欢迎访问作物学报,今天是

作物学报 ›› 2025, Vol. 51 ›› Issue (5): 1166-1177.doi: 10.3724/SP.J.1006.2025.44175

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

芥菜型油菜不同组织硫苷含量的QTL定位与候选基因分析

张金泽1,周庆国2,肖莉晶1,金海润1,欧阳青静1,龙旭1,晏中彬1,田恩堂1,*   

  1. 1贵州大学农学院, 贵州贵阳550025; 2安徽鼎科种业有限责任公司, 安徽合肥230001
  • 收稿日期:2024-10-20 修回日期:2025-01-23 接受日期:2025-01-23 出版日期:2025-05-12 网络出版日期:2025-02-17
  • 基金资助:
    本研究由贵州省科技支撑计划项目(黔科合支撑[2022]重点031), 国家自然科学基金项目(32160483, 32360497)和贵州大学国家自然科学基金后补助项目([2023]093号)。

QTL mapping and candidate gene analysis of glucosinolate content in various tissues of Brassica juncea

ZHANG Jin-Ze1,ZHOU Qing-Guo2,XIAO Li-Jing1,JIN Hai-Run1,OU-YANG Qing-Jing1,LONG Xu1,YAN Zhong-Bin1,TIAN En-Tang1,*   

  1. 1 College of Agriculture, Guizhou University, Guiyang 550025, Guizhou, China; 2Anhui Dingke Seed Industry Co, Ltd., Hefei 230001, Anhui, China
  • Received:2024-10-20 Revised:2025-01-23 Accepted:2025-01-23 Published:2025-05-12 Published online:2025-02-17
  • Supported by:
    This study was supported by the Guizhou Provincial Science and Technology Support Program (Guizhou Kehe Support [2022] Key 031), the National Natural Science Foundation of China (32160483, 32360497), and the Post-Funded Project for National Natural Science Foundation of Guizhou University ([2023] No. 093).

摘要:

硫苷是重要的次生代谢物,在油菜生长发育、病虫害防御等方面都具有重要作用。本研究以芥菜型油菜品系YufengZCQichi881为亲本创建的包含197RIL-F8株系的作图群体(QY-RIL群体)为研究材料,测定了全部株系的叶片、茎秆、花蕾和种子的硫苷含量。结果表明,同一株系不同组织间硫苷含量存在显著差异,而不同株系间相同组织的硫苷含量也存在较大变异,且总体呈正态分布。相关性分析发现,花蕾、茎秆与种子间硫苷含量呈显著正相关关系,而叶片的硫苷含量仅与花蕾呈显著正相关关系。此外,本研究还对叶片、茎秆、花蕾和种子硫苷含量的调控基因进行了QTL定位,分别鉴定到910918QTL,包含6个在多组织中共同检测到的cQTL (consensus QTL)。结合cQTL区间序列信息及所含基因的表达分析结果,初步筛选出6个候选基因,其中BjuB018426 (GTR1)BjuB020498 (GTR2)参与硫苷从营养组织向生殖组织的转运,可能是造成本研究中硫苷含量差异分布的重要调控基因。本研究结果可为解析芥菜型油菜不同组织的硫苷合成与分配机制及选育不同硫苷含量的多功能品种奠定基础。

关键词: 芥菜型油菜, 硫苷含量, QTL定位, 候选基因

Abstract:

