Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-26T03:01:43.234Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of seed amino acid content and its correlation network analysis in wild soybean (Glycine soja) germplasm in Japan

Published online by Cambridge University Press:  08 March 2021

Awatsaya Chotekajorn ,
Takuyu Hashiguchi ,
Masatsugu Hashiguchi ,
Hidenori Tanaka Open the ORCID record for Hidenori Tanaka [Opens in a new window]  and
Ryo Akashi
Awatsaya Chotekajorn
Affiliation:
Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki889-2192, Japan
Takuyu Hashiguchi
Affiliation:
Faculty of Agriculture, University of Miyazaki, Miyazaki889-2192, Japan
Masatsugu Hashiguchi
Affiliation:
Faculty of Agriculture, University of Miyazaki, Miyazaki889-2192, Japan
Hidenori Tanaka*
Affiliation:
Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki889-2192, Japan
Ryo Akashi
Affiliation:
Faculty of Agriculture, University of Miyazaki, Miyazaki889-2192, Japan
*
*Corresponding author. E-mail: htanaka@cc.miyazaki-u.ac.jp
Get access Rights & Permissions [Opens in a new window]

Abstract

Wild soybean (Glycine soja) is a valuable genetic resource for soybean improvement. Seed composition profiles provide beneficial information for the effective conservation and utilization of wild soybeans. Therefore, this study aimed to assess the variation in free amino acid abundance in the seeds of wild soybean germplasm collected in Japan. The free amino acid content in the seeds from 316 accessions of wild soybean ranged from 0.965 to 5.987 mg/g seed dry weight (DW), representing a 6.2-fold difference. Three amino acids had the highest coefficient of variation (CV): asparagine (1.15), histidine (0.95) and glutamine (0.94). Arginine (0.775 mg/g DW) was the predominant amino acid in wild soybean seeds, whereas the least abundant seed amino acid was glutamine (0.008 mg/g DW). A correlation network revealed significant positive relationships among most amino acids. Wild soybean seeds from different regions of origin had significantly different levels of several amino acids. In addition, a significant correlation between latitude and longitude of the collection sites and the total free amino acid content of seeds was observed. Our study reports diverse phenotypic data on the free amino acid content in seeds of wild soybean resources collected from throughout Japan. This information will be useful in conservation programmes for Japanese wild soybean and for the selection of accessions with favourable characteristics in future legume crop improvement efforts.

Keywords

crop wild relativefree amino acidsGlycine sojawild soybeans
Type
Research Article
Information
Plant Genetic Resources , Volume 19 , Issue 1 , February 2021 , pp. 35 - 43
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of NIAB

