Comparative transcriptome analysis of vegetable soybean grain discloses genes essential for grain quality.
Grain quality
Large grain
Soluble sugar
Transcriptome analysis
Vegetable soybean
Journal
BMC plant biology
ISSN: 1471-2229
Titre abrégé: BMC Plant Biol
Pays: England
ID NLM: 100967807
Informations de publication
Date de publication:
03 Jun 2024
03 Jun 2024
Historique:
received:
25
04
2023
accepted:
29
05
2024
medline:
3
6
2024
pubmed:
3
6
2024
entrez:
2
6
2024
Statut:
epublish
Résumé
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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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.
Identifiants
pubmed: 38825702
doi: 10.1186/s12870-024-05214-1
pii: 10.1186/s12870-024-05214-1
doi:
Substances chimiques
Sucrose
57-50-1
Types de publication
Journal Article
Comparative Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
491Subventions
Organisme : Zhejiang Provincial Natural Science Foundation
ID : LTGN23C150002
Organisme : Zhejiang Provincial Natural Science Foundation
ID : LGN21C150008
Organisme : Zhejiang Provincial Important Science & Technology Specific Projects
ID : 2022C02016
Informations de copyright
© 2024. The Author(s).
Références
Lee JY, Popp MP, Wolfe EJ, Nayga RM Jr., Popp JS, Chen P, Seo HS. Information and order of information effects on consumers’ acceptance and valuation for genetically modified edamame soybean. PLoS ONE. 2018;13(10):e0206300.
pubmed: 30356270
pmcid: 6200256
doi: 10.1371/journal.pone.0206300
Djanta MKA, Agoyi EE, Agbahoungba S, Quenum FJ-B, Chadare FJ, Assogbadjo AE, Agbangla C, Sinsin B. Vegetable soybean, edamame: Research, production, utilization and analysis of its adoption in Sub-saharan Africa. J Hortic Forestry. 2020;12(1):1–12.
doi: 10.5897/JHF2019.0604
Shurtleff W, Aoyagi A. History of Edamame, Vegetable soybeans, and vegetable-type soybeans (1000 BCE to 2021): extensively annotated bibliography. and Sourcebook: Soyinfo Center; 2021.
Dhakal K, Zhu Q, Zhang B, Li M, Li S. Analysis of shoot architecture traits in edamame reveals potential strategies to improve harvest efficiency. Front Plant Sci. 2021;12:614926.
pubmed: 33746998
pmcid: 7965963
doi: 10.3389/fpls.2021.614926
Zhang Q, Li Y, Chin KL, Qi Y. Vegetable soybean: seed composition and production research. Italian J Agron. 2017;12(3).
Wang Z, Yu D, Morota G, Dhakal K, Singer W, Lord N, Huang H, Chen P, Mozzoni L, Li S. Genome-wide association analysis of sucrose and alanine contents in edamame beans. Front Plant Sci. 2023;13:1086007.
Krober OA, Cartter JL. Quantitative interrelations of protein and nonprotein constituents of soybeans 1. Crop Sci. 1962;2(2):171–2.
doi: 10.2135/cropsci1962.0011183X000200020028x
Hymowitz T, Collins F. Variability of sugar content in seed of Glycine max (L.) Merrill and G. Soja Sieb. And Zucc. 1. Agron J. 1974;66(2):239–40.
doi: 10.2134/agronj1974.00021962006600020017x
Li Y-S, Du M, Zhang Q-Y, Wang G-H, Hashemi M, Liu X-B. Greater differences exist in seed protein, oil, total soluble sugar and sucrose content of vegetable soybean genotypes [Glycine max (L.) Merrill] in Northeast China. Aust J Crop Sci. 2012;6(12):1681–6.
Zeng A, Chen P, Shi A, Wang D, Zhang B, Orazaly M, Florez-Palacios L, Brye K, Song Q, Cregan P. Identification of quantitative trait loci for sucrose content in soybean seed. Crop Sci. 2014;54(2):554–64.
Crittenden RG, Playne MJ. Production, properties and applications of food-grade oligosaccharides. Trends Food Sci Technol. 1996;7(11):353–61.
doi: 10.1016/S0924-2244(96)10038-8
Rivero-Urgëll M, Santamaría-Orleans A. Oligosaccharides: application in infant food. Early Hum Dev. 2001;65(Suppl):S43–52.
pubmed: 11755034
doi: 10.1016/S0378-3782(01)00202-X
Zhan S, Ho SC. Meta-analysis of the effects of soy protein containing isoflavones on the lipid profile. Am J Clin Nutr. 2005;81(2):397–408.
