Analysis of differential expression of glycinin and β-conglycinin genes in Europeanand Canadian soybean varieties

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Abstract

Soybean (Glycine max (L.) Merr.) is a major source of high-quality protein for food and feed. The seed storage proteins, glycinin (11S) and β-conglycinin (7S), differ in amino acid composition and functional properties, and their ratio is a key determinant of soybean quality. This study aimed to analyze the differential expression of glycinin and β-conglycinin genes in European and Canadian soybean varieties to assess their storage protein profiles. Six European and Canadian soybean varieties (ESG 1813, RTZh Spida, Me 5, Mu 5 - France; N.S. Katya - Serbia; Сassidy - Canada) were grown in 2023-2024 at All-Russian Research Institute of Soybean, Amur region. Total RNA was isolated from mature seeds, cDNA was synthesized, and expression levels of Gy1-Gy5, Gy7, Cg1, Cg3, and Cg4 genes were analyzed by quantitative real-time PCR. The Tubulin gene was used as an internal reference. Relative expression was calculated using the 2-ΔΔCt method. All primers showed high specificity confirmed by single melting peaks. Gy7 gene expression was not detected in any variety, consistent with its loss or suppression during breeding. Most varieties displayed conserved expression profiles for Gy1, Gy2, and Gy3. Two varieties exhibited unique expression patterns: Cassidy (Canada) showed significantly increased Gy4 expression, and Mu 5 (France) showed significantly increased Gy5 expression (p < 0.05). RTZh Spida (France) demonstrated significantly elevated Cg3 expression. Based on total glycinin/β-conglycinin transcript ratios, varieties were clearly divided into high-glycinin (Mu 5 - 86%, Cassidy - 82%, Me 5 - 64%) and high-β-conglycinin (N.S. Katya - 67%, RTZh Spida - 63%, ESG 1813 - 53%) groups. The observed 11S/7S ratio range (0.5-6.1) substantially exceeded previously reported values for American and Brazilian cultivars. The identified donor varieties (Cassidy - high Gy4 expression, Mu 5 - high Gy5 expression, RTZh Spida - high Cg3 expression) represent valuable genetic resources for Russian breeding programs. They can be used to introgress regulatory alleles for targeted manipulation of storage protein composition, enabling the development of new soybean cultivars with optimized protein quality and technological properties adapted to agroclimatic conditions of the Russian Federation.

About the authors

Anastasiya A. Katrushenko

All-Russian Research Institute of Soybean

Author for correspondence.
Email: lsi@vniisoi.ru
ORCID iD: 0009-0008-3104-1696
SPIN-code: 8077-7588

Junior Researcher, Biotechnology Laboratory

19 Ignatyevskoye highway, Blagoveshchensk, Amur Region, 675027, Russian Federation

Alena A. Ivaniy

All-Russian Research Institute of Soybean

Email: iaa@vniisoi.ru
ORCID iD: 0009-0004-7304-7771
SPIN-code: 2512-8754

Junior Researcher, Biotechnology Laboratory

19 Ignatyevskoye highway, Blagoveshchensk, Amur Region, 675027, Russian Federation

Andrei A. Penzin

All-Russian Research Institute of Soybean

Email: paa@vniisoi.ru
ORCID iD: 0000-0002-8578-9818
SPIN-code: 1467-9500

PhD in Agricultural Sciences, Leading Researcher, Head of the Biotechnology Laboratory

