Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Unique and Conserved MicroRNAs in Wheat Chromosome 5D Revealed by Next-Generation Sequencing

  • Kuaybe Yucebilgili Kurtoglu,

    Affiliation Faculty of Engineering and Natural Sciences, Sabanci University, Orhanlı, Tuzla, Istanbul, Turkey

  • Melda Kantar,

    Affiliation Faculty of Engineering and Natural Sciences, Sabanci University, Orhanlı, Tuzla, Istanbul, Turkey

  • Stuart J. Lucas,

    Affiliation Sabanci University Nanotechnology Research and Application Center (SUNUM), Sabanci University, Tuzla, Istanbul, Turkey

  • Hikmet Budak

    budak@sabanciuniv.edu

    Affiliations Faculty of Engineering and Natural Sciences, Sabanci University, Orhanlı, Tuzla, Istanbul, Turkey, Sabanci University Nanotechnology Research and Application Center (SUNUM), Sabanci University, Tuzla, Istanbul, Turkey

Unique and Conserved MicroRNAs in Wheat Chromosome 5D Revealed by Next-Generation Sequencing

  • Kuaybe Yucebilgili Kurtoglu, 
  • Melda Kantar, 
  • Stuart J. Lucas, 
  • Hikmet Budak
PLOS
x

Abstract

MicroRNAs are a class of short, non-coding, single-stranded RNAs that act as post-transcriptional regulators in gene expression. miRNA analysis of Triticum aestivum chromosome 5D was performed on 454 GS FLX Titanium sequences of flow-sorted chromosome 5D with a total of 3,208,630 good quality reads representing 1.34x and 1.61x coverage of the short (5DS) and long (5DL) arms of the chromosome respectively. In silico and structural analyses revealed a total of 55 miRNAs; 48 and 42 miRNAs were found to be present on 5DL and 5DS respectively, of which 35 were common to both chromosome arms, while 13 miRNAs were specific to 5DL and 7 miRNAs were specific to 5DS. In total, 14 of the predicted miRNAs were identified in wheat for the first time. Representation (the copy number of each miRNA) was also found to be higher in 5DL (1,949) compared to 5DS (1,191). Targets were predicted for each miRNA, while expression analysis gave evidence of expression for 6 out of 55 miRNAs. Occurrences of the same miRNAs were also found in Brachypodium distachyon and Oryza sativa genome sequences to identify syntenic miRNA coding sequences. Based on this analysis, two other miRNAs: miR1133 and miR167 were detected in B. distachyon syntenic region of wheat 5DS. Five of the predicted miRNA coding regions (miR6220, miR5070, miR169, miR5085, miR2118) were experimentally verified to be located to the 5D chromosome and three of them : miR2118, miR169 and miR5085, were shown to be 5D specific. Furthermore miR2118 was shown to be expressed in Chinese Spring adult leaves. miRNA genes identified in this study will expand our understanding of gene regulation in bread wheat.

Introduction

MicroRNAs (miRNAs), a class of short (∼21 nt) non-coding, single stranded RNAs, are highly conserved across plant species. Plant miRNAs have been shown to play a critical role in diverse biological processes including growth, development, adaptation to biotic and abiotic stresses, signal transduction and protein degradation as well as their own biogenesis [1][13]. They act as post-transcriptional regulators in gene expression via target specific cleavage and translational repression [14][16]. Mature miRNAs are processed from long primary transcripts (pri-miRNAs) in a multistep manner. Pri-miRNAs that are generated from miRNA genes in the nucleus are then cleaved by Dicer-like1 nuclease (DCL1) to produce a precursor (pre-miRNA) that folds into a hairpin structure. This hairpin is further cleaved to excise a double stranded miRNA/miRNA* fragment from the stem of the hairpin [17], [18]. The duplex is then methylated by HEN1 and exported to the cytoplasm by a protein called HASTY, an exportin-5 homologue [19]. Shortly after this, the single-stranded miRNA or miRNA* is incorporated into the RNA-induced silencing complex (RISC). In turn the RISC complex regulates specific target mRNAs, usually by cleavage at the miRNA complementary sequence [16], [18], [20], [21].

Since the first plant miRNA was discovered in Arabidopsis [22], more than 3000 plant miRNAs have been found either by direct cloning of small RNA libraries or bioinformatic prediction based on sequence and secondary structure conservation [23]. To date, 3228 plant miRNAs have been identified in various plants and submitted to miRBase (release 19.0, August 2012).

Due to the high abundance of small interfering RNAs (siRNAs), which comprise the majority of the plant small RNA pool and resemble miRNAs in length and sequence-specific function, miRNA identification in plants is complicated. The major difference between miRNAs and siRNAs is the processing of their precursors; miRNAs are derived from imperfectly paired single-stranded stem-loop structures [23], [24], whereas siRNAs are derived from long, perfectly paired double-stranded RNAs [25], [26]. They also differ in mode of action; miRNAs function at the post-transcriptional level through mRNA degradation or transcriptional repression, while siRNAs trigger DNA methylation, histone modification and mRNA degradation at transcriptional and post-transcriptional levels [7], [27], [28]. In order to distinguish miRNAs from other RNAs and confidently annotate miRNAs, stringent criteria have been specified [29]. miRNAs are highly conserved between species. Thus sequence and secondary structure homology have been utilized to predict the novel miRNAs conserved in other organisms by computational analysis. This approach is also useful in detecting miRNAs expressed at very low levels. Predicted miRNAs ultimately have to be verified experimentally to be confirmed as ‘miRNA’ [29].

Furthermore, the bread wheat genome (∼17 Gb) is known to contain highly repetitive sequences [30]. Recent studies have shown that repeat elements, especially those transposable elements (TEs) containing inverted repeats that could fold into hairpin-like structures, have contributed to miRNA biogenesis [31], [32].Co-localization of TEs with miRNAs was initially studied in Arabidopsis thaliana (A. thaliana) and Oryza sativa (O. sativa), and it is proposed that some miRNA genes have been derived from DNA transposons,frequently the miniature-inverted TEs (MITEs) [33]. Additionally Li and colleagues found a number of miRNAs homologous to TEs in plant species including bread wheat, supporting the idea of domestication of TEs into miRNA genes [34]. Some of the plant miRNAs deposited in miRBase were also found to be TE derived [33].

In this study we utilize next-generation sequencing data of flow-sorted individual chromosome arms for computational identification of miRNAs located on wheat chromosome 5D. Improvements in chromosome sorting techniques have facilitated genomic studies of the polyploid wheat genome by reducing the template to a manageable size [35]. By this approach, putative miRNAs have been identified at the subgenomic level, and these miRNAs were mined for the purpose of understanding their roles in the regulation of growth, development and biological processes.

Results

Identification and characterization of conserved miRNAs in long and short arms of wheat chromosome 5D

A total of 5,940 known plant mature miRNA sequences derived from 67 plant species were obtained from miRBase. After elimination of duplicate mature miRNA sequences, 3,228 mature miRNAs were used as query in BLASTn searches against 937,264 454 GS FLX sequence reads (1.34x coverage) for the short arm and 2,271,366 reads (1.61x coverage) forthe long arm respectively, corresponding to a total of 3,208,630 T. aestivum chromosome 5D sequences.

