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  • Research
  • Open Access

Genotype- and tissue-specific miRNA profiles and their targets in three alfalfa (Medicago sativa L) genotypes

  • 1,
  • 2,
  • 2,
  • 3,
  • 3,
  • 1,
  • 3,
  • 2, 4Email author and
  • 1Email author
BMC Genomics201819 (Suppl 10) :913

https://doi.org/10.1186/s12864-018-5280-y

  • Published:

Abstract

Background

Alfalfa (Medicago sativa L.) is a forage legume with significant agricultural value worldwide. MicroRNAs (miRNAs) are key components of post-transcriptional gene regulation and essentially regulate many aspects of plant growth and development. Although miRNAs were reported in alfalfa, their expression profiles in different tissues and the discovery of novel miRNAs as well as their targets have not been described in this plant species.

Results

To identify tissue-specific miRNA profiles in whole plants, shoots and roots of three different alfalfa genotypes (Altet-4, NECS-141and NF08ALF06) were used. Small RNA libraries were generated and sequenced using a high-throughput sequencing platform. Analysis of these libraries enabled identification of100 miRNA families; 21 of them belong to the highly conserved families while the remaining 79 families are conserved at the minimum between M. sativa and the model legume and close relative, M. truncatula. The profiles of the six abundantly expressed miRNA families (miR156, miR159, miR166, miR319, miR396 and miR398) were relatively similar between the whole plants, roots and shoots of these three alfalfa genotypes. In contrast, robust differences between shoots and roots for miR160 and miR408 levels were evident, and their expression was more abundant in the shoots. Additionally, 17 novel miRNAs were identified and the relative abundance of some of these differed between tissue types. Further, the generation and analysis of degradome libraries from the three alfalfa genotypes enabled confirmation of 69 genes as targets for 31 miRNA families in alfalfa.

Conclusions

The miRNA profiles revealed both similarities and differences in the expression profiles between tissues within a genotype as well as between the genotypes. Among the highly conserved miRNA families, miR166 was the most abundantly expressed in almost all tissues from the three genotypes. The identification of conserved and novel miRNAs as well as their targets in different tissues of multiple genotypes increased our understanding of miRNA-mediated gene regulation in alfalfa and could provide valuable insights for practical research and plant improvement applications in alfalfa and related legume species.

Introduction

Alfalfa (Medicago sativa L.) is an important forage legume species with global adaptation, high forage quality and the capacity for harvesting biomass multiple times during the growing season. Alfalfa is an autotetraploid (2n = 4x = 32), perennial outcrossing species with high levels of genetic diversity in cultivated and non-cultivated populations. Besides its use as a forage, alfalfa also has potential crop for biofuel production [1]. Alfalfa has the capacity for symbiotic nitrogen fixation and can also contribute to reduce soil erosion [2, 3].

Endogenous non-coding RNAs of approximately 21–22 nucleotides represent plant miRNAs that silence gene expression by binding to complementary sequences of its target mRNA at the post-transcriptional level. Such targeting results in mRNA cleavage and degradation or repression of translation, with the former being more prevalent in plants [47]. The miRNA analyses in different plant species highlight the important regulatory roles of miRNAs in multiple organs (roots, stems, leaves and flowers), differentiation and development, leaf polarity, transition from juvenile to vegetative stages and vegetative to reproductive phases, and regulation of plant responses to biotic and abiotic stresses [810].

Several investigations have shown that plant miRNAs can be classified into conserved and novel lineage- or species-specific miRNAs. Conserved miRNAs and their corresponding target genes are commonly found in all or most angiosperms, with some also being described in gymnosperms as well as primitive land plants such as ferns [11, 12]. However, miRNA analysis in several legumes including M. truncatula, soybean (Glycine max L), chickpea (Cicer arietinum L.), common bean (Phaseolus vulgaris), and Lotus japonicus indicate the presence of miRNAs that seem to be specific to certain legumes and there could have important gene regulatory roles [1319]. Although recent attempts were made to report miRNAs from alfalfa (M. sativa) [2022], these do not include the discovery of novel miRNAs, and most importantly, the miRNA target genes have not been confirmed in this legume species. Understanding miRNAs and their target gene regulation in various tissues can provide further insights into the miRNA target networks operating in a tissue-specific manner in alfalfa.

In order to identify conserved miRNAs as well as novel miRNAs from alfalfa, we constructed and sequenced small RNA libraries from whole clonally propagated plants, roots and shoots of three alfalfa genotypes (Altet-4, NECS-141 and NF08ALF06). The sequenced reads were mapped to known miRNAs in M. truncatula, deposited in the miRBase to identify and annotate the miRNAs in alfalfa. Degradome libraries were constructed and sequenced from these three genotypes to characterize the miRNA gene targets.

Materials and methods

Plant materials and growth conditions

Three alfalfa genotypes NECS-141, Altet-4 and NF08ALF06 were evaluated in this study. NECS-141 is the genotype being used to sequence the tetraploid alfalfa genome [23]. Altet-4 is an aluminum tolerant genotype used to develop a mapping population [24]. NF08ALF06 is a commercial breeding line with good agronomic performance (Forage Genetics International). The three alfalfa genotypes (NECS-141, Altet-4 and NF08ALF06) were clonally propagated and grown in tissue culture. After 13 d of growth in rooting media, these were transferred to medium at pH 7 for 96 h as previously described [25]. The rooting media contains 0.55 g/L Murashige & Skoog Basal Medium with Vitamins (PhytoTechnology #M519), 1 ml Plant Preservative Mixture, PPM (PhytoTechnology), adjust the pH to 5.8, and add 12 g/L Gelzan. The plants were placed in a Conviron growth chamber (24 °C, 18 h /6 h day/night cycle, 100 μmol light intensity) for root development and growth. An additional 20 clonally propagated plants of these genotypes were grown in a Conviron growth chamber as previously described and used to evaluate the tissue-specific expression of the miRNAs. Tissue samples were harvested and immediately flash frozen in liquid nitrogen and stored at − 80 °C.

Small RNA library construction and sequencing

Total RNA was isolated from the whole plants, roots and shoots of three alfalfa genotypes using TRIzol ® Reagent (Invitrogen), according to the manufacturer’s instructions. The quality of total RNA was monitored on 1% agarose gel and their concentrations were measured using Nanodrop spectrophotometer. Small RNA libraries were generated as described previously [26] by following the protocol described for the Illumina Truseq® Small RNA Preparation kit (Illumina, San Diego, USA). Briefly, 1 μg total RNA per sample was ligated sequentially with 5′ and 3’ RNA adaptors. The ligated products were converted into cDNAs and then amplified using PCR. The amplified products were sequenced using an Illumina Hiseq® Analyzer.

Identification of conserved and novel miRNAs

The raw sequencing reads were processed as follows: adaptor sequences were trimmed off from the raw reads to obtain small RNAs. These reads were then mapped to ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNAs (snRNA), and the aligned and mapped reads were not used for further analysis. The remaining reads were aligned to miRBase v 20 [27] to identify miRNAs in M. sativa. The reads with 100% sequence identity were designated as conserved miRNA homologs. To identify novel miRNAs, the presence of the miRNA-star (miRNA*) sequences coupled with the predictable hairpin-like structure for the precursor sequences were used.

