1
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Rao X, Barros J. Modeling lignin biosynthesis: a pathway to renewable chemicals. TRENDS IN PLANT SCIENCE 2024; 29:546-559. [PMID: 37802691 DOI: 10.1016/j.tplants.2023.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/01/2023] [Accepted: 09/18/2023] [Indexed: 10/08/2023]
Abstract
Plant biomass contains lignin that can be converted into high-value-added chemicals, fuels, and materials. The precise genetic manipulation of lignin content and composition in plant cells offers substantial environmental and economic benefits. However, the intricate regulatory mechanisms governing lignin formation challenge the development of crops with specific lignin profiles. Mathematical models and computational simulations have recently been employed to gain fundamental understanding of the metabolism of lignin and related phenolic compounds. This review article discusses the strategies used for modeling plant metabolic networks, focusing on the application of mathematical modeling for flux network analysis in monolignol biosynthesis. Furthermore, we highlight how current challenges might be overcome to optimize the use of metabolic modeling approaches for developing lignin-engineered plants.
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Affiliation(s)
- Xiaolan Rao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan 430062, China.
| | - Jaime Barros
- Division of Plant Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211, USA.
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2
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Kusch L, Diaz-Pier S, Klijn W, Sontheimer K, Bernard C, Morrison A, Jirsa V. Multiscale co-simulation design pattern for neuroscience applications. Front Neuroinform 2024; 18:1156683. [PMID: 38410682 PMCID: PMC10895016 DOI: 10.3389/fninf.2024.1156683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 01/19/2024] [Indexed: 02/28/2024] Open
Abstract
Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.
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Affiliation(s)
- Lionel Kusch
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wouter Klijn
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Kim Sontheimer
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Christophe Bernard
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Forschungszentrum Jülich GmbH, IAS-6/INM-6, JARA, Jülich, Germany
- Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
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3
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Li R, Chen S, Matsumoto H, Gouda M, Gafforov Y, Wang M, Liu Y. Predicting rice diseases using advanced technologies at different scales: present status and future perspectives. ABIOTECH 2023; 4:359-371. [PMID: 38106429 PMCID: PMC10721578 DOI: 10.1007/s42994-023-00126-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023]
Abstract
The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.
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Affiliation(s)
- Ruyue Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058 China
| | - Sishi Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| | - Haruna Matsumoto
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Department of Nutrition and Food Science, National Research Centre, Giza, 12622 Egypt
| | - Yusufjon Gafforov
- Central Asian Center for Development Studies, New Uzbekistan University, Tashkent, 100000 Uzbekistan
| | - Mengcen Wang
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
- Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo, 060-8589 Japan
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
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4
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Jaramillo-Botero A, Colorado J, Quimbaya M, Rebolledo MC, Lorieux M, Ghneim-Herrera T, Arango CA, Tobón LE, Finke J, Rocha C, Muñoz F, Riascos JJ, Silva F, Chirinda N, Caccamo M, Vandepoele K, Goddard WA. The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization. FRONTIERS IN PLANT SCIENCE 2022; 13:992663. [PMID: 36311093 PMCID: PMC9614048 DOI: 10.3389/fpls.2022.992663] [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: 07/12/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
The OMICAS alliance is part of the Colombian government's Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program's kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS' R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.
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Affiliation(s)
- Andres Jaramillo-Botero
- Chemistry and Chemical Engineering Division, California Institute of Technology, Pasadena, CA, United States
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
| | - Julian Colorado
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Facultad de Ingeniería, Departamento de Ingeniería Electrónica, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Mauricio Quimbaya
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Facultad de Ingeniería y Ciencias, Departamento de Ciencias Naturales y Matemáticas, Pontificia Universidad Javeriana, Cali, Colombia
| | - Maria Camila Rebolledo
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- CIRAD, UMR AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
- International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Mathias Lorieux
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- International Center for Tropical Agriculture (CIAT), Cali, Colombia
- DIADE, University of Montpellier, CIRAD, IRD, Montpellier, France
| | - Thaura Ghneim-Herrera
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Facultad de Ciencias Naturales, Departamento de Ciencias Biológicas, Universidad Icesi, Cali, Colombia
| | - Carlos A. Arango
