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"Starch"

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"Starch"

Research Articles
Two Complementary Genes, SBE3 and GBSS1 Contribute to High Amylose Content in Japonica Cultivar Dodamssal
Cheryl C. Adeva, Hyun-Sook Lee, Sun-Ha Kim, Yun-A Jeon, Kyu-Chan Shim, Ngoc Ha Luong, Ju-Won Kang, Chang-Soo Kim, Jun-Hyeon Cho, Sang-Nag Ahn
Plant Breed. Biotech. 2020;8(4):354-367.   Published online December 1, 2020
DOI: https://doi.org/10.9787/PBB.2020.8.4.354

Quantitative trait loci (QTLs) for the starch-related traits amylose content (AC) and resistant starch (RS) content have received much attention due to the potential benefits of grains high in these starch levels. In this study, QTLs associated with AC and RS content were identified using 92 recombinant inbred lines (RILs) developed from a cross between two closely related japonica cultivars ‘Dodamssal’ and ‘Hwayeong’. One QTL on chromosome 2 for RS content and 2 QTLs for AC on chromosomes 2 and 6 were detected. The F2 population derived from a cross between Hwayeong and two selected RILs were used to analyze the interaction between starch branching enzyme 3 (SBE3) and granule-bound starch synthase 1 (GBSS1). The combined effect of SBE3 and GBSS1 in the F2 population suggested that these two genes behaved in an additive manner in increasing AC. Haplotype analysis based on two SNPs in GBSS1 classified 117 rice accessions into three groups. At the first SNP site, all indica, Korean landrace, and weedy rice accessions had the Wxa allele at the 5ʹ splice site of intron 1, whereas japonica accessions had the mutated Wxb allele. This suggests that this splice-donor mutation is prevalent in japonica cultivars, but rare or absent in landrace and weedy rice cultivars. Landrace or weedy rice accessions harboring the Wxa allele could be employed in breeding programs to manipulate AC in cultivated japonica rice considering the difficulty and time to introduce desirable indica traits into japonica due to reproductive barriers.

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  • Improved Processing Quality in a tongil-type Waxy Rice Cultivar Associated with a Rare granule-bound starch synthase I Allele
    Seong-Gyu Jang, Ji-Min Kim, Ji-Yoon Lee, Jun-Hyeon Cho, Youngho Kwon, So-Myeong Lee, Jisu Choi, Ju-Won Kang, Jong-Min Jeong, Jong-Hee Lee, Dong-Soo Park, Soon-Wook Kwon, Sumin Jo
    Korean Journal of Breeding Science.2026; 58(2): 123.     CrossRef
  • Evaluation of glycemic response and starch digestibility in Korean rice toward the development of low GI rice
    Chang-Min Lee, O-Young Jeong, Hyun-Su Park, Jeonghwan Seo, Songhee Park, Keon-Mi Lee, Mina Jin, Il-Ryong Choi, Jae-Hyuk Han
    Frontiers in Plant Science.2025;[Epub]     CrossRef
  • Evolution and association analysis of SSIIIa in rice landraces of Yunnan Province
    Xia Li, Xiaomeng Yang, Li’e Yang, Hafiz Ghulam Muhu-Din Ahmed, Chunlian Yao, Jiazhen Yang, Luxiang Wang, Tao Yang, Xiaoying Pu, Yawen Zeng
    Biologia.2024; 79(7): 2203.     CrossRef
  • Allelic Diversity at Protein Disulfide Isomerase Like 1-1 (PDIL1-1) Gene is Associated with Amylose Content in Japonica Rice
    Cheryl Adeva, Ju-Won Kang, Kyu-Chan Shim, Ngoc Ha Luong, Hyun-Sook Lee, Jong-Hee Lee, Sang-Nag Ahn
    Plant Breeding and Biotechnology.2023; 11(1): 56.     CrossRef
  • Quality Characteristics of Rice-Based Ice Creams with Different Amylose Contents
    Gi-Un Seong, Ji-Yoon Kim, Jung-Soo Kim, Sae-Ul Jeong, Jun-Hyeon Cho, Ji-Yoon Lee, Sais-Beul Lee, Nkulu-Rolly Kabange, Dong-Soo Park, Kwang-Deog Moon, Ju-Won Kang
    Foods.2023; 12(7): 1518.     CrossRef
  • Molecular Breeding of Zheyou810, an Indica–Japonica Hybrid Rice Variety with Superior Quality and High Yield
    Jian Song, Yongtao Cui, Honghuan Fan, Liqun Tang, Jianjun Wang
    Agriculture.2023; 13(9): 1807.     CrossRef
  • Interaction of starch branching enzyme 3 and granule-bound starch synthase 1 alleles increases amylose content and alters physico-chemical properties in japonica rice (Oryza sativa L.)
    Kyu-Chan Shim, Cheryl Adeva, Ju-Won Kang, Ngoc Ha Luong, Hyun-Sook Lee, Jun-Hyeon Cho, HyunJung Kim, Thomas H. Tai, Sang-Nag Ahn
    Frontiers in Plant Science.2022;[Epub]     CrossRef
  • QTL Mapping of Mineral Element Contents in Rice Using Introgression Lines Derived from an Interspecific Cross
    Cheryl Adeva, Yeo-Tae Yun, Kyu-Chan Shim, Ngoc Ha Luong, Hyun-Sook Lee, Ju-Won Kang, Hyun-Jung Kim, Sang-Nag Ahn
    Agronomy.2022; 13(1): 76.     CrossRef
  • Evaluation of Hardness and Retrogradation of Cooked Rice Based on Its Pasting Properties Using a Novel RVA Testing
    Sumiko Nakamura, Junji Katsura, Yasuhiro Maruyama, Ken’ichi Ohtsubo
    Foods.2021; 10(5): 987.     CrossRef
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Environmental Stability and Correlation of Soybean Seed Starch with Protein and Oil Contents
Sanjeev K. Dhungana, Krishnanand P. Kulkarni, Minsu Kim, Bo-Keun Ha, Sungtaeg Kang, Jong Tae Song, Dong-Hyun Shin, Jeong-Dong Lee
Plant Breed. Biotech. 2017;5(4):293-303.   Published online December 1, 2017
DOI: https://doi.org/10.9787/PBB.2017.5.4.293

