Identification of miRNAs and their target genes in response to salt stress in Brassica napus

Yue WANG, Ting ZHOU, Cai-peng YUE, Jin-yong HUANG, Ying-peng HUA

CHINESE JOURNAL OF OIL CROP SCIENCES ›› 2022, Vol. 44 ›› Issue (1) : 103-115.

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CHINESE JOURNAL OF OIL CROP SCIENCES ›› 2022, Vol. 44 ›› Issue (1) : 103-115. DOI: 10.19802/j.issn.1007-9084.2020299

Identification of miRNAs and their target genes in response to salt stress in Brassica napus

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Brassica napus L. is one of the important oil crops in the world. However soil salt stress severely inhibited its growth and development. Previous studies have shown that under salt stress, plants increase their own salt stress resistance by expressing some miRNAs and regulating the expression of their target genes. In this study, rapeseed was cultured under 200 mmol·L-1 NaCl treatment and control, and took the shoot and root to construct 12 small RNA libraries for high-throughput sequencing. Through the differential expression analysis of miRNAs in the whole genome, a total of 26 differentially expressed miRNAs were identified and 171 corresponding target genes were predicted. Combining the differential expression of genes in the early transcriptome and the results of the differential expression of miRNAs in this study, it is speculated that miRNA156-SPL15-WRKY, miRNA397-LAC12-xylogen, miR169-NFYA5 and miR399-UBC29-ubiquitination pathways might be involved in the regulation of salt stress resistance in B. napus.

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Yue WANG , Ting ZHOU , Cai-peng YUE , Jin-yong HUANG , Ying-peng HUA. Identification of miRNAs and their target genes in response to salt stress in Brassica napus[J]. CHINESE JOURNAL OF OIL CROP SCIENCES, 2022, 44(1): 103-115 https://doi.org/10.19802/j.issn.1007-9084.2020299
土壤盐碱化是全球重要的环境和农业问题。据估计,盐胁迫可能影响全球一半的可耕地。土壤盐胁迫严重抑制了作物生长发育并显著降低了作物的产量[1],其主要原因是盐胁迫会导致高渗透胁迫、氧化应激、营养失衡、离子毒性、生物膜紊乱,并严重破坏细胞分裂及关键代谢等过程,导致水势下降,从而限制水分的吸收和细胞的扩张[2]。为了在盐胁迫条件下生存下来,植物已经进化出各种复杂的机制来缓解盐胁迫对自身造成的损伤[3,4]。因此,了解盐胁迫的分子机制对于提高作物耐盐性具有重要意义[5]
MicroRNA(miRNA)是一类由内源基因编码的长度约为18~24 nt的非编码单链RNA分子,在转录后和翻译水平负调控基因表达[6]。越来越多的证据表明,miRNA参与了植物对非生物胁迫的响应[6~10],如温度[11]、干旱[12]和盐[13,14]胁迫。在拟南芥盐胁迫反应中,miR156、miR167、miR168和miR396等多个miRNA存在差异表达[15]。过表达水稻miR319a的匍匐禾草(Agrostis stolonifera)表现出更强的耐盐性[16],其原因是盐胁迫诱导miR319表达升高,导致至少4个miR319的靶基因(AsPCF5、AsPCF6、AsPCF8AsTCP14)和AsNAC60的表达下调,从而提高了植株的盐胁迫抗性。过表达miR396和miR394的棉花和拟南芥表现出高度的盐胁迫敏感性[17~19],其原因是miR396和miR394可能作为一种负调控因子,分别以GRF和LCR等调控蛋白为靶点。其中GRF抑制拟南芥的应激反应基因AtDREB2A的表达,增加了植物对渗透胁迫的耐受性,LCR则以ABA依赖性方式参与拟南芥盐胁迫抗性反应。盐胁迫激活WRKY并调节植物中胁迫耐受性相关途径越来越受关注[15]。前人研究表明,拟南芥RPD3-like组蛋白去乙酰化酶HDA9通过调节WRKY53的DNA结合能力和转录活性来降低拟南芥的盐胁迫抗性[20]。近期研究发现苹果中MdSPL13与MdWRKY100启动子区结合,并正向调节MdWRKY100的表达,通过促进基因转录,如抗氧化剂生物合成,糖代谢和脯氨酸生物合成有关基因的转录,进一步维持ROS和渗透平衡,从而增强盐胁迫的耐受性[21]。植物在盐胁迫下的生存需要对胁迫条件的快速感知和适应。前人研究[22]表明RACK1A诱导的miR393生物发生,下调了生长素信号,可能以此调节拟南芥对盐度的适应。最近研究表明[23]通过表达TIR1,增加植物生长素信号传导,可能会触发植物生长素介导的下游途径,以通过渗透压调节和增加Na+外排来增强植物对盐胁迫的抗性。这一发现使我们更好地理解miR393介导生长素调节的盐胁迫反应。上述研究结果均表明,miRNA在植物的盐胁迫抗性反应中发挥重要作用。
油菜(AnAnCnCn, 2n=4x=38)是世界上重要的油料作物之一,起源于其二倍体祖先Brassica rapa (ArAr, 2n=2x=20)和Brassica oleracea (CoCo, 2n=2x=18)的自发杂交[24]。目前,在拟南芥以及甘蓝型油菜等作物中,已有一些耐盐性相关的基因被克隆与鉴定。如脯氨酸合成的关键基因P5CS1 [25],通过缓解渗透胁迫来参与油菜盐胁迫响应;控制油菜离子稳态的Na+/H+逆向转运蛋白基因BnNHX1 [26],可在液泡中隔离Na+和K+,并通过维持Na+ / K+稳态并增强渗透和抗氧化调节能力,赋予转基因油菜耐盐性。还有诸多转录因子参与调控耐盐性,如WRKY家族[27]NAC家族[28]等。尽管前人已经对油菜的盐胁迫抗性展开了系列研究,但由于油菜基因组庞大复杂,耐盐机制中许多问题仍有待探索。
本研究的目的是鉴定油菜生长发育过程中对响应盐胁迫的miRNA及其核心靶基因并研究其在盐胁迫中发挥的作用。本研究在200 mmol·L-1 NaCl(T)和无盐处理对照(C)条件下培养油菜,分别从地上部(S)和根(R)中取样,提取总RNA后构建了12个miRNA文库。通过使用高通量测序技术分析了12个miRNA测序文库,最终鉴定到了稳定差异表达的26个miRNA,并根据与相应的转录组表达谱的比较预测了它们的靶基因。这些鉴定到的差异表达miRNA及其核心靶基因有助于深入了解植物盐胁迫抗性的分子机制,并为油菜盐胁迫抗性的遗传改良提供了优异的基因资源。

