanced bioengineering techniques can be used to address gaps in our current understanding of vascular pathophysiology, including integrating in vitro, in vivo, and in silico studies across multiple tissue systems and size scales. Improving our understanding of the antiatherogenic exercise effects will enable engaging, targeted, and individualized exercise recommendations to promote cardiovascular health rather than treating cardiovascular disease that results from a sedentary lifestyle.
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Affiliation(s)
- Gurneet S Sangha
- Fischell Department of Bioengineering, University of Maryland, College Park, Maryland
| | - Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana.,Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Steven J Prior
- Department of Kinesiology, University of Maryland School of Public Health, College Park, Maryland.,Baltimore Veterans Affairs Geriatric Research, Education, and Clinical Center, Baltimore, Maryland
| | - Sushant M Ranadive
- Department of Kinesiology, University of Maryland School of Public Health, College Park, Maryland
| | - Alisa M Clyne
- Fischell Department of Bioengineering, University of Maryland, College Park, Maryland
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22
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Calderan-Rodrigues MJ, de Barros Dantas LL, Cheavegatti Gianotto A, Caldana C. Applying Molecular Phenotyping Tools to Explore Sugarcane Carbon Potential. FRONTIERS IN PLANT SCIENCE 2021; 12:637166. [PMID: 33679852 PMCID: PMC7935522 DOI: 10.3389/fpls.2021.637166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 01/27/2021] [Indexed: 05/21/2023]
Abstract
Sugarcane (Saccharum spp.), a C4 grass, has a peculiar feature: it accumulates, gradient-wise, large amounts of carbon (C) as sucrose in its culms through a complex pathway. Apart from being a sustainable crop concerning C efficiency and bioenergetic yield per hectare, sugarcane is used as feedstock for producing ethanol, sugar, high-value compounds, and products (e.g., polymers and succinate), and bioelectricity, earning the title of the world's leading biomass crop. Commercial cultivars, hybrids bearing high levels of polyploidy, and aneuploidy, are selected from a large number of crosses among suitable parental genotypes followed by the cloning of superior individuals among the progeny. Traditionally, these classical breeding strategies have been favoring the selection of cultivars with high sucrose content and resistance to environmental stresses. A current paradigm change in sugarcane breeding programs aims to alter the balance of C partitioning as a means to provide more plasticity in the sustainable use of this biomass for metabolic engineering and green chemistry. The recently available sugarcane genetic assemblies powered by data science provide exciting perspectives to increase biomass, as the current sugarcane yield is roughly 20% of its predicted potential. Nowadays, several molecular phenotyping tools can be applied to meet the predicted sugarcane C potential, mainly targeting two competing pathways: sucrose production/storage and biomass accumulation. Here we discuss how molecular phenotyping can be a powerful tool to assist breeding programs and which strategies could be adopted depending on the desired final products. We also tackle the advances in genetic markers and mapping as well as how functional genomics and genetic transformation might be able to improve yield and saccharification rates. Finally, we review how "omics" advances are promising to speed up plant breeding and reach the unexplored potential of sugarcane in terms of sucrose and biomass production.