After using UNAfold,an implementation of the Zuker folding algorithm, 55 different miRNAs were identified from their predicted pre-miRNA stem-loop structures. Of these,13 miRNAs were found to be specifically present in the long arm, whereas 7 were specific to the short arm of 5D. (Figure 1, Data S1: Table 1).

thumbnail
Figure 1. Identified pre-miRNA stem-loop structures of selected miRNAs on chromosome 5D.

Mature miRNA sequence start and end points are designated with arrows. Structures are predicted using UNAFold (an implementation of Zuker algorithm).

https://doi.org/10.1371/journal.pone.0069801.g001

thumbnail
Table 1. miRBASE deposited targets for homologs of 5D T.aestivum miRNAs.

https://doi.org/10.1371/journal.pone.0069801.t001

Allowing for up to 3 mismatches from a known plant mature miRNA, 654 and 428 potential mature miRNA sequences were identified in 5DL and 5DS respectively; overall, the 55 5D miRNAs contained 926 potential mature miRNA sequences. Corresponding stem-loop structures for each new miRNA sequence that passed the 3 mismatch criteria were analyzed for miRNA characteristics.

Average sequence length for identified pre-miRNAs (130.022±39.39 nt, with a median of 122 nt) and mature miRNAs (20.9±1–36 and a median of 20), and average %GC content for pre-miRNAs (40.45% ±8.60 with a median of 38.022%, and minimum and maximum values of 26.06% and 67.01%) were calculated. Minimal folding free-energy index(MFEI), which is calculated from the minimal folding-free energy (MFE), sequence length and %GC content of the pre-miRNA, differentiates miRNAs with typically higher MFEIs (>0.67) from other types of cellular ssRNAs for which MFEIs were previously characterized; transfer RNAs (0.64), ribosomal RNAs (0.59), and mRNAs (0.62–0.66) [36]. The descriptive statistics for predicted wheat pre-miRNA MFEI values were; average:1.19±0.27, median: 1.19±0.27, minimum: 0.68, and maximum: 2.07. The low negative MFE values show higher stability of the predicted miRNA. The MFEs of the miRNAs we identified had an average of −61.82±23.44 and median of −59.7 kcal/mol; a minimum of −161.5 and a maximum of −23.4, which correlates well with previous plant miRNA identification studies [10], [11], [37]. Average MFEI values of 5DL and 5DS miRNAs were 1.19±0.27 with a median of 1.16 and 1.19±0.27 with a median of 1.19 respectively (Data S2: Table 2 and 3).

thumbnail
Table 2. Potential target genes and their predicted functions for 14 newly identified miRNAs in wheat chromosome 5D.

https://doi.org/10.1371/journal.pone.0069801.t002

thumbnail
Table 3. List of ESTs deposited in GenBank that have high homology to predicted 5D miRNAs.

https://doi.org/10.1371/journal.pone.0069801.t003

miRNA representation analysis

According to the results 5DL showed higher variety and representation of miRNAs than 5DS, as might be expected from its larger size. Twelve and 5 miRNAs were represented by only one putative pre-miRNA in the 5DS and 5DL arms, respectively. Eleven miRNAs were only detected at a single locus throughout the whole chromosome. The absolute copy number of each miRNA cannot be determined with certainty as some genomic miRNAs may be covered by more than one sequence read, while others may not be covered at all; however, the representation of each miRNA within the dataset provides a useful estimate of its prevalence on the chromosome. 5D miRNAs with the highest apparent representation (over 100 copies) were miR1117, miR1120, miR1139, miR1436, miR5049 in 5DS; miR1117, miR1120, miR1122, miR1131, miR1135, miR1136, miR1436, miR5049 in 5DL (Data S3; Table 1). The amount of 5D miRNA representation differed widely between miRNAs, and was found to be as high as 117 and 206 copies of a single putative miRNA present in 5DS and 5DL respectively.

Potential miRNA targets

Predicted 5D miRNAs were searched manually in miRBase to identify those with confirmed target mRNAs in other plant species. Targets were found for 3 miRNAs; for one miRNA unique to 5DL, and two miRNAs unique to 5DS; none of miRNAs with known targets were identified in sequence reads from both arms (Table 1). As a further analysis, using psRNATarget software, possible targets were retrieved for one predicted T. aestivum mature miRNA sequence corresponding to each identified miRNA family. In this analysis, possible targets were predicted for a total of 55 miRNA sequences, 48 (out of 48) from 5DL, 40 (out of 42) from 5DS and 33 (out of 35) miRNAs located in both chromosome arms (Data S4: Table 1,2). Putative wheat miRNA target genes varied in sequence and function, and most of them were classified as transcription factors, functional proteins in plant metabolism, and protein subunits. Potential targets of the newly identified miRNAs were listed in Table 2.

Elimination of known repeat sequences encoding miRNAs

The high representation of some of the putative miRNAs detected on chromosome 5D suggests that some or all of their apparent loci could be repetitive sequences. Therefore, all putative pre-miRNA hairpin sequences detected above were compared with a database of known wheat repetitive elements (see Materials and Methods). As a result, 83.84% and 84.38% of the 5DL and 5DS sequences were masked as repeats. Both Class I and Class II TEs were present in potential miRNA sequences with Class II DNA transposons being the predominant repeat elements; 81.54% and 81.98% of putative miRNA sequences were classified as DNA transposon elements in 5DL and 5DS, respectively (Data S5: Table 2 and 3). The composition of the repeats present in both chromosome arms was very similar and mostly consisted of MITEs from the Mariner family, followed by CACTA elements (Data S5: Table 1). Interestingly,the 5DL chromosome arm sequences were masked slightly less than 5DS chromosome arm sequences. The distribution of repeat elements also showed slight differences between 5DL and 5DS; differences in composition and distribution of TEs between different chromosomes, and even different regions of the same chromosome in wheat species have been reported previously [38][40].

Evidence for wheat chromosome 5D miRNA expression

Unlike siRNAs, miRNAs are generated from pri-miRNA transcripts, which are capped and polyadenylated in the same manner as protein-coding mRNAs [41]. Therefore, pri-miRNA sequences may be found in EST databases, albeit rarely [42]. As of January 2013, 1,286,372 T.aestivum ESTs had been submitted to the NCBI database (http://www.ncbi.nlm.nih.gov/dbEST/), making this a useful resource for attempting to confirm expression of putative wheat miRNAs. For each new miRNA detected in long and short chromosome arms, one corresponding pre-miRNA sequence was searched against the expressed sequence tag (EST) databases of T. aestivum using NCBI BLASTn. Hits above a threshold query coverage of 99% and maximum identity of 98%were recorded for each potential miRNA. To identify candidate pre-miRNA coding ESTs, all EST matches were compared to the non-redundant protein database at NCBI using blastx. All ESTs matching any protein sequence at an e-value of 1e−03 or lower were considered to be protein-coding, and were eliminated. A total of 6 (out of 55) miRNAs; 4 (miR1136, miR1436, miR167, miR5205) from 5DL, 4 (miR1122, miR1136, miR1436, miR1439) from 5DS, and 2(miR1136 and miR1436) of which were located in both chromosome arms matched an EST with no significant similarity to known proteins (Table 3), suggesting that these putative pre-miRNA sequences are transcribed. The remaining putative pre-miRNAs may also be transcribed, but absent from the available EST databases.