1 材料与方法

1.1 植物材料及试验设置

油菜种子为中双11,由郑州大学黄进勇教授提供,在25℃黑暗条件下催芽约24 h,挑选露白一致的油菜种子均匀摆放在铺有滤纸的育苗盘中,放于温室25℃±3℃培养架上培养,幼苗长到5 cm左右选取生长一致的健壮幼苗,移栽到盛有适量霍格兰营养液的黑色塑料盆中水培生长,每5 d更换一次营养液[29]。试验在光照培养室中进行,生长环境设定如下:光照强度150 μmol/(m2·s),室温24℃(白天)/ 22℃(夜晚),光照周期16 h(光照)/8 h(黑暗),相对湿度60%。
全转录组测序是在种子萌发7 d后,选取长势一致的油菜幼苗,将其培养在不含NaCl的条件下生长10 d,随后转移到含200 mmol·L-1 NaCl的溶液中培养12 h直至取样。处理组和对照组油菜幼苗分别取其地上部和根部,每个部位均包含3个生物学重复。

1.2 mRNA文库构建和测序

mRNA文库构建、测序详情以及本文所用的转录组数据参考本课题组已经发表的文章[30]。本文中重点分析油菜盐胁迫响应的miRNA及其靶基因。

1.3 sRNA文库的构建和测序

使用TRIzol法提取油菜总RNA,用Nanophotometer®分光光度计(IMPLEN, CA, USA)检测RNA纯度,使用Qubit® RNA检测试剂盒在Qubit®2.0荧光计(Life Technologies, CA, USA)中检测其浓度。为了评估RNA的完整性,使用Agilent Bioanalyzer 2000 system (Agilent Technologies, CA, USA)的RNA Nano 6000检测试剂盒测定RNA的完整性值。随后使用Illumina®的NEBNext®Multiplex小RNA文库制备试剂盒(NEB,USA)构建小RNA文库,将索引代码添加到每个样本的属性序列中,每个样品共使用约3.0 µg RNA。在miRNA的3′和5′端添加特定的索引代码,然后使用M‐MuLV逆转录酶(RNase H‐)作为催化剂合成第一链cDNA。使用LongAmp Taq 2× Master Mix、Illumina小RNA引物和index接头引物进行PCR扩增。PCR产物经8%聚丙烯酰胺凝胶电泳纯化,回收140~160 bp的DNA片段。使用Agilent Bioanalyzer 2100 system(Agilent Technologies, CA,USA)对文库质量进行评估。随后使用TruSeq SR Kit v3-cBOt-HS(Illumina)在cBot集群生成系统上对索引代码样本进行聚类。集群生成后,在Illumina Hiseq 2500平台上对库进行测序,生成50 bp的单端reads。

