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Zhang X, Yang M, Liu Z, Yang F, Zhang L, Guo Y, Huo D. Genetic analysis of yield components in buckwheat using high-throughput sequencing analysis and wild resource populations. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2024; 30:1313-1328. [PMID: 39184561 PMCID: PMC11341512 DOI: 10.1007/s12298-024-01491-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 06/06/2024] [Accepted: 07/15/2024] [Indexed: 08/27/2024]
Abstract
Fagopyrum tataricum, an important medicinal and edible crop, possesses significant agricultural and economic value. However, the development of buckwheat varieties and yields has been hindered by the delayed breeding progress despite the abundant material resources in China. Current research indicates that quantitative trait loci (QTLs) play a crucial role in controlling plant seed type and yield. To address these limitations, this study constructed recombinant inbred lines (RILs) utilizing both cultivated species and wild buckwheat as raw materials. In total, 84,521 Single Nucleotide Polymorphism (SNP) markers were identified through Genotyping-by-Sequencing (GBS) technology, and high-resolution and high-density SNP genetic maps were developed, which had significant value for QTL mapping, gene cloning and comparative mapping of buckwheat. In this study, we successfully identified 5 QTLs related to thousand grain weight (TGW), 9 for grain length (GL), and 1 for grain width (GW) by combining seed type and TGW data from 202 RIL populations in four different environments, within which one co-located QTL for TGW were discovered on the first chromosome. Transcriptome analysis during different grain development stages revealed 59 significant expression differences between the two materials, which can serve as candidate genes for further investigation into the regulation of grain weight and yield enhancement. The mapped major loci controlling TGW, GL and GW will be valuable for gene cloning and reveal the mechanism underlying grain development and marker-assisted selection in Tartary buckwheat.
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Affiliation(s)
- Xiao Zhang
- College of Biological Sciences and Technology, Taiyuan Normal University, Jinzhong, 030619 China
| | - Miao Yang
- College of Biological Sciences and Technology, Taiyuan Normal University, Jinzhong, 030619 China
| | - Zhang Liu
- Center for Agricultural Genetic Resources Research, Shanxi Agricultural University, Taiyuan, 030031 China
| | - Fan Yang
- College of Biological Sciences and Technology, Taiyuan Normal University, Jinzhong, 030619 China
| | - Lei Zhang
- College of Biological Sciences and Technology, Taiyuan Normal University, Jinzhong, 030619 China
| | - Yajing Guo
- College of Biological Sciences and Technology, Taiyuan Normal University, Jinzhong, 030619 China
| | - Dongao Huo
- College of Biological Sciences and Technology, Taiyuan Normal University, Jinzhong, 030619 China
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Khassanova G, Jatayev S, Gabdola A, Kuzbakova M, Zailasheva A, Kylyshbayeva G, Schramm C, Schleyer K, Philp-Dutton L, Sweetman C, Anderson P, Jenkins CLD, Soole KL, Shavrukov Y. Haplotypes of ATP-Binding Cassette CaABCC6 in Chickpea from Kazakhstan Are Associated with Salinity Tolerance and Leaf Necrosis via Oxidative Stress. Biomolecules 2024; 14:823. [PMID: 39062537 PMCID: PMC11275178 DOI: 10.3390/biom14070823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 06/30/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
Salinity tolerance was studied in chickpea accessions from a germplasm collection and in cultivars from Kazakhstan. After NaCl treatment, significant differences were found between genotypes, which could be arranged into three groups. Those that performed poorest were found in group 1, comprising five ICC accessions with the lowest chlorophyll content, the highest leaf necrosis (LN), Na+ accumulation, malondialdehyde (MDA) content, and a low glutathione ratio GSH/GSSG. Two cultivars, Privo-1 and Tassay, representing group 2, were moderate in these traits, while the best performance was for group 3, containing two other cultivars, Krasnokutsky-123 and Looch, which were found to have mostly green plants and an exact opposite pattern of traits. Marker-trait association (MTA) between 6K DArT markers and four traits (LN, Na+, MDA, and GSH/GSSG) revealed the presence of four possible candidate genes in the chickpea genome that may be associated with the three groups. One gene, ATP-binding cassette, CaABCC6, was selected, and three haplotypes, A, D1, and D2, were identified in plants from the three groups. Two of the most salt-tolerant cultivars from group 3 were found to have haplotype D2 with a novel identified SNP. RT-qPCR analysis confirmed that this gene was strongly expressed after NaCl treatment in the parental- and breeding-line plants of haplotype D2. Mass spectrometry of seed proteins showed a higher accumulation of glutathione reductase and S-transferase, but not peroxidase, in the D2 haplotype. In conclusion, the CaABCC6 gene was hypothesized to be associated with a better response to oxidative stress via glutathione metabolism, while other candidate genes are likely involved in the control of chlorophyll content and Na+ accumulation.