Identification of chromosome specific expression of miRNAs in O.sativa and B.distachyon

To find out whether miRNA coding sequences identified in wheat chromosome 5D are conserved in other grass species, the 5D miRNA sequences were used to search thecomplete genome sequences of B.distachyon and O.sativa, using the sameprocedure described for identification of conserved miRNAs in chromosome 5D, except that 100% identity of the mature miRNA sequence was required. miRNAs that were found to be specifically present in one or more chromosomes were recorded. In our ongoing analysis of the same dataset used in this study, syntenic regions of B.distachyon and O.sativa for both 5DL and 5DS chromosome arms have been delineated (Lucas et al., unpublished). According to these results, chromosome arm 5DL has regions syntenic to chromosomes Bd1& Bd4, and O.sativachromosomes 3& 9, whereas chromosome arm 5DS was found to have syntenic regions to chromosome Bd4 and O.sativa 12. Based on this analysis, miR1133 and miR167 were found to be present not only on 5DS but also in the syntenic region of Bd4 (Table 4). None of the 5DL specific miRNAs gave hits to the corresponding syntenic regions of B.distachyon and O.sativa chromosomes.

Experimental Evidence for localization of predicted pre-miRNA coding regions on 5D chromosome

In order to verify 5D chromosome localization, five of the predicted pre-miRNA coding regions,were amplified from flow sorted 5D chromosome arms by PCR. screening. Our experimental results supported our in silico predictions: 5DS was verified to harbour regions coding for pre-miR2118 and pre-miR5070, and 5DL was confirmeded to contain both of the above plus pre-miR6220, pre-miR5085 and miR169 coding regions. Furthermore, in order to confirm that these pre-miRNAs are specifically located on chromosome 5D, we also screened gDNA from CS and nullitetrasomic lines. pre-miR169, pre-miR5085 and pre-miR2118 coding regions were found to be 5D chromosome-specific. miR2118 was shown to be located on both arms of the 5D chromosome (5D specific), while miR5085 and miR169 were found to be specific to the long arm (Figure 2). Furthermore, to map the chromosomal positions of 5DL specific miRNAs, gDNA of group-5 deletion lines were also screened. Coding regions of both 5DL specific pre-miRNAs (pre-miR5085, pre-miR169) were found to be located between the breakpoint of 5DL-7 (FL : 0.29) and the centromere (Figure 3). Quantification with real-time PCR using CS gDNA suggested that coding regions of the selected pre-miRNAs had variable copy number: pre-miR169, pre-miR5085 and pre-miR5070 were shown to have approximately 8.6, 2.2 and 1.5 fold more copies than pre-miR6220. Gene copy number of pre-miR6220 was also separately evaluated with qRT-PCR in nullitetrasomic lines in comparison to CS, to determine its gene copy number restricted to the 5D chromosome. Approximately 9% of pre-miR6220 coding sequence copies from the whole wheat genome were observed to be located on chromosome 5D (Figure 4).

thumbnail
Figure 2. Pre-miRNA coding regions on long and short arms of the 5D chromosome.

PCR screening of pre-miRNA coding sequences in flow sorted 5D short and long chromosome arms (5DS and 5DL); Triticum aestivum L. cv Chinese Spring (CS) and nullitetrasomic lines (N5D-T5A and N5D-T5B) (A) pre-miR169 (B) pre-miR5085 (C) pre miR5070 (D) pre-miR6220 (E) pre-miR2118.

https://doi.org/10.1371/journal.pone.0069801.g002

thumbnail
Figure 3. Screening of pre-miRNA coding regions specific to 5DL with wheat group-5 deletion series.

PCR screening of 5DL specific pre-miRNA coding sequences (A) pre-miR169 (B) pre-miR5085 in Triticum aestivum L. cv Chinese Spring (CS) deletion lines (5DS-2, 5DS-5, 5DL-5, 5DL7) (C) Fraction length values of deletion lines.

https://doi.org/10.1371/journal.pone.0069801.g003

thumbnail
Figure 4. Quantification of miRNA gene copy number.

q-RT PCR (A) Five miRNA coding regions (pre-miR169, pre-miR5085, pre-miR6220 and pre-miR5070) in Triticum aestivum L. cv Chinese Spring (CS) B) Levels of non 5D-specific miR5070, detected by qRT-PCR in nullitetrasomic line (N5D-T5A) and Triticum aestivum L. cv Chinese Spring (CS).

https://doi.org/10.1371/journal.pone.0069801.g004

Experimental evidence for expression of pre-miR2118 from wheat 5D chromosome

In order to show expression of selected pre-miRNAs (pre-miR2118, pre-miR169, pre-miR5085, pre-miR6220, pre-miR5070), RT-PCR and qRT-PCR was performed using Chinese Spring cDNA. Expression of pre-miR2118 in adult leaves of wheat, grown under standard greenhouse conditions was shown. Expression was not unequivocally confirmed for the other 5D-specific pre-miRNAs, but as individual miRNA expression is frequently tissue/developmental stage/environmental condition specific, their expression may be detectable under specific conditions that were not tested here, most probably stress conditions (Figure 5).

thumbnail
Figure 5. Evidence for pre-miR2118 expression in wheat.

Expression of pre-miR2118 in Triticum aestivum L. cv Chinese Spring (CS) (A) Endpoint PCR results (B) qRT-PCR results.

https://doi.org/10.1371/journal.pone.0069801.g005

Discussion

The advent of next-generation high-throughput sequencing, chromosome sorting techniques and complementary bioinformatics tools have provided better approaches to identify miRNAs systematically at the sub-genomic level. The development of chromosome sorting techniques allows chromosome based sequencing, followed by identification of putative miRNA genes. Being one of the most important cereal crops in the world, understanding wheat genes and their regulation is a high priority; identifying wheat miRNAs and their targets is an important step in characterizing gene expression and regulation at the post-transcriptional level. Small RNA library sequences enable the identification of novel miRNAs which are present under the conditions in which the library RNA was collected [43], [44]; searching chromosomal sequences for miRNAs is a complementary strategy, with the advantage of detecting potential miRNAs present in the genome that are only expressed at low levels, or under conditions not represented by the small RNA libraries.

In this study, flow-sorted wheat chromosome 5D 454 sequence reads from T. aestivum L. var. “Chinese Spring” were used, and using in-house Perl scripts (see Materials and Methods) the first identification of conserved miRNAs in this chromosome was performed. To date 3,228 unique plant miRNA sequences have been deposited in miRBase. After a BLAST search based on sequence homology and conservation of pre-miRNA secondary structure, 55 putative conserved miRNAs were identified in5D, in which 13 miRNAs were specifically found to be present on 5DL and 7 on 5DS.The remaining 35 were found in both arms (Data S1: Table 1). Considering the total read count of 937,264 reads and 2,271,366 reads for 5DS and 5DL respectively, together with all analysis, it is notable that the long arm of the chromosome was shown to have a higher variety and representation of miRNAs compared to short arm. This is in accordance with the previous EST mapping studies in which 5DS has mapped roughly half the number of ESTs mapped on 5DL [45]. Bearing in mind the relative size of the chromosome arms, distribution of putative miRNA sequences seems to be consistent across the chromosome [46].