1.4 数据过滤和读取

测序完成后,首先对原始reads进行过滤,去除reads中的3’接头序列、由于接头自连等原因导致没有插入片段的reads、剪切3’端测序质量较低的碱基(质量值小于20)、含未知碱基N的reads、长度过短的reads(<18 nt)和长度过长的reads(>32 nt)以及含有poly A尾的reads。计算数据的Q20、Q30和GC含量,以便进一步分析。然后,将高质量的sRNA标记通过Bowtie无错配的方式对比到油菜参考序列上,分析其在油菜参考基因组(中双11)上的表达和分布。

1.5 miRNA分类,靶基因预测及其差异表达分析

对过滤后的reads进行分类,分析sRNA数据的组成,选择筛选18~32 nt的reads作为后续分析的有效性数据,并进行该区间sRNA长度分布的统计[31]。参照miRBase 20.0,使用mirdeep2和srna-tolls-cli获得潜在的miRNA并绘制二级结构。使用自定义脚本获得miRNA计数和碱基偏置。整合miREvo和miRdeep2,通过探索不加注释的小RNA标签的二级结构、Dicer裂解位点和最小自由能,预测新的miRNA。psRobot中通过psRobot_tar对miRNA的靶基因进行预测。miRNA表达水平通过每百万转录量(TPM)来估计。使用DESeq R软件包(1.8.3)将校正后的p值设为0.05作为鉴别差异表达miRNA的阈值。差异表达的miRNA及其靶基因通过Wallenius non-central hyper-geometric distribution进行GO分析。利用KEGG(http://www.kegg.jp/)数据库对miRNA及其靶基因的代谢途径富集进行分析[30,32],并使用KOBAS来检测KEGG通路中miRNA及其靶基因的富集情况。

2 结果与分析

2.1 sRNA序列分析

为了探究油菜盐胁迫条件下miRNA的调控机制,提取了在200 mmol∙L-1盐浓度下生长的油菜总RNA并进行了小RNA测序。使用Illumina测序技术,在对照组地上部(control shoot, CS)、处理组地上部(treatment shoot, TS)、对照组根部(control root, CR)和处理组根部(treatment root, TR)小RNA文库中分别获得约35 704 556、40 977 538、45 958 866和41 322 651条raw reads(表1)。对raw reads进行过滤后,单个样品的clean reads比率、Q20、Q30以及GC含量分别在90%、98%以及39%以上。总共获得了164 070 484个clean reads(表2)。
Table 1 Results of sRNA sequencing of B. napus library

表1 油菜文库小RNA测序结果

Read type Total Known miRNA Novel miRNA Ribosomal RNA Transfer RNA Small Nucleolar RNA Small Nuclear RNA Repbase Exon Intron Unknown
CR-1 10,671,419

724,524

(6.79%)

8027

(0.08%)

1,206,853

(11.31%)

292,719

(2.74%)

12,913

(0.12%)

4415

(0.04%)

196,833

(1.84%)

71,915

(0.67%)

58,371

(0.55%)

8,094,849

(75.86%)

CR-2 21,824,907

1,458,902

(6.68%)

18,806

(0.09%)

2,687,449

(12.31%)

539,622

(2.47%)

18,375

(0.08%)

4948

(0.02%)

155,441

(0.71%)

135,205

(0.62%)

109,721

(0.5%)

16,696,438

(76.5%)

CR-3 8,826,325

632,478

(7.17%)

5302

(0.06%)

889,018

(10.07%)

196,670

(2.23%)

6985

(0.08%)

2830

(0.03%)

59,018

(0.67%)

56,971

(0.65%)

47,288

(0.54%)

6,929,765

(78.51%)

CS-1 13,311,272

1,758,631

(13.21%)

14,990

(0.11%)

1,164,394

(8.75%)

181,656

(1.36%)

9785

(0.07%)

4157

(0.03%)

60,482

(0.45%)

107,119

(0.8%)

78,948

(0.59%)

9,931,110

(74.61%)

CS-2 19,907,571

2,758,297

(13.86%)

25,686

(0.13%)

1,731,062

(8.7%)

263,914

(1.33%)

19,987

(0.1%)

5651

(0.03%)

90,780

(0.46%)

186,702

(0.94%)

113,969

(0.57%)

14,711,523

(73.9%)

CS-3 12,740,023

1,827,330

(14.34%)

13,004

(0.1%)

