作物学报 ›› 2023, Vol. 49 ›› Issue (12): 3277-3288.doi: 10.3724/SP.J.1006.2023.34031
张红梅1(), 熊雅文2, 许文静3, 张威1, 王琼1, 刘晓庆1, 刘慧3, 崔晓艳1, 陈新1, 陈华涛1,2,*()
ZHANG Hong-Mei1(), XIONG Ya-Wen2, XU Wen-Jing3, ZHANG Wei1, WANG Qiong1, LIU Xiao-Qing1, LIU Hui3, CUI Xiao-Yan1, CHEN Xin1, CHEN Hua-Tao1,2,*()
摘要:
为解析大豆R6期籽粒氨基酸含量的遗传机制, 本研究利用264份大豆种质资源材料在2020年和2021年测定了与菜用大豆食味品质相关的精氨酸、丙氨酸、谷氨酸和天冬氨酸含量, 并进行全基因组关联分析(GWAS)。结果表明, 2年共检测到89个与大豆R6期籽粒4种氨基酸含量显著关联的SNP位点, 其中有5个标记能同时被2年或2个性状重复检测到, 分别为S03_40647948 (Chr.3)、S05_2727464 (Chr.5)、S10_4122977 (Chr.10)、S17_34559022 (Chr.17)和S19_48541685 (Chr.19), 单个位点可以解释11.25%~28.19%的表型变异解, 其中Chr.17上的标记S17_34559022在2020年和2021年被共同检测到与谷氨酸含量显著关联, 属稳定遗传的位点。共挖掘出9个候选基因, 其中ZIP蛋白(zinc finger family protein)、转录因子bHLH (bHLH DNA-binding superfamily protein)、生长素反应蛋白家族(auxin-responsive protein family)和天冬氨酸蛋白酶家族蛋白(aspartyl protease family protein), 可能是影响菜用大豆氨基酸代谢的重要基因。本研究挖掘到的5个氨基酸含量主效SNP位点和9个候选基因, 有助于解析大豆R6期籽粒氨基酸含量的遗传基础及其调控机制, 为菜用大豆食味品质遗传改良奠定了基础。
[1] | Akazawa T, Yanagisawa Y, Sasahara T. Concentrations of water-soluble nitrogen and amino acids as criteria for discriminating vegetable-type and grain-type soybean cultivars. Breed Sci, 1997, 47: 39-44. |
[2] |
Ferh W R, Caviness C E, Burmood D T, Pennington J S. Stages of development descriptions for soybean (Glycine max (L.) Merrill). Crop Sci, 1971, 11: 929-931.
doi: 10.2135/cropsci1971.0011183X001100060051x |
[3] | Fehr W R, Caviness C E. Stages of Soybean Development. Ames: Iowa State University of Science and Technology, Iowa State University, 1977. pp 1-12. |
[4] |
Dong D K, Fu X J, Yuan F J, Chen P Y, Zhu S L, Li B Q, Yang Q H, Yu X M, Zhu D H. Genetic diversity and population structure of vegetable soybean (Glycine max (L.) Merr.) in China as revealed by SSR markers. Genet Resour Crop Evol, 2014, 61: 173-183.
doi: 10.1007/s10722-013-0024-y |
[5] | Mohamed A, Rao M S. Vegetable soybeans as a functional food. In: Liu K, eds. Soybeans as Functional Foods and Ingredients, New York: AOAC Press, 2004. pp 209-238. |
[6] |
Monteiro S T, Minekawa Y, Kosugi Y, Akazawa T, Oda K. Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery. ISPRS J Photogram, 2007, 62: 2-12.