Saldivar X, Wang Y-J, Chen P, Hou A. Changes in chemical composition during soybean seed development. Food Chem. 2011;124(4):1369–75.
Yu X, Yuan F, Fu X, Zhu D. Profiling and relationship of water-soluble sugar and protein compositions in soybean seeds. Food Chem. 2016;196:776–82.
pubmed: 26593554
doi: 10.1016/j.foodchem.2015.09.092
Hou AC, Li PAJ, Mozzoni D, Zhang L, Shi B. A. Genetic variability of seed sugar content in worldwide soybean germplasm collections. Crop Sci. 2009;49(3):903–12.
Shangguan L, Song C, Leng X, Kayesh E, Sun X, Fang J. Mining and comparison of the genes encoding the key enzymes involved in sugar biosynthesis in apple, grape, and sweet orange. Entia Horticulturae. 2014;165:311–8.
doi: 10.1016/j.scienta.2013.11.026
Lin H, Rao J, Shi J, Hu C, Cheng F, Wilson ZA, Zhang D, Quan S. Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars. J Integr Plant Biol. 2014;56(9):826–36.
pubmed: 24942044
doi: 10.1111/jipb.12228
Chen Z, Zhong W, Zhou Y, Ji P, Wan Y, Shi S, Yang Z, Gong Y, Mu F, Chen S. Integrative analysis of metabolome and transcriptome reveals the improvements of seed quality in vegetable soybean (Glycine max (L.) Merr). Phytochemistry. 2022;200:113216.
pubmed: 35487251
doi: 10.1016/j.phytochem.2022.113216
Kumar R, Saini M, Taku M, Debbarma P, Mahto RK, Ramlal A, Sharma D, Rajendran A, Pandey R, Gaikwad K. Identification of quantitative trait loci (QTLs) and candidate genes for seed shape and 100-seed weight in soybean [Glycine max (L.) Merr]. Front Plant Sci. 2023;13:1074245.
Li J, Zhang Y, Ma R, Huang W, Hou J, Fang C, Wang L, Yuan Z, Sun Q, Dong X, et al. Identification of ST1 reveals a selection involving hitchhiking of seed morphology and oil content during soybean domestication. Plant Biotechnol J. 2022;20(6):1110–21.
pubmed: 35178867
pmcid: 9129076
doi: 10.1111/pbi.13791
Duan Z, Zhang M, Zhang Z, Liang S, Fan L, Yang X, Yuan Y, Pan Y, Zhou G, Liu S, et al. Natural allelic variation of GmST05 controlling seed size and quality in soybean. Plant Biotechnol J. 2022;20(9):1807–18.
pubmed: 35642379
pmcid: 9398382
doi: 10.1111/pbi.13865
Lu X, Xiong Q, Cheng T, Li Q-T, Liu X-L, Bi Y-D, Li W, Zhang W-K, Ma B, Lai Y-C, et al. A PP2C-1 allele underlying a quantitative trait locus enhances soybean 100-seed weight. Mol Plant. 2017;10(5):670–84.
pubmed: 28363587
doi: 10.1016/j.molp.2017.03.006
Liu N, Niu Y, Zhang G, Feng Z, Bo Y, Lian J, Wang B, Gong Y. Genome sequencing and population resequencing provide insights into the genetic basis of domestication and diversity of vegetable soybean. Hortic Res. 2022;9:uhab052.
pubmed: 35031802
pmcid: 8788355
doi: 10.1093/hr/uhab052
Valliyodan B, Cannon SB, Bayer PE, Shu S, Brown AV, Ren L, Jenkins J, Chung CYL, Chan TF, Daum CG. Construction and comparison of three reference-quality genome assemblies for soybean. Plant J. 2019;100(5):1066–82.
pubmed: 31433882
doi: 10.1111/tpj.14500
Purcell LC, Salmeron M, Ashlock L. Soybean growth and development. Ark Soybean Prod Handb. 2014;197:1–8.
Pedersen P, Kumudini S, Board J, Conley S: Soybean growth and development: Iowa State University, University Extension Ames, IA; 2004.28.