A total of37 miRNA families found in T.aestivum have previously been deposited in miRBase [42][44], [47]. Of the putative miRNAs reported here, 17 (out of 48) in 5DL and 14 (out of 42) in 5DS, making a total of 18 (out of 55) in the whole chromosome were previously reported to be T. aestivum miRNAs in miRBase. The remaining 37 putative miRNAs have not yet been confirmed to be expressed in T. aestivum. Conversely, 19 previously reported T.aestivum miRNAs were not detected in our dataset, meaning that the coding sequences for these miRNAs are probably not located on chromosome 5D.

Compared to a previous study also carried out by our group, 36 predicted miRNAs (out of 48) in 5DL and 24 (out of 42) in 5DS were also present in wheat chromosome 4A [48] (Data S1: Table 1 and 2).

According to the representation analysis 12 (out of 42), 5 (out of 48), 11 (out of 55) potential miRNAs were represented only once in 5DS, 5DL and the entire chromosome respectively. All of the highly represented miRNAs with over hundred copies were previously identified miRNAs. Eight out of 14 newly identified miRNAs with 10 or fewer copies were classed as “low represented” (Data S3: Table 1). These low represented miRNAs are more likely to be functional miRNA genes, compared to those with higher copy numbers which are probably repeat elements [34]. However, computational miRNAs remains putative until they are experimentally validated. Moreover, miRNA copy number may have a role in the level of regulation of its target; only if that miRNA is highly expressed it is more likely to have a greater effect on target regulation.

Considering the high repetitive content of wheat genome, repeat analysis was performed for the putative miRNAs detected on chromosome 5D. According to the results, Class II DNA Transposons were found to be the predominant repeats found in putative miRNAs from both arms, most frequently MITEs from the Mariner subfamily. CACTA sequences, Harbinger and Mutator sub-families were also detected in masked miRNA sequences. Since MITEs possess mostly palindromic terminal inverted repeat (TIR) sequences that can fold into miRNA-like hairpin structures [14],MITE-derived hairpins could be processed by DCL1, giving rise to mature miRNA sequences [33], [49]. Previously, a number of miRNA genes were found to be derived from TE sequences including osa-miR437 and osa-miR818, both of whichwere also found in T.aestivum chromosome 5D[49][53]. Due to its repeat rich nature, wheat may have utilized the step-wise model proposed by Piriyapongsa and Jordan [33]to explain how miRNAs could have evolved from TEs,(particularly MITEs) [49], [54].Furthermore, miR5021 corresponds to degenerate trinucleotide repeats and has not been confirmed in any species apart from A.thaliana, and so apparent matches to this miRNA are not likely to be true miRNA coding sequences. On the other hand, non-repetitive miRNAs(or non-repeat related miRNAs) all had low representation, with less than 20 hits across the chromosome. However, three of the highly represented miRNAs (miR1122, miR1136, miR1436) with more than 100 copies also gave EST hits (Table 3). In total,6 out of 55 putative miRNAs gave hits to ESTs, again suggesting their expression from wheat chromosome 5D. Expression of the remaining putative pre-miRNAs cannot be ruled out, as the EST database is unlikely to be exhaustive.

According to other research in our lab, chromosome arm 5DL has syntenic regions to chromosomes Bd1& Bd4 and O.sativa chromosomes 3 and 9, whereas 5DS was found to have syntenic regions to Bd4 and O.sativa chromosome 12 (Lucas et al., unpublished), but most of the miRNAs detected in this study were not syntenically conserved. This indicates that even conserved miRNA sequences have undergone more chromosomal translocations than conserved protein-coding genes since the separation of wheat from B.distachyon.

Target prediction of miRNAs is widely accepted as an important step towards understanding the role of miRNAs in regulation. All of the putative wheat miRNAs on chromosome 5D were found to have predicted or experimentally confirmed targets, involved in biological or metabolic processes and in stress responses (Data S4: Table 1, 2). The majority of the predicted targets of newly identified miRNAs are involved in a broad range of biological and molecular functions, such as hydrolase activity (miR3700; TC412324), nucleic acid binding transcription factor activity (miR5205; TC413453), transferase activity (miR5568; TC446402, TC395950), oxidoreductase activity (miR482; CO348589), metal ion binding activity (miR6197; AL821953) and response to stresses (miR5387; BE637541) such as drought (Figure 6).

thumbnail
Figure 6. Distribution of possible target functions.

Possible functions of newly identified 14 miRNA targets are shown on the pie-chart graph.

https://doi.org/10.1371/journal.pone.0069801.g006

Independent studies in different plant species including A. thaliana, O. sativa,and Populus trichocarpashowed drought stress responsiveness of miR160,miR167, miR169, miR1125, and miR398, which were also found in wheat chromosome 5D[55][57].

In addition to this; other drought related proteins such as late embryogenesis abundant protein (LEA) [58], [59], HVA22 [60], aquaporin [61] and calmodulin-like protein [62] were also found to be targeted by miR5387, miR6197/miR1118, miR1117/miR437 and miR1133 respectively.

Our target analysis show that the majority of the miRNAs have more than one potential regulatory target, conversely one target could be regulated by more than one miRNA. This observation supports the idea that miRNA studies should focus on a regulatory network in which more than one miRNA with different targets are involved (Table 2) [63].

Size distribution of miRNAs is important to their function. Previous studies have shown that 22 nt miRNAs are more likely to trigger siRNA biogenesis from their target transcripts [64]. Experimental analysis showed that the Argonaute (AGO) proteins have important roles to sort and load mature miRNA duplexes and 22 nt mature miRNA sequences were most effectively sorted and loaded onto the AGO (Figure 7) [65].

thumbnail
Figure 7. Distribution of different sizes of miRNAs on 5D chromosome.

Sizes and numbers of miRNAs on 5D chromosome distribution is shown on the graph.

https://doi.org/10.1371/journal.pone.0069801.g007

In this study, five pre-miRNA (miR169, miR5085, miR2118, miR6220, miR2118) coding sequences were verified to be located to the 5D chromosome (Figure 2). qRT analysis showed that the gene copy numbers of these miRNAs were highly variant (Figure 4). Three of these pre-miRNAs (miR169, miR5085, miR2118) were shown to be 5D specific (Figure 2). 5DL specific pre-miRNA (miR169, miR5085) coding sequences were shown to be located between the centromer and the breakpoint present in 5DL-7 (FL : 0.29) deletion line (Figure 3). Comparative quantification of gene copy number of pre-miR5070 in CS and nullitetrasomic lines revealed approximately %9 of the miR5070 coding regions were located on 5D chromosome (Figure 4). 5D-specific pre-miR2118 was shown to be expressed from the leaf tissue of CS grown under standard greenhouse conditions (Figure 5). Homologs of this miRNA have been previously shown to be involved in disease resistance and production of secondary siRNAs [66][68]. Other miRNAs included in this study (miR169, miR5085, miR6220, miR2118) may also be expressed, under stress conditions, in other wheat tissues and/or at different developmental stages. For instance, in several reports, miR169 was implicated in a broad range of stress responsive mechanisms including nitrogen starvation, arsenic, salt and drought stresses and response to virus infection [69][73].

Conclusion

Here we performed the first systematic identification of miRNAs in T. aestivum chromosome 5D through the use of next-generation sequencing data. In this study we found 55 putative miRNAs, of which 14 were not previously identified in wheat. Their potential targets were also predicted, and drought-related miRNA targets were detected. Moreover, in silico expression analysis of the predicted miRNAs gave EST hits for 6 out of 55 miRNAs. Furthermore we verified the 5D chromosome localization of 5 miRNAs, 3 of which were found to be 5D specific. Among these, expression of miR2118 was experimentally shown.

