Open Access

Identification of miRNAs and their targets from Brassica napus by high-throughput sequencing and degradome analysis

  • Miao Y Xu2,
  • Yun Dong1, 2, 3,
  • Qiu X Zhang2,
  • Lan Zhang2,
  • Yan Z Luo2,
  • Jie Sun1,
  • Yun L Fan2 and
  • Lei Wang2Email author
Contributed equally
BMC Genomics201213:421

DOI: 10.1186/1471-2164-13-421

Received: 17 September 2011

Accepted: 25 July 2012

Published: 24 August 2012

Abstract

Background

MicroRNAs (miRNAs) are endogenous regulators of a broad range of physiological processes and act by either degrading mRNA or blocking its translation. Oilseed rape (Brassica napus) is one of the most important crops in China, Europe and other Asian countries with publicly available expressed sequence tags (ESTs) and genomic survey sequence (GSS) databases, but little is known about its miRNAs and their targets. To date, only 46 miRNAs have been identified in B. napus.

Results

Forty-one conserved and 62 brassica-specific candidate B. napus miRNAs, including 20 miRNA* sequences, were identified using Solexa sequencing technology. Furthermore, 33 non-redundant mRNA targets of conserved brassica miRNAs and 19 new non-redundant mRNA targets of novel brassica-specific miRNAs were identified by genome-scale sequencing of mRNA degradome.

Conclusions

This study describes large scale cloning and characterization of B. napus miRNAs and their potential targets, providing the foundation for further characterization of miRNA function in the regulation of diverse physiological processes in B. napus.

Background

Endogenous small RNAs (sRNAs) are known to be important regulators of gene expression at the transcriptional and post-transcriptional levels. They fall into a number of different classes in plants: transacting siRNAs (tasiRNAs), heterochromatin-associated siRNAs, natural antisense siRNAs (nat-siRNAs) and miRNAs [1]. These classes of non-coding RNAs are distinguished by their biogenesis pathways and the types of genomic loci from which they arise [2].

miRNAs are non-coding RNAs of approximately 21 nucleotides that have been identified as important regulators of gene expression in both animals and plants [25]. Plant miRNAs are generated from hairpin-structured non-coding transcripts by DCL1(DICER-LIKE 1), which cleaves a short (21 bp) duplex from the stem region [6]. The duplex is incorporated into an AGO1 complex and the miRNA* strand is subsequently degraded. The mature miRNA strand guides the AGO1 complex (RNA-induced silencing complex, RISC) to protein-coding RNAs, which are cleaved by AGO1 at a specific position (opposite to the 10th and 11th nucleotides of the miRNA) [7]. Recent findings have shown that the inhibition of gene expression via translational arrest by the miRNA-guided AGO complex is more common in plants than was previously believed [8]. The mature miRNAs function within large complexes to negatively regulate specific target mRNAs. Plant miRNAs generally interact with their targets through perfect or near-perfect complementarity and direct mRNA target degradation [9, 10]. Due to their evolutionary conservation, miRNAs have been found to exist in both plants [9, 11] and animals [1214]. Conserved miRNA molecules can also be found in ferns, mosses and fungi [15, 16].

In plants, miRNAs not only post-transcriptionally regulate their own targets but also interact with each other in regulatory networks to affect many aspects of development, such as developmental timing [1721], senescence [2224], leaf morphogenesis [2531], reproductive development [3235], and modulation of root architecture [3642]. miRNAs are also reported to be involved in plant responses to biotic and abiotic stresses [31]. To date an increasing number of miRNAs have been identified and deposited in miRBase V17.0 (http://www.mirbase.org/). Among these miRNAs, there are 19,724 plant miRNAs and miRNAs*, from a total of 153 species. The species with the fastest growing number of miRNAs is Brachypodium distachyon, with 120 miRNAs being recently added. Initially, miRNAs were identified by the traditional Sanger sequencing method, which used for relatively small-size cDNA libraries of plant sRNAs from Arabidopsis, rice and poplar (Populus spp.). Comparison of miRNAs from these species led to the conclusion that plant miRNAs are highly conserved [16]. This was supported by observations that even ferns shared common miRNAs with flowering plants [43]. However, it was also noticed that a small number of miRNAs were not present in the genomes of some species, suggesting that they have evolved more recently [25]. As non-conserved miRNAs are often expressed at a lower level than conserved miRNAs, many non-conserved miRNAs were not found in small-scale sequencing projects. However, high-throughput sequencing technologies have allowed the identification of many non-conserved miRNAs in several species [4447]. To date, hundreds of miRNAs have been isolated by direct cloning or by deep sequencing in higher plants [48]. Elucidating the function of these tiny molecules requires efficient approaches to identify their targets. Originally, plant miRNA targets have been studied via computational prediction, which is based on either perfect or near -perfect sequence complementarity between miRNA and the target mRNA or sequence conservation among different species [10]. However, target prediction is very challenging, especially when a high level of mismatches exists in miRNA:target pairing [49]. Recently, a new method called degradome sequencing, which combines high-throughput RNA sequencing with bioinformatic tools, has been successfully established to screen for miRNA targets in Arabidopsis[5052]. Using degradome sequencing, many of the previously validated and predicted targets of miRNAs and tasiRNAs were verified [50, 51, 53, 54], indicating that it is an efficient strategy to identify sRNA targets on a large scale in plants.

Rape (Brassica napus) is one of the most important oil crops, and also is one of the major economic crops. However, unlike Arabidopsis and other plants, much less is known about its miRNA classification and miRNA targets, especially the roles of miRNAs in the developmental process of Brassica napus. Currently, miRBase lists 46 miRNAs forming 17 miRNA families in Brassica napus. The exploration of sRNA-based regulatory networks in Brassica napus is an important step towards our better understanding of sRNA-based genic regulation. Here, we describe the high-throughput sequencing analysis of sRNAs from a cultivated variety of B. napus, cv Westar, using the Illumina Solexa platform.

The sRNAs library was prepared for Solexa sequencing from greenhouse cultivated rape plants, and produced more than 2 million unique sequences. The most abundant classes were represented by 21 and 24 nt-long sRNAs. Forty-one conserved B. napus miRNAs and 62 candidate novel B. napus-specific miRNAs were firstly identified. Twelve conserved miRNAs and 10 B. napus-specific candidates were further verified by real-time RT-PCR. To identify miRNA targets, a degradome sequencing approach was used, which globally identifies the remnants of sRNA-directed target cleavage by sequencing the 5 ends of uncapped RNAs [50, 51]. We identified a total of 33 non-redundant target ESTs for 25 conserved miRNAs, and 19 non-redundant target ESTs for 17 B. napus-specific miRNAs. Approximately 70% of the identified targets for conserved miRNAs were transcriptional factors.

Results and discussion

Sequencing B. napus miRNAs using Solexa technology

We used Solexa technology to deeply sequence B. napus sRNAs. Total RNAs from different B. napus tissues were pooled and submitted for small RNA sequencing. A total of 13,020,106 reads were generated from the sequencing machine. After removing adaptor sequences, filtering out low quality tags and cleaning up sequences derived from adaptor-adaptor ligation, 2,149,116 unique sequences were obtained. Among these unique sequences, 73,931 (3.44%) were found to be similar to known miRNAs (Table 1).
Table 1

Statistics of sRNA sequences from B. napus

 

Redundant

Non-redundant

 

Number of counts

% of total

Number of unique sequence

% of total

Raw reads

13020106

\

2149116

\

Adaptor removed

30673

0.24

25713

1.2

Junk Filter a

5182

0.04

2362

0.11

Length filter

1379794

10.6

646357

30.08

Simple sequence filter b

69175

0.53

11859

0.55

Copy number <3

1219472

9.37

1130389

52.6

Hit mRNA, RFam, Repbase

9877816

75.87

57637

2.68

Mappable

437994

3.36

30400

1.41

a the sequences are filtered out if they contain 3 Ns (N is undetermined nucleotide) and only A and C without G and T, vice versa; b the simple sequences are filtered out if they contain 2 Ns (N is undetermined nucleotide), 7 consecutive As, 8 consecutive Cs, 6 consecutive Gs or 7 consecutive Ts and 10 repeats of any dimer, 6 repeats of any trimer, or 5 repeats of any tetramer. These numbers were from the statistics of miRbase ver16.