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Affiliation(s)
| | | | | | - Camila Caldana
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- *Correspondence: Camila Caldana,
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23
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Bhadra-Lobo S, Kim MK, Lun DS. Assessment of transcriptomic constraint-based methods for central carbon flux inference. PLoS One 2020; 15:e0238689. [PMID: 32903284 PMCID: PMC7480874 DOI: 10.1371/journal.pone.0238689] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 08/21/2020] [Indexed: 11/18/2022] Open
Abstract
MOTIVATION Determining intracellular metabolic flux through isotope labeling techniques such as 13C metabolic flux analysis (13C-MFA) incurs significant cost and effort. Previous studies have shown transcriptomic data coupled with constraint-based metabolic modeling can determine intracellular fluxes that correlate highly with 13C-MFA measured fluxes and can achieve higher accuracy than constraint-based metabolic modeling alone. These studies, however, used validation data limited to E. coli and S. cerevisiae grown on glucose, with significantly similar flux distribution for central metabolism. It is unclear whether those results apply to more diverse metabolisms, and therefore further, extensive validation is needed. RESULTS In this paper, we formed a dataset of transcriptomic data coupled with corresponding 13C-MFA flux data for 21 experimental conditions in different unicellular organisms grown on varying carbon substrates and conditions. Three computational flux-balance analysis (FBA) methods were comparatively assessed. The results show when uptake rates of carbon sources and key metabolites are known, transcriptomic data provides no significant advantage over constraint-based metabolic modeling (average correlation coefficients, transcriptomic E-Flux2 0.725 and SPOT 0.650 vs non-transcriptomic pFBA 0.768). When uptake rates are unknown, however, predictions obtained utilizing transcriptomic data are generally good and significantly better than those obtained using constraint-based metabolic modeling alone (E-Flux2 0.385 and SPOT 0.583 vs pFBA 0.237). Thus, transcriptomic data coupled with constraint-based metabolic modeling is a promising method to obtain intracellular flux estimates in microorganisms, particularly in cases where uptake rates of key metabolites cannot be easily determined, such as for growth in complex media or in vivo conditions.
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Affiliation(s)
- Siddharth Bhadra-Lobo
- Center for Computational and Integrative Biology, Rutgers, The State University of New Jersey, Camden, NJ, United States of America
- * E-mail:
| | - Min Kyung Kim
- Center for Computational and Integrative Biology, Rutgers, The State University of New Jersey, Camden, NJ, United States of America
| | - Desmond S. Lun
- Center for Computational and Integrative Biology, Rutgers, The State University of New Jersey, Camden, NJ, United States of America
- Department of Computer Science, Rutgers, The State University of New Jersey, Camden, NJ, United States of America
- Department of Plant Biology, Rutgers, The State University of New Jersey, New Brunswick, NJ, United States of America
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24
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Correa SM, Fernie AR, Nikoloski Z, Brotman Y. Towards model-driven characterization and manipulation of plant lipid metabolism. Prog Lipid Res 2020; 80:101051. [PMID: 32640289 DOI: 10.1016/j.plipres.2020.101051] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 06/20/2020] [Accepted: 06/21/2020] [Indexed: 01/09/2023]
Abstract
Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.
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Affiliation(s)
- Sandra M Correa
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel; Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín 050010, Colombia.
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Zoran Nikoloski
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modelling Group, Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm 14476, Germany.
| | - Yariv Brotman
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
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25
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Benes B, Guan K, Lang M, Long SP, Lynch JP, Marshall-Colón A, Peng B, Schnable J, Sweetlove LJ, Turk MJ. Multiscale computational models can guide experimentation and targeted measurements for crop improvement. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:21-31. [PMID: 32053236 DOI: 10.1111/tpj.14722] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 01/23/2020] [Indexed: 05/18/2023]
Abstract
Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.