The findings from this study will contribute to future research on wheat miRNA function.

Materials and Methods

miRNA reference set

A total of 5,940 miRNAs from 67 plant species deposited in the current release of miRBase have been downloaded (http://www.mirbase.org/) and compared to bread wheat (Triticum aestivum L.) chromosome 5D survey sequence data in order to find all miRNAs represented in T.aestivum.After identical miRNA sequences had been removed,3,228 unique sequences corresponding to 1,556 miRNA families were remained. To date 42 miRNAs were shown to be present in T.aestivum[42][44].

Wheat chromosome 5DS & 5DL sequences

Long and short arm of T.aestivum (cv Chinese Spring) flow sorted chromosome 5D were sequenced by using GS Titanium Rapid Library Preparation Kit, the GS FLX Titanium LV emPCR Kit and GS FLX Titanium Sequencing (XLR70) Kit following the manufacturer’s instructions(Roche Diagnostics). A total of 3,208,630 reads; 937,264 from 5DS, 2,271,366 for 5DL were obtained representing 1.34x and 1.61x coverage for 5DS and 5DL respectively. All sequence reads were submitted to the EBI Sequence Read Archive, accession number ERP002330 (http://www.ebi.ac.uk/ena/data/view/ERP002330).Two separate databases were generated from 454 GS FLX sequence reads using the BLAST+ stand-alone toolkit, version 2.2.25 [74].

Computational prediction of conserved miRNAs in wheat chromosome 5D

Conserved miRNA sequences were identified with the strategy described in [3], [48], [75] using two in-house Perl scripts: SUmirFind and SUmirFold (For current versions of both scripts please contact S.Lucas (slucas@sabanciuniv.edu). A total of 3228 mature miRNA queries were blasted separately against the sequence databases generated for 5DS and 5DL. SUmirFind uses blastn algorithm with parameters optimized for short-query sequences (word size:7;dust filter: off; e-value: 1,000). The program also eliminates the hits giving more than 3 mismatches to a published mature miRNA query sequence. SUmirFind results were recorded in table format including miRNAs giving hit to sequence reads. These read sequences were subjected to UNAFnew2, edited version of UNAFold (an implementation of Zuker algorithm [76]) using the other Perl script, SUmirFold. The secondary structures of the hit sequences were first predicted and sequences with more than 6 mismatches to the mature miRNA were removed. The remaining sequences containing the miRNA sequences were re-folded and checked whether they fit the putative miRNA criteria described in [17], [37], [48], [77]. After the manual elimination of multi-branch loops, following characteristics were determined and given in a table format: the new miRNA sequence, conserved miRNA sequence, pre-miRNA sequence, sequence ID, mature miRNA length, pre-miRNA length, number of mismatches to the query, pre-miRNA stem-loop start and end sites, hairpin location, MFE (ΔG kcal/mol), %GC content and MFEI. Maximum, minimum, and average of these values were calculated separately first and later combined for 5DS and 5DL (Data S2: Table 2 and Table 3).

miRNA representation analysis for both arms in chromosome 5D

The number of sequence reads of 5DS and 5DL which contained potential T. aestivum miRNA stem-loop structures were counted and recorded for each miRNA. In order to prevent over-representation, identical hits for the same miRNA were removed. Representation was analyzed both individually and collectively for 5DS and 5DL (Data S3:Table 1).

Potential miRNA targets

First, all T. aestivum predicted miRNAs were searched in miRBase and known targets of homologous miRNAs were listed (Table 1).T. aestivum miRNA target prediction was also performed using an online software psRNAtarget containing DFCI Gene Index Release 12. (http://plantgrn.-noble.org/psRNATarget/; [10], [17], [37], [48], [75]) (Data S4: Table 1, 2). Possible target functions of newly identified miRNAs were searched manually using QuickGO (http://www.ebi.ac.uk/QuickGO/), a web based browser for gene ontology terms and annotations which are provided by the UniProt-GOA project at the EBI, and were listed in Table 2.

Elimination of known repeat sequences encoding miRNAs

In order to screen and mask repetitive elements in all 5DL and 5DS survey sequence reads againsta custom repeat library assembled from the Triticeae Repeat Sequence Database (TREP, release10), RepeatMasker version 3.2.9was used. Sequences matching known repeats were masked and compared with potential T. aestivum miRNA sequences to show miRNA representation on repeat regions 5D chromosome arms.

Retroelements and DNA transposons that are present in short and long arms of 5D were listed and compared between each other and potential miRNAreads (Data S5: Table 1, 2 and 3).

EST analysis for potential miRNAs

For the in silico expression of identified miRNAs was analyzed by blasting predicted miRNAs, as queries, against T. aestivum EST database in NCBI. All EST matches were compared to the non-redundant protein database at NCBI using blastx in order to find candidate pre-miRNA coding ESTs. Hits with an e-value of less than or equal to 1E-03were considered to be protein-coding, and were eliminated. Predicted miRNA and the accession codes of corresponding EST hits were listed for both arms of chromosome 5D(Table 3).

Searching for 5D specific expression of miRNAs in O.sativa and B.distachyon

All B.distachyon (International Brachypodium Initiative 2010) and O.sativa(International Rice Genome Sequencing Project, last updated in 2010) genomic sequences were downloaded and separate databases were generated for each organism. Predicted 5D miRNAs corresponding to 654 (for 5DL), 428 (for 5DS) unique mature miRNA sequences were blasted against the databases. Predicted miRNAs giving hits to specific chromosomes of B.distachyon were listed (Table 4).

Plant materials and growth conditions

Triticum aestivum L. cv. Chinese Spring (AABBDD), its nullitetrasomic and 5D deletion line series were grown in normal greenhouse conditions (16-h light at 22oC and 8-h dark at 18oC). Seeds were surface sterilized and vernalized in petri dishes for 3–4 days at 4oC. Seedlings were transferred to pots containing soil supplemented with 200 ppm N, 100 ppm P and 20 ppm S. Leaf tissue was collected from adult plants (CS, nullitetrasomic and deletion series) and stored at −80oC.

Two lines of the nulli-tetrasomic series (N5D-T5A and N5D-T5B : with the genomic constitution of AABBAA and AABBBB, respectively) from Kansas State University were used. These lines lacked homoeologous 5D chromosomes (nullisomic condition) that were replaced by another homoeologous chromosome pair (tetrasomic condition) : 5A and 5B in N5D-T5A and N5D-T5B, respectively.

Four homozygous lines from the group-5 wheat chromosome deletion series (5DS-2, 5DS-5, 5DL-5, 5DL-7) with different deletion breakpoints were also retrieved from the Kansas State University wheat collection and used. The length of the remaining chromosome arm in each deletion line is referred as the 'fraction length' (FL). Corresponding FL values of each deletion line used are given in Figure 3c.