SRNAs with known function were commonly 20–24 nt in size [53]; therefore, we analyzed the size distribution patterns of the original and unique reads (Figure 1). The majority of sRNAs were 21 nt in size, followed by 24 nt and 23 nt (Figure 1a), which is consistent with the typical size distribution of sRNAs from other plants. The 21 nt class showed the highest redundancy, whereas the 24 nt class showed lower redundancy (Figure 1a and b).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-421/MediaObjects/12864_2011_Article_4797_Fig1_HTML.jpg
Figure 1

Size distribution of sequenced small RNAs.

Identification of conserved B. napus miRNAs

Conserved families of miRNAs are found in many plant species and have important functions in plant development and responses to stresses [55]. In this study, to identity the conserved miRNAs in B. napus, our dataset was mapped onto the the genome and ESTs of B. napus, B. rapa and B. oleracea, allowing one or two mismatches between sequences. all retained sequences were compared to known miRNAs from miRBase 17.0 (http://www.mirbase.org/), and secondary structures of these matched miRNAs were predicted. Based on genome mapping and the miRbase results and hairpin prediction, a total of 55conserved miRNAs derived from B. napus were identified, including 41 miRNAs and miRNAs* (22 families) were firstly identified together with 14 already in miRbase (Additional file 1: Figure S1, Additional file 2: Table S2). 41conserved miRNAs and miRNAs* were potentially generated from 26 non-redundant ESTs and 3 genomic survey sequence (GSS) loci (Table 2; Additional file 3: Table S3). The precursors of four miRNAs named Bna-miR166f, Bna-miR824*, Bna–miR1140b and Bna–miR1140b* were matched in the genome of B. rapa (Additional file 3: Table S3).
Table 2

Conserved miRNAs in B. napus

miRNA

miR sequence (5'→3')

miR length (nt)

Reads

Precursor from EST

Mature miRNA position

Stem-loop position

 

miR start

miR end

Precursor start

Precursor end

Bna--miR159*

GGGCTCCTTATAGTTCAAACG

21

79

EX039355

189

209

186

368

Bna--miR159b

TTTGGATTGAAGGGAGCTCTT

21

66

EV097138

224

244

216

408

Bna-miR160a

TGCCTGGCTCCCTGTATGCCA

21

4447

ES904429

153

173

149

237

Bna-miR160a*

GCGTACAGAGTAGTCAAGCATA

22

7

ES904429

214

235

149

237

Bna-miR161*

GCAAGTCGACTTTGGCTCTG

20

97

BZ512955

431

451

329

463

Bna-miR162a

TCGATAAACCTCTGCATCCAG

21

193

DY025212

495

515

420

523

Bna-miR162b

TCGATAAACCTCTGCATCCAG

21

EV208491

295

315

204

329

Bna-miR162b*

GGAGGCAGCGGTTCATCGATC

21

32

EV208491

222

242

212

323

Bna-miR165a

TCGGACCAGGCTTCATCCCCC

21

2205

FP063045

53

73

34

160

Bna--miR166a*

GGAATGTTGTTTGGCTCGAAG

21

29

DX911364

806

826

697

831

Bna-miR166e

GGAATGTTGTCTGGCTCGAGG

21

328

CU967744

48

68

39

185

Bna-miR167d

TGAAGCTGCCAGCATGATCTA

21

6110

CT022223

721

741

635

758

Bna-miR167e

TGAAGCTGCCAGCATGATCTA

21

AC189327

249

269

153

286

Bna-miR167e*

GATCATGTTCGTAGTTTCACC

21

47

AC189327

169

189

153

286

Bna-miR167f

TGAAGCTGCCAGCATGATCTT

21

160

ES910254

45

65

43

134

Bna-miR168b

TTCGCTTGGTGCAGGTCGGGA

21

14

DU984956

357

377

258

396

Bna-miR169n

GCAAGTTGACTTTGGCTCTGT

21

1463

CU944678

404

424

354

528

Bna-miR169n*

TGAGCCAAAGATGACTTGCCG

21

11

CU944678

459

479

356

530

Bna-miR171a*

AGATATTAGTGCGGTTCAATC

21

7

DX044654

128

148

119

219

    

DU980843

223

243

159

246

Bna--miR171f*

TATTGGCCTGGTTCACTCAGA

21

34

DU106747

666

686

647

756

Bna-miR172a

GGAATCTTGATGATGCTGCAT

21

95

EV092015

731

751

637

769

Bna-miR172a*

GTGGCATCATCAAGATTCACA

21

3

EV092015

654

674

629

777

Bna-miR172b

AGAATCTTGATGATGCTGCAT

21

223

CU946172

157

177

69

189

Bna-miR319a

GAGCTTTCTTCGGTCCACTC

20

105

ES908144

308

327

301

477

Bna-miR319b-1

ATCTGCCGACTCATCCATCCA

21

11

CN829704

153

173

76

312

Bna-miR319b-2

GAGATTCTTTCAGTCCAGTCA

21

3

CN829704

103

123

74

310

Bna-miR390d

AAGCTCAGGAGGGATAGCGCC

21

1157

EE544982

541

561

471

581

Bna-miR390d*

CGCTGTCCATCCTGAGTTTCA

21

348

EE544982

441

461

421

531

Bna -miR393*

ATCATGCGATCTCTTCGGATT

21

30

DU101699

224

242

116

242

Bna-miR396

AATAAAGCTGTGGGAAGATAC

21

24

DU106522

54

74

35

216

Bna-miR398

TGTGTTCTCAGGTCACCCCTG

21

66

EE426846

89

109

1

123

Bna-miR399d

TGCCAAAGGAGATTTGCCCGG

21

71

CX190537

106

126

9

146

Bna-miR399f

TGCCAAAGGAGAGTTGCCCTG

21

62

EE556998

584

604

475

635

Bna-miR400

TATGAGAGTATTATAAGTCAC

21

25

CX189066

239

259

212

346

Bna-miR408a

ACAGGGAACAAGCAGAGCATG

21

305

ES903146

59

79

49

157

Bna-miR408a*

ATGCACTGCCTCTTCCCTGGC

21

141

ES903146

128

148

49

157

Bna -miR2111c

TAATCTGCATCCTGAGGTTTA

21

35

BH986382

394

414

375

493

    

DX056967

280

300

176

326

The read number of the conserved miRNAs was highly variable, indicating different expression levels of these miRNAs. Among them, Bna-miR159, Bna-miR166a, Bna-miR164, Bna-miR171f and Bna-miR168 had relatively high number of reads, indicating that these miRNAs are likely to be expressed at a higher level, whereas Bna-miR169 family members had a low number of reads, and are, therefore, likely to be expressed at a lower level (Additional file 4: Table S1). The relative expression level of a few known miRNA families, such as miR159, miR167, miR160, miR165 and miR390, were similar to that of Arabidopsis[44] (Table 2).

Brassica-specific miRNAs

A distinct feature of miRNAs is the ability of their pre-miRNA sequences to adopt the canonical stem-loop hairpin structure. After removal of conserved miRNAs, the rest sRNA reads were predicted for each mapped locus for potential stem-loop structures. From this analysis, we identified 62 miRNA and miRNA*candidates (47 families) that were potentially generated from 62 EST or GSS loci (Additional file 5: Figure S2, Table 3).
Table 3

Candidate new brassica-specific miRNAs

miRNA

miR sequence (5'→3')

miR length (nt)