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Affiliation(s)
- Bedrich Benes
- Computer Graphics Technology and Computer Science, Purdue University, Knoy Hall of Technology, West Lafayette, IN, 47906, USA
| | - Kaiyu Guan
- College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA
- National Center of Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana Champaign, Urbana, IL, USA
| | - Meagan Lang
- National Center of Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, USA
| | - Stephen P Long
- Carl R. Woese Institute for Genomic Biology, University of Illinois, 1206 West Gregory Drive, Urbana, IL, 61801, USA
- Lancaster Environment Centre, University of Lancaster, Lancaster, LA1 1YX, UK
| | - Jonathan P Lynch
- Department of Plant Science, The Pennsylvania State University, University Park, PA, 16802, USA
- School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK
| | - Amy Marshall-Colón
- National Center of Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana Champaign, Urbana, IL, USA
- Department of Plant Biology, University of Illinois Urbana-Champaign, 265 Morrill Hall, MC-116, 505 South Goodwin Ave., Urbana, IL, 61801, USA
| | - Bin Peng
- College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA
- National Center of Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, USA
| | - James Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, 68583, USA
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Matthew J Turk
- National Center of Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, USA
- School of Information Sciences, University of Illinois, Urbana-Champaign, Urbana, IL, USA
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26
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Tivendale ND, Hanson AD, Henry CS, Hegeman AD, Millar AH. Enzymes as Parts in Need of Replacement - and How to Extend Their Working Life. TRENDS IN PLANT SCIENCE 2020; 25:661-669. [PMID: 32526171 DOI: 10.1016/j.tplants.2020.02.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 02/11/2020] [Accepted: 02/14/2020] [Indexed: 06/11/2023]
Abstract
Enzymes catalyze reactions in vivo at different rates and each enzyme molecule has a lifetime limit before it is degraded and replaced to enable catalysis to continue. Considering these rates together as a unitless ratio of catalytic cycles until replacement (CCR) provides a new quantitative tool to assess the replacement schedule of and energy investment into enzymes as they relate to function. Here, we outline the challenges of determining CCRs and new approaches to overcome them and then assess the CCRs of selected enzymes in bacteria and plants to reveal a range of seven orders of magnitude for this ratio. Modifying CCRs in plants holds promise to lower cellular costs, to tailor enzymes for particular environments, and to breed enzyme improvements for crop productivity.
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Affiliation(s)
- Nathan D Tivendale
- ARC Centre for Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, M316, Perth, WA 6009, Australia
| | - Andrew D Hanson
- Horticultural Sciences Department, University of Florida, PO Box 110690, Gainesville, FL 32611-0690, USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA; Computation Institute, The University of Chicago, Chicago, IL 60637, USA
| | - Adrian D Hegeman
- Department of Horticultural Science, Department of Plant and Microbial Biology, and The Microbial and Plant Genomics Institute, University of Minnesota, 1970 Folwell Avenue, Saint Paul, MN 55108-6007, USA
| | - A Harvey Millar
- ARC Centre for Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, M316, Perth, WA 6009, Australia.
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Perez De Souza L, Alseekh S, Brotman Y, Fernie AR. Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Rev Proteomics 2020; 17:243-255. [PMID: 32380880 DOI: 10.1080/14789450.2020.1766975] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
INTRODUCTION Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system. AREAS COVERED This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches. EXPERT OPINION Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.
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Affiliation(s)
- Leonardo Perez De Souza
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany
| | - Saleh Alseekh
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany.,Department of plant metabolomics, Centre of Plant Systems Biology and Biotechnology , Plovdiv, Bulgaria
| | - Yariv Brotman
- Department of Life Sciences, Ben-Gurion University of the Negev , Beersheba, Israel
| | - Alisdair R Fernie
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany.,Department of plant metabolomics, Centre of Plant Systems Biology and Biotechnology , Plovdiv, Bulgaria
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28
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Multiple Metabolic Innovations and Losses Are Associated with Major Transitions in Land Plant Evolution. Curr Biol 2020; 30:1783-1800.e11. [DOI: 10.1016/j.cub.2020.02.086] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 02/04/2020] [Accepted: 02/27/2020] [Indexed: 12/31/2022]
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29
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Khan S, Ince-Dunn G, Suomalainen A, Elo LL. Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases. J Clin Invest 2020; 130:20-28. [PMID: 31895050 PMCID: PMC6934214 DOI: 10.1172/jci129202] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.
