1
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Tcherkez G, Abadie C, Dourmap C, Lalande J, Limami AM. Leaf day respiration: More than just catabolic CO 2 production in the light. PLANT, CELL & ENVIRONMENT 2024; 47:2631-2639. [PMID: 38528759 DOI: 10.1111/pce.14904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/08/2024] [Accepted: 03/18/2024] [Indexed: 03/27/2024]
Abstract
Summary statementDay respiration is a net flux resulting from several CO2‐generating and CO2‐fixing reactions, not only related to catabolism but also to anabolism. We review pieces of evidence that decarboxylating reactions are partly fed by carbon sources disconnected from current photosynthesis and how they reflect various metabolic pathways.
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Affiliation(s)
- Guillaume Tcherkez
- Institut de recherche en horticulture et semences, Université d'Angers, INRAe, Beaucouzé, France
- Research school of biology, ANU College of Science, Australian National University, Canberra, Australia
| | - Cyril Abadie
- Institut de recherche en horticulture et semences, Université d'Angers, INRAe, Beaucouzé, France
- Ecophysiologie et génomique fonctionnelle de la vigne, Institut des Sciences de la Vigne et du Vin, INRAe, Université de Bordeaux, Villenave-d'Ornon, France
| | - Corentin Dourmap
- Institut de recherche en horticulture et semences, Université d'Angers, INRAe, Beaucouzé, France
| | - Julie Lalande
- Institut de recherche en horticulture et semences, Université d'Angers, INRAe, Beaucouzé, France
| | - Anis M Limami
- Institut de recherche en horticulture et semences, Université d'Angers, INRAe, Beaucouzé, France
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2
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Goelzer A, Rajjou L, Chardon F, Loudet O, Fromion V. Resource allocation modeling for autonomous prediction of plant cell phenotypes. Metab Eng 2024; 83:86-101. [PMID: 38561149 DOI: 10.1016/j.ymben.2024.03.009] [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: 11/15/2023] [Revised: 02/19/2024] [Accepted: 03/29/2024] [Indexed: 04/04/2024]
Abstract
Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO2 and O2, and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.
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Affiliation(s)
- Anne Goelzer
- Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France.
| | - Loïc Rajjou
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000, Versailles, France
| | - Fabien Chardon
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000, Versailles, France
| | - Olivier Loudet
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000, Versailles, France
| | - Vincent Fromion
- Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France.
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3
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Loo EPI, Durán P, Pang TY, Westhoff P, Deng C, Durán C, Lercher M, Garrido-Oter R, Frommer WB. Sugar transporters spatially organize microbiota colonization along the longitudinal root axis of Arabidopsis. Cell Host Microbe 2024; 32:543-556.e6. [PMID: 38479394 DOI: 10.1016/j.chom.2024.02.014] [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: 06/22/2023] [Revised: 02/01/2024] [Accepted: 02/21/2024] [Indexed: 04/13/2024]
Abstract
Plant roots are functionally heterogeneous in cellular architecture, transcriptome profile, metabolic state, and microbial immunity. We hypothesized that axial differentiation may also impact spatial colonization by root microbiota along the root axis. We developed two growth systems, ArtSoil and CD-Rhizotron, to grow and then dissect Arabidopsis thaliana roots into three segments. We demonstrate that distinct endospheric and rhizosphere bacterial communities colonize the segments, supporting the hypothesis of microbiota differentiation along the axis. Root metabolite profiling of each segment reveals differential metabolite enrichment and specificity. Bioinformatic analyses and GUS histochemistry indicate microbe-induced accumulation of SWEET2, 4, and 12 sugar uniporters. Profiling of root segments from sweet mutants shows altered spatial metabolic profiles and reorganization of endospheric root microbiota. This work reveals the interdependency between root metabolites and microbial colonization and the contribution of SWEETs to spatial diversity and stability of microbial ecosystem.
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Affiliation(s)
- Eliza P-I Loo
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Molecular Physiology, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany.
| | - Paloma Durán
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany
| | - Tin Yau Pang
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Computer Science and Department of Biology, 40225 Düsseldorf, Germany; Heinrich Heine University Düsseldorf, Medical Faculty and University Hospital Düsseldorf, Division of Cardiology, Pulmonology and Vascular Medicine, 40225 Düsseldorf, Germany
| | - Philipp Westhoff
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Plant Metabolism and Metabolomics Laboratory, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany
| | - Chen Deng
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Molecular Physiology, 40225 Düsseldorf, Germany
| | - Carlos Durán
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Martin Lercher
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Computer Science and Department of Biology, 40225 Düsseldorf, Germany; Heinrich Heine University Düsseldorf, Medical Faculty and University Hospital Düsseldorf, Division of Cardiology, Pulmonology and Vascular Medicine, 40225 Düsseldorf, Germany
| | - Ruben Garrido-Oter
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany; Earlham Institute, Norwich NR4 7UZ, UK
| | - Wolf B Frommer
- Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Molecular Physiology, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany; Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, 464-8601 Nagoya, Japan.
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4
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Chowdhury NB, Simons-Senftle M, Decouard B, Quillere I, Rigault M, Sajeevan KA, Acharya B, Chowdhury R, Hirel B, Dellagi A, Maranas C, Saha R. A multi-organ maize metabolic model connects temperature stress with energy production and reducing power generation. iScience 2023; 26:108400. [PMID: 38077131 PMCID: PMC10709110 DOI: 10.1016/j.isci.2023.108400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/30/2023] [Accepted: 11/03/2023] [Indexed: 02/18/2024] Open
Abstract
Climate change has adversely affected maize productivity. Thereby, a holistic understanding of metabolic crosstalk among its organs is important to address this issue. Thus, we reconstructed the first multi-organ maize metabolic model, iZMA6517, and contextualized it with heat and cold stress transcriptomics data using expression distributed reaction flux measurement (EXTREAM) algorithm. Furthermore, implementing metabolic bottleneck analysis on contextualized models revealed differences between these stresses. While both stresses had reducing power bottlenecks, heat stress had additional energy generation bottlenecks. We also performed thermodynamic driving force analysis, revealing thermodynamics-reducing power-energy generation axis dictating the nature of temperature stress responses. Thus, a temperature-tolerant maize ideotype can be engineered by leveraging the proposed thermodynamics-reducing power-energy generation axis. We experimentally inoculated maize root with a beneficial mycorrhizal fungus, Rhizophagus irregularis, and as a proof-of-concept demonstrated its efficacy in alleviating temperature stress. Overall, this study will guide the engineering effort of temperature stress-tolerant maize ideotypes.
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Affiliation(s)
- Niaz Bahar Chowdhury
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | | | - Berengere Decouard
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France
| | - Isabelle Quillere
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France
| | - Martine Rigault
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France
| | | | - Bibek Acharya
- Chemical and Biological Engineering, Iowa State University, Ames, IA, USA
| | - Ratul Chowdhury
- Chemical and Biological Engineering, Iowa State University, Ames, IA, USA
| | - Bertrand Hirel
- Centre de Versailles-Grignon, Institut National de Recherche pour l’Agriculture, Versailles, France
| | - Alia Dellagi
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France
| | - Costas Maranas
- Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Rajib Saha
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
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5
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Kitashova A, Brodsky V, Chaturvedi P, Pierides I, Ghatak A, Weckwerth W, Nägele T. Quantifying the impact of dynamic plant-environment interactions on metabolic regulation. JOURNAL OF PLANT PHYSIOLOGY 2023; 290:154116. [PMID: 37839392 DOI: 10.1016/j.jplph.2023.154116] [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: 08/23/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 10/17/2023]
Abstract
A plant's genome encodes enzymes, transporters and many other proteins which constitute metabolism. Interactions of plants with their environment shape their growth, development and resilience towards adverse conditions. Although genome sequencing technologies and applications have experienced triumphantly rapid development during the last decades, enabling nowadays a fast and cheap sequencing of full genomes, prediction of metabolic phenotypes from genotype × environment interactions remains, at best, very incomplete. The main reasons are a lack of understanding of how different levels of molecular organisation depend on each other, and how they are constituted and expressed within a setup of growth conditions. Phenotypic plasticity, e.g., of the genetic model plant Arabidopsis thaliana, has provided important insights into plant-environment interactions and the resulting genotype x phenotype relationships. Here, we summarize previous and current findings about plant development in a changing environment and how this might be shaped and reflected in metabolism and its regulation. We identify current challenges in the study of plant development and metabolic regulation and provide an outlook of how methodological workflows might support the application of findings made in model systems to crops and their cultivation.
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Affiliation(s)
- Anastasia Kitashova
- LMU Munich, Faculty of Biology, Plant Evolutionary Cell Biology, 82152, Planegg, Germany.
| | - Vladimir Brodsky
- LMU Munich, Faculty of Biology, Plant Evolutionary Cell Biology, 82152, Planegg, Germany.
| | - Palak Chaturvedi
- University of Vienna, Molecular Systems Biology Lab (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, Djerassiplatz 1, 1030, Vienna, Austria.
| | - Iro Pierides
- University of Vienna, Molecular Systems Biology Lab (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, Djerassiplatz 1, 1030, Vienna, Austria.
| | - Arindam Ghatak
- University of Vienna, Molecular Systems Biology Lab (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, Djerassiplatz 1, 1030, Vienna, Austria; Vienna Metabolomics Center, University of Vienna, Djerassiplatz 1, 1030, Vienna, Austria.
| | - Wolfram Weckwerth
- University of Vienna, Molecular Systems Biology Lab (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, Djerassiplatz 1, 1030, Vienna, Austria; Vienna Metabolomics Center, University of Vienna, Djerassiplatz 1, 1030, Vienna, Austria.
| | - Thomas Nägele
- LMU Munich, Faculty of Biology, Plant Evolutionary Cell Biology, 82152, Planegg, Germany.