doi: 10.1016/j.isprsjprs.2006.12.002 |
[7] | Hounsome N, Hounsome B, Tomos D, Edwards-Jones G. Plant metabolites and nutritional quality of vegetables. J Food Sci, 2008, 73: R48-R65. |
[8] | Ong P K, Liu S Q. Handbook of Vegetables and Vegetable Processing:Flavor and Sensory Characteristics of Vegetables. Hoboken: John Wiley & Sons Ltd, 2018. pp 135-156. |
[9] | 王丹英, 汪自强, 方勇, 徐律平. 菜用大豆食味品质及其与内含物关系研究. 金华职业技术学院学报, 2002, 2(3): 15-17. |
Wang D Y, Wang Z Q, Fang Y, Xu L P. Studies on the relationship between vegetable soybean eating quality and its components. J Jinhua Polytechnic, 2002, 2(3): 15-17. (in Chinese with English abstract) | |
[10] | Shanmugasundaram S, Cheng S T, Huang M T, Yan M R. Quality requirement and improvement of vegetable soybean. In: Shanmugasundaram S, ed. Vegetable Soybean: Research Needs for Production and Quality Improvement. Taiwan: Asian Vegetable Research and Development Center, 1991. pp 92-102. |
[11] | 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.). Phytochem Lett, 2022, 200: 113216-113228. |
[12] |
Guo L, Huang L, Cheng X, Gao Y, Zhang X, Yuan X, Xue C, Chen X. Volatile flavor profile and sensory properties of vegetable soybean. Molecules, 2022, 29, 27: 939.
doi: 10.3390/molecules27010029 |
[13] |
Zhao X, Dong H, Chang H, Zhao J, Teng W, Qiu L, Li W, Han Y. Genome wide association mapping and candidate gene analysis for hundred seed weight in soybean [Glycine max (L.) Merrill]. BMC Genomics, 2019, 20: 648.
doi: 10.1186/s12864-019-6009-2 pmid: 31412769 |
[14] |
Sayama T, Tanabata T, Saruta M, Yamada T, Anai T, Kaga A, Ishimoto M. Confirmation of the pleiotropic control of leaflet shape and number of seeds per pod by the Ln gene in induced soybean mutants. Breed Sci, 2017, 67: 363-369.
doi: 10.1270/jsbbs.16201 |
[15] |
Assefa T, Otyama P I, Brown A V, Kalberer S R, Kulkarni R S, Cannon S B. Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean. BMC Genomics, 2019, 20: 527.
doi: 10.1186/s12864-019-5907-7 pmid: 31242867 |
[16] |
Liu W, Kim M Y, Kang Y J, Van K, Lee Y H, Srinives P, Yuan D L, Lee S H. QTL identification of flowering time at three different latitudes reveals homeologous genomic regions that control flowering in soybean. Theor Appl Genet, 2011, 123: 545-553.
doi: 10.1007/s00122-011-1606-8 pmid: 21660531 |
[17] |
Wang J J, Hu B, Jing Y, Hu X, Guo Y, Chen J, Liu Y, Hao J, Li W, Ning H. Detecting QTL and candidate genes for plant height in soybean via linkage analysis and GWAS. Front Plant Sci, 2022, 12: 803820.
doi: 10.3389/fpls.2021.803820 |
[18] |
Kumar V, Goyal V, Mandlik R, Kumawat S, Sudhakaran S, Padalkar G, Rana N, Deshmukh R, Roy J, Sharma T R, Sonah H. Pinpointing genomic regions and candidate genes associated with seed oil and protein content in soybean through an integrative transcriptomic and QTL Meta-analysis. Cells, 2022, 12: 97.
doi: 10.3390/cells12010097 |
[19] |
Zhang H M, Zhang G W, Zhang W, Wang Q, Xu W J, Liu X Q, Cui X Y, Chen X, Chen H T. Identification of loci governing soybean seed protein content via genome-wide association study and selective signature analyses. Front Plant Sci, 2022, 13: 1045953.
doi: 10.3389/fpls.2022.1045953 |
[20] |
Panthee D R, Pantalone V R, Saxton A M, West D R, Sams C E. Genomic regions associated with amino acid composition in soybean. Mol Breed, 2006, 17: 79-89.