1,071,149

(8.41%)

154,355

(1.21%)

11,518

(0.09%)

3,750

(0.03%)

54,815

(0.43%)

113,233

(0.89%)

74,717

(0.59%)

9,416,152

(73.91%)

TR-1 14,041,921

1,094,034

(7.79%)

10,027

(0.07%)

933,178

(6.65%)

333,787

(2.38%)

17,183

(0.12%)

5482

(0.04%)

114,748

(0.82%)

96,049

(0.68%)

83,830

(0.6%)

11,353,603

(80.86%)

TR-2 15,453,219

1,334,615

(8.64%)

10,345

(0.07%)

1,028,761

(6.66%)

286,273

(1.85%)

20,802

(0.13%)

5192

(0.03%)

230,234

(1.49%)

113,410

(0.73%)

90,019

(0.58%)

12,333,568

(79.81%)

TR-3 11,482,398

1,022,945

(8.91%)

6952

(0.06%)

802,521

(6.99%)

206,254

(1.8%)

14,802

(0.13%)

3615

(0.03%)

115,120

(1.0%)

85,078

(0.74%)

66,935

(0.58%)

9,158,176

(79.76%)

TS-1 13,420,039

1,681,408

(12.53%)

12,961

(0.1%)

1,279,250

(9.53%)

442,353

(3.3%)

16,170

(0.12%)

3583

(0.03%)

71,301

(0.53%)

109,646

(0.82%)

82,047

(0.61%)

9,721,320

(72.44%)

TS-2 11,799,897

1,607,229

(13.62%)

9271

(0.08%)

1,415,308

(11.99%)

229,078

(1.94%)

15,229

(0.13%)

2582

(0.02%)

66,275

(0.56%)

112,288

(0.95%)

64,245

(0.54%)

8,278,392

(70.16%)

TS-3 10,484,620

1,495,122

(14.26%)

9063

(0.09%)

853,612

(8.14%)

190,008

(1.81%)

10,073

(0.1%)

2831

(0.03%)

74,202

(0.71%)

77,089

(0.74%)

64,089

(0.61%)

7,708,531

(73.52%)

注:CR:对照组根部;CS:对照组地上部;TR:处理组根部;TS:处理组地上部;1、2、3分别代表不同的重复
Note: CR: control root; CS: control shoot; TR: treatment root; TS: treatment shoot; 1, 2, 3 represent different repetitions respectively
Table 2 Annotation classification of small RNA

表2 小RNA注释分类

Category Total reads Total bases Error rate /% Q20 /% Q30 /% GC content /% Useful reads Clean reads Clean bases
CR-1 13 154 604 291 035 914 0.0235 98.69 95.55 43.12 10 671 419 10 681 492 239 019 646
CR-2 26 957 726 598 132 265 0.0236 98.66 95.49 43.22 21 824 907 21 844 428 491 159 419
CR-3 11 034 556 243 176 274 0.0236 98.67 95.53 42.43 8 826 325 8 835 328 197 239 995
CS-1 15 806 746 359 492 264 0.0234 98.73 95.76 39.34 13 311 272 13 318 196 303 252 592
CS-2 23 759 486 538 885 516 0.0234 98.72 95.67 39.98 19 907 571 19 919 184 452 987 087
CS-3 15 091 694 343 031 941 0.0235 98.67 95.56 39.58 12 740 023 12 746 750 289 962 903
TR-1 16 854 778 383 270 232 0.0235 98.69 95.59 39.74 14 041 921 14 050 220 319 345 392
TR-2 18 649 198 419 597 126 0.0236 98.67 95.54 41.00 15 453 219 15 464 404 348 639 253
TR-3 13 925 993 312 629 146 0.0234 98.71 95.68 41.26 11 482 398 11 490 602 259 040 892
TS-1 16 299 805 377 200 623 0.0234 98.73 95.69 40.45 13 420 039 13 426 808 306 419 825
TS-2 14 532 333 330 847 865 0.0123 99.63 98.74 42.15 11 799 897 11 801 873 267 923 980
TS-3 12 764 861 288 123 155 0.0235 98.68 95.61 40.40 10 484 620 10 491 199 235 944 750
注:CR:对照组根部;CS:对照组地上部;TR:处理组根部;TS:处理组地上部;1、2、3分别代表不同的重复
Note: CR: control root; CS: control shoot; TR: treatment root; TS: treatment shoot; 1, 2, 3 represent different repetitions respectively
在本研究的转录组中,小RNA序列长度在18~32 nt范围内,最丰富的是21~24 nt(图1A)。这些小RNA由已知的miRNA、预测的miRNA、核糖体RNA(rRNAs)、转移核糖核酸(tRNAs)、核小RNA(snRNAs)、核仁小RNA(snoRNAs)、反作用小干扰RNA(TAS)和未知的片段组成(表2)。本研究发现,在总的小RNA中有6.68%~14.34%是已知的miRNA,而73.91%~80.86%是未知的miRNA。未知的miRNA数量如此之多,表明在油菜中仍有很多的miRNA有待鉴定。从地上部和根中分别鉴定出2897和2435个miRNA(图1B),其中,不论在地上部还是根中,已知的miRNA数量总是少于预测的新的miRNA。最丰富的miRNA序列长度为24 nt(12.91%),其次是21 nt(8.40%)和23 nt(8.85%)(图1A),这些长度与被子植物中典型的miRNA分布相似。样品中长度为24 nt的miRNA首位碱基以“U”为主,这符合miRNA首位碱基偏向“U”的特征(图1C,D)。
Fig. 1 Base length distribution of miRNAs (A), numbers of miRNAs (B), preference distribution of the first base of different length miRNAs (C), base preference distribution of miRNAs (D) in shoots and roots of B. napus under different treatments

