水稻幼穗响应稻曲病菌毒素胁迫早期的转录组分析

doi:10.16819/j.1001-7216.2022.210714

摘要/Abstract

摘要：

【目的】由稻曲病菌引起的稻曲病不仅造成水稻减产，而且还会产生对动物和植物有毒的真菌毒素。探明水稻幼穗对稻曲病菌毒素胁迫响应的分子机制，可为发掘水稻抗稻曲病基因以及抗病分子育种开辟新的思路。【方法】用稻曲病菌毒素处理水稻幼穗，采用转录组测序技术对水稻幼穗进行转录组测序，以水稻9311基因组作为参考基因组进行对比，利用TPM法计算基因表达量，设定参数（差异倍数的绝对值不小于2，且q值不大于0.05）筛选差异表达基因。结合基因差异表达分析、富集功能分析，鉴定水稻响应胁迫的关键基因，并利用实时荧光定量PCR技术对差异表达基因进行验证。【结果】在稻曲病菌毒素胁迫12 h后，水稻幼穗出现2526个差异表达基因（DEG）;通过GO富集、KEGG代谢途经和KOG功能分析，将差异基因划分为GO功能下的64个条目、32个代谢途径和KOG功能下23个类别，包括淀粉和蔗糖代谢、苯丙类生物合成、碳代谢、糖酵解/糖异生、氨基糖和核苷酸糖代谢等生物学过程。DEG中有66个植物转录因子，分属7种植物转录因子家族，包括WRKY和Myb两大转录因子。分析二萜类生物合成与淀粉和蔗糖代谢途径相关基因发现，OsCPS2、OsKSL4和细胞色素P450等基因表达量上调，而淀粉酶、β-呋喃果糖苷酶和UDP-焦磷酸化酶等基因表达量下调，推测这些基因在水稻响应稻曲病菌毒素胁迫时发挥重要的作用。【结论】稻曲病菌毒素作为非生物胁迫因素对水稻幼穗具有毒性;通过干扰淀粉和蔗糖代谢等途径而影响种子营养物质的合成，降低水稻抵抗病原菌侵染水稻的能力。

关键词: 水稻, 稻曲病菌, 真菌毒素, 转录组分析

Abstract:

【Objective】 Rice false smut caused by Ustilaginoidea virens not only leads to rice yield loss, but also produces mycotoxins which is toxic to animals and plants. In order to elucidate the molecular mechanism behind the response to exposure to mycotoxin the young panicles of rice were mycotoxin-treated with U. virens mycotoxin, which may provide a new idea for exploring rice false smut resistance genes and molecular breeding for disease resistance. 【Method】Transcriptome sequencing was performed with mycotoxin-treated young panicles as materials. The genome of 9311 was used as the reference genome to align the sequences. The expression level of genes was calculated by the term of Transcripts Per Million (TPM). According to the database, the differentially expressed genes were screened by parameters (|LOG₂ fold change|≥1 and q-value≤0.05). Combined with differential expression analysis and function annotation, the key genes in response to mycotoxin were identified in rice panicles. The differentially expressed genes were verified by qRT-PCR. 【Result】2526 differentially expressed genes (DEG) were identified in young panicles of rice after 12 h treated with mycotoxin. Through GO enrichment, KEGG metabolic pathway and KOG function analysis, the DEGs were divided into 64 terms under GO function, 32 metabolic pathways and 23 categories under KOG function, including starch and sucrose metabolism, phenylpropyl biosynthesis, carbon metabolism, glycolysis/gluconeogenesis, amino sugar and nucleoside sugar metabolism and other biological processes. There were 66 plant transcription factors in DEGs, which belonged to 7 family of plant transcription factors, including WRKY and Myb. The expression levels of OsCPS2, OSKSL4 and cytochrome P450 were up-regulated, while the expression levels of amylase, beta-fructofuranosidase and UGPase were down-regulated when we analyzed genes involved in diterpenoid biosynthesis and starch and sucrose metabolism pathways. Therefore, it is speculated that these genes play an important role in rice response to mycotoxin of U. virens stress. 【Conclusion】As an abiotic stress factor, mycotoxin of U. virens had a toxic effect on young panicles of rice. It affects the synthesis of nutrients in seeds by interfering with metabolism of starch and sucrose, and thus reduces the resistance of rice to pathogen infection.