doi: 10.1007/s11032-005-2519-5 |
[21] | Vaughn J N, Nelson R L, Song Q, Cregan P B, Li Z. The genetic architecture of seed composition in soybean is refined by genome-wide association scans across multiple populations. G3: Genes Genom Genet, 2014, 4: 2283-2294. |
[22] |
Warrington C, Abdel-Haleem H, Hyten D, Cregan P, Orf J, Killam A, Bajjalieh N, Li Z, Boerma H. QTL for seed protein and amino acids in the Benning × Danbaekkong soybean population. Theor Appl Genet, 2015, 128: 839-850.
doi: 10.1007/s00122-015-2474-4 pmid: 25673144 |
[23] | 汪桂凤, 钟宣伯, 查霆, 周启政, 何梦迪, 唐桂香. 菜用大豆种质资源评价与筛选. 大豆科学, 2019, 38: 169-180. |
Wang G F, Zhong X B, Zha T, Zhou Q Z, He M D, Tang G X. Evaluation and screening of fresh soybean germplasm. Soybean Sci, 2019, 38: 169-180. (in Chinese with English abstract) | |
[24] |
Abe T, Ujiie T, Sasahara T. Varietal differences in free amino acid and sugar concentrations in immature seeds of soybean under raw and boiling treatments. J Jpn Soc Food Sci, 2004, 51: 172-176.
doi: 10.3136/nskkk.51.172 |
[25] |
Tseng Y H, Lee Y L, Li R C, Mau J L. Non-volatile flavour components of ganoderma tsugae. Food Chem, 2005, 90: 409-415.
doi: 10.1016/j.foodchem.2004.03.054 |
[26] |
Ye Z, Shang Z X, Li M Q, Zhang X T, Ren H B, Hu X S, Yi J J. Effect of ripening and variety on the physiochemical quality and flavor of fermented Chinese chili pepper (Paojiao). Food Chem, 2022, 368: 130797.
doi: 10.1016/j.foodchem.2021.130797 |
[27] |
Guo J, Rahman A, Mulvaney M J, Hossain M M, Basso K, Fethiere R, Babar M A. Evaluation of edamame genotypes suitable for growing in Florida. Agron J, 2020, 112: 693-707.
doi: 10.1002/agj2.v112.2 |
[28] |
Flores D, Giovanni M, Kirk L, Liles G. Capturing and explaining sensory differences among organically grown vegetable soybean varieties grown in Northern California. J Food Sci, 2019, 84: 613-622.
doi: 10.1111/1750-3841.14443 pmid: 30741493 |
[29] |
Zhang J, Wang X, Lu Y, Bhusal S J, Song Q, Cregan P B, Yen Y, Brown M, Jiang G L. Genome-wide scan for seed composition provides insights into soybean quality improvement and the impacts of domestication and breeding. Mol Plant, 2018, 11: 460-472.
doi: S1674-2052(17)30386-6 pmid: 29305230 |
[30] |
Zhang S, Hao D, Zhang S, Zhang D, Wang H, Du H, Kan G, Yu D. Genome-wide association mapping for protein, oil and water-soluble protein contents in soybean. Mol Genet Genomics, 2021, 296: 91-102.
doi: 10.1007/s00438-020-01704-7 |
[31] |
Zhang W, Xu W, Zhang H, Liu X, Cui X, Li S, Song L, Zhu Y, Chen X, Chen H. Comparative selective signature analysis and high-resolution GWAS reveal a new candidate gene controlling seed weight in soybean. Theor Appl Genet, 2021, 134: 1329-1341.
doi: 10.1007/s00122-021-03774-6 pmid: 33507340 |
[32] |
Xiao X, Hou Y, Liu Y, Liu Y, Zhao H, Dong L, Du J, Wang Y, Bai G, Luo G. Classification and analysis of corn steep liquor by UPLC/Q-TOF MS and HPLC. Talanta, 2013, 107: 344-348.