图1 不同处理条件下油菜地上部和根中miRNA碱基长度分布(A)、数量(B)、首位碱基偏好性(C)、各位点碱基偏好性(D)

Full size|PPT slide

2.2 油菜中保守miRNA及新miRNA的鉴定

为了鉴定油菜地上部和根中的保守miRNA,将小RNA序列与miRBase数据库中已知的植物miRNA序列进行比较。通过BLASTN搜索和进一步的序列分析,共发现了26个miRNA家族中的91个miRNA。最大的miRNA家族miR169有10个成员(图2)。从文库中可以看出,这些miRNA家族的丰度存在明显差异。为了比较它们表达水平,计算了12个文库中每个miRNA的每百万读次的平均转录量(TPM)[33,34]。TPM标准化后,bna-miR395f的表达水平最低,而bna-miR171f的相对表达水平最高,其次是bna-miR166b和bna-miR169j。为了识别新的miRNA,使用miREvo和miRDeep2程序预测二级结构和Dicer裂解位点,并测量最小自由能。总共从12个文库中预测了2116个新miRNA。新的成熟miRNA的长度为18~24 nt,约60%为24 nt。
Fig. 2 Number of conserved miRNAs of each miRNA family in shoots and roots of B. napus
Note: The abscissa and ordinate represent different conserved miRNA families found in 12 small libraries and the numbers of members in each family, respectively

图2 油菜地上部和根中每个miRNA家族的保守miRNA数量

注:横坐标和纵坐标分别表示在12个小文库中发现的不同保守miRNA家族和每个家族成员的数量

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2.3 不同盐处理下油菜miRNA的差异表达分析

为了确定miRNA的表达与盐胁迫的关系,对miRNA的表达量进行了分析,筛选出了表达量相对较高的15个miRNA,并分析它们在油菜地上部和根中的表达情况(图3A)。使用fold‐change进行计算,将处理组地上部和根部中miRNA的表达与对照组地上部和根部中miRNA表达进行比较,规定|fold change|≥2且p<0.05条件下的miRNA在盐胁迫条件下存在表达差异[35,36]
Fig. 3 Proportion of miRNA in shoots and roots of the top 15 with differential expression under different treatments (A), differential expressions of miRNA between treatment shoot and control shoot (B), and differential expressions of miRNA between control root and treatment root (C)

图3 不同处理下差异表达量前15的miRNA在地上部和根中所占的比例(A),地上部处理组和对照组之间差异表达miRNA的表达量(B)和根部处理组和对照组之间差异表达miRNA的表达量(C)

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与CS文库中的表达相比,TS文库中有9个miRNA表达下调,6个miRNA表达上调(图3B),与CR文库中的表达相比,TR文库中有25个miRNA表达下调,13个miRNA表达上调(图3C)。这些差异表明,盐胁迫影响着这些miRNA的表达。

2.4 差异表达miRNA的靶基因数量分析

为了进一步解释miRNA在盐胁迫反应中的作用,我们对miRNA的靶基因进行了分析,并统计了每个差异表达miRNA的靶基因数目,其中靶基因数目最多的是bna-miR156dbna-miR156ebna-miR156f,分别都含有85个受调控的靶基因(图4)。
Fig. 4 Numbers of target genes of miRNAs
Note: The abscissa indicates differentially expressed miRNAs in shoots and roots of B. napus, and the ordinate indicates the number of target genes of the differentially expressed miRNAs