Glucosinolates are critical secondary metabolites that play a pivotal role in the growth and development of rapeseed, as well as in its defense against diseases and pests. In this studya mapping population comprising 197 recombinant inbred lines (RILs) of Brassica juncea was utilized to investigate glucosinolate distribution and genetic regulation. The glucosinolate contents in leaves, stems, budsand seeds were quantified across all linesThe results revealed significant differences in glucosinolate content among different tissues within the same line and considerable variation across lines within the species, exhibiting an overall normal distribution. Correlation analysis showed a strong positive correlation in glucosinolate content among buds, stems, and seeds, as well as a notable positive correlation between leaves and flower buds. To further elucidate the genetic regulation of glucosinolate content, quantitative trait locus (QTL) mapping was conducted, resulting in the identification of 9, 10, 9, and 18 QTLs associated with glucosinolate content in leaves, stems, buds, and seeds, respectively. By integrating QTL interval sequence information with gene expression data, six candidate genes were preliminarily identified. Among them, BjuB018426 (GTR1) and BjuB020498 (GTR2) were implicated in the transport of glucosinolates from vegetative tissues to reproductive tissues, suggesting their potential roles as key regulatory genes underlying the differential distribution of glucosinolates observed in this study. These findings provide valuable insights into the mechanisms governing glucosinolate synthesis and distribution across various tissues in Brassica juncea. Moreover, they offer a foundational basis for breeding multifunctional varieties with tailored glucosinolate profiles to meet diverse agricultural and industrial needs.

Key words: Brassica juncea, glucosinolate contents, QTL mapping, candidate genes

[1] Essoh A P, Monteiro F, Pena A R, Pais M S, Moura M, Romeiras M M. Exploring glucosinolates diversity in Brassicaceae: a genomic and chemical assessment for deciphering abiotic stress tolerance. Plant Physiol Biochem, 2020, 150: 151–161.

[2] Raboanatahiry N, Li H X, Yu L J, Li M T. Rapeseed (Brassica napus): processing, utilization, and genetic improvement. Agronomy, 2021, 11: 1776.

[3] Xiao M L, Wang H D, Li X N, Mason A S, Fu D H. Rapeseed as an ornamental. Horticulturae, 2022, 8: 27.

[4] Meng L B, Zhang Y H, Yu S P, Ogundeji A O, Zhang S, Li S M. Temporal assessment of biofumigation using mustard and oilseed rape tissues on Verticillium dahliae, soil microbiome and yield of eggplant. Agronomy, 2022, 12: 2963.

[5] Zheng Q, Liu K D. Worldwide rapeseed (Brassica napus L.) research: a bibliometric analysis during 2011–2021. Oil Crop Sci, 2022, 7: 157–165.

[6] Liu W B, Li S, Tao J B, Liu X Y, Yin G Y, Xia Y, Wang T, Zhang H Y. CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data. Sci Data, 2024, 11: 356.

[7] Kang L, Qian L W, Zheng M, Chen L Y, Chen H, Yang L, You L, Yang B, Yan M L, Gu Y G, et al. Genomic insights into the origin, domestication and diversification of Brassica juncea. Nat Genet, 2021, 53: 1392–1402.

[8] Nguyen V P T, Stewart J, Lopez M, Ioannou I, Allais F. Glucosinolates: natural occurrence, biosynthesis, accessibility, isolation, structures, and biological activities. Molecules, 2020, 25: 4537.

[9] Kanstrup C, Jimidar C C, Tomas J, Cutolo G, Crocoll C, Schuler M, Klahn P, Tatibouët A, Nour-Eldin H H. Artificial fluorescent glucosinolates (F-GSLs) are transported by the glucosinolate transporters GTR1/2/3. Int J Mol Sci, 2023, 24: 920.

[10] Mann A, Kumari J, Kumar R, Kumar P, Pradhan A K, Pental D, Bisht N C. Targeted editing of multiple homologues of GTR1 and GTR2 genes provides the ideal low-seed, high-leaf glucosinolate oilseed mustard with uncompromised defence and yield. Plant Biotechnol J, 2023, 21: 2182–2195.

[11] Lou P, Zhao J J, He H J, Hanhart C, Pino Del Carpio D, Verkerk R, Custers J, Koornneef M, Bonnema G. Quantitative trait loci for glucosinolate accumulation in Brassica rapa leaves. New Phytol, 2008, 179: 1017–1032.