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Affiliation(s)
- Gulmira Khassanova
- Faculty of Agronomy, S.Seifullin Kazakh Agrotechnical Research University, Astana 010000, Kazakhstan; (S.J.); (A.G.); (M.K.); (A.Z.)
- A.I. Barayev Research and Production Centre of Grain Farming, Shortandy 021601, Kazakhstan
| | - Satyvaldy Jatayev
- Faculty of Agronomy, S.Seifullin Kazakh Agrotechnical Research University, Astana 010000, Kazakhstan; (S.J.); (A.G.); (M.K.); (A.Z.)
| | - Ademi Gabdola
- Faculty of Agronomy, S.Seifullin Kazakh Agrotechnical Research University, Astana 010000, Kazakhstan; (S.J.); (A.G.); (M.K.); (A.Z.)
| | - Marzhan Kuzbakova
- Faculty of Agronomy, S.Seifullin Kazakh Agrotechnical Research University, Astana 010000, Kazakhstan; (S.J.); (A.G.); (M.K.); (A.Z.)
| | - Aray Zailasheva
- Faculty of Agronomy, S.Seifullin Kazakh Agrotechnical Research University, Astana 010000, Kazakhstan; (S.J.); (A.G.); (M.K.); (A.Z.)
| | - Gulnar Kylyshbayeva
- Faculty of Natural Sciences, Central Asian Innovation University, Shymkent 160000, Kazakhstan;
| | - Carly Schramm
- College of Science and Engineering, Biological Sciences, Flinders University, Adelaide, SA 5042, Australia; (C.S.); (K.S.); (L.P.-D.); (C.S.); (P.A.); (C.L.D.J.); (K.L.S.)
| | - Kathryn Schleyer
- College of Science and Engineering, Biological Sciences, Flinders University, Adelaide, SA 5042, Australia; (C.S.); (K.S.); (L.P.-D.); (C.S.); (P.A.); (C.L.D.J.); (K.L.S.)
| | - Lauren Philp-Dutton
- College of Science and Engineering, Biological Sciences, Flinders University, Adelaide, SA 5042, Australia; (C.S.); (K.S.); (L.P.-D.); (C.S.); (P.A.); (C.L.D.J.); (K.L.S.)
| | - Crystal Sweetman
- College of Science and Engineering, Biological Sciences, Flinders University, Adelaide, SA 5042, Australia; (C.S.); (K.S.); (L.P.-D.); (C.S.); (P.A.); (C.L.D.J.); (K.L.S.)
| | - Peter Anderson
- College of Science and Engineering, Biological Sciences, Flinders University, Adelaide, SA 5042, Australia; (C.S.); (K.S.); (L.P.-D.); (C.S.); (P.A.); (C.L.D.J.); (K.L.S.)
| | - Colin L. D. Jenkins
- College of Science and Engineering, Biological Sciences, Flinders University, Adelaide, SA 5042, Australia; (C.S.); (K.S.); (L.P.-D.); (C.S.); (P.A.); (C.L.D.J.); (K.L.S.)
| | - Kathleen L. Soole
- College of Science and Engineering, Biological Sciences, Flinders University, Adelaide, SA 5042, Australia; (C.S.); (K.S.); (L.P.-D.); (C.S.); (P.A.); (C.L.D.J.); (K.L.S.)
| | - Yuri Shavrukov
- College of Science and Engineering, Biological Sciences, Flinders University, Adelaide, SA 5042, Australia; (C.S.); (K.S.); (L.P.-D.); (C.S.); (P.A.); (C.L.D.J.); (K.L.S.)