图4 差异表达miRNA的靶基因数量

注:横坐标表示油菜地上部和根中差异表达的miRNA,纵坐标表示差异表达的miRNA 的靶基因数量

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2.5 miRNA靶基因GO注释和KEGG分析

在CS和TS小RNA文库中,鉴定出17个差异表达的miRNA,共含有93个靶基因;其中已知miRNA的靶基因有89个,新miRNA的靶基因有4个。在CR和TR小RNA文库中,鉴定出38个差异表达的miRNA,共含有684个预测靶基因,其中已知miRNA的靶基因有544个,新miRNA的靶基因有140个。大多数的预测靶基因编码的一些与胁迫相关的转录因子,其中包括SPL、LAC、UBC、NFY以及其它蛋白质如AGO2、超氧化物歧化酶等。为了了解miRNA的生物学功能,所有推测的靶基因都通过Blast2GO软件进行了GO功能分类,以进一步探讨miRNA在响应盐胁迫中的作用。
已知miRNA和新miRNA的靶基因被分为三个GO类别:细胞组分(CC)、生物学过程(BP)和分子功能(MF)。图5(A)是在CS和TS小RNA文库中鉴定到的miRNA靶基因GO富集图,图5(B)是在CR和TR小RNA文库中鉴定到的miRNA靶基因GO富集图。CS和TS小RNA文库中共有93个靶基因参与了1835个GO条目(图5A)。其中BP中高度富集的GO条目是细胞过程(cellular process);CC中高度富集的GO条目是细胞(cell);MF中高度富集的GO条目是结合活性(binding)。CR和TR两个小RNA库共有684个靶基因参与了4941生物过程(图5B)。其中BP中高度富集的GO条目是细胞过程(cellular process);CC中高度富集的GO条目是细胞(cell);MF中高度富集的GO条目是结合活性(binding)。四个数据库中都发现了刺激反应(response to stimulus),表明这些靶基因可以在应对盐胁迫中发挥作用。已知KEGG代谢通路被分为7大类:代谢(M-etabolism)、遗传信息处理(genetic information processing)、环境信息处理(environmental in-formation processing)、细胞过程(cellular processes)、生物体系统(organismal systems)、人类疾病(human diseases)、药物开发(drug development)。图6是差异表达miRNA靶基因参与的pathway通路,其中A图为CS和TS小RNA文库中鉴定到的差异表达miRNA靶基因参与的pathway通路,B图为CR和TR小RNA文库中鉴定到的差异表达miRNA靶基因参与的pathway通路。在CS和TS小RNA文库中的生物体系统(organismal systems)中有18个靶基因参与了免疫防御系统(immune system)。在CR和TR两个小RNA库在CS和TS小RNA文库中的生物体系统(organismal systems)中有21个靶基因参与了免疫防御系统(immune system)。上述结果表明这些靶基因可以积极参与油菜盐胁迫抗性反应。
Fig. 5 Functional classification of different expression miRNA targets
Note: A: GO enrichment map of different expression miRNA target genes identified in CS (control shoot) and TS (treatment shoot) small RNA libraries; B: GO enrichment map of different expression miRNA target genes identified in CR (control root) and TR (control root) small RNA libraries

图5 差异表达miRNA靶基因功能分类

注:A:CS和TS小RNA文库中鉴定到的差异表达miRNA靶基因GO富集图;B:CR和TR小RNA文库中鉴定到的差异表达miRNA靶基因GO富集图

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Fig. 6 Pathway involved in differentially expressed miRNAs target genes
Note: A: Pathway of different expression miRNA target genes identified in CS (control shoot) and TS (treatment shoot) small RNA libraries; B:Pathway of different expression miRNA target genes identified in CR (control root) and TR (control root) small RNA libraries

图6 差异表达miRNA靶基因参与的pathway通路

注:A:CS和TS小RNA文库中鉴定到的差异表达miRNA靶基因参与的pathway通路;B:CR和TR小RNA文库中鉴定到的差异表达miRNA靶基因参与的pathway通路