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Cunha E, Silva M, Chaves I, Demirci H, Lagoa DR, Lima D, Rocha M, Rocha I, Dias O. The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber. PLoS Comput Biol 2023; 19:e1011499. [PMID: 37729340 PMCID: PMC10545120 DOI: 10.1371/journal.pcbi.1011499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/02/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023] Open
Abstract
Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.
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Affiliation(s)
- Emanuel Cunha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Silva
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Inês Chaves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
| | - Huseyin Demirci
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- SnT/University of Luxembourg, Luxembourg
| | | | - Diogo Lima
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
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7
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Nègre D, Larhlimi A, Bertrand S. Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism? PLoS One 2023; 18:e0289757. [PMID: 37647283 PMCID: PMC10468094 DOI: 10.1371/journal.pone.0289757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 09/01/2023] Open
Abstract
In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.
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Affiliation(s)
- Delphine Nègre
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
- Nantes Université, École Centrale Nantes, CNRS, Nantes, France
| | | | - Samuel Bertrand
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
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8
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Murali S, Ibrahim M, Rajendran H, Shagun S, Masakapalli SK, Raman K, Srivastava S. Genome-scale metabolic model led engineering of Nothapodytes nimmoniana plant cells for high camptothecin production. FRONTIERS IN PLANT SCIENCE 2023; 14:1207218. [PMID: 37600193 PMCID: PMC10433906 DOI: 10.3389/fpls.2023.1207218] [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: 04/17/2023] [Accepted: 07/04/2023] [Indexed: 08/22/2023]
Abstract
Camptothecin (CPT) is a vital monoterpene indole alkaloid used in anti-cancer therapeutics. It is primarily derived from Camptotheca acuminata and Nothapodytes nimmoniana plants that are indigenous to Southeast Asia. Plants have intricate metabolic networks and use them to produce secondary metabolites such as CPT, which is a prerequisite for rational metabolic engineering design to optimize their production. By reconstructing metabolic models, we can predict plant metabolic behavior, facilitating the selection of suitable approaches and saving time, cost, and energy, over traditional hit and trial experimental approaches. In this study, we reconstructed a genome-scale metabolic model for N. nimmoniana (NothaGEM iSM1809) and curated it using experimentally obtained biochemical data. We also used in silico tools to identify and rank suitable enzyme targets for overexpression and knockout to maximize camptothecin production. The predicted over-expression targets encompass enzymes involved in the camptothecin biosynthesis pathway, including strictosidine synthase and geraniol 10-hydroxylase, as well as targets related to plant metabolism, such as amino acid biosynthesis and the tricarboxylic acid cycle. The top-ranked knockout targets included reactions responsible for the formation of folates and serine, as well as the conversion of acetyl CoA and oxaloacetate to malate and citrate. One of the top-ranked overexpression targets, strictosidine synthase, was chosen to generate metabolically engineered cell lines of N. nimmoniana using Agrobacterium tumefaciens-mediated transformation. The transformed cell line showed a 5-fold increase in camptothecin production, with a yield of up to 5 µg g-1.
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Affiliation(s)
- Sarayu Murali
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Maziya Ibrahim
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India
| | - Hemalatha Rajendran
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Shagun Shagun
- School of Biosciences and Bioengineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India
| | - Shyam Kumar Masakapalli
- School of Biosciences and Bioengineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India
| | - Karthik Raman
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India
| | - Smita Srivastava
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
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9
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Marin de Mas I, Herand H, Carrasco J, Nielsen LK, Johansson PI. A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism. Bioengineering (Basel) 2023; 10:bioengineering10050576. [PMID: 37237646 DOI: 10.3390/bioengineering10050576] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant.
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Affiliation(s)
- Igor Marin de Mas
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark
| | - Helena Herand
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Jorge Carrasco
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
| | - Lars K Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane 4072, Australia
| | - Pär I Johansson
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark
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10
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Wendering P, Nikoloski Z. Toward mechanistic modeling and rational engineering of plant respiration. PLANT PHYSIOLOGY 2023; 191:2150-2166. [PMID: 36721968 PMCID: PMC10069892 DOI: 10.1093/plphys/kiad054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Plant respiration not only provides energy to support all cellular processes, including biomass production, but also plays a major role in the global carbon cycle. Therefore, modulation of plant respiration can be used to both increase the plant yield and mitigate the effects of global climate change. Mechanistic modeling of plant respiration at sufficient biochemical detail can provide key insights for rational engineering of this process. Yet, despite its importance, plant respiration has attracted considerably less modeling effort in comparison to photosynthesis. In this update review, we highlight the advances made in modeling of plant respiration, emphasizing the gradual but important change from phenomenological to models based on first principles. We also provide a detailed account of the existing resources that can contribute to resolving the challenges in modeling plant respiration. These resources point at tangible improvements in the representation of cellular processes that contribute to CO2 evolution and consideration of kinetic properties of underlying enzymes to facilitate mechanistic modeling. The update review emphasizes the need to couple biochemical models of respiration with models of acclimation and adaptation of respiration for their effective usage in guiding breeding efforts and improving terrestrial biosphere models tailored to future climate scenarios.
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Affiliation(s)
- Philipp Wendering
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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11
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Vidya Muthulakshmi M, Srinivasan A, Srivastava S. Antioxidant Green Factories: Toward Sustainable Production of Vitamin E in Plant In Vitro Cultures. ACS OMEGA 2023; 8:3586-3605. [PMID: 36743063 PMCID: PMC9893489 DOI: 10.1021/acsomega.2c05819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/14/2022] [Indexed: 06/18/2023]
Abstract
Vitamin E is a dietary supplement synthesized only by photosynthetic organisms and, hence, is an essential vitamin for human well-being. Because of the ever-increasing demand for natural vitamin E and limitations in existing synthesis modes, attempts to improve its yield using plant in vitro cultures have gained traction in recent years. With inflating industrial production costs, integrative approaches to conventional bioprocess optimization is the need of the hour for multifold vitamin E productivity enhancement. In this review, we briefly discuss the structure, isomers, and important metabolic routes of biosynthesis for vitamin E in plants. We then emphasize its vital role in human health and its industrial applications and highlight the market demand and supply. We illustrate the advantages of in vitro plant cell/tissue culture cultivation as an alternative to current commercial production platforms for natural vitamin E. We touch upon the conventional vitamin E metabolic pathway engineering strategies, such as single/multigene overexpression and chloroplast engineering. We highlight the recent progress in plant systems biology to rationally identify metabolic bottlenecks and knockout targets in the vitamin E biosynthetic pathway. We then discuss bioprocess optimization strategies for sustainable vitamin E production, including media/process optimization, precursor/elicitor addition, and scale-up to bioreactors. We culminate the review with a short discussion on kinetic modeling to predict vitamin E production in plant cell cultures and suggestions on sustainable green extraction methods of vitamin E for reduced environmental impact. This review will be of interest to a wider research fraternity, including those from industry and academia working in the field of plant cell biology, plant biotechnology, and bioprocess engineering for phytochemical enhancement.
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Affiliation(s)
- M. Vidya Muthulakshmi
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
| | - Aparajitha Srinivasan
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
| | - Smita Srivastava
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
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12
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Broddrick JT, Ware MA, Jallet D, Palsson BO, Peers G. Integration of physiologically relevant photosynthetic energy flows into whole genome models of light-driven metabolism. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 112:603-621. [PMID: 36053127 PMCID: PMC9826171 DOI: 10.1111/tpj.15965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 08/25/2022] [Accepted: 08/31/2022] [Indexed: 06/01/2023]
Abstract
Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome-scale metabolic models capture species-specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non-photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non-radiative energy dissipation processes, cross-compartment electron shuttling, and non-photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico. Overall, we present a methodology for incorporating a common, informative data type into computational models of light-driven metabolism and show its utilization within the design-build-test-learn cycle for engineering of photosynthetic organisms.
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Affiliation(s)
- Jared T. Broddrick
- Division of Biological SciencesUniversity of California, San DiegoLa JollaCA92093USA
- Department of BioengineeringUniversity of California, San DiegoLa JollaCA92093USA
- Space Biosciences Research BranchNASA Ames Research CenterMoffett FieldCA94035USA
| | - Maxwell A. Ware
- Department of BiologyColorado State UniversityFort CollinsCO80524USA
| | - Denis Jallet
- Department of BiologyColorado State UniversityFort CollinsCO80524USA
| | - Bernhard O. Palsson
- Department of BioengineeringUniversity of California, San DiegoLa JollaCA92093USA
| | - Graham Peers
- Department of BiologyColorado State UniversityFort CollinsCO80524USA
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13
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Beilsmith K, Henry CS, Seaver SMD. Genome-scale modeling of the primary-specialized metabolism interface. CURRENT OPINION IN PLANT BIOLOGY 2022; 68:102244. [PMID: 35714443 DOI: 10.1016/j.pbi.2022.102244] [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: 10/16/2021] [Revised: 04/21/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
Environmental challenges and development require plants to reallocate resources between primary and specialized metabolites to survive. Genome-scale metabolic models, which map carbon flux through metabolic pathways, are a valuable tool in the study of tradeoffs that arise at this interface. Due to annotation gaps, models that characterize all the enzymatic steps in individual specialized pathways and their linkages to each other and to central carbon metabolism are difficult to construct. Recent studies have successfully curated subsystems of specialized metabolism and characterized the interfaces where flux is diverted to the precursors of glucosinolates, terpenes, and anthocyanins. Although advances in metabolite profiling can help to constrain models at this interface, quantitative analysis remains challenging because of the different timescales on which specialized metabolites from constitutive and reactive pathways accumulate.
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Affiliation(s)
- Kathleen Beilsmith
- Data Science and Learning Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA
| | - Christopher S Henry
- Data Science and Learning Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA
| | - Samuel M D Seaver
- Data Science and Learning Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA.
