芥菜型油菜不同组织硫苷含量的QTL定位与候选基因分析

doi:10.3724/SP.J.1006.2025.44175

Abstract

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 study, a 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, buds, and seeds were quantified across all lines. The 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, glucosinolate transport

ZHANG Jin-Ze, ZHOU Qing-Guo, XIAO Li-Jing, JIN Hai-Run, OU-YANG Qing-Jing, LONG Xu, YAN Zhong-Bin, TIAN En-Tang. QTL mapping and candidate gene analysis of glucosinolate content in various tissues of Brassica juncea[J].Acta Agronomica Sinica, 2025, 51(5): 1166-1177.

Figures/Tables 7

Table 1

Fig. 1

Fig. 2

Fig. 3

Table 2

Information on QTLs for glucosinolate content traits in different tissues"

组织 Tissue	性状位点 Trait locus	染色体 Chromosome	QTL峰值 QTL peak (cM)	QTL区间 QTL interval (cM)	最近的SNP标记 Recent SNP marker	LOD值 LOD score	表型贡献率 PVE (%)
叶片 Leaves	qL-A02	A02	35.86	31.97-38.91	Marker 4,424,668	3.44	7.85
	qL-A03	A03	27.14	23.50-31.22	Marker 1,757,168	11.46	26.40
	qL-A04	A04	33.40	28.29-37.03	Marker 4,955,778	4.14	9.60
	qL-A05	A05	43.18	39.57-48.06	Marker 470,368	3.51	8.10
	qL-B02	B02	54.16	49.39-58.75	Marker 51,065	3.51	8.10
	qL-B03	B03	95.18	93.13-98.55	Marker 4,697,670	3.29	7.59
	qL-B05	B05	15.33	11.89-19.87	Marker 3,595,171	3.15	7.29
	qL-B06	B06	32.54	26.25-35.41	Marker 3,645,990	3.67	8.44
	qL-B08	B08	92.18	87.52-93.50	Marker 982,815	3.45	7.80
茎秆 Stems	qS-A02	A02	37.27	33.69-44.78	Marker 4,545,960	4.39	10.13
	qS-A03	A03	26.36	21.44-32.09	Marker 1,838,334	7.02	16.20
	qS-A04	A04	35.05	30.16-36.40	Marker 5,062,389	4.20	9.60
	qS-A06	A06	76.01	73.67-80.43	Marker 1,382,718	3.45	7.80
	qS-A08	A08	13.60	11.00-17.51	Marker 731,978	3.87	9.00
	qS-A09	A09	0.53	0-7.19	Marker 4,053,639	3.00	6.90
	qS-B04	B04	3.47	0-8.52	Marker 2,409,165	5.64	12.90
	qS-B05	B05	13.22	9.48-17.74	Marker 3,360,685	3.81	8.70
	qS-B06	B06	32.54	29.94-34.90	Marker 3,645,990	3.69	8.40
	qS-B08	B08	90.87	86.75-96.93	Marker 920,085	4.20	9.60
花蕾 Buds	qF-A02	A02	35.86	31.97-39.78	Marker 4,424,668	2.61	6.08
	qF-A03	A03	26.11	21.69-31.48	Marker 1,944,874	9.39	22.35
	qF-A04	A04	31.35	28.00-36.08	Marker 4,981,184	3.17	7.20
	qF-A07	A07	38.85	33.93-44.88	Marker 2,098,338	3.42	7.80
	qF-A09	A09	48.23	43.38-52.24	Marker 3,898,423	3.18	7.50
	qF-B01	B01	33.91	29.17-37.58	Marker 1,517,061	5.31	12.00
	qF-B05	B05	11.89	6.32-15.33	Marker 3,601,337	4.32	9.90
	qF-B06	B06	31.51	29.09-34.90	Marker 3,631,838	3.90	8.85
	qF-B07	B07	15.49	11.13-20.50	Marker 2,661,101	3.48	8.10
种子 Seeds (S1)	qS1-A01	A01	96.91	91.17-100.11	Marker 3,090,840	3.10	7.44
	qS1-A02	A02	33.69	28.47-39.78	Marker 4,441,071	4.25	10.13
	qS1-A03	A03	23.76	20.92-27.14	Marker 1,809,778	8.11	19.20
	qS1-A04	A04	34.19	32.26-37.03	Marker 4,999,692	2.95	9.00
	qS1-A08	A08	16.73	13.09-23.10	Marker 602,965	3.11	7.38
	qS1-B03	B03	94.66	91.57-98.55	Marker 4,754,822	3.00	7.20
	qS1-B05	B05	14.01	9.48-17.74	Marker 3,441,132	3.72	8.70
	qS1-B06	B06	28.09	25.21-35.16	Marker 3,847,963	3.33	7.89
	qS1-B08	B08	91.13	79.77-92.72	Marker 1,000,761	3.15	7.35
种子 Seeds (S2)	qS2-A02	A02	35.04	31.97-39.78	Marker 4,487,079	4.30	9.90
	qS2-A03	A03	25.58	23.76-31.73	Marker 1,900,789	6.39	15.06
	qS2-A04	A04	28.90	27.14-33.40	Marker 4,925,233	3.75	9.00
	qS2-B01	B01	5.01	0.87-9.59	Marker 1,451,547	3.06	7.20
	qS2-B03	B03	96.73	93.13-100.63	Marker 4,648,733	3.42	8.10
	qS2-B04	B04	49.07	46.08-54.87	Marker 2,242,137	4.12	9.68
	qS2-B05	B05	11.89	8.17-15.33	Marker 3,601,337	3.74	8.78
	qS2-B06	B06	34.12	31.51-37.73	Marker 3,783,270	3.69	8.70
	qS2-B08	B08	93.50	88.29-97.48	Marker 856,065	3.00	6.36

