1
|
Nguyen HA, Martre P, Collet C, Draye X, Salon C, Jeudy C, Rincent R, Muller B. Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:2510-2526. [PMID: 38520390 DOI: 10.1093/jxb/erae009] [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: 04/26/2023] [Accepted: 03/21/2024] [Indexed: 03/25/2024]
Abstract
Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.
Collapse
Affiliation(s)
- Hong Anh Nguyen
- LEPSE, Université de Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
| | - Pierre Martre
- LEPSE, Université de Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
| | - Clothilde Collet
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Xavier Draye
- Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Christophe Salon
- Agroécologie, AgroSup Dijon, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Christian Jeudy
- Agroécologie, AgroSup Dijon, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Renaud Rincent
- GDEC, Université Clermont-Auvergne, INRAE, Clermont-Ferrand, France
| | - Bertrand Muller
- LEPSE, Université de Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
| |
Collapse
|
2
|
Baykalov P, Bussmann B, Nair R, Smith AG, Bodner G, Hadar O, Lazarovitch N, Rewald B. Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models. PLANT METHODS 2023; 19:122. [PMID: 37932745 PMCID: PMC10629126 DOI: 10.1186/s13007-023-01101-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/27/2023] [Indexed: 11/08/2023]
Abstract
BACKGROUND Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset. RESULTS The best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives. CONCLUSIONS Although correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts-limiting the need for model retraining.
Collapse
Affiliation(s)
- Pavel Baykalov
- Institute of Forest Ecology, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
- Vienna Scientific Instruments GmbH, Alland, Austria
| | - Bart Bussmann
- IDLab, Department of Computer Science, University of Antwerp - Imec, Antwerp, Belgium
| | - Richard Nair
- Dept. Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
- Discipline of Botany, School of Natural Sciences, Trinity College, Dublin, Ireland
| | | | - Gernot Bodner
- Institute of Agronomy, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
| | - Ofer Hadar
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Naftali Lazarovitch
- Wyler Department for Dryland Agriculture, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Beersheba, Israel
| | - Boris Rewald
- Institute of Forest Ecology, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria.
- Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic.
| |
Collapse
|
3
|
Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
Collapse
Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
| |
Collapse
|
4
|
Lopez-Valdivia I, Yang X, Lynch JP. Large root cortical cells and reduced cortical cell files improve growth under suboptimal nitrogen in silico. PLANT PHYSIOLOGY 2023:kiad214. [PMID: 37040571 DOI: 10.1093/plphys/kiad214] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Suboptimal nitrogen availability is a primary constraint to plant growth. We used OpenSimRoot, a functional-structural plant/soil model, to test the hypothesis that larger root cortical cell size (CCS), reduced cortical cell file number (CCFN), and their interactions with root cortical aerenchyma (RCA) and lateral root branching density (LRBD) are useful adaptations to suboptimal soil nitrogen availability in maize (Zea mays). Reduced CCFN increased shoot dry weight over 80%. Reduced respiration, reduced nitrogen content, and reduced root diameter accounted for 23%, 20%, and 33% of increased shoot biomass, respectively. Large CCS increased shoot biomass by 24% compared with small CCS. When simulated independently, reduced respiration and reduced nutrient content increased the shoot biomass by 14% and 3%, respectively. However, increased root diameter resulting from large CCS decreased shoot biomass by 4% due to an increase in root metabolic cost. Under moderate N stress, integrated phenotypes with reduced CCFN, large CCS, and high RCA improved shoot biomass in silt loam and loamy sand soils. In contrast, integrated phenotypes composed of reduced CCFN, large CCS and reduced lateral root branching density had the greatest growth in silt loam, while phenotypes with reduced CCFN, large CCS and high LRBD were the best performers in loamy sands. Our results support the hypothesis that larger CCS, reduced CCFN, and their interactions with RCA and LRBD could increase nitrogen acquisition by reducing root respiration and root nutrient demand. Phene synergisms may exist between CCS, CCFN, and LRBD. CCS and CCFN merit consideration for breeding cereal crops with improved nitrogen acquisition, which is critical for global food security.
