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Farooqi MQU, Nawaz G, Wani SH, Choudhary JR, Rana M, Sah RP, Afzal M, Zahra Z, Ganie SA, Razzaq A, Reyes VP, Mahmoud EA, Elansary HO, El-Abedin TKZ, Siddique KHM. Recent developments in multi-omics and breeding strategies for abiotic stress tolerance in maize ( Zea mays L.). FRONTIERS IN PLANT SCIENCE 2022; 13:965878. [PMID: 36212378 PMCID: PMC9538355 DOI: 10.3389/fpls.2022.965878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 06/12/2023]
Abstract
High-throughput sequencing technologies (HSTs) have revolutionized crop breeding. The advent of these technologies has enabled the identification of beneficial quantitative trait loci (QTL), genes, and alleles for crop improvement. Climate change have made a significant effect on the global maize yield. To date, the well-known omic approaches such as genomics, transcriptomics, proteomics, and metabolomics are being incorporated in maize breeding studies. These approaches have identified novel biological markers that are being utilized for maize improvement against various abiotic stresses. This review discusses the current information on the morpho-physiological and molecular mechanism of abiotic stress tolerance in maize. The utilization of omics approaches to improve abiotic stress tolerance in maize is highlighted. As compared to single approach, the integration of multi-omics offers a great potential in addressing the challenges of abiotic stresses of maize productivity.
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Affiliation(s)
| | - Ghazala Nawaz
- Department of Botanical and Environmental Sciences, Kohat University of Science and Technology, Kohat, Pakistan
| | - Shabir Hussain Wani
- Mountain Research Centre for Field Crops, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India
| | - Jeet Ram Choudhary
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Maneet Rana
- Division of Crop Improvement, ICAR-Indian Grassland and Fodder Research Institute, Jhansi, India
| | - Rameswar Prasad Sah
- Division of Crop Improvement, ICAR-National Rice Research Institute, Cuttack, India
| | - Muhammad Afzal
- College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Zahra Zahra
- Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, United States
| | | | - Ali Razzaq
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | | | - Eman A. Mahmoud
- Department of Food Industries, Faculty of Agriculture, Damietta University, Damietta, Egypt
| | - Hosam O. Elansary
- Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Floriculture, Ornamental Horticulture, and Garden Design Department, Faculty of Agriculture (El-Shatby), Alexandria University, Alexandria, Egypt
- Department of Geography, Environmental Management, and Energy Studies, University of Johannesburg, Johannesburg, South Africa
| | - Tarek K. Zin El-Abedin
- Department of Agriculture & Biosystems Engineering, Faculty of Agriculture (El-Shatby), Alexandria University, Alexandria, Egypt
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Tapia R, Abd-Elrahman A, Osorio L, Whitaker VM, Lee S. Combining canopy reflectance spectrometry and genome-wide prediction to increase response to selection for powdery mildew resistance in cultivated strawberry. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5322-5335. [PMID: 35383379 DOI: 10.1093/jxb/erac136] [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: 12/14/2021] [Accepted: 04/01/2022] [Indexed: 06/14/2023]
Abstract
High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet-visible-near infrared region (350-1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (rA) with powdery mildew visual ratings with average rA values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics-genomics approach in strawberry breeding.
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Affiliation(s)
- Ronald Tapia
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Amr Abd-Elrahman
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32603, USA
| | - Luis Osorio
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Vance M Whitaker
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Seonghee Lee
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, 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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Kim M, Lee C, Hong S, Kim SL, Baek JH, Kim KH. High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops. Int J Mol Sci 2021; 22:ijms22158266. [PMID: 34361030 PMCID: PMC8347144 DOI: 10.3390/ijms22158266] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/28/2022] Open
Abstract
Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.
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Affiliation(s)
- Minsu Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Chaewon Lee
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
- Department of Crop Science and Biotechnology, Chonbuk National University, Jeonju 54896, Korea
| | - Subin Hong
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Song Lim Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Jeong-Ho Baek
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Kyung-Hwan Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
- Correspondence:
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Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning. SENSORS 2020; 20:s20185293. [PMID: 32947919 PMCID: PMC7570686 DOI: 10.3390/s20185293] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/23/2020] [Accepted: 09/14/2020] [Indexed: 11/16/2022]
Abstract
Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R2 = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R2 = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices.
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Zandalinas SI, Fritschi FB, Mittler R. Signal transduction networks during stress combination. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:1734-1741. [PMID: 31665392 DOI: 10.1093/jxb/erz486] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 10/18/2019] [Indexed: 05/18/2023]
Abstract
Episodes of heat waves combined with drought can have a devastating impact on agricultural production worldwide. These conditions, as well as many other types of stress combinations, impose unique physiological and developmental demands on plants and require the activation of dedicated pathways. Here, we review recent RNA sequencing studies of stress combination in plants, and conduct a meta-analysis of the transcriptome response of plants to different types of stress combination. Our analysis reveals that each different stress combination is accompanied by its own set of stress combination-specific transcripts, and that the response of different transcription factor families is unique to each stress combination. The alarming rate of increase in global temperatures, coupled with the predicted increase in future episodes of extreme weather, highlight an urgent need to develop crop plants with enhanced tolerance to stress combination. The uniqueness and complexity of the physiological and molecular response of plants to each different stress combination, highlighted here, demonstrate the daunting challenge we face in accomplishing this goal. Dedicated efforts combining field experimentation, omics, and network analyses, coupled with advanced phenotyping and breeding methods, will be needed to address specific crops and particular stress combinations relevant to maintaining our future food chain secured.
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Affiliation(s)
- Sara I Zandalinas
- Bond Life Sciences Center, Interdisciplinary Plant Group, and Division of Plant Sciences, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, MO USA
| | - Felix B Fritschi
- Bond Life Sciences Center, Interdisciplinary Plant Group, and Division of Plant Sciences, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, MO USA
| | - Ron Mittler
- Bond Life Sciences Center, Interdisciplinary Plant Group, and Division of Plant Sciences, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, MO USA
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Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. MOLECULAR PLANT 2020; 13:187-214. [PMID: 31981735 DOI: 10.1016/j.molp.2020.01.008] [Citation(s) in RCA: 239] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 05/18/2023]
Abstract
Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
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Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crops Science/College of Agronomy, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Jian Zhang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - John H Doonan
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | | | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
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