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14
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The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility. PLoS Comput Biol 2022; 18:e1010203. [PMID: 35759507 PMCID: PMC9269948 DOI: 10.1371/journal.pcbi.1010203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 07/08/2022] [Accepted: 05/14/2022] [Indexed: 11/30/2022] Open
Abstract
The topology of metabolic networks is recognisably modular with modules weakly connected apart from sharing a pool of currency metabolites. Here, we defined modules as sets of reversible reactions isolated from the rest of metabolism by irreversible reactions except for the exchange of currency metabolites. Our approach identifies topologically independent modules under specific conditions associated with different metabolic functions. As case studies, the E.coli iJO1366 and Human Recon 2.2 genome-scale metabolic models were split in 103 and 321 modules respectively, displaying significant correlation patterns in expression data. Finally, we addressed a fundamental question about the metabolic flexibility conferred by reversible reactions: “Of all Directed Topologies (DTs) defined by fixing directions to all reversible reactions, how many are capable of carrying flux through all reactions?”. Enumeration of the DTs for iJO1366 model was performed using an efficient depth-first search algorithm, rejecting infeasible DTs based on mass-imbalanced and loopy flux patterns. We found the direction of 79% of reversible reactions must be defined before all directions in the network can be fixed, granting a high degree of flexibility. Genome-scale metabolic reconstructions represent all biochemical reactions that an organism can accomplish. These reconstructions are complex and often difficult to study in great detail. A way to overcome this limitation is to focus on specific pathways or subsystems. We present a novel method to identify metabolic modules based on the network topology. The method relies on reaction directions and ignores currency metabolites, which artificially connect distant metabolic reactions. In this way, topologically independent modules are built, where inputs and outputs are controlled by irreversible reactions. The method is automatic and unbiased, and, the result is a set of condition specific modules with defined metabolic functions. As a proof-of-concept we generated biologically relevant modules for the E.coli and Human genome-scale metabolic reconstructions supported by transcriptomic data. Finally, we applied the novel approach to study the network flexibility conferred by reversible reactions. In the case of the E. coli model, we found that the direction of 79% of structurally reversible reactions (those not directionally constrained by surrounding irreversible reactions) must be fixed to determine all the reaction directions in the network. Therefore, reversible reactions operate practically independent of each other.
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15
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Sampaio M, Rocha M, Dias O. Exploring synergies between plant metabolic modelling and machine learning. Comput Struct Biotechnol J 2022; 20:1885-1900. [PMID: 35521559 PMCID: PMC9052043 DOI: 10.1016/j.csbj.2022.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/03/2022] Open
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16
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Sugimoto K, Zager JJ, Aubin BS, Lange B, Howe GA. Flavonoid deficiency disrupts redox homeostasis and terpenoid biosynthesis in glandular trichomes of tomato. PLANT PHYSIOLOGY 2022; 188:1450-1468. [PMID: 34668550 PMCID: PMC8896623 DOI: 10.1093/plphys/kiab488] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 09/23/2021] [Indexed: 05/11/2023]
Abstract
Glandular trichomes (GTs) are epidermal structures that provide the first line of chemical defense against arthropod herbivores and other biotic threats. The most conspicuous structure on leaves of cultivated tomato (Solanum lycopersicum) is the type-VI GT (tVI-GT), which accumulates both flavonoids and volatile terpenoids. Although these classes of specialized metabolites are derived from distinct metabolic pathways, previous studies with a chalcone isomerase 1 (CHI1)-deficient mutant called anthocyanin free (af) showed that flavonoids are required for terpenoid accumulation in tVI-GTs. Here, we combined global transcriptomic and proteomic analyses of isolated trichomes as a starting point to show that the lack of CHI1 is associated with reduced levels of terpenoid biosynthetic transcripts and enzymes. The flavonoid deficiency in af trichomes also resulted in the upregulation of abiotic stress-responsive genes associated with DNA damage and repair. Several lines of biochemical and genetic evidence indicate that the terpenoid defect in af mutants is specific for the tVI-GT and is associated with the absence of bulk flavonoids rather than loss of CHI1 per se. A newly developed genome-scale model of metabolism in tomato tVI-GTs helped identify metabolic imbalances caused by the loss of flavonoid production. We provide evidence that flavonoid deficiency in this cell type leads to increased production of reactive oxygen species (ROS), which may impair terpenoid biosynthesis. Collectively, our findings support a role for flavonoids as ROS-scavenging antioxidants in GTs.
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Affiliation(s)
- Koichi Sugimoto
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, Michigan, 48824, USA
| | - Jordan J Zager
- Institute of Biological Chemistry and M.J. Murdock Metabolomics Laboratory, Washington State University, Pullman, Washington, 99164-7411, USA
| | - Brian St Aubin
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, Michigan, 48824, USA
| | - Bernd Markus Lange
- Institute of Biological Chemistry and M.J. Murdock Metabolomics Laboratory, Washington State University, Pullman, Washington, 99164-7411, USA
| | - Gregg A Howe
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, Michigan, 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, 48824, USA
- Plant Resilience Institute, Michigan State University, East Lansing, Michigan, 48824, USA
- Author for communication:
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17
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Zamani Amirzakaria J, Marashi SA, Malboobi MA, Lohrasebi T, Forouzan E. Critical assessment of genome-scale metabolic models of Arabidopsis thaliana. Mol Omics 2022; 18:328-335. [PMID: 35081193 DOI: 10.1039/d1mo00351h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Genome-scale metabolic models (GEMs) have enabled researchers to perform systems-level studies of living organisms. Flux balance analysis (FBA), as a constraint-based technique, enables computation of reaction fluxes and prediction of the metabolic phenotypes of a cell under a set of specified conditions. The quality of a GEM is important for obtaining accurate predictions. In this study, we evaluated the quality of five available GEMs for Arabidopsis thaliana from various points of views. To do this, we inspected some of their important features, including the number of reactions with well-defined gene-protein-reaction rules, number of blocked reactions, mass-unbalanced reactions, prediction accuracy in the simulation of key metabolic functions and existence of erroneous energy generating cycles (EGCs). All of the models were found to include some mass-unbalanced reactions. Moreover, four out of five models were found to include EGCs. However, Aracell includes the maximum number of blocked reactions, which suggests the presence of several incomplete pathways. These results clearly show that simulation by using these models may result in erroneous predictions and all of the publicly available GEMs for A. thaliana require extensive curations before being applied in practice.
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Affiliation(s)
- Javad Zamani Amirzakaria
- Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
| | - Mohammad Ali Malboobi
- Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
| | - Tahmineh Lohrasebi
- Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
| | - Esmail Forouzan
- Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
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18
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Chowdhury NB, Schroeder WL, Sarkar D, Amiour N, Quilleré I, Hirel B, Maranas CD, Saha R. Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:275-291. [PMID: 34554248 DOI: 10.1093/jxb/erab435] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.
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Affiliation(s)
- Niaz Bahar Chowdhury
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Wheaton L Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Nardjis Amiour
- Institut National de Recherche pour l'Agriculure, l'Alimentation et l'Envionnement (INRAE), Centre de Versailles-Grignon, Versailles cedex, France
| | - Isabelle Quilleré
- Institut National de Recherche pour l'Agriculure, l'Alimentation et l'Envionnement (INRAE), Centre de Versailles-Grignon, Versailles cedex, France
| | - Bertrand Hirel
- Institut National de Recherche pour l'Agriculure, l'Alimentation et l'Envionnement (INRAE), Centre de Versailles-Grignon, Versailles cedex, France
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Root and Rhizobiome Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
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19
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Shameer S, Wang Y, Bota P, Ratcliffe RG, Long SP, Sweetlove LJ. A hybrid kinetic and constraint-based model of leaf metabolism allows predictions of metabolic fluxes in different environments. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:295-313. [PMID: 34699645 DOI: 10.1111/tpj.15551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 10/08/2021] [Accepted: 10/20/2021] [Indexed: 06/13/2023]
Abstract
While flux balance analysis (FBA) provides a framework for predicting steady-state leaf metabolic network fluxes, it does not readily capture the response to environmental variables without being coupled to other modelling formulations. To address this, we coupled an FBA model of 903 reactions of soybean (Glycine max) leaf metabolism with e-photosynthesis, a dynamic model that captures the kinetics of 126 reactions of photosynthesis and associated chloroplast carbon metabolism. Successful coupling was achieved in an iterative formulation in which fluxes from e-photosynthesis were used to constrain the FBA model and then, in turn, fluxes computed from the FBA model used to update parameters in e-photosynthesis. This process was repeated until common fluxes in the two models converged. Coupling did not hamper the ability of the kinetic module to accurately predict the carbon assimilation rate, photosystem II electron flux, and starch accumulation of field-grown soybean at two CO2 concentrations. The coupled model also allowed accurate predictions of additional parameters such as nocturnal respiration, as well as analysis of the effect of light intensity and elevated CO2 on leaf metabolism. Predictions included an unexpected decrease in the rate of export of sucrose from the leaf at high light, due to altered starch-sucrose partitioning, and altered daytime flux modes in the tricarboxylic acid cycle at elevated CO2 . Mitochondrial fluxes were notably different between growing and mature leaves, with greater anaplerotic, tricarboxylic acid cycle and mitochondrial ATP synthase fluxes predicted in the former, primarily to provide carbon skeletons and energy for protein synthesis.
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Affiliation(s)
- Sanu Shameer
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Yu Wang
- Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Pedro Bota
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - R George Ratcliffe
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Stephen P Long
- Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
| | - Lee J Sweetlove
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
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20
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Krantz M, Zimmer D, Adler SO, Kitashova A, Klipp E, Mühlhaus T, Nägele T. Data Management and Modeling in Plant Biology. FRONTIERS IN PLANT SCIENCE 2021; 12:717958. [PMID: 34539712 PMCID: PMC8446634 DOI: 10.3389/fpls.2021.717958] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/29/2021] [Indexed: 05/25/2023]
Abstract
The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.