doi: 10.1016/j.talanta.2013.01.044 pmid: 23598232 |
[33] |
Mao T, Jiang Z, Han Y, Teng W, Zhao X, Li W, Morris B. Identification of quantitative trait loci underlying seed protein and oil contents of soybean across multi-genetic backgrounds and environments. Plant Breed, 2013, 132: 630-641.
doi: 10.1111/pbr.2013.132.issue-6 |
[34] |
Chapman A, Pantalone V R, Ustun A, Allen F L, Landau-Ellis D, Trigiano R N, Gresshoff P M. Quantitative trait loci for agronomic and seed quality traits in an F2 and F4:6 soybean population. Euphytica, 2003, 129: 387-393.
doi: 10.1023/A:1022282726117 |
[35] |
Xu W, Wang Q, Zhang W, Zhang H, Liu X, Song Q, Zhu Y, Cui X, Chen X, Chen H. Using transcriptomic and metabolomic data to investigate the molecular mechanisms that determine protein and oil contents during seed development in soybean. Front Plant Sci, 2022, 13: 1012394.
doi: 10.3389/fpls.2022.1012394 |
[36] |
Orf J H, Chase K, Adler F R, Mansur L M, Lark K G. Genetics of soybean agronomic traits. Crop Sci, 1999, 39: 1642-1651.
doi: 10.2135/cropsci1999.3961642x |
[37] |
Yoshikawa T, Okumoto Y, Ogata D, Sayama T, Teraishi M, Terai M, Toda T, Yamada K, Yagasaki K, Yamada N, Tsukiyama T, Yamada T, Tanisaka T. Transgressive segregation of isoflavone contents under the control of four QTLs in a cross between distantly related soybean varieties. Breed Sci, 2010, 60: 243-254.
doi: 10.1270/jsbbs.60.243 |
[38] |
Hyten D L, Pantalone V R, Sams C E, Saxton A M, Landau-Ellis D, Stefaniak T R, Schmidt. Seed quality QTL in a prominent soybean population. Theor Appl Genet, 2004, 109: 552-561.
doi: 10.1007/s00122-004-1661-5 pmid: 15221142 |
[39] |
Kuroda Y, Kaga A, Tomooka N, Yano H, Takada Y, Kato S, Vaughan D. QTL affecting fitness of hybrids between wild and cultivated soybeans in experimental fields. Ecol Evol, 2013, 3: 2150-2168.
doi: 10.1002/ece3.606 pmid: 23919159 |
[40] |
Du W, Wang M, Fu S, Yu D. Mapping QTLs for seed yield and drought susceptibility index in soybean (Glycine max L.) across different environments. J Genet Genomics, 2009, 36: 721-731.
doi: 10.1016/S1673-8527(08)60165-4 |
[41] |
Wang X, Jiang G L, Green M, Scott R A, Hyten D L, Cregan P B. Quantitative trait locus analysis of saturated fatty acids in a population of recombinant inbred lines of soybean. Mol Breed, 2012, 30: 1163-1179.
doi: 10.1007/s11032-012-9704-0 |
[42] |
Ju Y, Liu C, Lu W, Zhang Q, Sodmergen. Arabidopsis mitochondrial protein slow embryo development1 is essential for embryo development. Biochem Biophys Res Commun, 2016, 474: 371-376.
doi: 10.1016/j.bbrc.2016.04.114 |
[43] |
Rao V, Virupapuram V. Arabidopsis F-box protein At1g08710 interacts with transcriptional protein ADA2b and imparts drought stress tolerance by negatively regulating seedling growth. Biochem Biophys Res Commun, 2021, 536: 45-51.
doi: 10.1016/j.bbrc.2020.12.054 |
[44] |
Lantcheva A, Zhiponova M, Revalska M, Heyman J, Dincheva I, Badjakov I, De Geyter N, Boycheva I, Goormachtig S, De Veylder L. A common F-box gene regulates the leucine homeostasis of Medicago truncatula and Arabidopsis thaliana. Protoplasma, 2022, 259: 277-290.