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Wang B, Bu Y, Zhang G, Liu N, Feng Z, Gong Y. Comparative transcriptome analysis of vegetable soybean grain discloses genes essential for grain quality. BMC PLANT BIOLOGY 2024; 24:491. [PMID: 38825702 PMCID: PMC11145879 DOI: 10.1186/s12870-024-05214-1] [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: 04/25/2023] [Accepted: 05/29/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Vegetable soybean is an important vegetable crop in world. Seed size and soluble sugar content are considered crucial indicators of quality in vegetable soybean, and there is a lack of clarity on the molecular basis of grain quality in vegetable soybean. RESULTS In this context, we performed a comprehensive comparative transcriptome analysis of seeds between a high-sucrose content and large-grain variety (Zhenong 6, ZN6) and a low-sucrose content and small-grain variety (Williams 82, W82) at three developmental stages, i.e. stage R5 (Beginning Seed), stage R6 (Full Seed), and stage R7 (Beginning Maturity). The transcriptome analysis showed that 17,107 and 13,571 differentially expressed genes (DEGs) were identified in ZN6 at R6 (vs. R5) and R7 (vs. R6), respectively, whereas 16,203 and 16,032 were detected in W82. Gene expression pattern and DEGs functional enrichment proposed genotype-specific biological processes during seed development. The genes participating in soluble sugar biosynthesis such as FKGP were overexpressed in ZN6, whereas those responsible for lipid and protein metabolism such as ALDH3 were more enhanced in W82, exhibiting different dry material accumulation between two genotypes. Furthermore, hormone-associated transcriptional factors involved in seed size regulation such as BEH4 were overrepresented in ZN6, exhibiting different seed size regulation processes between two genotypes. CONCLUSIONS Herein, we not only discovered the differential expression of genes encoding metabolic enzymes involved in seed composition, but also identified a type of hormone-associated transcriptional factors overexpressed in ZN6, which may regulate seed size and soluble content. This study provides new insights into the underlying causes of differences in the soybean metabolites and appearance, and suggests that genetic data can be used to improve its appearance and textural quality.
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Affiliation(s)
- Bin Wang
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China.
- Key Laboratory of Vegetable Legumes Germplasm Enhancement and Molecular Breeding in Southern China of Ministry of Agriculture and Rural Affairs, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China.
| | - Yuanpeng Bu
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China
- Key Laboratory of Vegetable Legumes Germplasm Enhancement and Molecular Breeding in Southern China of Ministry of Agriculture and Rural Affairs, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China
| | - Guwen Zhang
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China
- Key Laboratory of Vegetable Legumes Germplasm Enhancement and Molecular Breeding in Southern China of Ministry of Agriculture and Rural Affairs, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China
| | - Na Liu
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China
- Key Laboratory of Vegetable Legumes Germplasm Enhancement and Molecular Breeding in Southern China of Ministry of Agriculture and Rural Affairs, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China
| | - Zhijuan Feng
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China
- Key Laboratory of Vegetable Legumes Germplasm Enhancement and Molecular Breeding in Southern China of Ministry of Agriculture and Rural Affairs, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China
| | - Yaming Gong
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China.
- Key Laboratory of Vegetable Legumes Germplasm Enhancement and Molecular Breeding in Southern China of Ministry of Agriculture and Rural Affairs, 198, Shiqiao Rd, Hangzhou, 310021, Zhejiang, China.
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Gao W, Ma R, Li X, Liu J, Jiang A, Tan P, Xiong G, Du C, Zhang J, Zhang X, Fang X, Yi Z, Zhang J. Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean ( Glycine max L.). Int J Mol Sci 2024; 25:2857. [PMID: 38474104 DOI: 10.3390/ijms25052857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean.
