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Wang X, Cheng L, Xiong C, Whalley WR, Miller AJ, Rengel Z, Zhang F, Shen J. Understanding plant-soil interactions underpins enhanced sustainability of crop production. TRENDS IN PLANT SCIENCE 2024:S1360-1385(24)00126-2. [PMID: 38897884 DOI: 10.1016/j.tplants.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024]
Abstract
The Green Revolution transformed agriculture with high-yielding, stress-resistant varieties. However, the urgent need for more sustainable agricultural development presents new challenges: increasing crop yield, improving nutritional quality, and enhancing resource-use efficiency. Soil plays a vital role in crop-production systems and ecosystem services, providing water, nutrients, and physical anchorage for crop growth. Despite advancements in plant and soil sciences, our understanding of belowground plant-soil interactions, which impact both crop performance and soil health, remains limited. Here, we argue that a lack of understanding of these plant-soil interactions hinders sustainable crop production. We propose that targeted engineering of crops and soils can provide a fresh approach to achieve higher yields, more efficient sustainable crop production, and improved soil health.
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Affiliation(s)
- Xin Wang
- State Key Laboratory of Nutrient Use and Management, Department of Plant Nutrition, College of Resources and Environmental Sciences, China Agricultural University, Key Laboratory of Plant-Soil Interactions, Ministry of Education, Beijing 100193, China; State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lingyun Cheng
- State Key Laboratory of Nutrient Use and Management, Department of Plant Nutrition, College of Resources and Environmental Sciences, China Agricultural University, Key Laboratory of Plant-Soil Interactions, Ministry of Education, Beijing 100193, China
| | - Chuanyong Xiong
- State Key Laboratory of Nutrient Use and Management, Department of Plant Nutrition, College of Resources and Environmental Sciences, China Agricultural University, Key Laboratory of Plant-Soil Interactions, Ministry of Education, Beijing 100193, China; Horticultural Research Institute, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
| | - William R Whalley
- Rothamsted Research, West Common, Harpenden, Hertfordshire, AL5 2JQ, UK
| | - Anthony J Miller
- Biochemistry and Metabolism Department, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Zed Rengel
- Soil Science and Plant Nutrition, UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA 6009, Australia; Institute for Adriatic Crops and Karst Reclamation, 21000 Split, Croatia
| | - Fusuo Zhang
- State Key Laboratory of Nutrient Use and Management, Department of Plant Nutrition, College of Resources and Environmental Sciences, China Agricultural University, Key Laboratory of Plant-Soil Interactions, Ministry of Education, Beijing 100193, China
| | - Jianbo Shen
- State Key Laboratory of Nutrient Use and Management, Department of Plant Nutrition, College of Resources and Environmental Sciences, China Agricultural University, Key Laboratory of Plant-Soil Interactions, Ministry of Education, Beijing 100193, China.
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Hufnagel B, Bernardino KC, Malosetti M, Sousa SM, Silva LA, Guimaraes CT, Coelho AM, Santos TT, Viana JHM, Schaffert RE, Kochian LV, Eeuwijk FA, Magalhaes JV. Multi-trait association mapping for phosphorous efficiency reveals flexible root architectures in sorghum. BMC PLANT BIOLOGY 2024; 24:562. [PMID: 38877425 PMCID: PMC11179229 DOI: 10.1186/s12870-024-05183-5] [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: 02/27/2024] [Accepted: 05/22/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND On tropical regions, phosphorus (P) fixation onto aluminum and iron oxides in soil clays restricts P diffusion from the soil to the root surface, limiting crop yields. While increased root surface area favors P uptake under low-P availability, the relationship between the three-dimensional arrangement of the root system and P efficiency remains elusive. Here, we simultaneously assessed allelic effects of loci associated with a variety of root and P efficiency traits, in addition to grain yield under low-P availability, using multi-trait genome-wide association. We also set out to establish the relationship between root architectural traits assessed in hydroponics and in a low-P soil. Our goal was to better understand the influence of root morphology and architecture in sorghum performance under low-P availability. RESULT In general, the same alleles of associated SNPs increased root and P efficiency traits including grain yield in a low-P soil. We found that sorghum P efficiency relies on pleiotropic loci affecting root traits, which enhance grain yield under low-P availability. Root systems with enhanced surface area stemming from lateral root proliferation mostly up to 40 cm soil depth are important for sorghum adaptation to low-P soils, indicating that differences in root morphology leading to enhanced P uptake occur exactly in the soil layer where P is found at the highest concentration. CONCLUSION Integrated QTLs detected in different mapping populations now provide a comprehensive molecular genetic framework for P efficiency studies in sorghum. This indicated extensive conservation of P efficiency QTL across populations and emphasized the terminal portion of chromosome 3 as an important region for P efficiency in sorghum. Increases in root surface area via enhancement of lateral root development is a relevant trait for sorghum low-P soil adaptation, impacting the overall architecture of the sorghum root system. In turn, particularly concerning the critical trait for water and nutrient uptake, root surface area, root system development in deeper soil layers does not occur at the expense of shallow rooting, which may be a key reason leading to the distinctive sorghum adaptation to tropical soils with multiple abiotic stresses including low P availability and drought.
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Affiliation(s)
- Barbara Hufnagel
- Embrapa Maize and Sorghum, Sete Lagoas, Minas Gerais, 35701-970, Brazil
- CIRAD, UMR AGAP Institut, Petit-Bourg, Guadeloupe, F-97170, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | | | - Marcos Malosetti
- Biometris, Wageningen University and Research Center, Wageningen, 6700AC, The Netherlands
- BASF - Nunhems, Nunhem, The Netherlands
| | - Sylvia M Sousa
- Embrapa Maize and Sorghum, Sete Lagoas, Minas Gerais, 35701-970, Brazil
| | - Lidianne A Silva
- Embrapa Maize and Sorghum, Sete Lagoas, Minas Gerais, 35701-970, Brazil
- Universidade Federal do Acre, Rio Branco, Acre, 69920-900, Brazil
| | | | | | | | - Joao H M Viana
- Embrapa Maize and Sorghum, Sete Lagoas, Minas Gerais, 35701-970, Brazil
| | | | - Leon V Kochian
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, S7N 4J8, Canada
| | - Fred A Eeuwijk
- Biometris, Wageningen University and Research Center, Wageningen, 6700AC, The Netherlands
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Zhang S, Wu Y, Skaro M, Cheong JH, Bouffier-Landrum A, Torrres I, Guo Y, Stupp L, Lincoln B, Prestel A, Felt C, Spann S, Mandal A, Johnson N, Arnold J. Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology. Sci Rep 2024; 14:10866. [PMID: 38740920 DOI: 10.1038/s41598-024-61181-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 05/02/2024] [Indexed: 05/16/2024] Open
Abstract
The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.
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Affiliation(s)
- Shufan Zhang
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Yue Wu
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Michael Skaro
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | | | | | - Isaac Torrres
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Yinping Guo
- Genetics Department, University of Georgia, Athens, GA, USA
| | - Lauren Stupp
- Genetics Department, University of Georgia, Athens, GA, USA
| | - Brooke Lincoln
- Genetics Department, University of Georgia, Athens, GA, USA
| | - Anna Prestel
- Genetics Department, University of Georgia, Athens, GA, USA
| | - Camryn Felt
- Genetics Department, University of Georgia, Athens, GA, USA
| | - Sedona Spann
- School of Earth and Sustainability and Department of Biological Sciences, North Arizona University, Flagstaff, AZ, USA
| | - Abhyuday Mandal
- Statistics Department, University of Georgia, Athens, GA, USA
| | - Nancy Johnson
- School of Earth and Sustainability and Department of Biological Sciences, North Arizona University, Flagstaff, AZ, USA
| | - Jonathan Arnold
- Genetics Department, University of Georgia, Athens, GA, USA.
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4
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Kawa D, Thiombiano B, Shimels MZ, Taylor T, Walmsley A, Vahldick HE, Rybka D, Leite MFA, Musa Z, Bucksch A, Dini-Andreote F, Schilder M, Chen AJ, Daksa J, Etalo DW, Tessema T, Kuramae EE, Raaijmakers JM, Bouwmeester H, Brady SM. The soil microbiome modulates the sorghum root metabolome and cellular traits with a concomitant reduction of Striga infection. Cell Rep 2024; 43:113971. [PMID: 38537644 PMCID: PMC11063626 DOI: 10.1016/j.celrep.2024.113971] [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: 01/26/2023] [Revised: 01/17/2024] [Accepted: 02/29/2024] [Indexed: 04/10/2024] Open
Abstract
Sorghum bicolor is among the most important cereals globally and a staple crop for smallholder farmers in sub-Saharan Africa. Approximately 20% of sorghum yield is lost annually in Africa due to infestation with the root parasitic weed Striga hermonthica. Existing Striga management strategies are not singularly effective and integrated approaches are needed. Here, we demonstrate the functional potential of the soil microbiome to suppress Striga infection in sorghum. We associate this suppression with microbiome-mediated induction of root endodermal suberization and aerenchyma formation and with depletion of haustorium-inducing factors, compounds required for the initial stages of Striga infection. We further identify specific bacterial taxa that trigger the observed Striga-suppressive traits. Collectively, our study describes the importance of the soil microbiome in the early stages of root infection by Striga and pinpoints mechanisms of Striga suppression. These findings open avenues to broaden the effectiveness of integrated Striga management practices.
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Affiliation(s)
- Dorota Kawa
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA 95616, USA; Plant Stress Resilience, Department of Biology, Utrecht University, 3508 TC Utrecht, the Netherlands; Environmental and Computational Plant Development, Department of Biology, Utrecht University, 3508 TC Utrecht, the Netherlands.
| | - Benjamin Thiombiano
- Plant Hormone Biology Group, Green Life Sciences Cluster, Swammerdam Institute for Life Science, University of Amsterdam, 1098 XH Amsterdam, the Netherlands
| | - Mahdere Z Shimels
- Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, 6708 PB Wageningen, the Netherlands
| | - Tamera Taylor
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA 95616, USA; Plant Biology Graduate Group, University of California, Davis, Davis, CA 95616, USA
| | - Aimee Walmsley
- Plant Hormone Biology Group, Green Life Sciences Cluster, Swammerdam Institute for Life Science, University of Amsterdam, 1098 XH Amsterdam, the Netherlands
| | - Hannah E Vahldick
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA 95616, USA
| | - Dominika Rybka
- Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, 6708 PB Wageningen, the Netherlands
| | - Marcio F A Leite
- Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, 6708 PB Wageningen, the Netherlands
| | - Zayan Musa
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA 95616, USA
| | - Alexander Bucksch
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA; Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA; Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA
| | - Francisco Dini-Andreote
- Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, 6708 PB Wageningen, the Netherlands; Department of Plant Science, The Pennsylvania State University, University Park, PA 16802, USA; Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Mario Schilder
- Plant Hormone Biology Group, Green Life Sciences Cluster, Swammerdam Institute for Life Science, University of Amsterdam, 1098 XH Amsterdam, the Netherlands
| | - Alexander J Chen
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA 95616, USA
| | - Jiregna Daksa
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA 95616, USA
| | - Desalegn W Etalo
- Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, 6708 PB Wageningen, the Netherlands; Wageningen University and Research, Laboratory of Phytopathology, Wageningen, the Netherlands
| | - Taye Tessema
- Ethiopian Institute of Agricultural Research, 3G53+6XC Holeta, Ethiopia
| | - Eiko E Kuramae
- Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, 6708 PB Wageningen, the Netherlands; Ecology and Biodiversity, Department of Biology, Utrecht University, 3584 CH Utrecht, the Netherlands
| | - Jos M Raaijmakers
- Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, 6708 PB Wageningen, the Netherlands
| | - Harro Bouwmeester
- Plant Hormone Biology Group, Green Life Sciences Cluster, Swammerdam Institute for Life Science, University of Amsterdam, 1098 XH Amsterdam, the Netherlands
| | - Siobhan M Brady
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA 95616, USA.