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2.6 油菜地上部和根中差异表达miRNA的分析

通过分析油菜地上部和根中miRNA的差异表达谱,图7A和图7B分别是盐胁迫条件下,油菜地上部中上调表达和下调表达的miRNA;图7C和图7D是盐胁迫条件下,油菜根中上调和下调表达的miRNA。在地上部中,发现NaCl处理后bna-miR397abna-miR397b表达显著上调,bna-miR169a、bna-miR169bbna-miR169n的表达显著下调。在根中,bna-miR156abna-miR156dbna-miR156ebna-miR156f以及bna-miR824的表达显著上调,而bna-miR169jbna-miR169ibna-miR169hbna-miR169k、bna-miR169gbna-miR169abna-miR169b的表达并没有显著变化,bna-miR169m、bna-miR169n、bna-miR169c、bna-miR169e、bna-miR169fbna-miR169dbna-miR169b表达显著下调。
Fig. 7 Differentially expressed miRNAs in B. napus under salt stress
Note: A: Up-regulated expressed miRNA in the shoots; B: Down-regulated expressed miRNAs in the shoots; C: Up-regulated expressed miRNA in the roots; D: Down-regulated expressed miRNA in the roots. * indicates that the miRNA expression level is higher under this treatment condition

图7 盐胁迫条件下油菜的差异表达miRNA

注:A~D为miRNA不同类型的表达差异。A:地上部中上调表达;B:地上部下调表达;C:根中上调表达;D: 根中下调表达;*表示在这种处理条件下此miRNA表达量较高

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2.7 靶基因表达分析

前人研究表明有多个miRNA参与植物盐胁迫反应[23,37~41],分别为miR397-LACbna-miR397a、bna-miR397b)、miR156-SPLbna-miR156a、bna-miR156d、bna-miR156f、bna-miR156e)、miR169-NFYbna-miR169m、bna-miR169g、bna-miR169n)以及miR399-UBC(bna-miR399b)。其中转录因子miR156的靶基因是SPL [37],在本研究中(图8)发现miR156在根中表达量上调,而在本课题组前期转录组测序结果中发现SPL15表达量下调[30],因此miR156可能通过调控SPL15来响应盐胁迫,从而缓解盐胁迫给植物带来的损伤。LAC是miR397的靶基因,其可以通过调控木质素含量来响应外界非生物胁迫,差异表达分析显示miR397在地上部中表达上调,LAC12在本课题组前期转录组测序结果中表达量下调[30],miRNA397可能通过调控LAC12的表达,促进原生木质部细胞壁增厚,从而响应盐胁迫。转录因子NFY是miR169的靶基因[42],它是非生物胁迫反应的重要调节因子,在本研究中发现miR169在地上部和根中的表达量都下调,而在本课题组前期转录组测序结果中NFYA5的表达量显著上调[30],miR169可能通过调控NFYA5的表达参与盐胁迫反应。UBC是miR399的靶基因,差异表达分析显示miR399在根中表达量下调,而在本课题组前期转录组测序结果中UBC29的表达量显著性上调[30],miR399可能通过调控UBC29的表达从而参与异常蛋白质降解过程,提高作物的耐盐性。
Fig. 8 Impact on miRNA397 target gene LAC (A), miRNA156 target gene SPL (B), miRNA169 target gene NFY (C), and miRNA399 target gene UBC (D) under salt stress
Note: * indicates that the gene expression level is higher under this condition

图8 盐胁迫条件下对miRNA397靶基因LAC的影响(A)、miRNA156靶基因SPL的影响(B)、miRNA169靶基因NFY的影响(C)、miRNA399靶基因UBC的影响(D)

