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King J, Dreisigacker S, Reynolds M, Bandyopadhyay A, Braun HJ, Crespo-Herrera L, Crossa J, Govindan V, Huerta J, Ibba MI, Robles-Zazueta CA, Saint Pierre C, Singh PK, Singh RP, Achary VMM, Bhavani S, Blasch G, Cheng S, Dempewolf H, Flavell RB, Gerard G, Grewal S, Griffiths S, Hawkesford M, He X, Hearne S, Hodson D, Howell P, Jalal Kamali MR, Karwat H, Kilian B, King IP, Kishii M, Kommerell VM, Lagudah E, Lan C, Montesinos-Lopez OA, Nicholson P, Pérez-Rodríguez P, Pinto F, Pixley K, Rebetzke G, Rivera-Amado C, Sansaloni C, Schulthess U, Sharma S, Shewry P, Subbarao G, Tiwari TP, Trethowan R, Uauy C. Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future opportunities. GLOBAL CHANGE BIOLOGY 2024; 30:e17440. [PMID: 39185562 DOI: 10.1111/gcb.17440] [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: 01/23/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 08/27/2024]
Abstract
The use of plant genetic resources (PGR)-wild relatives, landraces, and isolated breeding gene pools-has had substantial impacts on wheat breeding for resistance to biotic and abiotic stresses, while increasing nutritional value, end-use quality, and grain yield. In the Global South, post-Green Revolution genetic yield gains are generally achieved with minimal additional inputs. As a result, production has increased, and millions of hectares of natural ecosystems have been spared. Without PGR-derived disease resistance, fungicide use would have easily doubled, massively increasing selection pressure for fungicide resistance. It is estimated that in wheat, a billion liters of fungicide application have been avoided just since 2000. This review presents examples of successful use of PGR including the relentless battle against wheat rust epidemics/pandemics, defending against diseases that jump species barriers like blast, biofortification giving nutrient-dense varieties and the use of novel genetic variation for improving polygenic traits like climate resilience. Crop breeding genepools urgently need to be diversified to increase yields across a range of environments (>200 Mha globally), under less predictable weather and biotic stress pressure, while increasing input use efficiency. Given that the ~0.8 m PGR in wheat collections worldwide are relatively untapped and massive impacts of the tiny fraction studied, larger scale screenings and introgression promise solutions to emerging challenges, facilitated by advanced phenomic and genomic tools. The first translocations in wheat to modify rhizosphere microbiome interaction (reducing biological nitrification, reducing greenhouse gases, and increasing nitrogen use efficiency) is a landmark proof of concept. Phenomics and next-generation sequencing have already elucidated exotic haplotypes associated with biotic and complex abiotic traits now mainstreamed in breeding. Big data from decades of global yield trials can elucidate the benefits of PGR across environments. This kind of impact cannot be achieved without widescale sharing of germplasm and other breeding technologies through networks and public-private partnerships in a pre-competitive space.
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Affiliation(s)
- Julie King
- School of Biosciences, The University of Nottingham, Loughborough, UK
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Anindya Bandyopadhyay
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Hans-Joachim Braun
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
- Colegio de Postgraduados, Montecillos, Mexico
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Julio Huerta
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
- Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Campo Experimental Valle de México, Texcoco, Mexico
| | - Maria Itria Ibba
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | | | - Carolina Saint Pierre
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Pawan K Singh
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
- Huazhong Agricultural University, Wuhan, Hubei, China
| | - V Mohan Murali Achary
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Sridhar Bhavani
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Gerald Blasch
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Shifeng Cheng
- Chinese Academy of Agricultural Science (AGIS), Shenzhen, China
| | - Hannes Dempewolf
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), Ibaraki, Japan
| | | | - Guillermo Gerard
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Surbhi Grewal
- School of Biosciences, The University of Nottingham, Loughborough, UK
| | | | | | - Xinyao He
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Sarah Hearne
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - David Hodson
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Phil Howell
- National Institute of Agricultural Botany (NIAB), Cambridge, UK
| | | | - Hannes Karwat
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | | | - Ian P King
- School of Biosciences, The University of Nottingham, Loughborough, UK
| | - Masahiro Kishii
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), Ibaraki, Japan
| | | | - Evans Lagudah
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Agriculture and Food, Canberra, Australian Capital Territory, Australia