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Facultad de Ciencias Naturales, Departamento de Ciencias Químicas, Universidad Icesi, Cali, Colombia
| | - Luis E. Tobón
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Facultad de Ingeniería y Ciencias, Departamento de Electrónica y Ciencias de la Computación, Pontificia Universidad Javeriana, Cali, Colombia
| | - Jorge Finke
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Facultad de Ingeniería y Ciencias, Departamento de Electrónica y Ciencias de la Computación, Pontificia Universidad Javeriana, Cali, Colombia
| | - Camilo Rocha
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Facultad de Ingeniería y Ciencias, Departamento de Electrónica y Ciencias de la Computación, Pontificia Universidad Javeriana, Cali, Colombia
| | - Fernando Muñoz
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Centro de Investigación de la Caña de Azúcar de Colombia, Centro de Investigación de la Caña de Azúcar (CENICAÑA), Cali, Colombia
| | - John J. Riascos
- Facultad de Ingeniería y Ciencias, Departamento de Electrónica y Ciencias de la Computación, Pontificia Universidad Javeriana, Cali, Colombia
- Vlaams Instituut voor Biotechnologie, Bioinformatics Systems Biology, Ghent University, Gent, Belgium
| | - Fernando Silva
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- Centro de Investigación de la Caña de Azúcar de Colombia, Centro de Investigación de la Caña de Azúcar (CENICAÑA), Cali, Colombia
| | - Ngonidzashe Chirinda
- Optimización Multiescala In-Silico de Cultivos Agrícolas Sostenibles (ÓMICAS) Alliance, Pontificia Universidad Javeriana, Cali, Colombia
- International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Mario Caccamo
- National Institute of Agricultural Botanics (NIAB), Cambridge, United Kingdom
| | - Klaas Vandepoele
- Vlaams Instituut voor Biotechnologie, Bioinformatics Systems Biology, Ghent University, Gent, Belgium
| | - William A. Goddard
- Chemistry and Chemical Engineering Division, California Institute of Technology, Pasadena, CA, United States
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5
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Islam MM, Goertzen A, Singh PK, Saha R. Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling. iScience 2022; 25:104483. [PMID: 35712079 PMCID: PMC9194136 DOI: 10.1016/j.isci.2022.104483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/08/2022] [Accepted: 05/23/2022] [Indexed: 11/18/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a major research focus because of its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism to the surrounding environment, often relying on diverse nutrient sources. Because traditional experimental techniques appear exhaustive to find a viable therapeutic strategy, a highly curated and omics-informed PDAC genome-scale metabolic model was reconstructed using patient-specific transcriptomics data. From the model-predictions, several new metabolic functions were explored as potential therapeutic targets in addition to the known metabolic hallmarks of PDAC. Significant downregulation in the peroxisomal beta oxidation pathway, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were correlated with potential drug combinations targeting genes with poor prognosis in PDAC. Overall, this study provides a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies.
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Affiliation(s)
- Mohammad Mazharul Islam
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Andrea Goertzen
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Pankaj K. Singh
- Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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6
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Beck AE, Kleiner M, Garrell AK. Elucidating Plant-Microbe-Environment Interactions Through Omics-Enabled Metabolic Modelling Using Synthetic Communities. FRONTIERS IN PLANT SCIENCE 2022; 13:910377. [PMID: 35795346 PMCID: PMC9251461 DOI: 10.3389/fpls.2022.910377] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/16/2022] [Indexed: 05/10/2023]
Abstract
With a growing world population and increasing frequency of climate disturbance events, we are in dire need of methods to improve plant productivity, resilience, and resistance to both abiotic and biotic stressors, both for agriculture and conservation efforts. Microorganisms play an essential role in supporting plant growth, environmental response, and susceptibility to disease. However, understanding the specific mechanisms by which microbes interact with each other and with plants to influence plant phenotypes is a major challenge due to the complexity of natural communities, simultaneous competition and cooperation effects, signalling interactions, and environmental impacts. Synthetic communities are a major asset in reducing the complexity of these systems by simplifying to dominant components and isolating specific variables for controlled experiments, yet there still remains a large gap in our understanding of plant microbiome interactions. This perspectives article presents a brief review discussing ways in which metabolic modelling can be used in combination with synthetic communities to continue progress toward understanding the complexity of plant-microbe-environment interactions. We highlight the utility of metabolic models as applied to a community setting, identify different applications for both flux balance and elementary flux mode simulation approaches, emphasize the importance of ecological theory in guiding data interpretation, and provide ideas for how the integration of metabolic modelling techniques with big data may bridge the gap between simplified synthetic communities and the complexity of natural plant-microbe systems.