Seed starch content (SSC) is a decisive factor influencing soy food quality. Variation in SSC affects the composition of major components, oil, and protein in soybean seeds. Therefore, understanding G × E interaction of SSC is important to produce soybeans with stable SSC. In the present study, G × E interactions of 17 soybean genotypes having different SSC (0.24–1.48%) and correlation of SSC with crude protein (CP) and crude fat (CF) were investigated. The genotypes were evaluated for SSC and other traits at two planting dates across three locations over two years (2015 and 2016). The genotype × year, genotype × location, and genotype × year × location interactions were found to be significant (P ≤ 0.001) for SSC, CP, and CF. The average SSC content was found to be higher in 2015 than in 2016. Late planted soybeans contained higher SSC than the early planting soybeans. The SSC was negatively affected by the average daily mean and minimum temperatures and cloudiness during the pod-filling stage. Based on the mean rank, IT189276 (1.39%) was observed to be the most stable genotype among the high starch containing soybeans. Significant (P ≤ 0.0001) negative correlations were found between SSC and CP as well as CP and CF contents. However, a significant (P ≤ 0.05) positive correlation was observed between SSC and CF content. Results of this study showed that SSC affects the seed protein and oil contents and is significantly influenced by the growing environments.

Citations

Citations to this article as recorded by  
  • Soybean as an animal protein analogue: Hormetic effect, popularity and consumer preference
    V Jayasri, Ayyagari Ramlal, Sreeramanan Subramaniam, Aparna Nautiyal, Praveen Gupta, Dhandapani Raju, S K Lal, Ambika Rajendran, Ankita Rajendra Parab
    Food Chemistry Advances.2026; 11: 101272.     CrossRef
  • Effects of Allelic Variation in Storage Protein Genes on Seed Composition and Agronomic Traits of Soybean in the Omsk Oblast of Western Siberia
    Ilya V. Strembovskiy, Pavel Yu. Kroupin, Lyudmila V. Omel’yanuk, Andrey V. Arkhipov, Yana S. Meglitskaya, Mikhail S. Bazhenov, Akimbek M. Asanov, Mariya E. Mukhordova, Oksana A. Yusova, Yuliya I. Yaschenko, Gennady I. Karlov, Mikhail G. Divashuk
    Agronomy.2025; 15(11): 2533.     CrossRef
  • Comparative Evaluation of Nutritional Quality and In Vitro Protein Digestibility in Selected Vegetable Soybean Genotypes at R6 and R8 Maturity
    Kanneboina Soujanya, T. Supraja, Aparna Kuna, Ramakrishnan M. Nair, S. Triveni, Kalenahalli Yogendra
    Foods.2025; 14(14): 2549.     CrossRef
  • Traditional Legume Seed Fermentation Processes: What is the Individual Impact of the Cooking and Fermentation Stages on the Degradation of Anti-Nutritional Factors?
    Charlène Gbedo, Elodie Arnaud, Caroline Strub
    Food Reviews International.2025; 41(5): 1290.     CrossRef
  • The effect of ethyl methanesulfonate (EMS) and environmental factors on soybean traits
    Khaled Ramadan, Souhail Nader, Loubna Mokrani, Ghrood Al Aswd, Samir Abou-Isba, Abdulkarim Dakah
    BMC Plant Biology.2025;[Epub]     CrossRef
  • Unveiling Diversity for Quality Traits in the Indian Landraces of Horsegram [Macrotyloma uniflorum (Lam.) Verdc.]
    Manju Kumari, Siddhant Ranjan Padhi, Sushil Kumar Chourey, Vishal Kondal, Swapnil S. Thakare, Ankita Negi, Veena Gupta, Mamta Arya, Jeshima Khan Yasin, Rakesh Singh, Chellapilla Bharadwaj, Atul Kumar, Kailash Chandra Bhatt, Rakesh Bhardwaj, Jai Chand Rana
    Plants.2023; 12(22): 3803.     CrossRef
  • Genetic variation in four maturity genes and photoperiod insensitivity effects on the yield components and on the growth duration periods of soybean