Table 2

Table 3

Candidate genes related to glucosinolate content in major effect intervals"

cQTL名称 cQTL name	区间范围 Interval range (cM)	候选基因ID Candidate gene ID	拟南芥同源基因信息 Information of Arabidopsis homologous genes	基因功能 Gene function
cQTLA02	33.69-38.78	BjuA043503/BjuB010312	AT1G74080, MYB122	参与吲哚族硫苷生物合成与调控
		BjuA043503/BjuB010312	AT1G74080, MYB122	Involved in indole glucosinolate biosynthesis and regulation
		BjuA005541/BjuB010311	AT1G74100, SOT16	硫苷合成中硫基团转移
		BjuA005541/BjuB010311	AT1G74100, SOT16	Sulfate group transfer in glucosinolate biosynthesis
cQTLA04	32.26-33.40	BjuA024283/BjuO013418	AT3G27785, MYB118	调控参与硫苷合成的下游基因
		BjuA024283/BjuO013418	AT3G27785, MYB118	Regulates downstream genes involved in glucosinolate biosynthesis
		BjuA015922/BjuB024226	AT2G22300, CAMTA3	响应钙信号调控硫苷合成
		BjuA015922/BjuB024226	AT2G22300, CAMTA3	Responds to calcium signals to regulate glucosinolate biosynthesis
cQTLB05	11.89-15.33	BjuA002613	AT2G22330, CYP79B3	参与色氨酸途径, 生成吲哚硫苷的前体物质
		BjuA002613	AT2G22330, CYP79B3	Involved in the tryptophan pathway, producing precursor substances for indole glucosinolates
		BjuO011663/BjuA003553	AT4G30530, GGP1	负责转化前体物质的酶
		BjuO011663/BjuA003553	AT4G30530, GGP1	Enzyme responsible for converting precursor substances
cQTLB06	31.51-34.90	BjuA040529/BjuB003228	AT4G21990, APR3	提供硫苷合成所需的还原性硫
		BjuA040529/BjuB003228	AT4G21990, APR3	Provides reduced sulfur needed for glucosinolate biosynthesis
		BjuA003915/BjuA040691	AT4G17880, MYC4	调节硫苷合成相关基因的表达
		BjuA003915/BjuA040691	AT4G17880, MYC4	Regulates the expression of genes related to glucosinolate biosynthesis
cQTLA03	23.76-27.14	BjuB020498	AT5G62680, GTR2	参与硫苷从源到库组织的运输
		BjuB020498		Involved in transporting glucosinolates from source to sink tissues
		BjuA009868		介导硫苷长距离运输
		BjuA009868		Mediates long-distance transport of glucosinolates
cQTLB08	88.29-92.72	BjuB018426/BjuA022496	AT3G47960, GTR1	将蛋氨酸和色氨酸衍生的硫苷转运到种子
cQTLB08	88.29-92.72	BjuB018426/BjuA022496	AT3G47960, GTR1	Transports glucosinolates derived from methionine and tryptophan to seeds

Table 3

Fig. 4

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性状 Trait	组织 Tissue	最小值 Min.	最大值 Max.	平均值 Mean	标准差 SD	变异系数 CV (%)	偏度 Skewness	峰度 Kurtosis
硫苷含量 Glucosinolate contents (μmol g^-1)	叶片Leaves	17.62	82.36	37.70	9.74	25.84	1.08	1.93
	茎秆Stems	0.09	12.97	5.50	3.36	60.65	-0.42	-0.18
	花蕾Buds	27.21	85.39	60.30	10.36	17.18	0.36	-1.02
	种子Seeds (S1)	81.09	190.25	134.75	20.88	15.50	0.07	-0.39
	种子Seeds (S2)	50.70	197.10	130.99	20.13	15.37	-0.21	1.18