注:*表示在这种处理条件下此基因表达量较高

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3 讨论

盐渍土使植物暴露在渗透胁迫下,渗透胁迫对植物最重要的后果之一是产生大量活性氧,随后是氧化损伤,例如蛋白质、脂质、色素和DNA的降解。生长在含盐条件下的植物吸收有害离子,尤其是钠离子和氯离子,其大量积累对细胞有毒,并加剧渗透胁迫。这些离子破坏膜完整性、细胞代谢、酶结构、细胞生长和光合作用[43]。近年来,miRNA在基因表达调控中的作用越来越清晰,许多miRNA如miR156、miR169、miR393、miR396、miR397及其靶基因[44]等在拟南芥[13]、大豆[45]、大麦[46]等多种植物的盐胁迫响应中发挥重要作用。
前人研究表明油菜中有56个基因编码假定的转录因子在非生物胁迫下会发生改变[47]。在这些基因中,已经显示在盐胁迫下上调超过5倍的基因来自AP2-EREBP家族(ATERF11, CBF4/DREB1D, CBF1/DREB1B, ATERF4/RAP2.5),碱性-螺旋-环-螺旋(bHLH)家族(AtbHLH17),碱性区亮氨酸拉链(bZIP)家族(AtbZIP55/GBF3),C2H2家族(ZAT10、ZAT12、RHL41、ZAT6ZAT102/RHL41),NAC家族(ANAC036、ANAC029/ATNAP、ANAC055/ATNAC3、ANAC047、ANAC072/RD26、ANAC002/ATAF1、ANAC019ANAC032),WRKY家族(ATWRKY53,ATWRKY40ATWRKY33),热激家族(ATHSFA1E)以及Homeobox家族(ATHB-7)。此后,由转录因子和其他蛋白质组成的复杂基因调控网络控制着许多基因的表达。但是在本研究中,发现SPL15在盐胁迫条件下表达量下调,而LAC12NFYA5UBC29在盐胁迫条件下表达量显著上调。遗憾的是,目前对油菜盐度胁迫下miRNA及其作用靶点的研究还不够全面。
为了鉴定miRNA及其靶基因在油菜盐胁迫响应中发挥的重要作用,在本研究中,鉴定了来自油菜的数百万个小RNA序列,以了解盐胁迫条件下miRNA对植物生长发育的影响。分别在200 mmol·L-1 NaCl(T)和无盐处理对照(C)条件下培养油菜品种中双11,取其地上部(S)和根(R)构建了12个小RNA文库,并用Illumina基因组分析仪对其进行测序,从CS、TS、CR和TR库中共得到198 831 780个 raw reads。进一步对全基因组的miRNA进行差异表达分析,一共鉴定到了26个差异表达的miRNA。这些差异表达的miRNA导致了下游多个基因的表达发生显著变化,并且植物对盐胁迫的响应伴随着一系列广泛的细胞内过程,包括信号感知、信号转导、转录和蛋白质生物合成等。这说明不同的植物通过多种miRNA介导的调控策略来响应胁迫[48],例如miR159、miR393、miR396和miR398等与盐胁迫有关[49~52]
在本研究中鉴定出miR156-SPL15、miR397-LAC12、miR169-NFYA5和miR399-UBC29在盐胁迫条件下发挥作用。有研究表明miR156及其靶基因SPL在植物非生物胁迫的响应中发挥着重要的作用[53],在本研究中,miR156特异性的在根中上调表达,这一结果与前人的研究结果形成对比,即拟南芥在盐胁迫下miR156的表达下调[21],并通过调控其靶基因SPL15参与到盐胁迫响应中;苹果在盐胁迫条件下miR156的表达下调,其靶基因SPL13通过激活WRKY100,促进与抗氧化剂生物合成,糖代谢和脯氨酸生物合成有关基因的转录,进一步维持ROS和渗透平衡,从而增强盐胁迫的耐受性[21]。这表明不同植物对盐胁迫的响应存在差异[54]。miR397及其靶基因LAC可以通过调控木质素含量来参与非生物胁迫(58),在本研究中,鉴定到LAC12是miR397a的靶基因,该基因可以导致木质素含量升高,进一步改变Na+迁移途径从而提高植物对盐胁迫的抗性。已有研究表明,番茄和玉米根系中高浓度的NaCl均能提高LAC的转录水平,并指出盐胁迫下根系转录水平的提高可能是植物的普遍响应。因此,LAC可能在植物根系适应盐胁迫过程中发挥重要作用[55,56]。越来越多的证据表明NFY是非生物胁迫反应的重要调节因子[16,17,43,57~61],并受到miR169的调控。在本研究中miR169 家族成员 miR169g 和 miR169n 在根中的表达量下调,这和前人之前的研究一致,但是过表达miR396c的水稻和拟南芥对盐胁迫抗性都降低,而过表达miR396a/b转基因植株的耐盐性提高。这表明同一个miRNA家族的不同成员在不同的植物中采用不同的方式参与盐胁迫响应[60]。然而,盐胁迫条件下油菜中NFY的作用还没有被详细研究。前人研究表明UBC [42,62]定位在内质网上,是miR399的靶基因,其表达受高盐度或ABA诱导并参与异常蛋白质的降解过程[42],且在盐胁迫条件下UBC转录水平升高,通过调控泛素化途径参与植物盐胁迫抗性反应。这表明,植物在盐胁迫反应中可能存在着共同的调控机制。这些结果为进一步研究作物抵抗盐胁迫的分子机制提供了理论依据。

4 结论

植物的耐盐过程是复杂的,由多种因素决定,miRNA的参与进一步增加了这一过程的复杂性。本研究通过油菜miRNA测序以及转录组组测序技术发掘盐胁迫响应相关的 miRNA及其核心靶基因,丰富了油菜地上部和根部miRNA 的情况。共筛选出26个与盐胁迫应激反应相关的候选 miRNA,并且同一家族的不同 miRNA 表达模式有所不同。差异表达的 miRNA 的靶基因显著富集的通路不同。这些miRNA的鉴定及其功能意义的阐明拓宽了我们对盐胁迫反应中转录后基因调控的理解,为探索油菜耐盐机制提供重要的参考。

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