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amir, R, Galili, G and Cohen, H (2018) The metabolic roles of free amino acids during seed development. Plant Science 275: 11–18.CrossRefGoogle ScholarPubMed
Angelovici, R, Fait, A, Zhu, X, Szymanski, J, Feldmesser, E, Fernie, AR and Galili, G (2009) Deciphering transcriptional and metabolic networks associated with lysine metabolism during Arabidopsis seed development. Plant Physiology 151: 2058–2072.CrossRefGoogle ScholarPubMed
Angelovici, R, Batushansky, A, Deason, N, Gonzalez-Jorge, S, Gore, MA, Fait, A and DellaPenna, D (2017) Network-guided GWAS improves identification of genes affecting free amino acids. Plant Physiology 173: 872–886.CrossRefGoogle ScholarPubMed
Bligh, EG and Dyer, WJ (1959) A rapid method of total lipid extraction and purification. Canadian Journal of Biochemistry and Physiology 37: 911–917.CrossRefGoogle ScholarPubMed
Carter, TE Jr, Nelson, RL, Sneller, CH and Cui, Z (2004) Genetic diversity in soybean. In: Boerma, HR and Specht, JE (eds.) Soybean: Improvement, Production, and Uses. Madison: American Society for Agronomy, pp. 303–416.Google Scholar
Chebrolu, KK, Fritschi, FB, Ye, S, Krishnan, HB, Smith, JR and Gillman, JD (2016) Impact of heat stress during seed development on soybean seed metabolome. Metabolomics 12: 28.CrossRefGoogle Scholar
Dempewolf, H, Baute, G, Anderson, J, Kilian, B, Smith, C and Guarino, L (2017) Past and future use of wild relatives in crop breeding. Crop Science 57: 1070–1082.CrossRefGoogle Scholar
Fukui, J, Taira, H, Kaizuma, N and Taira, H (1972) Subgeneric and specific differences in the content and amino acid composition of the seed protein in the genus Glycine. Japanese Journal of Breeding 22: 197–202.CrossRefGoogle Scholar
Hashiguchi, M, Abe, J, Aoki, T, Anai, T, Suzuki, A and Akashi, R (2012) The National BioResource Project (NBRP) Lotus and Glycine in Japan. Breeding Science 61: 453–461.CrossRefGoogle ScholarPubMed
Hermann, FJ (1962) A revision of the genus Glycine and its immediate allies. USDA Technical Bulletins 1268: 1–79.Google Scholar
Hymowitz, T (1970) On the domestication of the soybean. Economic Botany 24: 408–421.CrossRefGoogle Scholar
Hyten, DL, Song, Q, Zhu, Y, Choi, IY, Nelson, RL, Costa, JM, Specht, JE, Shoemaker, RC and Cregan, PB (2006) Impacts of genetic bottlenecks on soybean genome diversity. Proceedings of the National Academy of Sciences of the USA 103: 16666–16671.CrossRefGoogle ScholarPubMed
Kaizuma, N and Fukui, J (1974) Specific characteristics and varietal differences for seed protein percentage and sulfur-containing amino acid contents in Japanese wild soybean (Glycine soja), and its significance on the soybean (G. max) breeding program. Japanese Journal of Breeding 24: 65–72.CrossRefGoogle Scholar
Kofsky, J, Zhang, H and Song, BH (2018) The untapped genetic reservoir: the past, current, and future applications of the wild soybean (Glycine soja). Frontiers in Plant Science 9: 949.CrossRefGoogle Scholar
Kuroda, Y, Kaga, A, Tomooka, N and Vaughan, DA (2006) Population genetic structure of Japanese wild soybean (Glycine soja) based on microsatellite variation. Molecular Ecology 15: 959–974.CrossRefGoogle ScholarPubMed
La, T, Large, E, Taliercio, E, Song, Q, Gillman, JD, Xu, D, Nguyen, HT, Shannon, G and Scaboo, A (2019) Characterization of select wild soybean accessions in the USDA germplasm collection for seed composition and agronomic traits. Crop Science 59: 233–251.CrossRefGoogle Scholar
Labanauskas, CK, Shouse, P, Stolzy, LH and Handy, MF (1981) Protein and free amino acids in field-grown cowpea seeds as affected by water stress at various growth stages. Plant and Soil 63: 355–368.CrossRefGoogle Scholar
Leamy, LJ, Lee, CR, Mjuacic, I, Quigley, CV, Nelson, RL, Luo, Y, Chen, Y, Peregrine, EK and Song, B (2016) Environmental versus geographical effects on genomic variation in wild soybean (Glycine soja) across its native range in northeast Asia. Ecology and Evolution 6: 6332–6344.CrossRefGoogle ScholarPubMed
Leamy, LJ, Zhang, H, Li, C, Chen, CY and Song, BH (2017) A genome-wide association study of seed composition traits in wild soybean (Glycine soja). BMC Genomics 18: 18.CrossRefGoogle Scholar
Li, YH, Zhou, G, Ma, J, Jiang, W, Jin, LG, Zhang, Z, Guo, Y, Zhang, J, Sui, Y, Zheng, L and Zhang, SS (2014) De novo assembly of soybean wild relatives for pan-genome analysis of diversity and agronomic traits. Nature Biotechnology 32: 1045–1052.CrossRefGoogle ScholarPubMed
Lin, H, Rao, J, Shi, J, Hu, C, Cheng, F, Wilson, ZA, Zhang, D and Quan, S (2014) Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars. Journal of Integrative Plant Biology 56: 826–836.CrossRefGoogle ScholarPubMed
Liu, B, Fujita, T, Yan, ZH, Sakamoto, S, Xu, D and Abe, J (2007) QTL mapping of domestication-related traits in soybean (Glycine max). Annals of Botany 100: 1027–1038.CrossRefGoogle Scholar
Nayyar, H, Kaur, S, Singh, S and Upadhyaya, HD (2006) Differential sensitivity of Desi (small-seeded) and Kabuli (large-seeded) chickpea genotypes to water stress during seed filling: effects on accumulation of seed reserves and yield. Journal of the Science of Food and Agriculture 86: 2076–2082.CrossRefGoogle Scholar
QGIS Development Team (2020) QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available at http://qgis.osgeo.org.Google Scholar
Rai, VK (2002) Role of amino acids in plant responses to stresses. Biologia Plantarum 45: 481–487.CrossRefGoogle Scholar
R Core Team (2020) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Toubiana, D, Semel, Y, Tohge, T, Beleggia, R, Cattivelli, L, Rosental, L, Nikoloski, Z, Zamir, D, Fernie, AR and Fait, A (2012) Metabolic profiling of a mapping population exposes new insights in the regulation of seed metabolism and seed, fruit, and plant relations. PLoS Genetics 8: e1002612.CrossRefGoogle ScholarPubMed
Wang, J, Zhou, P, Shi, X, Yang, N, Yan, L, Zhao, Q, Yang, C and Guan, Y (2019) Primary metabolite contents are correlated with seed protein and oil traits in near-isogenic lines of soybean. The Crop Journal 7: 651–659.CrossRefGoogle Scholar
Wee, CD, Hashiguchi, M, Ishigaki, G, Muguerza, M, Oba, C, Abe, J, Harada, K and Akashi, R (2017) Evaluation of seed components of wild soybean (Glycine soja) collected in Japan using near-infrared reflectance spectroscopy. Plant Genetic Resources: Characterization and Utilization 16: 94–102.CrossRefGoogle Scholar
Yobi, A, Bagaza, C, Batushansky, A, Shrestha, V, Emery, ML, Holden, S, Turner-Hissong, S, Miller, ND, Mawhinney, TP and Angelovici, R (2020) The complex response of free and bound amino acids to water stress during the seed setting stage in Arabidopsis. The Plant Journal 102: 838–855.CrossRefGoogle ScholarPubMed
Yoshida, H, Kondo, K, Yamamoto, H, Kageyama, N, Ozawa, SI, Shimbo, K, Muramatsu, T, Imaizumi, A, Mizukoshi, T, Masuda, J and Nakayama, D (2015) Validation of an analytical method for human plasma free amino acids by high-performance liquid chromatography ionization mass spectrometry using automated precolumn derivatization. Journal of Chromatography B 998: 88–96.CrossRefGoogle ScholarPubMed
Supplementary material: Image

Chotekajorn et al. supplementary material

Figure S1

Download Chotekajorn et al. supplementary material(Image)
Image 357 KB
Supplementary material: File

Chotekajorn et al. supplementary material

Table S1

Download Chotekajorn et al. supplementary material(File)
File 65.6 KB