Plant DNA and RNA material

RNA isolation from frozen CS leaf tissue was carried out using TRI Reagent (Sigma,MO USA) according to the manufacturer’s instructions. Quality and quantity of isolated RNA was measured using a Nanodrop ND-100 spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA). Integrity of the isolated RNA was confirmed by separating the major rRNA bands in agarose gels. DNase treatment of 1 µg of total RNA was performed in 10 µl reaction mixture with 1 U of DNase I dioxyribonuclease I (Fermentas). First strand cDNA was synthesized from 100 ng of DNase treated RNA with RevertAid H- M-MuLV RT (Fermantas).

Genomic DNA isolation from frozen leaf tissue of wheat (CS, nullitetrasomic and deletion series) was performed using Wizard® Genomic DNA Purification Kit (Madison, WI, USA) according to the manufacturer's instructions.

Flow sorted chromosome arms (5DS and 5DL) were obtained from and J. Doležel and colleagues (IEB, Olomouc, Czech Republic; unpublished). All nucleic acid samples were stored at −20°C.

End point PCR and RT-PCR screening of predicted pre-miRNAs

To experimentally validate 5D chromosome localization of selected pre-miRNAs (miR169, miR5085, miR2118, miR5070, miR6220), PCR screening was carried out using DNA from flow-sorted 5D chromosome arms. To identify 5D chromosome specific miRNAs, screening of gDNA from CS and nullitetrasomic lines (N5D-T5A and N5D-T5B) for these pre-miRNAs was also performed. Additionally, using group-5 deletion series wheat gDNA, 5DL specific pre-miRNAs were screened to determine their location on the chromosome arm.

To check the expression of these pre-miRNAs in adult leaf tissue of wheat plants grown under standard greenhouse conditions, cDNA synthesized from CS RNA was used for RT-PCR.

PCR reactions were performed using 1 ul (10 ng/ul) DNA/cDNA template and were performed in a 20 µl PCR mix including 2 µl 10X Taq buffer (final concentration 1X), 1,6 µl 2.5 mM dNTP (final concentration 0.2 mM), 0,6 µl 10 µM primer (final concentration 300 nM) and 0,1 µl of 5U/µl Taq polymerase (0.5 U). 2.5 mM MgCl2 (stock concentration : 25 mM) was used for the amplification of miR6220, miR5070 and miR2118 and this value was optimized to 2 mM and 3 mM for the miR5085 and miR169 amplicons. Thermal cycling setup was adjusted as follows : heated to 95oC for 5 minutes; followed by 35 cycles of 95oC for 1 minute, 50oC/60,5oC/62oC for 30 seconds and 72oC for 30 seconds, followed by 72oC for 10 minutes. For amplification of miR2118 and miR5070, the annealing temperature was optimized to 50oC and 60,5oC, respectively. The annealing temperatures for the remaining miRNAs were optimized to 62oC. Primers used for PCR analysis are listed in Data S6 : Table 2.

Separation of PCR products (with 1∶5 ul 6X loading dye) was performed using 3% agarose gels run at 100V.

Quantitative real time PCR

To quantify pre-miRNA gene copy number and expression in CS, qRT-PCR was performed using FastStart Universal SYBR Green Master (ROX) (Mannheim, Germany) on an Icycler Multicolor Real-time PCR Detection Systems (Bio-Rad Laboratories). For quantification of pre-miR5070, which is located both on 5D and other wheat chromosomes, nullitetrasomic lines were used along with CS to quantify its 5D specific gene copy number. Normalization was performed with BF474284 primers (Forward Primer : CCATACTTGCATCCCCATCT; Reverse Primer : GTGTTGGATGAGCGCATTT), located to the long arm of wheat chromosome 1A.

Using 1ul of DNA/cDNA, quantitative PCR reactions were performed as 20 µL including 10 µL 2× Master mix and 0.6 µL forward/reverse primer mix (300 nM from each). Specified qRT-PCR thermal setup was adjusted as follows: heated to 95°C for 10 min, followed by 40 cycles of 95°C for 15 s, 56/58°C for 30 sec, and 72°C for 30 s, followed by 72°C for 7 min. The annealing temperature was optimized to 56°C for mi6220 and miR2118 quantification. The annealing temperatures for the remaining miRNAs were optimized to 58°C. The melting curves were generated by collecting fluorescence signals from 55°C to 95°C as the temperature increased 0.5°C with a dwell time of 10 seconds for 80 cycles. (Pre-miR2118 gene copy number quantification could not be performed due to the presence of an additional nonspecific band).

For analysis of quantification, PCR efficiency calculations were performed using the program LinRegPCR retrieved from the publication of Rujiter and his colleagues [78].

Supporting Information

Data S1.

Table 1. List of miRNAs that are found in 5DL, 5DS and 4A chromosome. Table 2: List of newly identified miRNAs for both chromosome arms.

https://doi.org/10.1371/journal.pone.0069801.s001

(XLSX)

Data S2.

Table 1. MFEI table of both chromosome arms for unique pre-miRNA sequences. Table 2: MFEI table of 5D long chromosome arm with representative miRNA sequences. Table 3: MFEI table of 5D short chromosome arm with representative miRNA sequences.

https://doi.org/10.1371/journal.pone.0069801.s002

(XLSX)

Data S3.

Table 1. Representation of 5D chromosome miRNAs.

https://doi.org/10.1371/journal.pone.0069801.s003

(XLSX)

Data S4.

Table 1. psRNATarget results for 5DL chromosome arm. Table 2: psRNATarget results for 5DS chromosome arm.

https://doi.org/10.1371/journal.pone.0069801.s004

(XLSX)

Data S5.

Table 1. Repeat Families of 5D chromosome. Table 2: RepeatMasker program results for 5D long chromosome arm. Table 3: RepeatMasker program results for 5D short chromosome arm.

https://doi.org/10.1371/journal.pone.0069801.s005

(XLSX)

Data S6.

Table 1. Pre-miRNA sequences selected for experimental validation. Table 2: Primer sequences.

https://doi.org/10.1371/journal.pone.0069801.s006

(XLSX)

Acknowledgments

The authors would like to thank the IWGSC (www.wheatgenome.org) for use of sequence data. Special thanks to Kansas State University for providing nullitetrasomic and deletion lines used in this study.

Author Contributions

Conceived and designed the experiments: HB. Performed the experiments: KK MK. Analyzed the data: KK MK SJL. Contributed reagents/materials/analysis tools: KK MK HB. Wrote the paper: KK MK HB.