Degradome library construction and analyses

Degradome libraries from the alfalfa genotypes NECS-141, Altet-4 and NF08ALF06 were constructed as previously described to identify potential target mRNAs [28]. Briefly, the cleaved 5′ monophosphate containing polyadenylated mRNA fragments were ligated to an RNA oligo-nucleotide adapter containing MmeI recognition site at its 3′ end. The ligated products were converted into cDNA using reverse transcriptase and the product was amplified using only 5 PCR cycles. The PCR product was eluted, digested with MmeI restriction enzyme and then ligated to a double-stranded DNA adapter. The ligated product was again purified and amplified using 15 cycles of PCR. The final PCR product was sequenced. The reads were processed for quality and then aligned to the transcriptome assembly of M. truncatula to identify potential miRNA targets using the SeqTar pipeline [29].

Results and discussion

The analyses of small RNA libraries

High-throughput sequencing has been used to identify miRNAs and their target mRNAs in plants [15, 30, 31]. To catalogue conserved and novel miRNAs in alfalfa, a total of eight small RNA libraries from the whole plants, roots and shoots of Altet-4, NECS-141 and NF08ALF06 genotypes were constructed and sequenced. After removal of the adapter sequences and low-quality reads, the total reads ranging between 11 to 42 million, and unique reads ranging between 1.8 to 8.5 million reads from these nine libraries were obtained (Table 1). However, the quality of the small RNA library generated from the shoots of NF08ALF06 did not meet the threshold criteria, therefore only NECS-141 and Altet-4 were used for the miRNA analyses of shoot tissues.
Table 1

The mapping of total and unique reads obtained from different small RNA libraries

 

Altet-4 whole plants

NECS-141 whole plants

NF08ALF06 whole plants

Altet-4 Roots

NECS-141 Roots

NF08ALF06 Roots

Altet-4 Shoots

NCES-141 Shoots

Total reads

Unique reads

Total reads

Unique reads

Total reads

Unique reads

Total reads

Unique reads

Total reads

Unique reads

Total reads

Unique reads

Total reads

Unique reads

Total reads

Unique reads

cdna

6,858,719

336,266

6,197,308

430,819

8,142,985

493,929

9,352,477

276,276

19,665,339

732,738

8,930,063

352,167

17,117,689

909,491

10,943,411

669,572

ncRNAs

6,810,937

261,134

5,666,067

213,396

7,722,727

284,785

9,633,993

271,496

18,454,661

272,596

8,834,028

237,827

14,718,199

289,087

7,855,629

128,514

pre-miRBase

567,518

3182

943,976

3888

1,162,233

4005

147,326

2409

1,102,879

5780

426,327

3213

2,520,492

7051

3,840,771

6449

repeats

5,451,840

162,552

4,218,992

148,297

5,756,349

183,418

8,267,063

158,310

15,744,403

180,030

7,536,403

146,043

10,855,687

192,377

3,798,312

104,708

genome

8,951,430

1,142,594

9,878,838

2,398,705

11,387,413

2,053,582

11,557,742

784,140

29,143,549

5,078,322

11,588,832

1,488,546

28,744,231

5,246,027

29,192,098

5,834,731

total

12,008,892

2,343,120

11,645,217

3,348,188

15,733,102

3,739,163

14,377,336

1,860,736

33,335,201

6,947,622

14,378,859

2,708,737

42,196,888

8,564,218

34,441,313

7,748,996

Quantification of miRNA abundances between the genotypes and tissues was preceded by normalizing the expression levels of miRNA families to reads per ten million (RPTM). The normalized miRNA family read frequencies ranged between 1 to 552,267 RPTM for the whole plants, between 1 to 134,679 RPTM for the root samples, and 1 to 165,310 RPTM for the shoot samples (Table 2). The range of miRNA read frequencies varied slightly between the three genotypes. As expected, the most conserved miRNAs appeared to be the most abundantly expressed in all tissues and genotypes, with the exception of miR169, miR393, miR395 and miR172 which exhibited low abundances. Specifically, miR172 levels in roots and shoots of the three genotypes were extremely low and in most cases was below 20 RPTM (Table 2). The miRNA families with the lowest expression levels, and in some cases as low as 1 RPTM, were largely represented by the non-conserved miRNAs or miRNAs that have been reported exclusively from M. truncatula (miRBase) that include miR2601, miR2674, miR5207, miR5241, miR5243, miR5244, miR5255, miR5257, miR5269, miR5282, miR5289, miR5294, miR5296, miR5299, miR5561, miR5744, and miR7701 (Table 2). miR5207 is the only miRNA that was also reported from Gossypium raimondii (miRBase). The majority of the miRNA families identified are 21 nt long, although some cases including miR2601 and miR2603 were represented by 22 nucleotides. Further, a total of 23 miRNA families included between miR5267 to miR5299 were 24 nt long. The fact that these small RNAs were initially identified in M. truncatula (miRBase), and could be identified in several independent small RNA libraries from three different alfalfa genotypes (Table 2), suggests that these sequences and their associated processing are conserved between alfalfa and its close relative M. truncatula. However, their extremely low abundances coupled with their longer read lengths could also indicate that these may be 24-nt long siRNAs. Additional studies are needed to assess the precise nature of these small RNAs, i.e., miRNAs or siRNAs.
Table 2

Identified miRNA families and their frequencies (reads per ten million [RPTM]) in whole plants, roots and shoots of three alfalfa genotypes (miRNA-stars were marked in bold)

 