Collapse
Affiliation(s)
- Ivan Lopez-Valdivia
- Department of Plant Science, The Pennsylvania State University, University Park, PA, U.S.A., 16802
| | - Xiyu Yang
- Department of Plant Science, The Pennsylvania State University, University Park, PA, U.S.A., 16802
| | - Jonathan P Lynch
- Department of Plant Science, The Pennsylvania State University, University Park, PA, U.S.A., 16802
| |
Collapse
|
5
|
Wu Q, Wu J, Hu P, Zhang W, Ma Y, Yu K, Guo Y, Cao J, Li H, Li B, Yao Y, Cao H, Zhang W. Quantification of the three-dimensional root system architecture using an automated rotating imaging system. PLANT METHODS 2023; 19:11. [PMID: 36732764 PMCID: PMC9896698 DOI: 10.1186/s13007-023-00988-1] [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: 07/22/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Crop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time. RESULTS We proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline. CONCLUSIONS The results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops.
Collapse
Affiliation(s)
- Qian Wu
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
| | - Jie Wu
- Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Pengcheng Hu
- School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD, 4072, Australia
| | - Weixin Zhang
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Kun Yu
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
| | - Yan Guo
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jing Cao
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
| | - Huayong Li
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
| | - Baiming Li
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
| | - Yuyang Yao
- College of Electronics & Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China
| | - Hongxin Cao
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
| | - Wenyu Zhang
- IGRB-IAI Joint Laboratory of Germplasm Resources Innovation & Information Utilization, YuanQi-IAI Joint Laboratory for Agricultural Digital Twin, Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.
| |
Collapse
|
6
|
Boter M, Pozas J, Jarillo JA, Piñeiro M, Pernas M. Brassica napus Roots Use Different Strategies to Respond to Warm Temperatures. Int J Mol Sci 2023; 24:ijms24021143. [PMID: 36674684 PMCID: PMC9863162 DOI: 10.3390/ijms24021143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/23/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Elevated growth temperatures are negatively affecting crop productivity by increasing yield losses. The modulation of root traits associated with improved response to rising temperatures is a promising approach to generate new varieties better suited to face the environmental constraints caused by climate change. In this study, we identified several Brassica napus root traits altered in response to warm ambient temperatures. Different combinations of changes in specific root traits result in an extended and deeper root system. This overall root growth expansion facilitates root response by maximizing root-soil surface interaction and increasing roots' ability to explore extended soil areas. We associated these traits with coordinated cellular events, including changes in cell division and elongation rates that drive root growth increases triggered by warm temperatures. Comparative transcriptomic analysis revealed the main genetic determinants of these root system architecture (RSA) changes and uncovered the necessity of a tight regulation of the heat-shock stress response to adjusting root growth to warm temperatures. Our work provides a phenotypic, cellular, and genetic framework of root response to warming temperatures that will help to harness root response mechanisms for crop yield improvement under the future climatic scenario.
Collapse
|
7
|
Rangarajan H, Hadka D, Reed P, Lynch JP. Multi-objective optimization of root phenotypes for nutrient capture using evolutionary algorithms. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:38-53. [PMID: 35426959 PMCID: PMC9544003 DOI: 10.1111/tpj.15774] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/05/2022] [Accepted: 04/10/2022] [Indexed: 05/11/2023]
Abstract
Root phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three-dimensional functional-structural plant model, SimRoot, to the Borg Multi-Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade-offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non-mobile nutrients and for maize (a monocot) and bean (a dicot). Functional-structural plant models can be used with multi-objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.
Collapse
Affiliation(s)
- Harini Rangarajan
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | | | - Patrick Reed
- Civil and Environmental EngineeringCornell UniversityIthacaNew YorkUSA
| | - Jonathan P. Lynch
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| |
Collapse
|
8
|
Lube V, Noyan MA, Przybysz A, Salama K, Blilou I. MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision. PLANT METHODS 2022; 18:38. [PMID: 35346267 PMCID: PMC8958799 DOI: 10.1186/s13007-022-00864-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. RESULTS We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. CONCLUSION MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.