Reads

Precursor from EST

Mature miRNA position

Stem-loop position

 

miR start

miR end

Precursor start

Precursor end

Bna-miRC1

CCATACTAAATCTGGATCATTT

22

115

CU943501

519

540

519

634

Bna-miRC2

ATAAATCCCAAGCATCATCCA

21

1011

EV202910

179

199

173

261

Bna-miRC3

TGGGATTGGCTTTGGGCTTTTC

22

12

CU940792

108

129

82

281

Bna-miRC4

TTTCAGTCGTCATAGGTTAGT

21

11

GT084890

55

75

51

158

Bna-miRC5-1

TGTGTTGTGATGATAATCCGA

21

306

CU971106

285

305

132

350

Bna-miRC5-1*

AATCGGATTATCATCACAACA

21

7

CU971106

93

113

29

291

Bna-miRC5-2

TCAACCAAATACACATTGTGG

21

4

CU971106

52

72

35

306

Bna-miRC5-3

TTATCATCACAACACTAGATC

21

536

CU971106

76

96

19

281

Bna-miRC5-3*

TCTTGTGTTGTGATGATAATC

21

216

CU971106

288

308

136

349

Bna-miRC5-4

TGATAATCCGACTTCTATGAC

21

29

CU971106

272

292

122

356

Bna-miRC5-5

TTGGTTTGGATCTTGGAAATC

21

8

CU971106

123

143

42

304

Bna-miRC5-6

TCGGATTATCATCACAACACT

21

182

CU971106

89

109

27

289

Bna-miRC6

ATAGATCCTTCTGATGACGCA

21

16

DU099814

306

326

254

327

Bna-miRC7

CAAATCCTGTCATCCCTACCA

21

89

GT079632

102

122

102

229

Bna-miRC8

CAGGAGAGATTGTTGGATCCA

21

3

CU931338

337

357

337

443

Bna-miRC9

TGCCTGGCTCCCTGTATACCA

21

118

EV193539

387

407

380

474

Bna-miRC10

TCAATGTTGGCTCAATTATGT

21

12

CU934632

731

751

666

751

Bna-miRC11

GGCGAGTCACCGGTGTCGGTC

21

6

FP328714

415

435

406

534

Bna-miRC12

GGGTCGATATGAGAACACATG

21

15

EE426846

15

35

1

123

Bna-miRC13

ACCCTGTTGAGCTTGTCTCTA

21

3

CU980942

490

510

449

524

Bna-miRC14

CAGCTGGACGACTTAGTAGAC

21

7

CU943399

123

143

103

229

Bna-miRC15-1

ACATTGGACTACATATATTAC

21

8

ES901619

392

412

299

430

Bna-miRC15-2

TCAATACATTGGACTACATAT

21

9

ES901619

387

407

299

430

Bna-miRC16

GTTTTGAGAGATTGGGAAGCT

21

3

EV146378

77

97

58

216

Bna-miRC17a-1

TTTCCAAATGTAGACAAAGCA

21

7132

ES913560

96

116

31

137

Bna-miRC17a-1*

CTTTGTCTATCGTTTGGAAAAG

22

782

ES913560

53

74

31

137

Bna-miRC18

TCGCGATCTTAGATCCTCTAA

21

41

EV179238

441

461

288

564

Bna-miRC19

CGAGTTGGTCGGGAAAGACGG

21

12

DU102764

104

124

35

128

Bna-miRC20

CTCTCGTGGAGCGTCTCGAGG

21

3

EV192419

700

720

567

746

Bna-miRC21

GGAGGCAGCGGTTGATCGATC

21

7

DY025212

429

449

420

523

Bna-miRC22a-1

CAAGTAGACGACTTTCCAGAC

21

10

CU945922

359

379

298

403

Bna-miRC22a-2

CGTGGTCGTCCAAGTAGACGA

21

13

CU945922

363

383

292

397

Bna-miRC22a-3

TTGGACGACTTTGTAGACGAC

21

9

CU945922

303

323

297

402

Bna-miRC23a-1

TCAGAACCAAACCCAGAACAAG

22

54

CU958057

25

46

3

241

Bna-miRC23a-2

TTACAGAACAGCAACAAGCTGT

22

150

CU958057

47

68

7

238

Bna-miRC23a-3

TATCTACTGCTTATGCCACCA

21

65

CU958057

54

74

1

215

Bna-miRC23a-3*

GATGCATAACCACTAGATACG

21

8

CU958057

140

160

1

215

Bna-miRC24

TTAGGATTGAGATCTTAGCGA

21

7

EV176533

225

245

214

395

Bna-miRC25

TTGGACTGAAGGGAACTCCCT

21

23719

FP023833

319

339

168

343

Bna-miRC25*

AGAGTTTCCTTAAGTCCATTC

21

34

FP023833

173

193

167

343

Bna-miRC26

TGAGCCAAAGATGACTTGTCG

21

45

BZ021311

68

88

66

323

Bna-miRC27

TAAGATGATGGAACACTGGCC

21

25

EE438385

18

38

14

279

Bna-miRC28

ATGGATCCGCCGGATAAGGAT

21

6

CU965419

466

486

350

511

Bna-miRC29

TTGAGGTTTTGAGGACTGGCC

21

6

EV093069

644

664

564

668

Bna-miRC30

TCCTGGACGACTTTCAAGTAAG

22

9

CZ888137

161

182

20

250

Bna-miRC31

AGATCATCCTGCGGCTTCATT

21

26

EV134163

290

310

233

335

Bna-miRC32

GCAAGTTGACTTTGGCTCCGT

21

51

BZ021311

52

72

13

184

Bna-miRC33

TTTTGCCTACTCCTCCCATACC

22

268

CU981257

103

124

95

223

Bna-miRC34

ATCCTCGGGACACAGATTACC

21

113

EV076017

357

377

339

459

Bna-miRC35

ATGGTGTAGGTACTGAGCAGA

21

13

EV194620

298

318

294

400

Bna-miRC36

CGTCCGGGGAAAGCAAAGTCG

21

11

EV088144

141

161

64

186

Bna-miRC37

TGATTTATCCAAGGGTTCAGG

21

31

DU101557

509

529

367

608

Bna-miRC38

CAAGTAGACTACTTTCCAGACG

22

9

GT084321

52

73

1

92

Bna-miRC39

TAAGATGATGGGACGTTGGATC

22

11

DY002174

42

63

40

306

Bna-miRC40

CGCTCACAGCATCTGAACTCT

21

21

CD842549

99

119

78

243

Bna-miRC41

TTTTGGAGAAGGCTGTAGGCA

21

13

DU109430

791

811

780

890

Bna-miRC42

TTCCCCGGACGACTTTAAATT

21

15

EV088144

90

110

3

125

Bna-miRC43

TGTGAATGATGCGGGAGATGT

21

15

CN829704

219

239

69

315

Bna-miRC44

TTGGCCACAACGGATTTAACA

21

9

EV006438

66

86

66

141

Bna-miRC45

TTTCATCTTAGAGAATGTTGTC

22

42

EV178795

578

599

478

617

Bna-miRC46

ACTTGTCTCACTCATCAGTT

20

7

EV063926

5

24

3

215

Bna-miRC47

CAAATGTAGACAAAGCAAAAC

21

4

ES913560

100

120

31

137

Generally, new species-specific miRNAs are considered to be young miRNAs that have evolved recently, and are often expressed at a lower level than conserved miRNAs, as was reported for Arabidopsis and wheat [44, 46, 56]. This observation is also true for many of the new B. napus miRNAs identified here. However, few new miRNAs were expressed at a high level, which was opposite with this observation (Figure 2). In some cases we observed considerable inconsistency between the level of miRNAs identified by Solexa sequencing and quantitative RT-PCR (qRT-PCR) analysis, however, though we do not know the explanation for these differences. It is possible that the primers used for stem-loop real-time reactions can bind miRNA species with a few mismatches that were not considered by the bioinformatic analysis.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-421/MediaObjects/12864_2011_Article_4797_Fig2_HTML.jpg
Figure 2

Expression levels of Bna-miRNAs by two methods. (a) Profile of qRT-PCR Ct values for Bna-miRNAs; (b) Profile of sequencing frequencies for Bna-miRNAs.

Stem-loop qRT-PCR validation and measurement of B. napus miRNAs

To verify the existence of the newly identified rape miRNA candidates, the same RNA preparation used in the Solexa sequencing was subjected to stem-loop qRT-PCR [57]. Finally, Twelve conserved miRNAs and 10 brassica-specific candidates, which were randomly selected, could be readily detected by qRT-PCR (Figure 2a), suggesting that miRNAs are bona fide miRNAs. Most results of qRT-PCR analysis agreed with the sequencing data, as in the cases of Bna-miR159, Bna-miR159b, Bna-miR160a, Bna-miR165a, Bna-miR166e, Bna-miR167f, Bna-miR169a, Bna-miR171a*, Bna-miR390d, Bna-miR400, Bna-miR1140b, Bna-miRC2, Bna-miRC5-1, Bna-miRC5-6, Bna-miRC17a-1, Bna-miRC18, Bna-miRC21, Bna-miRC22a-1, Bna-miRC30 and Bna-miRC45. In some cases, however, a discrepancy was also observed between the qRT-PCR and sequencing data (Bna-miR162a and Bna-miRC9; Figure 2a, b; Table 2, 3). The results suggested that Solexa sequencing was capable of successfully discovering candidate novel miRNAs from this species with high accuracy and efficiency.