[12] 赵卫国, 塔娜, 王灏. 甘蓝型油菜种子硫代葡萄糖苷含量的QTL定位及候选基因分析. 西北植物学报, 2024, 44: 1261–1272.
Zhao W G, Ta N, Wang H. QTL mapping and candidate gene identification of seed glucosinolate content in Brassica napus. Acta Bot Boreali-Occident Sin, 2024, 44: 1261–1272 (in Chinese with English abstract).

[13] Wei D Y, Cui Y X, Mei J Q, Qian L W, Lu K, Wang Z M, Li J N, Tang Q L, Qian W. Genome-wide identification of loci affecting seed glucosinolate contents in Brassica napus L. J Integr Plant Biol, 2019, 61: 611–623.

[14] Antonious G F, Bomford M, Vincelli P. Screening Brassica species for glucosinolate content. J Environ Sci Health B, 2009, 44: 311–316.

[15] 王倩, 杨旭, 张金泽, 肖莉晶, 余坤江, 田恩堂. 芥菜型油菜茎秆抗倒伏相关性状的组织观察与QTL初定位. 植物遗传资源学报, 2024, 25: 431–439.

Wang Q, Yang X, Zhang J Z, Xiao L J, Yu K J, Tian E T. Microstructure observation and QTL mapping of traits related to stalk lodging resistance in Brassica juncea. J Plant Genet Resour, 2024, 25: 431–439 (in Chinese with English abstract).

[16] 杨旭, 余坤江, 向阳, 代文东, 杜才富, 田恩堂. 芥菜型油菜RIL群体QTL定位能力评价与分析. 分子植物育种, 网络首发[2023–11–01]. http://kns.cnki.net/kcms/detail/46.1068.S.20231101.0933.002.
Yang X, Yu K J, Xiang Y, Dai W D, Du C F, Tian E T. Evaluation and analysis of QTL mapping ability in RIL population of Brassica juncea. Mol Plant Breed, Published online [2023–11–01], http://kns.cnki.net/kcms/detail/46.1068.S.20231101.0933.002 (in Chinese with English abstract).

[17] 李培武, 周海燕. 油菜硫代葡萄糖苷检测技术研究进展. 中国油料作物学报, 2008, 30: 127–131.
Li P W, Zhou H Y. A review on analytical methods for glucosinolates. Chin J Oil Crop Sci, 2008, 30: 127–131 (in Chinese with English abstract).

[18] 李东华, 叶春苗. 萝卜籽中活性成分提取及抑菌效果的研究. 沈阳化工大学学报, 2013, 27(1): 25–29.
Li D H, Ye C M. Active constituents and bacteriostatic offect in radish seed. J Shenyang Univ Chem Technol, 2013, 27(1): 25–29 (in Chinese with English abstract).

[19] 晏伟. 芥菜型油菜主要脂肪酸性状的QTL定位与分析. 贵州大学硕士学位论文, 贵州贵阳, 2022.
Yan W. QTL Mapping and Analysis of Main Fatty Acid Traits in Brassica juncea. MS Thesis of Guizhou University, Guiyang, Guizhou, China, 2022 (in Chinese with English abstract).

[20] Manrique-Carpintero N C, Coombs J J, Cui Y H, Veilleux R E, Buell C R, Douches D. Genetic map and QTL analysis of agronomic traits in a diploid potato population using single nucleotide polymorphism markers. Crop Sci, 2015, 55: 2566–2579.

[21] Voorrips R E. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered, 2002, 93: 77–78.

[22] Kim D, Langmead B, Salzberg S L. HISAT: a fast spliced aligner with low memory requirements. Nat Methods, 2015, 12: 357–360.

[23] Pertea M, Kim D, Pertea G M, Leek J T, Salzberg S L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc, 2016, 11: 1650–1667.

