Available online at www.sciencedirect.com

) 60–66www.elsevier.com/locate/gene

Gene 414 (2008

Identification of conserved microRNAs and their target genesin tomato (Lycopersicon esculentum)

Zujun Yin, Chunhe Li, Xiulan Han, Fafu Shen ⁎

State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China

Received 28 November 2007; received in revised form 12 February 2008; accepted 14 February 2008

Received by TAvailable online

akashi Gojobori

20 February 2008

Abstract

MicroRNAs (miRNAs) are a class of non-coding RNAs that have important gene regulation roles in various organisms. To date, a total of 1279plant miRNAs have been deposited in the miRNA miRBase database (Release 10.1). Many of them are conserved during the evolution of landplants suggesting that the well-conserved miRNAs may also retain homologous target interactions. Recently, little is known about theexperimental or computational identification of conserved miRNAs and their target genes in tomato. Here, using a computational homology searchapproach, 21 conserved miRNAs were detected in the Expressed Sequence Tags (EST) and Genomic Survey Sequence (GSS) databases.Following this, 57 potential target genes were predicted by searching the mRNA database. Most of the target mRNAs appeared to be involved inplant growth and development. Our findings verified that the well-conserved tomato miRNAs have retained homologous target interactionsamongst divergent plant species. Some miRNAs express diverse combinations in different cell types and have been shown to regulate cell-specifictarget genes coordinately. We believe that the targeting propensity for genes in different biological processes can be explained largely by theirprotein connectivity.© 2008 Elsevier B.V. All rights reserved.

Keywords: Conserved microRNAs; Tomato; Target genes; MFEI; Homology

1. Introduction

MicroRNAs (miRNAs) are single-stranded non-codingRNAs that ranging in length from 19 nucleotides (nt) to 25 nt

Abbreviations: 3′ UTR, 3′ untranslated region; 5′RLM-RACE, 5′ RNAligase mediated rapid amplification of cDNA ends; ΔG, folding free energiesAGO1, Argonaute-1; Ap2, APETALA2; ARF, auxin response transcriptionfactor; DCL1, Dicer-like protein; EREBPs, ethylene-responsive element bindingproteins; EST, expressed sequence tag; GH3, Grim helix 3; GSS, genomicsurvey sequence; MFE, minimal folding free energy; MFEI, minimal foldingfree energy index; miRNA, microRNA; miRNA⁎, opposite miRNA sequencent, nucleotide(s); pre-miRNA, microRNA precursor; pri-miRNAs, microRNAprimary; Pro, proline; RISC, RNA-induced silencing complex; SBP, Squamosapromoter Binding Proteins; Ser, serine; SPL, SBP-like proteins; Thr, threonine⁎ Corresponding author. State Key Laboratory of Crop Biology, Shandong

Agricultural University, Shandong, 271018, PR China. Tel.: +86 538 8242903fax: +86 538 8242226.

E-mail address: ffshen@sdau.edu.cn (F. Shen).

0378-1119/$ - see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.gene.2008.02.007

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that modulating gene expression in both plants and animals, anda large number of them are evolutionarily conserved acrossspecies boundaries (Carrington and Ambros, 2003). In plants,miRNA genes originate mostly from independent transcriptionalunits, which are transcribed by RNA polymerase II into longprimary transcripts (pri-miRNAs) (Chen, 2005; Zhang et al.,2006b). Subsequently the pri-miRNA is cut into miRNAprecursors (pre-miRNAs) with stem-loop (hairpin) structure(s).The loop region of the hairpin is removed by the ribonucleaseIII-like enzyme Dicer (DCL1) (Kurihara and Watanabe, 2004),and the remainder is exported to the cytoplasm by Hasty (Parket al., 2005). The mature miRNA is incorporated into the RNA-induced silencing complex (RISC) and guides RISC tocomplementary mRNA targets. Eventually, the RISC inhibitstranslation elongation or triggers the degradation of targetmRNA (Lin et al., 2005). An increasing number of studiessupport the idea that plant miRNAs repress the expression of

61Z. Yin et al. / Gene 414 (2008) 60–66

target RNAs through direct target cleavage or, in a few cases, bytranslational repression (Schwab et al., 2005; Axtell et al., 2007).