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5
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Bakala HS, Devi J, Singh G, Singh I. Drought and heat stress: insights into tolerance mechanisms and breeding strategies for pigeonpea improvement. PLANTA 2024; 259:123. [PMID: 38622376 DOI: 10.1007/s00425-024-04401-6] [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: 01/13/2024] [Accepted: 03/29/2024] [Indexed: 04/17/2024]
Abstract
MAIN CONCLUSION Pigeonpea has potential to foster sustainable agriculture and resilience in evolving climate change; understanding bio-physiological and molecular mechanisms of heat and drought stress tolerance is imperative to developing resilience cultivars. Pigeonpea is an important legume crop that has potential resilience in the face of evolving climate scenarios. However, compared to other legumes, there has been limited research on abiotic stress tolerance in pigeonpea, particularly towards drought stress (DS) and heat stress (HS). To address this gap, this review delves into the genetic, physiological, and molecular mechanisms that govern pigeonpea's response to DS and HS. It emphasizes the need to understand how this crop combats these stresses and exhibits different types of tolerance and adaptation mechanisms through component traits. The current article provides a comprehensive overview of the complex interplay of factors contributing to the resilience of pigeonpea under adverse environmental conditions. Furthermore, the review synthesizes information on major breeding techniques, encompassing both conventional methods and modern molecular omics-assisted tools and techniques. It highlights the potential of genomics and phenomics tools and their pivotal role in enhancing adaptability and resilience in pigeonpea. Despite the progress made in genomics, phenomics and big data analytics, the complexity of drought and heat tolerance in pigeonpea necessitate continuous exploration at multi-omic levels. High-throughput phenotyping (HTP) is crucial for gaining insights into perplexed interactions among genotype, environment, and management practices (GxExM). Thus, integration of advanced technologies in breeding programs is critical for developing pigeonpea varieties that can withstand the challenges posed by climate change. This review is expected to serve as a valuable resource for researchers, providing a deeper understanding of the mechanisms underlying abiotic stress tolerance in pigeonpea and offering insights into modern breeding strategies that can contribute to the development of resilient varieties suited for changing environmental conditions.
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Affiliation(s)
- Harmeet Singh Bakala
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, 141004, India
| | - Jomika Devi
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, 141004, India
| | - Gurjeet Singh
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, 141004, India.
- Texas A&M University, AgriLife Research Center, Beaumont, TX, 77713, USA.
| | - Inderjit Singh
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, 141004, India
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Nagle MF, Yuan J, Kaur D, Ma C, Peremyslova E, Jiang Y, Niño de Rivera A, Jawdy S, Chen JG, Feng K, Yates TB, Tuskan GA, Muchero W, Fuxin L, Strauss SH. GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa. G3 (BETHESDA, MD.) 2024; 14:jkae026. [PMID: 38325329 PMCID: PMC10989874 DOI: 10.1093/g3journal/jkae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/09/2024]
Abstract
Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.
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Affiliation(s)
- Michael F Nagle
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Jialin Yuan
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Damanpreet Kaur
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Cathleen Ma
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Ekaterina Peremyslova
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Yuan Jiang
- Statistics Department, Oregon State University, 239 Weniger Hall, Corvallis, OR 97331, USA
| | - Alexa Niño de Rivera
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Sara Jawdy
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Jin-Gui Chen
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Kai Feng
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Timothy B Yates
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Gerald A Tuskan
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Li Fuxin
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Steven H Strauss
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [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: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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8
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Ruppel M, Nelson SK, Sidberry G, Mitchell M, Kick D, Thomas SK, Guill KE, Oliver MJ, Washburn JD. RootBot: High-throughput root stress phenotyping robot. APPLICATIONS IN PLANT SCIENCES 2023; 11:e11541. [PMID: 38106535 PMCID: PMC10719875 DOI: 10.1002/aps3.11541] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/05/2023] [Accepted: 05/06/2023] [Indexed: 12/19/2023]
Abstract
Premise Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil. Methods and Results RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data. Conclusions The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.
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Affiliation(s)
- Mia Ruppel
- Department of Biomedical, Biological, and Chemical EngineeringUniversity of MissouriColumbiaMissouriUSA
| | - Sven K. Nelson
- Director of Plant ScienceHeliponix, LLCEvansvilleIndianaUSA
- Plant Genetics Research UnitUSDA‐ARSColumbiaMissouriUSA
| | - Grace Sidberry
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
| | - Madison Mitchell
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
| | - Daniel Kick
- Plant Genetics Research UnitUSDA‐ARSColumbiaMissouriUSA
| | - Shawn K. Thomas
- Division of Biological SciencesUniversity of MissouriColumbiaMissouriUSA
| | - Katherine E. Guill
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
| | - Melvin J. Oliver
- Division of Plant Science and TechnologyUniversity of MissouriColumbiaMissouriUSA
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Liu Z, Li P, Ren W, Chen Z, Olukayode T, Mi G, Yuan L, Chen F, Pan Q. Hybrid performance evaluation and genome-wide association analysis of root system architecture in a maize association population. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:194. [PMID: 37606710 DOI: 10.1007/s00122-023-04442-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 08/04/2023] [Indexed: 08/23/2023]
Abstract
KEY MESSAGE The genetic architecture of RSA traits was dissected by GWAS and coexpression networks analysis in a maize association population. Root system architecture (RSA) is a crucial determinant of water and nutrient uptake efficiency in crops. However, the maize genetic architecture of RSA is still poorly understood due to the challenges in quantifying root traits and the lack of dense molecular markers. Here, an association mapping panel including 356 inbred lines were crossed with a common tester, Zheng58, and the test crosses were phenotyped for 12 RSA traits in three locations. We observed a 1.3 ~ sixfold phenotypic variation for measured RSA in the association panel. The association panel consisted of four subpopulations, non-stiff stalk (NSS) lines, stiff stalk (SS), tropical/subtropical (TST), and mixed. Zheng58 × TST has a 2.1% higher crown root number (CRN) and 8.6% less brace root number (BRN) than Zheng58 × NSS and Zheng58 × SS, respectively. Using a genome-wide association study (GWAS) with 1.25 million SNPs and correction for population structure, 191 significant SNPs were identified for root traits. Ninety (47%) of the significant SNPs showed positive allelic effects, and 101 (53%) showed negative effects. Each locus could explain 0.39% to 11.8% of phenotypic variation. By integrating GWAS results and comparing coexpression networks, 26 high-priority candidate genes were identified. Gene GRMZM2G377215, which belongs to the COBRA-like gene family, affected root growth and development. Gene GRMZM2G468657 encodes the aspartic proteinase nepenthesin-1, related to root development and N-deficient response. Collectively, our research provides progress in the genetic dissection of root system architecture. These findings present the further possibility for the genetic improvement of root traits in maize.
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Affiliation(s)
- Zhigang Liu
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions of MOE, China Agricultural University, Beijing, China
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, Canada
| | - Pengcheng Li
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou, China
| | - Wei Ren
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions of MOE, China Agricultural University, Beijing, China
| | - Zhe Chen
- College of Resources and Environment, Jilin Agricultural University, Changchun, China
| | - Toluwase Olukayode
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, Canada
| | - Guohua Mi
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions of MOE, China Agricultural University, Beijing, China
| | - Lixing Yuan
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions of MOE, China Agricultural University, Beijing, China
| | - Fanjun Chen
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions of MOE, China Agricultural University, Beijing, China
- Sanya Institute of China Agricultural University, Sanya, China
| | - Qingchun Pan
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions of MOE, China Agricultural University, Beijing, China.
- Sanya Institute of China Agricultural University, Sanya, China.
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10
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Murphy KM, Dowd T, Khalil A, Char SN, Yang B, Endelman BJ, Shih PM, Topp C, Schmelz EA, Zerbe P. A dolabralexin-deficient mutant provides insight into specialized diterpenoid metabolism in maize. PLANT PHYSIOLOGY 2023; 192:1338-1358. [PMID: 36896653 PMCID: PMC10231366 DOI: 10.1093/plphys/kiad150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 01/19/2023] [Accepted: 02/02/2023] [Indexed: 06/01/2023]
Abstract
Two major groups of specialized metabolites in maize (Zea mays), termed kauralexins and dolabralexins, serve as known or predicted diterpenoid defenses against pathogens, herbivores, and other environmental stressors. To consider the physiological roles of the recently discovered dolabralexin pathway, we examined dolabralexin structural diversity, tissue-specificity, and stress-elicited production in a defined biosynthetic pathway mutant. Metabolomics analyses support a larger number of dolabralexin pathway products than previously known. We identified dolabradienol as a previously undetected pathway metabolite and characterized its enzymatic production. Transcript and metabolite profiling showed that dolabralexin biosynthesis and accumulation predominantly occur in primary roots and show quantitative variation across genetically diverse inbred lines. Generation and analysis of CRISPR-Cas9-derived loss-of-function Kaurene Synthase-Like 4 (Zmksl4) mutants demonstrated dolabralexin production deficiency, thus supporting ZmKSL4 as the diterpene synthase responsible for the conversion of geranylgeranyl pyrophosphate precursors into dolabradiene and downstream pathway products. Zmksl4 mutants further display altered root-to-shoot ratios and root architecture in response to water deficit. Collectively, these results demonstrate dolabralexin biosynthesis via ZmKSL4 as a committed pathway node biochemically separating kauralexin and dolabralexin metabolism, and suggest an interactive role of maize dolabralexins in plant vigor during abiotic stress.
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Affiliation(s)
- Katherine M Murphy
- Department of Plant Biology, University of California-Davis, Davis, CA 95616, USA
| | - Tyler Dowd
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Ahmed Khalil
- Section of Cell and Developmental Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Si Nian Char
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA
| | - Bing Yang
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA
| | - Benjamin J Endelman
- Department of Plant Biology, University of California-Davis, Davis, CA 95616, USA
| | - Patrick M Shih
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Plant and Microbial Biology, UC Berkeley, Berkeley, CA 94720, USA
| | | | - Eric A Schmelz
- Section of Cell and Developmental Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Philipp Zerbe
- Department of Plant Biology, University of California-Davis, Davis, CA 95616, USA
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11
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Dossa EN, Shimelis H, Mrema E, Shayanowako ATI, Laing M. Genetic resources and breeding of maize for Striga resistance: a review. FRONTIERS IN PLANT SCIENCE 2023; 14:1163785. [PMID: 37235028 PMCID: PMC10206272 DOI: 10.3389/fpls.2023.1163785] [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: 02/11/2023] [Accepted: 04/07/2023] [Indexed: 05/28/2023]
Abstract
The potential yield of maize (Zea mays L.) and other major crops is curtailed by several biotic, abiotic, and socio-economic constraints. Parasitic weeds, Striga spp., are major constraints to cereal and legume crop production in sub-Saharan Africa (SSA). Yield losses reaching 100% are reported in maize under severe Striga infestation. Breeding for Striga resistance has been shown to be the most economical, feasible, and sustainable approach for resource-poor farmers and for being environmentally friendly. Knowledge of the genetic and genomic resources and components of Striga resistance is vital to guide genetic analysis and precision breeding of maize varieties with desirable product profiles under Striga infestation. This review aims to present the genetic and genomic resources, research progress, and opportunities in the genetic analysis of Striga resistance and yield components in maize for breeding. The paper outlines the vital genetic resources of maize for Striga resistance, including landraces, wild relatives, mutants, and synthetic varieties, followed by breeding technologies and genomic resources. Integrating conventional breeding, mutation breeding, and genomic-assisted breeding [i.e., marker-assisted selection, quantitative trait loci (QTL) analysis, next-generation sequencing, and genome editing] will enhance genetic gains in Striga resistance breeding programs. This review may guide new variety designs for Striga-resistance and desirable product profiles in maize.