Targets of known B. napus miRNAs

In B. napus, many conserved miRNA targets have been predicted previously [58, 59], but few miRNA targets were identified experimentally. We therefore employed the recently developed high-throughput experimental approach [50, 51, 60] allowed us to identify target genes for known miRNAs and candidate new miRNAs identified in this work. The poly-A fraction of a balanced total RNA mix from leaf, petiole, stalk and root tissue was analyzed for the identification of target transcripts of known and new miRNAs. We obtained a total number of 8, 356, 060 reads with an average size of ~20 nt, representing the 5 ends of uncapped, polyadenylated RNAs. After initial processing, 6,999,869 reads were obtained, and could be mapped to mRNAs. Previous studies established that the 5 terminal nucleotide of miRNA-cleaved mRNA fragments would correspond to the nucleotide that is complementary to the 10th nucleotide of the miRNA. Therefore, the cleaved RNA targets should have distinct peaks in the degradome sequence reads at the predicted cleavage site relative to other regions of the transcript [50, 51]. CleaveLand pipeline [60] was used to identify cleaved targets for both known and new miRNAs in B. napus. The abundance of the sequenced tags was plotted for each transcript, and the results are shown in Figures 3, Additional file 6: Figure S3 and Additional file 7: Figure S4. The cleaved target transcripts were categorized into five classes (categories 0, 1, 2, 3 and 4). There were 31 non-redundant ESTs identified as known miRNAs targets, covering 17 miRNA families (Table 4). Nine target ESTs were classified as category 0 (Figure 3a). Category 0 targets are transcripts where the degradome reads corresponding to the expected miRNA-mediated cleavage site were the most abundant reads matching the transcript and there is only one peak on the transcript with more than one raw read at the position. Transcripts of one target (EV184491, for Bna-miR156a) fall into category 1 (Figure 3b), where the total abundance of degradome sequences at the cleavage position is equal to the maximum on the transcript, and there is more than one raw read at the position and more than one maximum position on the transcript. 3 target ESTs were classified as Category 2 (Figure 3c), where abundance at the cleavage position is less than the maximum but higher than the median for the transcript and more than one raw read at the position. 2 target ESTs were classed as Category 3 (Figure 3d), where abundance at the cleavage position is equal to or less than the median for the transcript and more than one raw read at the position. Among the identified targets the most abundant category was category 4 (18 target ESTs), where there is only one raw read at the cleavage position (Figure 3e). Using these classifications we identified targets for 17 conserved miRNA families out of 25. Many highly conserved miRNAs were identified in B. napus (Table 2) did not have detectable sliced targets in the degradome sequencing data (e.g. miR161, miR166, miR168 and miR397). It is possible that the levels of conserved miRNAs (e.g. miR161) or sliced targets are below the detection level in this growth stage, and may be present in other specific stages or tissues that have not yet been analyzed. Alternatively, these miRNAs inhibit target gene expression through translational arrest rather than mRNA cleavage.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-421/MediaObjects/12864_2011_Article_4797_Fig3_HTML.jpg
Figure 3

Confirmed microRNA (miRNA) targets using degradome sequencing are presented in the form of target plots (t-plots). We used normalized numbers in plotting the cleavages on target mRNAs, which were referred to as ‘target plots’ (t-plots) by German et al. [51]. Signature abundance throughout the length of the indicated transcripts is shown. Representative t-plots for class 0 (a), class I (b), class II (c), class III (d), and class VI (e) categories are shown. Arrows indicate signatures consistent with miRNA-directed cleavage. miRNA:mRNA alignments along with the detected cleavage frequencies (normalized numbers) are shown. The frequencies of degradome tags with 5′ends at the indicated positions are shown in black, with the frequency at position 10 of the inset miRNA target alignment highlighted in red. The underlined nucleotide on the target transcript indicates the cleavage site detected in the degradome.

Table 4

Targets of conserved B. napus miRNAs

miRNA

Target EST

Category

Cleavge site

Reads mapping to the expected cleavage site

Percentage of expected reads to total reads mapped to the full length of EST (%)

Target site location

Target annotation

Bna-miR156a

EL625881

4

455

5

19

3UTR

A. thaliana SPL3 transcription factor

 

EV190718

2

681

3

38

3UTR

A. thaliana SPL10 transcription factor

 

EV184491

1

747

2

100

3UTR

A. thaliana O-fucosyltransferase family protein

Bna-miR156c

EV190718

2

682

3

38

3UTR

A. thaliana SPL10 transcription factor

Bna-miR159

EV087133

4

439

2

20

ORF

A. thaliana MYB65

 

EV223870

4

279

5

25

ORF

metallo-beta-lactamase family protein

 

EV136053

4

685

3

21

ORF

pyruvate, phosphate dikinase (PPDK)

Bna-miR160a

EV006535

0

256

2

50

ORF

auxin response factor

Bna-miR164b

ES906443

4

68

3

20

ORF

NAC domain-containing protein

Bna-miR167b

ES962471

4

264

3

25

ORF

Auxin response factor 8

Bna-miR167c

EV208388

4

657

3

27

ORF

B. rapa IAA-amino acid hydrolase 3

Bna-miR169a

EE543166

0

247

3

50

3UTR

A. thaliana NF-YA3

 

CN729971

4

406

3

17

3UTR

A. thaliana NF-YA5

Bna-miR169e

ES980547

2

459

5

36

3UTR

A. thaliana NF-YA3

 

EE543166

0

247

3

50

3′UTR

A. thaliana NF-YA3

 

CN729971

4

406

4

22

3′UTR

A. thaliana NF-YA5

Bna-miR169l

ES980547

2

459

5

36

3′UTR

A. thaliana NF-YA3

 

ES959135

4

352

3

27

3UTR

B. napus clone bncbf-b2

 

EE543166

0

247

3

50

3UTR

A. thaliana NF-YA3

 

CN729971

4

406

4

22

3UTR

A. thaliana NF-YA5

Bna-miR171b

ES907976

3

609

3

33

ORF

B. napus SCL1

 

ES902868

3

673

4

34

ORF

A. thaliana SCL6-IV

Bna-miR171f

ES902868

4

676

3

21

ORF

A. thaliana SCL6-IV

 

ES907976

4

612

4

25

ORF

B. napus SCL1

Bna-miR171g

ES902868

4

676

3

23

ORF

A. thaliana SCL6-IV

 

ES907976

4

612

2

18

ORF

B. napus SCL1

Bna-miR172f

FG568924

4

488

2

20

ORF

B. napus APETALA2

 

EV197066

0

677

6

50

ORF

A. thaliana AP2-like protein (At2g28550)

 

DY025256

4

609

5

24

ORF

A. thaliana AP2-like protein (SMZ)

Bna-miR390a

EV220086

0

433

4

50

ORF

cytochrome P450 family 78, subfamily A

Bna-miR390d

EV220086

0

433

4

50

ORF

cytochrome P450 family 78, subfamily A

Bna-miR393

EL628991

2

246

10

17

ORF

A. thaliana auxin signaling F-box 3

 

EV176346

0

564

6

50

ORF

A. thaliana auxin signaling F-box 3

Bna-miR396a

ES923674

4

571

1

20

ORF

A. thaliana bHLH74 transcription factor

Bna-miR397b

ES906654

4

736

3

20

ORF

A. thaliana laccase-4 (IRX12)

 

EE460611

4

445

5

25

ORF

A. thaliana 60S ribosomal protein L15

Bna-miR399

EV157460

4

268

3

23

5UTR

A. lyrata PHO2/UBC24

 

CX281881

4

581

5

23

ORF

B. napus genes for ITS1, ITS2

Bna-miR408a

EE417826

4

457

2

20

ORF

A. thaliana peptide chain release factor 1

Bna-miR408b

EV075738

4

63

2

22

5UTR

A. thaliana plantacyanin

Bna-miR824

EV112524

0

319

4

50

ORF

A. thaliana MADS-box protein AGL16

miR1140b

EV217683

0

473

3

50

ORF

T. aestivum mRNA for glycosyltransferase

 