Whole plants

Roots

Shoots

Altet-4

NECS-141

NF08ALF06

Altet-4

NECS-141

NF08ALF06

Altet-4

NECS-141

miR156-5p

4712

7243

6436

1001

3466

3145

19,808

47,306

miR156-3p

3262

4012

2992

545

755

548

4634

6420

miR159-3p

6315

11,050

8484

3910

23,465

10,549

61,929

103,370

miR160-5p

225

417

351

20

277

113

3505

8706

miR162-3p

140

229

292

194

454

361

533

517

miR164-5p

108

275

306

6

77

57

48

431

miR166-3p

336,905

552,267

534,054

34,634

111,596

134,679

101,118

131,196

miR166-5p

544

960

614

228

508

438

800

1216

miR167-5p

218

470

722

107

240

357

699

1389

miR167-3p

2

1

0

0

0

0

0

0

miR168-5p

1121

1980

1691

735

2960

1317

3460

5049

miR168-3p

672

691

768

182

443

194

5550

5638

miR169-5p

19

34

35

47

55

35

46

59

miR169-3p

7

12

5

6

18

7

2

2

miR171-3p

51

120

232

44

238

316

60

85

miR171e-5p

26

39

44

22

37

42

7

6

miR172-3p

62

138

240

0

1

1

2

3

miR172-5p

3

8

20

1

1

2

2

2

miR319-3p

1631

3689

2101

1607

6281

3323

4330

10,864

miR319-5p

46

72

74

3

20

14

129

559

miR390-5p

95

410

318

86

656

234

121

382

miR393-5p

11

24

34

4

8

10

22

45

miR395-3p

3

8

7

12

13

7

2

0

miR396-5p

12,185

21,926

22,411

2835

14,549

8121

39,236

58,336

miR396-3p

250

437

437

76

312

188

323

356

miR397-5p

57

28

15

37

16

11

94

61

miR398a-5p

19

16

25

0

2

1

4

3

miR398-3p

3814

3223

2272

2101

4086

3176

35,538

26,478

miR399-3p

17

11

11

25

26

13

62

43

miR408-3p

2656

1301

1096

977

737

570

6380

2866

miR408-5p

17

7

12

12

14

8

55

35

miR482-3p

28

27

49

18

19

45

41

105

miR482-5p

7

10

10

11

19

13

9

12

miR530-5p

2

7

8

0

1

1

2

4

miR1507–3

963

1789

1701

881

1596

1230

1778

3349

miR1510-5p

1959

4278

3520

523

3505

1429

12,496

34,705

miR1510-3p

96

151

167

52

118

63

256

617

miR2111

47

20

10

44

15

42

278

22

miR2118

5607

11,948

16,134

106

610

307

79,977

165,310

miR2199

95

15

42

21

18

30

156

13

miR2585

57

7

74

28

1

22

239

10

miR2587

0

6

9

0

10

10

13

28

miR2590

15

41

42

23

55

25

109

177

miR2592

393

1350

395

119

1612

268

1224

1742

miR2601-5p

0

0

0

0

0

0

1

1

miR2603-5p

0

8

1

1

1

1

5

24

miR2629-5p

2

5

4

1

3

7

2

5

miR2632-5p

0

1

0

0

0

0

1

18

miR2634-3p

5

3

7

6

4

15

9

6

miR2643-3p

1502

2689

2106

382

1462

948

9682

24,971

miR2651-3p

27

52

22

4

21

7

40

49

miR2661-5p

3

4

5

2

4

5

13

9

miR2666-3p

0

21

0

0

14

0

0

29

miR2674-3p

0

0

1

0

0

0

0

0

miR2678-3p

2

6

4

0

4

4

4

12

miR4414-3p

2

4

4

0

1

1

3

7

miR4414-5p

1

3

4

1

1

0

5

7

miR5037-5p

4

3

13

3

8

24

2

4

miR5204-3p

4

10

6

3

28

17

6

10

miR5205-5p

7

22

14

0

6

6

15

6

miR5207-5p

0

0

0

0

0

1

0

1

miR5208-3p

2

1

1

0

0

0

1

1

miR5208d-5p

0

0

1

0

1

0

1

1

miR5211-5p

432

85

23

559

71

41

292

59

miR5213-5p

801

836

887

181

891

829

1397

1379

miR5214-3p

63

155

153

97

414

452

153

201

miR5225-5p

4

2

8

3

1

8

1

1

miR5230-5p

1

2

1

0

1

0

6

1

miR5231-5p

10

7

7

3

11

1

43

69

miR5232-5p

67

253

419

56

503

417

602

3964

miR5237-3p

2

2

0

0

2

1

6

4

miR5238-5p

2

0

2

1

2

1

0

0

miR5239-5p

347

269

430

16

52

72

622

773

miR5241-3p

0

0

0

0

0

0

0

1

miR5243-3p

0

0

0

0

0

0

0

1

miR5244-3p

0

1

1

0

0

0

0

1

miR5248-5p

0

2

1

0

2

1

0

3

miR5255-3p

0

1

1

0

0

0

0

1

miR5257-5p

1

0

0

0

0

0

0

0

miR5261-3p

76

89

93

22

302

127

283

227

miR5266-5p

0

0

0

4

2

3

0

1

miR5267-5p

1

3

1

0

1

1

0

2

miR5269-3p

0

1

1

1

0

0

0

0

miR5271-5p

1

1

1

1

2

2

1

1

miR5272-5p

17

22

12

12

34

21

18

18

miR5273-3p

1

3

1

1

3

1

4

2

miR5277-3p

60

108

62

75

99

48

16

20

miR5279-5p

3

19

13

1

16

8

8

7

miR5281-3p

29

47

29

35

69

18

141

150

miR5282-3p

0

0

0

0

0

0

1

0

miR5284-3p

20

52

50

4

14

17

10

23

miR5285-5p

0

0

1

1

1

0

2

3

miR5286-3p

2

0

2

1

3

2

3

2

miR5287-3p

6

10

14

8

9

4

17

19

miR5289-3p

0

1

0

1

0

0

0

0

miR5290-3p

0

5

1

1

2

1

2

6

miR5291-3p

0

1

1

0

3

1

0

1

miR5292-3p

16

35

21

6

34

21

53

82

miR5294-3p

0

0

1

0

0

0

0

0

miR5295-3p

9

29

13

3

15

9

7

6

miR5296-3p

1

0

0

0

0

0

0

0

miR5297-3p

0

1

2

1

0

1

1

1

miR5298-3p

4

4

1

4

0

1

3

15

miR5299-3p

0

1

0

0

1

1

0

0

miR5558-5p

539

1938

1820

220

415

412

1103

1276

miR5559-5p

7

3

0

0

0

0

8

5

miR5561-3p

5

14

18

0

5

5

4

5

miR5561-5p

0

0

0

1

0

0

0

0

miR5743-5p

19

113

6

0

1

1

70

398

miR5744-5p

0

0

0

0

0

1

0

0

miR5745-3p

28

39

41

69

144

171

126

113

miR5752-3p

0

4

0

0

1

0

8

11

miR5754-5p

0

6

19

0

1

3

2

41

miR7696-5p

0

1

1

0

1

0

0

1

miR7696-3p

174

95

253

40

138

184

1173

255

miR7701-3p

0

1

0

0

0

0

0

0

MicroRNA profiles in alfalfa plants, roots and shoots

A total of 100 known miRNA families were identified from the small RNA libraries of the three alfalfa genotypes (Table 2). Of these, 21 families were represented by the highly conserved miRNAs, whereas the remaining 79 families could be considered as Medicago-specific miRNA families. The identification of these 79 miRNA families in alfalfa was based on their expression in M. truncatula (miRbase), therefore, these are conserved at least between M. truncatula and alfalfa.

Among the highly conserved miRNA families, miR166 was the most highly expressed family in seven of the eight samples that were surveyed in this study. The only exception to this trend was observed in the shoots of NECS-141 in which the miR2118 family was the most abundant followed by the miR166 family. The miRNA families, miR396 and miR2118 represents the second and third most abundantly expressed in the whole plants, while miR159 and miR396 were the second and third most highly expressed miRNAs in roots. Several additional miRNA families including miR398, miR160, miR168, miR319, miR408, miR1510 and miR2643 were also highly expressed but miR169, miR171, miR393, miR397 and miR395 were expressed at relatively very low levels (Table 2). On the other hand, miR159, miR156, miR319, miR398 miR1507 and miR1510 were highly expressed but miR164, miR169, miR172, miR393, miR397, miR399 and miR482 were expressed at very low levels in roots of these genotypes. Interestingly, miR160 was not sequenced from the roots of three alfalfa genotypes.

Overall, the conserved miRNA families such as the miR156, miR159, miR166, miR168, miR319, miR396, miR398 and miR408 were more highly expressed in the plants, roots and shoots of all three alfalfa genotypes. Among the legume-specific families, miR1507, miR1510, miR2118, miR2592, miR2643, miR5213, miR5232, miR5558 and miR7696 (Table 2) were also abundant in all tissues of alfalfa genotypes. Conversely, some conserved miRNA families represented by miR169 and miR393 recorded very low abundances in all samples. Other notable differences between roots and shoots include relatively low expression levels of miR160, miR167, and miR408 in roots compared to the shoots of alfalfa genotypes (Table 2).