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Affiliation(s)
- Ashley E. Beck
- Department of Biological and Environmental Sciences, Carroll College, Helena, MT, United States
- *Correspondence: Ashley E. Beck,
| | - Manuel Kleiner
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
| | - Anna-Katharina Garrell
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, United States
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7
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Yang X, Liu D, Lu H, Weston DJ, Chen JG, Muchero W, Martin S, Liu Y, Hassan MM, Yuan G, Kalluri UC, Tschaplinski TJ, Mitchell JC, Wullschleger SD, Tuskan GA. Biological Parts for Plant Biodesign to Enhance Land-Based Carbon Dioxide Removal. BIODESIGN RESEARCH 2021; 2021:9798714. [PMID: 37849951 PMCID: PMC10521660 DOI: 10.34133/2021/9798714] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 11/07/2021] [Indexed: 10/19/2023] Open
Abstract
A grand challenge facing society is climate change caused mainly by rising CO2 concentration in Earth's atmosphere. Terrestrial plants are linchpins in global carbon cycling, with a unique capability of capturing CO2 via photosynthesis and translocating captured carbon to stems, roots, and soils for long-term storage. However, many researchers postulate that existing land plants cannot meet the ambitious requirement for CO2 removal to mitigate climate change in the future due to low photosynthetic efficiency, limited carbon allocation for long-term storage, and low suitability for the bioeconomy. To address these limitations, there is an urgent need for genetic improvement of existing plants or construction of novel plant systems through biosystems design (or biodesign). Here, we summarize validated biological parts (e.g., protein-encoding genes and noncoding RNAs) for biological engineering of carbon dioxide removal (CDR) traits in terrestrial plants to accelerate land-based decarbonization in bioenergy plantations and agricultural settings and promote a vibrant bioeconomy. Specifically, we first summarize the framework of plant-based CDR (e.g., CO2 capture, translocation, storage, and conversion to value-added products). Then, we highlight some representative biological parts, with experimental evidence, in this framework. Finally, we discuss challenges and strategies for the identification and curation of biological parts for CDR engineering in plants.
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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
| | - 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
| | - Haiwei Lu
- Department of Academic Education, Central Community College-Hastings, Hastings, NE 68902USA
| | - 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
| | - 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
| | - 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
| | - 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
| | - Yang Liu
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - 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
| | - 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
| | - 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
| | - 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
| | - Julie C. Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Stan D. Wullschleger
- Environmental Sciences Division and Climate Change Science Institute, 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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8
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Jansson C, Faiola C, Wingler A, Zhu XG, Kravchenko A, de Graaff MA, Ogden AJ, Handakumbura PP, Werner C, Beckles DM. Crops for Carbon Farming. FRONTIERS IN PLANT SCIENCE 2021; 12:636709. [PMID: 34149744 PMCID: PMC8211891 DOI: 10.3389/fpls.2021.636709] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 04/26/2021] [Indexed: 05/03/2023]
Abstract
Agricultural cropping systems and pasture comprise one third of the world's arable land and have the potential to draw down a considerable amount of atmospheric CO2 for storage as soil organic carbon (SOC) and improving the soil carbon budget. An improved soil carbon budget serves the dual purpose of promoting soil health, which supports crop productivity, and constituting a pool from which carbon can be converted to recalcitrant forms for long-term storage as a mitigation measure for global warming. In this perspective, we propose the design of crop ideotypes with the dual functionality of being highly productive for the purposes of food, feed, and fuel, while at the same time being able to facilitate higher contribution to soil carbon and improve the below ground ecology. We advocate a holistic approach of the integrated plant-microbe-soil system and suggest that significant improvements in soil carbon storage can be achieved by a three-pronged approach: (1) design plants with an increased root strength to further allocation of carbon belowground; (2) balance the increase in belowground carbon allocation with increased source strength for enhanced photosynthesis and biomass accumulation; and (3) design soil microbial consortia for increased rhizosphere sink strength and plant growth-promoting (PGP) properties.
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Affiliation(s)
- Christer Jansson
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Celia Faiola
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, United States
| | - Astrid Wingler
- School of Biological, Earth & Environmental Sciences and Environmental Research Institute, University College Cork, Cork, Ireland
| | - Xin-Guang Zhu
- National Key Laboratory for Plant Molecular Genetics, Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Alexandra Kravchenko
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, United States
| | - Marie-Anne de Graaff
- Department of Biological Sciences, Boise State University, Boise, ID, United States
| | - Aaron J. Ogden
- Pacific Northwest National Laboratory, Richland, WA, United States
| | | | | | - Diane M. Beckles
- Department of Plant Sciences, University of California, Davis, Davis, CA, United States
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9
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Introduction to emerging technologies in plant science. Emerg Top Life Sci 2021; 5:177-178. [PMID: 34027975 DOI: 10.1042/etls20200269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 11/17/2022]
Abstract
In recent years, an array of new technologies is propelling plant science in exciting directions and facilitating the integration of data across multiple scales. These tools come at a critical time. With an expanding global population and the need to provide food in sustainable ways, we as a civilization will be asking more of plants and plant biologists than ever before. This special issue on emerging technologies in plant science brings together a set of reviews that spotlight a range of approaches that are changing how we ask questions and allow scientific inquiry from macromolecular to ecosystem scales.
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