Plant miRNAs negatively regulate their target genes, whichfunction in a range of developmental processes (Guo et al., 2005;Laufs et al., 2004; Mallory et al., 2004) as well as in response toenvironmental stimuli (Jones-Rhoades and Bartel, 2004; Sunkarand Zhu, 2004). Identifying miRNAs and their target genes istherefore central to understanding their function and theunderlying control mechanisms. Recently, computationalapproaches have been used wildly as rapid, accurate, andaffordable methods for the identification of miRNAs. Zhanget al. (2006d) classified these computational approaches into fivemajor categories: homology search-based, gene search-based,neighbor stem-loop search-based, comparative genomic algo-rithm-based, and phylogenetic shadowing-based. The homologysearch-based approach, which is based on conserved sequencesand secondary structures, searches nucleotide databases usingthe BLAST program, and has been used to identify hundreds ofnew miRNAs in the genomes of model species, such as Arabi-dopsis thaliana (Adai et al., 2005) and Oryza sativa (Li et al.,2005, Zhang et al., 2006a). At present, the genomic sequencesare available for only a few plant species. Zhang et al. (2005)developed an efficient strategy for identifying plant miRNAsusing expressed sequenced tag (EST) analysis. Using thisapproach, more than 700 miRNAs have been identified in plantsand viruses (Zhang et al., 2006a; Pan et al., 2007). In order toexpand the scope of the search, Zhang et al. (2007), usingpreviously knownmiRNA sequences to blast the cotton ESTandGenomic Survey Sequences (GSS) databases, detected 30potential miRNAs. With the development of computationalmethods, several computer software programs have beendeveloped to help identify plant potential miRNA target genesin mRNA sequences. These programs include MIRcheck(Jones-Rhoades and Bartel, 2004) and miRU (Zhang, 2005).Because almost all miRNAs show perfect or near-perfectcomplementarity with their targets in plants, it is much easierto predict miRNA targets using a BLAST search of mRNAdatabase for plants than it is for in animals. More and morestudies have shown this is a powerful approach that has beenused to successfully select potential miRNA targets in mRNAsequences for experimental validation (Zhang et al., 2006d,Gleave et al., 2008).

To date, a total of 1279 plant miRNAs have been discoveredand are deposited in the current edition of the miRNA Registry(http://microrna.sanger.ac.uk/sequences/index.shtml). How-ever, the majority have been identified from A. rabidopsis,O. sativa and Populus trichocarpa where genome sequencesare available. In addition, many of them are conserved duringthe evolution of land plants. This finding raises the question ofwhether the well-conserved miRNAs retain homologous targetinteractions and perform analogous molecular functionsamongst divergent plant species.

Tomato (Lycopersicon esculentum L.) is an importantvegetable crop of significant economic importance in horticul-tural industries worldwide (Leonardi et al., 2000). In addition toits economic value, the tomato plant is an excellent experimentalsystem. Little is known, however, about the experimental or

computational identification ofmiRNAs in tomato, and nothing isknown about their target genes.Only recently, Pilcher et al. (2007)and Itaya et al. (2008) discovered some novel putativemiRNAs insRNAs library of tomato fruit and leaf tissue through directcloning. In this study, using a combined computational approach,a total of 21 conserved miRNAs and 57 potential target geneshave been detected in tomato. Our results verified that the highlyconserved tomato miRNAs have retained homologous targetinteractions amongst divergent plant species. These newmiRNAsand their targets could help guide experimental design andimprove our understanding of the possible roles of miRNAs inregulating the growth and development of tomato.

2. Materials and methods

2.1. Sequences of miRNAs, expressed sequence tags (EST),genomic survey sequences (GSS) and mRNA

To search for potential conserved miRNAs, the sequences ofpreviously identified mature miRNAs and their pre-miRNAswere downloaded from the miRNA Registry Database (Release9.2, May 2007; http://microrna.sanger.ac.uk/sequences/index.shtml). This set contained 1110 miRNAs from 9 species[A. thaliana (186), Glycine max (22), Medicago truncatula(30), O. sativa (243), Physcomitrella patens (220), Populustrichocarpa (215), Saccharum officinarum (16), Sorghumbicolor (72) and Zea mays (96)]. The tomato EST, GSS, andmRNA databases were obtained from the NCBI (http://www.ncbi.nlm.nih.gov/).

2.2. Prediction of potential miRNAs

The procedure used to search for potential miRNAs in tomatois shown in Fig. 1. To avoid overlap, repeat miRNA sequenceswithin the above species were removed. The remainingsequences were used as BLAST search queries against thetomato EST and GSS databases. The BLAST search was carriedout using BLAST 2.2.14. Adjusted blast parameter settings wereas follows: an expect value cutoff of 10; a low-complexitysequence filter; 1000 descriptions and alignments; and auto-matically adjusted parameters for short input sequences toimprove the veracity of outputs. If the matched sequences wereless than the previously known mature miRNA sequences, thenon-aligned parts were inspected and compared manually todetermine the number of matching nucleotides. All BLASTresults were saved. EST or GSS sequences which have only 0–3 nt mismatches compared with the query miRNA sequenceswere chosen manually.