Several miRNA families including miR482, miR1507, miR2118, miR4416 are conserved in M. truncatula, soybean, chickpea (miRBase). These miRNA families are known to regulate NBS-LRR genes that are involved in pathogen resistance. The miRNA-guided cleavage on the NBS-LRR genes initiates the generation of phasiRNAs [16, 18, 32]. In alfalfa, miR482, miR1507 and miR2118 were detected in all three tissues (Table 2), but not miR4416. Both miR2118 and miR1507 families were more abundantly expressed in all tissues and genotypes compared with miR482 family. Remarkably, miR2118 was the top most highly expressed miRNA family in shoots of NECS-141. By contrast, miR2118 levels were very low in roots of three alfalfa genotypes. On the other hand, miR1507 family displayed approximately similar levels in three tissues of alfalfa genotypes.

The miRNA-star sequences corresponding to the 12 of the 21 highly conserved miRNA families were also recovered from almost all libraries (Table 2). Additionally, miRNA-stars for the miR1510, miR4414, miR5208, and miR7696 were also detected. Furthermore, the miRNA-star expression levels for miR156, miR166 and miR168 were very high (Table 2). Intriguingly, like miR168, miR168 star levels differed greatly between different tissue. In shoots of NECS-141, miR168 star levels were slightly more than that of miR168, while both in whole plants and roots, the star levels were approximately half of the levels of miR168.

miRNA diversity in alfalfa compared with other legumes

Several miRNA families are specifically reported from the leguminous plants such as the M. truncatula, Glycine max, Lotus japonicus, Phaseolus vulgaris, Cicer arietinum, Vigna unguiculata and Acacia auriculiformis [14, 16, 18, 19, 32, 33]. These lineage-specific miRNAs include miR1507, miR1508, miR1509, miR1510, miR1512, miR1514, miR1520, miR1521, miR2118, miR2086, miR2109, miR2199, miR4414, miR5213, miR5232, and miR5234 among others (miRBase). The majority of these were reported from M. truncatula and soybean, since these legume species have been the subject of multiple studies exploring small RNAs. Most of these legume-specific miRNAs were also identified in alfalfa and a few of them including miR1507, miR1510, miR2118, miR2592, miR2643, miR5211, miR5213, miR5214, miR5232, miR5239, miR5277, miR5558, and miR7696 were specifically highly expressed in all three genotypes (Table 2).

Identification of novel miRNAs from alfalfa

The sequencing of the small RNAs from multiple tissues of three different alfalfa genotypes would allow us to identify the novel miRNAs more confidently. Novel miRNA identification was dependent on sequencing of the miRNA complementary strand (miRNA-star) coupled with the predictable fold back structure for the primary miRNA transcript. Because a stable assembly of the tetraploid alfalfa genome sequence is not available, the small RNAs were mapped to the M. truncatula genome. Mapping of the small RNAs from the three alfalfa genotypes onto the M. truncatula genome enabled the identification of novel miRNAs more confidently because they have been sequenced from M. sativa and mapped on to the M. truncatula, suggesting their conservation between M. sativa and M. truncatula. Moreover, the novel miRNA identification in this study is more robust as it includes sequencing of these small RNAs from three different genotypes. We have identified a total of 17 novel miRNAs which have been sequenced from all of the three genotypes (Table 3 and Fig. 1). Among these, t50582913 was highly expressed followed by t50063038. In roots, t50582913 was highly expressed in NECS-141 and Altet-4 but not in NF08ALF06. In shoots, t50063038 was highly expressed followed by the t50582913 and t51235783.
Table 3

Identified novel miRNAs based on sequencing both 5′ and 3′ reads and the most abundant ones that is marked in bold denotes potential novel miRNA based on their greater abundances

miR-5p

miR-5p_seq

miR-3p

miR-3p_seq

Altet-4 Plants

NECS-141 Plants

NF08ALF06 Plants

Altet-4 Roots

NECS-141 Roots

NF08ALF06 Roots

Altet-4 Shoots

NECS-141 Shoots

t61680599

UUUCUUUGACUGGUUUUUGAAU

t21108041

CAAAAGCCUGUCAAUGAAAAUG

0

31

0

0

312

0

0

32

t46402976

UAGCAUCAAGCGUCGCGUCGAU

t28372577

CGACCCGAGGCUUAUGCGAUC

115

97

145

81

479

229

335

315

t59820880

UUGGCAGAAUCACGGUGUGCC

t29809748

CGGUGGCAUCGUGAUUUUGAC

0

6

25

1

6

8

1

47

t21870702

CAACUCGGUCCUUCUGUUAAC

t44359413

UAACAGAAGGACUGAGUUGCC

0

11

3

1

41

12

24

103

t62603216

UUUUCAAGUUGGUCCCUUACG

t44814359

UAAGGGACCAACUUGAAAACU

77

178

196

7

240

107

529

899

t8901469

ACCUGGAGACAGAGAUGCAAU

t45832108

UACGUCUCUGUCUUUCGGGUUG

1

55

28

2

222

28

6

247

t12927907

AGGAUAACAAUGUUGCAUAAG

t47767430

UAUGUAGCACUGUUUUUCUGA

13

43

85

14

273

147

83

262

t63076572

UUUUUAGAUACAUUGAAUAAU

t47960370

UAUUCAAUGUAUCUAAAAAG

10

10

14

4

40

2

208

177

t53501433

UGAUUAUUCUACUACCCGGACC

t50063038

UCCGGGUAGCAGAAUAAUCAUC

350

371

78

45

371

18

17,057

20,494

t12458129

AGCGGUUGGUACAAUGCAAUAu

t50582913

UCGCCUUGUACCAACCUACUGC

544

915

0

123

1148

0

881

1453

t40560414

GGUCCUGAUGUUUUUUAGAGC

t51235783

UCUCAAAGACAUAAGGAACCUC

19

281

0

24

762

0

269

1655

t55270980

UGUCUUUAGCUUCCGAAACAa

t55621674

UGUUCCGGUAGAUGAAGUCAC

4

4

0

2

23

0

24

40

t14211567

AGUUAAUUGUGUUGCAUGAGUU

t57726911

UUCAGCAACAUGAGUUAACUCA

17

26

60

3

48

22

42

50

t8194733

ACAUUUUAGAUUGUUGAGGAA

t27568341

CCUCAAUUAUCUAUUAUGUUU

0

0

3

0

3

6

6

0

t62313817

UUUGUUAAACAUUUGUUUCC

t311560

AAAACAAAUGUUUAGCUAAG

0

6

0

0

15

1

0

12

t55268921

UGUCUUGGUUUCAAAAAGAAGu

t52170136

UCUUUUUGCAAACCAACUCAAU

4

19

13

1

29

4

9

56

t51870988

UCUUAUUUUCGACAUUGCAAAG

t59475847

UUGCAGGUCGAGAAUAAAAUG

19

99

71

1

9

1

353

1072

Fig. 1
Fig. 1

The predicted foldback structures using the novel miRNA precursor sequences. a The fold-back structures for six novel miRNAs. b The distribution of small RNA reads on the precursors of the novel miRNAs depicted in Fig. 1a

Identification of miRNA targets in alfalfa

Although the alfalfa is one of the important legumes agronomically, the genome sequencing and annotations are not available so far. Due to this, studies have utilized the well-studied and closely related M. truncatula genome annotations as a model for alfalfa studies. The nucleotide identity for some genes was greater than 97% between M. sativa and M. truncatula [34]). Thus, using M. truncatula transcript annotations can facilitate identification of miRNA targets in alfalfa. We used SeqTar algorithm (Zheng et al., 2012) to identify miRNA targets by allowing a maximum of 4 mismatches between miRNAs and their potential target transcripts.