| | - Caixia Lan
- Huazhong Agricultural University, Wuhan, Hubei, China
| | | | - Paul Nicholson
- John Innes Centre (JIC), Norwich Research Park, Norwich, UK
| | | | - Francisco Pinto
- Department of Plant Sciences, Centre for Crop Systems Analysis, Wageningen University Research, Wageningen, The Netherlands
| | - Kevin Pixley
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Greg Rebetzke
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Agriculture and Food, Canberra, Australian Capital Territory, Australia
| | - Carolina Rivera-Amado
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Carolina Sansaloni
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Urs Schulthess
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
- CIMMYT-China Joint Center for Wheat and Maize Improvement, Henan Agricultural University, Zhengzhou, China
| | | | | | - Guntar Subbarao
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), Ibaraki, Japan
| | - Thakur Prasad Tiwari
- International Maize and Wheat Improvement Center (CIMMYT) and Affiliates, Texcoco, Mexico
| | - Richard Trethowan
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, University of Sydney, Narrabri, New South Wales, Australia
| | - Cristobal Uauy
- John Innes Centre (JIC), Norwich Research Park, Norwich, UK
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Chang-Brahim I, Koppensteiner LJ, Beltrame L, Bodner G, Saranti A, Salzinger J, Fanta-Jende P, Sulzbachner C, Bruckmüller F, Trognitz F, Samad-Zamini M, Zechner E, Holzinger A, Molin EM. Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding. FRONTIERS IN PLANT SCIENCE 2024; 15:1319938. [PMID: 38699541 PMCID: PMC11064034 DOI: 10.3389/fpls.2024.1319938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/13/2024] [Indexed: 05/05/2024]
Abstract
Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.
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Affiliation(s)
- Ignacio Chang-Brahim
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Lorenzo Beltrame
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Gernot Bodner
- Department of Crop Sciences, Institute of Agronomy, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
| | - Anna Saranti
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Jules Salzinger
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Phillipp Fanta-Jende
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Christoph Sulzbachner
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Felix Bruckmüller
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Friederike Trognitz
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Elisabeth Zechner
- Verein zur Förderung einer nachhaltigen und regionalen Pflanzenzüchtung, Zwettl, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Eva M. Molin
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
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Pugh NA, Young A, Ojha M, Emendack Y, Sanchez J, Xin Z, Puppala N. Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms. FRONTIERS IN PLANT SCIENCE 2024; 15:1339864. [PMID: 38444530 PMCID: PMC10912196 DOI: 10.3389/fpls.2024.1339864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024]
Abstract
Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.
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Affiliation(s)
- N. Ace Pugh
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Andrew Young
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Manisha Ojha
- Agricultural Science Center at Clovis, New Mexico State University, Clovis, NM, United States
| | - Yves Emendack
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Jacobo Sanchez
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Zhanguo Xin
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Naveen Puppala
- Agricultural Science Center at Clovis, New Mexico State University, Clovis, NM, United States
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Jafari M, Daneshvar MH. Machine learning-mediated Passiflora caerulea callogenesis optimization. PLoS One 2024; 19:e0292359. [PMID: 38266002 PMCID: PMC10807783 DOI: 10.1371/journal.pone.0292359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/19/2023] [Indexed: 01/26/2024] Open
Abstract
Callogenesis is one of the most powerful biotechnological approaches for in vitro secondary metabolite production and indirect organogenesis in Passiflora caerulea. Comprehensive knowledge of callogenesis and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. In the present investigation, the callogenesis responses (i.e., callogenesis rate and callus fresh weight) of P. caerulea were predicted based on different types and concentrations of plant growth regulators (PGRs) (i.e., 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), 1-naphthaleneacetic acid (NAA), and indole-3-Butyric Acid (IBA)) as well as explant types (i.e., leaf, node, and internode) using multilayer perceptron (MLP). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and explant types for maximizing callogenesis responses. Furthermore, sensitivity analysis was conducted to assess the importance of each input variable on the callogenesis responses. The results showed that MLP had high predictive accuracy (R2 > 0.81) in both training and testing sets for modeling all studied parameters. Based on the results of the optimization process, the highest callogenesis rate (100%) would be obtained from the leaf explant cultured in the medium supplemented with 0.52 mg/L IBA plus 0.43 mg/L NAA plus 1.4 mg/L 2,4-D plus 0.2 mg/L BAP. The results of the sensitivity analysis showed the explant-dependent impact of the exogenous application of PGRs on callogenesis. Generally, the results showed that a combination of MLP and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