Collapse
Affiliation(s)
- Vinicius Lube
- Laboratory of Plant Cell and Developmental Biology (LPCDB), Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | | | - Alexander Przybysz
- Sensors Lab, Advanced Membranes and Porous Materials Center (AMPMC), Computer, Electrical and Mathematical Science and Engineering (CEMSE), KAUST, Thuwal, Saudi Arabia
| | - Khaled Salama
- Sensors Lab, Advanced Membranes and Porous Materials Center (AMPMC), Computer, Electrical and Mathematical Science and Engineering (CEMSE), KAUST, Thuwal, Saudi Arabia
| | - Ikram Blilou
- Laboratory of Plant Cell and Developmental Biology (LPCDB), Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
| |
Collapse
|
9
|
Liao Q, Chebotarov D, Islam MS, Quintana MR, Natividad MA, De Ocampo M, Beredo JC, Torres RO, Zhang Z, Song H, Price AH, McNally KL, Henry A. Aus rice root architecture variation contributing to grain yield under drought suggests a key role of nodal root diameter class. PLANT, CELL & ENVIRONMENT 2022; 45:854-870. [PMID: 35099814 DOI: 10.1111/pce.14272] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
The aus rice variety group originated in stress-prone regions and is a promising source for the development of new stress-tolerant rice cultivars. In this study, an aus panel (~220 genotypes) was evaluated in field trials under well-watered and drought conditions and in the greenhouse (basket, herbicide and lysimeter studies) to investigate relationships between grain yield and root architecture, and to identify component root traits behind the composite trait of deep root growth. In the field trials, high and stable grain yield was positively related to high and stable deep root growth (r = 0.16), which may indicate response to within-season soil moisture fluctuations (i.e., plasticity). When dissecting component traits related to deep root growth (including angle, elongation and branching), the number of nodal roots classified as 'large-diameter' was positively related to deep root growth (r = 0.24), and showed the highest number of colocated genome-wide association study (GWAS) peaks with grain yield under drought. The role of large-diameter nodal roots in deep root growth may be related to their branching potential. Two candidate loci that colocated for yield and root traits were identified that showed distinct haplotype distributions between contrasting yield/stability groups and could be good candidates to contribute to rice improvement.
Collapse
Affiliation(s)
- Qiong Liao
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
- Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China, College of Resources and Environmental Sciences, Hunan Agricultural University, Changsha, China
| | - Dmytro Chebotarov
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
| | - Mohammad S Islam
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
| | - Marinell R Quintana
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
| | - Mignon A Natividad
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
| | - Marjorie De Ocampo
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
| | - Joseph C Beredo
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
| | - Rolando O Torres
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
| | - Zhenhua Zhang
- Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China, College of Resources and Environmental Sciences, Hunan Agricultural University, Changsha, China
| | - Haixing Song
- Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China, College of Resources and Environmental Sciences, Hunan Agricultural University, Changsha, China
| | - Adam H Price
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
| | - Kenneth L McNally
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
| | - Amelia Henry
- Rice Breeding Innovations, International Rice Research Institute, Pili Drive, UPLB Compound, Los Baños, Laguna, Philippines, 4031, Philippines
| |
Collapse
|
10
|
Hou F, Liu K, Zhang N, Zou C, Yuan G, Gao S, Zhang M, Pan G, Ma L, Shen Y. Association mapping uncovers maize ZmbZIP107 regulating root system architecture and lead absorption under lead stress. FRONTIERS IN PLANT SCIENCE 2022; 13:1015151. [PMID: 36226300 PMCID: PMC9549328 DOI: 10.3389/fpls.2022.1015151] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 05/22/2023]
Abstract
Lead (Pb) is a highly toxic contaminant to living organisms and the environment. Excessive Pb in soils affects crop yield and quality, thus threatening human health via the food chain. Herein, we investigated Pb tolerance among a maize association panel using root bushiness (BSH) under Pb treatment as an indicator. Through a genome-wide association study of relative BSH, we identified four single nucleotide polymorphisms (SNPs) and 30 candidate genes associated with Pb tolerance in maize seedlings. Transcriptome analysis showed that four of the 30 genes were differentially responsive to Pb treatment between two maize lines with contrasting Pb tolerance. Among these, the ZmbZIP107 transcription factor was confirmed as the key gene controlling maize tolerance to Pb by using gene-based association studies. Two 5' UTR_variants in ZmbZIP107 affected its expression level and Pb tolerance among different maize lines. ZmbZIP107 protein was specifically targeted to the nucleus and ZmbZIP107 mRNA showed the highest expression in maize seedling roots among different tissues. Heterologous expression of ZmbZIP107 enhanced rice tolerance to Pb stress and decreased Pb absorption in the roots. Our study provided the basis for revelation of the molecular mechanism underlying Pb tolerance and contributed to cultivation of Pb-tolerant varieties in maize.
