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Lin F, Chhapekar SS, Vieira CC, Da Silva MP, Rojas A, Lee D, Liu N, Pardo EM, Lee YC, Dong Z, Pinheiro JB, Ploper LD, Rupe J, Chen P, Wang D, Nguyen HT. Breeding for disease resistance in soybean: a global perspective. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3773-3872. [PMID: 35790543 PMCID: PMC9729162 DOI: 10.1007/s00122-022-04101-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 04/11/2022] [Indexed: 05/29/2023]
Abstract
KEY MESSAGE This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.
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Affiliation(s)
- Feng Lin
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824 USA
| | - Sushil Satish Chhapekar
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
| | - Caio Canella Vieira
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
- Fisher Delta Research Center, University of Missouri, Portageville, MO 63873 USA
| | - Marcos Paulo Da Silva
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701 USA
| | - Alejandro Rojas
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701 USA
| | - Dongho Lee
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
- Fisher Delta Research Center, University of Missouri, Portageville, MO 63873 USA
| | - Nianxi Liu
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun,, 130033 Jilin China
| | - Esteban Mariano Pardo
- Instituto de Tecnología Agroindustrial del Noroeste Argentino (ITANOA) [Estación Experimental Agroindustrial Obispo Colombres (EEAOC) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)], Av. William Cross 3150, C.P. T4101XAC, Las Talitas, Tucumán, Argentina
| | - Yi-Chen Lee
- Fisher Delta Research Center, University of Missouri, Portageville, MO 63873 USA
| | - Zhimin Dong
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, Changchun,, 130033 Jilin China
| | - Jose Baldin Pinheiro
- Departamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz” (ESALQ/USP), PO Box 9, Piracicaba, SP 13418-900 Brazil
| | - Leonardo Daniel Ploper
- Instituto de Tecnología Agroindustrial del Noroeste Argentino (ITANOA) [Estación Experimental Agroindustrial Obispo Colombres (EEAOC) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)], Av. William Cross 3150, C.P. T4101XAC, Las Talitas, Tucumán, Argentina
| | - John Rupe
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701 USA
| | - Pengyin Chen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
- Fisher Delta Research Center, University of Missouri, Portageville, MO 63873 USA
| | - Dechun Wang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824 USA
| | - Henry T. Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211 USA
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Ribeiro AV, Cira TM, MacRae IV, Koch RL. Effects of feeding injury from Popillia japonica (Coleoptera: Scarabaeidae) on soybean spectral reflectance and yield. FRONTIERS IN INSECT SCIENCE 2022; 2:1006092. [PMID: 38468790 PMCID: PMC10926407 DOI: 10.3389/finsc.2022.1006092] [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: 07/28/2022] [Accepted: 10/05/2022] [Indexed: 03/13/2024]
Abstract
Remote sensing has been shown to be a promising technology for the detection and monitoring of plant stresses including insect feeding. Popillia japonica Newman, is an invasive insect species in the United States, and a pest of concern to soybean, Glycine max (L.) Merr., in the upper Midwest. To investigate the effects of P. japonica feeding injury (i.e., defoliation) on soybean canopy spectral reflectance and yield, field trials with plots of caged soybean plants were established during the summers of 2020 and 2021. In each year, field-collected P. japonica adults were released into some of the caged plots, creating a gradient of infestation levels and resulting injury. Estimates of injury caused by P. japonica, ground-based hyperspectral readings, total yield, and yield components were obtained from the caged plots. Injury was greatest in the upper canopy of soybean in plots infested with P. japonica. Overall mean canopy injury (i.e., across lower, middle, and upper canopy) ranged from 0.23 to 6.26%, which is representative of injury levels observed in soybean fields in the Midwest United States. Feeding injury from P. japonica tended to reduce measures of soybean canopy reflectance in near infra-red wavelengths (~700 to 1000 nm). These results indicate that remote sensing has potential for detection of injury from P. japonica and could facilitate scouting for this pest. Effects of P. japonica injury on total yield were not observed, but a reduction in seed size was detected in one of the two years. The threat to soybean yield posed by P. japonica alone appears minimal, but this pest adds to the guild of other defoliating insects in soybean whose combined effects could threaten yield. The results of this research will guide refinement of management recommendations for this pest in soybean and hold relevance for other cropping systems.
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Affiliation(s)
- Arthur V. Ribeiro
- Department of Entomology, University of Minnesota, Saint Paul, MN, United States
| | - Theresa M. Cira
- Department of Entomology, University of Minnesota, Saint Paul, MN, United States
| | - Ian V. MacRae
- Department of Entomology, University of Minnesota, Northwest Research and Outreach Center, Crookston, MN, United States
| | - Robert L. Koch
- Department of Entomology, University of Minnesota, Saint Paul, MN, United States
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Optimization of Ultrasonic-Assisted Extraction of Active Components and Antioxidant Activity from Polygala tenuifolia: A Comparative Study of the Response Surface Methodology and Least Squares Support Vector Machine. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27103069. [PMID: 35630542 PMCID: PMC9144772 DOI: 10.3390/molecules27103069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/29/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
Abstract
Dried roots of Polygala tenuifolia (YuanZhi in Chinese) are widely used in Chinese herbal medicine. These components in YuanZhi have significant anti-oxidation properties owing to high levels of 3,6'-disinapoylsucrose (DISS) and Polygalaxanthone III (PolyIII). In order to efficiently extract natural medicines, response surface methodology (RSM) and least squares support vector machine (LSSVM) were used for the modeling and optimization of ultrasound-assisted extraction of DISS and PolyIII together to determine the antioxidant activity of the extracts obtained from YuanZhi. For the optimal combination of the comprehensive yield of DISS and PolyIII (Y), the Box-Behnken design (BBD) was used to improve extraction time (X1), extraction temperature (X2), liquid-solid ratio (X3), and ethanol concentration (X4). The optimal process parameters were determined to be as follows: extraction time, 93 min; liquid-solid ratio, 40 mL/g; extraction temperature, 48 °C; and ethanol concentration, 67%. With these conditions, the predictive optimal combination comprehensive evaluation value is 13.0217. It was clear that the LS-SVM model had higher accuracy in predictive and optimization capabilities, with higher antioxidant activity and lower relative deviations values, than did RSM. Hence, the LS-SVM model proved to be more effective for the analysis and improvement of the extraction process.
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