References

  1. 1. Guo H-S, Xie Q, Fei J-F, Chua N-H (2005) MicroRNA Directs mRNA Cleavage of the Transcription Factor NAC1 to Downregulate Auxin Signals for Arabidopsis Lateral Root Development. The Plant Cell Online 17: 1376–1386.
  2. 2. Jones-Rhoades MW, Bartel DP (2004) Computational Identification of Plant MicroRNAs and Their Targets, Including a Stress-Induced miRNA. Molecular cell 14: 787–799.
  3. 3. Kantar M, Unver T, Budak H (2010) Regulation of barley miRNAs upon dehydration stress correlated with target gene expression. Functional & Integrative Genomics 10: 493–507.
  4. 4. Kim J, Jung J-H, Reyes JL, Kim Y-S, Kim S-Y, et al. (2005) microRNA-directed cleavage of ATHB15 mRNA regulates vascular development in Arabidopsis inflorescence stems. The Plant Journal 42: 84–94.
  5. 5. Lu Y, Yang X (2010) Computational Identification of Novel MicroRNAs and Their Targets in Vigna unguiculata. Comparative and Functional Genomics 2010.
  6. 6. Rodriguez RE, Mecchia MA, Debernardi JM, Schommer C, Weigel D, et al. (2010) Control of cell proliferation in Arabidopsis thaliana by microRNA miR396. Development 137: 103–112.
  7. 7. Sunkar R, Chinnusamy V, Zhu J, Zhu J-K (2007) Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends in plant science 12: 301–309.
  8. 8. Unver T, Bakar M, Shearman R, Budak H (2010) Genome-wide profiling and analysis of Festuca arundinacea miRNAs and transcriptomes in response to foliar glyphosate application. Molecular Genetics and Genomics 283: 397–413.
  9. 9. Wang J-W, Czech B, Weigel D (2009) miR156-Regulated SPL Transcription Factors Define an Endogenous Flowering Pathway in Arabidopsis thaliana. Cell 138: 738–749.
  10. 10. Zhang B, Wang Q, Pan X (2007) MicroRNAs and their regulatory roles in animals and plants. Journal of Cellular Physiology 210: 279–289.
  11. 11. Zhang B, Wang Q, Wang K, Pan X, Liu F, et al. (2007) Identification of cotton microRNAs and their targets. Gene 397: 26–37.
  12. 12. Ruiz-Ferrer V, Voinnet O (2009) Roles of Plant Small RNAs in Biotic Stress Responses. Annual Review of Plant Biology 60: 485–510.
  13. 13. Budak H, Akpinar A (2011) Dehydration stress-responsive miRNA in Brachypodium distachyon: evident by genome-wide screening of microRNAs expression. OMICS: A Journal of Integrative Biology 15: 791–799.
  14. 14. Bartel DP (2004) MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell 116: 281–297.
  15. 15. Chen J, Li WX, Xie D, Peng JR, Ding SW (2004) Viral Virulence Protein Suppresses RNA Silencing–Mediated Defense but Upregulates the Role of MicroRNA in Host Gene Expression. The Plant Cell Online 16: 1302–1313.
  16. 16. Puzey JR, Karger A, Axtell M, Kramer EM (2012) Deep Annotation of Populus trichocarpa microRNAs from Diverse Tissue Sets. PLoS ONE 7: e33034.
  17. 17. Lucas SJ, Budak H (2012) Sorting the Wheat from the Chaff: Identifying miRNAs in Genomic Survey Sequences of Triticum aestivum Chromosome 1AL. PLoS ONE 7: e40859.
  18. 18. Voinnet O (2009) Origin, Biogenesis, and Activity of Plant MicroRNAs. Cell 136: 669–687.
  19. 19. Park MY, Wu G, Gonzalez-Sulser A, Vaucheret H, Poethig RS (2005) Nuclear processing and export of microRNAs in Arabidopsis. Proceedings of the National Academy of Sciences of the United States of America 102: 3691–3696.
  20. 20. Jones-Rhoades MW, Bartel DP, Bartel B (2006) MicroRNAs and their regulatory roles in plants. Annual Review of Plant Biology 57: 19–53.
  21. 21. Vaucheret H (2008) Plant ARGONAUTES. Trends in plant science 13: 350–358.
  22. 22. Llave C, Xie Z, Kasschau KD, Carrington JC (2002) Cleavage of Scarecrow-like mRNA Targets Directed by a Class of Arabidopsis miRNA. Science 297: 2053–2056.
  23. 23. Unver T, Namuth-Covert DM, Budak H (2009) Review of Current Methodological Approaches for Characterizing MicroRNAs in Plants. International Journal of Plant Genomics 2009.
  24. 24. Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP (2002) MicroRNAs in plants. Genes & Development 16: 1616–1626.
  25. 25. Czech B, Hannon GJ (2011) Small RNA sorting: matchmaking for Argonautes. Nat Rev Genet 12: 19–31.
  26. 26. Yan YS, Zhang YM, Yang K, Sun ZX, Fu YP, et al. (2011) Small RNAs from MITE-derived stem-loop precursors regulate abscisic acid signaling and abiotic stress responses in rice. Plant Journal 65: 820–828.
  27. 27. Guleria P, Mahajan M, Bhardwaj J, Yadav SK (2011) Plant Small RNAs: Biogenesis, Mode of Action and Their Roles in Abiotic Stresses. Genomics, Proteomics & Bioinformatics 9: 183–199.
  28. 28. Filipowicz W, Bhattacharyya SN, Sonenberg N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nature Reviews Genetics 9: 102–114.
  29. 29. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, et al. (2008) Criteria for Annotation of Plant MicroRNAs. The Plant Cell Online 20: 3186–3190.
  30. 30. Smith DB, Flavell RB (1975) Characterisation of the wheat genome by renaturation kinetics. Chromosoma 50: 223–242.
  31. 31. Sun J, Zhou M, Mao Z, Li C (2012) Characterization and Evolution of microRNA Genes Derived from Repetitive Elements and Duplication Events in Plants. PLoS ONE 7: e34092.
  32. 32. Yuan Z, Sun X, Liu H, Xie J (2011) MicroRNA Genes Derived from Repetitive Elements and Expanded by Segmental Duplication Events in Mammalian Genomes. PLoS ONE 6: e17666.
  33. 33. Piriyapongsa J, Jordan IK (2008) Dual coding of siRNAs and miRNAs by plant transposable elements. RNA 14: 814–821.
  34. 34. Li Y, Li C, Xia J, Jin Y (2011) Domestication of Transposable Elements into MicroRNA Genes in Plants. PLoS ONE 6: e19212.
  35. 35. Doležel J, Kubaláková M, Paux E, Bartoš J, Feuillet C (2007) Chromosome-based genomics in the cereals. Chromosome Research 15: 51–66.
  36. 36. Schwab R, Palatnik JF, Riester M, Schommer C, Schmid M, et al. (2005) Specific Effects of MicroRNAs on the Plant Transcriptome. Developmental cell 8: 517–527.
  37. 37. Jin W, Li N, Zhang B, Wu F, Li W, et al. (2008) Identification and verification of microRNA in wheat (Triticum aestivum). Journal of Plant Research 121: 351–355.
  38. 38. Sabot F, Guyot R, Wicker T, Chantret N, Laubin B, et al. (2005) Updating of transposable element annotations from large wheat genomic sequences reveals diverse activities and gene associations. Molecular Genetics and Genomics 274: 119–130.
  39. 39. Li W, Zhang P, Fellers JP, Friebe B, Gill BS (2004) Sequence composition, organization, and evolution of the core Triticeae genome. The Plant Journal 40: 500–511.
  40. 40. Paux E, Roger D, Badaeva E, Gay G, Bernard M, et al. (2006) Characterizing the composition and evolution of homoeologous genomes in hexaploid wheat through BAC-end sequencing on chromosome 3B. The Plant Journal 48: 463–474.