ES912747

0

119

1

50

ORF

A. thaliana two-component response regulator ARR8 (RR3)

Bna-miR2111b

EV221566

0

337

3

50

ORF

A. thaliana F-box/kelch-repeat protein

Most of the identified targets of the conserved B. napus miRNAs belong to diverse gene families of transcription factors, such as SPLs, ARFs, MYBs, NF-Y subunits, NAC-domain proteins, AP2-like factors, SCLs and MADS-box factors (Table 4). Many of these transcription factors are known to regulate diverse aspects of plant growth and development. For example, SPLs and AP2-like factors targeted by miR156 and miR172, respectively, have been shown to play an important role in phase changes (from juvenile to adult and from vegetative to the reproductive phase) in Arabidopsis[21]. Another important family of transcription factors is the MADS-box gene family,which is known to play a critical role in determining organ specificity during flower development in Arabidopsis[61]. One MADS-box gene (AtAGL16-like protein) was validated as a target for miR824 in B. napus (Table 4). MADS-box factors in B. napus have also been identified to play important roles in petal identity [62]. Similarly, three SCL6s targeted by miR171 play an important role in the regulation of shoot branch production in Arabidopsis[63]. Besides their possible involvement in plant development, miRNA targets identified in this study could also play fundamental roles in biotic and abiotic stress resistance in B. napus. NF-YA transcription factor genes were validated as targets of for miR169 family numbers. The AtNF-YA5 transcription factor, whose transcript is a target of miR169, has been implicated in drought stress responses in Arabidopsis[64]. Over-expression of a miR169-resistant AtNF-YA5 transgene significantly improves drought resistance by promoting stomatal closure under drought stress [64]. Furthermore, NF-YA factors in Petunia hybrida and Antirrhinum majus were validated to play important roles in floral organ identity [65]. NF- YA mRNAs were identified as targets of miR169 in B. napus (Table 4). In addition, laccases (enzymes involved in cell wall metabolism), plantacyanin-like proteins (involved in reproduction and seed setting) and F-box proteins involved in auxin-stimulated protein degradation (TIR1-like) were among the confirmed targets in B. napus (Table 4). Bna-miR1140 is a brassica-specific miRNA identified in our previous work.

Brassica-specific miRNA targets

Out of the 62 candidate new miRNAs, we only identified targets for only 17 miRNAs from the degradome sequencing data, plus 19 non-redundant target ESTs for candidate new brassica-specific miRNAs (Table 5). The abundance of the sequence tags for candidate brassica-specific miRNA target transcripts was plotted as a function of its position in the target genes (Additional file 7: Figure S4). We found there was no clear correlation between the expression level of the new miRNAs and their ability to target an mRNA for cleavage. We found candidate new miRNAs, such as Bna-miRC8, Bna-miRC13, Bna-miRC16, target mRNAs despite their low abundance and that target mRNAs. Consistent with our observation, no clear inverse correlations between the miRNA abundance and the cleavage frequency of target transcripts in Arabidopsis, rice and grapevine have been reported [53, 66, 67]. The new B. napus miRNAs target different genes with a wide variety of predicted functions. For instance, Bna-miRC16 targets chlorophyll a/b-binding protein gene, Bna-miRC20-1 targets photosystem II reaction center W-like protein gene and Bna-miRC21 targets photosystem I subunit XI gene, which are all involved in photosynthesis. Bna-miRC17a-1 targets cinnamyl alcohol dehydrogenase (CAD), which is likely to be involved in pathogen resistance and plant development [68]. Several specific targets, such as PPR-containing protein (required for normal plant development), ferrochelatase (involved in the heme biosynthetic pathway), GF14 omega proteins (potential roles in signaling), FtsH-like protease (an ATP-dependent zinc metalloprotease, related to photo-oxidative damage), glycosyl hydrolase family proteins (involved in plant cell wall architecture), Histone H2A and Histone H2B (involved in compacting DNA strands and chromatin regulation) were found as targets of rape-specific miRNAs in B. napus.
Table 5

Targets of candidate novel B. napus miRNAs

miRNA

Target EST

Category

Cleavage site

Reads mapping to the expected cleavage site

Percentage of expected reads to total reads mapped to the full length of EST (%)

Target site location

Target annotation

Bna-miRC2

EV142354

1

347

6

30

ORF

A. lyrata PPR-containing protein

Bna-miRC5-2

EV077017

0

326

4

50

ORF

A. lyrata exostosin family protein

Bna-miRC5-5Bna-miRC8

EV154449 FG574835

2 4

296 119

3 2

27 20

ORF 5UTR

A. thaliana alpha-tubulin 6

A. thaliana uncharacterized protein

Bna-miRC9

EV006535

0

256

2

50

ORF

A. thaliana auxin response factor 17

Bna-miRC13

EV022057

1

105

3

30

ORF

A. thaliana protein PIR

Bna-miRC15-1

EV191962

0

132

5

50

5UTR

A. thaliana ferrochelatase 1

Bna-miRC15-2

EV054423

2

615

3

33

ORF

A. lyrata ferrochelatase 1

Bna-miRC16

GR445128

3

416

4

27

ORF

B.juncea chlorophyll a/b-binding protein

Bna-miRC17a-1

CD818234

1

647

5

36

ORF

A. thaliana cinnamyl alcohol dehydrogenase

Bna-miRC18

GT074945

2

341

12

30

ORF

B.napus GF14 omega

Bna-miRC20-1

GR442870

0

578

3

50

ORF

A. thaliana histone H2B-like protein

 

ES987065

0

39

3

50

ORF

B.rapa photosystem II center W-like protein

Bna-miRC21

GT079646

2

65

3

33

5UTR

A. thaliana photosystem I subunit XI

Bna-miRC22a-1

EV044066

3

505

4

27

ORF

A. thaliana OST3/OST6 family protein

Bna-miRC26

EV077764

0

593

5

50

ORF

A.thaliana uncharacterized protein (AT3G51610)

Bna-miRC30

EV025081

2

329

4

36

ORF

Glycosyl hydrolase family protein

 

CX189212

3

269

3

27

ORF

Glycosyl hydrolase family protein

Bna-miRC47

ES992448

0

517

6

50

ORF

A. thaliana prenylcysteine oxidase (FCLY)

Verification of miRNA-guided cleavage of target mRNAs in B. napus

To verify the miRNA-guided target cleavage, RLM-RACE experiment was performed to detect cleavage product of 5 predicted Bna-miRNAs (primers were listed in Additional file 8: Table S4). As shown in Figure 4, all five of the Bna-miRNAs guided the target cleavage, often at the tenth nucleotide, or eleventh nucleotide (Figure 4). Thus, all the five predicted targets were found to have specific cleavage sites corresponding to the complementary sequences of miRNA.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-421/MediaObjects/12864_2011_Article_4797_Fig4_HTML.jpg
Figure 4

Mapping of the mRNA cleavage sites by RNA ligase-mediated 5RACE. Each top strand (black) depicts a miRNA complementary site, and each bottom strand depicts the miRNA (red). Watson-Crick pairing (vertical dashes) and G:U wobble pairing (circles) are indicated. The arrows indicate the 5 termini of mRNA fragments isolated from B. napus, as identified by cloned 5 RACE products, with the frequency of clones with the predicted cleavage site shown.

Conclusion

Here, 41 conserved data and 62 brassica-specific candidate miRNAs, including 20 miRNA* sequences were firstly identified. The sequencing results were further confirmed using stem-loop quantitative RT-PCR. The data will be updated to incorporate future miRBase updates. Our approach leads to the identification of several conserved and specific brassica miRNA targets in the available EST and genomic databases. 33 non-redundant mRNA targets for the conserved brassica miRNAs and 19 non-redundant mRNA targets of new brassica-specific miRNAs were identified. Validated miRNA targets in B. napus are potentially involved in diverse biological processes, including phase transitions, flowering, hormone signaling, photosynthesis, metabolism and biotic and abiotic stress resistance. Our data will be a useful resource for further investigation of the evolution of small RNA-based regulation in Brassica napus and related species. More importantly, this study will serve as a foundation for future research on the functional roles of miRNAs and their target genes in this important oil crop.

Methods

Plant materials

The dihaploid B. napus line, Westar, was grown in a glasshouse at 22–25°C with a 16 h light/8 h dark photoperiod and light intensity of >8000 lx. Leaves, petiole, stalk, roots and shoot apices from one month-old seedlings were collected and used for RNA extraction. A balanced RNA mix was used for small RNA expression and degradome analysis.

RNA extraction and preparation of sRNA and degradome cDNA libraries for Solexa sequencing

B. napus total RNA from different tissues was extracted using Trizol (Invitrogen). The total RNA balanced mix sample was size-fractionated by 15% denaturing polyacrylamide gel electrophoresis, after which the small RNA fragments of 18–28 nt were isolated from the gel and purified. The small RNA molecules were then ligated to a 5' adaptor and a 3' adaptor sequentially and then converted to cDNA by RT-PCR following the Illumina protocol. The concentration of the sample was adjusted to ~10 nM and a total of 10 μL was used in a sequencing reaction. The purified cDNA library was sequenced on an Illumina GAIIx.