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Affiliation(s)
- Marziyeh Jafari
- Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, Iran
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
| | - Mohammad Hosein Daneshvar
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
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5
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Ortiz R. Challenges for crop improvement. Emerg Top Life Sci 2023; 7:197-205. [PMID: 37905719 DOI: 10.1042/etls20230106] [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: 09/24/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023]
Abstract
The genetic improvement of crops faces the significant challenge of feeding an ever-increasing population amidst a changing climate, and when governments are adopting a 'more with less' approach to reduce input use. Plant breeding has the potential to contribute to the United Nations Agenda 2030 by addressing various sustainable development goals (SDGs), with its most profound impact expected on SDG2 Zero Hunger. To expedite the time-consuming crossbreeding process, a genomic-led approach for predicting breeding values, targeted mutagenesis through gene editing, high-throughput phenomics for trait evaluation, enviromics for including characterization of the testing environments, machine learning for effective management of large datasets, and speed breeding techniques promoting early flowering and seed production are being incorporated into the plant breeding toolbox. These advancements are poised to enhance genetic gains through selection in the cultigen pools of various crops. Consequently, these knowledge-based breeding methods are pursued for trait introgression, population improvement, and cultivar development. This article uses the potato crop as an example to showcase the progress being made in both genomic-led approaches and gene editing for accelerating the delivery of genetic gains through the utilization of genetically enhanced elite germplasm. It also further underscores that access to technological advances in plant breeding may be influenced by regulations and intellectual property rights.
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Affiliation(s)
- Rodomiro Ortiz
- Department of Plant Breeding (VF), Swedish University of Agricultural Sciences (SLU), Box 190 Sundsvagen 10, SE 23422 Lomma, Sweden
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6
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Rezaei H, Mirzaie-asl A, Abdollahi MR, Tohidfar M. Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis. PLoS One 2023; 18:e0293754. [PMID: 37922261 PMCID: PMC10624318 DOI: 10.1371/journal.pone.0293754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/18/2023] [Indexed: 11/05/2023] Open
Abstract
The important feature of petunia in tissue culture is its unpredictable and genotype-dependent callogenesis, posing challenges for efficient regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a powerful tool to analyze callogenesis data, extract key parameters, and predict optimal conditions for petunia callogenesis, facilitating more controlled and productive tissue culture processes. The study aimed to develop a predictive model for callogenesis in petunia using ML algorithms and to optimize the concentrations of phytohormones to enhance callus formation rate (CFR) and callus fresh weight (CFW). The inputs for the model were BAP, KIN, IBA, and NAA, while the outputs were CFR and CFW. Three ML algorithms, namely MLP, RBF, and GRNN, were compared, and the results revealed that GRNN (R2≥83) outperformed MLP and RBF in terms of accuracy. Furthermore, a sensitivity analysis was conducted to determine the relative importance of the four phytohormones. IBA exhibited the highest importance, followed by NAA, BAP, and KIN. Leveraging the superior performance of the GRNN model, a genetic algorithm (GA) was integrated to optimize the concentration of phytohormones for maximizing CFR and CFW. The genetic algorithm identified an optimized combination of phytohormones consisting of 1.31 mg/L BAP, 1.02 mg/L KIN, 1.44 mg/L NAA, and 1.70 mg/L IBA, resulting in 95.83% CFR. To validate the reliability of the predicted results, optimized combinations of phytohormones were tested in a laboratory experiment. The results of the validation experiment indicated no significant difference between the experimental and optimized results obtained through the GA. This study presents a novel approach combining ML, sensitivity analysis, and GA for modeling and predicting callogenesis in petunia. The findings offer valuable insights into the optimization of phytohormone concentrations, facilitating improved callus formation and potential applications in plant tissue culture and genetic engineering.