[24]  张金泽, 周庆国, 王倩, 肖莉晶, 金海润, 欧阳青静, 余坤江, 田恩堂. 芥菜型油菜响应菌核病侵染表达特性与高抗性关联基因分析. 作物学报, 网络首发[2024-11-13], http://kns.cnki.net/kcms/detail/11.1809.s.20241113.1017.004.
Zhang J Z, Zhou Q G, Yang X, Wang Q, Xiao L J, Jin H R, Ou-Yang Q J, Yu K J, Tian E T. Analysis of genes associated with expression characteristics and high resistance in response to Sclerotinia sclerotiorum infection in Brassica juncea. Acta Agron Sin, Published online [2024-11-13], http://kns.cnki.net/kcms/detail/11.1809.s.20241113.1017.004 (in Chinese with English abstract).

[25] Halkier B A, Gershenzon J. Biology and biochemistry of glucosinolates. Annu Rev Plant Biol, 2006, 57: 303333.

[26] Zhang L, Kawaguchi R, Enomoto T, Nishida S, Burow M, Maruyama-Nakashita A. Glucosinolate catabolism maintains glucosinolate profiles and transport in sulfur-starved Arabidopsis. Plant Cell Physiol, 2023, 64: 15341550.

[27] Brown P D, Tokuhisa J G, Reichelt M, Gershenzon J. Variation of glucosinolate accumulation among different organs and developmental stages of Arabidopsis thaliana. Phytochemistry, 2003, 62: 471–481.

[28] 田志涛, 赵永国, LENKA Havlickova, HE Zhesi, REA L Harper, IAN Bancroft, 邹锡玲, 张学昆, 陆光远. 甘蓝型油菜种子和角果皮中硫苷含量的动态变化及转录组关联分析. 中国农业科学, 2018, 51: 635–651.
Tian Z T, Zhao Y G, Havlickova L, He Z S, Harper R, Bancroft I, Zou X L, Zhang X K, Lu G Y. Dynamic and associative transcriptomic analysis of glucosinolate content in seeds and silique walls of Brassica napus. Sci Agric Sin, 2018, 51: 635–651 (in Chinese with English abstract).

[29] 李培武. 甘蓝型油菜叶片与种子硫苷相关性研究. 华中农业大学博士学位论文, 湖北武汉, 2007.
Li P W. Correlation Between Glucosinolates in Leaves and Seeds of Brassica napus L. PhD Dissertation of Huazhong Agricultural University, Wuhan, Hubei, China, 2007 (in Chinese with English abstract)..

[30] Rout K, Sharma M, Gupta V, Mukhopadhyay A, Sodhi Y S, Pental D, Pradhan A K. Deciphering allelic variations for seed glucosinolate traits in oilseed mustard (Brassica juncea) using two bi-parental mapping populations. Theor Appl Genet, 2015, 128: 657–666.

[31] He Y J, Fu Y, Hu D X, Wei D Y, Qian W. QTL mapping of seed glucosinolate content responsible for environment in Brassica napus. Front Plant Sci, 2018, 9: 891.

[32] Rahman H, Kebede B, Zimmerli C, Yang R C. Genetic study and QTL mapping of seed glucosinolate content in Brassica rapa L. Crop Sci, 2014, 54: 537–543.

[33] Fu Y, Lu K, Qian L W, Mei J Q, Wei D Y, Peng X H, Xu X F, Li J N, Frauen M, Dreyer F, et al. Development of genic cleavage markers in association with seed glucosinolate content in canola. Theor Appl Genet, 2015, 128: 1029–1037.

[34] Schnug E, Ceynowa J. Phytopathological aspects of glucosinolates in oilseed rape. J Agron Crop Sci, 1990, 165: 319–328.

[35] Du L C, Ann Halkier B. Biosynthesis of glucosinolates in the developing silique walls and seeds of Sinapis alba. Phytochemistry, 1998, 48: 1145–1150.

[36] Chen S, Petersen B L, Olsen C E, Schulz A, Halkier B A. Long-distance phloem transport of glucosinolates in Arabidopsis. Plant Physiol, 2001, 127: 194–201.

[37] Petersen B L, Chen S X, Hansen C H, Olsen C E, Halkier B A. Composition and content of glucosinolates in developing Arabidopsis thaliana. Planta, 2002, 214: 562–571.

