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Review of Current Methodological Approaches for Characterizing MicroRNAs in Plants

Review of Current Methodological Approaches for Characterizing MicroRNAs in Plants Hindawi Publishing Corporation International Journal of Plant Genomics Volume 2009, Article ID 262463, 11 pages doi:10.1155/2009/262463 Review Article Review of Current Methodological Approaches for Characterizing MicroRNAs in Plants 1 2 1 Turgay Unver, Deana M. Namuth-Covert, and Hikmet Budak Biological Sciences & Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli, Tuzla, 34956 Istanbul, Turkey Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA Correspondence should be addressed to Hikmet Budak, budak@sabanciuniv.edu Received 17 February 2009; Revised 19 June 2009; Accepted 16 July 2009 Recommended by Andrew James Advances in molecular biology have led to some surprising discoveries. One of these includes the complexities of RNA and its role in gene expression. One particular class of RNA called microRNA (miRNA) is the focus of this paper. We will first briefly look at some of the characteristics and biogenesis of miRNA in plant systems. The remainder of the paper will go into details of three different approaches used to identify and study miRNA. These include two reverse genetics approaches: computation (bioinformatics) and experimental, and one rare forward genetics approach. We also will summarize how to measure and quantify miRNAs, and how to detect their possible targets in plants. Strengths and weaknesses of each methodological approach are discussed. Copyright © 2009 Turgay Unver et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction processed in the nucleus from longer stem-loop structures called pre-miRNAs, that are approximately 70 nucleotides in 1.1. MicroRNA Description. With the advent of more length. miRNA genes are transcribed by RNA polymerase II, advanced molecular techniques, a newly discovered phe- with some miRNA genes residing in intron sequences. After nomenon of gene expression involving RNA has been transcription, the 5 end of pri-miRNA is capped and the 3 characterized. MicroRNAs (miRNAs) are small endogenous, end is polyadenylated. Based upon sequence homologies, the noncoding regulatory RNA sequences. They have been stem and loop structures are formed with base pairings. Pri- found to play key roles in regulatory functions of gene miRNA is thought to be longer than conserved stem-loop expression for most eukaryotes [1–3]. These endogenous structures (pre-miRNA), therefore some processing takes RNA sequences are the interest of intensive research in place next. various model organisms, ranging from plants to mammals. Pri-miRNA is processed by an enzyme complex called In plants, miRNAs are involved in a number of biological microprocessor which includes Dicer like-1 nucleases mechanisms including plant growth, development, and (members of an RNase III endonuclease family) and HYL1, a defense response against abiotic stress. Evidence indicates dsRNA binding protein, bound to the pri-miRNA complex. that miRNAs regulate gene expression at posttranscriptional In Arabidopsis thaliana all of the miRNA biogenesis steps levels in various organisms [3–6]. Further research has are processed by one of the four Dicer-like RNase III shown that miRNA sequences in plants are deeply conserved endonucleases [7]. Among these, Dicer-like-1 is specialized [5] and have near perfect complementarities with their for miRNA processing, while other Dicer-like enzymes are specific messenger RNA (mRNA) targets. As a result of involved with another type of small RNA biogenesis and this complementarity, a plant miRNA guides the cleavage, accumulation known as short interfering RNAs (siRNAs) degradation, or translational inhibition of its target mRNA, [8]. However, Dicer like-4 dependence for the accumulation thereby affecting gene expression. Figure 1 and the following text illustrate the biogenesis of several plant miRNAs has also been detected [9]. of miRNAs. MicroRNAs are 21–24 nucleotides long and are MicroRNAs are structurally and functionally similar to 2 International Journal of Plant Genomics miRNA gene Helicase 5 Cap RNA Pol II RISC AGO Pri-miRNA Mature miRNA with RISC complex Microprocessor AGO Pre-miRNA Dicer like-1 HEN1 CH Mature miRNA 3 Export to Target mRNA hybridization CH cytoplasm Figure 1: MicroRNA biogenesis. AGO siRNA, but miRNAs originate from long stem loop double stranded RNAs (dsRNAs) [10]. At this point, the structure is now referred to as “pre-miRNA” and has one final step remaining. The pre- miRNA complex is methylated with HEN1 enzyme and then exported into the cytoplasm with the help of HASTY, an Translational miRNA transporter. After methylation, the structure is called mRNA degredation inhibition mRNA cleavage “mature miRNA”. The HASTY protein might be a plant ortholog of an animal enzyme known as Exportin-5, which has been shown in animal systems to also transport mature miRNAs to the cytoplasm [2, 11]. AGO The mature miRNA structure (miRNA) is next loaded into an RNA-induced ribonucleoprotein silencing complex Figure 2: MicroRNA mechanism in plants. (RISC) to cleave its specific target mRNA or to inhibit the translation of its target transcript (Figure 2)[12]. The RISC complex includes Arganoute (AGO1) (PAZ, RNA- binding domain and RNaseH-like P-element induced wimpy testis (PIWI) domain containing protein) and miRNA is target DNA methyltransferases to catalyse methylation of degraded. The AGO protein family is the most important DNA [17]. Histone H3 lysine 9 is an important target for and key component of the miRNA-RISC complex [7]. epigenetic modifications in plants. H3K9 methylation is Their ability to suppress protein synthesis and association associated with epigenetic regulation of gene expression and with miRNAs was demonstrated in human [13]. Single- heterochromatin modification [18]. stranded miRNA in the RISC is able to target a specific To summarize the basic principles of miRNA biogenesis, mRNA sequence, having sequence complementarity and by although we see many similarities between plant and animal the Piwi domain. The AGO component can then cleave systems, there are also ample differences between plant and the miRNA-mRNA duplex (or siRNA-mRNA duplex) [7], animal miRNA characteristics and biogenesis. thereby slowing gene expression of that particular mRNA. First, it is known that plant miRNAs are mostly generated On the other hand, translational inhibition and mRNA from noncoding transcriptional units [5]incontrastwith degradation are also other ways gene expression is regulated some of the animal miRNAs which are processed from by miRNA. This can occur via deadenylation of the 3 poly introns and protein coding genetic sequences [19]. Com- (A) tail and decapping of the 5 end in mRNAs, which pared to animals, plants have a more complex small RNA leads to progressive mRNA decay and degradation [14]. population in their transcriptomes. Due to the abundance It has been demonstrated that in Drosophyla melanogaster of plant-specific RNA Polymerase IV and RNA Polymerase S2 cells, the P-body protein GW182, which is a key V-dependent siRNA and trans-acting siRNAs, plant miRNAs component marking mRNAs for decay, interacts with the are represented in the pool of small RNAs. By contrast, AGO1 [15]. Furthermore, RNA directed-DNA methylation animal small RNA populations are generally filled with revealing epigenetic regulation of gene expression has been miRNAs in their transcriptomes [8]. Plant miRNAs have a demonstrated in Arabidopsis,reviewedin[16]. This process unique 5 end which differsfromanimal miRNA5 end is initiated with RNA signal through cleavage of dsRNA sequences. To repress translation, plant miRNAs tend to bind by Dicer Like family 3 (DCL3) proteins. These signals to the protein-coding region of target mRNAs [20–22], but 3 poly- International Journal of Plant Genomics 3 animal miRNAs bind to the 3 untranslatedregion(3 UTR) have limitations. First, most of the miRNAs are tissue and of their target mRNA transcripts [23]. time specific, and generally their expression level is low. To date, 1763 miRNAs have been identified in plants, In addition, they mostly express in response to specific including 187 from Arabidopsis, 377 from rice, 234 from environmental stimuli. They also coexist with their cleaved Populus, 98 from maize, 72 from sorghum, 230 from and degraded target mRNAs, hence cloning small RNAs Physcomitrella, 38 from Medicago truncatula,78from (miRNA and siRNA) is difficult, whereas computational soybean, 37 from Pinus taeda,58from Selaginella approaches are effective because of no need for cloning. Since moellendorffii,44from Brassica napus and 16 from forward genetics, or the genetic screening approach, is time sugar-cane (miRBase release 13.0, March, 2009, http:// consuming, expensive, and less efficient, it is rarely used microrna.sanger.ac.uk/sequences/). These plant miRNAs for plant miRNA identification. Next generation massive have been identified via computational (bioinformat- sequencing techniques such as pyrosequencing and Illumina ics) and/or experimental methods. For instance, through are also applied to identify new miRNAs in plants [40, 41]. sequence homology analysis, 30 potential miRNAs were Here we summarize the main approaches to each strategy predicted from cotton [24] and an additional 58 wheat for identifying plant miRNAs, starting with computational miRNAs have been identified by Yao et al. [25]. The (bioinformatics) approaches. majority of plant miRNAs studied to date negatively regulate their target gene expression at the posttranscriptional level. They are involved in regulating developmental processes 2. Computational Approach [3, 26, 27] responding to environmental stresses [27, 28] and play a variety of important biological and metabolic 2.1. Sequence and Structure Conservation in miRNAs. processes [29–31]. Some examples of these processes include Once potential miRNA sequences have been cloned and the regulation of plant development (miR172, floral organ sequenced, the sequence data can be imported into a variety of software programs for computational analysis. These specification, and miR166, leaf polarity), root initiation and development (ath-miR164), signal transduction (i.e., bioinformatics tools search for sequence and structure con- miR159, miR160, miR164, and miR167), and also plant servation of miRNAs [42] using homology searches with pre- viously known/identified miRNAs. To date a number of com- environmental response (miR391 and miR395), as reviewed by Zhang et al. [32]. putational