    I. M. Raievska, A. S. Schogolev
    Regulatory Mechanisms in Biosystems.2023; 14(1): 55.     CrossRef
  • Soybean genetic resources contributing to sustainable protein production
    Bingfu Guo, Liping Sun, Siqi Jiang, Honglei Ren, Rujian Sun, Zhongyan Wei, Huilong Hong, Xiaoyan Luan, Jun Wang, Xiaobo Wang, Donghe Xu, Wenbin Li, Changhong Guo, Li-Juan Qiu
    Theoretical and Applied Genetics.2022; 135(11): 4095.     CrossRef
  • Correlations between soybean seed quality traits using a genome-wide association study panel grown in Canadian and Ukrainian mega-environments
    Huilin Hong, Mohsen Yoosefzadeh-Najafabadi, Istvan Rajcan
    Canadian Journal of Plant Science.2022; 102(5): 1040.     CrossRef
  • Control of seed born mycobiota associated with Glycine max L. Merr. seeds by a combination of traditional medicinal plants extracts
    SULAIMAN A. AL YOUSEF
    BIOCELL.2021; 45(5): 1403.     CrossRef
  • Application of near infrared spectroscopy for determination of relationship between crop year, maturity group, location, and carbohydrate composition in soybeans
    Mukti Singh, Michael J. Bowman, Mark A. Berhow, Neil P. J. Price, Sean X. Liu
    Crop Science.2021; 61(4): 2409.     CrossRef
  • Comparison of sugars, lipids and phenolics content in the grains of organically and conventionally grown soybean in Serbia
    Jelena M. Golijan, Danijel D. Milinčić, Radivoj B. Petronijević, Mirjana B. Pešić, Sladjana P. Stanojević, Miroljub B. Barać, Slavoljub Lekić, Aleksandar Ž. Kostić
    Zemdirbyste-Agriculture.2021; 108(1): 51.     CrossRef
  • Protein, Amino Acid, Oil, Fatty Acid, Sugar, Anthocyanin, Isoflavone, Lutein, and Antioxidant Variations in Colored Seed-Coated Soybeans
    Sanjeev Kumar Dhungana, Jeong-Hyun Seo, Beom-Kyu Kang, Ji-Hee Park, Jun-Hoi Kim, Jung-Sook Sung, In-Youl Baek, Sang-Ouk Shin, Chan-Sik Jung
    Plants.2021; 10(9): 1765.     CrossRef
  • Morpho-chemical evaluation of soybean genotypes across tropical agroecosystem
    A Krisnawati, M M Adie
    IOP Conference Series: Earth and Environmental Science.2019; 230: 012106.     CrossRef
  • Assessment of Phenotypic Variations and Correlation among Seed Composition Traits in Mutagenized Soybean Populations
    Zhou Zhou, Naoufal Lakhssassi, Mallory A. Cullen, Abdelhalim El Baz, Tri D. Vuong, Henry T. Nguyen, Khalid Meksem
    Genes.2019; 10(12): 975.     CrossRef
  • Genome-wide association study of seed protein, oil and amino acid contents in soybean from maturity groups I to IV
    Sungwoo Lee, Kyujung Van, Mikyung Sung, Randall Nelson, Jonathan LaMantia, Leah K. McHale, M. A. Rouf Mian
    Theoretical and Applied Genetics.2019; 132(6): 1639.     CrossRef
  • Insight Into the Prospects for the Improvement of Seed Starch in Legume—A Review
    Rupesh Tayade, Krishnanand P. Kulkarni, Hyun Jo, Jong Tae Song, Jeong-Dong Lee
    Frontiers in Plant Science.2019;[Epub]     CrossRef
  • Dynamic Transcriptome Changes Related to Oil Accumulation in Developing Soybean Seeds
    Songnan Yang, Long Miao, Jianbo He, Kai Zhang, Yan Li, Junyi Gai
    International Journal of Molecular Sciences.2019; 20(9): 2202.     CrossRef
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Modification of Starch Composition Using RNAi Targeting Soluble Starch Synthase I in Japonica Rice
Hye Jung Lee, Moo-Geun Jee, Joonki Kim, Franz M.C. Nogoy, Marjohn C. Niño, Dal-A Yu, Me Sun Kim, Mingmao Sun, Kwon-Kyoo Kang, Illsup Nou, Yong-Gu Cho
Plant Breed. Biotech. 2014;2(3):301-312.   Published online September 30, 2014
DOI: https://doi.org/10.9787/PBB.2014.2.3.301