Collapse
|
11
|
Fonta JE, Vejchasarn P, Henry A, Lynch JP, Brown KM. Many paths to one goal: Identifying integrated rice root phenotypes for diverse drought environments. FRONTIERS IN PLANT SCIENCE 2022; 13:959629. [PMID: 36072326 PMCID: PMC9441928 DOI: 10.3389/fpls.2022.959629] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/28/2022] [Indexed: 05/02/2023]
Abstract
Drought is a major source of yield loss in the production of rice (Oryza sativa L.), and cultivars that maintain yield under drought across environments and drought stress scenarios are urgently needed. Root phenotypes directly affect water interception and uptake, so plants with root systems optimized for water uptake under drought would likely exhibit reduced yield loss. Deeper nodal roots that have a low metabolic cost per length (i.e., cheaper roots) via smaller root diameter and/or more aerenchyma and that transport water efficiently through smaller diameter metaxylem vessels may be beneficial during drought. Subsets of the Rice Diversity Panel 1 and Azucena × IR64 recombinant inbred lines were grown in two greenhouse and two rainout shelter experiments under drought stress to assess their shoot, root anatomical, and root architectural phenotypes. Root traits and root trait plasticity in response to drought varied with genotype and environment. The best-performing groups in the rainout shelter experiments had less plasticity of living tissue area in nodal roots than the worst performing groups. Root traits under drought were partitioned into similar groups or clusters via the partitioning-around-medoids algorithm, and this revealed two favorable integrated root phenotypes common within and across environments. One favorable integrated phenotype exhibited many, deep nodal roots with larger root cross-sectional area and more aerenchyma, while the other favorable phenotype exhibited many, deep nodal roots with small root cross-sectional area and small metaxylem vessels. Deeper roots with high theoretical axial hydraulic conductance combined with reduced root metabolic cost contributed to greater shoot biomass under drought. These results reflect how some root anatomical and architectural phenes work in concert as integrated phenotypes to influence the performance of plant under drought stress. Multiple integrated root phenotypes are therefore recommended to be selected in breeding programs for improving rice yield across diverse environments and drought scenarios.
Collapse
Affiliation(s)
- Jenna E. Fonta
- Intercollege Graduate Degree Program in Plant Biology, Huck Institutes of the Life Sciences, Penn State University, University Park, PA, United States
- Department of Plant Science, The Pennsylvania State University, University Park, PA, United States
| | - Phanchita Vejchasarn
- Rice Department, Ministry of Agriculture, Ubon Ratchathani Rice Research Center, Ubon Ratchathani, Thailand
| | - Amelia Henry
- Rice Breeding Innovations Platform, International Rice Research Institute (IRRI), Los Baños, Philippines
| | - Jonathan P. Lynch
- Department of Plant Science, The Pennsylvania State University, University Park, PA, United States
| | - Kathleen M. Brown
- Department of Plant Science, The Pennsylvania State University, University Park, PA, United States
- *Correspondence: Kathleen M. Brown,
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|