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Affiliation(s)
- Maria Krantz
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Zimmer
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Stephan O. Adler
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Anastasia Kitashova
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| | - Edda Klipp
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Thomas Nägele
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
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21
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Sundermann EM, Lercher MJ, Heckmann D. Modeling photosynthetic resource allocation connects physiology with evolutionary environments. Sci Rep 2021; 11:15979. [PMID: 34354112 PMCID: PMC8342476 DOI: 10.1038/s41598-021-94903-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 07/15/2021] [Indexed: 11/12/2022] Open
Abstract
The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extent do they reflect ancestral environments? Here, we explore these questions for C3, C4, and C3–C4 intermediate plants of the model genus Flaveria. We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns. Models of C4 plants optimized for conditions experienced by evolutionary ancestors perform better than models accounting for experimental growth conditions, indicating low phenotypic plasticity. Supporting this interpretation, the model predicts that C4 species need to re-allocate more nitrogen between photosynthetic components than C3 species to adapt to new environments. We thus hypothesize that observed resource distribution patterns in C4 plants still reflect optimality in ancestral environments, allowing the quantitative inference of these environments from today’s plants. Our work allows us to quantify environmental effects on photosynthetic resource allocation and performance in the light of evolutionary history.
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Affiliation(s)
- Esther M Sundermann
- Institute for Computer Science and Department of Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany.
| | - David Heckmann
- Institute for Computer Science and Department of Biology, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany.
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22
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Elucidation of trophic interactions in an unusual single-cell nitrogen-fixing symbiosis using metabolic modeling. PLoS Comput Biol 2021; 17:e1008983. [PMID: 33961619 PMCID: PMC8143392 DOI: 10.1371/journal.pcbi.1008983] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 05/24/2021] [Accepted: 04/20/2021] [Indexed: 12/15/2022] Open
Abstract
Marine nitrogen-fixing microorganisms are an important source of fixed nitrogen in oceanic ecosystems. The colonial cyanobacterium Trichodesmium and diatom symbionts were thought to be the primary contributors to oceanic N2 fixation until the discovery of the unusual uncultivated symbiotic cyanobacterium UCYN-A (Candidatus Atelocyanobacterium thalassa). UCYN-A has atypical metabolic characteristics lacking the oxygen-evolving photosystem II, the tricarboxylic acid cycle, the carbon-fixation enzyme RuBisCo and de novo biosynthetic pathways for a number of amino acids and nucleotides. Therefore, it is obligately symbiotic with its single-celled haptophyte algal host. UCYN-A receives fixed carbon from its host and returns fixed nitrogen, but further insights into this symbiosis are precluded by both UCYN-A and its host being uncultured. In order to investigate how this syntrophy is coordinated, we reconstructed bottom-up genome-scale metabolic models of UCYN-A and its algal partner to explore possible trophic scenarios, focusing on nitrogen fixation and biomass synthesis. Since both partners are uncultivated and only the genome sequence of UCYN-A is available, we used the phylogenetically related Chrysochromulina tobin as a proxy for the host. Through the use of flux balance analysis (FBA), we determined the minimal set of metabolites and biochemical functions that must be shared between the two organisms to ensure viability and growth. We quantitatively investigated the metabolic characteristics that facilitate daytime N2 fixation in UCYN-A and possible oxygen-scavenging mechanisms needed to create an anaerobic environment to allow nitrogenase to function. This is the first application of an FBA framework to examine the tight metabolic coupling between uncultivated microbes in marine symbiotic communities and provides a roadmap for future efforts focusing on such specialized systems. Reduction of dinitrogen gas to biologically useful forms via nitrogen fixation is a key component of the biogeochemical cycle. In the marine environment, the cyanobacteria UCYN-A (Candidatus Atelocyanobacterium thalassa) has been found to be a primary contributor to biological nitrogen fixation at a global scale. UCYN-A exhibits a highly streamlined genome which lacks genes coding for essential cyanobacterial processes such as the energy-generating TCA cycle, oxygen-producing photosystem II, the carbon-fixing RuBisCo and de novo production pathways for numerous amino acids and nucleotides. Thus, it exists in a symbiosis with unicellular planktonic algae where it exchanges fixed nitrogen for fixed carbon with its host. However, both UCYN-A and its symbiotic partner remain uncultured under laboratory conditions. This necessitates implementing a computational approach to glean insights into UCYN-A’s unique physiology and metabolic processes governing the symbiotic association. To this end, we develop a constraints-based framework that infers all possible trophic scenarios consistent with the observed data. Possible mechanisms employed by UCYN-A to accommodate diazotrophy with daytime carbon fixation by the host (i.e., two mutually incompatible processes) are also elucidated. We envision that the developed framework using UCYN-A and its algal host will be used as a roadmap and motivate the study of similarly unique microbial systems in the future.
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23
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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: 8] [Impact Index Per Article: 2.7] [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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24
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Hao Y, Mabry ME, Edger PP, Freeling M, Zheng C, Jin L, VanBuren R, Colle M, An H, Abrahams RS, Washburn JD, Qi X, Barry K, Daum C, Shu S, Schmutz J, Sankoff D, Barker MS, Lyons E, Pires JC, Conant GC. The contributions from the progenitor genomes of the mesopolyploid Brassiceae are evolutionarily distinct but functionally compatible. Genome Res 2021; 31:799-810. [PMID: 33863805 PMCID: PMC8092008 DOI: 10.1101/gr.270033.120] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 03/05/2021] [Indexed: 01/08/2023]
Abstract
The members of the tribe Brassiceae share a whole-genome triplication (WGT), and one proposed model for its formation is a two-step pair of hybridizations producing hexaploid descendants. However, evidence for this model is incomplete, and the evolutionary and functional constraints that drove evolution after the hexaploidy are even less understood. Here, we report a new genome sequence of Crambe hispanica, a species sister to most sequenced Brassiceae. Using this new genome and three others that share the hexaploidy, we traced the history of gene loss after the WGT using the Polyploidy Orthology Inference Tool (POInT). We confirm the two-step formation model and infer that there was a significant temporal gap between those two allopolyploidizations, with about a third of the gene losses from the first two subgenomes occurring before the arrival of the third. We also, for the 90,000 individual genes in our study, make parental subgenome assignments, inferring, with measured uncertainty, from which of the progenitor genomes of the allohexaploidy each gene derives. We further show that each subgenome has a statistically distinguishable rate of homoeolog losses. There is little indication of functional distinction between the three subgenomes: the individual subgenomes show no patterns of functional enrichment, no excess of shared protein-protein or metabolic interactions between their members, and no biases in their likelihood of having experienced a recent selective sweep. We propose a "mix and match" model of allopolyploidy, in which subgenome origin drives homoeolog loss propensities but where genes from different subgenomes function together without difficulty.
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Affiliation(s)
- Yue Hao
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Makenzie E Mabry
- Division of Biological Sciences, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Patrick P Edger
- Department of Horticulture, Michigan State University, East Lansing, Michigan 48824, USA
- Genetics and Genome Sciences, Michigan State University, East Lansing, Michigan 48824, USA
| | - Michael Freeling
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Chunfang Zheng
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Lingling Jin
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5C9, Canada
| | - Robert VanBuren
- Department of Horticulture, Michigan State University, East Lansing, Michigan 48824, USA
- Plant Resilience Institute, Michigan State University, East Lansing, Michigan 48824, USA
| | - Marivi Colle
- Department of Horticulture, Michigan State University, East Lansing, Michigan 48824, USA
| | - Hong An
- Division of Biological Sciences, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - R Shawn Abrahams
- Division of Biological Sciences, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Jacob D Washburn
- Plant Genetics Research Unit, USDA-ARS, Columbia, Missouri 65211, USA
| | - Xinshuai Qi
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA
| | - Kerrie Barry
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Christopher Daum
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Shengqiang Shu
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Jeremy Schmutz
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | - David Sankoff
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Michael S Barker
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA
| | - Eric Lyons
- School of Plant Sciences, University of Arizona, Tucson, Arizona 85721, USA
- BIO5 Institute, University of Arizona, Tucson, Arizona 85721, USA
| | - J Chris Pires
- Division of Biological Sciences, University of Missouri-Columbia, Columbia, Missouri 65211, USA
- Informatics Institute, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Gavin C Conant
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, USA
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
- Division of Animal Sciences, University of Missouri-Columbia, Columbia, Missouri 65211, USA
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25
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Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava. Sci Rep 2021; 11:8758. [PMID: 33888810 PMCID: PMC8062692 DOI: 10.1038/s41598-021-88129-3] [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: 10/12/2020] [Accepted: 04/08/2021] [Indexed: 02/02/2023] Open
Abstract
The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.
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26
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Environment-coupled models of leaf metabolism. Biochem Soc Trans 2021; 49:119-129. [PMID: 33492365 PMCID: PMC7925006 DOI: 10.1042/bst20200059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/30/2020] [Accepted: 12/17/2020] [Indexed: 12/15/2022]
Abstract
The plant leaf is the main site of photosynthesis. This process converts light energy and inorganic nutrients into chemical energy and organic building blocks for the biosynthesis and maintenance of cellular components and to support the growth of the rest of the plant. The leaf is also the site of gas–water exchange and due to its large surface, it is particularly vulnerable to pathogen attacks. Therefore, the leaf's performance and metabolic modes are inherently determined by its interaction with the environment. Mathematical models of plant metabolism have been successfully applied to study various aspects of photosynthesis, carbon and nitrogen assimilation and metabolism, aided suggesting metabolic intervention strategies for optimized leaf performance, and gave us insights into evolutionary drivers of plant metabolism in various environments. With the increasing pressure to improve agricultural performance in current and future climates, these models have become important tools to improve our understanding of plant–environment interactions and to propel plant breeders efforts. This overview article reviews applications of large-scale metabolic models of leaf metabolism to study plant–environment interactions by means of flux-balance analysis. The presented studies are organized in two ways — by the way the environment interactions are modelled — via external constraints or data-integration and by the studied environmental interactions — abiotic or biotic.