methods have been reported for the identification of plant miRNAs [5, 27, 33, 39, 43, 44]. Research in plants has revealed that short length sequences of mature miRNAs are 1.2. Strategies for miRNA Identification and Characterization. conserved and have high complementarities to their target mRNAs [27]. Hence, candidate miRNAs can be detected In reverse genetics strategies, researchers are utilizing known sequences to discover functions or phenotypes. miRNA using the conserved complementarities of miRNA to target identification largely relies on two main reverse genetics mRNA, if the mRNA target sequence is known. On the other hand, it has also been shown that the secondary struc- strategies: (1) computer-based (bioinformatics) and (2) experimental approaches. A third identification approach, tures of miRNA precursor (pre-miRNA) are relatively more conserved than pri-miRNA sequences (precursor of pre- forward genetics, is rarely used in miRNA discovery. Forward miRNA) (Figure 1)[45]. Recent bioinformatics tools were genetics is the classical approach where researchers have a known phenotype, but the DNA sequence (genotype) coding used to identify miRNA utilizing both sequence and sec- ondary structure alignments; one of these tools is miRAlign for that particular phenotype is unknown. miRNA identification using bioinformatics tools is one in which more properties of miRNA structure conservation of the most widely used methods, contributing considerably are considered (http://bioinfo.au.tsinghua.edu.cn/miralign) [43, 45]. Since the characteristic patterns of the conservation to the prediction of new miRNAs in both animal and plant systems. This is largely due to the low cost, high efficiency, of miRNAs are searched by algorithms, the major challenge is finding miRNAs which are species specific and unrelated fast and comprehensive methodology of bioinformatics. The to previously known organisms. main theory behind this approach is finding homologous sequences of known miRNAs both within a single genome and across genomes of related organisms [33, 34]. Sequence and structure homologies are used for computer-based 2.2. Bioinformatics Tools Used for Identifying miRNA and predictions of miRNAs. Computational strategies provide Its Target mRNA. Severalprogramshavebeendesignedfor avaluableand efficient manner to predict miRNA genes the identification of miRNAs and their targets. Here we and their targets. The software-based approach is applied summarize five of the most commonly and widely used to animals, human, fungi, and plants [35–38]. For example software tools for identifying miRNAs and miRNA targets. Zhang et al. identified 338 new possible miRNAs in 60 In this section, the softwares and databases are exemplified different plant species [31] and Adai et al. have predicted 43 and their use in identifying plant miRNAs is described. The new miRNAs in Arabidopsis [39]. first one, miRBase, is currently a database of all known In contrast, cloning and sequencing of small RNA miRNA sequences. Following the description of miRBase, libraries represents an experimental approach to identify the plant miRNA-mRNA target finder called miRU will be and characterize miRNAs. However, in contrast with bioin- explained. The secondary structure for a given pre-miRNA formatics, such approaches for miRNA identification also sequence can be predicted with appropriate criteria using 4 International Journal of Plant Genomics a third software, RNAmFold. Another program, micro- the minimum free energy (MFE) secondary structure using HARVESTER, can be applied to find homology of a given the algorithm originally proposed by Zuker and Stiegler [48]. miRNA in one plant species with a candidate miRNA in Equilibrium base-pairing probabilities of MFE structures are another plant species. Also mentioned in this section is calculated via McCaskill’s partition function (PF) algorithm findmiRNA, which is used for finding possible miRNAs in [49]. a given precursor miRNA sequence. After each of these software tools (described below), an example will be given. 2.2.4. micro-HARVESTER (http://www-ab.informatik.uni- tuebingen.de/brisbane/tb/index.php). micro-HARVESTER is a computational tool that searches for miRNA homologs in 2.2.1. miRBase (http://microrna.sanger.ac.uk/). miRBase is a a given miRNA sequence query. Due to sequence similarity, central online database for all (plant, animal, virus, fungus the search step is followed by a set of structural filters. This to date) miRNAs including sequences, nomenclature, and method is a sensitive approach to identify miRNA candidates target mRNA prediction data from all species. Currently with higher specificity. The approach uses a BLAST search the 13.0 version of the online database (March, 2009) to generate the first set of candidates and then the process consists of 8619 miRNA total entries from 103 species. These entries represent 9539 hairpin precursor miRNAs, expressing continues with a series of filters based on structural features specific to plant miRNAs to achieve the desired specificity 9169 mature miRNA products with 1763 plant miRNAs. [50]. The database has three main functions. miRBase::Registry is where individual data is uploaded to the database prior to publication of novel miRNAs. miRBase::Sequences 2.2.5. findmiRNA (http://sundarlab.ucdavis.edu/mirna/)(A provides miRNA sequences, nomenclature, and references. Resource of Predicted miRNA and Precursor Candidates for miRBase::Targets provides the prediction of the mRNA target the Arabidopsis Genome). findmiRNA algorithm is used for from all published animal miRNAs [46]. predicting potential miRNAs in a given set of candidate precursor sequences which have corresponding target sites in the transcriptome. Generally the algorithm is based on the 2.2.2. miRU (http://bioinfo3.noble.org/miRNA/miRU.htm). complementarity existing between plant miRNAs and their miRU is known as a potential plant mRNA target finder. mRNA targets to identify initial putative miRNA. Then the Using this database, a mature miRNA sequence from a plant software analyzes the candidate miRNA precursor sequence species is uploaded. The miRU system searches for potential with regard to forming a stem-loop structure [39]. Since complementary target sites in miRNA-target recognition the tool identifies any sequence with the potential to form with acceptable mismatches. Specifically, the user enters a hairpin structures, it has limitations such as the possibility of mature miRNA sequence in the 5 to 3 direction (entered identifing tRNAs, foldback elements, and retrotransposons sequence can be in a range of 19–28 nucleotides long) [39]. then the dataset should be selected for prediction of mRNA target in the intended organism of interest. The allowable complementary mismatches between the target mRNA and 2.2.6. MiRCheck (http://web.wi.mit.edu/bartel/pub/software the uploaded miRNA sequence can be adjusted or limited by .html). MiRCheck is an algorithm designed to identify the user. The output report provides information for each 20 mers which encode potential plant miRNAs [27]. Entries predicted miRNA target including gene identifier, target site should be (1) putative miRNA hairpin sequences, (2) puta- position, mismatch score, number of mismatches, and target tive hairpin secondary structures, and (3) 20-mer potential complementary sequence with color highlighted mismatches plant miRNA sequences within the hairpin for MIRcheck [31]. miRU is a very useful software for identifying mRNA algorithm. This software requires data of miRNA comple- targets of specific plant miRNAs. However not all plant mentarities that should be conserved between homologous species are available at this time. mRNAs in Arabidopsis and Oryza sativa. Researchers use this software to check their candidate miRNAs if they have potential to encode miRNA. 2.2.3. RNA mFold (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold To briefly summarize, the largest limitation of most .cgi). The algorithm utilized in the RNAmFold bioinformat- bioinformatic methods is the need to start from a known ics tool predicts secondary structures of single stranded RNA homologue and depend heavily on conservation of sec- or DNA sequences. It is currently packaged in the Vienna ondary structure and mature miRNA sequences. More RNA websuite, a collection of tools for folding, designing, advanced methods using hidden Markov models can over- and analyzing of RNA sequences [47]. The package also come this limitation. As an example, Kadri et al. [51]have provides additional analysis of folding parts using the bar- developed a novel approach, Hierarchical Hidden Markov riers program and structural RNA alignments. The package Model (HHMM) that utilizes region-based structural infor- includes basic programs such as RNAFold for structure mation of miRNA precursors. They used this model for prediction of single sequences, RNAalifold for consensus computational miRNA hairpin prediction in the absence of miRNA structure prediction on a set of aligned sequences, conservation in human [51]. RNAinverse for seqence design, RNAcofold and RNAup for RNA-RNA interaction analysis, LocARNA for the generation of structural alignment and barriers, and treekin for folding 2.3. EST Database Analysis Used for miRNA Prediction. Now kinetics analysis. The RNAmFold tool is used for predicting we will describe how the above tools are utilized in the search International Journal of Plant Genomics 5 of miRNAs. One such example is with Expressed sequence OsSet1 with 0–2 base substitutions. The Patscan algorithm tags (ESTs). ESTs are partial sequences of complementary was used in consideration of 20 mers on the same arm of DNA (cDNA) cloned into plasmid vectors [52]. RNA is their putative hairpins. the starting material from which the cDNA clone is made, Bonnetetal. [35] also applied computational approaches using reverse transcriptase. Many important plant genes to detect miRNAs and then applied EST analysis to confirm using EST databases have been cloned [53, 54]. It is well 91 newly identified miRNAs in Oryza sativa and Arabidopsis known that miRNAs are deeply conserved from species thaliana.Zhang et al.[31] have taken previously all-known to species, which allows researchers the ability to predict Arabidopsis miRNAs (miRBase Release 3.0, April 2004) and orthologues of previously known miRNAs by utilizing EST searched the EST databases (using Basic Local Alignment databases. Availability of ESTs in databases for identifying Search Tool for nucleotide analyzes (BLASTn) 2.2.9 (May new plant miRNAs increases with coverage of the genome 1, 2004)) to find ESTs matched with miRNAs. They found and number of sequences. Currently, GeneBank release a total of 18 694 BLAST hits in the databases and removed 171.0 April 2009 (http://www.ncbi.nlm.nih.gov/Genbank/) the EST sequences with high numbers (more than 2) of contains 103 335 431 EST sequences, representing more mismatched hits. Their result came to a total of 812 ESTs with than 1370 different organisms. The number of ESTs 0 to 2 mismatches. They used those ESTs to predict secondary available for a specific organism can be found at structures with RNA mFold software. Finally they identified http://www.ncbi.nlm.nih.gov/dbEST/dbEST summary.html. 338 new potential miRNAs in 60 plant species. This particular website is the best to utilize because conserved candidate miRNAs and their precursors can be predicted 2.4. Sample Method to Identify miRNA in a Plant Species using this resource. The largest number of plant ESTs is Using Computational Approach. In this section, we work from maize (Zea mays) (2 018 530), thale cress (Arabidopsis through an example of using ESTs and the software thaliana) (1 527 298), soybean (Glycine max) (1 386 618), tools discussed previously, to identify plant miRNAs. rice (Oryza sativa) (1 248 955), wheat (Triticum aestivum) In order to predict the plant miRNAs, EST sequences (1 064 111), oilseed rape (Brassica napus) (596 471), and should be downloaded from the GenBank database barley (Hordeum vulgare) (525 527). (http://www.ncbi.nlm.nih.gov/)(Figure 3(a)). Searching for To identify homologous miRNAs across plant species, miRNA-like sequences includes two major procedures: EST analysis approaches have been developed using sequence searching pre-miRNA-like sequences and identifying conservation of known miRNAs. An extra filtering which pre-miRNAs and miRNAs. First, the RNAfold (http:// provides structure prediction (secondary structure), such rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi)[47]program as the “Zuker folding” algorithm with RNAmFold software or mFold program (http://frontend.bioinfo.rpi.edu/ [55], has also been applied [27, 31, 39]. applications/mfold/cgi-bin/rna-form1.cgi)[55]can be used Zhang et al. [31] reported an EST database analysis for to find potential miRNA hairpin structures from the predicting new plant miRNA genes using the BLAST algo- databased EST sequences [56]. According to Weibo et al. rithm to search known plant miRNAs (taken from miRBase [57], strict criteria should be adopted in the identification 3.1 April, 2004). Their additional filter was the Zuker folding of pre-miRNA-like sequences from hairpin structure algorithm (mFold 3.1) to predict the secondary structure sequences. These are of putative miRNA sequences. The Zuker algorithm outputs (a) 60 nucleotides is the minimum length of pre-miRNA used to analyze the results included the number of structures, free energy (ΔG kcal/mol), miRNA-like helicity, the number sequences; of arms per structure, size of helices within arms, and size (b) the stem of the hairpin structure (including the GU and symmetry of internal loops within arms. Then hairpin wobble pairs) includes at least 17 base pairs; stem-loop structures of predicted putative miRNAs were (c) −15 kcal/mol should be the maximum free energy of analyzed for structure filtering with known miRNAs using the secondary structure; computational strategies [42]. The highest score of stem- (d) the secondary structure must not compromise multi- loop structures was considered as new miRNAs and then the ESTs (with high similarity, E value less than e-100) were branch loops; assigned as miRNA clones. (e) the GC content of pre-miRNA should be between 24 Jones-Rhoades and Bartel [10] have also analyzed EST and 71%. databases to look for possible miRNAs in other plant species using known Arabidopsis miRNAs. They also revised the Following these criteria assures that the processed computational strategy with possible miRNA targets to sequences are similar to real pre-miRNAs, according to increase sensitivity of the approach. Their first step for widely accepted characteristics. Second, the real pre- identifying miRNAs in the genome was detecting genomic miRNAs might be identified from a large number of portions containing imperfect inverted repeats using the pre-miRNA-like sequences using a program such as Genom- “EINVERT” algorithm. Then they used the RNAmFold soft- icSVM (http://geneweb.go3.icpcn.com/genomicSVM/). This ware to predict secondary structures of miRNA candidates. program was developed using the “Support Vector They checked all 20 mers within the inverted repeats against Machine” model [57]. Alternatively, miRCheck (http://web MiRCheck. After MiRCheck analysis they applied Patscan to .wi.mit.edu/bartel/pub/softwareWebTools.html) can be ap- identify 20 mers in AtSet1 that matched at least one 20 mer in plied (Figure 3(a)) for confirming sequences to identify, if 6 International Journal of Plant Genomics ESTs of the organism GSSs of the organism Nucleic acid isolation And/or Total RNA isolation using trizol from NCBI from NCBI reagent according to manufacturers instructions All known plant mature miRNA Total RNAs are enriched Small-sized RNA sequences from miRBase with small-sized RNAs enrichment Recovery of small RNAs, 16 to 28 nt long from denaturing gel BLAST using algorithm (15% polyacrylamide) parameters Adaptor ligation by using poly(A) polymerase Synthesis of first strand cDNA Additionally apply BLASTX to Remove repeat sequences and using MMLV reverse transcriptase remove protein coding sequences sequences with > 4 mismatches cDNAs are amplified using 17.93D primers (the cDNAs can be pol(G) tailed at 3 end and amplified with primerse HindIII (dT) and BamHI(dC) 5 -GGAATTCGGATC16-3 ) Prediction of secondary structure Cloning of miRNAs mFold or RNAfold Using BigDye terminator cycle sequencing kit (PE applied biosystems) cloned fragments Apply the criteria mentioned in are sequenced on DNA sequencer the text Analysis of results using Check the pre-miRNA sequences bioinformatic tools (a) (b) Figure 3: (a) Flow chart for computational approaches in identifying plant miRNAs. (b) Flow chart for experimental approaches in identifying plant miRNAs. the entries contain 20 mers which encode potential plant environmental conditions, at different plant developmental miRNAs. stages and tissues. Therefore specific time points, tissues, and/or biotic and abiotic stressed induced plant samples are used for miRNA cloning. The most common plant species 3. Experimental Approaches used for direct cloning are Arabidopsis thaliana [5, 58, 59], Oryza sativa (rice) [60], (cottonwood) [61], and Triticum Computational methods for identifying miRNAs in plants aestivum (wheat) [24]. are rapid, less expensive and relatively easy compared with experimental procedures. However, these bioinformatic approaches can only identify conserved miRNAs among 3.2. An Example of the Direct Cloning Experimental Approach. organisms and DNA or RNA sequence information is Total RNA is extracted from the organism of interest [40, required in order to run the softwares. On the other 41]. Next, small RNAs approximately 16–28 nucleotides hand, the computationally predicted miRNAs should also long are selected from the total RNA and excised from be confirmed via experimental methods. These experimental a polyacrylamide gel. Next, these small RNAs are ligated method options are described next. with an adaptor and reverse transcribed [62]. Resulting cDNAs are amplified with Real-Time PCR (RT-PCR) using 3.1. Direct Cloning and Sequencing of Small RNA Libraries. primers designed for adaptor sites. Finally, the RT-PCR Direct cloning of small RNAs from plants is one of the products are concatamerized and cloned [63]. Selected basic approaches of miRNA discovery. Scientists have used clones are sequenced and the sequence data is then analyzed this methodology to isolate and clone small RNAs from (Figure 3(b)). These experimental procedures for identifying various plant species such as Arabidopsis and rice [5, 20, 21, miRNA have been successfully applied and detailed by 58, 59]. Identification of miRNAs using the direct cloning Elbashir et al. [63], Lau et al. [62], and Park et al. [58]. approach basically involves the creation of a cDNA library Alternatively, new high-throughput technologies such as and includes six steps: (1) isolation of total RNA from plant 454 pyrosequencing and Solexa sequencing can be used for tissue, (2) recovery of small RNAs from an acrylamide gel, identification of plant miRNAs [40, 41]. (3) adaptor ligation, (4) reverse transcription, RT-PCR, (5) The most important advantage of high-throughput cloning, and (6) sequencing methods. Expression of several deep sequencing technology compared to computational miRNAs is broad but many of them are detected in certain approaches is the opportunity for finding nonconserved and International Journal of Plant Genomics 7 species specific miRNAs. To identify conserved and noncon- different strategies, end-point PCR, SYBR Green I assay, and served miRNAs in tomato, Moxon et al. (2008) used the TaqMan UPL procedure. Each of these strategies is described pyrosequencing approach [40]. On the other hand, Szittya next. et al. (2008) successfully used Solexa sequencing to find new miRNAs in barrel medic (Medicago truncatula)[41]. They 3.3.1. End-Point PCR. A nontemplate control should be have identified 25 conserved and 26 novel nonconserved included with each experiment to insure the expected miRNAs using 1 563 959 distinct sequences and 2 168 937 banding pattern for specific cDNA of miRNA amplification. reads [41]. Experimental approaches also provide to detect A PCR master mix is prepared and the following components and measure the specific miRNAs expressed in plants. Using added to nuclease-free eppendorf tubes: 15.4 µLnuclease- the following methods plant miRNAs are efficiently detected free water, 2 µL10 × PCR buffer, 0.4 µL10mMdNTP and quantified. mix, 0.4 µL forward primer (10 µM), 0.4 µL reverse primer (10 µM), and 0.4 µL Advantage 2 Polymerase mix. Then 19 µL of the PCR master mix should be aliquot into different 3.3. miRNA Detection and Quantification Methods. Efficient tubes and 1 µL RT product is added to reaction mixtures. and suitable miRNA detection and quantification are essen- ◦ After that, the thermal cycler is set up as 94 Cfor 2minutes, tial to understand miRNA function in specific conditions, ◦ ◦ followed by 20–40 cycles of 94 Cfor 15 seconds and60 Cfor cell and tissue types. Northern hybridization, cloning, and 1 minute. Finally the PCR reaction products are analyzed by microarray analysis are widely used to detect and quantify electrophoresis on a 4% agarose gel in 1× TAE gel. miRNAs in plants, but these techniques are less sensitive and are not high throughput compared with quantitative 3.3.2. SYBR Green I Assay. SYBR Green I master mix is real-time reverse transcription PCR (qRT-PCR) and end- prepared according to real-time qPCR system (5 × LightCy- poind PCR. Effective and sensitive qRT-PCR detection can cler for Roche Diagnostics or 2X Master mix for Stratagene circumvent these limitations. Several methods have been Mx3005p) by adding 1 µL forward primer (10 µM), 1 µL developed to detect and quantify miRNA for mammalian reverse primer (10 µM), 12 µL nuclease-free water (for 5 cells [64–66]. Recently Varkonyi-Gasic et al. [67]described × LightCycler for Roche Diagnostics) or (6 µLnuclease- a protocol for an end-point and real-time looped RT-PCR free water for 2X Master mix for Stratagene Mx3005p), procedure. Their approach includes two steps. In the first 4 µL SYBR Green I master mix for 5 × LightCycler for step, a stem-loop RT primer is designed, following the Roche Diagnostics or (10 µL SYBR Green I master mix for strategy developed by Chen et al. [68] and is hybridized Stratagene Mx3005p). Using nuclease-free eppendorf tubes with the candidate miRNA. The second step includes the for each qPCR reactions, 18 µL prepared mixtures containing specific amplification of the miRNA, using a forward primer master mix and primers are pipetted into each tube. Adding specific for the miRNA and a universal reverse primer, 2 µL RT products to tubes, the reaction is started. qPCR which is designed for the stem-loop RT primer sequence. machine is set up as 95 C for 5 minutes, followed by 35– The clues for designing the reverse RT primers and miRNA ◦ ◦ 45 cycles of 95 C for 5 seconds, 60 C for 10 seconds, and specific forward primers are that the specificity of stem- 72 C for 1 second. For melting curve analysis samples are loop RT primers for a certain miRNA is conferred by a ◦ ◦ ◦ denaturated at 95 C, and then cooled to 65 Cat20 C six nucleotide extension at the 3 end. This extension is per second. Fluorescence signals are collected at 530 nm the reverse complement of the last six nucleotides at the ◦ ◦ ◦ wavelengths continuously from 65 Cto95 Cat0.2 Cper 3 end of the miRNA. Forward RT primers are specifically second. Finally results are analyzed using the LightCycler or designed for individual miRNA sequences. At the primer’s Stratagene software. 5 end 5–7 random and relatively GC-rich nucleotides are added to increase the template’s melting temperature [67]. 3.3.3. miRNA TaqMan UPL Probe Procedure. To perform To perform end-point and real-time looped RT-PCR a TaqMan assay for miRNA detection and quantification miRNA quantification experiments, total RNA is isolated using Universal Probe Library (UPL) probes, first a 5× from a plant sample using the TRizol reagent according to LightCycler TaqMan master mix (Roche Diagnostics) is manufacturer’s protocol (Invitrogen, Carlsbad, CA). Then prepared according to manufacturer’s instructions. Next the stem-loop RT PCR reaction is performed by mixing the the following components are added to a nuclease-free component as follows: 0.5 µL 10 mM dNTP mix, 11.15 µL eppendorf tube: 11.8 µL nuclease-free water, 4 µLTaqMan nuclease-free water, 1 µL of appropriate stem-loop RT primer master mix, 1 µL forward primer (10 µM), 1 µLreverse (1 µM), and the mix is heated at 65 Cfor 5minutes and primer (10 µM), 0.2 µLUPL probeno. 21 (10 µM). Then chilled on ice for 2 minutes. Additional components are real-time qRT-PCR is performed with cycling temperatures added to the mixture, 4 µL5× First-Strand buffer, 2 µL and resulting data analyzed as described above with the 0.1 M DTT, 0.1 µL RNaseOUT (40 units/µL), and 0.25 µL, SYBR Green I assay protocol. An educational eLesson SuperScript III RT (200 units/µL). Finally the pulsed RT and animation further describing the real time PCR reaction incubation is set up as 30 minutes at 16 C, followed technique can be found at the Plant and Soil Sciences ◦ ◦ by pulsed RT of 60 cycles at 30 C for 30 seconds, 42 Cfor eLibrary (http://plantandsoil.unl.edu/croptechnology2005/ 30 seconds, and 50 C for 1 second. RT products can be used pages/index.jsp?what=topicsD&topicOrder=1&information to detect and quantify individual miRNAs in plants via three ModuleId=1057077340). 8 International Journal of Plant Genomics 3.4. Forward Genetics. miRNAs were first discovered via 1 for non-G:U wobble pairs, 2 for each bulged or loop mutant analysis [64] in animals. However to date, there is nucleotide in the miRNA or target site. They have reported only one example using a forward genetics experimental scored complementary results of ≤2 in conserved miRNA approach to identify miRNA in plants. Baker et al. [69] target mRNA sites in both Arabidopsis and O. sativa. identified an miRNA loss of function allele by a transposon insertion upstream of the predicted MIR164c stem-loop. The 4.2. Experimental Approaches for Prediction of miRNA Targets. miRNA mutant resulted in a flower phenotype with extra As with computational approaches, experimental approaches petals. Since highly conserved plant miRNAs are encoded have been utilized widely to predict plant miRNA-mRNA by gene families, functional redundancy restricts the loss target sites. Genome-wide expression profiling to search of function of an miRNA gene, making mutation searches for miRNA targets can be applied on expression arrays. highly inefficient. Overexpression of miRNA genes and In one example, array data showed that five transcripts precursors or construction of miRNA resistant transgenic encoding TCP genesweredownregulated viaoverexpression plants have the potential to better provide a clear assessment of miR319a (miR-JAW) in Arabidopsis. Those five TCP tran- of overlapping functions of other miRNA family members. scription factor mRNAs show up to five mismatches, or four If this comes to fruitition, then forward genetics approaches mismatches when G:Uwobblecounts0.5 mismatch [71]. may become more viable in identifying miRNAs. Additionally Schwab et al. [22] overexpressed four different miRNAs in each Arabidposis plant and examined each expression profile to experimentally establish parameters 4. Identification of miRNA Targets for target cleavage guided by plant miRNAs. However, they found no new target mRNAs other than previously identified So far we have described computational (bioinformatics) and by computational approaches. Two new targets, not found experimental approaches used to identify miRNA sequences. through bioinformatics, were detected, but their cleaved Now we will describe methods utilized to identify their products were not confirmed via 5 RACE experiments. targets, mRNA sequences cleaved or targeted by miRNA. Specific miRNA targets in plant genomes and transcriptomes have been identified with both experimental and compu- 4.3. 5 RACE Experiment. At present, the most powerful tational approaches. Predicting miRNA targets in plants is method to confirm miRNA-mRNA targets is the 5 RACE much easier due to the high and significant complemen- procedure (Random Amplification of cDNA Ends). 5 RACE tarities to miRNA-mRNA targets [34]. The ability for plant has been used by many researchers to identify miRNA targets miRNAtotargetmRNAwithperfect sequence complemen- in plants [3, 21, 71, 72]. Cleaved mRNA products in plants tary matches was first shown with miR171 [59]. It was shown have two diagnostic properties. One is that the 5 phosphate that miR171 has perfect antisense complementarity with of a cleaved mRNA product can be ligated to an RNA adaptor three SCARECROW-like (SCL) transcription factors in the with T4 RNA ligase. Second, in general, the precise target Arabidopsis genome. Additionally, this particular miRNA is cleavage position is that mRNA target nucleotides pair with transcribed from an intergenic locus and lacks a stem-loop the tenth nucleotide of miRNA [21, 73]. Cleaved mRNA structure [5, 21]. Predicting conserved miRNA targets in products by miRNA guided activity can be amplified with lig- different organisms has revealed that homologous mRNAs ation of an oligo-nucleotide adaptor to the 5 end, followed are targeted by conserved miRNAs within an miRNA family, by reverse transcription and PCR amplification with a gene yet allowing more gaps and more mismatches between specific primer [21]. A modified 5 RACE procedure can be an individual miRNA and its target [27]. Next we will appliedasfollows.Total RNAisisolatedand polyAmRNAis summarize some of the bioinformatic and experimental prepared (Qiatex mRNA midi kit, Qiagen, CA) and directly methods utilized to find mRNA targets of known miRNAs ligated to an RNA oligo adaptor (supplied by GeneRacer in plants. kit, Invitrogen, CA). Oligo dT is used to synthesize the first strand of cDNA with reverse transcriptase. This first cDNA strand is amplified with GenRacer 5 and 3 primers for 4.1. Computer-Based Procedures for Predicting mRNA nongene specific amplification (according to manufacturer’s Sequences Targeted by miRNA. Several algorithms are used procedures, Invitrogen, CA, USA or Clontech, RL, USA). for predicting putative miRNA-mRNA targets in plants; Then the 5 RACE PCR and 5 nested PCR are performed for this purpose mirU is one of the widely used softwares. using specific primers supplied with kits. RACE products are The mirU system using given miRNA sequences searches gel purified, cloned, and sequenced. for potential mRNA targets with tolerable mismatches [70]. Additionally, Jones-Rhoades and Bartel [10] have developed a more refined method by using the MIR check algorithm to 5. Concluding Remarks predict miRNA targets specifically in Arabidopsis and Oryza sativa. The MirCheck software allows for more mismatches miRNA studies in plants have already explained a number and gaps in miRNA-mRNA complexes in these two species. of biological events in response to both biotic and abiotic This software also needs miRNA complementarities that stresses. Improved understanding of molecular mechanisms should be conserved between homologous mRNAs in of miRNA in plants will lead to the development of novel and Arabidopsis and Oryza sativa [10]. They have scored the more precise techniques that will help better understanding miRNA complementary sites as 0.5 for G:U wobble pairs, some posttranscriptional gene silencing in response to both International Journal of Plant Genomics 9 biotic and abiotic stresses. Accumulating knowledge on the [13] R. S. Pillai, C. G. Artus, and W. 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Review of Current Methodological Approaches for Characterizing MicroRNAs in Plants

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Copyright © 2009 Turgay Unver et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Hindawi Publishing Corporation International Journal of Plant Genomics Volume 2009, Article ID 262463, 11 pages doi:10.1155/2009/262463 Review Article Review of Current Methodological Approaches for Characterizing MicroRNAs in Plants 1 2 1 Turgay Unver, Deana M. Namuth-Covert, and Hikmet Budak Biological Sciences & Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli, Tuzla, 34956 Istanbul, Turkey Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA Correspondence should be addressed to Hikmet Budak, budak@sabanciuniv.edu Received 17 February 2009; Revised 19 June 2009; Accepted 16 July 2009 Recommended by Andrew James Advances in molecular biology have led to some surprising discoveries. One of these includes the complexities of RNA and its role in gene expression. One particular class of RNA called microRNA (miRNA) is the focus of this paper. We will first briefly look at some of the characteristics and biogenesis of miRNA in plant systems. The remainder of the paper will go into details of three different approaches used to identify and study miRNA. These include two reverse genetics approaches: computation (bioinformatics) and experimental, and one rare forward genetics approach. We also will summarize how to measure and quantify miRNAs, and how to detect their possible targets in plants. Strengths and weaknesses of each methodological approach are discussed. Copyright © 2009 Turgay Unver et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction processed in the nucleus from longer stem-loop structures called pre-miRNAs, that are approximately 70 nucleotides in 1.1. MicroRNA Description. With the advent of more length. miRNA genes are transcribed by RNA polymerase II, advanced molecular techniques, a newly discovered phe- with some miRNA genes residing in intron sequences. After nomenon of gene expression involving RNA has been transcription, the 5 end of pri-miRNA is capped and the 3 characterized. MicroRNAs (miRNAs) are small endogenous, end is polyadenylated. Based upon sequence homologies, the noncoding regulatory RNA sequences. They have been stem and loop structures are formed with base pairings. Pri- found to play key roles in regulatory functions of gene miRNA is thought to be longer than conserved stem-loop expression for most eukaryotes [1–3]. These endogenous structures (pre-miRNA), therefore some processing takes RNA sequences are the interest of intensive research in place next. various model organisms, ranging from plants to mammals. Pri-miRNA is processed by an enzyme complex called In plants, miRNAs are involved in a number of biological microprocessor which includes Dicer like-1 nucleases mechanisms including plant growth, development, and (members of an RNase III endonuclease family) and HYL1, a defense response against abiotic stress. Evidence indicates dsRNA binding protein, bound to the pri-miRNA complex. that miRNAs regulate gene expression at posttranscriptional In Arabidopsis thaliana all of the miRNA biogenesis steps levels in various organisms [3–6]. Further research has are processed by one of the four Dicer-like RNase III shown that miRNA sequences in plants are deeply conserved endonucleases [7]. Among these, Dicer-like-1 is specialized [5] and have near perfect complementarities with their for miRNA processing, while other Dicer-like enzymes are specific messenger RNA (mRNA) targets. As a result of involved with another type of small RNA biogenesis and this complementarity, a plant miRNA guides the cleavage, accumulation known as short interfering RNAs (siRNAs) degradation, or translational inhibition of its target mRNA, [8]. However, Dicer like-4 dependence for the accumulation thereby affecting gene expression. Figure 1 and the following text illustrate the biogenesis of several plant miRNAs has also been detected [9]. of miRNAs. MicroRNAs are 21–24 nucleotides long and are MicroRNAs are structurally and functionally similar to 2 International Journal of Plant Genomics miRNA gene Helicase 5 Cap RNA Pol II RISC AGO Pri-miRNA Mature miRNA with RISC complex Microprocessor AGO Pre-miRNA Dicer like-1 HEN1 CH Mature miRNA 3 Export to Target mRNA hybridization CH cytoplasm Figure 1: MicroRNA biogenesis. AGO siRNA, but miRNAs originate from long stem loop double stranded RNAs (dsRNAs) [10]. At this point, the structure is now referred to as “pre-miRNA” and has one final step remaining. The pre- miRNA complex is methylated with HEN1 enzyme and then exported into the cytoplasm with the help of HASTY, an Translational miRNA transporter. After methylation, the structure is called mRNA degredation inhibition mRNA cleavage “mature miRNA”. The HASTY protein might be a plant ortholog of an animal enzyme known as Exportin-5, which has been shown in animal systems to also transport mature miRNAs to the cytoplasm [2, 11]. AGO The mature miRNA structure (miRNA) is next loaded into an RNA-induced ribonucleoprotein silencing complex Figure 2: MicroRNA mechanism in plants. (RISC) to cleave its specific target mRNA or to inhibit the translation of its target transcript (Figure 2)[12]. The RISC complex includes Arganoute (AGO1) (PAZ, RNA- binding domain and RNaseH-like P-element induced wimpy testis (PIWI) domain containing protein) and miRNA is target DNA methyltransferases to catalyse methylation of degraded. The AGO protein family is the most important DNA [17]. Histone H3 lysine 9 is an important target for and key component of the miRNA-RISC complex [7]. epigenetic modifications in plants. H3K9 methylation is Their ability to suppress protein synthesis and association associated with epigenetic regulation of gene expression and with miRNAs was demonstrated in human [13]. Single- heterochromatin modification [18]. stranded miRNA in the RISC is able to target a specific To summarize the basic principles of miRNA biogenesis, mRNA sequence, having sequence complementarity and by although we see many similarities between plant and animal the Piwi domain. The AGO component can then cleave systems, there are also ample differences between plant and the miRNA-mRNA duplex (or siRNA-mRNA duplex) [7], animal miRNA characteristics and biogenesis. thereby slowing gene expression of that particular mRNA. First, it is known that plant miRNAs are mostly generated On the other hand, translational inhibition and mRNA from noncoding transcriptional units [5]incontrastwith degradation are also other ways gene expression is regulated some of the animal miRNAs which are processed from by miRNA. This can occur via deadenylation of the 3 poly introns and protein coding genetic sequences [19]. Com- (A) tail and decapping of the 5 end in mRNAs, which pared to animals, plants have a more complex small RNA leads to progressive mRNA decay and degradation [14]. population in their transcriptomes. Due to the abundance It has been demonstrated that in Drosophyla melanogaster of plant-specific RNA Polymerase IV and RNA Polymerase S2 cells, the P-body protein GW182, which is a key V-dependent siRNA and trans-acting siRNAs, plant miRNAs component marking mRNAs for decay, interacts with the are represented in the pool of small RNAs. By contrast, AGO1 [15]. Furthermore, RNA directed-DNA methylation animal small RNA populations are generally filled with revealing epigenetic regulation of gene expression has been miRNAs in their transcriptomes [8]. Plant miRNAs have a demonstrated in Arabidopsis,reviewedin[16]. This process unique 5 end which differsfromanimal miRNA5 end is initiated with RNA signal through cleavage of dsRNA sequences. To repress translation, plant miRNAs tend to bind by Dicer Like family 3 (DCL3) proteins. These signals to the protein-coding region of target mRNAs [20–22], but 3 poly- International Journal of Plant Genomics 3 animal miRNAs bind to the 3 untranslatedregion(3 UTR) have limitations. First, most of the miRNAs are tissue and of their target mRNA transcripts [23]. time specific, and generally their expression level is low. To date, 1763 miRNAs have been identified in plants, In addition, they mostly express in response to specific including 187 from Arabidopsis, 377 from rice, 234 from environmental stimuli. They also coexist with their cleaved Populus, 98 from maize, 72 from sorghum, 230 from and degraded target mRNAs, hence cloning small RNAs Physcomitrella, 38 from Medicago truncatula,78from (miRNA and siRNA) is difficult, whereas computational soybean, 37 from Pinus taeda,58from Selaginella approaches are effective because of no need for cloning. Since moellendorffii,44from Brassica napus and 16 from forward genetics, or the genetic screening approach, is time sugar-cane (miRBase release 13.0, March, 2009, http:// consuming, expensive, and less efficient, it is rarely used microrna.sanger.ac.uk/sequences/). These plant miRNAs for plant miRNA identification. Next generation massive have been identified via computational (bioinformat- sequencing techniques such as pyrosequencing and Illumina ics) and/or experimental methods. For instance, through are also applied to identify new miRNAs in plants [40, 41]. sequence homology analysis, 30 potential miRNAs were Here we summarize the main approaches to each strategy predicted from cotton [24] and an additional 58 wheat for identifying plant miRNAs, starting with computational miRNAs have been identified by Yao et al. [25]. The (bioinformatics) approaches. majority of plant miRNAs studied to date negatively regulate their target gene expression at the posttranscriptional level. They are involved in regulating developmental processes 2. Computational Approach [3, 26, 27] responding to environmental stresses [27, 28] and play a variety of important biological and metabolic 2.1. Sequence and Structure Conservation in miRNAs. processes [29–31]. Some examples of these processes include Once potential miRNA sequences have been cloned and the regulation of plant development (miR172, floral organ sequenced, the sequence data can be imported into a variety of software programs for computational analysis. These specification, and miR166, leaf polarity), root initiation and development (ath-miR164), signal transduction (i.e., bioinformatics tools search for sequence and structure con- miR159, miR160, miR164, and miR167), and also plant servation of miRNAs [42] using homology searches with pre- viously known/identified miRNAs. To date a number of com- environmental response (miR391 and miR395), as reviewed by Zhang et al. [32]. putational methods have been reported for the identification of plant miRNAs [5, 27, 33, 39, 43, 44]. Research in plants has revealed that short length sequences of mature miRNAs are 1.2. Strategies for miRNA Identification and Characterization. conserved and have high complementarities to their target mRNAs [27]. Hence, candidate miRNAs can be detected In reverse genetics strategies, researchers are utilizing known sequences to discover functions or phenotypes. miRNA using the conserved complementarities of miRNA to target identification largely relies on two main reverse genetics mRNA, if the mRNA target sequence is known. On the other hand, it has also been shown that the secondary struc- strategies: (1) computer-based (bioinformatics) and (2) experimental approaches. A third identification approach, tures of miRNA precursor (pre-miRNA) are relatively more conserved than pri-miRNA sequences (precursor of pre- forward genetics, is rarely used in miRNA discovery. Forward miRNA) (Figure 1)[45]. Recent bioinformatics tools were genetics is the classical approach where researchers have a known phenotype, but the DNA sequence (genotype) coding used to identify miRNA utilizing both sequence and sec- ondary structure alignments; one of these tools is miRAlign for that particular phenotype is unknown. miRNA identification using bioinformatics tools is one in which more properties of miRNA structure conservation of the most widely used methods, contributing considerably are considered (http://bioinfo.au.tsinghua.edu.cn/miralign) [43, 45]. Since the characteristic patterns of the conservation to the prediction of new miRNAs in both animal and plant systems. This is largely due to the low cost, high efficiency, of miRNAs are searched by algorithms, the major challenge is finding miRNAs which are species specific and unrelated fast and comprehensive methodology of bioinformatics. The to previously known organisms. main theory behind this approach is finding homologous sequences of known miRNAs both within a single genome and across genomes of related organisms [33, 34]. Sequence and structure homologies are used for computer-based 2.2. Bioinformatics Tools Used for Identifying miRNA and predictions of miRNAs. Computational strategies provide Its Target mRNA. Severalprogramshavebeendesignedfor avaluableand efficient manner to predict miRNA genes the identification of miRNAs and their targets. Here we and their targets. The software-based approach is applied summarize five of the most commonly and widely used to animals, human, fungi, and plants [35–38]. For example software tools for identifying miRNAs and miRNA targets. Zhang et al. identified 338 new possible miRNAs in 60 In this section, the softwares and databases are exemplified different plant species [31] and Adai et al. have predicted 43 and their use in identifying plant miRNAs is described. The new miRNAs in Arabidopsis [39]. first one, miRBase, is currently a database of all known In contrast, cloning and sequencing of small RNA miRNA sequences. Following the description of miRBase, libraries represents an experimental approach to identify the plant miRNA-mRNA target finder called miRU will be and characterize miRNAs. However, in contrast with bioin- explained. The secondary structure for a given pre-miRNA formatics, such approaches for miRNA identification also sequence can be predicted with appropriate criteria using 4 International Journal of Plant Genomics a third software, RNAmFold. Another program, micro- the minimum free energy (MFE) secondary structure using HARVESTER, can be applied to find homology of a given the algorithm originally proposed by Zuker and Stiegler [48]. miRNA in one plant species with a candidate miRNA in Equilibrium base-pairing probabilities of MFE structures are another plant species. Also mentioned in this section is calculated via McCaskill’s partition function (PF) algorithm findmiRNA, which is used for finding possible miRNAs in [49]. a given precursor miRNA sequence. After each of these software tools (described below), an example will be given. 2.2.4. micro-HARVESTER (http://www-ab.informatik.uni- tuebingen.de/brisbane/tb/index.php). micro-HARVESTER is a computational tool that searches for miRNA homologs in 2.2.1. miRBase (http://microrna.sanger.ac.uk/). miRBase is a a given miRNA sequence query. Due to sequence similarity, central online database for all (plant, animal, virus, fungus the search step is followed by a set of structural filters. This to date) miRNAs including sequences, nomenclature, and method is a sensitive approach to identify miRNA candidates target mRNA prediction data from all species. Currently with higher specificity. The approach uses a BLAST search the 13.0 version of the online database (March, 2009) to generate the first set of candidates and then the process consists of 8619 miRNA total entries from 103 species. These entries represent 9539 hairpin precursor miRNAs, expressing continues with a series of filters based on structural features specific to plant miRNAs to achieve the desired specificity 9169 mature miRNA products with 1763 plant miRNAs. [50]. The database has three main functions. miRBase::Registry is where individual data is uploaded to the database prior to publication of novel miRNAs. miRBase::Sequences 2.2.5. findmiRNA (http://sundarlab.ucdavis.edu/mirna/)(A provides miRNA sequences, nomenclature, and references. Resource of Predicted miRNA and Precursor Candidates for miRBase::Targets provides the prediction of the mRNA target the Arabidopsis Genome). findmiRNA algorithm is used for from all published animal miRNAs [46]. predicting potential miRNAs in a given set of candidate precursor sequences which have corresponding target sites in the transcriptome. Generally the algorithm is based on the 2.2.2. miRU (http://bioinfo3.noble.org/miRNA/miRU.htm). complementarity existing between plant miRNAs and their miRU is known as a potential plant mRNA target finder. mRNA targets to identify initial putative miRNA. Then the Using this database, a mature miRNA sequence from a plant software analyzes the candidate miRNA precursor sequence species is uploaded. The miRU system searches for potential with regard to forming a stem-loop structure [39]. Since complementary target sites in miRNA-target recognition the tool identifies any sequence with the potential to form with acceptable mismatches. Specifically, the user enters a hairpin structures, it has limitations such as the possibility of mature miRNA sequence in the 5 to 3 direction (entered identifing tRNAs, foldback elements, and retrotransposons sequence can be in a range of 19–28 nucleotides long) [39]. then the dataset should be selected for prediction of mRNA target in the intended organism of interest. The allowable complementary mismatches between the target mRNA and 2.2.6. MiRCheck (http://web.wi.mit.edu/bartel/pub/software the uploaded miRNA sequence can be adjusted or limited by .html). MiRCheck is an algorithm designed to identify the user. The output report provides information for each 20 mers which encode potential plant miRNAs [27]. Entries predicted miRNA target including gene identifier, target site should be (1) putative miRNA hairpin sequences, (2) puta- position, mismatch score, number of mismatches, and target tive hairpin secondary structures, and (3) 20-mer potential complementary sequence with color highlighted mismatches plant miRNA sequences within the hairpin for MIRcheck [31]. miRU is a very useful software for identifying mRNA algorithm. This software requires data of miRNA comple- targets of specific plant miRNAs. However not all plant mentarities that should be conserved between homologous species are available at this time. mRNAs in Arabidopsis and Oryza sativa. Researchers use this software to check their candidate miRNAs if they have potential to encode miRNA. 2.2.3. RNA mFold (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold To briefly summarize, the largest limitation of most .cgi). The algorithm utilized in the RNAmFold bioinformat- bioinformatic methods is the need to start from a known ics tool predicts secondary structures of single stranded RNA homologue and depend heavily on conservation of sec- or DNA sequences. It is currently packaged in the Vienna ondary structure and mature miRNA sequences. More RNA websuite, a collection of tools for folding, designing, advanced methods using hidden Markov models can over- and analyzing of RNA sequences [47]. The package also come this limitation. As an example, Kadri et al. [51]have provides additional analysis of folding parts using the bar- developed a novel approach, Hierarchical Hidden Markov riers program and structural RNA alignments. The package Model (HHMM) that utilizes region-based structural infor- includes basic programs such as RNAFold for structure mation of miRNA precursors. They used this model for prediction of single sequences, RNAalifold for consensus computational miRNA hairpin prediction in the absence of miRNA structure prediction on a set of aligned sequences, conservation in human [51]. RNAinverse for seqence design, RNAcofold and RNAup for RNA-RNA interaction analysis, LocARNA for the generation of structural alignment and barriers, and treekin for folding 2.3. EST Database Analysis Used for miRNA Prediction. Now kinetics analysis. The RNAmFold tool is used for predicting we will describe how the above tools are utilized in the search International Journal of Plant Genomics 5 of miRNAs. One such example is with Expressed sequence OsSet1 with 0–2 base substitutions. The Patscan algorithm tags (ESTs). ESTs are partial sequences of complementary was used in consideration of 20 mers on the same arm of DNA (cDNA) cloned into plasmid vectors [52]. RNA is their putative hairpins. the starting material from which the cDNA clone is made, Bonnetetal. [35] also applied computational approaches using reverse transcriptase. Many important plant genes to detect miRNAs and then applied EST analysis to confirm using EST databases have been cloned [53, 54]. It is well 91 newly identified miRNAs in Oryza sativa and Arabidopsis known that miRNAs are deeply conserved from species thaliana.Zhang et al.[31] have taken previously all-known to species, which allows researchers the ability to predict Arabidopsis miRNAs (miRBase Release 3.0, April 2004) and orthologues of previously known miRNAs by utilizing EST searched the EST databases (using Basic Local Alignment databases. Availability of ESTs in databases for identifying Search Tool for nucleotide analyzes (BLASTn) 2.2.9 (May new plant miRNAs increases with coverage of the genome 1, 2004)) to find ESTs matched with miRNAs. They found and number of sequences. Currently, GeneBank release a total of 18 694 BLAST hits in the databases and removed 171.0 April 2009 (http://www.ncbi.nlm.nih.gov/Genbank/) the EST sequences with high numbers (more than 2) of contains 103 335 431 EST sequences, representing more mismatched hits. Their result came to a total of 812 ESTs with than 1370 different organisms. The number of ESTs 0 to 2 mismatches. They used those ESTs to predict secondary available for a specific organism can be found at structures with RNA mFold software. Finally they identified http://www.ncbi.nlm.nih.gov/dbEST/dbEST summary.html. 338 new potential miRNAs in 60 plant species. This particular website is the best to utilize because conserved candidate miRNAs and their precursors can be predicted 2.4. Sample Method to Identify miRNA in a Plant Species using this resource. The largest number of plant ESTs is Using Computational Approach. In this section, we work from maize (Zea mays) (2 018 530), thale cress (Arabidopsis through an example of using ESTs and the software thaliana) (1 527 298), soybean (Glycine max) (1 386 618), tools discussed previously, to identify plant miRNAs. rice (Oryza sativa) (1 248 955), wheat (Triticum aestivum) In order to predict the plant miRNAs, EST sequences (1 064 111), oilseed rape (Brassica napus) (596 471), and should be downloaded from the GenBank database barley (Hordeum vulgare) (525 527). (http://www.ncbi.nlm.nih.gov/)(Figure 3(a)). Searching for To identify homologous miRNAs across plant species, miRNA-like sequences includes two major procedures: EST analysis approaches have been developed using sequence searching pre-miRNA-like sequences and identifying conservation of known miRNAs. An extra filtering which pre-miRNAs and miRNAs. First, the RNAfold (http:// provides structure prediction (secondary structure), such rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi)[47]program as the “Zuker folding” algorithm with RNAmFold software or mFold program (http://frontend.bioinfo.rpi.edu/ [55], has also been applied [27, 31, 39]. applications/mfold/cgi-bin/rna-form1.cgi)[55]can be used Zhang et al. [31] reported an EST database analysis for to find potential miRNA hairpin structures from the predicting new plant miRNA genes using the BLAST algo- databased EST sequences [56]. According to Weibo et al. rithm to search known plant miRNAs (taken from miRBase [57], strict criteria should be adopted in the identification 3.1 April, 2004). Their additional filter was the Zuker folding of pre-miRNA-like sequences from hairpin structure algorithm (mFold 3.1) to predict the secondary structure sequences. These are of putative miRNA sequences. The Zuker algorithm outputs (a) 60 nucleotides is the minimum length of pre-miRNA used to analyze the results included the number of structures, free energy (ΔG kcal/mol), miRNA-like helicity, the number sequences; of arms per structure, size of helices within arms, and size (b) the stem of the hairpin structure (including the GU and symmetry of internal loops within arms. Then hairpin wobble pairs) includes at least 17 base pairs; stem-loop structures of predicted putative miRNAs were (c) −15 kcal/mol should be the maximum free energy of analyzed for structure filtering with known miRNAs using the secondary structure; computational strategies [42]. The highest score of stem- (d) the secondary structure must not compromise multi- loop structures was considered as new miRNAs and then the ESTs (with high similarity, E value less than e-100) were branch loops; assigned as miRNA clones. (e) the GC content of pre-miRNA should be between 24 Jones-Rhoades and Bartel [10] have also analyzed EST and 71%. databases to look for possible miRNAs in other plant species using known Arabidopsis miRNAs. They also revised the Following these criteria assures that the processed computational strategy with possible miRNA targets to sequences are similar to real pre-miRNAs, according to increase sensitivity of the approach. Their first step for widely accepted characteristics. Second, the real pre- identifying miRNAs in the genome was detecting genomic miRNAs might be identified from a large number of portions containing imperfect inverted repeats using the pre-miRNA-like sequences using a program such as Genom- “EINVERT” algorithm. Then they used the RNAmFold soft- icSVM (http://geneweb.go3.icpcn.com/genomicSVM/). This ware to predict secondary structures of miRNA candidates. program was developed using the “Support Vector They checked all 20 mers within the inverted repeats against Machine” model [57]. Alternatively, miRCheck (http://web MiRCheck. After MiRCheck analysis they applied Patscan to .wi.mit.edu/bartel/pub/softwareWebTools.html) can be ap- identify 20 mers in AtSet1 that matched at least one 20 mer in plied (Figure 3(a)) for confirming sequences to identify, if 6 International Journal of Plant Genomics ESTs of the organism GSSs of the organism Nucleic acid isolation And/or Total RNA isolation using trizol from NCBI from NCBI reagent according to manufacturers instructions All known plant mature miRNA Total RNAs are enriched Small-sized RNA sequences from miRBase with small-sized RNAs enrichment Recovery of small RNAs, 16 to 28 nt long from denaturing gel BLAST using algorithm (15% polyacrylamide) parameters Adaptor ligation by using poly(A) polymerase Synthesis of first strand cDNA Additionally apply BLASTX to Remove repeat sequences and using MMLV reverse transcriptase remove protein coding sequences sequences with > 4 mismatches cDNAs are amplified using 17.93D primers (the cDNAs can be pol(G) tailed at 3 end and amplified with primerse HindIII (dT) and BamHI(dC) 5 -GGAATTCGGATC16-3 ) Prediction of secondary structure Cloning of miRNAs mFold or RNAfold Using BigDye terminator cycle sequencing kit (PE applied biosystems) cloned fragments Apply the criteria mentioned in are sequenced on DNA sequencer the text Analysis of results using Check the pre-miRNA sequences bioinformatic tools (a) (b) Figure 3: (a) Flow chart for computational approaches in identifying plant miRNAs. (b) Flow chart for experimental approaches in identifying plant miRNAs. the entries contain 20 mers which encode potential plant environmental conditions, at different plant developmental miRNAs. stages and tissues. Therefore specific time points, tissues, and/or biotic and abiotic stressed induced plant samples are used for miRNA cloning. The most common plant species 3. Experimental Approaches used for direct cloning are Arabidopsis thaliana [5, 58, 59], Oryza sativa (rice) [60], (cottonwood) [61], and Triticum Computational methods for identifying miRNAs in plants aestivum (wheat) [24]. are rapid, less expensive and relatively easy compared with experimental procedures. However, these bioinformatic approaches can only identify conserved miRNAs among 3.2. An Example of the Direct Cloning Experimental Approach. organisms and DNA or RNA sequence information is Total RNA is extracted from the organism of interest [40, required in order to run the softwares. On the other 41]. Next, small RNAs approximately 16–28 nucleotides hand, the computationally predicted miRNAs should also long are selected from the total RNA and excised from be confirmed via experimental methods. These experimental a polyacrylamide gel. Next, these small RNAs are ligated method options are described next. with an adaptor and reverse transcribed [62]. Resulting cDNAs are amplified with Real-Time PCR (RT-PCR) using 3.1. Direct Cloning and Sequencing of Small RNA Libraries. primers designed for adaptor sites. Finally, the RT-PCR Direct cloning of small RNAs from plants is one of the products are concatamerized and cloned [63]. Selected basic approaches of miRNA discovery. Scientists have used clones are sequenced and the sequence data is then analyzed this methodology to isolate and clone small RNAs from (Figure 3(b)). These experimental procedures for identifying various plant species such as Arabidopsis and rice [5, 20, 21, miRNA have been successfully applied and detailed by 58, 59]. Identification of miRNAs using the direct cloning Elbashir et al. [63], Lau et al. [62], and Park et al. [58]. approach basically involves the creation of a cDNA library Alternatively, new high-throughput technologies such as and includes six steps: (1) isolation of total RNA from plant 454 pyrosequencing and Solexa sequencing can be used for tissue, (2) recovery of small RNAs from an acrylamide gel, identification of plant miRNAs [40, 41]. (3) adaptor ligation, (4) reverse transcription, RT-PCR, (5) The most important advantage of high-throughput cloning, and (6) sequencing methods. Expression of several deep sequencing technology compared to computational miRNAs is broad but many of them are detected in certain approaches is the opportunity for finding nonconserved and International Journal of Plant Genomics 7 species specific miRNAs. To identify conserved and noncon- different strategies, end-point PCR, SYBR Green I assay, and served miRNAs in tomato, Moxon et al. (2008) used the TaqMan UPL procedure. Each of these strategies is described pyrosequencing approach [40]. On the other hand, Szittya next. et al. (2008) successfully used Solexa sequencing to find new miRNAs in barrel medic (Medicago truncatula)[41]. They 3.3.1. End-Point PCR. A nontemplate control should be have identified 25 conserved and 26 novel nonconserved included with each experiment to insure the expected miRNAs using 1 563 959 distinct sequences and 2 168 937 banding pattern for specific cDNA of miRNA amplification. reads [41]. Experimental approaches also provide to detect A PCR master mix is prepared and the following components and measure the specific miRNAs expressed in plants. Using added to nuclease-free eppendorf tubes: 15.4 µLnuclease- the following methods plant miRNAs are efficiently detected free water, 2 µL10 × PCR buffer, 0.4 µL10mMdNTP and quantified. mix, 0.4 µL forward primer (10 µM), 0.4 µL reverse primer (10 µM), and 0.4 µL Advantage 2 Polymerase mix. Then 19 µL of the PCR master mix should be aliquot into different 3.3. miRNA Detection and Quantification Methods. Efficient tubes and 1 µL RT product is added to reaction mixtures. and suitable miRNA detection and quantification are essen- ◦ After that, the thermal cycler is set up as 94 Cfor 2minutes, tial to understand miRNA function in specific conditions, ◦ ◦ followed by 20–40 cycles of 94 Cfor 15 seconds and60 Cfor cell and tissue types. Northern hybridization, cloning, and 1 minute. Finally the PCR reaction products are analyzed by microarray analysis are widely used to detect and quantify electrophoresis on a 4% agarose gel in 1× TAE gel. miRNAs in plants, but these techniques are less sensitive and are not high throughput compared with quantitative 3.3.2. SYBR Green I Assay. SYBR Green I master mix is real-time reverse transcription PCR (qRT-PCR) and end- prepared according to real-time qPCR system (5 × LightCy- poind PCR. Effective and sensitive qRT-PCR detection can cler for Roche Diagnostics or 2X Master mix for Stratagene circumvent these limitations. Several methods have been Mx3005p) by adding 1 µL forward primer (10 µM), 1 µL developed to detect and quantify miRNA for mammalian reverse primer (10 µM), 12 µL nuclease-free water (for 5 cells [64–66]. Recently Varkonyi-Gasic et al. [67]described × LightCycler for Roche Diagnostics) or (6 µLnuclease- a protocol for an end-point and real-time looped RT-PCR free water for 2X Master mix for Stratagene Mx3005p), procedure. Their approach includes two steps. In the first 4 µL SYBR Green I master mix for 5 × LightCycler for step, a stem-loop RT primer is designed, following the Roche Diagnostics or (10 µL SYBR Green I master mix for strategy developed by Chen et al. [68] and is hybridized Stratagene Mx3005p). Using nuclease-free eppendorf tubes with the candidate miRNA. The second step includes the for each qPCR reactions, 18 µL prepared mixtures containing specific amplification of the miRNA, using a forward primer master mix and primers are pipetted into each tube. Adding specific for the miRNA and a universal reverse primer, 2 µL RT products to tubes, the reaction is started. qPCR which is designed for the stem-loop RT primer sequence. machine is set up as 95 C for 5 minutes, followed by 35– The clues for designing the reverse RT primers and miRNA ◦ ◦ 45 cycles of 95 C for 5 seconds, 60 C for 10 seconds, and specific forward primers are that the specificity of stem- 72 C for 1 second. For melting curve analysis samples are loop RT primers for a certain miRNA is conferred by a ◦ ◦ ◦ denaturated at 95 C, and then cooled to 65 Cat20 C six nucleotide extension at the 3 end. This extension is per second. Fluorescence signals are collected at 530 nm the reverse complement of the last six nucleotides at the ◦ ◦ ◦ wavelengths continuously from 65 Cto95 Cat0.2 Cper 3 end of the miRNA. Forward RT primers are specifically second. Finally results are analyzed using the LightCycler or designed for individual miRNA sequences. At the primer’s Stratagene software. 5 end 5–7 random and relatively GC-rich nucleotides are added to increase the template’s melting temperature [67]. 3.3.3. miRNA TaqMan UPL Probe Procedure. To perform To perform end-point and real-time looped RT-PCR a TaqMan assay for miRNA detection and quantification miRNA quantification experiments, total RNA is isolated using Universal Probe Library (UPL) probes, first a 5× from a plant sample using the TRizol reagent according to LightCycler TaqMan master mix (Roche Diagnostics) is manufacturer’s protocol (Invitrogen, Carlsbad, CA). Then prepared according to manufacturer’s instructions. Next the stem-loop RT PCR reaction is performed by mixing the the following components are added to a nuclease-free component as follows: 0.5 µL 10 mM dNTP mix, 11.15 µL eppendorf tube: 11.8 µL nuclease-free water, 4 µLTaqMan nuclease-free water, 1 µL of appropriate stem-loop RT primer master mix, 1 µL forward primer (10 µM), 1 µLreverse (1 µM), and the mix is heated at 65 Cfor 5minutes and primer (10 µM), 0.2 µLUPL probeno. 21 (10 µM). Then chilled on ice for 2 minutes. Additional components are real-time qRT-PCR is performed with cycling temperatures added to the mixture, 4 µL5× First-Strand buffer, 2 µL and resulting data analyzed as described above with the 0.1 M DTT, 0.1 µL RNaseOUT (40 units/µL), and 0.25 µL, SYBR Green I assay protocol. An educational eLesson SuperScript III RT (200 units/µL). Finally the pulsed RT and animation further describing the real time PCR reaction incubation is set up as 30 minutes at 16 C, followed technique can be found at the Plant and Soil Sciences ◦ ◦ by pulsed RT of 60 cycles at 30 C for 30 seconds, 42 Cfor eLibrary (http://plantandsoil.unl.edu/croptechnology2005/ 30 seconds, and 50 C for 1 second. RT products can be used pages/index.jsp?what=topicsD&topicOrder=1&information to detect and quantify individual miRNAs in plants via three ModuleId=1057077340). 8 International Journal of Plant Genomics 3.4. Forward Genetics. miRNAs were first discovered via 1 for non-G:U wobble pairs, 2 for each bulged or loop mutant analysis [64] in animals. However to date, there is nucleotide in the miRNA or target site. They have reported only one example using a forward genetics experimental scored complementary results of ≤2 in conserved miRNA approach to identify miRNA in plants. Baker et al. [69] target mRNA sites in both Arabidopsis and O. sativa. identified an miRNA loss of function allele by a transposon insertion upstream of the predicted MIR164c stem-loop. The 4.2. Experimental Approaches for Prediction of miRNA Targets. miRNA mutant resulted in a flower phenotype with extra As with computational approaches, experimental approaches petals. Since highly conserved plant miRNAs are encoded have been utilized widely to predict plant miRNA-mRNA by gene families, functional redundancy restricts the loss target sites. Genome-wide expression profiling to search of function of an miRNA gene, making mutation searches for miRNA targets can be applied on expression arrays. highly inefficient. Overexpression of miRNA genes and In one example, array data showed that five transcripts precursors or construction of miRNA resistant transgenic encoding TCP genesweredownregulated viaoverexpression plants have the potential to better provide a clear assessment of miR319a (miR-JAW) in Arabidopsis. Those five TCP tran- of overlapping functions of other miRNA family members. scription factor mRNAs show up to five mismatches, or four If this comes to fruitition, then forward genetics approaches mismatches when G:Uwobblecounts0.5 mismatch [71]. may become more viable in identifying miRNAs. Additionally Schwab et al. [22] overexpressed four different miRNAs in each Arabidposis plant and examined each expression profile to experimentally establish parameters 4. Identification of miRNA Targets for target cleavage guided by plant miRNAs. However, they found no new target mRNAs other than previously identified So far we have described computational (bioinformatics) and by computational approaches. Two new targets, not found experimental approaches used to identify miRNA sequences. through bioinformatics, were detected, but their cleaved Now we will describe methods utilized to identify their products were not confirmed via 5 RACE experiments. targets, mRNA sequences cleaved or targeted by miRNA. Specific miRNA targets in plant genomes and transcriptomes have been identified with both experimental and compu- 4.3. 5 RACE Experiment. At present, the most powerful tational approaches. Predicting miRNA targets in plants is method to confirm miRNA-mRNA targets is the 5 RACE much easier due to the high and significant complemen- procedure (Random Amplification of cDNA Ends). 5 RACE tarities to miRNA-mRNA targets [34]. The ability for plant has been used by many researchers to identify miRNA targets miRNAtotargetmRNAwithperfect sequence complemen- in plants [3, 21, 71, 72]. Cleaved mRNA products in plants tary matches was first shown with miR171 [59]. It was shown have two diagnostic properties. One is that the 5 phosphate that miR171 has perfect antisense complementarity with of a cleaved mRNA product can be ligated to an RNA adaptor three SCARECROW-like (SCL) transcription factors in the with T4 RNA ligase. Second, in general, the precise target Arabidopsis genome. Additionally, this particular miRNA is cleavage position is that mRNA target nucleotides pair with transcribed from an intergenic locus and lacks a stem-loop the tenth nucleotide of miRNA [21, 73]. Cleaved mRNA structure [5, 21]. Predicting conserved miRNA targets in products by miRNA guided activity can be amplified with lig- different organisms has revealed that homologous mRNAs ation of an oligo-nucleotide adaptor to the 5 end, followed are targeted by conserved miRNAs within an miRNA family, by reverse transcription and PCR amplification with a gene yet allowing more gaps and more mismatches between specific primer [21]. A modified 5 RACE procedure can be an individual miRNA and its target [27]. Next we will appliedasfollows.Total RNAisisolatedand polyAmRNAis summarize some of the bioinformatic and experimental prepared (Qiatex mRNA midi kit, Qiagen, CA) and directly methods utilized to find mRNA targets of known miRNAs ligated to an RNA oligo adaptor (supplied by GeneRacer in plants. kit, Invitrogen, CA). Oligo dT is used to synthesize the first strand of cDNA with reverse transcriptase. This first cDNA strand is amplified with GenRacer 5 and 3 primers for 4.1. Computer-Based Procedures for Predicting mRNA nongene specific amplification (according to manufacturer’s Sequences Targeted by miRNA. Several algorithms are used procedures, Invitrogen, CA, USA or Clontech, RL, USA). for predicting putative miRNA-mRNA targets in plants; Then the 5 RACE PCR and 5 nested PCR are performed for this purpose mirU is one of the widely used softwares. using specific primers supplied with kits. RACE products are The mirU system using given miRNA sequences searches gel purified, cloned, and sequenced. for potential mRNA targets with tolerable mismatches [70]. Additionally, Jones-Rhoades and Bartel [10] have developed a more refined method by using the MIR check algorithm to 5. Concluding Remarks predict miRNA targets specifically in Arabidopsis and Oryza sativa. The MirCheck software allows for more mismatches miRNA studies in plants have already explained a number and gaps in miRNA-mRNA complexes in these two species. of biological events in response to both biotic and abiotic This software also needs miRNA complementarities that stresses. Improved understanding of molecular mechanisms should be conserved between homologous mRNAs in of miRNA in plants will lead to the development of novel and Arabidopsis and Oryza sativa [10]. They have scored the more precise techniques that will help better understanding miRNA complementary sites as 0.5 for G:U wobble pairs, some posttranscriptional gene silencing in response to both International Journal of Plant Genomics 9 biotic and abiotic stresses. Accumulating knowledge on the [13] R. S. Pillai, C. G. Artus, and W. 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