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Affiliation(s)
- Emeline Nanou Dossa
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Hussein Shimelis
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Emmanuel Mrema
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
- Tanzania Agricultural Research Institute, Tumbi Center, Tabora, Tanzania
| | | | - Mark Laing
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
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12
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Yu Q, Tang H, Zhu L, Zhang W, Liu L, Wang N. A method of cotton root segmentation based on edge devices. FRONTIERS IN PLANT SCIENCE 2023; 14:1122833. [PMID: 36875594 PMCID: PMC9982017 DOI: 10.3389/fpls.2023.1122833] [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: 12/13/2022] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.
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Affiliation(s)
- Qiushi Yu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Hui Tang
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Lingxiao Zhu
- College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Wenjie Zhang
- College of Modern Science And Technology, Hebei Agricultural University, Baoding, China
| | - Liantao Liu
- College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Nan Wang
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
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13
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Shrestha N, Hu H, Shrestha K, Doust AN. Pearl millet response to drought: A review. FRONTIERS IN PLANT SCIENCE 2023; 14:1059574. [PMID: 36844091 PMCID: PMC9955113 DOI: 10.3389/fpls.2023.1059574] [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: 10/01/2022] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
The C4 grass pearl millet is one of the most drought tolerant cereals and is primarily grown in marginal areas where annual rainfall is low and intermittent. It was domesticated in sub-Saharan Africa, and several studies have found that it uses a combination of morphological and physiological traits to successfully resist drought. This review explores the short term and long-term responses of pearl millet that enables it to either tolerate, avoid, escape, or recover from drought stress. The response to short term drought reveals fine tuning of osmotic adjustment, stomatal conductance, and ROS scavenging ability, along with ABA and ethylene transduction. Equally important are longer term developmental plasticity in tillering, root development, leaf adaptations and flowering time that can both help avoid the worst water stress and recover some of the yield losses via asynchronous tiller production. We examine genes related to drought resistance that were identified through individual transcriptomic studies and through our combined analysis of previous studies. From the combined analysis, we found 94 genes that were differentially expressed in both vegetative and reproductive stages under drought stress. Among them is a tight cluster of genes that are directly related to biotic and abiotic stress, as well as carbon metabolism, and hormonal pathways. We suggest that knowledge of gene expression patterns in tiller buds, inflorescences and rooting tips will be important for understanding the growth responses of pearl millet and the trade-offs at play in the response of this crop to drought. Much remains to be learnt about how pearl millet's unique combination of genetic and physiological mechanisms allow it to achieve such high drought tolerance, and the answers to be found may well be useful for crops other than just pearl millet.
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Affiliation(s)
- Nikee Shrestha
- Department of Plant Biology, Ecology and Evolution, Oklahoma State University, Stillwater, OK, United States
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Hao Hu
- Department of Plant Biology, Ecology and Evolution, Oklahoma State University, Stillwater, OK, United States
| | - Kumar Shrestha
- Department of Plant Biology, Ecology and Evolution, Oklahoma State University, Stillwater, OK, United States
| | - Andrew N. Doust
- Department of Plant Biology, Ecology and Evolution, Oklahoma State University, Stillwater, OK, United States
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14
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Pierz LD, Heslinga DR, Buell CR, Haus MJ. An image-based technique for automated root disease severity assessment using PlantCV. APPLICATIONS IN PLANT SCIENCES 2023; 11:e11507. [PMID: 36818784 PMCID: PMC9934521 DOI: 10.1002/aps3.11507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/31/2022] [Accepted: 09/23/2022] [Indexed: 06/18/2023]
Abstract
PREMISE Plant disease severity assessments are used to quantify plant-pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis. METHODS Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot. RESULTS Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (R 2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output. DISCUSSION Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.
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Affiliation(s)
- Logan D. Pierz
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
| | - Dilyn R. Heslinga
- Department of HorticultureMichigan State UniversityEast LansingMichigan48824USA
| | - C. Robin Buell
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
- Department of Crop and Soil SciencesUniversity of GeorgiaAthensGeorgia30602USA
| | - Miranda J. Haus
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
- Department of HorticultureMichigan State UniversityEast LansingMichigan48824USA
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15
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Ren W, Zhao L, Liang J, Wang L, Chen L, Li P, Liu Z, Li X, Zhang Z, Li J, He K, Zhao Z, Ali F, Mi G, Yan J, Zhang F, Chen F, Yuan L, Pan Q. Genome-wide dissection of changes in maize root system architecture during modern breeding. NATURE PLANTS 2022; 8:1408-1422. [PMID: 36396706 DOI: 10.1038/s41477-022-01274-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 10/12/2022] [Indexed: 05/12/2023]
Abstract
Appropriate root system architecture (RSA) can improve maize yields in densely planted fields, but little is known about its genetic basis in maize. Here we performed root phenotyping of 14,301 field-grown plants from an association mapping panel to study the genetic architecture of maize RSA. A genome-wide association study identified 81 high-confidence RSA-associated candidate genes and revealed that 28 (24.3%) of known root-related genes were selected during maize domestication and improvement. We found that modern maize breeding has selected for a steeply angled root system. Favourable alleles related to steep root system angle have continuously accumulated over the course of modern breeding, and our data pinpoint the root-related genes that have been selected in different breeding eras. We confirm that two auxin-related genes, ZmRSA3.1 and ZmRSA3.2, contribute to the regulation of root angle and depth in maize. Our genome-wide identification of RSA-associated genes provides new strategies and genetic resources for breeding maize suitable for high-density planting.
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Affiliation(s)
- Wei Ren
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Longfei Zhao
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Jiaxing Liang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Lifeng Wang
- Cereal Crops Research Institute, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Limei Chen
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, China
| | - Pengcheng Li
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou, China
| | - Zhigang Liu
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Xiaojie Li
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Zhihai Zhang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Jieping Li
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Kunhui He
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Zheng Zhao
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Farhan Ali
- Cereal Crops Research Institute, Pirsabak, Nowshera, Pakistan
| | - Guohua Mi
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Fusuo Zhang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Fanjun Chen
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China.
- Sanya Institute of China Agricultural University, Sanya, China.
| | - Lixing Yuan
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China.
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, China.
| | - Qingchun Pan
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China.
- Sanya Institute of China Agricultural University, Sanya, China.
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16
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Abbas M, Abid MA, Meng Z, Abbas M, Wang P, Lu C, Askari M, Akram U, Ye Y, Wei Y, Wang Y, Guo S, Liang C, Zhang R. Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway. PHYSIOLOGIA PLANTARUM 2022; 174:e13787. [PMID: 36169590 DOI: 10.1111/ppl.13787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/12/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.
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Affiliation(s)
- Mubashir Abbas
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Ali Abid
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhigang Meng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Manzar Abbas
- School of Agriculture, Forestry and Food Engineering, Yibin University, Yibin, China
| | - Peilin Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chao Lu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Askari
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Umar Akram
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yulu Ye
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yunxiao Wei
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Sandui Guo
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chengzhen Liang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Rui Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
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17
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Wilhelm J, Wojciechowski T, Postma JA, Jollet D, Heinz K, Böckem V, Müller-Linow M. Assessing the Storage Root Development of Cassava with a New Analysis Tool. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:9767820. [PMID: 37228350 PMCID: PMC10204708 DOI: 10.34133/2022/9767820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/28/2022] [Indexed: 05/27/2023]
Abstract
Storage roots of cassava plants crops are one of the main providers of starch in many South American, African, and Asian countries. Finding varieties with high yields is crucial for growing and breeding. This requires a better understanding of the dynamics of storage root formation, which is usually done by repeated manual evaluation of root types, diameters, and their distribution in excavated roots. We introduce a newly developed method that is capable to analyze the distribution of root diameters automatically, even if root systems display strong variations in root widths and clustering in high numbers. An application study was conducted with cassava roots imaged in a video acquisition box. The root diameter distribution was quantified automatically using an iterative ridge detection approach, which can cope with a wide span of root diameters and clustering. The approach was validated with virtual root models of known geometries and then tested with a time-series of excavated root systems. Based on the retrieved diameter classes, we show plausibly that the dynamics of root type formation can be monitored qualitatively and quantitatively. We conclude that this new method reliably determines important phenotypic traits from storage root crop images. The method is fast and robustly analyses complex root systems and thereby applicable in high-throughput phenotyping and future breeding.
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Affiliation(s)
- Jens Wilhelm
- Institute of Plant Sciences, IBG-2, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Tobias Wojciechowski
- Institute of Plant Sciences, IBG-2, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Johannes A. Postma
- Institute of Plant Sciences, IBG-2, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Dirk Jollet
- Institute of Plant Sciences, IBG-2, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | | | - Vera Böckem
- Institute of Plant Sciences, IBG-2, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Mark Müller-Linow
- Institute of Plant Sciences, IBG-2, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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18
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Seidenthal K, Panjvani K, Chandnani R, Kochian L, Eramian M. Iterative image segmentation of plant roots for high-throughput phenotyping. Sci Rep 2022; 12:16563. [PMID: 36195610 PMCID: PMC9532414 DOI: 10.1038/s41598-022-19754-9] [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: 01/24/2022] [Accepted: 09/02/2022] [Indexed: 11/24/2022] Open
Abstract
Accurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems. Various approaches to image segmentation exist but many of them are not well suited to the thin and reticulated structures characteristic of root systems. The findings presented here describe an approach to RSA segmentation that takes advantage of the inherent structural properties of the root system, a segmentation network architecture we call ITErRoot. We have also generated a novel 2D root image dataset which utilizes an annotation tool developed for producing high quality ground truth segmentation of root systems. Our approach makes use of an iterative neural network architecture to leverage the thin and highly branched properties of root systems for accurate segmentation. Rigorous analysis of model properties was carried out to obtain a high-quality model for 2D root segmentation. Results show a significant improvement over other recent approaches to root segmentation. Validation results show that the model generalizes to plant species with fine and highly branched RSA’s, and performs particularly well in the presence of non-root objects.
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Affiliation(s)
- Kyle Seidenthal
- Department of Computer Science, University of Saskatchewan, 110 Science Place, Saskatoon, SK, S7N 5C9, Canada
| | - Karim Panjvani
- Global Institute for Food Security, University of Saskatchewan, 421 Downey Road, Saskatoon, SK, S7N 4L8, Canada
| | - Rahul Chandnani
- Global Institute for Food Security, University of Saskatchewan, 421 Downey Road, Saskatoon, SK, S7N 4L8, Canada
| | - Leon Kochian
- Global Institute for Food Security, University of Saskatchewan, 421 Downey Road, Saskatoon, SK, S7N 4L8, Canada
| | - Mark Eramian
- Department of Computer Science, University of Saskatchewan, 110 Science Place, Saskatoon, SK, S7N 5C9, Canada.
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19
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Barnhart MH, Masalia RR, Mosley LJ, Burke JM. Phenotypic and transcriptomic responses of cultivated sunflower seedlings (Helianthus annuus L.) to four abiotic stresses. PLoS One 2022; 17:e0275462. [PMID: 36178944 PMCID: PMC9524668 DOI: 10.1371/journal.pone.0275462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 09/18/2022] [Indexed: 11/19/2022] Open
Abstract
Plants encounter and respond to numerous abiotic stresses during their lifetimes. These stresses are often related and could therefore elicit related responses. There are, however, relatively few detailed comparisons between multiple different stresses at the molecular level. Here, we investigated the phenotypic and transcriptomic response of cultivated sunflower (Helianthus annuus L.) seedlings to three water-related stresses (i.e., dry-down, an osmotic challenge, and salt stress), as well as a generalized low-nutrient stress. All four stresses negatively impacted seedling growth, with the nutrient stress having a more divergent response from control as compared to the water-related stresses. Phenotypic responses were consistent with expectations for growth in low-resource environments, including increased (i.e., less negative) carbon fractionation values and leaf C:N ratios, as well as increased belowground biomass allocation. The number of differentially expressed genes (DEGs) under stress was greater in leaf tissue, but roots exhibited a higher proportion of DEGs unique to individual stresses. Overall, the three water-related stresses had a more similar transcriptomic response to each other vs. nutrient stress, though this pattern was more pronounced in root vs. leaf tissue. In contrast to our DEG analyses, co-expression network analysis revealed that there was little indication of a shared response between the four stresses in despite the majority of DEGs being shared between multiple stresses. Importantly, osmotic stress, which is often used to simulate drought stress in experimental settings, had little transcriptomic resemblance to true water limitation (i.e., dry-down) in our study, calling into question its utility as a means for simulating drought.