doi: 10.1007/s00709-021-01662-w |
[45] |
Xu Y P, Zhao Y, Song X Y, Ye Y F, Wang R G, Wang Z L, Ren X L, Cai X Z. Ubiquitin extension protein uep1 modulates cell death and resistance to various pathogens in tobacco. Phytopathology, 2019, 109: 1257-1269.
doi: 10.1094/PHYTO-06-18-0212-R |
[46] |
Nawaz G, Han Y, Usman B, Liu F, Qin B, Li R. Knockout of OsPRP1, a gene encoding proline-rich protein, confers enhanced cold sensitivity in rice (Oryza sativa L.) at the seedling stage. 3 Biotech, 2019, 9: 254.
doi: 10.1007/s13205-019-1787-4 pmid: 31192079 |
[47] |
Wang R, Chong K, Wang T. Divergence in spatial expression patterns and in response to stimuli of tandem-repeat paralogues encoding a novel class of proline-rich proteins in Oryza sativa. J Exp Bot, 2006, 57: 2887-2897.
doi: 10.1093/jxb/erl057 |
[48] |
Kant S, Bi Y M, Zhu T, Rothstein S J. SAUR39, a small auxin-up RNA gene, acts as a negative regulator of auxin synthesis and transport in rice. Plant Physiol, 2009, 151: 691-701.
doi: 10.1104/pp.109.143875 pmid: 19700562 |
[49] |
Chae K, Isaacs C G, Reeves P H, Maloney G S, Muday G K, Nagpal P, Reed J W. Arabidopsis SMALL AUXIN UP RNA63 promotes hypocotyl and stamen filament elongation. Plant J, 2012, 71: 684-697.
doi: 10.1111/tpj.2012.71.issue-4 |
[50] |
Hou K, Wu W, Gan S S. SAUR36, a small auxin-up RNA gene, is involved in the promotion of leaf senescence in Arabidopsis. Plant Physiol, 2013, 161: 1002-1009.
doi: 10.1104/pp.112.212787 |
[51] |
He S L, Hsieh H L, Jauh G Y. Small auxin up RNA62/75 are required for the translation of transcripts essential for pollen tube growth. Plant Physiol, 2018, 178: 626-640.
doi: 10.1104/pp.18.00257 |
[52] |
Gao H, Li R, Guo Y. Arabidopsis aspartic proteases A36 and A39 play roles in plant reproduction. Plant Signal Behav, 2017, 12: e1304343.
doi: 10.1080/15592324.2017.1304343 |
[53] |
Hao Y, Zong X, Ren P, Qian Y, Fu A. Basic Helix-Loop-Helix (bHLH) transcription factors regulate a wide range of functions in Arabidopsis. Int J Mol Sci, 2021, 22: 7152.
doi: 10.3390/ijms22137152 |
[54] |
Frova C, Krajewski P, Fonzo N D, Villa M, Sari-Gorla M. Genetic analysis of drought tolerance in maize by molecular markers: I. Yield components. Theor Appl Genet, 1999, 99: 280-288.
doi: 10.1007/s001220051233 |
[55] |
张华, 田蕊, 褚佳豪, 邢馨竹, 陈士亮, 李喜焕, 张彩英. 大豆需磷关键时期磷高效利用遗传位点挖掘. 植物遗传资源学报, 2020, 21: 991-1001.