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Affiliation(s)
- Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Gulayse Ince-Dunn
- Research Programs Unit, Stem Cells and Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anu Suomalainen
- Research Programs Unit, Stem Cells and Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Neuroscience Center, HiLife, University of Helsinki, Helsinki, Finland
- Helsinki University Hospital, HUSlab, Helsinki, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
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30
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Traustason B, Cheeks M, Dikicioglu D. Computer-Aided Strategies for Determining the Amino Acid Composition of Medium for Chinese Hamster Ovary Cell-Based Biomanufacturing Platforms. Int J Mol Sci 2019; 20:E5464. [PMID: 31684012 PMCID: PMC6862603 DOI: 10.3390/ijms20215464] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 10/30/2019] [Accepted: 10/31/2019] [Indexed: 01/07/2023] Open
Abstract
Chinese hamster ovary (CHO) cells are used for the production of the majority of biopharmaceutical drugs, and thus have remained the standard industry host for the past three decades. The amino acid composition of the medium plays a key role in commercial scale biologics manufacturing, as amino acids constitute the building blocks of both endogenous and heterologous proteins, are involved in metabolic and non-metabolic pathways, and can act as main sources of nitrogen and carbon under certain conditions. As biomanufactured proteins become increasingly complex, the adoption of model-based approaches become ever more popular in complementing the challenging task of medium development. The extensively studied amino acid metabolism is exceptionally suitable for such model-driven analyses, and although still limited in practice, the development of these strategies is gaining attention, particularly in this domain. This paper provides a review of recent efforts. We first provide an overview of the widely adopted practice, and move on to describe the model-driven approaches employed for the improvement and optimization of the external amino acid supply in light of cellular amino acid demand. We conclude by proposing the likely prevalent direction the field is heading towards, providing a critical evaluation of the current state and the future challenges and considerations.
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Affiliation(s)
- Bergthor Traustason
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK.
| | - Matthew Cheeks
- Cell Sciences, Biopharmaceutical Development, AstraZeneca, Cambridge CB21 6GH, UK.
| | - Duygu Dikicioglu
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK.
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31
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A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis. Trends Pharmacol Sci 2019; 40:763-773. [PMID: 31511194 DOI: 10.1016/j.tips.2019.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 07/31/2019] [Accepted: 08/11/2019] [Indexed: 12/15/2022]
Abstract
Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.
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Shameer S, Ratcliffe RG, Sweetlove LJ. Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light. PLANT PHYSIOLOGY 2019; 180:1947-1961. [PMID: 31213510 PMCID: PMC6670072 DOI: 10.1104/pp.19.00624] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 06/04/2019] [Indexed: 05/04/2023]
Abstract
Key aspects of leaf mitochondrial metabolism in the light remain unresolved. For example, there is debate about the relative importance of exporting reducing equivalents from mitochondria for the peroxisomal steps of photorespiration versus oxidation of NADH to generate ATP by oxidative phosphorylation. Here, we address this and explore energetic coupling between organelles in the light using a diel flux balance analysis model. The model included more than 600 reactions of central metabolism with full stoichiometric accounting of energy production and consumption. Different scenarios of energy availability (light intensity) and demand (source leaf versus a growing leaf) were considered, and the model was constrained by the nonlinear relationship between light and CO2 assimilation rate. The analysis demonstrated that the chloroplast can theoretically generate sufficient ATP to satisfy the energy requirements of the rest of the cell in addition to its own. However, this requires unrealistic high light use efficiency and, in practice, the availability of chloroplast-derived ATP is limited by chloroplast energy dissipation systems, such as nonphotochemical quenching, and the capacity of the chloroplast ATP export shuttles. Given these limitations, substantial mitochondrial ATP synthesis is required to fulfill cytosolic ATP requirements, with only minimal, or zero, export of mitochondrial reducing equivalents. The analysis also revealed the importance of exporting reducing equivalents from chloroplasts to sustain photorespiration. Hence, the chloroplast malate valve and triose phosphate-3-phosphoglycerate shuttle are predicted to have important metabolic roles, in addition to their more commonly discussed contribution to the avoidance of photooxidative stress.
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Affiliation(s)
- Sanu Shameer
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
| | - R George Ratcliffe
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, United Kingdom
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