Previous studies have revealed that conserved miRNAs are strongly associated with the regulation of genes that encode transcription factors [35]. These transcription factors in turn regulate key developmental processes and pathways in plants. Degradome sequencing has been very effective in identifying plant miRNA targets. Besides identifying the conserved targets, this approach can also identify non-conserved targets for the conserved miRNAs [28, 36, 37]. Degradome sequencing was used in this study to identify the cleaved mRNA fragments corresponding to the miRNA recognition sites in all three alfalfa genotypes. Approximately 30 million degradome reads were obtained from the transcripts of each of the alfalfa genotypes (Table 4) and these reads were analysed using SeqTar program. In total, we have identified 69 targets for 31 miRNA families that included 16 highly conserved families (Table 5). With respect to the conserved miRNAs, 33 targets for 16 conserved miRNA families were identified (Table 5). The known targets for miR162, miR165/166, miR398 and miR399 families were not identified in this study. Although miR165/166 family is the most abundantly expressed as scored from their read frequencies in almost all libraries but the cleaved fragments from the HD-Zip target transcripts were not recovered from degradome libraries of alfalfa genotypes.
Table 4

Mapping of the reads obtained from the degradome libraries

Database

Altet-4

NECS-141

NF08ALF06

Total reads

Unique reads

Total reads

Unique reads

Total reads

Unique reads

M. truncatula genome

852,790

487,582

1,541,055

791,294

3,091,832

1,021230

M. sativa genome

1,488,681

957,866

2,691,763

1,435,659

4,591,130

1,877,041

Cds

770,970

426,278

1,436,059

727,330

2,928,098

933,425

ncRNA

231,076

22,907

186,813

18,014

1,305,681

36,585

Repeats

171,358

16,804

116,741

16,759

636,675

23,958

Pre-miRBase

34,631

837

35,979

1045

50,136

1192

Total

28,674,678

2,286,693

30,573,270

3,137,327

30,812,606

3,885,547

Table 5

miRNA targets identified in the degradome libraries generated from three alfalfa genotypes. #Mis. is number of mismatches on the miRNA complementary site; Valid reads is Reads corresponding to the expected cleavage site; Total reads is Total reads mapped to the cDNA of the gene; Percent is Percent reads at the expected cleavage site

genotypes

miRNA id#

Target gene

#Mis.

Valid reads

Total reads

Percent

Target gene annotation

Altet-4

miR156e

Medtr7g028740.2

0

4

23

17.4

squamosa promoter-binding-like protein

Altet-4

miR156a

Medtr7g444860.1

0

2

28

7.1

squamosa promoter-binding-like protein

Altet-4

miR156a

Medtr3g099080.1

0

1

3

33.3

squamosa promoter-binding 13A-like protein

Altet-4

miR159b

Medtr8g042410.1

2.5

1

16

6.3

MYB transcription factor

Altet-4

miR160c

Medtr2g094570.3

1

4

21

19.1

auxin response factor 1

Altet-4

miR164d

Medtr2g064470.1

1

2

34

5.9

NAC transcription factor-like protein

Altet-4

miR164d

Medtr8g058330.1

2

5

49

10.2

protein transporter Sec61 subunit alpha-like protein

Altet-4

miR167b-5p

Medtr8g079492.3

4

4

62

6.5

auxin response factor 2

Altet-4

miR169e-5p

Medtr2g099490.2

2

1

20

5

CCAAT-binding transcription factor

Altet-4

miR171f

Medtr0092s0100.2

1.5

5

24

20.8

GRAS family transcription regulator

Altet-4

miR172a

Medtr4g094868.3

1

1

13

7.7

AP2 domain transcription factor

Altet-4

miR172a

Medtr5g016810.2

1

1

18

5.6

AP2 domain transcription factor

Altet-4

miR172a

Medtr2g093060.3

0

4

17

23.5

AP2-like ethylene-responsive transcription factor

Altet-4

miR319d-3p

Medtr2g078200.1

3

2

34

5.9

TCP family transcription factor

Altet-4

miR319d-3p

Medtr8g463380.1

3

2

7

28.6

TCP family transcription factor

Altet-4

miR393a

Medtr1g088950.1

1

11

83

13.3

transport inhibitor response-like protein

Altet-4

miR393a

Medtr7g083610.1

2

38

134

28.4

transport inhibitor response 1 protein

Altet-4

miR395j

Medtr1g102550.1

1

1

76

1.3

ATP sulfurylase

Altet-4

miR396b-5p

Medtr1g017490.2

3

47

100

47

growth-regulating factor

Altet-4

miR396b-5p

Medtr2g041430.3

3

5

12

41.7

growth-regulating factor-like protein

Altet-4

miR396b-5p

Medtr5g027030.1

3

5

15

33.3

growth-regulating factor

Altet-4

miR396a-5p

Medtr3g052060.1

2

1

1

100

hypothetical protein

Altet-4

miR398c

Medtr4g114870.1

3

8

23

34.8

plastocyanin-like domain protein

Altet-4

miR398a-3p

Medtr8g064810.1

3

5

36

13.9

protein disulfide isomerase (PDI)-like protein

Altet-4

miR408-3p

Medtr8g089110.1

3

3

9

33.3

basic blue-like protein

Altet-4

miR408-3p

Medtr8g007020.1

3.5

5

73

6.9

plastocyanin-like domain protein

Altet-4

miR408-3p

Medtr8g007035.1

3.5

5

123

4.1

plastocyanin-like domain protein

Altet-4

miR408-5p

Medtr3g074830.1

3.5

2

442

0.5

phosphate-responsive 1 family protein

Altet-4

miR1510a-5p

Medtr2g012770.1

1

1

5

20

disease resistance protein (TIR-NBS-LRR class)

Altet-4

miR2199

Medtr7g080780.2

2

2

8

25

helix loop helix DNA-binding domain protein

Altet-4

miR2643a

Medtr3g010590.1

1

1

15

6.7

F-box protein interaction domain protein

Altet-4

miR2643a

Medtr6g053240.1

3

2

4

50

F-box protein interaction domain protein

Altet-4

miR4414a-5p

Medtr3g117120.1

4

3

84

3.6

BZIP transcription factor bZIP124

Altet-4

miR5213-5p

Medtr6g084370.1

2

1

2

50

disease resistance protein (TIR-NBS-LRR class)

Altet-4

miR5213-5p

Medtr6g088245.1

3

1

5

20

disease resistance protein (TIR-NBS-LRR class)