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Affiliation(s)
- Weiran Gao
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Ronghan Ma
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Xi Li
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Jiaqi Liu
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Aohua Jiang
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Pingting Tan
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Guoxi Xiong
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Chengzhang Du
- Institute of Specialty Crop, Chongqing Academy of Agricultural Sciences, Chongqing 402160, China
| | - Jijun Zhang
- Institute of Specialty Crop, Chongqing Academy of Agricultural Sciences, Chongqing 402160, China
| | - Xiaochun Zhang
- Institute of Specialty Crop, Chongqing Academy of Agricultural Sciences, Chongqing 402160, China
| | - Xiaomei Fang
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Zelin Yi
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
| | - Jian Zhang
- College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
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5
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Li Y, Zhao W, Tang J, Yue X, Gu J, Zhao B, Li C, Chen Y, Yuan J, Lin Y, Li Y, Kong F, He J, Wang D, Zhao TJ, Wang ZY. Identification of the domestication gene GmCYP82C4 underlying the major quantitative trait locus for the seed weight in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:62. [PMID: 38418640 DOI: 10.1007/s00122-024-04571-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
KEY MESSAGE A major quantitative trait locus (QTL) for the hundred-seed weight (HSW) was identified and confirmed in the two distinct soybean populations, and the target gene GmCYP82C4 underlying this locus was identified that significantly associated with soybean seed weight, and it was selected during the soybean domestication and improvement process. Soybean is a major oil crop for human beings and the seed weight is a crucial goal of soybean breeding. However, only a limited number of target genes underlying the quantitative trait loci (QTLs) controlling seed weight in soybean are known so far. In the present study, six loci associated with hundred-seed weight (HSW) were detected in the first population of 573 soybean breeding lines by genome-wide association study (GWAS), and 64 gene models were predicted in these candidate QTL regions. The QTL qHSW_1 exhibits continuous association signals on chromosome four and was also validated by region association study (RAS) in the second soybean population (409 accessions) with wild, landrace, and cultivar soybean accessions. There were seven genes in qHSW_1 candidate region by linkage disequilibrium (LD) block analysis, and only Glyma.04G035500 (GmCYP82C4) showed specifically higher expression in flowers, pods, and seeds, indicating its crucial role in the soybean seed development. Significant differences in HSW trait were detected when the association panels are genotyped by single-nucleotide polymorphisms (SNPs) in putative GmCYP82C4 promoter region. Eight haplotypes were generated by six SNPs in GmCYP82C4 in the second soybean population, and two superior haplotypes (Hap2 and Hap4) of GmCYP82C4 were detected with average HSW of 18.27 g and 18.38 g, respectively. The genetic diversity of GmCYP82C4 was analyzed in the second soybean population, and GmCYP82C4 was most likely selected during the soybean domestication and improvement process, leading to the highest proportion of Hap2 of GmCYP82C4 both in landrace and cultivar subpopulations. The QTLs and GmCYP82C4 identified in this study provide novel genetic resources for soybean seed weight trait, and the GmCYP82C4 could be used for soybean molecular breeding to develop desirable seed weight in the future.
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Affiliation(s)
- Yang Li
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Wenqian Zhao
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Jiajun Tang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Xiuli Yue
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Jinbao Gu
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Biyao Zhao
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Cong Li
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Yanhang Chen
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Jianbo Yuan
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Yan Lin
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China
| | - Yan Li
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Fanjiang Kong
- Innovative Center of Molecular Genetics and Evolution, School of Life Sciences, Guangzhou University, Guangzhou, China
| | - Jin He
- College of Agriculture, Guizhou University, Guiyang, China
| | - Dong Wang
- Key Laboratory of Molecular Biology and Gene Engineering in Jiangxi Province, College of Life Science, Nanchang University, Nanchang, China
| | - Tuan-Jie Zhao
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China.
| | - Zhen-Yu Wang
- Institute of Nanfan & Seed Industry, Guangdong Academy of Science, Guangzhou, 510316, China.
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Tayade R, Imran M, Ghimire A, Khan W, Nabi RBS, Kim Y. Molecular, genetic, and genomic basis of seed size and yield characteristics in soybean. FRONTIERS IN PLANT SCIENCE 2023; 14:1195210. [PMID: 38034572 PMCID: PMC10684784 DOI: 10.3389/fpls.2023.1195210] [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: 03/28/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans' genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits' genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands.