The degradome library was constructed as previously described [51]. Similarly to the short RNA libraries, the degradome cDNA library was sequenced on an Illumina GAIIx.

Bioinformatic analyses

After masking adaptor sequences and removal of contaminated reads the clean reads were filtered for miRNA prediction with the ACGT101-miR-v3.5 package (LC Sciences, Houston, USA). First, reads that matched rRNA, tRNA, snRNA, snoRNA, repeat sequences, and other ncRNAs deposited in Rfam (http://www.sanger.ac.uk/software/Rfam)) [48] and the GenBank noncoding RNA database (http://www.ncbi.nlm.nih.gov/) were discarded. The retained 15–26 nt reads were mapped onto the the genome and ESTs of Brassica napus, Brassica rapa and Brassica oleracea using MapMi software under default parameters. Sequences with up to two mismatches were retained for miRNA prediction. After rigorous screening, all retained sequences of 15–26 nt with three or more copies in frequency were considered as potential miRNAs. We then attempted to align the predicted miRNAs to all rape known mature miRNA sequences in miRBase Version 17.0 [48] to identify novelty. Finally, Secondary structure prediction of individual miRNA was performed by MFOLD software (Version 2.38, http://mfold.rna.albany.edu/?q=mfold/RNA-Folding-Form) using the default folding conditions. [69].

The degradome analysis and the classification of target categories were performed using CleaveLand 2.0 [60]. Small RNA targets prediction was run against the transcriptome of interest. The alignment scores (using the [70] rubric) for each hit up to a user-defined cutoff were calculated, full RNA-RNA alignments were printed, and the 'cleavage site' associated with each prediction was also calculated. The cleavage site is simply the 10th nt of complementarity to the aligned small RNA. For randomized queries, no alignments were retained. However, concise records of each predicted target for the random queries were retained, including the predicted cleavage sites.

End-point and SYBR Green I real-time PCR assays of B. napus miRNAs

End-point and Real-time looped RT-PCR [57] were used to validate and measure the levels of B. napus miRNA. Stem–loop RT primers, universal reverse primer and miRNA-specific forward primers for Bna-miR159, Bna-miR159b, Bna-miR160a, Bna-miR162a, Bna-miR165a, Bna-miR166e, Bna-miR167f, Bna-miR169a, Bna-miR171a*, Bna-miR390d, Bna-miR400, Bna-miR1140b, Bna-miRC2, Bna-miRC5-1, Bna-miRC5-6, Bna-miRC9, Bna-miRC17a-1, Bna-miRC18, Bna-miRC21, Bna-miRC22a-1, Bna-miRC30and Bna-miRC45 were designed according to Varkonyi-Gasic et al.[57]. (Additional file 4: Table S1). 1 μg of total RNA was reverse-transcribed to cDNA using ReverTra Ace (TOYOBO, Osaka, Japan). Stem-loop pulsed reverse transcription and end-point PCR was performed according to Varkonyi-Gasic et al. [57]. Advantage 2 PCR Polymerase Mix (Clontech, Mountain View, CA, USA) was used to perform end-point PCR. qRT-PCR was performed using SYBR Premix Ex TaqTM of TaKaRa (TaKaRa Code: DRR041A) on an Applied Biosystems 7500 thermocycler (Applied Biosystems, Foster City, CA, USA). All reactions were run in triplicate. After the reaction, the threshold cycle (Ct) was determined using default threshold settings. The Ct is defined as the fractional cycle number at which the fluorescence passes the fixed threshold.

Modified 5′ RNA ligase-mediated RACE for the mapping of mRNA cleavage sites

Total RNA from different tissues was extracted using Trizol (Invitrogen). Poly(A) + mRNA was purified from all kinds of pooled tissue RNA using the PolyA kit (Promega, Madison, WI), according to manufacturer’s instructions. A small RNA adapter (5′GUUCAGAGUUCUACAGUCCGACGAUC- 3) was ligated to Poly(A) + mRNA. A modified procedure for 5′ RNA ligase-mediated RACE (RLM-5 RACE) was followed with the 5-Full RACE Kit (TaKaRa, Dalian), according to manufacturer’s instructions. Nested PCR amplifications were performed using the 5 small RNA nested primer (5 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA 3) and gene-specific nested primers (Additional file 8: Table S4). The amplification products were gel purified, cloned, and sequenced, and at least 10 independent clones were sequenced.

Notes

Declarations

Acknowledgements

This work was supported by the National Key Basic Research Program (Grant No. 2010CB125903) and National Natural Science Foundation of China (Grant No. 31270318). We gratefully acknowledge the English language editing from Dr. Mingbo Wang.

Authors’ Affiliations

(1)
College of Agriculture/Key Laboratory of Oasis Ecology Agriculture of BINTUAN, Shihezi University
(2)
Biotechnology Research Institute, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences
(3)
Crops institute, Gansu Academy of Agricultural Sciences