Altet-4

miR5239

Medtr3g018680.1

3

1

5

20

F-box/RNI superfamily protein, putative

Altet-4

miR5561-3p

Medtr2g045295.1

3

1

4

25

hypothetical protein

Altet-4

miR5752b

Medtr8g066820.1

4

9

423

2.1

PLATZ transcription factor family protein |

Altet-4

miR7696a-5p

Medtr1g072130.1

3

2

27

7.4

PHD finger protein, putative

Altet-4

miR7696c-3p

Medtr3g081480.1

3

2

21

9.5

endoplasmic reticulum vesicle transporter

Altet-4

miR7696d-5p

Medtr3g112250.1

3.5

8

36

22.2

hypothetical protein

Altet-4

miR7696c-3p

Medtr4g011600.2

3.5

1

26

3.9

sulfate transporter-like protein

Altet-4

miR7696c-3p

Medtr7g085650.4

3.5

1

6

16.7

sulfate adenylyltransferase subunit 1/adenylylsulfate kinase

Altet-4

miR7701-3p

Medtr6g011380.2

2

1

137

0.7

SPFH/band 7/PHB domain membrane-associated family protein

NF08ALF06

miR156e

Medtr7g028740.2

0

14

36

38.9

squamosa promoter-binding-like protein

NF08ALF06

miR156a

Medtr7g444860.1

0

1

144

0.7

squamosa promoter-binding-like protein

NF08ALF06

miR156h-3p

Medtr7g091370.1

3

1

11

9.1

heat shock transcription factor

NF08ALF06

miR159b

Medtr8g042410.1

2.5

4

30

13.3

MYB transcription factor

NF08ALF06

miR160c

Medtr2g094570.3

1

8

46

17.4

auxin response factor 1

NF08ALF06

miR160d

Medtr1g064430.2

0.5

3

24

12.5

auxin response factor 1

NF08ALF06

miR160d

Medtr3g073420.1

0.5

2

17

11.8

auxin response factor, putative

NF08ALF06

miR164d

Medtr2g064470.1

1

41

151

27.2

NAC transcription factor-like protein

NF08ALF06

miR164d

Medtr8g058330.1

2

5

115

4.4

protein transporter Sec61 subunit alpha-like protein

NF08ALF06

miR167b-5p

Medtr8g079492.3

4

9

133

6.8

auxin response factor 2

NF08ALF06

miR167a

Medtr4g076020.1

3.5

5

77

6.5

GRAS family transcription factor

NF08ALF06

miR171f

Medtr0092s0100.2

1.5

60

115

52.2

GRAS family transcription regulator

NF08ALF06

miR172a

Medtr4g094868.3

1

1

45

2.2

AP2 domain transcription factor

NF08ALF06

miR172a

Medtr5g016810.2

1

1

84

1.2

AP2 domain transcription factor

NF08ALF06

miR172a

Medtr2g093060.3

0

4

35

11.4

AP2-like ethylene-responsive transcription factor

NF08ALF06

miR172a

Medtr4g061200.4

1

1

28

3.6

AP2-like ethylene-responsive transcription factor

NF08ALF06

miR172a

Medtr7g100590.1

1

2

17

11.8

AP2 domain transcription factor

NF08ALF06

miR319d-3p

Medtr2g078200.1

3

2

126

1.6

TCP family transcription factor

NF08ALF06

miR319d-3p

Medtr8g463380.1

3

2

48

4.2

TCP family transcription factor

NF08ALF06

miR393a

Medtr1g088950.1

1

54

268

20.2

transport inhibitor response-like protein

NF08ALF06

miR393a

Medtr7g083610.1

2

472

771

61.2

transport inhibitor response 1 protein

NF08ALF06

miR393a

Medtr8g098695.2

4

1

46

2.2

transport inhibitor response 1 protein

NF08ALF06

miR396b-5p

Medtr1g017490.2

3

423

742

57

growth-regulating factor

NF08ALF06

miR396b-5p

Medtr2g041430.3

3

30

75

40

growth-regulating factor-like protein

NF08ALF06

miR396b-5p

Medtr5g027030.1

3

10

42

23.8

growth-regulating factor

NF08ALF06

miR396a-5p

Medtr3g011560.1

3

1

3

33.3

TNP1

NF08ALF06

miR396a-5p

Medtr3g052060.1

2

3

11

27.3

hypothetical protein

NF08ALF06

miR396a-5p

Medtr8g017000.1

3

1

2

50

Ulp1 protease family, carboxy-terminal domain protein

NF08ALF06

miR398c

Medtr4g114870.1

3

14

49

28.6

plastocyanin-like domain protein

NF08ALF06

miR398a-3p

Medtr8g064810.1

3

8

44

18.2

protein disulfide isomerase (PDI)-like protein

NF08ALF06

miR408-3p

Medtr8g089110.1

3

8

34

23.5

basic blue-like protein

NF08ALF06

miR408-3p

Medtr8g007020.1

3.5

10

375

2.7

plastocyanin-like domain protein

NF08ALF06

miR408-3p

Medtr8g007035.1

3.5

10

675

1.5

plastocyanin-like domain protein

NF08ALF06

miR408-5p

Medtr3g074830.1

3.5

27

948

2.9

phosphate-responsive 1 family protein

NF08ALF06

miR482-5p

Medtr1g064430.2

3.5

1

24

4.2

auxin response factor 1

NF08ALF06

miR530

Medtr3g072110.1

2.5

3

102

2.9

transmembrane amino acid transporter family protein

NF08ALF06

miR1507–3p

Medtr8g036195.1

2

4

9

44.4

NBS-LRR type disease resistance protein

NF08ALF06

miR1510a-5p

Medtr7g108860.4

3.5

21

1061

2

CS domain protein

NF08ALF06

miR2199

Medtr7g080780.2

2

1

26

3.9

helix loop helix DNA-binding domain protein

NF08ALF06

miR2643a

Medtr6g053240.1

3

25

33

75.8

F-box protein interaction domain protein

NF08ALF06

miR4414a-5p

Medtr3g117120.1

4

8

260

3.1

BZIP transcription factor bZIP124

NF08ALF06

miR5037c

Medtr4g070550.1

3

2

44

4.6

F-box protein interaction domain protein

NF08ALF06

miR5213-5p

Medtr4g014580.1

1.5

3

31

9.7

TIR-NBS-LRR class disease resistance protein

NF08ALF06

miR5238

Medtr3g077740.2

2.5

1

259

0.4

pantothenate kinase

NF08ALF06

miR5239

Medtr3g018680.1

3

4

43

9.3

F-box/RNI superfamily protein, putative

NF08ALF06

miR5561-3p

Medtr2g045295.1

3

1

12

8.3

hypothetical protein

NF08ALF06

miR5752a

Medtr8g066820.1

4

13

936

1.4

PLATZ transcription factor family protein

NF08ALF06

miR7696a-5p

Medtr1g072130.1

3

4

259

1.5

PHD finger protein, putative

NF08ALF06

miR7696c-3p

Medtr3g081480.1

3

2

46

4.4

endoplasmic reticulum vesicle transporter

NF08ALF06

miR7696c-5p

Medtr7g076830.1

3

3

103

2.9

DEAD-box ATP-dependent RNA helicase-like protein

NF08ALF06

miR7696d-5p

Medtr3g112250.1

3.5

5

30

16.7

hypothetical protein

NF08ALF06

miR7696c-3p

Medtr4g011600.2

3.5

1

103

1

sulfate transporter-like protein

NF08ALF06

miR7696c-3p

Medtr7g085650.4

3.5

2

10

20

sulfate adenylyltransferase subunit 1/adenylylsulfate kinase

NF08ALF06

miR7701-3p

Medtr3g108910.1

2.5

2

375

0.5

hypothetical protein

NF08ALF06

miR7701-3p

Medtr6g011380.2

2

2

86

2.3

SPFH/band 7/PHB domain membrane-associated family protein

NCES-141

miR156e

Medtr7g028740.2

0

18

46

39.1

squamosa promoter-binding-like protein

NCES-141

miR156a

Medtr7g444860.1

0

4

101

4

squamosa promoter-binding-like protein

NCES-141

miR156a

Medtr8g096780.1

0

1

11

9.1

squamosa promoter-binding 13A-like protein

NCES-141

miR156a

Medtr3g085180.1

1

1

2

50

squamosa promoter-binding-like protein

NCES-141

miR156h-3p

Medtr7g091370.1

3

2

5

40

heat shock transcription factor

NCES-141

miR159b

Medtr8g042410.1

2.5

3

36

8.3

MYB transcription factor

NCES-141

miR160c

Medtr2g094570.3

1

12

37

32.4

auxin response factor 1

NCES-141

miR164d

Medtr2g064470.1

1

33

100

33

NAC transcription factor-like protein

NCES-141

miR164d

Medtr8g058330.1

2

14

119

11.8

protein transporter Sec61 subunit alpha-like protein

NCES-141

miR167b-5p

Medtr8g079492.3

4

10

101

9.9

auxin response factor 2

NCES-141

miR167a

Medtr4g076020.1

3.5

4

45

8.9

GRAS family transcription factor

NCES-141

miR167b-3p

Medtr4g124900.2

3.5

1

154

0.7

auxin response factor 2

NCES-141

miR168a

Medtr6g477980.2

4

2

245

0.8

argonaute protein 1A

NCES-141

miR171f

Medtr0092s0100.2

1.5

36

70

51.4

GRAS family transcription regulator

NCES-141

miR172a

Medtr4g094868.3

1

2

50

4

AP2 domain transcription factor

NCES-141

miR172a

Medtr5g016810.2

1

2

56

3.6

AP2 domain transcription factor

NCES-141

miR172a

Medtr2g093060.3

0

1

19

5.3

AP2-like ethylene-responsive transcription factor

NCES-141

miR172a

Medtr4g061200.4

1

3

32

9.4

AP2-like ethylene-responsive transcription factor

NCES-141

miR319d-3p

Medtr2g078200.1

3

1

55

1.8

TCP family transcription factor

NCES-141

miR319d-3p

Medtr8g463380.1

3

1

26

3.9

TCP family transcription factor

NCES-141

miR393a

Medtr1g088950.1

1

38

222

17.1

transport inhibitor response-like protein

NCES-141

miR393a

Medtr7g083610.1

2

337

539

62.5

transport inhibitor response 1 protein

NCES-141

miR395j

Medtr1g102550.1

1

1

163

0.6

ATP sulfurylase

NCES-141

miR396b-5p

Medtr1g017490.2

3

201

352

57.1

growth-regulating factor

NCES-141

miR396b-5p

Medtr5g027030.1

3

6

16

37.5

growth-regulating factor

NCES-141

miR396b-5p

Medtr8g020560.1

3

1

7

14.3

growth-regulating factor-like protein

NCES-141

miR396a-5p

Medtr3g011560.1

3

1

1

100

TNP1

NCES-141

miR396a-5p

Medtr8g017000.1

3

1

1

100

Ulp1 protease family, carboxy-terminal domain protein

NCES-141

miR397-5p

Medtr7g062310.1

1.5

2

4

50

laccase/diphenol oxidase family protein

NCES-141

miR398c

Medtr4g114870.1

3

8

21

38.1

plastocyanin-like domain protein

NCES-141

miR398a-3p

Medtr8g064810.1

3

47

89

52.8

protein disulfide isomerase (PDI)-like protein

NCES-141

miR398c

Medtr5g089180.1

3

4

19

21.1

hypothetical protein

NCES-141

miR408-3p

Medtr8g089110.1

3

9

18

50

basic blue-like protein

NCES-141

miR408-3p

Medtr8g007020.1

3.5

7

209

3.4

plastocyanin-like domain protein

NCES-141

miR408-3p

Medtr8g007035.1

3.5

8

381

2.1

plastocyanin-like domain protein

NCES-141

miR408-5p

Medtr3g074830.1

3.5

14

703

2

phosphate-responsive 1 family protein

NCES-141

miR482-3p

Medtr5g027900.1

2.5

1

19

5.3

disease resistance protein (CC-NBS-LRR class) family protein

NCES-141

miR530

Medtr3g072110.1

2.5

1

119

0.8

transmembrane amino acid transporter family protein

NCES-141

miR1510a-5p

Medtr7g108860.4

3.5

17

746

2.3

CS domain protein

NCES-141

miR2643a

Medtr3g010620.1

1

2

72

2.8

F-box protein interaction domain protein

NCES-141

miR4414a-5p

Medtr3g117120.1

4

2

134

1.5

BZIP transcription factor bZIP124

NCES-141

miR5037c

Medtr4g070550.1

3

1

36

2.8

F-box protein interaction domain protein

NCES-141

miR5213-5p

Medtr6g084370.1

2

1

5

20

disease resistance protein (TIR-NBS-LRR class)

NCES-141

miR5213-5p

Medtr4g014580.1

1.5

1

18

5.6

TIR-NBS-LRR class disease resistance protein

NCES-141

miR5213-5p

Medtr6g088245.1

3

1

7

14.3

disease resistance protein (TIR-NBS-LRR class)

NCES-141

miR5238

Medtr3g077740.2

2.5

1

151

0.7

pantothenate kinase

NCES-141

miR5561-3p

Medtr2g045295.1

3

1

9

11.1

hypothetical protein

NCES-141

miR5752b

Medtr8g066820.1

4

8

765

1.1

PLATZ transcription factor family protein

NCES-141

miR7696a-5p

Medtr1g072130.1

3

2

135

1.5

PHD finger protein, putative

NCES-141

miR7696c-5p

Medtr7g076830.1

3

5

78

6.4

DEAD-box ATP-dependent RNA helicase-like protein

NCES-141

miR7696d-5p

Medtr3g112250.1

3.5

9

44

20.5

hypothetical protein

NCES-141

miR7696c-3p

Medtr4g011600.2

3.5

1

124

0.8

sulfate transporter-like protein

NCES-141

miR7696c-3p

Medtr7g085650.4

3.5

1

10

10

sulfate adenylyltransferase subunit 1/adenylylsulfate kinase

NCES-141

miR7701-3p

Medtr3g108910.1

2.5

2

444

0.5

hypothetical protein

The identified miRNA targets in all three genotypes include mainly transcription factors. Specifically, five members of the squamosa promoter-binding-like protein (SPL) targeted by the miR156 family, five members of the auxin response factors targeted by both miR160 and miR167 families, five members of the apetala2 (AP2)-domain containing transcription factors, four members of the growth-regulating factor (GRFs) family targeted by miR396, two members of the TCP family transcription factors targeted by miR319, and, a NAC domain-containing transcription factor-like protein (NAC) targeted by miR164 [35]. Additionally, transcripts encoding Argonaute targeted by miR168, laccase targeted by miR397, and three plantacyanin containing proteins targeted by miR408 were also identified. Although evidence indicates that that miR398 targets Cu/Zn superoxide dismutases and a copper chaperone for the superoxide dismutases (CCS) in plants [28, 38] these relationships were not apparent in the data from this study. On the other hand, we have identified three potentially non-conserved targets (plastocyanin, protein disulphide isomerase and a hypothetical protein) for miR398 in three alfalfa genotypes. In addition to the GRFs, our analyses revealed potential non-conserved targets for miR396 including TNP1, Ulp1 protease and hypothetical proteins (Table 5).

The analyses of legume-specific miRNAs and their targets have revealed an interesting miRNA: target networks between the miRNAs and the NBS-LRR genes [14, 16, 18, 32]. In this study, we identified NBS-LRR disease resistance genes as targets for four different miRNA families including miR482, miR1507, miR1510 and miR5213 in alfalfa (Table 5).

Degradome analyses has also been utilized to identify potential targets for several non-conserved miRNAs or miRNAs that are present only in closely related species such as the M. truncatula. To increase the confidence in identification of targets for the non-conserved miRNAs that are usually expressed at low abundances and the cleavage frequencies on those targets are relatively low, we considered as ‘targets’ only those for which the cleavages were detected at least in two of the three alfalfa genotypes. The transcripts for Medtr6g053240.1 (F-box protein interaction domain protein) had a cleavage frequency of approximately 75% and were targeted by the miR2643 in NF08ALF06 genotype. Additionally, two other F-box protein interaction domain protein genes were also identified as targets for miR2643 in alfalfa genotypes (Table 5). These results suggest that the F-box protein interaction domain protein family are regulated by this potential legume-specific miRNA. Another notable observation is that 6 different genes identified as potential targets for miR7696, and the cleavage frequency of a particular target gene (hypothetical protein, Medtr3g112250.1) was more abundant in all three alfalfa genotypes (Table 5).

Because some of the miRNA-stars are also highly expressed, we scrutinized the degradome reads for potential cleavages on the transcripts that are complementary to the miRNA-stars. This analysis has identified potential targets for at least four conserved miRNAs. Specifically, miR156-star targets a heat shock transcription factor, miR164-star targets a protein transporter Sec61 subunit alpha-like protein, miR167-star targets a GRAS family transcription factor, and, miR482-star targets an auxin response factor 1 in alfalfa (Table 5).

The confirmed targets of conserved miRNAs are known to regulate diverse developmental processes in the lifecycle of plants. For example, the SPL transcription factors (target of miR156) which regulate the transition from juvenile to adult phase of the life cycle in land plants [39]. Auxin receptors (TIR1 proteins) and ARFs targeted by miR393 and miR160, miR167, are components of the auxin signalling pathway that regulates several aspects of plant growth and development. The roles of NAC factors (targeted by miR164) include shoot meristem initiation and later root formation in Arabidopsis [40, 41]. Similarly, TCP family transcription factors have several different roles including regulating leaf morphogenesis [42, 43]. In Arabidopsis, seven out of nine GRFs are known targets for miR396 [44], and we have identified four GRFs as targets for miR396 in alfalfa (Table 5). By interacting with its coactivators called GRF-interacting factors (GIFs), this regulatory network (miR396-GRFs-GIFs) regulate leaf size, leaf growth and senescence in Arabidopsis [44]. The known targets for miR397 include laccase, which is involved in oxidative polymerization of lignin in plants [45]. Similarly, miR408 is targeting a family of plantacyanins, which could function in shuttling electron-transfer between proteins [46, 47].

The miR398 family is known to target CSDs and a copper chaperone for superoxide dismutase (CCS) genes in plants [28, 38]. In this study, we have identified plastocyanin-domain like proteins (plastocyanin is an essential electron carrier which shuttles the electrons between cytochrome b6f and PS I) represents a novel target for miR398. Protein disulphide isomerase (PDI) is a member of a family of dithiol/disulfide oxidoreductases, the thioredoxin superfamily, which functions in the formation of disulphide linkage between the cysteine residues for proper protein folding [48]. Our degradome analyses confirms that PDI represents a novel target for miR398 in alfalfa (Table 5). The other confirmed miRNA target transcripts include Leucine rich repeat resistance (LRR) proteins (TIR-NBS-LRR and CC-NBS-LRR) that play important roles in plant pathogen recognition and activation of plant innate immune responses [14, 16, 18, 32]. Yet another interesting target include the F-box protein interaction domain proteins that are regulated by miR2643, one of the very abundantly expressed miRNA in alfalfa.

Conclusions

The analyses of small RNA libraries from the whole plants, shoots and roots resulted in the identification of 100 miRNA families that included highly conserved miRNAs as well as miRNAs that are at least conserved between M. truncatula and alfalfa. The conserved miRNA profiles share some similarities and a few differences between genotypes and types of tissues (roots and shoots). The tissue-specific profiles were used to identify miRNAs that are highly abundant as well as those miRNAs that are expressed at low levels. Additionally, 17 novel miRNAs with varying levels of expression were also identified in alfalfa. The present study also reports identification of 69 targets for 31 miRNA families. In addition to the conserved targets for conserved miRNAs, a few non-conserved targets such as the PDI for miR398 were confirmed. Similarly, miR2643 is targeting three transcripts encoding F-box protein interaction domain containing proteins in alfalfa. In summary, the results from this study have increased our understanding of miRNAs and miRNA-mediated gene regulation in alfalfa that could result in potential tangible targets for practical applications in alfalfa and related legume species to increase biomass yield and address abiotic and biotic limitations to agricultural productivity.

Declarations

Acknowledgements

Not applicable.

Funding

This research was funded by the Noble Research Institute and Forage Genetics International, a hatch grant from NIFA-0229360 (OKL02844) to RS and MM, and the National Natural Science Foundation of China (numbers 31460295 and 31760314) to YZ. This work was also partially supported by the Neustadt-Sarkeys Distinguished Professorship to RS. Publication costs are funded by the Oklahoma Agricultural Experiment Station, Oklahoma State University, Stillwater.

Availability of data and materials

The small RNA and degradome datasets generated and analyzed in the present study are available in the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) under accession number GSE119460 available at: https://www.ncbi.nlm.nih.gov/geo/query/511acc.cgi?acc=GSE119460.

About this supplement

This article has been published as part of BMC Genomics Volume 19 Supplement 10, 2018: Proceedings of the 29th International Conference on Genome Informatics (GIW 2018): genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume-19-supplement-10.

Authors’ contributions

RS and MM conceived the idea and designed the study. CM and TH cultured the plants used in this study; RP isolated the RNA from samples and generated the small RNA libraries; YZ, SR, QW, SA and RS analyzed the small RNA and degradome libraries; RP, SA and RS wrote the manuscript; MM edited the manuscript. All authors reviewed and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK 74078, USA
(2)
Institute of Primate Translational Medicine, Kunming University of Science and Technology, 727 South Jingming Road, Kunming, 650500, Yunnan, China
(3)
Noble Research Institute, Ardmore, OK 73401, USA
(4)
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 727 South Jingming Road, Kunming, 650500, Yunnan, China

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Copyright

© The Author(s). 2018

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