[38] Tang Y S, Zhang G R, Jiang X Y, Shen S L, Guan M W, Tang Y H, Sun F J, Hu R, Chen S, Zhao H Y, et al. Genome-wide association study of glucosinolate metabolites (mGWAS) in Brassica napus L. Plants (Basel), 2023, 12: 639.

[39] Nour-Eldin H H, Halkier B A. Piecing together the transport pathway of aliphatic glucosinolates. Phytochem Rev, 2009, 8: 5367.

[40] Madsen S R, Olsen C E, Nour-Eldin H H, Halkier B A. Elucidating the role of transport processes in leaf glucosinolate distribution. Plant Physiol, 2014, 166: 14501462.

[41] Nambiar D M, Kumari J, Augustine R, Kumar P, Bajpai P K, Bisht N C. GTR1 and GTR2 transporters differentially regulate tissue-specific glucosinolate contents and defence responses in the oilseed crop Brassica juncea. Plant Cell Environ, 2021, 44: 2729–2743.

[42] Tan Z D, Xie Z Q, Dai L H, Zhang Y T, Zhao H, Tang S, Wan L L, Yao X, Guo L, Hong D F. Genome- and transcriptome-wide association studies reveal the genetic basis and the breeding history of seed glucosinolate content in Brassica napus. Plant Biotechnol J, 2022, 20: 211–225.