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Affiliation(s)
- Max H. Barnhart
- Department of Plant Biology, University of Georgia, Athens, GA, United States of America
- * E-mail:
| | - Rishi R. Masalia
- Department of Plant Biology, University of Georgia, Athens, GA, United States of America
| | - Liana J. Mosley
- Department of Plant Biology, University of Georgia, Athens, GA, United States of America
| | - John M. Burke
- Department of Plant Biology, University of Georgia, Athens, GA, United States of America
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20
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Li A, Zhu L, Xu W, Liu L, Teng G. Recent advances in methods for in situ root phenotyping. PeerJ 2022; 10:e13638. [PMID: 35795176 PMCID: PMC9252182 DOI: 10.7717/peerj.13638] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/06/2022] [Indexed: 01/17/2023] Open
Abstract
Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.
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Affiliation(s)
- Anchang Li
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
| | - Lingxiao Zhu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultrual University, Baoding, Hebei, China
| | - Wenjun Xu
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
| | - Liantao Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultrual University, Baoding, Hebei, China
| | - Guifa Teng
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
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21
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Urfan M, Sharma S, Hakla HR, Rajput P, Andotra S, Lehana PK, Bhardwaj R, Khan MS, Das R, Kumar S, Pal S. Recent trends in root phenomics of plant systems with available methods- discrepancies and consonances. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2022; 28:1311-1321. [PMID: 35910442 PMCID: PMC9334470 DOI: 10.1007/s12298-022-01209-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 06/03/2023]
Abstract
The phenotyping of plant roots is a challenging task and poses a major lacuna in plant root research. Roots rhizospheric zone is affected by several environmental cues among which salinity, drought, heavy metal and soil pH are key players. Among biological factors, fungal, nematode and bacterial interactions with roots are vital for improving nutrient uptake efficiency in plants. The subterranean nature of a plant root and the limited number of approaches for root phenotyping offers a great challenge to the plant breeders to select a desirable root trait under different stress conditions. Identification of key root traits can provide a basic understanding for generating crop plants with enhanced ability to withstand various biotic or abiotic stresses. For instance, crops with improved soil exploration potential, phosphate uptake efficiency, water use efficiency and others. Laboratory methods such as hydroponics, rhizotron, rhizoslide and luminescence observatory for roots do not provide precise and desired root quantification attributes. Though 3D imaging by X-ray computed tomography (X-ray-CT) and magnetic resonance imaging techniques are complex, however, it provides the most applicable and practically relevant data for quantifying root system architecture traits. This review outlines the current developments in root studies including recent approaches viz. X-ray-CT, MRI, thermal infrared imaging and minirhizotron. Although root phenotyping is a laborious procedure, it offers multiple advantages by removing discrepancies and providing the actual practical significance of plant roots for breeding programs.
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Affiliation(s)
- Mohammad Urfan
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, 180006 India
| | - Shubham Sharma
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, 180006 India
| | - Haroon Rashid Hakla
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, 180006 India
| | - Prakriti Rajput
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, 180006 India
| | - Sonali Andotra
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, 180006 India
| | | | - Renu Bhardwaj
- Department of Botanical and Environmental Sciences, Guru Nanak Dev University, Amritsar, 143001 India
| | - M Suhail Khan
- USBT, Guru Gobind Singh Indraprastha University, Dwarka, 110 078 New Delhi India
| | - Ranjan Das
- Department of Crop Physiology, Assam Agricultural University, Jorhat, 785013 India
| | - Sunil Kumar
- Department of Statistics, University of Jammu, Jammu, 180006 India
| | - Sikander Pal
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, 180006 India
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22
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Hostetler AN, Erndwein L, Reneau JW, Stager A, Tanner HG, Cook D, Sparks EE. Multiple brace root phenotypes promote anchorage and limit root lodging in maize. PLANT, CELL & ENVIRONMENT 2022; 45:1573-1583. [PMID: 35141927 DOI: 10.1111/pce.14289] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/20/2021] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
Plant mechanical failure (lodging) causes global yield losses of 7%-66% in cereal crops. We have previously shown that the above-ground nodal roots (brace roots) in maize are critical for anchorage. However, it is unknown how brace root phenotypes vary across genotypes and the functional consequence of this variation. This study quantifies the contribution of brace roots to anchorage, brace root traits, plant height, and root lodging susceptibility in 52 maize inbred lines. We show that the contribution of brace roots to anchorage and root lodging susceptibility varies among genotypes and this contribution can be explained by plant architectural variation. Additionally, supervised machine learning models were developed and show that multiple plant architectural phenotypes can predict the contribution of brace roots to anchorage and root lodging susceptibility. Together these data define the plant architectures that are important in lodging resistance and show that the contribution of brace roots to anchorage is a good proxy for root lodging susceptibility.
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Affiliation(s)
- Ashley N Hostetler
- Department of Plant and Soil Sciences, The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
| | - Lindsay Erndwein
- Department of Plant and Soil Sciences, The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
| | - Jonathan W Reneau
- Department of Plant and Soil Sciences, The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
| | - Adam Stager
- Department of Plant and Soil Sciences, The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
- Department of Mechanical Engineering, University of Delaware, Newark, Delaware, USA
| | - Herbert G Tanner
- Department of Mechanical Engineering, University of Delaware, Newark, Delaware, USA
| | - Douglas Cook
- Department of Mechanical Engineering, Brigham Young University, Provo, Utah, USA
| | - Erin E Sparks
- Department of Plant and Soil Sciences, The Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
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23
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Woods P, Lehner KR, Hein K, Mullen JL, McKay JK. Root Pulling Force Across Drought in Maize Reveals Genotype by Environment Interactions and Candidate Genes. FRONTIERS IN PLANT SCIENCE 2022; 13:883209. [PMID: 35498695 PMCID: PMC9051544 DOI: 10.3389/fpls.2022.883209] [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: 02/24/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.
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Affiliation(s)
- Patrick Woods
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
- Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
| | - Kevin R. Lehner
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
| | - Kirsten Hein
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
- Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
| | - Jack L. Mullen
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
| | - John K. McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
- Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
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24
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Rajurkar AB, McCoy SM, Ruhter J, Mulcrone J, Freyfogle L, Leakey ADB. Installation and imaging of thousands of minirhizotrons to phenotype root systems of field-grown plants. PLANT METHODS 2022; 18:39. [PMID: 35346269 PMCID: PMC8958774 DOI: 10.1186/s13007-022-00874-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/10/2022] [Indexed: 05/27/2023]
Abstract
BACKGROUND Roots are vital to plant performance because they acquire resources from the soil and provide anchorage. However, it remains difficult to assess root system size and distribution because roots are inaccessible in the soil. Existing methods to phenotype entire root systems range from slow, often destructive, methods applied to relatively small numbers of plants in the field to rapid methods that can be applied to large numbers of plants in controlled environment conditions. Much has been learned recently by extensive sampling of the root crown portion of field-grown plants. But, information on large-scale genetic and environmental variation in the size and distribution of root systems in the field remains a key knowledge gap. Minirhizotrons are the only established, non-destructive technology that can address this need in a standard field trial. Prior experiments have used only modest numbers of minirhizotrons, which has limited testing to small numbers of genotypes or environmental conditions. This study addressed the need for methods to install and collect images from thousands of minirhizotrons and thereby help break the phenotyping bottleneck in the field. RESULTS Over three growing seasons, methods were developed and refined to install and collect images from up to 3038 minirhizotrons per experiment. Modifications were made to four tractors and hydraulic soil corers mounted to them. High quality installation was achieved at an average rate of up to 84.4 minirhizotron tubes per tractor per day. A set of four commercially available minirhizotron camera systems were each transported by wheelbarrow to allow collection of images of mature maize root systems at an average rate of up to 65.3 tubes per day per camera. This resulted in over 300,000 images being collected in as little as 11 days for a single experiment. CONCLUSION The scale of minirhizotron installation was increased by two orders of magnitude by simultaneously using four tractor-mounted, hydraulic soil corers with modifications to ensure high quality, rapid operation. Image collection can be achieved at the corresponding scale using commercially available minirhizotron camera systems. Along with recent advances in image analysis, these advances will allow use of minirhizotrons at unprecedented scale to address key knowledge gaps regarding genetic and environmental effects on root system size and distribution in the field.
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Affiliation(s)
- Ashish B. Rajurkar
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Scott M. McCoy
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jeremy Ruhter
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jessica Mulcrone
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Luke Freyfogle
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Andrew D. B. Leakey
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL USA
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25
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Lopez-Guerrero MG, Wang P, Phares F, Schachtman DP, Alvarez S, van Dijk K. A glass bead semi-hydroponic system for intact maize root exudate analysis and phenotyping. PLANT METHODS 2022; 18:25. [PMID: 35246193 PMCID: PMC8897885 DOI: 10.1186/s13007-022-00856-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although there have been numerous studies describing plant growth systems for root exudate collection, a common limitation is that these systems require disruption of the plant root system to facilitate exudate collection. Here, we present a newly designed semi-hydroponic system that uses glass beads as solid support to simulate soil impedance, which combined with drip irrigation, facilitates growth of healthy maize plants, collection and analysis of root exudates, and phenotyping of the roots with minimal growth disturbance or root damage. RESULTS This system was used to collect root exudates from seven maize genotypes using water or 1 mM CaCl2, and to measure root phenotype data using standard methods and the Digital imaging of root traits (DIRT) software. LC-MS/MS (Liquid Chromatography-Tandem Mass Spectrometry) and GC-MS (Gas Chromatography-Mass Spectrometry) targeted metabolomics platforms were used to detect and quantify metabolites in the root exudates. Phytohormones, some of which are reported in maize root exudates for the first time, the benzoxazinoid DIMBOA (2,4-Dihydroxy-7-methoxy-1,4-benzoxazin-3-one), amino acids, and sugars were detected and quantified. After validating the methodology using known concentrations of standards for the targeted compounds, we found that the choice of the exudate collection solution affected the exudation and analysis of a subset of analyzed metabolites. No differences between collection in water or CaCl2 were found for phytohormones and sugars. In contrast, the amino acids were more concentrated when water was used as the exudate collection solution. The collection in CaCl2 required a clean-up step before MS analysis which was found to interfere with the detection of a subset of the amino acids. Finally, using the phenotypic measurements and the metabolite data, significant differences between genotypes were found and correlations between metabolites and phenotypic traits were identified. CONCLUSIONS A new plant growth system combining glass beads supported hydroponics with semi-automated drip irrigation of sterile solutions was implemented to grow maize plants and collect root exudates without disturbing or damaging the roots. The validated targeted exudate metabolomics platform combined with root phenotyping provides a powerful tool to link plant root and exudate phenotypes to genotype and study the natural variation of plant populations.
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Affiliation(s)
| | - Peng Wang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Felicia Phares
- Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Daniel P Schachtman
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Sophie Alvarez
- Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.
| | - Karin van Dijk
- Biochemistry Department, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.
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26
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Schneider HM, Lor VSN, Hanlon MT, Perkins A, Kaeppler SM, Borkar AN, Bhosale R, Zhang X, Rodriguez J, Bucksch A, Bennett MJ, Brown KM, Lynch JP. Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15). PLANT, CELL & ENVIRONMENT 2022; 45:837-853. [PMID: 34169548 PMCID: PMC9544310 DOI: 10.1111/pce.14135] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 06/05/2021] [Accepted: 06/16/2021] [Indexed: 05/06/2023]
Abstract
Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.