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Affiliation(s)
- Rupesh Tayade
- Upland Field Machinery Research Center, Kyungpook National University, Daegu, Republic of Korea
| | - Muhammad Imran
- Division of Biosafety, National Institute of Agriculture Science, Rural Development Administration, Jeonju, Jeollabul-do, Republic of Korea
| | - Amit Ghimire
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
| | - Waleed Khan
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
| | - Rizwana Begum Syed Nabi
- Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration, Miryang, Republic of Korea
| | - Yoonha Kim
- Upland Field Machinery Research Center, Kyungpook National University, Daegu, Republic of Korea
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
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7
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Duc NT, Ramlal A, Rajendran A, Raju D, Lal SK, Kumar S, Sahoo RN, Chinnusamy V. Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean. FRONTIERS IN PLANT SCIENCE 2023; 14:1206357. [PMID: 37771485 PMCID: PMC10523016 DOI: 10.3389/fpls.2023.1206357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/26/2023] [Indexed: 09/30/2023]
Abstract
Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.
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Affiliation(s)
- Nguyen Trung Duc
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
- Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Ayyagari Ramlal
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
- School of Biological Sciences, Universiti Sains Malaysia (USM), Georgetown, Penang, Malaysia
| | - Ambika Rajendran
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Dhandapani Raju
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - S. K. Lal
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Sudhir Kumar
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Rabi Narayan Sahoo
- Division of Agricultural Physics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Viswanathan Chinnusamy
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
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Jiang A, Liu J, Gao W, Ma R, Tan P, Liu F, Zhang J. Construction of a genetic map and QTL mapping of seed size traits in soybean. Front Genet 2023; 14:1248315. [PMID: 37693311 PMCID: PMC10485605 DOI: 10.3389/fgene.2023.1248315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023] Open
Abstract
Soybean seed size and seed shape traits are closely related to plant yield and appearance quality. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiang Chun 2 and JiYu 166 were selected as the mapping population to construct a molecular genetic linkage map, and the phenotypic data of hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean under three generations of F2 single plants and F2:3 and F2:4 lines were combined to detect the QTL (quantitative trait loci) for the corresponding traits by ICIM mapping. A soybean genetic map containing 455 markers with an average distance of 6.15 cM and a total length of 2799.2 cM was obtained. Forty-nine QTLs related to the hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean were obtained under three environmental conditions. A total of 10 QTLs were detected in more than two environments with a phenotypic variation of over 10%. Twelve QTL clusters were identified on chromosomes 1, 2, 5, 6, 8, 13, 18, and 19, with the majority of the overlapping intervals for hundred-grain weight and seed width. These results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean seed weight and seed shape. Eighteen candidate genes that may be involved in the regulation of soybean seed size were screened by gene functional annotation and GO enrichment analysis.
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Affiliation(s)
| | | | | | | | | | | | - Jian Zhang
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
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Ramlal A, Bhat I, Nautiyal A, Baweja P, Mehta S, Kumar V, Tripathi S, Mahto RK, Saini M, Mallikarjuna BP, Saluja S, Lal SK, Subramaniam S, Fawzy IM, Rajendran A. In silico analysis of angiotensin-converting enzyme inhibitory compounds obtained from soybean [ Glycine max (L.) Merr.]. Front Physiol 2023; 14:1172684. [PMID: 37324400 PMCID: PMC10264776 DOI: 10.3389/fphys.2023.1172684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/02/2023] [Indexed: 06/17/2023] Open
Abstract
Cardiovascular diseases (CVDs) are one of the major reasons for deaths globally. The renin-angiotensin-aldosterone system (RAAS) regulates body hypertension and fluid balance which causes CVD. Angiotensin-converting enzyme I (ACE I) is the central Zn-metallopeptidase component of the RAAS playing a crucial role in maintaining homeostasis of the cardiovascular system. The available drugs to treat CVD have many side effects, and thus, there is a need to explore phytocompounds and peptides to be utilized as alternative therapies. Soybean is a unique legume cum oilseed crop with an enriched source of proteins. Soybean extracts serve as a primary ingredient in many drug formulations against diabetes, obesity, and spinal cord-related disorders. Soy proteins and their products act against ACE I which may provide a new scope for the identification of potential scaffolds that can help in the design of safer and natural cardiovascular therapies. In this study, the molecular basis for selective inhibition of 34 soy phytomolecules (especially of beta-sitosterol, soyasaponin I, soyasaponin II, soyasaponin II methyl ester, dehydrosoyasaponin I, and phytic acid) was evaluated using in silico molecular docking approaches and dynamic simulations. Our results indicate that amongst the compounds, beta-sitosterol exhibited a potential inhibitory action against ACE I.