An increasing preference for good eating quality of rice among consumers has become one of the important considerations in rice breeding. Amylose content is a leading factor affecting eating quality of rice. Amylose composition is determined by the relative activity of soluble starch synthase (SSS) and granule-bound starch synthase (GBSS). This study focused on modifying the expression of SSSI gene which is responsible for amylopectin and amylose synthesis in rice by using RNA interference (RNAi) technology. The transgenic rice plants showed various amylose contents (11–17%) in rice grains. Favorable rice lines were selected according to genomic PCR, transgene expression and amylose contents analysis. A semi-quantitative RT-PCR was carried out to determine the expression level of SSSI gene after flowering of transgenic rice and wild type. Down-regulation of SSSI gene in transgenic plants was evident in the decreasing expression in rice grains. Accordingly, scanning electron microscopy (SEM) analysis revealed uniform size with smooth curves starch granules in down-regulation rice lines, in contrast with the non-uniform granules in wild type. Results indicated that RNAi-SSSI transgenic lines produced low amylose contents that fell between glutinous and non-glutinous rice. This study showed that down-regulation of endogenous SSSI may improve the eating quality in rice.

Citations

Citations to this article as recorded by  
  • Biochemical markers for low glycemic index and approaches to alter starch digestibility in rice
    Awadhesh Kumar, Upasana Sahoo, Milan Kumar Lal, Rahul Kumar Tiwari, Sangram Keshari Lenka, Nihar Ranjan Singh, Om Prakash Gupta, Rameswar Prasad Sah, Srigopal Sharma
    Journal of Cereal Science.2022; 106: 103501.     CrossRef
  • Soluble Starch Synthase Enzymes in Cereals: An Updated Review
    Ahsan Irshad, Huijun Guo, Shoaib Ur Rehman, Xueqing Wang, Chaojie Wang, Ali Raza, Chunyun Zhou, Yuting Li, Luxiang Liu
    Agronomy.2021; 11(10): 1983.     CrossRef
  • SSIIIa-RNAi suppression associated changes in rice grain quality and starch biosynthesis metabolism in response to high temperature
    Qian Zhao, Yu Ye, Zhanyu Han, Lujian Zhou, Xianyue Guan, Gang Pan, Muhammad-Asad-Ullah Asad, Fangmin Cheng
    Plant Science.2020; 294: 110443.     CrossRef
  • Physico-chemical characterization and transcriptome analysis of 5-methyltryptophan resistant lines in rice
    Franz Marielle Nogoy, Yu Jin Jung, Kwon-Kyoo Kang, Yong-Gu Cho, Wujun Ma
    PLOS ONE.2019; 14(9): e0222262.     CrossRef
  • Characterization of ‘GolSam’ Lines Developed from the Cross between Samgwang and 5MT Resistant Lines in Rice
    Franz Marielle Nogoy, Yu Jin Jung, Kwon Kyoo Kang, Yong-Gu Cho
    Plant Breeding and Biotechnology.2018; 6(3): 233.     CrossRef
  • Crop resistant starch and genetic improvement: a review of recent advances
    Jian Xia, Dong Zhu, Ruomei Wang, Yue Cui, Yueming Yan
    Theoretical and Applied Genetics.2018; 131(12): 2495.     CrossRef
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    Yu-Jin Jung, Franz Marielle Nogoy, Sang-Kyu Lee, Yong-Gu Cho, Kwon-Kyoo Kang
    Biotechnology and Bioprocess Engineering.2018; 23(1): 108.     CrossRef
  • Potential impact of genome editing in world agriculture
    Wendy Harwood, Jorge Martínez-Fortún, Dylan W. Phillips, Huw D. Jones
    Emerging Topics in Life Sciences.2017; 1(2): 117.     CrossRef
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    Sailila E. Abdula, Hye-Jung Lee, Hojin Ryu, Kwon Kyoo Kang, Illsup Nou, Mark E. Sorrells, Yong-Gu Cho
    Plant Molecular Biology Reporter.2016; 34(2): 501.     CrossRef
  • BrUGE1 transgenic rice showed improved growth performance with enhanced drought tolerance
    Sailila E. Abdula, Hye Jung Lee, Joonki Kim, Marjohn C. Niño, Yu-Jin Jung, Young-Chan Cho, Illsup Nou, Kwon-Kyoo Kang, Yong-Gu Cho
    Breeding Science.2016; 66(2): 226.     CrossRef
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