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Affiliation(s)
- Hannah M. Schneider
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Vai Sa Nee Lor
- Department of AgronomyUniversity of WisconsinMadisonWisconsinUSA
| | - Meredith T. Hanlon
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Alden Perkins
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | | | - Aditi N. Borkar
- School of Veterinary Medicine and ScienceUniversity of NottinghamSutton BoningtonUK
| | - Rahul Bhosale
- Future Food Beacon of Excellence and School of BiosciencesUniversity of NottinghamNottinghamUK
| | - Xia Zhang
- Department of AgronomyUniversity of WisconsinMadisonWisconsinUSA
| | - Jonas Rodriguez
- Department of AgronomyUniversity of WisconsinMadisonWisconsinUSA
| | - Alexander Bucksch
- Department of Plant BiologyUniversity of GeorgiaAthensGeorgiaUSA
- Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAthensGeorgiaUSA
- Institute of BioinformaticsUniversity of GeorgiaAthensGeorgiaUSA
| | - Malcolm J. Bennett
- Future Food Beacon of Excellence and School of BiosciencesUniversity of NottinghamNottinghamUK
| | - Kathleen M. Brown
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Jonathan P. Lynch
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
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27
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Griffiths M, Delory BM, Jawahir V, Wong KM, Bagnall GC, Dowd TG, Nusinow DA, Miller AJ, Topp CN. Optimisation of root traits to provide enhanced ecosystem services in agricultural systems: A focus on cover crops. PLANT, CELL & ENVIRONMENT 2022; 45:751-770. [PMID: 34914117 PMCID: PMC9306666 DOI: 10.1111/pce.14247] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 11/05/2021] [Accepted: 12/01/2021] [Indexed: 05/26/2023]
Abstract
Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical and biotic properties of soil. However, cover crops, a group of plants that provide ecosystem services, can be utilised during fallow periods or used as an intercrop to restore soil health. The effectiveness of ecosystem services provided by cover crops varies widely as very little breeding has occurred in these species. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. In this study, we provide an overview of the range of belowground ecosystem services provided by cover crop roots: (1) soil structural remediation, (2) capture of soil resources and (3) maintenance of the rhizosphere and building of organic matter content. Based on the ecosystem services described, we outline current and promising phenotyping technologies and breeding strategies in cover crops that can enhance agricultural sustainability through improvement of root traits.
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Affiliation(s)
| | | | | | - Kong M. Wong
- Donald Danforth Plant Science CenterSt. LouisMissouriUSA
| | | | - Tyler G. Dowd
- Donald Danforth Plant Science CenterSt. LouisMissouriUSA
| | | | - Allison J. Miller
- Donald Danforth Plant Science CenterSt. LouisMissouriUSA
- Department of BiologySaint Louis UniversitySt. LouisMissouriUSA
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28
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Groen SC, Joly-Lopez Z, Platts AE, Natividad M, Fresquez Z, Mauck WM, Quintana MR, Cabral CLU, Torres RO, Satija R, Purugganan MD, Henry A. Evolutionary systems biology reveals patterns of rice adaptation to drought-prone agro-ecosystems. THE PLANT CELL 2022; 34:759-783. [PMID: 34791424 PMCID: PMC8824591 DOI: 10.1093/plcell/koab275] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/02/2021] [Indexed: 05/24/2023]
Abstract
Rice (Oryza sativa) was domesticated around 10,000 years ago and has developed into a staple for half of humanity. The crop evolved and is currently grown in stably wet and intermittently dry agro-ecosystems, but patterns of adaptation to differences in water availability remain poorly understood. While previous field studies have evaluated plant developmental adaptations to water deficit, adaptive variation in functional and hydraulic components, particularly in relation to gene expression, has received less attention. Here, we take an evolutionary systems biology approach to characterize adaptive drought resistance traits across roots and shoots. We find that rice harbors heritable variation in molecular, physiological, and morphological traits that is linked to higher fitness under drought. We identify modules of co-expressed genes that are associated with adaptive drought avoidance and tolerance mechanisms. These expression modules showed evidence of polygenic adaptation in rice subgroups harboring accessions that evolved in drought-prone agro-ecosystems. Fitness-linked expression patterns allowed us to identify the drought-adaptive nature of optimizing photosynthesis and interactions with arbuscular mycorrhizal fungi. Taken together, our study provides an unprecedented, integrative view of rice adaptation to water-limited field conditions.
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Affiliation(s)
- Simon C Groen
- Author for correspondence: (S.C.G.), (M.D.P.), (A.H.)
| | | | | | - Mignon Natividad
- International Rice Research Institute, Los Baños, Laguna, Philippines, USA
| | - Zoë Fresquez
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, USA
| | | | | | - Carlo Leo U Cabral
- International Rice Research Institute, Los Baños, Laguna, Philippines, USA
| | - Rolando O Torres
- International Rice Research Institute, Los Baños, Laguna, Philippines, USA
| | - Rahul Satija
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, USA
- New York Genome Center, New York, USA
| | | | - Amelia Henry
- Author for correspondence: (S.C.G.), (M.D.P.), (A.H.)
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29
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Chiteri KO, Jubery TZ, Dutta S, Ganapathysubramanian B, Cannon S, Singh A. Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel. FRONTIERS IN PLANT SCIENCE 2022; 12:808001. [PMID: 35154202 PMCID: PMC8828542 DOI: 10.3389/fpls.2021.808001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
Mung bean [Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points (days 12, 15, and 18 after germination). A broad sense heritability for days 15 (0.22-0.73) and 18 (0.23-0.87) was higher than that for day 12 (0.24-0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake "steep, cheap, and deep" ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.
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Affiliation(s)
- Kevin O. Chiteri
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Talukder Zaki Jubery
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
| | - Somak Dutta
- Department of Statistics, Iowa State University, Ames, IA, United States
| | | | - Steven Cannon
- Department of Agronomy, Iowa State University, Ames, IA, United States
- USDA—Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA, United States
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States
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Genome-Wide Association Study of Root System Architecture in Maize. Genes (Basel) 2022; 13:genes13020181. [PMID: 35205226 PMCID: PMC8872597 DOI: 10.3390/genes13020181] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/16/2022] [Accepted: 01/18/2022] [Indexed: 01/05/2023] Open
Abstract
Roots are important plant organs for the absorption of water and nutrients. To date, there have been few genome-wide association studies of maize root system architecture (RSA) in the field. The genetic basis of maize RSA is poorly understood, and the maize RSA-related genes that have been cloned are very limited. Here, 421 maize inbred lines of an association panel were planted to measure the root systems at the maturity stage, and a genome-wide association study was performed. There was a strong correlation among eight RSA traits, and the RSA traits were highly correlated with the aboveground plant architecture traits (e.g., plant height and ear leaf length, r = 0.13–0.25, p < 0.05). The RSA traits of the stiff stalk subgroup (SS) showed lower values than those of the non-stiff stalk subgroup (NSS) and tropical/subtropical subgroup (TST). Using the RSA traits, the genome-wide association study identified 63 SNPs and 189 candidate genes. Among them, nine candidate genes co-localized between RSA and aboveground architecture traits. A further co-expression analysis identified 88 candidate genes having high confidence levels. Furthermore, we identified four highly reliable RSA candidate genes, GRMZM2G099797, GRMZM2G354338, GRMZM2G085042, and GRMZM5G812926. This research provides theoretical support for the genetic improvement of maize root systems, and it identified candidate genes that may act as genetic resources for breeding.
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Duncan KE, Topp CN. Phenotyping Complex Plant Structures with a Large Format Industrial Scale High-Resolution X-Ray Tomography Instrument. Methods Mol Biol 2022; 2539:119-132. [PMID: 35895201 DOI: 10.1007/978-1-0716-2537-8_12] [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] [Indexed: 06/15/2023]
Abstract
Phenotyping specific plant traits is difficult when the samples to be measured are architecturally complex. Inflorescence and root system traits are of great biological interest, but these structures present unique phenotyping challenges due to their often complicated and three-dimensional (3D) forms. We describe how a large industrial scale X-ray tomography (XRT) instrument can be used to scan architecturally complex plant structures for the goal of rapid and accurate measurement of traits that are otherwise cumbersome or not possible to capture by other means. The combination of a large imaging cabinet that can accommodate a wide range of sample size geometries and a variable microfocus reflection X-ray source allows noninvasive X-ray imaging and 3D volume generation of diverse sample types. Specific sample fixturing (mounting) and scanning conditions are presented. These techniques can be moderate to high throughput and still provide unprecedented levels of accuracy and information content in the 3D volume data they generate.
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Affiliation(s)
- Keith E Duncan
- Donald Danforth Plant Science Center, Saint Louis, MO, USA.
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Heredia MC, Kant J, Prodhan MA, Dixit S, Wissuwa M. Breeding rice for a changing climate by improving adaptations to water saving technologies. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:17-33. [PMID: 34218290 DOI: 10.1007/s00122-021-03899-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 06/25/2021] [Indexed: 06/13/2023]
Abstract
Climate change is expected to increasingly affect rice production through rising temperatures and decreasing water availability. Unlike other crops, rice is a main contributor to greenhouse gas emissions due to methane emissions from flooded paddy fields. Climate change can therefore be addressed in two ways in rice: through making the crop more climate resilient and through changes in management practices that reduce methane emissions and thereby slow global warming. In this review, we focus on two water saving technologies that reduce the periods lowland rice will be grown under fully flooded conditions, thereby improving water use efficiency and reducing methane emissions. Rice breeding over the past decades has mostly focused on developing high-yielding varieties adapted to continuously flooded conditions where seedlings were raised in a nursery and transplanted into a puddled flooded soil. Shifting cultivation to direct-seeded rice or to introducing non-flooded periods as in alternate wetting and drying gives rise to new challenges which need to be addressed in rice breeding. New adaptive traits such as rapid uniform germination even under anaerobic conditions, seedling vigor, weed competitiveness, root plasticity, and moderate drought tolerance need to be bred into the current elite germplasm and to what extent this is being addressed through trait discovery, marker-assisted selection and population improvement are reviewed.
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Affiliation(s)
| | | | - M Asaduzzaman Prodhan
- Japan International Research Center for Agricultural Sciences (JIRCAS), Tsukuba, Japan
| | - Shalabh Dixit
- International Rice Research Institute (IRRI), Los Baños, The Philippines
| | - Matthias Wissuwa
- Japan International Research Center for Agricultural Sciences (JIRCAS), Tsukuba, Japan.
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Tabb A, Holguín GA, Naegele R. Using Cameras for Precise Measurement of Two-Dimensional Plant Features: CASS. Methods Mol Biol 2022; 2539:87-94. [PMID: 35895199 DOI: 10.1007/978-1-0716-2537-8_10] [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] [Indexed: 06/15/2023]
Abstract
Images are used frequently in plant phenotyping to capture measurements. This chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern. The method is based on calibrating the camera using information available from the EXIF tags from the image, as well as visual information from the pattern. Code is provided to implement the method, as well as a dataset for testing. We include steps to verify protocol correctness by imaging an artifact. The use of this protocol for two-dimensional plant phenotyping will allow data capture from different cameras and environments, with comparison on the same physical scale. We abbreviate this method as CASS, CAmera aS Scanner.