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Affiliation(s)
- Ayyagari Ramlal
- Division of Genetics, ICAR-Indian Agricultural Research Institute (IARI), Pusa Campus, New Delhi, India
- School of Biological Sciences, Universiti Sains Malaysia (USM), Georgetown, Penang, Malaysia
| | - Isha Bhat
- Department of Biosciences, Jamia Millia Islamia, New Delhi, Delhi, India
| | - Aparna Nautiyal
- Department of Botany, Deshbandhu College, University of Delhi, New Delhi, India
| | - Pooja Baweja
- Department of Botany, Maitreyi College, University of Delhi, New Delhi, India
| | - Sahil Mehta
- Department of Botany, Hansraj College, University of Delhi, New Delhi, India
| | - Vikash Kumar
- Faculty of Agricultural Sciences, Institute of Applied Sciences and Humanities, GLA University, Mathura, Uttar Pradesh, India
| | - Shikha Tripathi
- ICAR- National Institute for Biotechnology, New Delhi, India
- Department of Botany, Institute of Science, Banaras Hindu University (BHU), Varanasi, Uttar Pradesh, India
| | - Rohit Kumar Mahto
- Division of Genetics, ICAR-Indian Agricultural Research Institute (IARI), Pusa Campus, New Delhi, India
- School of Biotechnology, Institute of Science, Banaras Hindu University (BHU), Varanasi, Uttar Pradesh, India
| | - Manisha Saini
- Division of Genetics, ICAR-Indian Agricultural Research Institute (IARI), Pusa Campus, New Delhi, India
| | - Bingi Pujari Mallikarjuna
- Division of Genetics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute (IARI), Regional Research Centre, Dharwad, Karnataka, India
| | - Shukla Saluja
- Department of Botany, Sri Venkateswara College, University of Delhi, New Delhi, India
| | - S. K. Lal
- Division of Genetics, ICAR-Indian Agricultural Research Institute (IARI), Pusa Campus, New Delhi, India
| | - Sreeramanan Subramaniam
- School of Biological Sciences, Universiti Sains Malaysia (USM), Georgetown, Penang, Malaysia
- Chemical Centre Biology (CCB), Universiti Sains Malaysia (USM), Bayan Lepas, Penang, Malaysia
- Institute of Nano Optoelectronics Research and Technology, Universiti Sains Malaysia (USM), Bayan Lepas, Penang, Malaysia
| | - Iten M. Fawzy
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Future University in Egypt, Cairo, Egypt
| | - Ambika Rajendran
- Division of Genetics, ICAR-Indian Agricultural Research Institute (IARI), Pusa Campus, New Delhi, India
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Duan Z, Li Q, Wang H, He X, Zhang M. Genetic regulatory networks of soybean seed size, oil and protein contents. FRONTIERS IN PLANT SCIENCE 2023; 14:1160418. [PMID: 36959925 PMCID: PMC10028097 DOI: 10.3389/fpls.2023.1160418] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
As a leading oilseed crop that supplies plant oil and protein for daily human life, increasing yield and improving nutritional quality (high oil or protein) are the top two fundamental goals of soybean breeding. Seed size is one of the most critical factors determining soybean yield. Seed size, oil and protein contents are complex quantitative traits governed by genetic and environmental factors during seed development. The composition and quantity of seed storage reserves directly affect seed size. In general, oil and protein make up almost 60% of the total storage of soybean seed. Therefore, soybean's seed size, oil, or protein content are highly correlated agronomical traits. Increasing seed size helps increase soybean yield and probably improves seed quality. Similarly, rising oil and protein contents improves the soybean's nutritional quality and will likely increase soybean yield. Due to the importance of these three seed traits in soybean breeding, extensive studies have been conducted on their underlying quantitative trait locus (QTLs) or genes and the dissection of their molecular regulatory pathways. This review summarized the progress in functional genome controlling soybean seed size, oil and protein contents in recent decades, and presented the challenges and prospects for developing high-yield soybean cultivars with high oil or protein content. In the end, we hope this review will be helpful to the improvement of soybean yield and quality in the future breeding process.
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Affiliation(s)
- Zongbiao Duan
- Hainan Yazhou Bay Seed Laboratory, Sanya, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Qing Li
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China
| | - Hong Wang
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China
| | - Xuemei He
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Min Zhang
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
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