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Affiliation(s)
- Hamed Rezaei
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Asghar Mirzaie-asl
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Mohammad Reza Abdollahi
- Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Masoud Tohidfar
- Department of Plant Biotechnology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran
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Jafari M, Daneshvar MH. Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms. BMC Biotechnol 2023; 23:27. [PMID: 37528396 PMCID: PMC10394921 DOI: 10.1186/s12896-023-00796-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/21/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Optimization of indirect shoot regeneration protocols is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and/or genome editing in Passiflora caerulea. Comprehensive knowledge of indirect shoot regeneration and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. MATERIALS AND METHODS In the present investigation, the indirect shoot regeneration responses (i.e., de novo shoot regeneration rate, the number of de novo shoots, and length of de novo shoots) of P. caerulea were predicted based on different types and concentrations of PGRs (i.e., TDZ, BAP, PUT, KIN, and IBA) as well as callus types (i.e., callus derived from different explants including leaf, node, and internode) using generalized regression neural network (GRNN) and random forest (RF). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and callus types for maximizing indirect shoot regeneration responses. Moreover, sensitivity analysis was conducted to assess the importance of each input variable on the studied parameters. RESULTS The results showed that both algorithms (RF and GRNN) had high predictive accuracy (R2 > 0.86) in both training and testing sets for modeling all studied parameters. Based on the results of optimization process, the highest de novo shoot regeneration rate (100%) would be obtained from callus derived from nodal segments cultured in the medium supplemented with 0.77 mg/L BAP plus 2.41 mg/L PUT plus 0.06 mg/L IBA. The results of the sensitivity analysis showed the explant-dependent impact of exogenous application of PGRs on indirect de novo shoot regeneration. CONCLUSIONS A combination of ML (GRNN and RF) and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
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Affiliation(s)
- Marziyeh Jafari
- Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, 7144113131, Iran.
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, 6341773637, Iran.
| | - Mohammad Hosein Daneshvar
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, 6341773637, Iran
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Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits. PLANTS (BASEL, SWITZERLAND) 2023; 12:2659. [PMID: 37514272 PMCID: PMC10383196 DOI: 10.3390/plants12142659] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.
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Affiliation(s)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
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Roychowdhury R, Das SP, Gupta A, Parihar P, Chandrasekhar K, Sarker U, Kumar A, Ramrao DP, Sudhakar C. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses. Genes (Basel) 2023; 14:1281. [PMID: 37372461 DOI: 10.3390/genes14061281] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/03/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.
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Affiliation(s)
- Rajib Roychowdhury
- Department of Plant Pathology and Weed Research, Institute of Plant Protection, Agricultural Research Organization (ARO)-The Volcani Institute, Rishon Lezion 7505101, Israel
| | - Soumya Prakash Das
- School of Bioscience, Seacom Skills University, Bolpur 731236, West Bengal, India
| | - Amber Gupta
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology, Faculty of Science, Maharaja Sayajirao University of Baroda, Vadodara 390002, Gujarat, India
| | - Parul Parihar
- Department of Biotechnology and Bioscience, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Kottakota Chandrasekhar
- Department of Plant Biochemistry and Biotechnology, Sri Krishnadevaraya College of Agricultural Sciences (SKCAS), Affiliated to Acharya N.G. Ranga Agricultural University (ANGRAU), Guntur 522034, Andhra Pradesh, India
| | - Umakanta Sarker
- Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
| | - Ajay Kumar
- Department of Botany, Maharshi Vishwamitra (M.V.) College, Buxar 802102, Bihar, India
| | - Devade Pandurang Ramrao
- Department of Biotechnology, Mizoram University, Pachhunga University College Campus, Aizawl 796001, Mizoram, India
| | - Chinta Sudhakar
- Plant Molecular Biology Laboratory, Department of Botany, Sri Krishnadevaraya University, Anantapur 515003, Andhra Pradesh, India
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Yoosefzadeh Najafabadi M, Hesami M, Rajcan I. Unveiling the Mysteries of Non-Mendelian Heredity in Plant Breeding. PLANTS (BASEL, SWITZERLAND) 2023; 12:1956. [PMID: 37653871 PMCID: PMC10221147 DOI: 10.3390/plants12101956] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 07/30/2023]
Abstract
Mendelian heredity is the cornerstone of plant breeding and has been used to develop new varieties of plants since the 19th century. However, there are several breeding cases, such as cytoplasmic inheritance, methylation, epigenetics, hybrid vigor, and loss of heterozygosity (LOH), where Mendelian heredity is not applicable, known as non-Mendelian heredity. This type of inheritance can be influenced by several factors besides the genetic architecture of the plant and its breeding potential. Therefore, exploring various non-Mendelian heredity mechanisms, their prevalence in plants, and the implications for plant breeding is of paramount importance to accelerate the pace of crop improvement. In this review, we examine the current understanding of non-Mendelian heredity in plants, including the mechanisms, inheritance patterns, and applications in plant breeding, provide an overview of the various forms of non-Mendelian inheritance (including epigenetic inheritance, cytoplasmic inheritance, hybrid vigor, and LOH), explore insight into the implications of non-Mendelian heredity in plant breeding, and the potential it holds for future research.
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Affiliation(s)
| | | | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada; (M.Y.N.); (M.H.)
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