Key words: rice, Ustilaginoidea virens, mycotoxin, transcriptome analysis

伏荣桃, 王剑, 陈诚, 赵黎宇, 陈雪娟, 卢代华. 水稻幼穗响应稻曲病菌毒素胁迫早期的转录组分析[J]. 中国水稻科学, 2022, 36(5): 447-458.

FU Rongtao, WANG Jian, CHEN Cheng, ZHAO Liyu, CHEN Xuejuan, LU Daihua. Transcriptome Analysis of Young Rice Panicles in Early Response to Exposure to Mycotoxin of Ustilaginoidea virens[J]. Chinese Journal OF Rice Science, 2022, 36(5): 447-458.

图/表 13

图1 稻曲病菌毒素处理后的稻穗症状

Fig. 1. Symptoms of rice panicles 14 days after exposure to mycotoxins.

表1 样本测序数据质量控制数据统计

Table 1. Statistics of transcriptone sequencing data.

样品名 Sample	过滤前数据 Total raw reads / bp	过滤后数据 Total clean reads / bp	过滤后数据Clean bases（G） / bp	Q30/%	N/%	GC/%	总对比数据 Total mapped reads /%
CK1-1	43 418 126	41 710 168	5.82	90.75	0.01	50.07	36 532 652(94.56%)
CK1-2	38 359 472	35 721 996	4.98	87.46	0.01	52.25	32 694 216(94.92%)
CK1-3	49 946 750	47 116 642	6.53	86.91	0.01	52.26	42 564 507(94.74%)
CL1-1	44 766 290	43 134 338	6.11	90.88	0.01	49.21	38 396 232(95.14%)
CL1-2	43 990 022	42 300 572	5.84	89.18	0.01	48.86	35 673 451(93.65%)
CL1-3	44 322 824	42 304 834	5.97	89.59	0.01	49.82	39 456 359(94.76%)

图2 差异表达基因的火山图分布横轴为基因在不同组样本间的差异表达倍数(Log2 B/A);纵轴为基因表达量变化的统计学显著程度(P值);图中每个点代表一个基因，其中红色表示上调基因，绿色表示下调基因，黑色表示非差异基因。

Fig. 2. Volcano-plot distribution of DEGs. Horizontal axis represents the multiple value of differetially expressed gene between different groups of samples; Vertical axis represents statistically significant degree(P value) of gene expression changes; Red, Up-regulated DEGs; Blue, Down-regulated DEGs; Black, Non-DEGs.

图3 差异表达基因的GO功能分类纵轴为功能分类; 横轴为该分类内基因个数(下)及其占被注释上基因总数的百分比(上)。不同颜色代表不同的分类。柱状图和坐标轴上浅色代表差异基因，深色代表所有基因。

Fig. 3. GO function classification of DEGs. Vertical axis is function classification; Horizontal axis is the number of genes in the classification (bottom) and their percentage in the total number of annotated genes (top). Light colors represent differentially expressed genes and dark colors represent all genes.

图4 差异表达基因KEEG代谢途径分类横轴代表的是不同的通路，左侧纵轴代表该通路内基因个数占被注释上基因总数的百分比(左)，右侧纵轴代表基因数目（浅色字体代表差异基因，深色字体代表所有基因）。

Fig. 4. KEGG pathway classification of DEGs. The horizontal axis represents different pathways; The left vertical axis represents the percentage of the number of genes in this pathway in the total number of annotated genes (left), the right vertical axis represents the number of genes (light color represents differentially expressed genes, dark color represents all genes).

图5 差异基因通路富集散点图纵轴表示功能注释信息，横轴表示通路对应的富集因子(Rich factor), 富集因子越大，表示富集的程度越高。点的颜色表示q值的大小，q值越小则颜色越接近红色。点的大小表示每个条目下包含的差异基因的个数，点越大则基因越多(富集程度最高的前30个通路)。

Fig. 5. Scatter plot of enriched KEGG pathways for DEGs. Vertical axis represents functional information; Horizontal axis shows the rich factor corresponding to pathway, and the greater the rich factor, the greater the enrichment degree. The size of the dots represents the number of DEGs under the term, and the larger the dots, the more genes (the top 30 pathways with the highest enrichment).

