The Ps-iaa4/5-like Family of Early Auxin-inducible Mrnas in Arabidopsis Thaliana.
Advances in molecular biology have led to some surprising discoveries. 1 of these includes the complexities of RNA and its role in gene expression. One particular course 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 constitute systems. The remainder of the paper will get into details of 3 unlike approaches used to identify and study miRNA. These include 2 reverse genetics approaches: ciphering (bioinformatics) and experimental, and ane rare forwards genetics arroyo. We also volition 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.
one. Introduction
one.i. MicroRNA Clarification
With the appearance of more advanced molecular techniques, a newly discovered phenomenon of gene expression involving RNA has been characterized. MicroRNAs (miRNAs) are modest endogenous, noncoding regulatory RNA sequences. They have been found to play central roles in regulatory functions of factor expression for nearly eukaryotes [1–3]. These endogenous RNA sequences are the interest of intensive enquiry in various model organisms, ranging from plants to mammals. In plants, miRNAs are involved in a number of biological mechanisms including plant growth, evolution, and defense response confronting abiotic stress. Testify indicates that miRNAs regulate factor expression at posttranscriptional levels in various organisms [3–6]. Farther research has shown that miRNA sequences in plants are securely conserved [5] and have virtually perfect complementarities with their specific messenger RNA (mRNA) targets. Every bit a result of this complementarity, a constitute miRNA guides the cleavage, degradation, or translational inhibition of its target mRNA, thereby affecting gene expression.
Effigy i and the following text illustrate the biogenesis of miRNAs. MicroRNAs are 21–24 nucleotides long and are processed in the nucleus from longer stem-loop structures called pre-miRNAs, that are approximately lxx nucleotides in length. miRNA genes are transcribed by RNA polymerase II, with some miRNA genes residing in intron sequences. After transcription, the end of pri-miRNA is capped and the cease is polyadenylated. Based upon sequence homologies, the stalk and loop structures are formed with base pairings. Pri-miRNA is idea to be longer than conserved stem-loop structures (pre-miRNA), therefore some processing takes identify next.
Pri-miRNA is processed past an enzyme complex called microprocessor which includes Dicer like-1 nucleases (members of an RNase III endonuclease family) and HYL1, a dsRNA binding protein, spring to the pri-miRNA circuitous. In Arabidopsis thaliana all of the miRNA biogenesis steps are processed by one of the four Dicer-like RNase Three endonucleases [7]. Among these, Dicer-similar-ane is specialized for miRNA processing, while other Dicer-similar enzymes are involved with another type of modest RNA biogenesis and accumulation known as short interfering RNAs (siRNAs) [8]. However, Dicer like-iv dependence for the accumulation of several establish miRNAs has likewise been detected [9]. MicroRNAs are structurally and functionally like to siRNA, but miRNAs originate from long stalk loop double stranded RNAs (dsRNAs) [ten].
At this point, the construction is at present referred to as "pre-miRNA" and has ane final pace remaining. The pre-miRNA complex is methylated with HEN1 enzyme so exported into the cytoplasm with the help of Jerky, an miRNA transporter. After methylation, the construction is called "mature miRNA". The HASTY poly peptide might be a plant ortholog of an animate being enzyme known equally Exportin-5, which has been shown in animal systems to likewise transport mature miRNAs to the cytoplasm [2, 11].
The mature miRNA structure (miRNA) is next loaded into an RNA-induced ribonucleoprotein silencing circuitous (RISC) to cleave its specific target mRNA or to inhibit the translation of its target transcript (Effigy 2) [12]. The RISC complex includes Arganoute (AGO1) (PAZ, RNA-binding domain and RNaseH-similar P-chemical element induced wimpy testis (PIWI) domain containing protein) and miRNA* is degraded. The Agone protein family is the most important and key component of the miRNA-RISC complex [7]. Their ability to suppress protein synthesis and clan with miRNAs was demonstrated in human [13]. Single-stranded miRNA in the RISC is able to target a specific mRNA sequence, having sequence complementarity and by the Piwi domain. The AGO component can then cleave the miRNA-mRNA duplex (or siRNA-mRNA duplex) [vii], thereby slowing gene expression of that detail mRNA.
On the other hand, translational inhibition and mRNA deposition are as well other ways gene expression is regulated by miRNA. This can occur via deadenylation of the poly (A) tail and decapping of the end in mRNAs, which leads to progressive mRNA decay and degradation [14]. It has been demonstrated that in Drosophyla melanogaster S2 cells, the P-trunk protein GW182, which is a key component mark mRNAs for disuse, interacts with the AGO1 [15]. Furthermore, RNA directed-Deoxyribonucleic acid methylation revealing epigenetic regulation of gene expression has been demonstrated in Arabidopsis, reviewed in [xvi]. This procedure is initiated with RNA signal through cleavage of dsRNA by Dicer Like family 3 (DCL3) proteins. These signals target Dna methyltransferases to catalyse methylation of DNA [17]. Histone H3 lysine 9 is an important target for epigenetic modifications in plants. H3K9 methylation is associated with epigenetic regulation of gene expression and heterochromatin modification [18].
To summarize the basic principles of miRNA biogenesis, although we run across many similarities between plant and brute systems, at that place are as well ample differences between institute and brute miRNA characteristics and biogenesis.
Get-go, it is known that institute miRNAs are mostly generated from noncoding transcriptional units [5] in contrast with some of the animal miRNAs which are processed from introns and protein coding genetic sequences [19]. Compared to animals, plants accept a more circuitous small RNA population in their transcriptomes. Due to the affluence of plant-specific RNA Polymerase IV and RNA Polymerase V-dependent siRNA and trans-acting siRNAs, plant miRNAs are represented in the pool of small RNAs. By contrast, animal small RNA populations are generally filled with miRNAs in their transcriptomes [8]. Plant miRNAs accept a unique cease which differs from animal miRNA end sequences. To repress translation, establish miRNAs tend to bind to the protein-coding region of target mRNAs [20–22], but animate being miRNAs bind to the untranslated region ( UTR) of their target mRNA transcripts [23].
To date, 1763 miRNAs have been identified in plants, including 187 from Arabidopsis, 377 from rice, 234 from Populus, 98 from maize, 72 from sorghum, 230 from Physcomitrella, 38 from Medicago truncatula, 78 from soybean, 37 from Pinus taeda, 58 from Selaginella moellendorffii, 44 from Brassica napus and 16 from sugar-cane (miRBase release 13.0, March, 2009, http://microrna.sanger.ac.uk/sequences/). These establish miRNAs take been identified via computational (bioinformatics) and/or experimental methods. For example, through sequence homology assay, xxx potential miRNAs were predicted from cotton [24] and an boosted 58 wheat miRNAs have been identified by Yao et al. [25]. The majority of plant miRNAs studied to appointment negatively regulate their target gene expression at the posttranscriptional level. They are involved in regulating developmental processes [3, 26, 27] responding to environmental stresses [27, 28] and play a diverseness of of import biological and metabolic processes [29–31]. Some examples of these processes include the regulation of found development (miR172, floral organ specification, and miR166, foliage polarity), root initiation and evolution (ath-miR164), signal transduction (i.e., miR159, miR160, miR164, and miR167), and too plant environmental response (miR391 and miR395), as reviewed by Zhang et al. [32].
1.2. Strategies for miRNA Identification and Characterization
In reverse genetics strategies, researchers are utilizing known sequences to discover functions or phenotypes. miRNA identification largely relies on two main reverse genetics strategies: (1) computer-based (bioinformatics) and (2) experimental approaches. A third identification arroyo, forward genetics, is rarely used in miRNA discovery. Forward genetics is the classical approach where researchers take a known phenotype, but the Dna sequence (genotype) coding for that particular phenotype is unknown.
miRNA identification using bioinformatics tools is one of the near widely used methods, contributing considerably to the prediction of new miRNAs in both beast and institute systems. This is largely due to the low cost, loftier efficiency, fast and comprehensive methodology of bioinformatics. The principal theory behind this approach is finding homologous sequences of known miRNAs both within a single genome and beyond genomes of related organisms [33, 34]. Sequence and construction homologies are used for computer-based predictions of miRNAs. Computational strategies provide a valuable and efficient manner to predict miRNA genes and their targets. The software-based approach is applied to animals, human, fungi, and plants [35–38]. For example Zhang et al. identified 338 new possible miRNAs in 60 different found species [31] and Adai et al. have predicted 43 new miRNAs in Arabidopsis [39].
In contrast, cloning and sequencing of small RNA libraries represents an experimental approach to identify and characterize miRNAs. Still, in contrast with bioinformatics, such approaches for miRNA identification likewise have limitations. Commencement, virtually of the miRNAs are tissue and time specific, and generally their expression level is depression. In addition, they mostly limited in response to specific environmental stimuli. They likewise coexist with their cleaved and degraded target mRNAs, hence cloning small RNAs (miRNA and siRNA) is hard, whereas computational approaches are effective because of no need for cloning. Since forrard genetics, or the genetic screening approach, is fourth dimension consuming, expensive, and less efficient, it is rarely used for plant miRNA identification. Adjacent generation massive sequencing techniques such every bit pyrosequencing and Illumina are also applied to identify new miRNAs in plants [40, 41].
Hither we summarize the main approaches to each strategy for identifying plant miRNAs, starting with computational (bioinformatics) approaches.
2. Computational Arroyo
2.1. Sequence and Construction Conservation in miRNAs
Once potential miRNA sequences have been cloned and sequenced, the sequence information can be imported into a diversity of software programs for computational analysis. These bioinformatics tools search for sequence and structure conservation of miRNAs [42] using homology searches with previously known/identified miRNAs. To date a number of computational methods take been reported for the identification of plant miRNAs [v, 27, 33, 39, 43, 44]. Research in plants has revealed that brusk length sequences of mature miRNAs are conserved and have high complementarities to their target mRNAs [27]. Hence, candidate miRNAs can be detected using the conserved complementarities of miRNA to target mRNA, if the mRNA target sequence is known. On the other hand, it has also been shown that the secondary structures of miRNA precursor (pre-miRNA) are relatively more conserved than pri-miRNA sequences (precursor of pre-miRNA) (Figure 1) [45]. Recent bioinformatics tools were used to identify miRNA utilizing both sequence and secondary structure alignments; one of these tools is miRAlign in which more properties of miRNA structure conservation are considered (http://bioinfo.au.tsinghua.edu.cn/miralign) [43, 45]. Since the feature patterns of the conservation of miRNAs are searched by algorithms, the major challenge is finding miRNAs which are species specific and unrelated to previously known organisms.
2.two. Bioinformatics Tools Used for Identifying miRNA and Its Target mRNA
Several programs have been designed for the identification of miRNAs and their targets. Here we summarize five of the most normally and widely used software tools for identifying miRNAs and miRNA targets. In this section, the softwares and databases are exemplified and their apply in identifying found miRNAs is described. The get-go one, miRBase, is currently a database of all known miRNA sequences. Post-obit the description of miRBase, the plant miRNA-mRNA target finder called miRU volition be explained. The secondary structure for a given pre-miRNA sequence tin can be predicted with appropriate criteria using a third software, RNAmFold. Another programme, micro-HARVESTER, can exist applied to observe homology of a given miRNA in one constitute species with a candidate miRNA in another plant species. Also mentioned in this section is findmiRNA, which is used for finding possible miRNAs in a given forerunner miRNA sequence. Subsequently each of these software tools (described below), an example will be given.
2.ii.i. miRBase (http://microrna.sanger.air-conditioning.u.k./)
miRBase is a central online database for all (plant, animal, virus, mucus to engagement) miRNAs including sequences, nomenclature, and target mRNA prediction data from all species. Currently the 13.0 version of the online database (March, 2009) consists of 8619 miRNA total entries from 103 species. These entries correspond 9539 hairpin precursor miRNAs, expressing 9169 mature miRNA products with 1763 constitute miRNAs. The database has three principal functions. miRBase::Registry is where individual information is uploaded to the database prior to publication of novel miRNAs. miRBase::Sequences provides miRNA sequences, nomenclature, and references. miRBase::Targets provides the prediction of the mRNA target from all published animate being miRNAs [46].
2.2.two. miRU (http://bioinfo3.noble.org/miRNA/miRU.htm)
miRU is known as a potential plant mRNA target finder. Using this database, a mature miRNA sequence from a institute species is uploaded. The miRU organization searches for potential complementary target sites in miRNA-target recognition with acceptable mismatches. Specifically, the user enters a mature miRNA sequence in the to direction (entered sequence tin be in a range of 19–28 nucleotides long) then the dataset should exist selected for prediction of mRNA target in the intended organism of interest. The commanded complementary mismatches between the target mRNA and the uploaded miRNA sequence tin exist adjusted or limited by the user. The output report provides information for each predicted miRNA target including cistron identifier, target site position, mismatch score, number of mismatches, and target complementary sequence with color highlighted mismatches [31]. miRU is a very useful software for identifying mRNA targets of specific establish miRNAs. However not all plant species are available at this time.
2.two.3. RNA mFold (http://rna.tbi.univie.air conditioning.at/cgi-bin/RNAfold.cgi)
The algorithm utilized in the RNAmFold bioinformatics tool predicts secondary structures of single stranded RNA or Deoxyribonucleic acid sequences. It is currently packaged in the Vienna RNA websuite, a collection of tools for folding, designing, and analyzing of RNA sequences [47]. The package too provides additional analysis of folding parts using the barriers programme and structural RNA alignments. The parcel includes basic programs such as RNAFold for structure prediction of single sequences, RNAalifold for consensus miRNA structure prediction on a set of aligned sequences, 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 kinetics assay. The RNAmFold tool is used for predicting the minimum costless energy (MFE) secondary structure using the algorithm originally proposed past Zuker and Stiegler [48]. Equilibrium base-pairing probabilities of MFE structures are calculated via McCaskill'due south partition function (PF) algorithm [49].
2.ii.four. micro-HARVESTER (http://world wide web-ab.informatik.uni-tuebingen.de/brisbane/tb/index.php)
micro-HARVESTER is a computational tool that searches for miRNA homologs in a given miRNA sequence query. Due to sequence similarity, the search pace is followed by a set up of structural filters. This method is a sensitive approach to identify miRNA candidates with college specificity. The arroyo uses a BLAST search to generate the kickoff set of candidates and and then the procedure continues with a series of filters based on structural features specific to establish miRNAs to achieve the desired specificity [50].
2.2.v. findmiRNA (http://sundarlab.ucdavis.edu/mirna/) (A Resource of Predicted miRNA and Precursor Candidates for the Arabidopsis Genome)
findmiRNA algorithm is used for predicting potential miRNAs in a given prepare of candidate forerunner sequences which have corresponding target sites in the transcriptome. By and large the algorithm is based on the complementarity existing between plant miRNAs and their mRNA targets to identify initial putative miRNA. Then the software analyzes the candidate miRNA forerunner sequence with regard to forming a stem-loop structure [39]. Since the tool identifies whatever sequence with the potential to form hairpin structures, it has limitations such equally the possibility of identifing tRNAs, foldback elements, and retrotransposons [39].
2.2.6. MiRCheck (http://spider web.wi.mit.edu/bartel/pub/software.html)
MiRCheck is an algorithm designed to place 20 mers which encode potential found miRNAs [27]. Entries should exist (one) putative miRNA hairpin sequences, (2) putative hairpin secondary structures, and (3) twenty-mer potential constitute miRNA sequences within the hairpin for MIRcheck algorithm. This software requires data of miRNA complementarities that should exist conserved between homologous mRNAs in Arabidopsis and Oryza sativa. Researchers use this software to check their candidate miRNAs if they accept potential to encode miRNA.
To briefly summarize, the largest limitation of virtually bioinformatic methods is the need to showtime from a known homologue and depend heavily on conservation of secondary construction and mature miRNA sequences. More advanced methods using hidden Markov models tin overcome this limitation. As an instance, Kadri et al. [51] accept adult a novel approach, Hierarchical Subconscious Markov Model (HHMM) that utilizes region-based structural information of miRNA precursors. They used this model for computational miRNA hairpin prediction in the absenteeism of conservation in human [51].
2.iii. EST Database Analysis Used for miRNA Prediction
Now we will draw how the above tools are utilized in the search of miRNAs. 1 such instance is with Expressed sequence tags (ESTs). ESTs are partial sequences of complementary DNA (cDNA) cloned into plasmid vectors [52]. RNA is the starting cloth from which the cDNA clone is made, using reverse transcriptase. Many important plant genes using EST databases have been cloned [53, 54]. It is well known that miRNAs are securely conserved from species to species, which allows researchers the power to predict orthologues of previously known miRNAs by utilizing EST databases. Availability of ESTs in databases for identifying new plant miRNAs increases with coverage of the genome and number of sequences. Currently, GeneBank release 171.0 Apr 2009 (http://www.ncbi.nlm.nih.gov/Genbank/) contains 103 335 431 EST sequences, representing more than 1370 different organisms. The number of ESTs available for a specific organism tin can be found at http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html. This particular website is the best to utilize considering conserved candidate miRNAs and their precursors can be predicted using this resource. The largest number of plant ESTs is from maize (Zea mays) (2 018 530), thale cress (Arabidopsis thaliana) (1 527 298), soybean (Glycine max) (one 386 618), rice (Oryza sativa) (1 248 955), wheat (Triticum aestivum) (1 064 111), oilseed rape (Brassica napus) (596 471), and barley (Hordeum vulgare) (525 527).
To identify homologous miRNAs across establish species, EST analysis approaches have been developed using sequence conservation of known miRNAs. An extra filtering which provides structure prediction (secondary structure), such as the "Zuker folding" algorithm with RNAmFold software [55], has likewise been applied [27, 31, 39].
Zhang et al. [31] reported an EST database analysis for predicting new plant miRNA genes using the Smash algorithm to search known constitute miRNAs (taken from miRBase 3.one April, 2004). Their additional filter was the Zuker folding algorithm (mFold 3.i) to predict the secondary structure of putative miRNA sequences. The Zuker algorithm outputs used to analyze the results included the number of structures, complimentary energy (G kcal/mol), miRNA-like helicity, the number of arms per structure, size of helices within arms, and size and symmetry of internal loops within artillery. Then hairpin stem-loop structures of predicted putative miRNAs were analyzed for structure filtering with known miRNAs using computational strategies [42]. The highest score of stalk-loop structures was considered as new miRNAs and then the ESTs (with high similarity, value less than east-100) were assigned every bit miRNA clones.
Jones-Rhoades and Bartel [10] have also analyzed EST databases to look for possible miRNAs in other plant species using known Arabidopsis miRNAs. They also revised the computational strategy with possible miRNA targets to increment sensitivity of the arroyo. Their get-go step for identifying miRNAs in the genome was detecting genomic portions containing imperfect inverted repeats using the "EINVERT" algorithm. Then they used the RNAmFold software to predict secondary structures of miRNA candidates. They checked all twenty mers within the inverted repeats against MiRCheck. After MiRCheck analysis they applied Patscan to identify 20 mers in AtSet1 that matched at to the lowest degree 1 20 mer in OsSet1 with 0–ii base substitutions. The Patscan algorithm was used in consideration of twenty mers on the same arm of their putative hairpins.
Bonnet et al. [35] also applied computational approaches to detect miRNAs and then applied EST analysis to confirm 91 newly identified miRNAs in Oryza sativa and Arabidopsis thaliana. Zhang et al. [31] have taken previously all-known Arabidopsis miRNAs (miRBase Release 3.0, April 2004) and searched the EST databases (using Bones Local Alignment Search Tool for nucleotide analyzes (BLASTn) 2.2.9 (May ane, 2004)) to discover ESTs matched with miRNAs. They plant a total of 18 694 BLAST hits in the databases and removed the EST sequences with high numbers (more than two) of mismatched hits. Their issue came to a total of 812 ESTs with 0 to 2 mismatches. They used those ESTs to predict secondary structures with RNA mFold software. Finally they identified 338 new potential miRNAs in 60 establish species.
2.iv. Sample Method to Identify miRNA in a Establish Species Using Computational Approach
In this section, we work through an example of using ESTs and the software tools discussed previously, to identify plant miRNAs. In order to predict the plant miRNAs, EST sequences should exist downloaded from the GenBank database (http://www.ncbi.nlm.nih.gov/) (Figure 3(a)). Searching for miRNA-like sequences includes two major procedures: searching pre-miRNA-like sequences and identifying pre-miRNAs and miRNAs. First, the RNAfold (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi) [47] program or mFold program (http://frontend.bioinfo.rpi.edu/applications/mfold/cgi-bin/rna-form1.cgi) [55] can exist used to notice potential miRNA hairpin structures from the databased EST sequences [56]. According to Weibo et al. [57], strict criteria should exist adopted in the identification of pre-miRNA-like sequences from hairpin structure sequences. These are (a) 60 nucleotides is the minimum length of pre-miRNA sequences; (b) the stem of the hairpin structure (including the GU wobble pairs) includes at least 17 base pairs; (c) xv kcal/mol should be the maximum costless energy of the secondary structure; (d) the secondary structure must not compromise multibranch loops; (e) the GC content of pre-miRNA should be betwixt 24 and 71%.
(a)
(b)
Post-obit these criteria assures that the processed sequences are like to real pre-miRNAs, according to widely accepted characteristics. Second, the existent pre-miRNAs might exist identified from a large number of pre-miRNA-like sequences using a program such as GenomicSVM (http://geneweb.go3.icpcn.com/genomicSVM/). This program was developed using the "Support Vector Car'' model [57]. Alternatively, miRCheck (http://web.wi.mit.edu/bartel/pub/softwareWebTools.html) can be applied (Effigy three(a)) for confirming sequences to identify, if the entries contain 20 mers which encode potential plant miRNAs.
three. Experimental Approaches
Computational methods for identifying miRNAs in plants are rapid, less expensive and relatively easy compared with experimental procedures. However, these bioinformatic approaches can merely place conserved miRNAs among organisms and Dna or RNA sequence information is required in order to run the softwares. On the other manus, the computationally predicted miRNAs should also be confirmed via experimental methods. These experimental method options are described next.
3.1. Straight Cloning and Sequencing of Minor RNA Libraries
Straight cloning of small RNAs from plants is one of the bones approaches of miRNA discovery. Scientists have used this methodology to isolate and clone small RNAs from various plant species such every bit Arabidopsis and rice [v, twenty, 21, 58, 59]. Identification of miRNAs using the direct cloning arroyo basically involves the cosmos of a cDNA library and includes half-dozen steps: (1) isolation of total RNA from plant tissue, (2) recovery of pocket-size RNAs from an acrylamide gel, (3) adaptor ligation, (4) reverse transcription, RT-PCR, (5) cloning, and (half dozen) sequencing methods. Expression of several miRNAs is wide but many of them are detected in sure environmental weather condition, at different plant developmental stages and tissues. Therefore specific time points, tissues, and/or biotic and abiotic stressed induced constitute samples are used for miRNA cloning. The near mutual institute species used for direct cloning are Arabidopsis thaliana [five, 58, 59], Oryza sativa (rice) [60], (cottonwood) [61], and Triticum aestivum (wheat) [24].
3.two. An Example of the Direct Cloning Experimental Approach
Total RNA is extracted from the organism of interest [twoscore, 41]. Side by side, small RNAs approximately 16–28 nucleotides long are selected from the total RNA and excised from a polyacrylamide gel. Adjacent, these small RNAs are ligated with an adaptor and contrary transcribed [62]. Resulting cDNAs are amplified with Real-Time PCR (RT-PCR) using primers designed for adaptor sites. Finally, the RT-PCR products are concatamerized and cloned [63]. Selected clones are sequenced and the sequence data is and so analyzed (Figure 3(b)). These experimental procedures for identifying miRNA have been successfully applied and detailed by Elbashir et al. [63], Lau et al. [62], and Park et al. [58]. Alternatively, new high-throughput technologies such equally 454 pyrosequencing and Solexa sequencing tin be used for identification of found miRNAs [xl, 41].
The nigh important reward of high-throughput deep sequencing applied science compared to computational approaches is the opportunity for finding nonconserved and species specific miRNAs. To identify conserved and nonconserved miRNAs in tomato, Moxon et al. (2008) used the pyrosequencing approach [40]. On the other hand, Szittya et al. (2008) successfully used Solexa sequencing to find new miRNAs in barrel medic (Medicago truncatula) [41]. They have identified 25 conserved and 26 novel nonconserved miRNAs using ane 563 959 distinct sequences and 2 168 937 reads [41]. Experimental approaches also provide to detect and measure the specific miRNAs expressed in plants. Using the post-obit methods plant miRNAs are efficiently detected and quantified.
3.three. miRNA Detection and Quantification Methods
Efficient and suitable miRNA detection and quantification are essential to understand miRNA function in specific conditions, jail cell and tissue types. Northern hybridization, cloning, and microarray analysis are widely used to detect and quantify miRNAs in plants, merely these techniques are less sensitive and are not high throughput compared with quantitative real-time contrary transcription PCR (qRT-PCR) and end-poind PCR. Effective and sensitive qRT-PCR detection tin circumvent these limitations. Several methods take been developed to notice and quantify miRNA for mammalian cells [64–66]. Recently Varkonyi-Gasic et al. [67] described a protocol for an terminate-point and existent-time looped RT-PCR process. Their approach includes two steps. In the first footstep, a stem-loop RT primer is designed, following the strategy developed past Chen et al. [68] and is hybridized with the candidate miRNA. The 2d stride includes the specific amplification of the miRNA, using a forward primer specific for the miRNA and a universal opposite primer, which is designed for the stem-loop RT primer sequence. The clues for designing the reverse RT primers and miRNA specific forward primers are that the specificity of stem-loop RT primers for a certain miRNA is conferred by a half-dozen nucleotide extension at the end. This extension is the reverse complement of the last 6 nucleotides at the end of the miRNA. Forward RT primers are specifically designed for individual miRNA sequences. At the primer's end v–7 random and relatively GC-rich nucleotides are added to increment the template'southward melting temperature [67].
To perform finish-bespeak and real-time looped RT-PCR miRNA quantification experiments, total RNA is isolated from a plant sample using the TRizol reagent according to manufacturer's protocol (Invitrogen, Carlsbad, CA). And so the stem-loop RT PCR reaction is performed by mixing the component equally follows: 0.5Fifty x mM dNTP mix, 11.15L nuclease-free h2o, iL of appropriate stem-loop RT primer (1M), and the mix is heated at 65°C for v minutes and chilled on ice for ii minutes. Boosted components are added to the mixture, fourL 5 First-Strand buffer, iiL 0.1 M DTT, 0.1L RNaseOUT (40 units/L), and 0.25Fifty, SuperScript III RT (200 units/L). Finally the pulsed RT reaction incubation is ready upwards as 30 minutes at xvi°C, followed by pulsed RT of 60 cycles at 30°C for 30 seconds, 42°C for 30 seconds, and 50°C for 1 second. RT products can be used to detect and quantify individual miRNAs in plants via iii different strategies, stop-betoken PCR, SYBR Green I assay, and TaqMan UPL procedure. Each of these strategies is described next.
three.3.1. End-Point PCR
A nontemplate command should be included with each experiment to insure the expected banding pattern for specific cDNA of miRNA amplification. A PCR master mix is prepared and the following components added to nuclease-complimentary eppendorf tubes: 15.4Fifty nuclease-free water, iiL ten PCR buffer, 0.four50 10 mM dNTP mix, 0.iv50 forrard primer (tenM), 0.4L contrary primer (10M), and 0.450 Reward 2 Polymerase mix. Then 19L of the PCR master mix should be aliquot into different tubes and 1L RT product is added to reaction mixtures. Subsequently that, the thermal cycler is set up as 94°C for ii minutes, followed by xx–twoscore cycles of 94°C for 15 seconds and 60°C for 1 infinitesimal. Finally the PCR reaction products are analyzed by electrophoresis on a four% agarose gel in 1 TAE gel.
3.3.2. SYBR Green I Assay
SYBR Green I master mix is prepared according to real-fourth dimension qPCR organization (five LightCycler for Roche Diagnostics or 2X Chief mix for Stratagene Mx3005p) by adding 1L forrad primer (10M), oneL reverse primer (10M), 12Fifty nuclease-free water (for five LightCycler for Roche Diagnostics) or (6L nuclease-free water for 2X Master mix for Stratagene Mx3005p), 4Fifty SYBR Light-green I master mix for 5 LightCycler for Roche Diagnostics or (10L SYBR Green I master mix for Stratagene Mx3005p). Using nuclease-gratis eppendorf tubes for each qPCR reactions, 18Fifty prepared mixtures containing master mix and primers are pipetted into each tube. Adding iiFifty RT products to tubes, the reaction is started. qPCR machine is fix as 95°C for 5 minutes, followed by 35–45 cycles of 95°C for five seconds, 60°C for 10 seconds, and 72°C for 1 2d. For melting curve analysis samples are denaturated at 95°C, and then cooled to 65°C at twenty°C per 2d. Fluorescence signals are nerveless at 530 nm wavelengths continuously from 65°C to 95°C at 0.2°C per second. Finally results are analyzed using the LightCycler or Stratagene software.
3.iii.iii. miRNA TaqMan UPL Probe Process
To perform a TaqMan analysis for miRNA detection and quantification using Universal Probe Library (UPL) probes, get-go a 5 LightCycler TaqMan master mix (Roche Diagnostics) is prepared according to manufacturer's instructions. Next the post-obit components are added to a nuclease-gratis eppendorf tube: eleven.850 nuclease-free water, four50 TaqMan chief mix, oneL forward primer (10G), one50 opposite primer (10M), 0.iiL UPL probe no. 21 (10M). Then real-time qRT-PCR is performed with cycling temperatures and resulting data analyzed as described above with the SYBR Green I assay protocol. An educational eLesson and animation farther describing the existent time PCR technique tin be found at the Establish and Soil Sciences eLibrary (http://plantandsoil.unl.edu/croptechnology2005/pages/index.jsp?what=topicsD&topicOrder=1&informationModuleId=1057077340).
3.4. Forwards Genetics
miRNAs were offset discovered via mutant analysis [64] in animals. However to date, there is only one instance using a forward genetics experimental approach to identify miRNA in plants. Bakery et al. [69] identified an miRNA loss of part allele by a transposon insertion upstream of the predicted MIR164c stem-loop. The miRNA mutant resulted in a flower phenotype with actress petals. Since highly conserved institute miRNAs are encoded by gene families, functional redundancy restricts the loss of function of an miRNA gene, making mutation searches highly inefficient. Overexpression of miRNA genes and precursors or construction of miRNA resistant transgenic plants accept the potential to better provide a clear assessment of overlapping functions of other miRNA family members. If this comes to fruitition, then frontwards genetics approaches may become more feasible in identifying miRNAs.
4. Identification of miRNA Targets
Then far nosotros have described computational (bioinformatics) and experimental approaches used to identify miRNA sequences. Now we volition describe methods utilized to identify their targets, mRNA sequences broken or targeted past miRNA. Specific miRNA targets in constitute genomes and transcriptomes take been identified with both experimental and computational approaches. Predicting miRNA targets in plants is much easier due to the high and significant complementarities to miRNA-mRNA targets [34]. The ability for plant miRNA to target mRNA with perfect sequence complementary matches was first shown with miR171 [59]. It was shown that miR171 has perfect antisense complementarity with 3 SCARECROW-like (SCL) transcription factors in the Arabidopsis genome. Additionally, this detail miRNA is transcribed from an intergenic locus and lacks a stem-loop structure [5, 21]. Predicting conserved miRNA targets in unlike organisms has revealed that homologous mRNAs are targeted by conserved miRNAs within an miRNA family, yet allowing more than gaps and more mismatches between an individual miRNA and its target [27]. Side by side nosotros will summarize some of the bioinformatic and experimental methods utilized to find mRNA targets of known miRNAs in plants.
4.1. Calculator-Based Procedures for Predicting mRNA Sequences Targeted past miRNA
Several algorithms are used for predicting putative miRNA-mRNA targets in plants; for this purpose mirU is one of the widely used softwares. The mirU system using given miRNA sequences searches for potential mRNA targets with tolerable mismatches [lxx]. Additionally, Jones-Rhoades and Bartel [ten] have developed a more refined method by using the MIR check algorithm to predict miRNA targets specifically in Arabidopsis and Oryza sativa. The MirCheck software allows for more mismatches and gaps in miRNA-mRNA complexes in these 2 species. This software besides needs miRNA complementarities that should be conserved between homologous mRNAs in Arabidopsis and Oryza sativa [x]. They have scored the miRNA complementary sites as 0.5 for One thousand:U wobble pairs, i for non-Yard:U wobble pairs, 2 for each bulged or loop nucleotide in the miRNA or target site. They accept reported scored complementary results of 2 in conserved miRNA target mRNA sites in both Arabidopsis and O. sativa.
four.2. Experimental Approaches for Prediction of miRNA Targets
As with computational approaches, experimental approaches have been utilized widely to predict plant miRNA-mRNA target sites. Genome-broad expression profiling to search for miRNA targets can be practical on expression arrays. In one example, array information showed that five transcripts encoding TCP genes were downregulated via overexpression of miR319a (miR-JAW) in Arabidopsis. Those five TCP transcription factor mRNAs show upwardly to five mismatches, or four mismatches when G:U wobble counts 0.5 mismatch [71]. Additionally Schwab et al. [22] overexpressed 4 different miRNAs in each Arabidposis plant and examined each expression profile to experimentally establish parameters for target cleavage guided past plant miRNAs. However, they found no new target mRNAs other than previously identified by computational approaches. Two new targets, not found through bioinformatics, were detected, but their cleaved products were not confirmed via RACE experiments.
iv.3. 5′ RACE Experiment
At present, the nigh powerful method to ostend miRNA-mRNA targets is the RACE procedure (Random Amplification of cDNA Ends). RACE has been used by many researchers to place miRNA targets in plants [three, 21, 71, 72]. Cleaved mRNA products in plants have two diagnostic properties. One is that the phosphate of a broken mRNA product can be ligated to an RNA adaptor with T4 RNA ligase. Second, in general, the precise target cleavage position is that mRNA target nucleotides pair with the tenth nucleotide of miRNA [21, 73]. Broken mRNA products by miRNA guided activity can be amplified with ligation of an oligo-nucleotide adaptor to the finish, followed by reverse transcription and PCR distension with a gene specific primer [21]. A modified RACE procedure can be applied as follows. Total RNA is isolated and polyA mRNA is prepared (Qiatex mRNA midi kit, Qiagen, CA) and directly ligated to an RNA oligo adaptor (supplied by GeneRacer kit, Invitrogen, CA). Oligo dT is used to synthesize the first strand of cDNA with reverse transcriptase. This beginning cDNA strand is amplified with GenRacer and primers for nongene specific amplification (according to manufacturer's procedures, Invitrogen, CA, USA or Clontech, RL, USA). Then the RACE PCR and nested PCR are performed using specific primers supplied with kits. RACE products are gel purified, cloned, and sequenced.
miRNA studies in plants accept already explained a number of biological events in response to both biotic and abiotic stresses. Improved understanding of molecular mechanisms of miRNA in plants will lead to the development of novel and more precise techniques that will assistance better understanding some posttranscriptional gene silencing in response to both biotic and abiotic stresses. Accumulating noesis on the roles of plant miRNA's in molecular biology is leading to the evolution of more efficient and reliable tools for their characterization. Building new algorithms and models will overcome drawbacks and limitations of current softwares used for miRNA and target mRNA identification. New bioinformatic tools should be generated to separate miRNA hairpin structures from tRNAs and retrotransposons and to differentiate miRNAs from siRNAs. New algorithms or new models to aid cleaved mRNA production analysis for target identification besides require improvement. Considering the discovery that miRNAs play important roles in numerous biological events, greater enquiry in this area is being stimulated.
Copyright
Copyright © 2009 Turgay Unver et al. This is an open up access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in whatsoever medium, provided the original work is properly cited.
Source: https://www.hindawi.com/journals/ijpg/2009/262463/
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