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Affiliation(s)
- Amy Tabb
- United States Department of Agriculture, Agricultural Research Service, Appalachian Fruit Research Station (USDA-ARS-AFRS), Kearneysville, WV, USA.
| | - Germán A Holguín
- Electrical Engineering Department, Universidad Tecnológica de Pereira, Pereira, Colombia
| | - Rachel Naegele
- United States Department of Agriculture, Agricultural Research Service, Sugarbeet and Bean Research Unit (USDA-ARS), East Lansing, MI, USA
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Kawa D, Taylor T, Thiombiano B, Musa Z, Vahldick HE, Walmsley A, Bucksch A, Bouwmeester H, Brady SM. Characterization of growth and development of sorghum genotypes with differential susceptibility to Striga hermonthica. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:7970-7983. [PMID: 34410382 PMCID: PMC8643648 DOI: 10.1093/jxb/erab380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 08/17/2021] [Indexed: 06/13/2023]
Abstract
Two sorghum varieties, Shanqui Red (SQR) and SRN39, have distinct levels of susceptibility to the parasitic weed Striga hermonthica, which have been attributed to different strigolactone composition within their root exudates. Root exudates of the Striga-susceptible variety Shanqui Red (SQR) contain primarily 5-deoxystrigol, which has a high efficiency for inducing Striga germination. SRN39 roots primarily exude orobanchol, leading to reduced Striga germination and making this variety resistant to Striga. The structural diversity in exuded strigolactones is determined by a polymorphism in the LOW GERMINATION STIMULANT 1 (LGS1) locus. Yet, the genetic diversity between SQR and SRN39 is broad and has not been addressed in terms of growth and development. Here, we demonstrate additional differences between SQR and SRN39 by phenotypic and molecular characterization. A suite of genes related to metabolism was differentially expressed between SQR and SRN39. Increased levels of gibberellin precursors in SRN39 were accompanied by slower growth rate and developmental delay and we observed an overall increased SRN39 biomass. The slow-down in growth and differences in transcriptome profiles of SRN39 were strongly associated with plant age. Additionally, enhanced lateral root growth was observed in SRN39 and three additional genotypes exuding primarily orobanchol. In summary, we demonstrate that the differences between SQR and SRN39 reach further than the changes in strigolactone profile in the root exudate and translate into alterations in growth and development.
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Affiliation(s)
- Dorota Kawa
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA, USA
| | - Tamera Taylor
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA, USA
- Plant Biology Graduate Group, University of California, Davis, Davis, CA, USA
| | - Benjamin Thiombiano
- Plant Hormone Biology Group, Green Life Sciences Cluster, Swammerdam Institute for Life Science, University of Amsterdam, Amsterdam, the Netherlands
| | - Zayan Musa
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA, USA
| | - Hannah E Vahldick
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA, USA
| | - Aimee Walmsley
- Plant Hormone Biology Group, Green Life Sciences Cluster, Swammerdam Institute for Life Science, University of Amsterdam, Amsterdam, the Netherlands
| | - Alexander Bucksch
- Department of Plant Biology, University of Georgia, Athens, GA, USA
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
- Warnell School of Forestry and Natural Resources, University of Georgia, GA, USA
| | - Harro Bouwmeester
- Plant Hormone Biology Group, Green Life Sciences Cluster, Swammerdam Institute for Life Science, University of Amsterdam, Amsterdam, the Netherlands
| | - Siobhan M Brady
- Department of Plant Biology and Genome Center, University of California, Davis, Davis, CA, USA
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Seethepalli A, Dhakal K, Griffiths M, Guo H, Freschet GT, York LM. RhizoVision Explorer: open-source software for root image analysis and measurement standardization. AOB PLANTS 2021; 13:plab056. [PMID: 34804466 PMCID: PMC8598384 DOI: 10.1093/aobpla/plab056] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 10/23/2021] [Indexed: 05/10/2023]
Abstract
Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.
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Affiliation(s)
| | - Kundan Dhakal
- Noble Research Institute, LLC, Ardmore, OK 73401, USA
| | | | - Haichao Guo
- Noble Research Institute, LLC, Ardmore, OK 73401, USA
| | | | - Larry M York
- Noble Research Institute, LLC, Ardmore, OK 73401, USA
- Biosciences Division and Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
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Aguilar JJ, Moore M, Johnson L, Greenhut RF, Rogers E, Walker D, O’Neil F, Edwards JL, Thystrup J, Farrow S, Windle JB, Benfey PN. Capturing in-field root system dynamics with RootTracker. PLANT PHYSIOLOGY 2021; 187:1117-1130. [PMID: 34618063 PMCID: PMC8566282 DOI: 10.1093/plphys/kiab352] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 06/30/2021] [Indexed: 06/13/2023]
Abstract
Optimizing root system architecture offers a promising approach to developing stress tolerant cultivars in the face of climate change, as root systems are critical for water and nutrient uptake as well as mechanical stability. However, breeding for optimal root system architecture has been hindered by the difficulty in measuring root growth in the field. Here, we describe the RootTracker, a technology that employs impedance touch sensors to monitor in-field root growth over time. Configured in a cylindrical, window shutter-like fashion around a planted seed, 264 electrodes are individually charged multiple times over the course of an experiment. Signature changes in the measured capacitance and resistance readings indicate when a root has touched or grown close to an electrode. Using the RootTracker, we have measured root system dynamics of commercial maize (Zea mays) hybrids growing in both typical Midwest field conditions and under different irrigation regimes. We observed rapid responses of root growth to water deficits and found evidence for a "priming response" in which an early water deficit causes more and deeper roots to grow at later time periods. Genotypic variation among hybrid maize lines in their root growth in response to drought indicated a potential to breed for root systems adapted for different environments. Thus, the RootTracker is able to capture changes in root growth over time in response to environmental perturbations.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Philip N Benfey
- Hi Fidelity Genetics, Durham, NC USA
- Duke University, Department of Biology, Durham, NC, USA and Howard Hughes Medical Institute (HHMI)
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Li M, Coneva V, Robbins KR, Clark D, Chitwood D, Frank M. Quantitative dissection of color patterning in the foliar ornamental coleus. PLANT PHYSIOLOGY 2021; 187:1310-1324. [PMID: 34618067 PMCID: PMC8566300 DOI: 10.1093/plphys/kiab393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/17/2021] [Indexed: 05/04/2023]
Abstract
Coleus (Coleus scutellarioides) is a popular ornamental plant that exhibits a diverse array of foliar color patterns. New cultivars are currently hand selected by both amateur and experienced plant breeders. In this study, we reimagine breeding for color patterning using a quantitative color analysis framework. Despite impressive advances in high-throughput data collection and processing, complex color patterns remain challenging to extract from image datasets. Using a phenotyping approach called "ColourQuant," we extract and analyze pigmentation patterns from one of the largest coleus breeding populations in the world. Working with this massive dataset, we can analyze quantitative relationships between maternal plants and their progeny, identify features that underlie breeder-selections, and collect and compare public input on trait preferences. This study is one of the most comprehensive explorations into complex color patterning in plant biology and provides insights and tools for exploring the color pallet of the plant kingdom.
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Affiliation(s)
- Mao Li
- Donald Danforth Plant Science Center, St Louis, Missouri 63132, USA
| | - Viktoriya Coneva
- Donald Danforth Plant Science Center, St Louis, Missouri 63132, USA
| | - Kelly R Robbins
- School of Integrative Plant Science, Cornell University, Ithaca, New York 14850, USA
| | - David Clark
- Department of Environmental Horticulture, University of Florida, Gainesville, Florida 32611-0670, USA
| | - Dan Chitwood
- Department of Horticulture, Michigan State University, East Lansing, Michigan 48824, USA
- Department of Computational Mathematics, Michigan State University, East Lansing, Michigan 48824, USA
| | - Margaret Frank
- Donald Danforth Plant Science Center, St Louis, Missouri 63132, USA
- School of Integrative Plant Science, Cornell University, Ithaca, New York 14850, USA
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Freschet GT, Pagès L, Iversen CM, Comas LH, Rewald B, Roumet C, Klimešová J, Zadworny M, Poorter H, Postma JA, Adams TS, Bagniewska‐Zadworna A, Bengough AG, Blancaflor EB, Brunner I, Cornelissen JHC, Garnier E, Gessler A, Hobbie SE, Meier IC, Mommer L, Picon‐Cochard C, Rose L, Ryser P, Scherer‐Lorenzen M, Soudzilovskaia NA, Stokes A, Sun T, Valverde‐Barrantes OJ, Weemstra M, Weigelt A, Wurzburger N, York LM, Batterman SA, Gomes de Moraes M, Janeček Š, Lambers H, Salmon V, Tharayil N, McCormack ML. A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements. THE NEW PHYTOLOGIST 2021; 232:973-1122. [PMID: 34608637 PMCID: PMC8518129 DOI: 10.1111/nph.17572] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 03/22/2021] [Indexed: 05/17/2023]
Abstract
In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I-VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.
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Affiliation(s)
- Grégoire T. Freschet
- CEFEUniv Montpellier, CNRS, EPHE, IRD1919 route de MendeMontpellier34293France
- Station d’Ecologie Théorique et ExpérimentaleCNRS2 route du CNRS09200MoulisFrance
| | - Loïc Pagès
- UR 1115 PSHCentre PACA, site AgroparcINRAE84914Avignon cedex 9France
| | - Colleen M. Iversen
- Environmental Sciences Division and Climate Change Science InstituteOak Ridge National LaboratoryOak RidgeTN37831USA
| | - Louise H. Comas
- USDA‐ARS Water Management Research Unit2150 Centre Avenue, Bldg D, Suite 320Fort CollinsCO80526USA
| | - Boris Rewald
- Department of Forest and Soil SciencesUniversity of Natural Resources and Life SciencesVienna1190Austria
| | - Catherine Roumet
- CEFEUniv Montpellier, CNRS, EPHE, IRD1919 route de MendeMontpellier34293France
| | - Jitka Klimešová
- Department of Functional EcologyInstitute of Botany CASDukelska 13537901TrebonCzech Republic
| | - Marcin Zadworny
- Institute of DendrologyPolish Academy of SciencesParkowa 562‐035KórnikPoland
| | - Hendrik Poorter
- Plant Sciences (IBG‐2)Forschungszentrum Jülich GmbHD‐52425JülichGermany
- Department of Biological SciencesMacquarie UniversityNorth RydeNSW2109Australia
| | | | - Thomas S. Adams
- Department of Plant SciencesThe Pennsylvania State UniversityUniversity ParkPA16802USA
| | - Agnieszka Bagniewska‐Zadworna
- Department of General BotanyInstitute of Experimental BiologyFaculty of BiologyAdam Mickiewicz UniversityUniwersytetu Poznańskiego 661-614PoznańPoland
| | - A. Glyn Bengough
- The James Hutton InstituteInvergowrie, Dundee,DD2 5DAUK
- School of Science and EngineeringUniversity of DundeeDundee,DD1 4HNUK
| | | | - Ivano Brunner
- Forest Soils and BiogeochemistrySwiss Federal Research Institute WSLZürcherstr. 1118903BirmensdorfSwitzerland
| | - Johannes H. C. Cornelissen
- Department of Ecological ScienceFaculty of ScienceVrije Universiteit AmsterdamDe Boelelaan 1085Amsterdam1081 HVthe Netherlands
| | - Eric Garnier
- CEFEUniv Montpellier, CNRS, EPHE, IRD1919 route de MendeMontpellier34293France
| | - Arthur Gessler
- Forest DynamicsSwiss Federal Research Institute WSLZürcherstr. 1118903BirmensdorfSwitzerland
- Institute of Terrestrial EcosystemsETH Zurich8092ZurichSwitzerland
| | - Sarah E. Hobbie
- Department of Ecology, Evolution and BehaviorUniversity of MinnesotaSt PaulMN55108USA
| | - Ina C. Meier
- Functional Forest EcologyUniversity of HamburgHaidkrugsweg 122885BarsbütelGermany
| | - Liesje Mommer
- Plant Ecology and Nature Conservation GroupDepartment of Environmental SciencesWageningen University and ResearchPO Box 476700 AAWageningenthe Netherlands
| | | | - Laura Rose
- Station d’Ecologie Théorique et ExpérimentaleCNRS2 route du CNRS09200MoulisFrance
- Senckenberg Biodiversity and Climate Research Centre (BiK-F)Senckenberganlage 2560325Frankfurt am MainGermany
| | - Peter Ryser
- Laurentian University935 Ramsey Lake RoadSudburyONP3E 2C6Canada
| | | | - Nadejda A. Soudzilovskaia
- Environmental Biology DepartmentInstitute of Environmental SciencesCMLLeiden UniversityLeiden2300 RAthe Netherlands
| | - Alexia Stokes
- INRAEAMAPCIRAD, IRDCNRSUniversity of MontpellierMontpellier34000France
| | - Tao Sun
- Institute of Applied EcologyChinese Academy of SciencesShenyang110016China
| | - Oscar J. Valverde‐Barrantes
- International Center for Tropical BotanyDepartment of Biological SciencesFlorida International UniversityMiamiFL33199USA
| | - Monique Weemstra
- CEFEUniv Montpellier, CNRS, EPHE, IRD1919 route de MendeMontpellier34293France
| | - Alexandra Weigelt
- Systematic Botany and Functional BiodiversityInstitute of BiologyLeipzig UniversityJohannisallee 21-23Leipzig04103Germany
| | - Nina Wurzburger
- Odum School of EcologyUniversity of Georgia140 E. Green StreetAthensGA30602USA
| | - Larry M. York
- Biosciences Division and Center for Bioenergy InnovationOak Ridge National LaboratoryOak RidgeTN37831USA
| | - Sarah A. Batterman
- School of Geography and Priestley International Centre for ClimateUniversity of LeedsLeedsLS2 9JTUK
- Cary Institute of Ecosystem StudiesMillbrookNY12545USA
| | - Moemy Gomes de Moraes
- Department of BotanyInstitute of Biological SciencesFederal University of Goiás1974690-900Goiânia, GoiásBrazil
| | - Štěpán Janeček
- School of Biological SciencesThe University of Western Australia35 Stirling HighwayCrawley (Perth)WA 6009Australia
| | - Hans Lambers
- School of Biological SciencesThe University of Western AustraliaCrawley (Perth)WAAustralia
| | - Verity Salmon
- Environmental Sciences Division and Climate Change Science InstituteOak Ridge National LaboratoryOak RidgeTN37831USA
| | - Nishanth Tharayil
- Department of Plant and Environmental SciencesClemson UniversityClemsonSC29634USA
| | - M. Luke McCormack
- Center for Tree ScienceMorton Arboretum, 4100 Illinois Rt. 53LisleIL60532USA
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Freschet GT, Pagès L, Iversen CM, Comas LH, Rewald B, Roumet C, Klimešová J, Zadworny M, Poorter H, Postma JA, Adams TS, Bagniewska-Zadworna A, Bengough AG, Blancaflor EB, Brunner I, Cornelissen JHC, Garnier E, Gessler A, Hobbie SE, Meier IC, Mommer L, Picon-Cochard C, Rose L, Ryser P, Scherer-Lorenzen M, Soudzilovskaia NA, Stokes A, Sun T, Valverde-Barrantes OJ, Weemstra M, Weigelt A, Wurzburger N, York LM, Batterman SA, Gomes de Moraes M, Janeček Š, Lambers H, Salmon V, Tharayil N, McCormack ML. A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements. THE NEW PHYTOLOGIST 2021. [PMID: 34608637 DOI: 10.1111/nph.17572.hal-03379708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I-VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.