References

  1. Schwach F, Moxon S, Moulton V, Dalmay T: Deciphering the diversity of small RNAs in plants: the long and short of it. Brief Funct Genomic Proteomic. 2009, 8 (6): 472-481. 10.1093/bfgp/elp024.View ArticlePubMedGoogle Scholar
  2. Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
  3. Chuck G, Candela H, Hake S: Big impacts by small RNAs in plant development. Curr Opin Plant Biol. 2009, 12 (1): 81-86. 10.1016/j.pbi.2008.09.008.View ArticlePubMedGoogle Scholar
  4. Dugas DV, Bartel B: MicroRNA regulation of gene expression in plants. Curr Opin Plant Biol. 2004, 7 (5): 512-520. 10.1016/j.pbi.2004.07.011.View ArticlePubMedGoogle Scholar
  5. Kidner CA, Martienssen RA: The developmental role of microRNA in plants. Curr Opin Plant Biol. 2005, 8 (1): 38-44. 10.1016/j.pbi.2004.11.008.View ArticlePubMedGoogle Scholar
  6. Kurihara Y, Watanabe Y: Arabidopsis micro-RNA biogenesis through Dicer-like 1 protein functions. Proc Natl Acad Sci U S A. 2004, 101 (34): 12753-12758. 10.1073/pnas.0403115101.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Mallory AC, Elmayan T, Vaucheret H: MicroRNA maturation and action–the expanding roles of ARGONAUTEs. Curr Opin Plant Biol. 2008, 11 (5): 560-566. 10.1016/j.pbi.2008.06.008.View ArticlePubMedGoogle Scholar
  8. Brodersen P, Sakvarelidze-Achard L, Bruun-Rasmussen M, Dunoyer P, Yamamoto YY, Sieburth L, Voinnet O: Widespread translational inhibition by plant miRNAs and siRNAs. Science. 2008, 320 (5880): 1185-1190. 10.1126/science.1159151.View ArticlePubMedGoogle Scholar
  9. Llave C, Kasschau KD, Rector MA, Carrington JC: Endogenous and silencing-associated small RNAs in plants. Plant Cell. 2002, 14 (7): 1605-1619. 10.1105/tpc.003210.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Rhoades MW, Reinhart BJ, Lim LP, Burge CB, Bartel B, Bartel DP: Prediction of plant microRNA targets. Cell. 2002, 110 (4): 513-520. 10.1016/S0092-8674(02)00863-2.View ArticlePubMedGoogle Scholar
  11. Tang G, Reinhart BJ, Bartel DP, Zamore PD: A biochemical framework for RNA silencing in plants. Genes Dev. 2003, 17 (1): 49-63. 10.1101/gad.1048103.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science. 2001, 294 (5543): 853-858. 10.1126/science.1064921.View ArticlePubMedGoogle Scholar
  13. Lau NC, Lim LP, Weinstein EG, Bartel DP: An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science. 2001, 294 (5543): 858-862. 10.1126/science.1065062.View ArticlePubMedGoogle Scholar
  14. Lee RC, Ambros V: An extensive class of small RNAs in Caenorhabditis elegans. Science. 2001, 294 (5543): 862-864. 10.1126/science.1065329.View ArticlePubMedGoogle Scholar
  15. Arazi T, Talmor-Neiman M, Stav R, Riese M, Huijser P, Baulcombe DC: Cloning and characterization of micro-RNAs from moss. Plant J. 2005, 43 (6): 837-848. 10.1111/j.1365-313X.2005.02499.x.View ArticlePubMedGoogle Scholar
  16. Axtell MJ, Bartel DP: Antiquity of microRNAs and their targets in land plants. Plant Cell. 2005, 17 (6): 1658-1673. 10.1105/tpc.105.032185.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Chuck G, Meeley R, Hake S: Floral meristem initiation and meristem cell fate are regulated by the maize AP2 genes ids1 and sid1. Development. 2008, 135 (18): 3013-3019. 10.1242/dev.024273.View ArticlePubMedGoogle Scholar
  18. Chuck G, Meeley R, Irish E, Sakai H, Hake S: The maize tasselseed4 microRNA controls sex determination and meristem cell fate by targeting Tasselseed6/indeterminate spikelet1. Nat Genet. 2007, 39 (12): 1517-1521. 10.1038/ng.2007.20.View ArticlePubMedGoogle Scholar
  19. Chuck G, Whipple C, Jackson D, Hake S: The maize SBP-box transcription factor encoded by tasselsheath4 regulates bract development and the establishment of meristem boundaries. Development. 2010, 137 (8): 1243-1250. 10.1242/dev.048348.View ArticlePubMedGoogle Scholar
  20. Wang JW, Czech B, Weigel D: miR156-regulated SPL transcription factors define an endogenous flowering pathway in Arabidopsis thaliana. Cell. 2009, 138 (4): 738-749. 10.1016/j.cell.2009.06.014.View ArticlePubMedGoogle Scholar
  21. Wu G, Park MY, Conway SR, Wang JW, Weigel D, Poethig RS: The sequential action of miR156 and miR172 regulates developmental timing in Arabidopsis. Cell. 2009, 138 (4): 750-759. 10.1016/j.cell.2009.06.031.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Kim JH, Woo HR, Kim J, Lim PO, Lee IC, Choi SH, Hwang D, Nam HG: Trifurcate feed-forward regulation of age-dependent cell death involving miR164 in Arabidopsis. Science. 2009, 323 (5917): 1053-1057. 10.1126/science.1166386.View ArticlePubMedGoogle Scholar
  23. Lim PO, Lee IC, Kim J, Kim HJ, Ryu JS, Woo HR, Nam HG: Auxin response factor 2 (ARF2) plays a major role in regulating auxin-mediated leaf longevity. J Exp Bot. 2010, 61 (5): 1419-1430. 10.1093/jxb/erq010.PubMed CentralView ArticlePubMedGoogle Scholar
  24. Schommer C, Palatnik JF, Aggarwal P, Chetelat A, Cubas P, Farmer EE, Nath U, Weigel D: Control of jasmonate biosynthesis and senescence by miR319 targets. PLoS Biol. 2008, 6 (9): e230-10.1371/journal.pbio.0060230.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Allen RS, Li J, Alonso-Peral MM, White RG, Gubler F, Millar AA: MicroR159 regulation of most conserved targets in Arabidopsis has negligible phenotypic effects. Silence. 2010, 1 (1): 18-10.1186/1758-907X-1-18.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Alonso-Peral MM, Li J, Li Y, Allen RS, Schnippenkoetter W, Ohms S, White RG, Millar AA: The microRNA159-regulated GAMYB-like genes inhibit growth and promote programmed cell death in Arabidopsis. Plant Physiol. 2010, 154 (2): 757-771. 10.1104/pp.110.160630.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Chitwood DH, Nogueira FT, Howell MD, Montgomery TA, Carrington JC, Timmermans MC: Pattern formation via small RNA mobility. Genes Dev. 2009, 23 (5): 549-554. 10.1101/gad.1770009.PubMed CentralView ArticlePubMedGoogle Scholar
  28. Donner TJ, Sherr I, Scarpella E: Regulation of preprocambial cell state acquisition by auxin signaling in Arabidopsis leaves. Development. 2009, 136 (19): 3235-3246. 10.1242/dev.037028.View ArticlePubMedGoogle Scholar
  29. Rodriguez RE, Mecchia MA, Debernardi JM, Schommer C, Weigel D, Palatnik JF: Control of cell proliferation in Arabidopsis thaliana by microRNA miR396. Development. 2010, 137 (1): 103-112. 10.1242/dev.043067.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Scarpella E, Barkoulas M, Tsiantis M: Control of leaf and vein development by auxin. Cold Spring Harb Perspect Biol. 2010, 2 (1): a001511-10.1101/cshperspect.a001511.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Todesco M, Rubio-Somoza I, Paz-Ares J, Weigel D: A collection of target mimics for comprehensive analysis of microRNA function in Arabidopsis thaliana. PLoS Genet. 2010, 6 (7): e1001031-10.1371/journal.pgen.1001031.PubMed CentralView ArticlePubMedGoogle Scholar
  32. Wollmann H, Mica E, Todesco M, Long JA, Weigel D: On reconciling the interactions between APETALA2, miR172 and AGAMOUS with the ABC model of flower development. Development. 2010, 137 (21): 3633-3642. 10.1242/dev.036673.PubMed CentralView ArticlePubMedGoogle Scholar
  33. Yan J, Cai X, Luo J, Sato S, Jiang Q, Yang J, Cao X, Hu X, Tabata S, Gresshoff PM, et al: The REDUCED LEAFLET genes encode key components of the trans-acting small interfering RNA pathway and regulate compound leaf and flower development in Lotus japonicus. Plant Physiol. 2010, 152 (2): 797-807. 10.1104/pp.109.140947.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Yant L, Mathieu J, Dinh TT, Ott F, Lanz C, Wollmann H, Chen X, Schmid M: Orchestration of the floral transition and floral development in Arabidopsis by the bifunctional transcription factor APETALA2. Plant Cell. 2010, 22 (7): 2156-2170. 10.1105/tpc.110.075606.PubMed CentralView ArticlePubMedGoogle Scholar
  35. Zhao L, Kim Y, Dinh TT, Chen X: miR172 regulates stem cell fate and defines the inner boundary of APETALA3 and PISTILLATA expression domain in Arabidopsis floral meristems. Plant J. 2007, 51 (5): 840-849. 10.1111/j.1365-313X.2007.03181.x.PubMed CentralView ArticlePubMedGoogle Scholar
  36. Gutierrez L, Bussell JD, Pacurar DI, Schwambach J, Pacurar M, Bellini C: Phenotypic plasticity of adventitious rooting in Arabidopsis is controlled by complex regulation of AUXIN RESPONSE FACTOR transcripts and microRNA abundance. Plant Cell. 2009, 21 (10): 3119-3132. 10.1105/tpc.108.064758.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Krouk G, Lacombe B, Bielach A, Perrine-Walker F, Malinska K, Mounier E, Hoyerova K, Tillard P, Leon S, Ljung K, et al: Nitrate-regulated auxin transport by NRT1.1 defines a mechanism for nutrient sensing in plants. Dev Cell. 2010, 18 (6): 927-937. 10.1016/j.devcel.2010.05.008.View ArticlePubMedGoogle Scholar