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

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[3]	XU Jian-Xia, DING Yan-Qing, CAO Ning, CHENG Bin, GAO Xu, LI Wen-Zhen, ZHANG Li-Yi. Genome-wide association analysis and prediction of candidate genes for plant height and internode number in Chinese sorghum [J]. Acta Agronomica Sinica, 2025, 51(3): 568-585.
[4]	YONG Rui, HU Wen-Jing, WU Di, WANG Zun-Jie, LI Dong-Sheng, ZHAO Die, YOU Jun-Chao, XIAO Yong-Gui, WANG Chun-Ping. Identification and validation of quantitative trait loci for grain number per spike showing pleiotropic effect on thousand grain weight in bread wheat (Triticum aestivum L.) [J]. Acta Agronomica Sinica, 2025, 51(2): 312-323.
[5]	GUO Shu-Hui, PAN Zhuan-Xia, ZHAO Zhan-Sheng, YANG Liu-Liu, HUANG-FU Zhang-Long, GUO Bao-Sheng, HU Xiao-Li, LU Ya-Dan, DING Xiao, WU Cui-Cui, LAN Gang, LYU Bei-Bei, TAN Feng-Ping, LI Peng-Bo. Genetic analysis of a major fiber length locus on chromosome D11 of upland cotton [J]. Acta Agronomica Sinica, 2025, 51(2): 383-394.
[6]	YANG Jing-Fa, YU Xin-Lian, YAO You-Hua, YAO Xiao-Hua, WANG Lei, WU Kun-Lun, LI Xin. QTL mapping of tiller angle in qingke (Hordeum vulgare L.) [J]. Acta Agronomica Sinica, 2025, 51(1): 260-272.
[7]	YE Liang, ZHU Ye-Lin, PEI Lin-Jing, ZHANG Si-Ying, ZUO Xue-Qian, LI Zheng-Zhen, LIU Fang, TAN Jing. Screening candidate resistance genes to ear rot caused by Fusarium verticillioides in maize by combined GWAS and transcriptome analysis [J]. Acta Agronomica Sinica, 2024, 50(9): 2279-2296.
[8]	HAN Li, TANG Sheng-Sheng, LI Jia, HU Hai-Bin, LIU Long-Long, WU Bin. Construction of SNP high-density genetic map and localization of QTL for β-glucan content in oats [J]. Acta Agronomica Sinica, 2024, 50(7): 1710-1718.
[9]	BI Jun-Ge, ZENG Zhan-Kui, LI Qiong, HONG Zhuang-Zhuang, YAN Qun-Xiang, ZHAO Yue, WANG Chun-Ping. QTL mapping and KASP marker development of grain quality-relating traits in two wheat RIL populations [J]. Acta Agronomica Sinica, 2024, 50(7): 1669-1683.
[10]	QIN Na, YE Zhen-Yan, ZHU Can-Can, FU Sen-Jie, DAI Shu-Tao, SONG Ying-Hui, JING Ya, WANG Chun-Yi, LI Jun-Xia. QTL mapping for flavonoid content and seed color in foxtail millet [J]. Acta Agronomica Sinica, 2024, 50(7): 1719-1727.
[11]	ZHENG Xue-Qing, WANG Xing-Rong, ZHANG Yan-Jun, GONG Dian-Ming, QIU Fa-Zhan. Mapping of QTL for ear-related traits and prediction of key candidate genes in maize [J]. Acta Agronomica Sinica, 2024, 50(6): 1435-1450.
[12]	MIAO Long, SHU Kuo, LI Juan, HUANG Ru, WANG Ye-Xing, Soltani Muhammad YOUSOF, XU Jing-Hao, WU Chuan-Lei, LI Jia-Jia, WANG Xiao-Bo, QIU Li-Juan. Identification and gene mapping of soybean mutant M_rstzin root-stem transition zone [J]. Acta Agronomica Sinica, 2024, 50(5): 1091-1103.
[13]	LI Yang-Yang, WU Dan, XU Jun-Hong, CHEN Zhuo-Yong, XU Xin-Yuan, XU Jin-Pan, TANG Zhong-Lin, ZHANG Ya-Ru, ZHU Li, YAN Zhuo-Li, ZHOU Qing-Yuan, LI Jia-Na, LIU Lie-Zhao, TANG Zhang-Lin. Identification of candidate genes associated with drought tolerance based on QTL and transcriptome sequencing in Brassica napus L. [J]. Acta Agronomica Sinica, 2024, 50(4): 820-835.
[14]	ZHANG Yue, WANG Zhi-Hui, HUAI Dong-Xin, LIU Nian, JIANG Hui-Fang, LIAO Bo-Shou, LEI Yong. Research progress on genetic basis and QTL mapping of oil content in peanut seed [J]. Acta Agronomica Sinica, 2024, 50(3): 529-542.
[15]	HAO Qian-Lin, YANG Ting-Zhi, LYU Xin-Ru, QIN Hui-Min, WANG Ya-Lin, JIA Chen-Fei, XIA Xian-Chun, MA Wu-Jun, XU Deng-An. QTL mapping and GWAS analysis of coleoptile length in bread wheat [J]. Acta Agronomica Sinica, 2024, 50(3): 590-602.