doi: 10.13430/j.cnki.jpgr.20191228001 |
Zhang H, Tian R, Chu J H, Xing X Z, Chen S L, li X H, Zhang C Y. Mining of genetic loci controlling phosphorus efficiency at crucial phosphorus requirement stages in soybean. J Plant Genet Resour, 2020, 21: 991-1001. (in Chinese with English abstract) |
[1] | 杨立达, 任俊波, 彭新月, 杨雪丽, 罗凯, 陈平, 袁晓婷, 蒲甜, 雍太文, 杨文钰. 施氮与种间距离下大豆/玉米带状套作作物生长特性及其对产量形成的影响[J]. 作物学报, 2024, 50(1): 251-264. |
[2] | 石宇欣, 刘欣玥, 孙建强, 李晓菲, 郭潇阳, 周雅, 邱丽娟. 利用CRISPR-CAS9技术编辑GmBADH1基因改变大豆耐盐性[J]. 作物学报, 2024, 50(1): 100-109. |
[3] | 袁晓婷, 王甜, 罗凯, 刘姗姗, 彭新月, 杨立达, 蒲甜, 王小春, 杨文钰, 雍太文. 带宽和株距对带状间作大豆物质积累分配及产量形成的影响[J]. 作物学报, 2024, 50(1): 161-171. |
[4] | 黄钰杰, 张啸天, 陈会丽, 王宏伟, 丁双成. 玉米ZmC2s基因家族鉴定及ZmC2-15耐热功能分析[J]. 作物学报, 2023, 49(9): 2331-2343. |
[5] | 南金生, 安江红, 柴明娜, 蒋屿潋, 朱志强, 杨燕, 韩冰. 淀粉特性及其表面结合蛋白与裸燕麦籽粒硬度的关系研究[J]. 作物学报, 2023, 49(9): 2552-2561. |
[6] | 张刁亮, 杨昭, 胡发龙, 殷文, 柴强, 樊志龙. 复种绿肥在不同灌水水平下对小麦籽粒品质和产量的影响[J]. 作物学报, 2023, 49(9): 2572-2581. |
[7] | 杨文宇, 吴成秀, 肖英杰, 严建兵. 基于Adaptive Lasso的两阶段全基因组关联分析方法[J]. 作物学报, 2023, 49(9): 2321-2330. |
[8] | 王兴荣, 张彦军, 涂奇奇, 龚佃明, 邱法展. 一个新的玉米细胞核雄性不育突变体ms6的鉴定与基因定位[J]. 作物学报, 2023, 49(8): 2077-2087. |
[9] | 王娟, 徐相波, 张茂林, 刘铁山, 徐倩, 董瑞, 刘春晓, 关海英, 刘强, 汪黎明, 何春梅. 一个新的玉米Miniature1基因等位突变体的鉴定与遗传分析[J]. 作物学报, 2023, 49(8): 2088-2096. |
[10] | 李星, 杨会, 骆璐, 李华东, 张昆, 张秀荣, 李玉颖, 于海洋, 王天宇, 刘佳琪, 王瑶, 刘风珍, 万勇善. 栽培种花生单仁重QTL定位分析[J]. 作物学报, 2023, 49(8): 2160-2170. |
[11] | 李刚, 周彦辰, 熊亚俊, 陈伊洁, 郭庆元, 高杰, 宋健, 王俊, 李英慧, 邱丽娟. 大豆叶型调控基因Ln及其同源基因单倍型分析[J]. 作物学报, 2023, 49(8): 2051-2063. |
[12] | 王媛, 王劲松, 董二伟, 刘秋霞, 武爱莲, 焦晓燕. 施氮量对高粱籽粒灌浆及淀粉累积的影响[J]. 作物学报, 2023, 49(7): 1968-1978. |
[13] | 张振, 石玉, 张永丽, 于振文, 王西芝. 土壤水分含量对小麦耗水特性和旗叶/根系衰老特性的影响[J]. 作物学报, 2023, 49(7): 1895-1905. |
[14] | 王让剑, 杨军, 张力岚, 高香凤. 茶树新梢中香叶醇樱草糖苷含量的全基因组关联分析[J]. 作物学报, 2023, 49(7): 1843-1859. |
[15] | 唐玉凤, 姚敏, 何昕, 官梅, 刘忠松, 官春云, 钱论文. 甘蓝型油菜SGR基因家族的全基因组鉴定与功能分析[J]. 作物学报, 2023, 49(7): 1829-1842. |
|