  41. 41. Lee Y, Kim M, Han J, Yeom KH, Lee S, et al. (2004) MicroRNA genes are transcribed by RNA polymerase II. EMBO J 23: 4051–4060.
  42. 42. Dryanova A, Zakharov A, Gulick PJ (2008) Data mining for miRNAs and their targets in the Triticeae. Genome 51: 433–443.
  43. 43. Yao Y, Guo G, Ni Z, Sunkar R, Du J, et al. (2007) Cloning and characterization of microRNAs from wheat (Triticum aestivum L.). Genome Biol 8: R96.
  44. 44. Wei B, Cai T, Zhang R, Li A, Huo N, et al. (2009) Novel microRNAs uncovered by deep sequencing of small RNA transcriptomes in bread wheat (Triticum aestivum L.) and Brachypodium distachyon (L.) Beauv. Functional & Integrative Genomics 9: 499–511.
  45. 45. Linkiewicz AM, Qi LL, Gill BS, Ratnasiri A, Echalier B, et al. (2004) A 2500-locus bin map of wheat homoeologous group 5 provides insights on gene distribution and colinearity with rice. Genetics 168: 665–676.
  46. 46. Šafář J, Šimková H, Kubaláková M, Číhalíková J, Suchánková P, et al. (2010) Development of chromosome-specific BAC resources for genomics of bread wheat. Cytogenetic and genome research 129: 211–223.
  47. 47. Schreiber AW, Shi BJ, Huang CY, Langridge P, Baumann U (2011) Discovery of barley miRNAs through deep sequencing of short reads. BMC Genomics 12: 129.
  48. 48. Kantar M, Akpınar B, Valárik M, Lucas S, Doležel J, et al. (2012) Subgenomic analysis of microRNAs in polyploid wheat. Functional & Integrative Genomics 12: 465–479.
  49. 49. Piriyapongsa J, Mariño-Ramírez L, Jordan IK (2007) Origin and Evolution of Human microRNAs From Transposable Elements. Genetics 176: 1323–1337.
  50. 50. Piriyapongsa J, Jordan IK (2007) A Family of Human MicroRNA Genes from Miniature Inverted-Repeat Transposable Elements. PLoS ONE 2: e203.
  51. 51. Mette MF, van der Winden J, Matzke M, Matzke AJM (2002) Short RNAs Can Identify New Candidate Transposable Element Families in Arabidopsis. Plant Physiology 130: 6–9.
  52. 52. Smalheiser NR, Torvik VI (2005) Mammalian microRNAs derived from genomic repeats. Trends in Genetics 21: 322–326.
  53. 53. Borchert GM, Lanier W, Davidson BL (2006) RNA polymerase III transcribes human microRNAs. Nat Struct Mol Biol 13: 1097–1101.
  54. 54. Vitulo N, Albiero A, Forcato C, Campagna D, Dal Pero F, et al. (2011) First Survey of the Wheat Chromosome 5A Composition through a Next Generation Sequencing Approach. PLoS ONE 6: e26421.
  55. 55. Liu HH, Tian X, Li YJ, Wu CA, Zheng CC (2008) Microarray-based analysis of stress-regulated microRNAs in Arabidopsis thaliana. RNA 14: 836–843.
  56. 56. Zhou L, Liu Y, Liu Z, Kong D, Duan M, et al. (2010) Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. Journal of experimental botany 61: 4157–4168.
  57. 57. Kantar M, Lucas SJ, Budak H (2011) miRNA expression patterns of Triticum dicoccoides in response to shock drought stress. Planta 233: 471–484.
  58. 58. Cheng Z, Targolli J, Huang X, Wu R (2002) Wheat LEA genes, PMA80 and PMA1959, enhance dehydration tolerance of transgenic rice (Oryza sativa L.). Molecular Breeding 10: 71–82.
  59. 59. Li Y, Meng F, Zhang C, Zhang N, Sun M, et al. (2012) Comparative analysis of water stress-responsive transcriptomes in drought-susceptible and -tolerant wheat (Triticum aestivum L.). Journal of Plant Biology 55: 349–360.
  60. 60. Shen Q, Chen C-N, Brands A, Pan S-M, Tuan-Hua D (2001) The stress- and abscisic acid-induced barley gene HVA22: developmental regulation and homologues in diverse organisms. Plant Molecular Biology 45: 327–340.
  61. 61. Kantar M, Lucas SJ, Budak H (2011) Drought stress: molecular genetics and genomics approaches. Advances in Botanical Research 57: 445–493.
  62. 62. Xu G-Y, Rocha PCF, Wang M-L, Xu M-L, Cui Y-C, et al. (2011) A novel rice calmodulin-like gene, OsMSR2, enhances drought and salt tolerance and increases ABA sensitivity in Arabidopsis. Planta 234: 47–59.
  63. 63. Peter M (2010) Targeting of mRNAs by multiple miRNAs: the next step. Oncogene 29: 2161–2164.
  64. 64. Chen HM, Chen LT, Patel K, Li YH, Baulcombe DC, et al. (2010) 22-Nucleotide RNAs trigger secondary siRNA biogenesis in plants. Proceedings of the National Academy of Sciences 107: 15269–15274.
  65. 65. Manavella PA, Koenig D, Weigel D (2012) Plant secondary siRNA production determined by microRNA-duplex structure. Proceedings of the National Academy of Sciences 109: 2461–2466.
  66. 66. Song X, Li P, Zhai J, Zhou M, Ma L, et al. (2012) Roles of DCL4 and DCL3b in rice phased small RNA biogenesis. The Plant Journal 69: 462–474.
  67. 67. Jagadeeswaran G, Zheng Y, Li YF, Shukla LI, Matts J, et al. (2009) Cloning and characterization of small RNAs from Medicago truncatula reveals four novel legume-specific microRNA families. New Phytol 184: 85–98.
  68. 68. Shivaprasad PV, Chen H-M, Patel K, Bond DM, Santos BACM, et al.. (2012) A MicroRNA Superfamily Regulates Nucleotide Binding Site–Leucine-Rich Repeats and Other mRNAs. The Plant Cell Online.
  69. 69. Srivastava S, Srivastava AK, Suprasanna P, D'Souza SF (2013) Identification and profiling of arsenic stress-induced microRNAs in Brassica juncea. J Exp Bot 64: 303–315.
  70. 70. Liang G, He H, Yu D (2012) Identification of nitrogen starvation-responsive microRNAs in Arabidopsis thaliana. PLoS One 7: e48951.
  71. 71. Singh K, Talla A, Qiu W (2012) Small RNA profiling of virus-infected grapevines: evidences for virus infection-associated and variety-specific miRNAs. Funct Integr Genomics 12: 659–669.
  72. 72. Yin Z, Li Y, Yu J, Liu Y, Li C, et al. (2012) Difference in miRNA expression profiles between two cotton cultivars with distinct salt sensitivity. Molecular Biology Reports 39: 4961–4970.
  73. 73. Zhang X, Zou Z, Gong P, Zhang J, Ziaf K, et al. (2011) Over-expression of microRNA169 confers enhanced drought tolerance to tomato. Biotechnol Lett 33: 403–409.
  74. 74. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, et al. (2009) BLAST+: architecture and applications. BMC Bioinformatics 10: 421.
  75. 75. Unver T, Budak H (2009) Conserved microRNAs and their targets in model grass species Brachypodium distachyon. Planta 230: 659–669.
  76. 76. Markham NR, Zuker M (2008) UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol 453: 3–31.
  77. 77. Zhang B, Pan X, Anderson TA (2006) Identification of 188 conserved maize microRNAs and their targets. FEBS Letters 580: 3753–3762.
  78. 78. Ruijter JM, Ramakers C, Hoogaars WMH, Karlen Y, Bakker O, et al. (2009) Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Research 37: e45.