Zhou Q, Guo JJ, He CT, Shen C, Huang YY, Chen JX, Guo JH, Yuan JG, Yang ZY. Comparative transcriptome analysis between low- and high-cadmium-accumulating genotypes of Pakchoi (Brassica chinensis L.) in response to cadmium stress. Environ Sci Technol. 2016;50(12):6485–94.
Fang R, Chen X, Zhang X, Wang B. Comparative transcriptome analyses shed light on the regulation of harvest duration and shelf life in sweet corn. Food Res Int. 2023;163:112188.
pubmed: 36596131
doi: 10.1016/j.foodres.2022.112188
Hayashi M, Crofts N, Oitome NF, Fujita N. Analyses of starch biosynthetic protein complexes and starch properties from developing mutant rice seeds with minimal starch synthase activities. BMC Plant Biol. 2018;18(1):59.
pubmed: 29636002
pmcid: 5894220
doi: 10.1186/s12870-018-1270-0
Zheng Z, Xu X, Crosley RA, Greenwalt SA, Sun Y, Blakeslee B, Wang L, Ni W, Sopko MS, Yao C. The protein kinase SnRK2. 6 mediates the regulation of sucrose metabolism and plant growth in Arabidopsis. Plant Physiol. 2010;153(1):99–113.
pubmed: 20200070
pmcid: 2862418
doi: 10.1104/pp.109.150789
Zhang Q, Liu M, Ruan J. Integrated transcriptome and metabolic analyses reveals novel insights into free amino acid metabolism in Huangjinya tea cultivar. Front Plant Sci. 2017;8:291.
pubmed: 28321230
pmcid: 5337497
Zhang J, Zhang W, Xing L. Effects of ultrasound on the taste components from aqueous extract of unsmoked bacon. Food Chem. 2021;365:130411.
pubmed: 34218106
doi: 10.1016/j.foodchem.2021.130411
Harrigan GG, Skogerson K, MacIsaac S, Bickel A, Perez T, Li X. Application of 1H NMR profiling to assess seed metabolomic diversity. A case study on a soybean era population. J Agric Food Chem. 2015;63(18):4690–7.
pubmed: 25940152
doi: 10.1021/acs.jafc.5b01069
Pan WJ, Xue HA, Huang SY, Yu JY, Ying ZH, Qu KX, Zhang ZX, Yin ZG, Qi HD, Yu GL, Zhang Y. Identification of candidate genes related to soluble sugar contents in soybean seeds using multiple genetic analyses. J Integr Agr. 2022;21(7):1886–902.
Li N, Xu R, Li Y. Molecular networks of seed size control in plants. Annu Rev Plant Biol. 2019;70:435–63.
pubmed: 30795704
doi: 10.1146/annurev-arplant-050718-095851
Fang N, Xu R, Huang L, Zhang B, Duan P, Li N, Luo Y, Li Y. SMALL GRAIN 11 controls grain size, grain number and grain yield in rice. Rice. 2016;9:1–11.
doi: 10.1186/s12284-016-0136-z
Hong Z, Ueguchi-Tanaka M, Fujioka S, Takatsuto S, Yoshida S, Hasegawa Y, Ashikari M, Kitano H, Matsuoka M. The rice brassinosteroid-deficient dwarf2 mutant, defective in the rice homolog of Arabidopsis DIMINUTO/DWARF1, is rescued by the endogenously accumulated alternative bioactive brassinosteroid, dolichosterone. Plant Cell. 2005;17(8):2243–54.
pubmed: 15994910
pmcid: 1182486
doi: 10.1105/tpc.105.030973
Jiang WB, Huang HY, Hu YW, Zhu SW, Wang ZY, Lin WH. Brassinosteroid regulates seed size and shape in Arabidopsis. Plant Physiol. 2013;162(4):1965–77.
pubmed: 23771896
pmcid: 3729775
doi: 10.1104/pp.113.217703
Morinaka Y, Sakamoto T, Inukai Y, Agetsuma M, Kitano H, Ashikari M, Matsuoka M. Morphological alteration caused by brassinosteroid insensitivity increases the biomass and grain production of rice. Plant Physiol. 2006;141(3):924–31.