图6 差异基因KOG功能分类横轴每种颜色代表一个KOG的功能分类;纵轴为注释到该分类下的基因数目。

Fig. 6. KOG function classification of DEGs. Each color on the horizontal axis represents a functional classification of KOG; Vertical axis is number of genes annotated under the classification.

表2 稻曲菌毒素影响的植物转录因子

Table 2. Plant transcription factors affected by mycotoxin from U. virens.

转录因子家族 Transcription factor family	受影响的转录因子数量 Number of affected transcription factors			主要的植物调控功能 Main regulatory functions in plants
转录因子家族 Transcription factor family	上调 Up-regulated	下调 Down-regulated	总数 Total	主要的植物调控功能 Main regulatory functions in plants
Myb	8	7	15	植物生长发育，抵御生物、非生物胁迫^{a, b, c}
WRKY	2	0	2	植物生长发育，抵御生物、非生物胁迫^{a, b, c}
HSF(Heat shock transcription factor)	6	3	9	抵御生物、非生物胁迫^{b, c}
bHLH(basic Helix loop helix)	1	3	4	植物生长发育，抵御生物、非生物胁迫^{a, b, c}
MADS-box	3	9	12	植物生长发育，抵御非生物胁迫^{a, c}
GATA	2	7	9	植物生长发育，抵御非生物胁迫^{a, c}
HOX	6	9	15	植物生长发育，抵御非生物胁迫^{a, c}
总数Total	28	38	66

图7 WRKY和Myb的差异表达基因热图

Fig. 7. Heatmap for DEGs of WRKY and Myb genes.

图8 二萜类生物合成途径差异基因分析热图

Fig. 8. Heatmap for DEGs analysis of diterpenoid biosynthesis pathway.

图9 淀粉和蔗糖代谢途径差异基因分析热图

Fig. 9. Heatmap for DEGs analysis of starch and sucrose metabolism.

表3 qRT-PCR验证引物

Table 3. Primers used for qRT-PCR verifying.

序号No.	基因名称 Gene name	基因登录号 Gene ID	引物序列 Primer sequence (5′-3′)
1	WRKY71	BGIOSGA007670	F: TGCAGGTGGTGAAAGATGGG
			R: GAAACAGGCTCGAATTTGCACT
2	WRKY24	BGIOSGA004722	F: AGAGATGGTGAGTGCCCTGT
			R: CGATGTCGCTCATGGTTTGG
3	Myb	BGIOSGA004072	F: CATCTCGCAAAACGGCGAAG
			R: GTTCGGACATCACATAGTCGC
4	Myb	BGIOSGA006005	F: TGCCTTGGATACGCGAAAGA
			R: CCGCTTCTTGAGGTGAGTGT
5	UGPase	BGIOSGA007245	F: TCTGGGATGGCCATAGAAAAACA
			R: GTACTGGACCAACAACGTGC
6	Chitinase	BGIOSGA006087	F: GTACTGGACCAACAACGTGC
			R: TTAGCAGGTGAGGTTGCTGC
7	Peroxidase	BGIOSGA037333	F: GGGATTTGCCATAAGCGAAACA
			R:CCACATTCTCGGTTGTTGCC
8	SBEII	BGIOSGA018719	F: GAGTCGAGCTGGAATTGTGTTG
			R: GCAGAGTGCCCACATTCATC
9	NADPH oxidase	BGIOSGA037531	F: AGATGAATTCGCTCCAATGATTTGT
			R: GGCCAAGCTGAAATTGTGGC
	UBI		F: CGCAAGAAGAAGTGTGGTCA
			R: ACGATTGATTTAACCAGTCCATGA

图10 差异表达基因的qRT-PCR验证 1-9：差异表达基因名称参照表3。

Fig. 10. Validation of DEGs using quantitative real time RT-PCR. 1-9, See table 3 for the DEG names of 1-9.

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