[1] 李文佳, 廖泳俊, 黄璐, 鲁清, 李少雄, 陈小平, 金晶炜, 王润风. 花生开花时间的全基因组关联分析及候选基因筛选[J]. 作物学报, 2025, 51(5): 1400-1408.
[2] 林伟津, 郭泽佳, 刘浩, 李海芬, 王润风, 黄璐, 余倩霞, 陈小平, 洪彦彬, 李少雄, 鲁清. 花生荚果产量相关性状QTL定位与候选基因分析[J]. 作物学报, 2025, 51(4): 969-981.
[3] 张金泽, 周庆国, 杨旭, 王倩, 肖莉晶, 金海润, 欧阳青静, 余坤江, 田恩堂. 芥菜型油菜响应菌核病侵染表达特性与高抗性关联基因分析[J]. 作物学报, 2025, 51(3): 621-631.
[4] 徐建霞, 丁延庆, 曹宁, 程斌, 高旭, 李文贞, 张立异. 中国高粱株高和节间数全基因组关联分析及候选基因预测[J]. 作物学报, 2025, 51(3): 568-585.
[5] 郭淑慧, 潘转霞, 赵战胜, 杨六六, 皇甫张龙, 郭宝生, 胡晓丽, 录亚丹, 丁霄, 吴翠翠, 兰刚, 吕贝贝, 谭逢平, 李朋波. 陆地棉D11染色体一个纤维长度主效位点的遗传解析[J]. 作物学报, 2025, 51(2): 383-394.
[6] 杨景发, 余鑫莲, 姚有华, 姚晓华, 王蕾, 吴昆仑, 李新. 青稞分蘖角度的QTL定位[J]. 作物学报, 2025, 51(1): 260-272.
[7] 叶靓, 朱叶琳, 裴琳婧, 张思颖, 左雪倩, 李正真, 刘芳, 谭静. 联合全基因组关联和转录组分析筛选玉米拟轮枝镰孢穗腐病的抗性候选基因[J]. 作物学报, 2024, 50(9): 2279-2296.
[8] 韩丽, 汤胜胜, 李佳, 胡海斌, 刘龙龙, 吴斌. 燕麦SNP高密度遗传图谱构建及β-葡聚糖含量QTL定位[J]. 作物学报, 2024, 50(7): 1710-1718.
[9] 毕俊鸽, 曾占奎, 李琼, 洪壮壮, 颜群翔, 赵越, 王春平. 两个RIL群体中小麦籽粒品质相关性状QTL定位及KASP标记开发[J]. 作物学报, 2024, 50(7): 1669-1683.
[10] 秦娜, 叶珍言, 朱灿灿, 付森杰, 代书桃, 宋迎辉, 景雅, 王春义, 李君霞. 谷子籽粒类黄酮含量和粒色的QTL定位[J]. 作物学报, 2024, 50(7): 1719-1727.
[11] 郑雪晴, 王兴荣, 张彦军, 龚佃明, 邱法展. 玉米果穗相关性状QTL定位及重要候选基因分析[J]. 作物学报, 2024, 50(6): 1435-1450.
[12] 张红梅, 张威, 王琼, 贾倩茹, 孟珊, 熊雅文, 刘晓庆, 陈新, 陈华涛. 大豆籽粒Ve含量的全基因组关联分析[J]. 作物学报, 2024, 50(5): 1223-1235.
[13] 苗龙, 舒阔, 李娟, 黄茹, 王业杏, Soltani Muhammad YOUSOF, 许竞好, 吴传磊, 李佳佳, 王晓波, 邱丽娟. 大豆根茎过渡区弯曲突变体Mrstz的鉴定与基因定位[J]. 作物学报, 2024, 50(5): 1091-1103.
[14] 张力岚, 杨军, 王让剑. 茶树橙花叔醇和芳樟醇樱草糖苷含量全基因组关联分析及候选基因预测[J]. 作物学报, 2024, 50(4): 871-886.
[15] 李阳阳, 吴丹, 许军红, 陈倬永, 徐昕媛, 徐金盼, 唐钟林, 张娅茹, 朱丽, 严卓立, 周清元, 李加纳, 刘列钊, 唐章林. 基于QTL和转录组测序鉴定甘蓝型油菜耐旱候选基因[J]. 作物学报, 2024, 50(4): 820-835.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 李绍清, 李阳生, 吴福顺, 廖江林, 李达模. 水稻孕穗期在淹涝胁迫下施肥的优化选择及其作用机理[J]. 作物学报, 2002, 28(01): 115 -120 .
[2] 王兰珍;米国华;陈范骏;张福锁. 不同产量结构小麦品种对缺磷反应的分析[J]. 作物学报, 2003, 29(06): 867 -870 .
[3] 袁美;杨光圣;傅廷栋;严红艳. 甘蓝型油菜生态型细胞质雄性不育两用系的研究Ⅲ. 8-8112AB的温度敏感性及其遗传[J]. 作物学报, 2003, 29(03): 330 -335 .
[4] 王永胜;王景;段静雅;王金发;刘良式. 水稻极度分蘖突变体的分离和遗传学初步研究[J]. 作物学报, 2002, 28(02): 235 -239 .
[5] 王丽燕;赵可夫. 玉米幼苗对盐胁迫的生理响应[J]. 作物学报, 2005, 31(02): 264 -268 .
[6] 田孟良;黄玉碧;谭功燮;刘永建;荣廷昭. 西南糯玉米地方品种waxy基因序列多态性分析[J]. 作物学报, 2008, 34(05): 729 -736 .
[7] 胡希远;李建平;宋喜芳. 空间统计分析在作物育种品系选择中的效果[J]. 作物学报, 2008, 34(03): 412 -417 .
[8] 王艳;邱立明;谢文娟;黄薇;叶锋;张富春;马纪. 昆虫抗冻蛋白基因转化烟草的抗寒性[J]. 作物学报, 2008, 34(03): 397 -402 .
[9] 郑希;吴建国;楼向阳;徐海明;石春海. 不同环境条件下稻米组氨酸和精氨酸的胚乳和母体植株QTL分析[J]. 作物学报, 2008, 34(03): 369 -375 .
[10] 邢光南, 周斌, 赵团结, 喻德跃, 邢邯, 陈受宜, 盖钧镒. 大豆抗筛豆龟蝽Megacota cribraria (Fabricius)的QTL分析[J]. 作物学报, 2008, 34(03): 361 -368 .