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Rai KK, Pandey N, Meena RP, Rai SP. Biotechnological strategies for enhancing heavy metal tolerance in neglected and underutilized legume crops: A comprehensive review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 208:111750. [PMID: 33396075 DOI: 10.1016/j.ecoenv.2020.111750] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 11/27/2020] [Accepted: 11/29/2020] [Indexed: 05/15/2023]
Abstract
Contamination of agricultural land and water by heavy metals due to rapid industrialization and urbanization including various natural processes have become one of the major constraints to crop growth and productivity. Several studies have reported that to counteract heavy metal stress, plants should be able to maneuver various physiological, biochemical and molecular processes to improve their growth and development under heavy metal stress. With the advent of modern biotechnological tools and techniques it is now possible to tailor legume and other plants overexpressing stress-induced genes, transcription factors, proteins, and metabolites that are directly involved in heavy metal stress tolerance. This review provides an in-depth overview of various biotechnological approaches and/or strategies that can be used for enhancing detoxification of the heavy metals by stimulating phytoremediation processes. Synthetic biology tools involved in the engineering of legume and other crop plants against heavy metal stress tolerance are also discussed herewith some pioneering examples where synthetic biology tools that have been used to modify plants for specific traits. Also, CRISPR based genetic engineering of plants, including their role in modulating the expression of several genes/ transcription factors in the improvement of abiotic stress tolerance and phytoremediation ability using knockdown and knockout strategies has also been critically discussed.
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Affiliation(s)
- Krishna Kumar Rai
- Centre of Advance Study in Botany, Department of Botany, Institute of Science, Banaras Hindu University (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Neha Pandey
- Centre of Advance Study in Botany, Department of Botany, Institute of Science, Banaras Hindu University (BHU), Varanasi 221005, Uttar Pradesh, India; Department of Botany, CMP PG College, University of Allahabad, Prayagraj, India
| | - Ram Prasad Meena
- Centre of Advance Study in Botany, Department of Botany, Institute of Science, Banaras Hindu University (BHU), Varanasi 221005, Uttar Pradesh, India; Department of Computer Science, IIT, Banaras Hindu University (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Shashi Pandey Rai
- Centre of Advance Study in Botany, Department of Botany, Institute of Science, Banaras Hindu University (BHU), Varanasi 221005, Uttar Pradesh, India.
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Daloso DDM, Williams TCR. Current Challenges in Plant Systems Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1346:155-170. [DOI: 10.1007/978-3-030-80352-0_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Alsiyabi A, Solis AG, Cahoon EB, Saha R. Dissecting the regulatory roles of ORM proteins in the sphingolipid pathway of plants. PLoS Comput Biol 2021; 17:e1008284. [PMID: 33507896 PMCID: PMC7872301 DOI: 10.1371/journal.pcbi.1008284] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 02/09/2021] [Accepted: 12/14/2020] [Indexed: 01/05/2023] Open
Abstract
Sphingolipids are a vital component of plant cellular endomembranes and carry out multiple functional and regulatory roles. Different sphingolipid species confer rigidity to the membrane structure, facilitate trafficking of secretory proteins, and initiate programmed cell death. Although the regulation of the sphingolipid pathway is yet to be uncovered, increasing evidence has pointed to orosomucoid proteins (ORMs) playing a major regulatory role and potentially interacting with a number of components in the pathway, including both enzymes and sphingolipids. However, experimental exploration of new regulatory interactions is time consuming and often infeasible. In this work, a computational approach was taken to address this challenge. A metabolic network of the sphingolipid pathway in plants was reconstructed. The steady-state rates of reactions in the network were then determined through measurements of growth and cellular composition of the different sphingolipids in Arabidopsis seedlings. The Ensemble modeling framework was modified to accurately account for activation mechanisms and subsequently used to generate sets of kinetic parameters that converge to the measured steady-state fluxes in a thermodynamically consistent manner. In addition, the framework was appended with an additional module to automate screening the parameters and to output models consistent with previously reported network responses to different perturbations. By analyzing the network's response in the presence of different combinations of regulatory mechanisms, the model captured the experimentally observed repressive effect of ORMs on serine palmitoyltransferase (SPT). Furthermore, predictions point to a second regulatory role of ORM proteins, namely as an activator of class II (or LOH1 and LOH3) ceramide synthases. This activating role was found to be modulated by the concentration of free ceramides, where an accumulation of these sphingolipid species dampened the activating effect of ORMs on ceramide synthase. The predictions pave the way for future guided experiments and have implications in engineering crops with higher biotic stress tolerance.
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Affiliation(s)
- Adil Alsiyabi
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
| | - Ariadna Gonzalez Solis
- Center for Plant Science Innovation & Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
| | - Edgar B. Cahoon
- Center for Plant Science Innovation & Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
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30
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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31
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Yang X, Medford JI, Markel K, Shih PM, De Paoli HC, Trinh CT, McCormick AJ, Ployet R, Hussey SG, Myburg AA, Jensen PE, Hassan MM, Zhang J, Muchero W, Kalluri UC, Yin H, Zhuo R, Abraham PE, Chen JG, Weston DJ, Yang Y, Liu D, Li Y, Labbe J, Yang B, Lee JH, Cottingham RW, Martin S, Lu M, Tschaplinski TJ, Yuan G, Lu H, Ranjan P, Mitchell JC, Wullschleger SD, Tuskan GA. Plant Biosystems Design Research Roadmap 1.0. BIODESIGN RESEARCH 2020; 2020:8051764. [PMID: 37849899 PMCID: PMC10521729 DOI: 10.34133/2020/8051764] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 10/30/2020] [Indexed: 10/19/2023] Open
Abstract
Human life intimately depends on plants for food, biomaterials, health, energy, and a sustainable environment. Various plants have been genetically improved mostly through breeding, along with limited modification via genetic engineering, yet they are still not able to meet the ever-increasing needs, in terms of both quantity and quality, resulting from the rapid increase in world population and expected standards of living. A step change that may address these challenges would be to expand the potential of plants using biosystems design approaches. This represents a shift in plant science research from relatively simple trial-and-error approaches to innovative strategies based on predictive models of biological systems. Plant biosystems design seeks to accelerate plant genetic improvement using genome editing and genetic circuit engineering or create novel plant systems through de novo synthesis of plant genomes. From this perspective, we present a comprehensive roadmap of plant biosystems design covering theories, principles, and technical methods, along with potential applications in basic and applied plant biology research. We highlight current challenges, future opportunities, and research priorities, along with a framework for international collaboration, towards rapid advancement of this emerging interdisciplinary area of research. Finally, we discuss the importance of social responsibility in utilizing plant biosystems design and suggest strategies for improving public perception, trust, and acceptance.
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Affiliation(s)
- Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - June I. Medford
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Kasey Markel
- Department of Plant Biology, University of California, Davis, Davis, CA, USA
| | - Patrick M. Shih
- Department of Plant Biology, University of California, Davis, Davis, CA, USA
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, CA, USA
| | - Henrique C. De Paoli
- Department of Biodesign, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Cong T. Trinh
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN 37996, USA
| | - Alistair J. McCormick
- SynthSys and Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Raphael Ployet
- Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria 0002, South Africa
| | - Steven G. Hussey
- Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria 0002, South Africa
| | - Alexander A. Myburg
- Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria 0002, South Africa
| | - Poul Erik Jensen
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, DK-1858, Frederiksberg, Copenhagen, Denmark
| | - Md Mahmudul Hassan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jin Zhang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- State Key Laboratory of Subtropical Silviculture, School of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Udaya C. Kalluri
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Hengfu Yin
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, Zhejiang 311400, China
| | - Renying Zhuo
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, Zhejiang 311400, China
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jin-Gui Chen
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - David J. Weston
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Yinong Yang
- Department of Plant Pathology and Environmental Microbiology and the Huck Institute of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Degao Liu
- Department of Genetics, Cell Biology and Development, Center for Precision Plant Genomics and Center for Genome Engineering, University of Minnesota, Saint Paul, MN 55108, USA
| | - Yi Li
- Department of Plant Science and Landscape Architecture, University of Connecticut, Storrs, CT 06269, USA
| | - Jessy Labbe
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Bing Yang
- Division of Plant Sciences, Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Jun Hyung Lee
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | | | - Stanton Martin
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Mengzhu Lu
- State Key Laboratory of Subtropical Silviculture, School of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China
| | - Timothy J. Tschaplinski
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Guoliang Yuan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Haiwei Lu
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Priya Ranjan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Julie C. Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Stan D. Wullschleger
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Gerald A. Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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32
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Küken A, Gennermann K, Nikoloski Z. Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:2168-2177. [PMID: 32656814 DOI: 10.1111/tpj.14890] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 05/06/2020] [Accepted: 06/09/2020] [Indexed: 06/11/2023]
Abstract
Availability of plant-specific enzyme kinetic data is scarce, limiting the predictive power of metabolic models and precluding identification of genetic factors of enzyme properties. Enzyme kinetic data are measured in vitro, often under non-physiological conditions, and conclusions elicited from modeling warrant caution. Here we estimate maximal in vivo catalytic rates for 168 plant enzymes, including photosystems I and II, cytochrome-b6f complex, ATP-citrate synthase, sucrose-phosphate synthase as well as enzymes from amino acid synthesis with previously undocumented enzyme kinetic data in BRENDA. The estimations are obtained by integrating condition-specific quantitative proteomics data, maximal rates of selected enzymes, growth measurements from Arabidopsis thaliana rosette with and fluxes through canonical pathways in a constraint-based model of leaf metabolism. In comparison to findings in Escherichia coli, we demonstrate weaker concordance between the plant-specific in vitro and in vivo enzyme catalytic rates due to a low degree of enzyme saturation. This is supported by the finding that concentrations of nicotinamide adenine dinucleotide (phosphate), adenosine triphosphate and uridine triphosphate, calculated based on our maximal in vivo catalytic rates, and available quantitative metabolomics data are below reported KM values and, therefore, indicate undersaturation of respective enzymes. Our findings show that genome-wide profiling of enzyme kinetic properties is feasible in plants, paving the way for understanding resource allocation.