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Affiliation(s)
- Grégoire T Freschet
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, 1919 route de Mende, Montpellier, 34293, France
- Station d'Ecologie Théorique et Expérimentale, CNRS, 2 route du CNRS, 09200, Moulis, France
| | - Loïc Pagès
- UR 1115 PSH, Centre PACA, site Agroparc, INRAE, 84914, Avignon cedex 9, France
| | - Colleen M Iversen
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Louise H Comas
- USDA-ARS Water Management Research Unit, 2150 Centre Avenue, Bldg D, Suite 320, Fort Collins, CO, 80526, USA
| | - Boris Rewald
- Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna, 1190, Austria
| | - Catherine Roumet
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, 1919 route de Mende, Montpellier, 34293, France
| | - Jitka Klimešová
- Department of Functional Ecology, Institute of Botany CAS, Dukelska 135, 37901, Trebon, Czech Republic
| | - Marcin Zadworny
- Institute of Dendrology, Polish Academy of Sciences, Parkowa 5, 62-035, Kórnik, Poland
| | - Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425, Jülich, Germany
- Department of Biological Sciences, Macquarie University, North Ryde, NSW, 2109, Australia
| | - Johannes A Postma
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425, Jülich, Germany
| | - Thomas S Adams
- Department of Plant Sciences, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Agnieszka Bagniewska-Zadworna
- Department of General Botany, Institute of Experimental Biology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznańskiego 6, 61-614, Poznań, Poland
| | - A Glyn Bengough
- The James Hutton Institute, Invergowrie, Dundee,, DD2 5DA, UK
- School of Science and Engineering, University of Dundee, Dundee,, DD1 4HN, UK
| | - Elison B Blancaflor
- Noble Research Institute, LLC, 2510 Sam Noble Parkway, Ardmore, OK, 73401, USA
| | - Ivano Brunner
- Forest Soils and Biogeochemistry, Swiss Federal Research Institute WSL, Zürcherstr. 111, 8903, Birmensdorf, Switzerland
| | - Johannes H C Cornelissen
- Department of Ecological Science, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1085, Amsterdam, 1081 HV, the Netherlands
| | - Eric Garnier
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, 1919 route de Mende, Montpellier, 34293, France
| | - Arthur Gessler
- Forest Dynamics, Swiss Federal Research Institute WSL, Zürcherstr. 111, 8903, Birmensdorf, Switzerland
- Institute of Terrestrial Ecosystems, ETH Zurich, 8092, Zurich, Switzerland
| | - Sarah E Hobbie
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, MN, 55108, USA
| | - Ina C Meier
- Functional Forest Ecology, University of Hamburg, Haidkrugsweg 1, 22885, Barsbütel, Germany
| | - Liesje Mommer
- Plant Ecology and Nature Conservation Group, Department of Environmental Sciences, Wageningen University and Research, PO Box 47, 6700 AA, Wageningen, the Netherlands
| | | | - Laura Rose
- Station d'Ecologie Théorique et Expérimentale, CNRS, 2 route du CNRS, 09200, Moulis, France
- Senckenberg Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, 60325, Frankfurt am Main, Germany
| | - Peter Ryser
- Laurentian University, 935 Ramsey Lake Road, Sudbury, ON, P3E 2C6, Canada
| | | | - Nadejda A Soudzilovskaia
- Environmental Biology Department, Institute of Environmental Sciences, CML, Leiden University, Leiden, 2300 RA, the Netherlands
| | - Alexia Stokes
- INRAE, AMAP, CIRAD, IRD, CNRS, University of Montpellier, Montpellier, 34000, France
| | - Tao Sun
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Oscar J Valverde-Barrantes
- International Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, FL, 33199, USA
| | - Monique Weemstra
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, 1919 route de Mende, Montpellier, 34293, France
| | - Alexandra Weigelt
- Systematic Botany and Functional Biodiversity, Institute of Biology, Leipzig University, Johannisallee 21-23, Leipzig, 04103, Germany
| | - Nina Wurzburger
- Odum School of Ecology, University of Georgia, 140 E. Green Street, Athens, GA, 30602, USA
| | - Larry M York
- Biosciences Division and Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Sarah A Batterman
- School of Geography and Priestley International Centre for Climate, University of Leeds, Leeds, LS2 9JT, UK
- Cary Institute of Ecosystem Studies, Millbrook, NY, 12545, USA
| | - Moemy Gomes de Moraes
- Department of Botany, Institute of Biological Sciences, Federal University of Goiás, 19, 74690-900, Goiânia, Goiás, Brazil
| | - Štěpán Janeček
- School of Biological Sciences, The University of Western Australia, 35 Stirling Highway, Crawley (Perth), WA 6009, Australia
| | - Hans Lambers
- School of Biological Sciences, The University of Western Australia, Crawley (Perth), WA, Australia
| | - Verity Salmon
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Nishanth Tharayil
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, 29634, USA
| | - M Luke McCormack
- Center for Tree Science, Morton Arboretum, 4100 Illinois Rt. 53, Lisle, IL, 60532, USA
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Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays). PLANT PHYSIOLOGY 2021; 187:739-757. [PMID: 34608967 PMCID: PMC8491025 DOI: 10.1093/plphys/kiab311] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 06/09/2021] [Indexed: 05/25/2023]
Abstract
The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.
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Affiliation(s)
- Suxing Liu
- Department of Plant Biology, University of Georgia, Athens, Georgia 30602, USA
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia 30602, USA
- Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, USA
| | | | - Meredith Hanlon
- Department of Plant Science, Pennsylvania State University, State College, Pennsylvania 16802, USA
| | - Jonathan P. Lynch
- Department of Plant Science, Pennsylvania State University, State College, Pennsylvania 16802, USA
| | - Alexander Bucksch
- Department of Plant Biology, University of Georgia, Athens, Georgia 30602, USA
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia 30602, USA
- Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, USA
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Danilevicz MF, Bayer PE, Nestor BJ, Bennamoun M, Edwards D. Resources for image-based high-throughput phenotyping in crops and data sharing challenges. PLANT PHYSIOLOGY 2021; 187:699-715. [PMID: 34608963 PMCID: PMC8561249 DOI: 10.1093/plphys/kiab301] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 05/26/2021] [Indexed: 05/06/2023]
Abstract
High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Benjamin J. Nestor
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, University of Western Australia, Perth, Western Australia 6009, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, Western Australia 6009, Australia
- Author for communication:
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Le Ru A, Ibarcq G, Boniface MC, Baussart A, Muños S, Chabaud M. Image analysis for the automatic phenotyping of Orobanche cumana tubercles on sunflower roots. PLANT METHODS 2021; 17:80. [PMID: 34289852 PMCID: PMC8293553 DOI: 10.1186/s13007-021-00779-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 07/07/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND The parasitic plant Orobanche cumana is one of the most important threats to sunflower crops in Europe. Resistant sunflower varieties have been developed, but new O. cumana races have evolved and have overcome introgressed resistance genes, leading to the recurrent need for new resistance methods. Screening for resistance requires the phenotyping of thousands of sunflower plants to various O. cumana races. Most phenotyping experiments have been performed in fields at the later stage of the interaction, requiring time and space. A rapid phenotyping screening method under controlled conditions would need less space and would allow screening for resistance of many sunflower genotypes. Our study proposes a phenotyping tool for the sunflower/O. cumana interaction under controlled conditions through image analysis for broomrape tubercle analysis at early stages of the interaction. RESULTS We optimized the phenotyping of sunflower/O. cumana interactions by using rhizotrons (transparent Plexiglas boxes) in a growth chamber to control culture conditions and Orobanche inoculum. We used a Raspberry Pi computer with a picamera for acquiring images of inoculated sunflower roots 3 weeks post inoculation. We set up a macro using ImageJ free software for the automatic counting of the number of tubercles. This phenotyping tool was named RhizOSun. We evaluated five sunflower genotypes inoculated with two O. cumana races and showed that automatic counting of the number of tubercles using RhizOSun was highly correlated with manual time-consuming counting and could be efficiently used for screening sunflower genotypes at the tubercle stage. CONCLUSION This method is rapid, accurate and low-cost. It allows rapid imaging of numerous rhizotrons over time, and it enables image tracking of all the data with time kinetics. This paves the way toward automatization of phenotyping in rhizotrons that could be used for other root phenotyping, such as symbiotic nodules on legumes.
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Affiliation(s)
- A Le Ru
- FRAIB, Castanet-Tolosan, France
| | - G Ibarcq
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | - M- C Boniface
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | | | - S Muños
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | - M Chabaud
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France.