19 Ignatyevskoye highway, Blagoveshchensk, Amur Region, 675027, Russian Federation

Anna P. Galichenko

All-Russian Research Institute of Soybean

Email: gap@vniisoi.ru
ORCID iD: 0000-0002-4445-3779
SPIN-code: 5274-8503

Senior Researcher, Breeding Laboratory

19 Ignatyevskoye highway, Blagoveshchensk, Amur Region, 675027, Russian Federation

Pavel D. Timkin

All-Russian Research Institute of Soybean

Email: tpd@vniisoi.ru
ORCID iD: 0000-0001-6655-1049
SPIN-code: 2729-2815

Junior Researcher, Biotechnology Laboratory

19 Ignatyevskoye highway, Blagoveshchensk, Amur Region, 675027, Russian Federation

References

  1. Duan Z, Li Q, Wang H, He X, Zhang M. Genetic regulatory networks of soybean seed size, oil, and protein content. Frontiers in Plant Science. 2023;14:1160418. doi: 10.3389/fpls.2023.1160418
  2. Liu S, Liu Z, Hou X, Li X. Genetic mapping and functional genomics of soybean seed protein. Molecular Breeding. 2023;43(4):29. doi: 10.1007/s11032-023-01373-5 EDN: JNNPZF
  3. Liu S, Zhang M, Feng F, Tian Z. Toward a “green revolution” for soybean. Molecular Plant. 2020;13(5):688-697. doi: 10.1016/j.molp.2020.03.002 EDN: QYFLTY
  4. Cabanos C, Matsuoka Y, Maruyama N. Soybean proteins/peptides: a review on their importance, biosynthesis, vacuolar sorting, and accumulation in seeds. Peptides. 2021;143:170598. doi: 10.1016/j.peptides.2021.170598 EDN: LYDRAD
  5. Kim W, Nott J, Kim S, Krishnan HB. Soybean seed proteomics: Methods for the isolation, detection, and identification of low abundance proteins. In: Jez J. (ed.) Methods in Enzymology. Volume 676. Academic Press; 2022. p.325-345. doi: 10.1016/bs.mie.2022.07.001
  6. Wijewardana C, Reddy KR, Bellaloui N. Soybean seed physiology, quality, and chemical composition under soil moisture stress. Food Chemistry. 2019;278:92-100. doi: 10.1016/j.foodchem.2018.11.035
  7. Hu Y, Liu Y, Wei JJ, Zhang WK, Chen SY, Zhang JS. Regulation of seed traits in soybean. aBIOTECH. 2023;4(4):372-385. doi: 10.1007/s42994-023-00122-8
  8. Zhang S, Du H, Ma Y, Li H, Kan G, Yu D. Linkage and association study discovered loci and candidate genes for glycinin and β-conglycinin in soybean (Glycine max L. Merr.). Theoretical and Applied Genetics. 2021;134(4):1201-1215. doi: 10.1007/s00122-021-03766-6 EDN: XKUIJX
  9. Guo B, Sun L, Jiang S, Ren H, Sun R, Wei Z, et al. Soybean genetic resources contributing to sustainable protein production. Theoretical and Applied Genetics. 2022;135(11):4095-4121. doi: 10.1007/s00122-022-04222-9 EDN: FZJUTT
  10. Song B, An L, Han Y, Gao H, Ren H, Zhao X, et al. Transcriptome profile of near-isogenic soybean lines for β-conglycinin α-subunit deficiency during seed maturation. PLoS One. 2016;11(8): e0159723. doi: 10.1371/journal.pone.0159723
  11. Lee S, Van K, Sung M, Nelson R, LaMantia J, McHale LK, et al. Genome-wide association study of seed protein, oil and amino acid contents in soybean from maturity groups I to IV. Theoretical and Applied Genetics. 2019;132(6):1639-1659. doi: 10.1007/s00122-019-03304-5 EDN: OTMCPE
  12. Diamond JS. Explanatory chapter: quantitative PCR. In: Methods in Enzymology. Volume 529. Academic Press; 2013. p.279-289. doi: 10.1016/B978-0-12-418687-3.00023-9
  13. Assfaw AT, Bunmi O, Paterne A, Chigeza G, Mushoriwa H. Genetic diversity and population structure analysis of soybean [Glycine max (L.) Merrill] genotypes based on agro-morphological traits and SNP markers. PLoS One. 2025;20(10):e0332895. doi: 10.1371/journal.pone.0332895 EDN: OSNCCJ
  14. Libault M, Thibivilliers S, Bilgin DD, Radwan O, Benitez M, Clough SJ, et al. Identification of four soybean reference genes for gene expression normalization. Plant Genome. 2008;1(1):44-54. doi: 10.3835/plantgenome2008.02.0091
  15. Rao X, Huang X, Zhou Z, Lin X. An improvement of the 2ˆ(-delta delta CT) method for quantitative real-time polymerase chain reaction data analysis. Biostatistics, Bioinformatics and Biomathematics. 2013;3(3):71-85.
  16. Li C, Zhang YM. Molecular evolution of glycinin and β-conglycinin gene families in soybean (Glycine max L. Merr.). Heredity. 2011;106(4):633-641. doi: 10.1038/hdy.2010.97
  17. Beilinson V, Chen Z, Shoemaker R, Fischer R, Goldberg R, Nielsen N. Genomic organization of glycinin genes in soybean. Theoretical and Applied Genetics. 2002;104:1132-1140. doi: 10.1007/s00122-002-0884-6 EDN: BDTIRL
  18. Kim WS, Gillman JD, Krishnan HB. Identification of a plant introduction soybean line with genetic lesions affecting two distinct glycinin subunits and evaluation of impacts on protein content and composition. Molecular Breeding. 2013;32(2):291-298. doi: 10.1007/s11032-013-9870-8
  19. Hou Y, Huang L, Xing G, Yuan X, Zhang X, Dai D, et al. Structure and functional properties of proteins from different soybean varieties as affected by the 11S/7S globulin ratio. Foods. 2025;14(5):755. doi: 10.3390/foods14050755 EDN: IHRJWH
  20. Galão OF, Teixeira AI, Moreira MA, Carrão-Panizzi MC, Visentainer JV. Variation in genetic and environmental effects of beta-conglycinin (7S) and glycinin (11S) protein fractions in conventional and GM soybean cultivars grown in Southern Brazil. Semina: Ciências Agrárias. 2013;34(2):683-692.
  21. Lakhssassi N, El Baze A, Knizia D, Salhi Y, Embaby MG, Anil E, et al. A sucrose-binding protein and β-conglycinins regulate soybean seed protein content and control multiple seed traits. Plant Physiology. 2024;196(2):1298-1321. doi: 10.1093/plphys/kiae307 EDN: FHSBTJ
  22. Yang Z, Li D, Chen L, Zhang W, Jiang L, Huang Z, et al. Structural characteristics, techno-functionalities, innovation applications and future prospects of soybean β-conglycinin/glycinin: a comprehensive review. Critical Reviews in Food Science and Nutrition. 2025;65(29):6410-6427. doi: 10.1080/10408398.2024.2440601
  23. Yang Y, Zhang L, Zuo H, Yang Y, Hu D, Zhang S, et al. GmGASA12 coordinates hormonal dynamics to enhance soybean water-soluble protein accumulation and seed size. Journal of Integrative Plant Biology. 2025;67(9):2401-2415. doi: 10.1111/jipb.13952 EDN: CKJKRX

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Copyright (c) 2026 Katrushenko A.A., Ivaniy A.A., Penzin A.A., Galichenko A.P., Timkin P.D.

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