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Affiliation(s)
- Anika Küken
- System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam-Golm, Germany
| | - Kristin Gennermann
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam-Golm, Germany
| | - Zoran Nikoloski
- System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam-Golm, Germany
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33
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Correa SM, Alseekh S, Atehortúa L, Brotman Y, Ríos-Estepa R, Fernie AR, Nikoloski Z. Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 104:76-95. [PMID: 33001507 DOI: 10.1111/tpj.14906] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 06/03/2020] [Accepted: 06/12/2020] [Indexed: 06/11/2023]
Abstract
Efficient approaches to increase plant lipid production are necessary to meet current industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires an understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom-up metabolic reconstruction of J. curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint-based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping stone for future rational optimization of other agronomically relevant traits in J. curcas.
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Affiliation(s)
- Sandra M Correa
- Genetics of Metabolic Traits Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Grupo de Biotecnología, Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín, 050010, Colombia
| | - Saleh Alseekh
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Centre for Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Lucía Atehortúa
- Grupo de Biotecnología, Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín, 050010, Colombia
| | - Yariv Brotman
- Genetics of Metabolic Traits Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Rigoberto Ríos-Estepa
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia, Medellín, 050010, Colombia
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
- Centre for Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Zoran Nikoloski
- Centre for Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, 14476, Germany
- Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, 14476, Germany
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Fitzpatrick TB, Chapman LM. The importance of thiamine (vitamin B 1) in plant health: From crop yield to biofortification. J Biol Chem 2020; 295:12002-12013. [PMID: 32554808 PMCID: PMC7443482 DOI: 10.1074/jbc.rev120.010918] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/17/2020] [Indexed: 12/14/2022] Open
Abstract
Ensuring that people have access to sufficient and nutritious food is necessary for a healthy life and the core tenet of food security. With the global population set to reach 9.8 billion by 2050, and the compounding effects of climate change, the planet is facing challenges that necessitate significant and rapid changes in agricultural practices. In the effort to provide food in terms of calories, the essential contribution of micronutrients (vitamins and minerals) to nutrition is often overlooked. Here, we focus on the importance of thiamine (vitamin B1) in plant health and discuss its impact on human health. Vitamin B1 is an essential dietary component, and deficiencies in this micronutrient underlie several diseases, notably nervous system disorders. The predominant source of dietary vitamin B1 is plant-based foods. Moreover, vitamin B1 is also vital for plants themselves, and its benefits in plant health have received less attention than in the human health sphere. In general, vitamin B1 is well-characterized for its role as a coenzyme in metabolic pathways, particularly those involved in energy production and central metabolism, including carbon assimilation and respiration. Vitamin B1 is also emerging as an important component of plant stress responses, and several noncoenzyme roles of this vitamin are being characterized. We summarize the importance of vitamin B1 in plants from the perspective of food security, including its roles in plant disease resistance, stress tolerance, and crop yield, and review the potential benefits of biofortification of crops with increased vitamin B1 content to improve human health.
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Affiliation(s)
- Teresa B Fitzpatrick
- Department of Botany and Plant Biology, University of Geneva, Geneva, Switzerland.
| | - Lottie M Chapman
- Department of Botany and Plant Biology, University of Geneva, Geneva, Switzerland
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Dittami SM, Corre E, Brillet-Guéguen L, Lipinska AP, Pontoizeau N, Aite M, Avia K, Caron C, Cho CH, Collén J, Cormier A, Delage L, Doubleau S, Frioux C, Gobet A, González-Navarrete I, Groisillier A, Hervé C, Jollivet D, KleinJan H, Leblanc C, Liu X, Marie D, Markov GV, Minoche AE, Monsoor M, Pericard P, Perrineau MM, Peters AF, Siegel A, Siméon A, Trottier C, Yoon HS, Himmelbauer H, Boyen C, Tonon T. The genome of Ectocarpus subulatus - A highly stress-tolerant brown alga. Mar Genomics 2020; 52:100740. [PMID: 31937506 DOI: 10.1016/j.margen.2020.100740] [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] [Received: 12/11/2019] [Accepted: 01/01/2020] [Indexed: 11/20/2022]
Abstract
Brown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kützing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies.
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Affiliation(s)
- Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France.
| | - Erwan Corre
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Loraine Brillet-Guéguen
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Agnieszka P Lipinska
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Noé Pontoizeau
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Meziane Aite
- Univ Rennes, Inria, CNRS, IRISA, 35000 Rennes, France
| | - Komlan Avia
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; Université de Strasbourg, INRA, SVQV UMR-A 1131, F-68000 Colmar, France
| | - Christophe Caron
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Chung Hyun Cho
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Alexandre Cormier
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Sylvie Doubleau
- IRD, UMR DIADE, 911 Avenue Agropolis, BP 64501, 34394 Montpellier, France
| | | | - Angélique Gobet
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Irene González-Navarrete
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
| | - Agnès Groisillier
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Cécile Hervé
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Didier Jollivet
- Sorbonne Université, CNRS, Adaptation and Diversity in the Marine Environment (ADME), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Hetty KleinJan
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Xi Liu
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Dominique Marie
- Sorbonne Université, CNRS, Adaptation and Diversity in the Marine Environment (ADME), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - André E Minoche
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Misharl Monsoor
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Pierre Pericard
- CNRS, Sorbonne Université, FR2424, ABiMS platform, Station Biologique de Roscoff, 29680 Roscoff, France
| | - Marie-Mathilde Perrineau
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; Scottish Association for Marine Science, Scottish Marine Institute, Oban PA37 1QA, United Kingdom
| | | | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, 35000 Rennes, France
| | - Amandine Siméon
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Camille Trottier
- Univ Rennes, Inria, CNRS, IRISA, 35000 Rennes, France; Laboratory of Digital Sciences of Nantes (LS2N) - University of Nantes, France
| | - Hwan Su Yoon
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Heinz Himmelbauer
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany; Department of Biotechnology, University of Natural Resources and Life Sciences (BOKU), Vienna, 1190 Vienna, Austria
| | - Catherine Boyen
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Thierry Tonon
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff, 29680 Roscoff, France; Centre for Novel Agricultural Products, Department of Biology, University of York, York YO10 5DD, United Kingdom
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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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37
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Zamani Amirzakaria J, Malboobi MA, Marashi SA, Lohrasebi T. In silico prediction of enzymatic reactions catalyzed by acid phosphatases. J Biomol Struct Dyn 2020; 39:3900-3911. [PMID: 32615050 DOI: 10.1080/07391102.2020.1785943] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In present work, we describe a methodology for prediction of an enzymatic reaction for which no experimental data are available except for a gene sequence. As a challenging case, we have developed the method for identifying the putative substrates of monoester phosphatases, commonly known as acid phosphatase enzymes, which have no strong substrate specificity. Finding a preferable substrate for each one is an important task to unravel pathways involved in plant phosphate metabolism. Having used an Arabidopsis thaliana haloacid dehalogenase (HAD)-related acid phosphatases, HRP9, with an experimentally known structure and preferred substrate as an instance, we firstly predicted the 3 D-structure of HRP1 for subsequent analysis. Then, molecular docking was used to find the best protein interaction with a ligand existing in a set of possible substrates compiled from genome scale metabolic networks of A. thaliana based on binding energy, binding mode as well as the distance between phosphoric ester and cofactor, Mg2+, localized in the active site of HRP1. Molecular dynamics simulation ratified stable protein-ligand complex model. Our analysis predicted HRP1 preferably bind to pyridoxamine-5'-phosphate (PMP). Thus, it is deduced that the conversion of PMP to pyridoxamine must be catalyzed by HRP1. This procedure is expected to make a reliable pipeline to predict the enzymatic reactions catalyzed by acid phosphatases. Taken as a whole, it could be applicable for discovery of the interacting ligands, inhibitors as well as interacting proteins which limits lab works or used for gap filling in biosystems.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Javad Zamani Amirzakaria
- Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Mohammad Ali Malboobi
- Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, Faculty of Science, University of Tehran, Tehran, Iran
| | - Tahmineh Lohrasebi
- Department of Plant Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
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Sarkar D, Maranas CD. SNPeffect: identifying functional roles of SNPs using metabolic networks. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:512-531. [PMID: 32167625 PMCID: PMC9328443 DOI: 10.1111/tpj.14746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/20/2020] [Indexed: 05/04/2023]
Abstract
Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.
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Affiliation(s)
- Debolina Sarkar
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
| | - Costas D. Maranas
- Department of Chemical EngineeringPennsylvania State UniversityUniversity ParkPAUSA
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39
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Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host-microbial interactions. Biochem Soc Trans 2020; 48:901-913. [PMID: 32379295 DOI: 10.1042/bst20190667] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/01/2020] [Accepted: 04/03/2020] [Indexed: 01/24/2023]
Abstract
Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.