  38. Marin E, Jouannet V, Herz A, Lokerse AS, Weijers D, Vaucheret H, Nussaume L, Crespi MD, Maizel A: miR390, Arabidopsis TAS3 tasiRNAs, and their AUXIN RESPONSE FACTOR targets define an autoregulatory network quantitatively regulating lateral root growth. Plant Cell. 2010, 22 (4): 1104-1117. 10.1105/tpc.109.072553.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Moreno-Risueno MA, Van Norman JM, Moreno A, Zhang J, Ahnert SE, Benfey PN: Oscillating gene expression determines competence for periodic Arabidopsis root branching. Science. 2010, 329 (5997): 1306-1311. 10.1126/science.1191937.PubMed CentralView ArticlePubMedGoogle Scholar
  40. Rubio-Somoza I, Cuperus JT, Weigel D, Carrington JC: Regulation and functional specialization of small RNA-target nodes during plant development. Curr Opin Plant Biol. 2009, 12 (5): 622-627. 10.1016/j.pbi.2009.07.003.View ArticlePubMedGoogle Scholar
  41. Vidal EA, Araus V, Lu C, Parry G, Green PJ, Coruzzi GM, Gutierrez RA: Nitrate-responsive miR393/AFB3 regulatory module controls root system architecture in Arabidopsis thaliana. Proc Natl Acad Sci U S A. 2010, 107 (9): 4477-4482. 10.1073/pnas.0909571107.PubMed CentralView ArticlePubMedGoogle Scholar
  42. Yoon EK, Yang JH, Lim J, Kim SH, Kim SK, Lee WS: Auxin regulation of the microRNA390-dependent transacting small interfering RNA pathway in Arabidopsis lateral root development. Nucleic Acids Res. 2010, 38 (4): 1382-1391. 10.1093/nar/gkp1128.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Floyd SK, Bowman JL: Gene regulation: ancient microRNA target sequences in plants. Nature. 2004, 428 (6982): 485-486. 10.1038/428485a.View ArticlePubMedGoogle Scholar
  44. Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, et al: High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One. 2007, 2 (2): e219-10.1371/journal.pone.0000219.PubMed CentralView ArticlePubMedGoogle Scholar
  45. Moxon S, Jing R, Szittya G, Schwach F: Rusholme Pilcher RL, Moulton V, Dalmay T: Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening. Genome Res. 2008, 18 (10): 1602-1609. 10.1101/gr.080127.108.PubMed CentralView ArticlePubMedGoogle Scholar
  46. Rajagopalan R, Vaucheret H, Trejo J, Bartel DP: A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 2006, 20 (24): 3407-3425. 10.1101/gad.1476406.PubMed CentralView ArticlePubMedGoogle Scholar
  47. Szittya G, Moxon S, Santos DM, Jing R, Fevereiro MP, Moulton V, Dalmay T: High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families. BMC Genomics. 2008, 9: 593-10.1186/1471-2164-9-593.PubMed CentralView ArticlePubMedGoogle Scholar
  48. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, 36: 154-158. 10.1093/nar/gkn221.View ArticleGoogle Scholar
  49. Jones-Rhoades MW, Bartel DP: Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell. 2004, 14 (6): 787-799. 10.1016/j.molcel.2004.05.027.View ArticlePubMedGoogle Scholar
  50. Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ: Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol. 2008, 18 (10): 758-762. 10.1016/j.cub.2008.04.042.PubMed CentralView ArticlePubMedGoogle Scholar
  51. German MA, Pillay M, Jeong DH, Hetawal A, Luo S, Janardhanan P, Kannan V, Rymarquis LA, Nobuta K, German R, et al: Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol. 2008, 26 (8): 941-946. 10.1038/nbt1417.View ArticlePubMedGoogle Scholar
  52. Gregory BD, O'Malley RC, Lister R, Urich MA, Tonti-Filippini J, Chen H, Millar AH, Ecker JR: A link between RNA metabolism and silencing affecting Arabidopsis development. Dev Cell. 2008, 14 (6): 854-866. 10.1016/j.devcel.2008.04.005.View ArticlePubMedGoogle Scholar
  53. Pantaleo V, Szittya G, Moxon S, Miozzi L, Moulton V, Dalmay T, Burgyan J: Identification of grapevine microRNAs and their targets using high-throughput sequencing and degradome analysis. Plant J. 2010, 62 (6): 960-976.PubMedGoogle Scholar
  54. Zhou M: GL, Li PC, Song XW, Wei LY, Chen ZY, Cao XF: Degradome sequencing reveals endogenous small RNA targets in rice (Oryza sativa L. ssp. indica). Front Biol. 2010, 5 (1): 67-90. 10.1007/s11515-010-0007-8.View ArticleGoogle Scholar
  55. Jones-Rhoades MW, Bartel DP, Bartel B: MicroRNAS and their regulatory roles in plants. Annu Rev Plant Biol. 2006, 57: 19-53. 10.1146/annurev.arplant.57.032905.105218.View ArticlePubMedGoogle Scholar
  56. Yao Y, Guo G, Ni Z, Sunkar R, Du J, Zhu JK, Sun Q: Cloning and characterization of microRNAs from wheat (Triticum aestivum L.). Genome Biol. 2007, 8 (6): 96-10.1186/gb-2007-8-6-r96.View ArticleGoogle Scholar
  57. Varkonyi-Gasic E, Wu R, Wood M, Walton EF, Hellens RP: Protocol: a highly sensitive RT-PCR method for detection and quantification of microRNAs. Plant Methods. 2007, 3: 12-10.1186/1746-4811-3-12.PubMed CentralView ArticlePubMedGoogle Scholar
  58. Wang L, Wang MB, Tu JX, Helliwell CA, Waterhouse PM, Dennis ES, Fu TD, Fan YL: Cloning and characterization of microRNAs from Brassica napus. FEBS Lett. 2007, 581 (20): 3848-3856. 10.1016/j.febslet.2007.07.010.View ArticlePubMedGoogle Scholar
  59. Xie FL, Huang SQ, Guo K, Xiang AL, Zhu YY, Nie L, Yang ZM: Computational identification of novel microRNAs and targets in Brassica napus. FEBS Lett. 2007, 581 (7): 1464-1474. 10.1016/j.febslet.2007.02.074.View ArticlePubMedGoogle Scholar
  60. Addo-Quaye C, Miller W, Axtell MJ: CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics. 2009, 25 (1): 130-131. 10.1093/bioinformatics/btn604.PubMed CentralView ArticlePubMedGoogle Scholar
  61. Kaufmann K, Muino JM, Jauregui R, Airoldi CA, Smaczniak C, Krajewski P, Angenent GC: Target genes of the MADS transcription factor SEPALLATA3: integration of developmental and hormonal pathways in the Arabidopsis flower. PLoS Biol. 2009, 7 (4): e1000090-PubMed CentralView ArticlePubMedGoogle Scholar
  62. Zhang Y, Wang X, Zhang W, Yu F, Tian J, Li D, Guo A: Functional analysis of the two Brassica AP3 genes involved in apetalous and stamen carpelloid phenotypes. PLoS One. 2011, 6 (6): e20930-10.1371/journal.pone.0020930.PubMed CentralView ArticlePubMedGoogle Scholar
  63. Wang L, Mai YX, Zhang YC, Luo Q, Yang HQ: MicroRNA171c-targeted SCL6-II, SCL6-III, and SCL6-IV genes regulate shoot branching in Arabidopsis. Mol Plant. 2010, 3 (5): 794-806. 10.1093/mp/ssq042.View ArticlePubMedGoogle Scholar
  64. Li WX, Oono Y, Zhu J, He XJ, Wu JM, Iida K, Lu XY, Cui X, Jin H, Zhu JK: The Arabidopsis NFYA5 transcription factor is regulated transcriptionally and posttranscriptionally to promote drought resistance. Plant Cell. 2008, 20 (8): 2238-2251. 10.1105/tpc.108.059444.PubMed CentralView ArticlePubMedGoogle Scholar
  65. Cartolano M, Castillo R, Efremova N, Kuckenberg M, Zethof J, Gerats T, Schwarz-Sommer Z, Vandenbussche M: A conserved microRNA module exerts homeotic control over Petunia hybrida and Antirrhinum majus floral organ identity. Nat Genet. 2007, 39 (7): 901-905. 10.1038/ng2056.View ArticlePubMedGoogle Scholar
  66. Jiao Y, Riechmann JL, Meyerowitz EM: Transcriptome-wide analysis of uncapped mRNAs in Arabidopsis reveals regulation of mRNA degradation. Plant Cell. 2008, 20 (10): 2571-2585. 10.1105/tpc.108.062786.PubMed CentralView ArticlePubMedGoogle Scholar
  67. Li YF, Zheng Y, Addo-Quaye C, Zhang L, Saini A, Jagadeeswaran G, Axtell MJ, Zhang W, Sunkar R: Transcriptome-wide identification of microRNA targets in rice. Plant J. 2010, 62 (5): 742-759. 10.1111/j.1365-313X.2010.04187.x.View ArticlePubMedGoogle Scholar
  68. Sattler SE, Saathoff AJ, Haas EJ, Palmer NA, Funnell-Harris DL, Sarath G, Pedersen JF: A nonsense mutation in a cinnamyl alcohol dehydrogenase gene is responsible for the Sorghum brown midrib6 phenotype. Plant Physiol. 2009, 150 (2): 584-595. 10.1104/pp.109.136408.PubMed CentralView ArticlePubMedGoogle Scholar
  69. Guerra-Assuncao JA, Enright AJ: MapMi: automated mapping of microRNA loci. BMC Bioinformatics. 2010, 11: 133-10.1186/1471-2105-11-133.PubMed CentralView ArticlePubMedGoogle Scholar
  70. Allen E, Xie Z, Gustafson AM, Carrington JC: microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell. 2005, 121 (2): 207-221. 10.1016/j.cell.2005.04.004.View ArticlePubMedGoogle Scholar

Copyright

© Xu et al.; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement