smRNAome profiling to identify conserved and novel microRNAs in Stevia rebaudiana Bertoni

  • Vibha Mandhan1,

    Affiliated with

    • Jagdeep Kaur1 and

      Affiliated with

      • Kashmir Singh1Email author

        Affiliated with

        BMC Plant Biology201212:197

        DOI: 10.1186/1471-2229-12-197

        Received: 11 April 2012

        Accepted: 29 October 2012

        Published: 1 November 2012

        Abstract

        Background

        MicroRNAs (miRNAs) constitute a family of small RNA (sRNA) population that regulates the gene expression and plays an important role in plant development, metabolism, signal transduction and stress response. Extensive studies on miRNAs have been performed in different plants such as Arabidopsis thaliana, Oryza sativa etc. and volume of the miRNA database, mirBASE, has been increasing on day to day basis. Stevia rebaudiana Bertoni is an important perennial herb which accumulates high concentrations of diterpene steviol glycosides which contributes to its high indexed sweetening property with no calorific value. Several studies have been carried out for understanding molecular mechanism involved in biosynthesis of these glycosides, however, information about miRNAs has been lacking in S. rebaudiana. Deep sequencing of small RNAs combined with transcriptomic data is a powerful tool for identifying conserved and novel miRNAs irrespective of availability of genome sequence data.

        Results

        To identify miRNAs in S. rebaudiana, sRNA library was constructed and sequenced using Illumina genome analyzer II. A total of 30,472,534 reads representing 2,509,190 distinct sequences were obtained from sRNA library. Based on sequence similarity, we identified 100 miRNAs belonging to 34 highly conserved families. Also, we identified 12 novel miRNAs whose precursors were potentially generated from stevia EST and nucleotide sequences. All novel sequences have not been earlier described in other plant species. Putative target genes were predicted for most conserved and novel miRNAs. The predicted targets are mainly mRNA encoding enzymes regulating essential plant metabolic and signaling pathways.

        Conclusions

        This study led to the identification of 34 highly conserved miRNA families and 12 novel potential miRNAs indicating that specific miRNAs exist in stevia species. Our results provided information on stevia miRNAs and their targets building a foundation for future studies to understand their roles in key stevia traits.

        Keywords

        Stevia rebaudiana Next generation sequencing Small RNAs Conserved and novel miRNA miRNA targets

        Background

        MicroRNAs (miRNAs) constitute a class of single stranded 19–24 nucleotide long non-coding RNAs that mediate endogenous gene silencing by binding to their target genes. miRNAs binds either to the open reading frame (ORF) or untranslated regions (UTRs) of target mRNAs and silencing occurs either by target cleavage or by inhibiting mRNA translation [1, 2] through perfect or partial complementarity respectively [3]. miRNA feature to regulate gene expression is exemplified by their indispensable roles in critical biological and cellular events in plants including lateral root formation, leaf morphology and polarity, hormonal control, flowering time [4], cell death and apoptosis [5]. These are also involved in plant adaptation to vast array of biotic and abiotic stresses such as cold, drought, salinity, nutrient deficiency [4, 6] and oxidative stress [7]. Majority of the targets of plant miRNAs were found to be transcription factors such as Squamosa-promoter Binding Protein (SBP), HD-Zip transcription factors [8] or regulatory stress response proteins like NAC domain protein and MYB proteins [7]. miRNAs along with other RNAs such as small interfering RNA (siRNA), CRISPR RNA , Piwi interacting RNA and trans-acting siRNAs (ta-siRNAs) constitute the complex smRNAome [9]. siRNAs and miRNAs are near similar in structure and function, however, several characters distinguish miRNAs from siRNAs which include the nature of precursor, conservation, target gene preference and genetic requirements for biogenesis [10].

        miRNA biogenesis involves a meshwork of various enzymes. These are produced from their own transcriptional units [11] generated by stepwise processing of RNA polymerase II dependent primary miRNA transcripts (pri-miRNAs) which are subsequently capped, spliced and polyadenylated [5]. In plants, RNA binding protein DWADDLE (DDL) stabilizes pri-miRNAs for their conversion into processing centers called nuclear dicing bodies (D bodies) to stem loop precursor miRNAs (pre-miRNAs). This conversion further requires concerted assistance of Serrate (C2-H2 zinc finger protein), HYPONASTIC LEAVES1 (HYL-1, a double stranded-RNA binding protein), nuclear cap binding complex (CBC) and Dicer-like 1 (DCL-1, RNase-III class of enzymes). DCL-1 possess both Drosha and Dicer like activities instigating the initial set of both the cuts responsible for above pri- to pre-miRNA conversion and further in mature miRNA processing for releasing miRNA/miRNA* duplex in the nucleus, supposed to be predominant location of DCL-1. Hua Enhancer 1 (HEN-1), a methyl transferase, adds methyl groups to the 3 ends of duplex for stabilization which is further exported in cytoplasm by HASTY (HST) proteins, plant ortholog of Exportin-5 [5]. Finally, depending on the thermodynamic stability of 5’end, miRNA strand is preferentially loaded into RNA induced silencing complex (RISC) whereas miRNA* strand is degraded. The RISC complex guides miRNA strand to its target mRNA which is then cleaved between 10th and 11th bases from 5 end of miRNA match. The cleaved halves of target mRNA are then degraded by exoribonucleases 4 (XRN-4) and secondary siRNAs [12, 13].

        miRNAs were initially discovered as developmental timing regulators in Caenorhabditis elegans[14] and later found to be ubiquitously present in wide range of animals, plants, some viruses and in unicellular organisms such as green algae, Chlamydomonas reinhardtii[15]. Increasing evidences showed that miRNA repertoire of plant or animal species includes a set of conserved (ancient, abundant) and non-conserved or novel (species specific, recently evolved) miRNAs [16]. A total of 18226 entries representing hairpin precursor miRNAs, expressing 21643 mature miRNA products, across 168 species have been deposited in the publicly available online repository for miRNA sequences, miRBase Release 18 [17]. However, the biggest challenge in plants is to identify novel miRNAs and to understand their mode of action, role in various metabolic processes.

        Stevia rebaudiana Bertoni (family Asteraceae) is one of 154 members of genus Stevia and one of the only two species that produce sweet stevoil glycosides [18]. S. rebaudiana is a perennial herb which accumulates up to 30% (w/w leaf dry weight) diterpenoid steviol glycosides (SGs) [19]. SGs are glucosylated derivatives of diterpenoid alcohol steviol and stevioside and rebaudioside A are the major SGs found in stevia. Sweetness indices of these and other related compounds ranges between 30 to 300 times higher than that of sucrose [20] and are used as non-calorific sweeteners in many countries of the world. In addition to being a sweetener, stevia has been suggested to exert beneficial effects on human health and is considered to have anti-hyperglycemic, anti-hypersensitive, anti-oxidant, anti-tumour/anti-inflammatory, immunomodulatory, antidiarrheal, antimicrobial and anti-rotavirus activities [18]. Stevioside has been reported to lower the postprandial blood glucose level of Type II diabetes patients and blood pressure in mildly hypertensive patients [21, 22]. SGs biosynthesis pathway is divided into two stages; early stage wherein geranylgeranyl diphosphate (GGDP) is synthesized and late stage, which is involved in the synthesis of SGs from GGDP. SGs being important compounds of stevia, imparting medicinal properties to the plant, so the molecular studies done on this plant were mainly focussed on understanding the biosynthesis and regulation of the genes involved in SG biosynthesis. A control over the regulation of these early and late genes can help to manipulate the diterepenoid contents [23]. Further, the unraveling of sRNA guided circuitry in stevia will enhance the value of its gene and EST information and improve our ability to devise strategies to enhance certain essential features of stevia that are less amenable to functional genomics analysis leading to its enhanced nutritive value. The identification of miRNA and their targets is important not only to help us learn more about the roles of miRNAs in stevia development and physiology but also to provide a framework for further designing RNAi based experiments for regulation of gene expression in this species.

        Genome of S. rebaudiana has not been sequenced yet and further there are only 5548 stevia expressed sequecend tags (ESTs) and 136 gene sequences available in the NCBI database. Thus, it was not possible to perform an extensive study to discover stevia miRNAs using computational analysis with this limited number of available sequences. Experimental cloning combined with computational prediction appears to be the most effective method to predict stevia miRNAs. The availability of high throughput next generation sequencing (NGS) technologies such as 454 and Illumina have further revolutionized sRNA discovery. Whereas, transcribed sequences such as ESTs has led to the identification of only conserved (abundant) miRNAs or miRNAs previously identified in other species, on the other hand, NGS provides high throughput tools to make new discoveries of additional species specific or lowly expressed miRNAs in plants irrespective of whether their genome is sequenced or not, e.g. Arabidopsis[16], Oryza sativa[24], Populus trichocarpa[25], Triticum aestivum[26], Brachypodium distachyon[27], Vitis vinifera[28], Arachis hypogaea[29], Citrus trifoliate[30] , Carthamus tinctorius[31] and many more. This huge list signifies that miRNAs have been extensively studied since years and the digging of smRNAome of any organism is of utmost importance to understand the sRNA mediated gene regulations and the diversity of sRNAs.

        Our research aimed to examine sRNA population in leaves of S. rebaudiana using Illumina platform to identify conserved and novel miRNAs. The downstream analysis of sRNA data generated by genome analyzer II led to the identification of 100 conserved miRNA sequences belonging to 34 miRNA families sharing very high homology with that already known in other species. In addition, 12 putative novel miRNA families were also identified with their precursors. A scan of the S. rebaudiana EST and nucleotide databases with miRNA sequences revealed their putative targets. Putative targets have been predicted for 12 out of above 34 conserved miRNA families and 5 novel miRNAs using computational methods.

        Results

        Deep sequencing of sRNAs

        Using 36 cycled single end sequencing by genome analyzer II [32], a total of 30,472,534 sequences were obtained from smRNAome of S. rebaudiana. After removing the low quality and adaptor sequences, 17,295,850 sequences were obtained. Among these sequences 15,327,722 sequences ranged from 16–28 nt in length. Further removing t/rRNAs, a total of 6,075,858 sRNA sequences were obtained with a size range of 19–24 nt (Table 1).
        Table 1

        Statistics of sequences obtained from the stevia sRNA library

        sRNA

        Redundant reads

        Unique reads

        Total sRNA sequenced in library

        30,472,534

        -

        Sequences matching 3’ adaptor sequence

        17,295,850

        -

        Sequences matching 3' adaptor sequences within size range specified: (16–28 nt)

        15327722

        2509190

        Filter valid miRNA size range: (19-24nt)

        7,020,896

        629987

        Filter t/rRNA

        6,075858

        627710

        Filter sequences matching miRBase-18-mature.fa

        6075552

        627678

        Although some sRNAs were high in abundance and present thousands of times in our dataset, majority of sRNAs were sequenced only once in our experiment e.g. 2,509,190 sequences out of 15,327,722 sequences were sequenced only once (Table 1). This suggested that (1) there is a significant variation in expression of various sRNAs in stevia; (2) sRNA survey in stevia is far from being exhausted. This ultimately directs that stevia contains a large and diverse sRNA population as was the case seen in C. trifoliate[30].

        In stevia, the size of sRNAs was not evenly distributed (Figure 1). Among these sequences, the number of 23nt long sequences was unusually greater than other longer and shorter sequences and accounted for 13.25% of the total sequence number. Class of 23–25 nt has been recently reported and named as long miRNAs discovered in Arabidopsis[33]. This result was unlike as seen in peanut [29], cotton [34], medicago [35] and early phase maize seedlings [36] where fraction of 23 nt was very small as compared to others.
        http://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-197/MediaObjects/12870_2012_1131_Fig1_HTML.jpg
        Figure 1

        Length distribution and abundance of sRNA sequences of S. rebaudiana .

        In total, 1,752,385 sequences of 24 nt length (11.43%) represented the second highest followed by 22nt sequences (6.83%) and finally 21 nt sequences (5.61%) which is the typical length of plant mature miRNAs (Figure 1). The latter trend of abundances of 24nt sequences followed by 22nt and 21nt was same as observed during seed germination in maize (36). Additionally, 27,63512 sequences with a length of 27nt were present, covering 18.02% in the stevia library. They may represent the degradation products during sRNA library construction.

        Identification of conserved stevia miRNAs

        The filtered sRNA dataset were compared with the currently known mature plant miRNAs in miRBASE v18 [37] using miRPROF. After multiple BLASTN searches and further sequence analysis, a total of 100 conserved miRNAs, belonging to 34 miRNA families (Table 2) were identified. Most of the miRNA families were found to be conserved in a variety of plant species e.g. using a comparative genomics based strategy homologs of miR319, miR156/157, miR169, miR165/166, miR394 and miR159 were found in 51,45,41,40,40 and 30 diverse plant species respectively [38]. Deep sequencing aids by providing an alternative way to estimate the expression profiles of miRNA genes [39]. It not only determines the abundance of various miRNA families but simultaneously distinguishes between different members of a given family. On the basis of similarity in mature miRNA sequence, these miRNA were grouped into families, with members often varying upto 1 or 2nt. Diversity of stevia miRNAs could also be found in the aspect of number of members each family contained (Figure 2).
        Table 2

        Profile and abundance of conserved miRNAs obtained from S. rebaudiana sRNA library

        miRNA family

        miRNA

        Sequence

        Length (nt)

        miRNA* sequenced

        Reads

        Homolog

        156/157

        miR156a

        UGACAGAAGAGAGUGAGCACAUC

        23

        N

        124

        bna-MIR156a

         

        miR156b

        UGACAGAAGAGAGUGAGCAUC

        21

        N

        124

        ath-MIR156a

         

        miR156c

        UUGACAGAAGAUAGAGAGCACAC

        23

        N

        124

        ath-MIR157c

         

        miR156d

        UGACAGAAGAGAGUGAGCACAU

        22

        N

        124

        rco-MIR156d

         

        miR156e

        UGACAGAAGAGAGUGAGCACUC

        22

        N

        3

        ath-MIR156e

         

        miR157a

        UUGACAGAAGAUAGAGAGCACAC

        23

        N

        1

        ath-MIR157a

        159

        miR159a

        UUUGGAUUGAAGGGAGCUCUAUC

        23

        N

        11759

        ath-MIR159a

         

        miR159b

        UUUGGAUUGAAGGGAGCUCAUC

        22

        N

        11827

        ath-MIR159b

         

        miR159c

        UUUGGAUUGAAGGGAGCUCUAAU

        23

        N

        12411

        vvi-MIR159c

         

        miR159d

        UUUGGAUUGAAGGGAGCUCUGUC

        23

        N

        80

        sof-MIR159d

         

        miR159e

        UUGGAUUGAAGGGAGCAUC

        19

        N

        6

        osa-MIR159e

         

        miR159f

        UUGGAUUGAAGGGAGCUCUAAU

        22

        N

        136

        osa-MIR159f

         

        miR159j

        UUUGGAUUGAAGGGAGCUCU

        20

        N

        74

        zma-MIR159j

         

        mirR159k

        UUUUGGAUUGAAGGGAGAUC

        20

        N

        74

        zma-MIR159k

        160

        miR160a

        UGCCUGGCUCCCUGUAUGCCAA

        22

        N

        1

        ath-MIR160a

        163

        miR163

        AGAUGACUUGGAACUUCGAUC

        21

        N

        2

        ath-MIR163

        164

        miR164c

        UGGAGAAGCAGGGUACGUGCAUC

        23

        N

        8

        osa-MIR164c

         

        miR164h

        UGGAGAAGCAGGGCACGUAUC

        21

        N

        2

        zma-MIR164h

        165

        miR165a

        UCGGACCAGACUUCAUCCCCCA

        22

        N

        5

        ath-MIR165a

         

        miR165b

        UCGGACCAGGCUUCAUCCCCCUC

        24

        N

        5

        ath-MIR165b

        166

        miR166a

        UCGGACCAGGCUUCAUUCCCCAU

        22

        Y

        412

        ath-MIR166a

         

        miR166b

        UCGGACCAGGCUUCAUUCCAUC

        23

        N

        398

        crt-MIR166b

         

        miR166c

        UCGGACCAGGCUUCAUUCCUCAU

        23

        Y

        357

        mtr-MIR166c

         

        miR166d

        UCGGACCAGGCUUCAUUCCCCAC

        23

        Y

        396

        ath-MIR166d

         

        miR166e

        UCGGACCAGGCUUCAUUCCCCA

        22

        Y

        292

        ath-MIR166e

         

        miR166f

        UCUCGGACCAGGCUUCAUUC

        20

        N

        379

        bdi-MIR166f

         

        miR166g

        UCGGACCAGGCUUCAUUCCUCUC

        23

        Y

        357

        osa-MIR166g

         

        miR166h

        UCGGACCAGGCUUCAUUCCUCGA

        23

        N

        357

        osa-MIR166h

         

        miR166i

        UCGGACCAGGCUUCAUUCCCC

        21

        N

        291

        ath-MIR166b

         

        miR166j

        UCGGACCAGGCUUCAUAUC

        19

        N

        291

        ptc-MIR166j

         

        miR166k

        UCGGACCAGGCUUCAUUCCU

        20

        N

        304

        sbi-MIR166k

         

        miR166l

        UCGGACCAGGCUUCAUUCCUCAC

        23

        N

        357

        zma-MIR166l

         

        miR166m

        UCGGACCAGGCUUCAUUCCUCC

        22

        N

        397

        zma-MIR166m

         

        miR166n

        UCGGACCAGGCUUCAUUCCUUAU

        23

        N

        370

        ptc-MIR166n

         

        miR166o

        UCGGACCAGGCUUCAUUCCUUCG

        23

        N

        286

        ptc-MIR166o

         

        miR166p

        UCGGACCAGGCUCCAUUCC

        19

        N

        1

        ptc-MIR166p

         

        miR166q

        UCGGACCAGGCUUCAUUCCUUUC

        23

        N

        286

        ptc-MIR166q

        167

        miR167a

        UGAAGCUGCCAGCAUGAUCUGAUC

        24

        N

        9629

        ccl-MIR167a

         

        miR167b

        UGAAGCUGCCAGCAUGAUCUAAUC

        24

        N

        9637

        bna-MIR167b

         

        miR167c

        GAAGCUGCCAGCAUGAUCUGAUC

        23

        N

        7862

        bdi-MIR167c

         

        miR167d

        UGAAGCUGCCAGCAUGAUCUGAU

        23

        N

        7853

        bdi-MIR167d

         

        miR167e

        UGAAGCUGCCAGCAUGAUCUGUC

        23

        N

        73

        osa-MIR167e

         

        miR167f

        UGAAGCUGCCAGCAUGAUCAUC

        22

        N

        21

        gma-MIR167f

         

        miR167g

        UGAAGCUGCCAGCAUGAUCUGA

        22

        N

        7806

        gma-MIR167g

         

        miR167h

        UGAAGCUGCCAGCAUGAUCUG

        21

        N

        13

        osa-MIR167h

         

        miR167i

        UGAAGCUGCCAGCAUGAUCUAC

        22

        N

        65

        sbi-MIR167i

         

        miR167j

        UGAAGCUGCCAGCAUGAUCUGC

        22

        N

        13

        osa-MIR167j

        168

        miR168a

        UCGCUUGGUGCAGGUCGGGAAUC

        23

        Y

        71

        ath-MIR168a

         

        miR168b

        UCGCUUGGUGCAGGUCGGGAAU

        22

        Y

        69

        ath-MIR168b

        169

        miR169f

        UAGCCAAGGAUGACUUGCCUAUC

        23

        N

        7

        osa-MIR169f

        171

        miR171a

        UGAUUGAGCCGUGCCAAUA

        19

        N

        10

        osa-MIR171b

         

        miR171b

        UGAGCCGUGCCAAUAUCAUC

        20

        N

        10

        ath-MIR171b

         

        miR171c

        UUAUAGAGCCGUGCCAAUA

        19

        N

        10

        osa-MIR171c

         

        miR171d

        UGAUUGAGCCGUGCCAAUC

        19

        N

        10

        osa-MIR171d

        172

        miR172a

        AGAAUCUUGAUGAUGCUGCAUUC

        23

        Y

        20

        ath-MIR172a

         

        miR172b

        AGAAUCUUGAUGAUGCUGCAUC

        22

        N

        20

        ath-MIR172b

         

        miR172c

        AGAAUCUUGAUGAUGCUGCAGAU

        23

        N

        24

        ath-MIR172c

         

        miR172d

        AGAAUCUUGAUGAUGCUGCAUAU

        23

        N

        25

        osa-miR172d

         

        miR172e

        AGAAUCUUGAUGAUGCUGCAUCA

        23

        N

        20

        tcc-MIR172e

         

        miR172f

        AGAAUCUUGAUGAUGCUGCA

        20

        N

        20

        ath-MIR172a

        319

        miR319a

        UUGGACUGAAGGGAGCUCCCUUC

        23

        N

        76

        ath-MIR319a

         

        miR319b

        UUGGACUGAAGGGAGCUCCAUC

        22

        N

        76

        ath-MIR319b

         

        miR319c

        UUGGACUGAAGGGAGCUCCCU

        21

        N

        78

        vvi-MIR319c

         

        miR319d

        UUGGACUGAAGGGAGCAUC

        19

        N

        23

        ptc-MIR319d

         

        miR319e

        UUGGACUGAAGGGAGCUCCC

        20

        N

        14

        ppt-MIR319e

         

        miR319f

        UUGGACUGAAGGGAGCUCCCC

        21

        N

        78

        vvi-MIR319f

         

        miR319g

        UUGGACUGAAGGGAGCUCCCACC

        23

        N

        528

        vvi-MIR319g

         

        miR319h

        UGGACUGAAGGGAGCUCAUC

        20

        N

        12

        ptc-MIR319g

        393

        miR393a

        UCCAAAGGGAUCGCAUUGAUCCC

        23

        N

        4

        ath-MIR393a

         

        miR393b

        UCCAAAGGGAUCGCAUUGAUCCAU

        24

        N

        4

        ath-MIR393b

         

        miR393c

        UCCAAAGGGAUCGCAUUUAUC

        21

        N

        2

        zma-MIR393a

         

        miR393d

        UCCAAAGGGAUCGCAUUGAUC

        21

        N

        2

        ptc-MIR393d

        394

        miR394a

        UUGGCAUUCUGUCCACCUCCAUC

        23

        N

        1554

        vvi-MIR394a

         

        miR394b

        UUGGCAUUCUGUCCACCUCCUC

        22

        N

        2

        ath-MIR394b

         

        miR394c

        UUUGGCAUUCUGUCCACCUCCAU

        23

        N

        1554

        vvi-MIR394c

        396

        miR396a

        UUCCACAGCUUUCUUGAACUAUC

        23

        Y

        23

        vvi-MIR396a

         

        miR396b

        UUCCACAGCUUUCUUGAACUUUC

        23

        Y

        17

        aqc-MIR396b

         

        miR396c

        UUCCACAGCUUUCUUGAACUUAU

        23

        N

        17

        osa-MIR396c

         

        miR396d

        UUCCACAGCUUUCUUGAACUUC

        22

        N

        17

        ptc-MIR396d

         

        miR396e

        UUCCACAGCUUUCUUGAACU

        20

        N

        29

        tcc-MIR396e

         

        miR396f

        UUCCACGGCUUUCUUGAACUGAU

        23

        N

        32

        ptc-MIR396f

         

        miR396g

        UUCCACGGCUUUCUUGAACAUC

        22

        N

        2

        ptc-MIR396g

        397

        miR397b

        AUUGAGUGCAGCGUUGAUGAAUC

        23

        N

        2

        bdi-MIR397b

        403

        miR403a

        UUAGAUUCACGCACAAACUCGUC

        23

        N

        1

        ptc-MIR403a

        408

        miR408a

        UGCACUGCCUCUUCCCUUA

        19

        N

        1

        sof-MIR408a

         

        miR408b

        UGCACUGCCUCUUCCCUGGAUC

        22

        N

        6

        ppt-MIR408b

        414

        miR414

        AUCAUCAUCAUCAUCAUCA

        19

        N

        1

        ath-MIR414

        482

        miR482a

        UCUUCCCCACACCUCCCAU

        19

        N

        1

        gso-MIR482a

        856

        miR856*

        UGAUGUUAAUUGUGGUAUC

        19

        Y

        1

        aly-MIR856

        858

        miR858

        UUCGUUGUCUGUUCGACCUUGA

        22

        N

        2

        ath-MIR858b

        894

        miR894

        CGUUUCACGUCAGGUUCACCA

        21

        N

        17

        ppt-MIR894

        1310

        miR1310

        AUCGGGGGCGCAACGCCUC

        19

        N

        2

        pta-MIR1310

        1317

        miR1317

        AAAUGAUCUUGGAGGUUAUC

        20

        N

        3

        osa-MIR1317

        1511

        miR1511

        AACCUGGCUCUGAUACCAUC

        20

        N

        1

        gma-MIR1511

        1850

        miR1850

        UGUUUAGUUGCCAUCAAUC

        19

        N

        2

        osa-MIR1850

        2111

        miR2111a

        UAAUCUGCAUCCUGAGGUUUAUC

        23

        N

        1

        ath-MIR2111a

        2916

        miR2916

        GGGGGCUCGAAGACGAUCAUC

        21

        N

        68

        peu-MIR2916

        3520

        miR3520

        AGAGAGACUUUUGAAUUAUC

        20

        N

        1

        ahy-MIR3520

        5084

        miR5084

        UAGAGUACUGUAGAGGAUC

        19

        N

        1

        tae-MIR5084

        5139

        miR5139

        AACCUGGCUCUGAUACCAUC

        20

        N

        2

        rgl-MIR5139

        http://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-197/MediaObjects/12870_2012_1131_Fig2_HTML.jpg
        Figure 2

        Number of identical miRNA members in each family in S. rebaudiana.

        Further, miRNA families displayed significant varied abundance from each other. For e.g. majority of stevia miRNAs were sequenced less than 1000 times whereas some miRNAs were detected in low counts i.e. less than 10 times (Figure 3). Among the 34 miRNA families, miR159 proved to be largest one with highest number of sequences. In addition, miR167 and miR394 were found to have some thousands to tens of thousands of redundancies while miR319, miR166 and miR156 had more than one hundred redundancies. The remaining families were infrequently sequenced (less than 100).
        http://static-content.springer.com/image/art%3A10.1186%2F1471-2229-12-197/MediaObjects/12870_2012_1131_Fig3_HTML.jpg
        Figure 3

        Abundance of stevia conserved miRNA families. Only one read was detected form miR157, 160, 403, 414, 482, 1511, 2111, 3520, 5084.

        A vast diversity was further seen in abundance of different members of a single family. For e.g. - as miR159a showed 11,759 sequenced clones i.e. more than 10,000 times. On the other hand, miR159e was sequenced just 6 times. As the samples were collected in the pre-flowering phase the existence of a dominant member in a miRNA family may suggest that the regulatory role of this family was performed by the dominant member in that particular developmental phase.

        Identification of novel stevia miRNAs

        In this study, due to unavailability of whole genome sequence of S. rebaudiana, we predicted potential novel miRNAs based on bioinformatics analysis using the stevia EST and nt sequences available in NCBI database. First, we removed the sequencing tags that can be annotated as non- coding RNAs including t or rRNAs and then filtered the known miRNA sequences (Table 1) from further analysis.

        A total of 6,075,552 redundant sequences survived the above filtration steps and 627,678 unique sequences were analyzed for novel miRNA prediction. A distinguishing feature of miRNAs is the ability of their pre miRNA sequences to adopt canonical stem loop hairpin structure. These unique sRNA sequences were mapped to EST and nt sequences by searching all S. rebaudiana sRNAs and predicting the secondary structure of sequences surrounding them (±150bp) (Additional file 1 Figure S1). We identified 12 sequences that satisfied the secondary structure and other criterions (Table 3).
        Table 3

        Novel miRNAs identified in S. rebaudiana

        miRNA

        Sequence

        Length

        Clones

        EST/nt accession no.

        Strand

        A+U content

        Mismatch

        miRNA start

        miRNA end

        EST/nt start

        EST/nt end

        Precursor

        Stv_1

        AAACGGUGAACAAGAAGAGG

        20

        1

        BG522782.1

        Plus

        55%

        0

        1

        20

        470

        489

        YES

        Stv_2

        UGUGAAGGCGGAUGACUGUAU

        21

        1

        BG523272.1

        Plus

        55%

        0

        1

        21

        216

        236

        YES

        Stv_3

        UGAGAUGUUCAAUGGAAAGUAA

        22

        1

        BG526716.1

        Plus

        68.2%

        0

        1

        22

        142

        163

        YES

        Stv_4

        GAGACUGAUUUCAGAACCGG

        20

        1

        BG524295.1

        Plus

        50%

        0

        1

        20

        240

        259

        YES

        Stv_5

        ACGUACGAUCAUUUGGAUAAGAU

        23

        1

        BG525786.1

        Plus

        65.2%

        0

        1

        23

        167

        189

        YES

        Stv_6

        UUGAACAAGUAUGGUCGUCCC

        21

        5

        AY215182.1

        Plus

        52.3%

        0

        1

        21

        435

        455

        YES

        Stv_7

        UCGUGCUGUUGGGAAGUGGA

        21

        1

        BG526771.1

        Plus

        42.3%

        0

        1

        20

        327

        346

        YES

        Stv_8

        CUGACGUGUCACCAUUACGGA

        21

        1

        BG525715.1

        Minus

        47.6%

        0

        1

        21

        323

        303

        YES

        Stv_9

        GGUAAAGCACUGUUUCGGUGC

        21

        1

        BG523023.1

        Plus

        47.6%

        0

        1

        21

        324

        344

        YES

        Stv_10

        UUGACACUUUCCCGGGACA

        19

        2

        BG522709.1

        Plus

        42.1%

        0

        1

        19

        95

        113

        YES

        Stv_11

        GGCGGGCUCAAAUGACGAAUCAU

        23

        1

        BG524443.1

        Plus

        47.5%

        1

        1

        23

        378

        400

        YES

        Stv_12

        CUCGCGCUUUGGUUGAAGAAC

        21

        1

        BG526387.1

        Plus

        47.6%

        0

        1

        21

        266

        286

        YES

        Corresponding hairpin loop structures are provided in supplementary information.

        A sRNA is considered as a potential miRNA candidate only if it meets the following strict criteria as defined by Zhang et al. [40]; 1) the sequence could fold into an appropriate stem loop hairpin secondary structure; 2) the small RNA sits in one arm of hairpin structure; 3) no more than 6 mismatches between mature miRNA sequence and its opposite miRNA * sequence in the secondary structure; 4) no loop or break in the miRNA or miRNA * sequences; 5) predicted secondary structure has negative minimal folding free energy; 6) minimum A+U% (30-70%) as mature miRNAs contain more A+U nucleotides than G+C [41] and 7) usually begins with 5' U which is one of the characteristic feature of miRNAs.

        Target prediction of miRNAs

        miRNA target prediction in plants is easier due to high and significant complementarities of miRNA- mRNA targets [42] and targets of most plant miRNAs possesses a single perfect or near perfect complementary site in coding region [43]. Assuming this to be generally the case, the Stevia transcript library (EST and nucleotide databases) was searched for complementarity with the sequences of identified conserved and novel miRNAs using the two target prediction web servers psRNATarget and TAPIR . Almost all the targets predicted through psRNA target server were further authenticated by TAPIR which showed the same targets. Further, TAPIR predicted additional putative targets. A deep insight into miRNA targets helps us in understanding the range of sRNA expression, regulation and their functional importance. To analyze probable sRNA targets is significant in plants because the complimentary sites of potential sRNA can exist anywhere along the target mRNA rather than at 3’UTR in case of animals [44].

        We predicted target genes and putative targets were identified for 12 out of 34 conserved families (Additional file 2 Table S1) and for 5 out of 12 stevia specific or novel miRNAs (Table 4). Further, mode of action of miRNAs on targets was found to be predominantly through cleavage which is usually the case seen in plants [1, 6]. Mostly multiple regulatory targets were predicted e.g. miR 414 has been predicted to have almost 70 targets in stevia EST and nucleotide transcript library. The predicted targets are mainly mRNA encoding enzymes regulating essential plant metabolic and signaling pathways which include transcription factors, elongation factors, protein kinases, heat shock proteins, F-box family proteins, catalases, peroxidases, aminopeptases, dehydrogenases, transferases, synthases, esterases, oxidoreductases etc.
        Table 4

        Putative target genes of novel miRNAs identified using psRNATarget and TAPIR programs

        Novel miRNA

        Target Acc.

        Tool used

        Score

        mfe

        Start

        Seed gap

        Seed mismatch

        Seed gu

        Gap

        Mismatch

        gu

        E

        UPE

        Inhibition

        Annotation

        e-value

        Stv_1

        BG523199

        psRNA/TAPIR

        2.5

        0.7

        55

        0

        0

        1

        0

        1

        1

        2

        9.668

        Cleavage

        AT1G55490 [Arabidopsis thaliana]

        4e-71

        Stv_2

        BG525025

        psRNA/TAPIR

        0

        1

        418

        0

        0

        0

        0

        0

        0

        0

        10.485

        Cleavage

        Phosphoglycerate kinase [Arabidopsis thaliana]

        8e-101

        Stv_7

        BG526734

        psRNA/TAPIR

        3.5

        0.78

        463

        0

        1

        0

        0

        1

        1

        3

        16.578

        Cleavage

        Pectinacetylesterase family protein [Arabidopsis thaliana]

        3e-108

        Stv_8

        BG525715

        psRNA/TAPIR

        2

        0.89

        303

        0

        1

        0

        0

        0

        0

        1.5

        16.677

        Cleavage

        Predicted protein [Populus trichocarpa]

        2e-06

        Stv_10

        BG521995

        TAPIR

        3.5

        0.82

        594

        0

        1

        1

        0

        0

        1

        -

        ---

        -------

        Ribulose-1,5-biphosphate carboxylase activase [Oryza sativa]

        1e-63

        mfe: free energy ratio; start: start position of the duplex on the mRNA; seed gap: no.of gaps in seed region; seed mismatch: no.of mismatches in seed region; seed gu: number of G–U pairs in seed region; mismatches: number of mismatches, gu: number of G–U pairs; gap: number of gaps introduced by bulges and loop structures; Score: score calculated for each miRNA–mRNA duplex taking into account all the above described parameters. E (Expectation): scores < 3.0 considered as a potential target of miRNA; UPE :Target accessibility - allowed maximum energy to unpair the target site.

        Discussion

        A sudden inclination of extensive research towards investigation of plant miRNAs is due to their indispensable roles in plant development and adaption. At present more than thousand miRNA genes among diverse plant species have been identified, annotated and some of them have been well characterised [37]. But still a large number of plant species are unexplored and stevia is one of them. Till date several agrotechniques, bioproduct extraction, phytochemical, biological and toxicological studies have been carried out on S. rebaudiana[18]. A large flux of research activities have diverted to understand the biosynthesis and possible manipulation of diterpenoids in stevia, particularly SGs which are mainly responsible for its ecological and commercial importance. But till date no research activity is reported for exploring the miRNA repertoire in stevia. A vast survey of miRNAs in stevia will provide useful information to elucidate their physiological functions, gene regulation, biogenesis and evolutionary roles in plants.

        Traditional strategies like genetic screening, microarray and bioinformatics prediction have miserably failed in species where genome sequence is not available such as stevia. The availability of high throughput NGS technologies has overshadowed this weakness by generating an accurate and comprehensive picture of smRNAome and identification of known miRNAs but also for successful discovery of novel miRNAs with high precision. It is also used as miRNA expression profiling tool to understand miRNA function in various molecular and physiological pathways. In this report, we describe the first screen for stevia miRNAs by deep sequencing with an aim of gaining insights into various roles of miRNAs. In total, 17,295,850 high quality sequences were generated from 30,472,534 raw sequences representing 57% of the total raw reads. RNA species 23, 24, 22 and 21 nt in length dominated the small RNA transcriptome with the 23 nt class being the most abundant in our library. This class of 23–25 nt has been named as long miRNAs. This finding is consistent with the working hypothesis that evolutionary change in structure of hairpin can change their affinity for DCL’s and hence the size of miRNAs. So, it is actually DCL3 which is responsible for pri-miRNA processing to generate 23 nt long miRNAs in contrast to the highly stringent DCL 1 for 20–21 nt canonical miRNAs. Further at times DCL2 can also process certain pri-miRNAs and generate 22 nt long miRNAs [33] justifying their presence.

        Further a total of 30, 472, 534 raw sequences generated a profile of 34 conserved miRNA families. Our data were in good harmony with previous studies of miRNA profiling based on exhaustive sequencing of sRNA populations (e.g. In grapevine 24 million reads gave 26 known miRNA families, in peanut 6,009,541 reads gave 22 know miRNA families and in C. trifoliate 13, 106,573 reads gave 42 known miRNA families) [ 28, 29,30]. This probably reflects the generally accepted high level of expression reported for conserved miRNAs. Stevia miRNA families showed varied abundance from each other. Among the 34 miRNA families mir159 showed the largest number of sequenced clones which are in agreement as miR159 is also the most abundant family in Arabidopsis[16]. Further mir159 and mir394 with highest abundance in stevia were among the moderately expressed miRNAs in Arachis hypogea[29] and among the lowly expressed miRNAs in C. trifoliate[30]. In stevia miR156 was among the lowly expressed miRNAs but usually miR156 represents one of the highly abundant miRNA families in diverse plant species e.g. Arachis hypogea[29], Brachypodium[27] and early maize seedlings [36]. The actual reason why these miRNA families showed varied abundances in different plant species in unknown. Thus this varied abundance of miRNA families suggested that miRNA genes would be differentially transcribed at this young leaves stage.

        Certain miRNA families are monocot or dicot specific which is further justified by the presence of miR403 (dicot specific miRNA) in stevia. The abundance of miR172 was 20 times low as compared to miR156 in our dataset which is consistent with the previous finding that these two miRNAs are conversely regulated [36]. Further, the largest miRNAs family size identified was miR166 that consisted of 17 members. miR156, miR159, miR167, miR319, miR396 and miR172 possessed 5, 8, 10, 8, 7 and 6 members respectively whereas other miRNA families such as miR157, miR160, miR169, miR858, miR894, miR2111 etc. had only one member detected in this library. The size of miRNAs families may be indicative of their function.

        Based on the results from the deep sequencing, different family members displayed drastically different expression levels. For example, the abundance of miR159 family varied from 6 reads to 11,759 reads in the deep sequencing. This was also the case for some other miRNA families, such as miR156 (from 3 read to 124 reads) miR167 (from 13 reads to 9,637 reads) and miR394 (from 2 reads to 1,554 reads). Abundance comparisons of different members in one miRNA family may provide useful information on the role that miRNAs play in the pre-flowering stage and even importance of the dominant member at that particular stage of plant growth. On the contrary 12 novel miRNAs were predicted according the criterion mentioned before. All the 12 followed secondary structure criteria forming a stem loop structure with miRNA sequence sitting at one arm of the hairpin and negative minimal folding energy. The A+U contents of predicted miRNAs were found in the range of 42.1- 68.2%. Out of these 12 sRNAs, sequences of stv_2, 3, 6, 7 and 10 start with 5’U. Though stv_7 and stv_10 contained less than 50% A+U content but they start with U. Keeping this in view, these two sRNAs could be putative stevia miRNAs. Earlier accepted findings state that non-conserved miRNAs are usually expressed at lower levels showing a tissue or development specific pattern [30] as was seen in case of stevia where the read number of novel miRNAs was much lower than that for conserved miRNAs. Majority of them had only 1–5 sequenced clones (Table 3). This suggests miRNAs identified in stevia might represent only a meager portion of novel miRNAs due to the fact that sRNA library was constructed from young plant leaves under normal conditions.

        It has been shown that plant miRNAs exhibit high degree of sequence complementarity to their targets which facilitates effective target prediction. Target genes identified seem to be associated not only with development but also with diverse physiological processes. It was found that target sites for conserved miRNAs in this plant were similar or functionally related to validated plant miRNA targets e.g. most members of the Squamosa Promoter Binding Protein Like (SPL) transcription factor family are targeted by miR156 in plants [8]. For instance, miR156 targets 11 of the 17 SPL genes in Arabidopsis. SPLs affect diverse developmental processes such as leaf development, shoot maturation, phase change and flowering in plants. Similarly, in stevia miR156 has been found to target SPL. Similarly, miR160 targets auxin response factor 10 (ARF) and miR414 targets Zinc finger related protein (Additional file 2 Table S1). miR414 is predicted to regulate multiple targets including RING/U-box domain-containing protein, phytochrome E, WD-repeat protein, vacuolar ATP synthase subunit E, SIN3 component, translation elongation factor, molecular chaperone Hsp90, ALF domain class transcription factor, programmed cell death 2 C-terminal domain-containing protein, resistance protein, NAC, DNA-damage repair protein and several proteins involved in synthesis i.e. 60S acidic ribosomal protein etc. Further, miR414 is predicted to have interesting stevia genes as targets like kaurene synthase (KS22-1), calmodulin and UDP-glycosyltransferase 73E1. Kaurene synthase is a vital late gene involved in steviol glycoside biosynthesis pathway. Further targets were predicted for certain more conserved miRNAs including miR166, miR167, miR319, miR 396 and miR408, miR856 and miR1310 (Additional file 2 Table S1). miRNAs regulate gene expression predominantly by cleavage (Additional file 2 Table S1) due to high complementarity of miRNA and targets.

        Putative targets were also predicted for some newly identified miRNAs. Stv_1 targets one of the chaperonin_like superfamily protein whose common function is to sequester non-native proteins inside their central cavity and promote folding by using energy derived from ATP hydrolysis. On the other hand, Stv_2 targets phosphoglycerate kinase (PGK) which is a monomeric enzyme that catalyzes the transfer of the high-energy phosphate group of 1,3-bisphosphoglycerate to ADP, forming ATP and 3-phosphoglycerate. This reaction represents the first of the two substrate-level phosphorylation events in the glycolytic pathway. Stv_7 targets pectinacetylesterase family protein which is located in cell wall or membrane and exhibits carboxylesterase activity. Further, Stv_10 is predicted to target ribulose-1,5-bisphosphate carboxylase activase(P-loop NTPases) involved in diverse cellular functions as well as lupus la ribonucleoprotein or RRM (RNA recognition motif) found in proteins involved in post-transcriptional gene expression processes including mRNA and rRNA processing, RNA export, and RNA stability. Additionally, we predicted a few genes with unknown function and hypothetical genes for miRNA targeting e.g. Stv_8 (Table 4). Target of only 5 novel miRNAs were predicted speculating that rest of the miRNA may be typically present in stevia and needs further experimental work to locate their gene targets.

        The above described information regarding predicted targets could be utilized efficiently to assess the regulatory roles of these novel miRNAs in stevia. Further if any of these putative novel miRNA is found to have control over the regulation of the early and late genes in steviol glycosides biosynthetic pathway as seen in case of conserved miRNA 414 can help to manipulate the diterepenoid contents.

        Conclusion

        The present study provides an important glimpse of sRNA abundance and diversity in stevia. We discovered, for the first time, 34 highly conserved miRNA families and 12 novel potential miRNAs from S. rebaudiana using high throughput solexa sequencing of sRNAs, which indicated that species specific miRNAs exist in stevia. The isolated sRNAs may be considered as stevia putative miRNAs based on their characteristic features. These results show that regulatory miRNAs exist in nutritionally important stevia and play a role in stevia growth, development and response to stress. Further prediction of target genes adds to our understanding of mechanisms regulating their cellular function and evolution. Most importantly, this study will serve as a foundation of future research into functional roles of miRNAs and will prove to be a major breakthrough if any of these miRNA will be targeting the genes involved in steviol glycosides pathway.

        Methods

        Plant material and RNA isolation

        Plants of S. rebaudiana were grown and maintained in Panjab University, Chandigarh, India. Young leaves were harvested by snap freezing method and stored at −80 °C till further use. Total RNA enriched with sRNA fraction was isolated using protocol developed by Ghawana et al. [45] combined with miRNAeasy spin kit (Qiagen, Germany). Briefly, 100 mg of tissue was ground with liquid nitrogen using a pestle and mortar. Powdered tissue was mixed with solution-I to make a homogeneous mixture. Solution-II was added and ground again for a while. Supernatant was extracted twice with 200μl of chloroform. To the upper aqueous phase, 1.5 volumes of 100% ethanol was added and mixed thoroughly. Then the samples were loaded onto miRNAeasy column and centrifuged. The column was washed once with wash buffer provided in the kit and on-column DNase digestion was done to remove residual DNA using the RNase-free DNase set (Qiagen, Germany). The column was washed again with wash buffer and clean RNA enriched with sRNA fragments was eluted using elution buffer.

        sRNA library preparation and sequencing

        RNA samples were sent to the Microarray and Genomic Core Facility, Huntsman cancer institute, University of Utah, Salt Lake City, USA, for preparation of the sRNA library and sequencing. Briefly, sRNAs were separated on polyacrylamide gel by electrophoresis and RNA bands corresponding to the size range 20 to 30 nucleotides (nt) were purified from the gel using Illumina’s Small RNA Sample Prep Kit. The 3 adapter (5-UCGUAUGCCGUCUUCUGCUUGUidT-3) and 5 adapter (5-GUUCAGAGUUCUACAGUCCGACGAUC-3) were ligated to sRNAs in two steps. Adapters were designed to ligate specifically to sRNAs containing 5 monophosphate and 3′ hydroxyl ends that were found in the DCL-catalyzed products [46]. Adapter ligated sRNAs were converted to cDNA and then amplified in PCR. PCR-generated DNA libraries were subjected to the 36 cycled single-end sequencing using the Genome Analyzer II (Illumina, USA).