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40
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Schroeder WL, Saha R. Introducing an Optimization- and explicit Runge-Kutta- based Approach to Perform Dynamic Flux Balance Analysis. Sci Rep 2020; 10:9241. [PMID: 32514037 PMCID: PMC7280247 DOI: 10.1038/s41598-020-65457-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 05/04/2020] [Indexed: 11/17/2022] Open
Abstract
In this work we introduce the generalized Optimization- and explicit Runge-Kutta-based Approach (ORKA) to perform dynamic Flux Balance Analysis (dFBA), which is numerically more accurate and computationally tractable than existing approaches. ORKA is applied to a four-tissue (leaf, root, seed, and stem) model of Arabidopsis thaliana, p-ath773, uniquely capturing the core-metabolism of several stages of growth from seedling to senescence at hourly intervals. Model p-ath773 has been designed to show broad agreement with published plant-scale properties such as mass, maintenance, and senescence, yet leaving reaction-level behavior unconstrainted. Hence, it serves as a framework to study the reaction-level behavior necessary for observed plant-scale behavior. Two such case studies of reaction-level behavior include the lifecycle progression of sulfur metabolism and the diurnal flow of water throughout the plant. Specifically, p-ath773 shows how transpiration drives water flow through the plant and how water produced by leaf tissue metabolism may contribute significantly to transpired water. Investigation of sulfur metabolism elucidates frequent cross-compartment exchange of a standing pool of amino acids which is used to regulate the proton flow. Overall, p-ath773 and ORKA serve as scaffolds for dFBA-based lifecycle modeling of plants and other systems to further broaden the scope of in silico metabolic investigation.
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Affiliation(s)
- Wheaton L Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, USA.
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Drought Stress Responses in Context-Specific Genome-Scale Metabolic Models of Arabidopsis thaliana. Metabolites 2020; 10:metabo10040159. [PMID: 32325728 PMCID: PMC7241242 DOI: 10.3390/metabo10040159] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 03/26/2020] [Accepted: 04/16/2020] [Indexed: 01/08/2023] Open
Abstract
Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the metabolic pathways of Arabidopsis thaliana which respond to drought stress by omics-based in silico analyses. We proposed an analysis pipeline to understand metabolism under specific conditions based on a genome-scale metabolic model (GEM). Context-specific GEMs under drought and well-watered control conditions were reconstructed using transcriptome data and examined using metabolome data. The metabolic fluxes throughout the metabolic network were estimated by flux balance analysis using the context-specific GEMs. We used in silico methods to identify an important reaction contributing to biomass production and clarified metabolic reaction responses under drought stress by comparative analysis between drought and control conditions. This proposed pipeline can be applied in other studies to understand metabolic changes under specific conditions using Arabidopsis GEM or other available plant GEMs.
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42
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Vos RA, Katayama T, Mishima H, Kawano S, Kawashima S, Kim JD, Moriya Y, Tokimatsu T, Yamaguchi A, Yamamoto Y, Wu H, Amstutz P, Antezana E, Aoki NP, Arakawa K, Bolleman JT, Bolton E, Bonnal RJP, Bono H, Burger K, Chiba H, Cohen KB, Deutsch EW, Fernández-Breis JT, Fu G, Fujisawa T, Fukushima A, García A, Goto N, Groza T, Hercus C, Hoehndorf R, Itaya K, Juty N, Kawashima T, Kim JH, Kinjo AR, Kotera M, Kozaki K, Kumagai S, Kushida T, Lütteke T, Matsubara M, Miyamoto J, Mohsen A, Mori H, Naito Y, Nakazato T, Nguyen-Xuan J, Nishida K, Nishida N, Nishide H, Ogishima S, Ohta T, Okuda S, Paten B, Perret JL, Prathipati P, Prins P, Queralt-Rosinach N, Shinmachi D, Suzuki S, Tabata T, Takatsuki T, Taylor K, Thompson M, Uchiyama I, Vieira B, Wei CH, Wilkinson M, Yamada I, Yamanaka R, Yoshitake K, Yoshizawa AC, Dumontier M, Kosaki K, Takagi T. BioHackathon 2015: Semantics of data for life sciences and reproducible research. F1000Res 2020; 9:136. [PMID: 32308977 PMCID: PMC7141167 DOI: 10.12688/f1000research.18236.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/05/2020] [Indexed: 01/08/2023] Open
Abstract
We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.
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Affiliation(s)
- Rutger A. Vos
- Institute of Biology Leiden, Leiden University, Leiden, The Netherlands
- Naturalis Biodiversity Center, Leiden, The Netherlands
| | | | - Hiroyuki Mishima
- Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shin Kawano
- Database Center for Life Science, Tokyo, Japan
| | | | | | - Yuki Moriya
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Hongyan Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Erick Antezana
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nobuyuki P. Aoki
- Faculty of Science and Engineering, SOKA University, Tokyo, Japan
| | - Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Jerven T. Bolleman
- SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Lausanne, Switzerland
| | - Evan Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Raoul J. P. Bonnal
- Istituto Nazionale Genetica Molecolare, Romeo ed Enrica Invernizzi, Milan, Italy
| | | | - Kees Burger
- Dutch Techcentre for Life Sciences, Utrecht, The Netherlands
| | - Hirokazu Chiba
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Kevin B. Cohen
- Computational Bioscience Program, University of Colorado School of Medicine, Denver, USA
- Université Paris-Saclay, LIMSI, CNRS, Paris, France
| | | | | | - Gang Fu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | | | | | | | - Naohisa Goto
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Tudor Groza
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - Colin Hercus
- Novocraft Technologies Sdn. Bhd., Selangor, Malaysia
| | - Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Kotone Itaya
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Jee-Hyub Kim
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Akira R. Kinjo
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Masaaki Kotera
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Kouji Kozaki
- The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan
| | | | - Tatsuya Kushida
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Giessen, Germany
- Gesellschaft für innovative Personalwirtschaftssysteme mbH (GIP GmbH), Offenbach, Germany
| | | | | | - Attayeb Mohsen
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Hiroshi Mori
- Center for Information Biology, National Institute of Genetics, Mishima, Japan
| | - Yuki Naito
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Naoki Nishida
- Department of Systems Science, Osaka University, Osaka, Japan
| | - Hiroyo Nishide
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Tazro Ohta
- Database Center for Life Science, Tokyo, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, USA
| | | | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Pjotr Prins
- University Medical Center Utrecht, Utrecht, The Netherlands
- University of Tennessee Health Science Center, Memphis, USA
| | - Núria Queralt-Rosinach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Shinya Suzuki
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Tsuyosi Tabata
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | | | - Kieron Taylor
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Mark Thompson
- Leiden University Medical Center, Leiden, The Netherlands
| | - Ikuo Uchiyama
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Bruno Vieira
- WurmLab, School of Biological & Chemical Sciences, Queen Mary University of London, London, UK
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Mark Wilkinson
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Kazutoshi Yoshitake
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, The Netherlands
| | - Kenjiro Kosaki
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | - Toshihisa Takagi
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
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43
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Belcour A, Girard J, Aite M, Delage L, Trottier C, Marteau C, Leroux C, Dittami SM, Sauleau P, Corre E, Nicolas J, Boyen C, Leblanc C, Collén J, Siegel A, Markov GV. Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway Drift. iScience 2020; 23:100849. [PMID: 32058961 PMCID: PMC6997860 DOI: 10.1016/j.isci.2020.100849] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 11/11/2019] [Accepted: 01/13/2020] [Indexed: 11/03/2022] Open
Abstract
Inferring genome-scale metabolic networks in emerging model organisms is challenged by incomplete biochemical knowledge and partial conservation of biochemical pathways during evolution. Therefore, specific bioinformatic tools are necessary to infer biochemical reactions and metabolic structures that can be checked experimentally. Using an integrative approach combining genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterols or mycosporine-like amino acid synthesis pathways, undergo substantial turnover. This phenomenon, here formally defined as "metabolic pathway drift," is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We present a proof of concept with a methodological approach to formalize the logical reasoning necessary to infer reactions and molecular structures, abstracting molecular transformations based on previous biochemical knowledge.
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Affiliation(s)
- Arnaud Belcour
- Univ Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France
| | - Jean Girard
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Méziane Aite
- Univ Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | | | - Cédric Leroux
- Sorbonne Université, CNRS, Plateforme METABOMER-Corsaire (FR2424), Station Biologique de Roscoff, Roscoff, France
| | - Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Erwan Corre
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, Roscoff, France
| | - Jacques Nicolas
- Univ Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France
| | - Catherine Boyen
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France
| | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M, UMR8227), Station Biologique de Roscoff (SBR), 29680 Roscoff, France.
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44
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Shaw R, Cheung CYM. Multi-tissue to whole plant metabolic modelling. Cell Mol Life Sci 2020; 77:489-495. [PMID: 31748916 PMCID: PMC11104929 DOI: 10.1007/s00018-019-03384-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 11/06/2019] [Accepted: 11/12/2019] [Indexed: 10/25/2022]
Abstract
Genome-scale metabolic models have been successfully applied to study the metabolism of multiple plant species in the past decade. While most existing genome-scale modelling studies have focussed on studying the metabolic behaviour of individual plant metabolic systems, there is an increasing focus on combining models of multiple tissues or organs to produce multi-tissue models that allow the investigation of metabolic interactions between tissues and organs. Multi-tissue metabolic models were constructed for multiple plants including Arabidopsis, barley, soybean and Setaria. These models were applied to study various aspects of plant physiology including the division of labour between organs, source and sink tissue relationship, growth of different tissues and organs and charge and proton balancing. In this review, we outline the process of constructing multi-tissue genome-scale metabolic models, discuss the strengths and challenges in using multi-tissue models, review the current status of plant multi-tissue and whole plant metabolic models and explore the approaches for integrating genome-scale metabolic models into multi-scale plant models.