pubmed: 16714407
pmcid: 1489896
doi: 10.1104/pp.106.077081
Tanabe S, Ashikari M, Fujioka S, Takatsuto S, Yoshida S, Yano M, Yoshimura A, Kitano H, Matsuoka M, Fujisawa Y. A novel cytochrome P450 is implicated in brassinosteroid biosynthesis via the characterization of a rice dwarf mutant, dwarf11, with reduced seed length. Plant Cell. 2005;17(3):776–90.
pubmed: 15705958
pmcid: 1069698
doi: 10.1105/tpc.104.024950
Yuan H, Fan S, Huang J, Zhan S, Wang S, Gao P, Chen W, Tu B, Ma B, Wang Y. 08SG2/OsBAK1 regulates grain size and number, and functions differently in Indica and Japonica backgrounds in rice. Rice. 2017;10(1):1–12.
doi: 10.1186/s12284-017-0165-2
Aya K, Hobo T, Sato-Izawa K, Ueguchi-Tanaka M, Kitano H, Matsuoka M. A novel AP2-type transcription factor, SMALL ORGAN SIZE1, controls organ size downstream of an auxin signaling pathway. Plant Cell Physiol. 2014;55(5):897–912.
pubmed: 24486766
doi: 10.1093/pcp/pcu023
Figueiredo DD, Batista RA, Roszak PJ, Hennig L, Köhler C. Auxin production in the endosperm drives seed coat development in Arabidopsis. Elife. 2016;5:e20542.
pubmed: 27848912
pmcid: 5135394
doi: 10.7554/eLife.20542
Hirano K, Yoshida H, Aya K, Kawamura M, Hayashi M, Hobo T, Sato-Izawa K, Kitano H, Ueguchi-Tanaka M, Matsuoka M. SMALL ORGAN SIZE 1 and SMALL ORGAN SIZE 2/DWARF AND LOW-TILLERING form a complex to integrate auxin and brassinosteroid signaling in rice. Mol Plant. 2017;10(4):590–604.
pubmed: 28069545
doi: 10.1016/j.molp.2016.12.013
Hughes R, Spielman M, Schruff MC, Larson TR, Graham IA, Scott RJ. Yield assessment of integument-led seed growth following targeted repair of auxin response factor 2. Plant Biotechnol J. 2008;6(8):758–69.
pubmed: 18643948
doi: 10.1111/j.1467-7652.2008.00359.x
Liu L, Tong H, Xiao Y, Che R, Xu F, Hu B, Liang C, Chu J, Li J, Chu C. Activation of Big Grain1 significantly improves grain size by regulating auxin transport in rice. Proc Natl Acad Sci USA. 2015;112(35):11102–7.
Noguero M, Le Signor C, Vernoud V, Bandyopadhyay K, Sanchez M, Fu C, Torres-Jerez I, Wen J, Mysore KS, Gallardo K. DASH transcription factor impacts Medicago truncatula seed size by its action on embryo morphogenesis and auxin homeostasis. Plant J. 2015;81(3):453–66.
pubmed: 25492260
pmcid: 4329604
doi: 10.1111/tpj.12742
Okushima Y, Mitina I, Quach HL, Theologis A. AUXIN RESPONSE FACTOR 2 (ARF2): a pleiotropic developmental regulator. Plant J. 2005;43(1):29–46.
pubmed: 15960614
doi: 10.1111/j.1365-313X.2005.02426.x
Schruff MC, Spielman M, Tiwari S, Adams S, Fenby N, Scott RJ. The AUXIN RESPONSE FACTOR 2 gene of Arabidopsis links auxin signalling, cell division, and the size of seeds and other organs. Development. 2006;133(2):251–61.
pubmed: 16339187
doi: 10.1242/dev.02194
Li Y, Du M, Zhang Q, Wang G, Jin J, Herbert S, Liu X. Planting date influences fresh pod yield and seed chemical compositions of vegetable soybean. HortScience. 2014;49(11):1376–80.
doi: 10.21273/HORTSCI.49.11.1376
Beljkaš B, Matić J, Milovanović I, Jovanov P, Mišan A, Šarić L. Rapid method for determination of protein content in cereals and oilseeds: validation, measurement uncertainty and comparison with the Kjeldahl method. Accred Qual Assur. 2010;15:555–61.
doi: 10.1007/s00769-010-0677-6
Ernst J, Bar-Joseph Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics. 2006;7(1):1–11.
doi: 10.1186/1471-2105-7-191