        Bioinformatics analysis

        Processing of raw sequences

        To analyze the sRNA data, online version of the UEA sRNA toolkit [47, 48] was used. Toolkit provides a package of various tools for the analysis of high-throughput sRNA data including the sequence preprocessing tool which aids in conversion of FASTQ to FASTA format, removal of adaptor sequences, extracting sequences of defined size range and their abundance calculation. The adaptor sequences were removed by finding exact matches of the 3 adaptor and optional 5 adaptor sequences on the raw sequences. Filter tool removed t/r-RNAs (non-coding) sequences from further analysis by comparing the t/rRNA sequences available at Rfam database, genomic tRNA database and EMBL release 95. sRNAs in the size range of 19nt to 24nt were extracted which constitutes the ideal miRNA size range.

        Identification of conserved miRNAs

        The online repository for miRNA sequences, miRBase Release 18, was used to search for known and conserved miRNAs using miRPROF (known miRNA expression profile) [47] allowing only 2 mismatches with mature miRNAs in miRBASE [17]. Parameters taken into consideration were sequences matching to viridiplantae mature miRNA v18 and further unchecked parameters including group by match signature, combine miRNA family members and combine mature and star sequences leaving all other parameters at their default settings.

        Prediction of novel miRNAs

        Stevia ESTs (5548) and nt (136) sequences were downloaded from the NCBI database. For detection of novel miRNAs of stevia, conserved miRNA sequences were removed from processing file and the rest were used to perform BLASTN searches against stevia sequences in order to obtain precursors for potential novel miRNAs. The selected EST and nt sequences which showed significant complementarity with the sRNA in the dataset were then folded into a secondary structure using RNA fold annotation tool provided in the UEA sRNA tool kit which uses Vienna RNA package to fold miRNA precursors using the minimum free energy algorithm [49] and yields a single optimal structure. The sRNA was considered to be a novel miRNA if a perfect stem loop structure was formed with sRNA sequence at one arm of the stem as well as keeping other criteria given by Meyers et al. [50] under consideration.

        Target gene prediction

        The potential targets of conserved and novel miRNAs were predicted using the psRNATarget program [51] and secondly checked with new web server called TAPIR [52], designed for the prediction of plant miRNA targets. psRNATarget program was executed with parameters at their default settings i.e. score or maximum expectation at 3, length of complementary scoring at 20, target accessibility at 25, flanking length around target 17 bp upstream and 13 bp downstream and keeping range of central mismatch leading to translational inhibition in between 9–11. Newly identified conserved and novel miRNA sequences were used as custom miRNA sequences and S. rebaudiana transcript library (EST and nucleotide databases) was used as custom plant database.

        All predicted target genes were evaluated under the psRNA target server by scoring, and the criteria used were as follows: each G:U wobble pairing was assigned 0.5 points, each indel was assigned 2.0 points, and all other non-canonical Watson-Crick pairings were assigned 1.0 point each. The total score for an alignment was calculated based on 20 nt. When the query exceeded 20 nt length, scores for all possible consecutive 20 nt subsequences were computed, and the minimum score was considered the total score for the query-subject alignment. Because targets complementary to the miRNA 5’ end appear to be critical, mismatches other than G: U wobbles at positions 2–7 at the 5’ end were further penalized by 0.5 points in the final score [53]. Sequences were considered to be miRNA targets if the total score was less than 3.0 points.

        For further validation of the above miRNA targets TAPIR was used keeping the score cutoff value (default 4) and the free energy ratio cutoff value (default 0.7) using FASTA search engine. TAPIR indicates several parameter values (names, free energy ratio, start position of the duplex on the mRNA, seed and non-seed mismatches, gaps and G–U pairs) including a full representation of the miRNA–mRNA duplex with an alignment string. Sensitivity and specificity tests for the TAPIR fast method was done with psRNA target server using various parameters. All the results showed that TAPIR with the FASTA search engine (score cutoff 4) has a higher rate of true positive, while keeping the false positives to values that are similar to those of psRNA target [54]. Once potential target mRNA sequences were obtained, BLAST was performed using these target sequence and the NCBI database as reference to predict functions of potential targets.

        Abbreviations

        miRNAs: 

        MicroRNAs

        sRNA: 

        Small RNA

        ORF: 

        Open reading frame

        UTRs: 

        Untranslated regions

        SBP: 

        Squamosa-promoter Binding Protein

        ta-siRNAs: 

        Trans-acting siRNAs

        pri-miRNA: 

        Primary miRNA

        DDL: 

        DWADDLE

        D bodies: 

        Dicing bodies

        pre-miRNAs: 

        Precursor miRNAs

        HYL-1: 

        HYPONASTIC LEAVES1

        CBC: 

        Cap binding complex

        DCL-1: 

        Dicer-like 1

        HEN-1: 

        Hua Enhancer 1

        RISC: 

        RNA induced silencing complex

        XRN-4: 

        Exoribonucleases 4

        SGs: 

        Steviol glycosides

        GGDP: 

        Geranylgeranyl diphosphate

        ESTs: 

        Expressed sequecend tags

        NGS: 

        Next generation sequencing

        Nt: 

        Nucleotide

        miRPROF: 

        Known miRNA expression profile.

        Declarations

        Acknowledgements

        Authors are thankful to Dr. Brian Dalley, Director, Microarray and Genomic Core Facility, Huntsman cancer institute, University of Utah, Salt Lake City- UT, USA for providing services for library preparation and GAII sequencing. Vibha Mandhan is thankful to University Grants commission, India for providing research fellowship.

        Authors’ Affiliations

        (1)
        Department of Biotechnology, Panjab University, Sector 14

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        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.

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