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Affiliation(s)
- Rahul Shaw
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, Singapore
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45
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Holzheu P, Kummer U. Computational systems biology of cellular processes in Arabidopsis thaliana: an overview. Cell Mol Life Sci 2020; 77:433-440. [PMID: 31768604 PMCID: PMC11105087 DOI: 10.1007/s00018-019-03379-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 11/11/2019] [Accepted: 11/12/2019] [Indexed: 02/06/2023]
Abstract
Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation and computational modeling. While there is already a large quantity of systems biology studies describing human, animal and especially microbial cell biological systems, plant biology has been lagging behind for many years. However, in the case of the model plant Arabidopsis thaliana, the steadily increasing amount of information on the levels of its genome, proteome and on a variety of its metabolic and signalling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated, showing also for plant sciences how fruitful systems biology research can be. In this review, we provide an overview over some of these recent studies which use different systems biological approaches to get a better understanding of the cell biology of A. thaliana. The approaches used in these are genome-scale metabolic modeling, as well as kinetic modeling of metabolic and signalling pathways. Furthermore, we selected several cases to exemplify how the modeling approaches have led to significant advances or new perspectives in the field.
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Affiliation(s)
- Pascal Holzheu
- INF 267 (Bioquant), Heidelberg University, 69120, Heidelberg, Germany
| | - Ursula Kummer
- INF 267 (Bioquant), Heidelberg University, 69120, Heidelberg, Germany.
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46
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Fachet M, Witte C, Flassig RJ, Rihko-Struckmann LK, McKie-Krisberg Z, Polle JEW, Sundmacher K. Reconstruction and analysis of a carbon-core metabolic network for Dunaliella salina. BMC Bioinformatics 2020; 21:1. [PMID: 31898485 PMCID: PMC6941287 DOI: 10.1186/s12859-019-3325-0] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 12/17/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The green microalga Dunaliella salina accumulates a high proportion of β-carotene during abiotic stress conditions. To better understand the intracellular flux distribution leading to carotenoid accumulation, this work aimed at reconstructing a carbon core metabolic network for D. salina CCAP 19/18 based on the recently published nuclear genome and its validation with experimental observations and literature data. RESULTS The reconstruction resulted in a network model with 221 reactions and 212 metabolites within three compartments: cytosol, chloroplast and mitochondrion. The network was implemented in the MATLAB toolbox CellNetAnalyzer and checked for feasibility. Furthermore, a flux balance analysis was carried out for different light and nutrient uptake rates. The comparison of the experimental knowledge with the model prediction revealed that the results of the stoichiometric network analysis are plausible and in good agreement with the observed behavior. Accordingly, our model provides an excellent tool for investigating the carbon core metabolism of D. salina. CONCLUSIONS The reconstructed metabolic network of D. salina presented in this work is able to predict the biological behavior under light and nutrient stress and will lead to an improved process understanding for the optimized production of high-value products in microalgae.
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Affiliation(s)
- Melanie Fachet
- Max Planck Institute for Dynamics of Complex Technical Systems, Process Systems Engineering, Sandtorstr. 1, Magdeburg, 39106, Germany
| | - Carina Witte
- Max Planck Institute for Dynamics of Complex Technical Systems, Process Systems Engineering, Sandtorstr. 1, Magdeburg, 39106, Germany
| | - Robert J Flassig
- Brandenburg University of Applied Sciences, Department of Engineering, Magdeburger Str. 50, Brandenburg an der Havel, 14770, Germany
| | - Liisa K Rihko-Struckmann
- Max Planck Institute for Dynamics of Complex Technical Systems, Process Systems Engineering, Sandtorstr. 1, Magdeburg, 39106, Germany.
| | - Zaid McKie-Krisberg
- Brooklyn College of the City University of New York, Department of Biology, 2900 Bedford Avenue, New York, NY 11210, USA
| | - Jürgen E W Polle
- Brooklyn College of the City University of New York, Department of Biology, 2900 Bedford Avenue, New York, NY 11210, USA
| | - Kai Sundmacher
- Max Planck Institute for Dynamics of Complex Technical Systems, Process Systems Engineering, Sandtorstr. 1, Magdeburg, 39106, Germany.,Otto von Guericke University Magdeburg, Process Systems Engineering, Universitätsplatz 2, Magdeburg, 39106, Germany
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47
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McGarrity S, Karvelsson ST, Sigurjónsson ÓE, Rolfsson Ó. Comparative Metabolic Network Flux Analysis to Identify Differences in Cellular Metabolism. Methods Mol Biol 2020; 2088:223-269. [PMID: 31893377 DOI: 10.1007/978-1-0716-0159-4_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Metabolic network flux analysis uses genome-scale metabolic reconstructions to integrate transcriptomics, proteomics, and/or metabolomics data to allow for comprehensive interpretation of genotype to metabolic phenotype relationships. The compilation of many Constraint-based model analysis methods into one MATLAB package, the COBRAtoolbox, has opened the possibility of using these methods to the many biologists with some knowledge of the commonly used statistical program, MATLAB. Here we outline the steps required to take a published genome-scale metabolic reconstruction and interrogate its consistency and biological feasibility. Subsequently, we demonstrate how mRNA expression data and metabolomics data, relating to one or more cell types or biological contexts, can be applied to constrain and generate metabolic models descriptive of metabolic flux phenotypes. Finally, we describe the comparison of the resulting models and model outputs with the aim of identifying metabolic biomarkers and changes in cellular metabolism.
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Affiliation(s)
- Sarah McGarrity
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Sigurður T Karvelsson
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Ólafur E Sigurjónsson
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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48
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Hattwell JPN, Hastings J, Casanueva O, Schirra HJ, Witting M. Using Genome-Scale Metabolic Networks for Analysis, Visualization, and Integration of Targeted Metabolomics Data. Methods Mol Biol 2020; 2104:361-386. [PMID: 31953826 DOI: 10.1007/978-1-0716-0239-3_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Interpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methods to analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented.
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Affiliation(s)
- Jake P N Hattwell
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Janna Hastings
- Department of Epigenetics, Babraham Institute, Cambridge, UK
| | | | | | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany. .,Analytical Food Chemistry, Technical University of Munich, Freising, Germany.
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49
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Blätke MA, Bräutigam A. Evolution of C4 photosynthesis predicted by constraint-based modelling. eLife 2019; 8:e49305. [PMID: 31799932 PMCID: PMC6905489 DOI: 10.7554/elife.49305] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 11/08/2019] [Indexed: 01/03/2023] Open
Abstract
Constraint-based modelling (CBM) is a powerful tool for the analysis of evolutionary trajectories. Evolution, especially evolution in the distant past, is not easily accessible to laboratory experimentation. Modelling can provide a window into evolutionary processes by allowing the examination of selective pressures which lead to particular optimal solutions in the model. To study the evolution of C4 photosynthesis from a ground state of C3 photosynthesis, we initially construct a C3 model. After duplication into two cells to reflect typical C4 leaf architecture, we allow the model to predict the optimal metabolic solution under various conditions. The model thus identifies resource limitation in conjunction with high photorespiratory flux as a selective pressure relevant to the evolution of C4. It also predicts that light availability and distribution play a role in guiding the evolutionary choice of possible decarboxylation enzymes. The data shows evolutionary CBM in eukaryotes predicts molecular evolution with precision.
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Affiliation(s)
- Mary-Ann Blätke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany
| | - Andrea Bräutigam
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany
- Computational Biology, Faculty of Biology, Bielefeld University, UniversitätsstraßeBielefeldGermany
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50
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Nègre D, Aite M, Belcour A, Frioux C, Brillet-Guéguen L, Liu X, Bordron P, Godfroy O, Lipinska AP, Leblanc C, Siegel A, Dittami SM, Corre E, Markov GV. Genome-Scale Metabolic Networks Shed Light on the Carotenoid Biosynthesis Pathway in the Brown Algae Saccharina japonica and Cladosiphon okamuranus. Antioxidants (Basel) 2019; 8:E564. [PMID: 31744163 PMCID: PMC6912245 DOI: 10.3390/antiox8110564] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/13/2019] [Accepted: 11/15/2019] [Indexed: 12/20/2022] Open
Abstract
Understanding growth mechanisms in brown algae is a current scientific and economic challenge that can benefit from the modeling of their metabolic networks. The sequencing of the genomes of Saccharina japonica and Cladosiphon okamuranus has provided the necessary data for the reconstruction of Genome-Scale Metabolic Networks (GSMNs). The same in silico method deployed for the GSMN reconstruction of Ectocarpus siliculosus to investigate the metabolic capabilities of these two algae, was used. Integrating metabolic profiling data from the literature, we provided functional GSMNs composed of an average of 2230 metabolites and 3370 reactions. Based on these GSMNs and previously published work, we propose a model for the biosynthetic pathways of the main carotenoids in these two algae. We highlight, on the one hand, the reactions and enzymes that have been preserved through evolution and, on the other hand, the specificities related to brown algae. Our data further indicate that, if abscisic acid is produced by Saccharina japonica, its biosynthesis pathway seems to be different in its final steps from that described in land plants. Thus, our work illustrates the potential of GSMNs reconstructions for formalizing hypotheses that can be further tested using targeted biochemical approaches.
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Affiliation(s)
- Delphine Nègre
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
- Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes, 9, Rue Bias, 44035 Nantes, France
| | - Méziane Aite
- Université de Rennes 1, Institute for Research in IT and Random Systems (IRISA), Equipe Dyliss, 35052 Rennes, France
| | - Arnaud Belcour
- Université de Rennes 1, Institute for Research in IT and Random Systems (IRISA), Equipe Dyliss, 35052 Rennes, France
| | - Clémence Frioux
- Université de Rennes 1, Institute for Research in IT and Random Systems (IRISA), Equipe Dyliss, 35052 Rennes, France
- Quadram Institute, Colney Lane, Norwich NR4 7UQ, UK
| | - Loraine Brillet-Guéguen
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Xi Liu
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Philippe Bordron
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Olivier Godfroy
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Agnieszka P. Lipinska
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Université de Rennes 1, Institute for Research in IT and Random Systems (IRISA), Equipe Dyliss, 35052 Rennes, France
| | - Simon M. Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Erwan Corre
- Sorbonne Université, CNRS, Plateforme ABiMS (FR2424), Station Biologique de Roscoff, 29680 Roscoff, France
| | - Gabriel V. Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
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