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Moussa AA, Mandozai A, Jin Y, Qu J, Zhang Q, Zhao H, Anwari G, Khalifa MAS, Lamboro A, Noman M, Bakasso Y, Zhang M, Guan S, Wang P. Genome-wide association screening and verification of potential genes associated with root architectural traits in maize (Zea mays L.) at multiple seedling stages. BMC Genomics 2021; 22:558. [PMID: 34284723 PMCID: PMC8290564 DOI: 10.1186/s12864-021-07874-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 07/05/2021] [Indexed: 01/26/2023] Open
Abstract
Background Breeding for new maize varieties with propitious root systems has tremendous potential in improving water and nutrients use efficiency and plant adaptation under suboptimal conditions. To date, most of the previously detected root-related trait genes in maize were new without functional verification. In this study, seven seedling root architectural traits were examined at three developmental stages in a recombinant inbred line population (RIL) of 179 RILs and a genome-wide association study (GWAS) panel of 80 elite inbred maize lines through quantitative trait loci (QTL) mapping and genome-wide association study. Results Using inclusive composite interval mapping, 8 QTLs accounting for 6.44–8.83 % of the phenotypic variation in root traits, were detected on chromosomes 1 (qRDWv3-1-1 and qRDW/SDWv3-1-1), 2 (qRBNv1-2-1), 4 (qSUAv1-4-1, qSUAv2-4-1, and qROVv2-4-1), and 10 (qTRLv1-10-1, qRBNv1-10-1). GWAS analysis involved three models (EMMAX, FarmCPU, and MLM) for a set of 1,490,007 high-quality single nucleotide polymorphisms (SNPs) obtained via whole genome next-generation sequencing (NGS). Overall, 53 significant SNPs with a phenotypic contribution rate ranging from 5.10 to 30.2 % and spread all over the ten maize chromosomes exhibited associations with the seven root traits. 17 SNPs were repeatedly detected from at least two growth stages, with several SNPs associated with multiple traits stably identified at all evaluated stages. Within the average linkage disequilibrium (LD) distance of 5.2 kb for the significant SNPs, 46 candidate genes harboring substantial SNPs were identified. Five potential genes viz. Zm00001d038676, Zm00001d015379, Zm00001d018496, Zm00001d050783, and Zm00001d017751 were verified for expression levels using maize accessions with extreme root branching differences from the GWAS panel and the RIL population. The results showed significantly (P < 0.001) different expression levels between the outer materials in both panels and at all considered growth stages. Conclusions This study provides a key reference for uncovering the complex genetic mechanism of root development and genetic enhancement of maize root system architecture, thus supporting the breeding of high-yielding maize varieties with propitious root systems. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07874-x.
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Affiliation(s)
- Abdourazak Alio Moussa
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China.
| | - Ajmal Mandozai
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | - Yukun Jin
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | - Jing Qu
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | - Qi Zhang
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | - He Zhao
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | - Gulaqa Anwari
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | | | - Abraham Lamboro
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | - Muhammad Noman
- College of Life Sciences, Jilin Agricultural University, Jilin, 130118, Changchun, China
| | - Yacoubou Bakasso
- Biology Department, Faculty of Sciences and Techniques, Abdou Moumouni University of Niamey, 10662, Niamey, Niger
| | - Mo Zhang
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | - Shuyan Guan
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China
| | - Piwu Wang
- College of Agronomy, Plant Biotechnology Center, Jilin Agricultural University, 130118, Changchun, Jilin, China.
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Dowd T, McInturf S, Li M, Topp CN. Rated-M for mesocosm: allowing the multimodal analysis of mature root systems in 3D. Emerg Top Life Sci 2021; 5:249-260. [PMID: 33555320 PMCID: PMC8166344 DOI: 10.1042/etls20200278] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/12/2021] [Accepted: 01/18/2021] [Indexed: 11/17/2022]
Abstract
A plants' water and nutrients are primarily absorbed through roots, which in a natural setting is highly dependent on the 3-dimensional configuration of the root system, collectively known as root system architecture (RSA). RSA is difficult to study due to a variety of factors, accordingly, an arsenal of methods have been developed to address the challenges of both growing root systems for imaging, and the imaging methods themselves, although there is no 'best' method as each has its own spectrum of trade-offs. Here, we describe several methods for plant growth or imaging. Then, we introduce the adaptation and integration of three complementary methods, root mesocosms, photogrammetry, and electrical resistance tomography (ERT). Mesocosms can allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. The recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Lastly, we demonstrate how 3-dimensional water availability can be measured using ERT inside of root mesocosms.
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Affiliation(s)
- Tyler Dowd
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Samuel McInturf
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Mao Li
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Christopher N Topp
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
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Zingaretti LM, Monfort A, Pérez-Enciso M. Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9812910. [PMID: 34056620 PMCID: PMC8139333 DOI: 10.34133/2021/9812910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 04/20/2021] [Indexed: 06/01/2023]
Abstract
Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.
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Affiliation(s)
- Laura M. Zingaretti
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193 Bellaterra, Barcelona, Spain
| | - Amparo Monfort
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193 Bellaterra, Barcelona, Spain
- Institut de Recerca i Tecnologia Agroalimentàries (IRTA), 08193 Barcelona, Spain
| | - Miguel Pérez-Enciso
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193 Bellaterra, Barcelona, Spain
- ICREA, Passeig de Lluís Companys 23, 08010 Barcelona, Spain
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Colom SM, Baucom RS. Below-ground competition favors character convergence but not character displacement in root traits. THE NEW PHYTOLOGIST 2021; 229:3195-3207. [PMID: 33220075 DOI: 10.1111/nph.17100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 11/17/2020] [Indexed: 06/11/2023]
Abstract
Character displacement can play a major role in species ecology and evolution; however, research testing whether character displacement can influence the evolution of root traits in plant systems remains scarce in the literature. Here we investigated the potential that character displacement may influence the evolution of root traits using two closely related morning glory species, Ipomoea purpurea and Ipomoea hederacea. We performed a field experiment where we grew the common morning glory, I. purpurea, in the presence and absence of competition from I. hederacea and examined the potential that the process of character displacement could influence the evolution of root traits. We found maternal line variation in root phenotypes and evidence that below-ground competition acts as an agent of selection on these traits. Our test of character displacement, however, showed evidence of character convergence on our measure of root architecture rather than displacement. These results suggest that plants may be constrained by their local environments to express a phenotype that enhances fitness. Therefore, the conditions of the competitive environment experienced by a plant may influence the potential for character convergence or displacement to influence the evolution of root traits.
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Affiliation(s)
- Sara M Colom
- University of Michigan, 4034 Biological Sciences Building, Ann Arbor, MI, 48109, USA
| | - Regina S Baucom
- University of Michigan, 4034 Biological Sciences Building, Ann Arbor, MI, 48109, USA
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Abstract
A semi-hydroponic phenotyping platform was constructed using inexpensive and easily obtained materials for characterizing root trait variability in a large set of chickpea (Cicer arietinum) germplasm. The system was designed to accommodate a large number of plants in a small area allowing relatively deeper root development, and thus serves as a high-throughput phenotyping tool for studying root dynamic growth. The root trait quantitative platform could provide accurate phenotyping data for parameterizing root models and for genome-wide association analyses or mapping studies of quantitative trait loci.
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Shao MR, Jiang N, Li M, Howard A, Lehner K, Mullen JL, Gunn SL, McKay JK, Topp CN. Complementary Phenotyping of Maize Root System Architecture by Root Pulling Force and X-Ray Imaging. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9859254. [PMID: 34870229 PMCID: PMC8603028 DOI: 10.34133/2021/9859254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/05/2021] [Indexed: 05/11/2023]
Abstract
The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.
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Affiliation(s)
- M. R. Shao
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - N. Jiang
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - M. Li
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - A. Howard
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - K. Lehner
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - J. L. Mullen
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - S. L. Gunn
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - J. K. McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - C. N. Topp
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
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Wild Isolates of Neurospora crassa Reveal Three Conidiophore Architectural Phenotypes. Microorganisms 2020; 8:microorganisms8111760. [PMID: 33182369 PMCID: PMC7695285 DOI: 10.3390/microorganisms8111760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/04/2020] [Accepted: 11/06/2020] [Indexed: 12/25/2022] Open
Abstract
The vegetative life cycle in the model filamentous fungus, Neurospora crassa, relies on the development of conidiophores to produce new spores. Environmental, temporal, and genetic components of conidiophore development have been well characterized; however, little is known about their morphological variation. We explored conidiophore architectural variation in a natural population using a wild population collection of 21 strains from Louisiana, United States of America (USA). Our work reveals three novel architectural phenotypes, Wild Type, Bulky, and Wrap, and shows their maintenance throughout the duration of conidiophore development. Furthermore, we present a novel image-classifier using a convolutional neural network specifically developed to assign conidiophore architectural phenotypes in a high-throughput manner. To estimate an inheritance model for this discrete complex trait, crosses between strains of each phenotype were conducted, and conidiophores of subsequent progeny were characterized using the trained classifier. Our model suggests that conidiophore architecture is controlled by at least two genes and has a heritability of 0.23. Additionally, we quantified the number of conidia produced by each conidiophore type and their dispersion distance, suggesting that conidiophore architectural phenotype may impact N. crassa colonization capacity.
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50
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Rosero A, Granda L, Berdugo-Cely JA, Šamajová O, Šamaj J, Cerkal R. A Dual Strategy of Breeding for Drought Tolerance and Introducing Drought-Tolerant, Underutilized Crops into Production Systems to Enhance Their Resilience to Water Deficiency. PLANTS (BASEL, SWITZERLAND) 2020; 9:E1263. [PMID: 32987964 PMCID: PMC7600178 DOI: 10.3390/plants9101263] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/19/2020] [Accepted: 09/22/2020] [Indexed: 02/06/2023]
Abstract
Water scarcity is the primary constraint on crop productivity in arid and semiarid tropical areas suffering from climate alterations; in accordance, agricultural systems have to be optimized. Several concepts and strategies should be considered to improve crop yield and quality, particularly in vulnerable regions where such environmental changes cause a risk of food insecurity. In this work, we review two strategies aiming to increase drought stress tolerance: (i) the use of natural genes that have evolved over time and are preserved in crop wild relatives and landraces for drought tolerance breeding using conventional and molecular methods and (ii) exploiting the reservoir of neglected and underutilized species to identify those that are known to be more drought-tolerant than conventional staple crops while possessing other desired agronomic and nutritive characteristics, as well as introducing them into existing cropping systems to make them more resilient to water deficiency conditions. In the past, the existence of drought tolerance genes in crop wild relatives and landraces was either unknown or difficult to exploit using traditional breeding techniques to secure potential long-term solutions. Today, with the advances in genomics and phenomics, there are a number of new tools available that facilitate the discovery of drought resistance genes in crop wild relatives and landraces and their relatively easy transfer into advanced breeding lines, thus accelerating breeding progress and creating resilient varieties that can withstand prolonged drought periods. Among those tools are marker-assisted selection (MAS), genomic selection (GS), and targeted gene editing (clustered regularly interspaced short palindromic repeat (CRISPR) technology). The integration of these two major strategies, the advances in conventional and molecular breeding for the drought tolerance of conventional staple crops, and the introduction of drought-tolerant neglected and underutilized species into existing production systems has the potential to enhance the resilience of agricultural production under conditions of water scarcity.
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Affiliation(s)
- Amparo Rosero
- Corporación Colombiana de Investigación Agropecuaria–AGROSAVIA, Centro de Investigación Turipaná, Km 13 vía Montería, 250047 Cereté, Colombia;
| | - Leiter Granda
- Department of Crop Science, Breeding and Plant Medicine, Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic; (L.G.); (R.C.)
| | - Jhon A. Berdugo-Cely
- Corporación Colombiana de Investigación Agropecuaria–AGROSAVIA, Centro de Investigación Turipaná, Km 13 vía Montería, 250047 Cereté, Colombia;
| | - Olga Šamajová
- Department of Cell Biology, Centre of the Region Haná for Biotechnological and Agricultural Research, Faculty of Science, Palacký University, Šlechtitelů 27, 783 71 Olomouc, Czech Republic; (O.Š.); (J.Š.)
| | - Jozef Šamaj
- Department of Cell Biology, Centre of the Region Haná for Biotechnological and Agricultural Research, Faculty of Science, Palacký University, Šlechtitelů 27, 783 71 Olomouc, Czech Republic; (O.Š.); (J.Š.)
| | - Radim Cerkal
- Department of Crop Science, Breeding and Plant Medicine, Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic; (L.G.); (R.C.)
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