The secondary structures of the selected entire EST or GSSsequences were predicted and generated using the web-basedsoftware Mfold 3.2 (publicly available at http://frontend.bioinfo.rpi.edu/applications/mfold/cgi-bin/rna-form1.cgi). The followingparameters were used in predicting the secondary structures: afixed folding temperature of 37 °C; ionic conditions set at 1 MNaCl with no divalent ions; and grid lines in the energy dot plotsturned on. Other parameters followed the default parameters.Generally, the lowest-energy structure corresponds to helices in

Fig. 1. Schematic representation of the miRNA gene search procedure used to identify tomato homology to known miRNAs.

62 Z. Yin et al. / Gene 414 (2008) 60–66

optimal foldings. Thus, in each case, only the lowest-energystructure was selected for manual inspection. According to thestructures, the potential pre-miRNA sequences were selected topredict their secondary structures and calculate the folding freeenergies. The following empirical criteria were used for selectingpotential miRNAs and pre-miRNAs: (a) predicted maturemiRNAs have only 0–3 nt mismatches compared with the querysequences; (b) the approximately 23 nt potential miRNA sequenceis not located on the terminal loop of the hairpin structure;(c) potential precursors have a higher minimal folding free energy(MFE) index (MFEI) than other types of RNAs. The MFEI wascalculated using the following equation:

MFEI ¼ MFE=length of the RNA sequenceð Þ � 100½ �= GþCð Þk

whereMFEdenotes the negative folding free energies (ΔG); (d) themiRNA has less than six mismatches with the opposite miRNAsequence (miRNA*) on the other arm; (e) potential miRNAs arelocated on the same arm of the stem-loop structure as their knownhomologs; and (f) there is no large loop or break in the miRNAsequence. Predicted miRNAs and their related information wererecorded. Closely related EST and GSS sequences were blastedagainst each other and analyzed. If a high degree of similarity(N98%) was observed, the sequences were deemed to have beencreated from the same sequences and considered as one miRNA.

Fig. 2. Schematic representation of the potential target genes search proce-dure by blasting mRNA databases of tomato with newly identified miRNAsequences.

2.3. Prediction of potential target genes and their functions

Fig. 2 summarizes the major steps employed in pre-dicting potential target genes and their functions. BLASTX

(http://www.ncbi.nlm.nih.gov/BLAST/) was used for the ana-lysis of potential targets. The criteria were as follows: (a) four orfewer mismatched nucleotides at complementary sites betweenmiRNA sequences and potential mRNA targets; (b) one mis-match in the complementary region of the miRNA at nucleotidepositions 2–12, but not at positions 10 or 11, which is a pre-dicted cleavage site; and (c) up to three additional mismatchesbetween 12 nt and 23 nt but with no more than two continuousmismatches within this region.

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3. Results and discussion

3.1. Identification of potential tomato miRNAs

The high degree of sequence conservation of many miRNAswithin the plant kingdom (Axtell and Bartel, 2005) provides ameans to identify conserved miRNAs from all plant species(Fahlgren et al., 2007). In this research, a homology searchapproach was adopted to identify conserved miRNAs in tomato.Following a set of strict filtering criteria, a total of 21 conservedmiRNAs were detected, of which 7 were identified in the ESTdatabase and 14 in the GSS database (Table 1). The lengths ofthese newly identified miRNAs varied from 20 nt to 22 nt, and atotal of 15 began with a 5' uridine, a characteristic feature ofmiRNAs (Supplementary Fig. 1).

During screening of the potential miRNAs, sequences of thecandidate pre-miRNAs were evaluated for their A+U content,which ranged from 45.52% to 83.33% (Table 1), in agreementwith previous results (Zhang et al., 2006a). Compared to thenumber of nucleotides in animal pre-miRNAs (typically 70–80 nt), the tomato pre-miRNAs were more diverse in structureand size (Table 1, Supplementary Fig. 1). The predicted hairpinstructures of the pre-miRNAs required 54–355 nt, with themajority (71.43%) requiring 70–160 nt, similar to what hasbeen observed in A. thaliana and O. sativa (Li et al., 2005). Thedifferent sizes and structures of the identified miRNAs suggestunique functions in the regulation of miRNA biogenesis or geneexpression. The location of mature miRNA sequences in theidentified miRNAs also showed diversity (Supplementary Fig.1), with sequences of miR156/157, miR160, miR167, miR168,miR169, miR437, miR869.1 and miR1030 being located at the 5′end of the pre-miRNAs and all others at the 3′ end.

The MFEI is a useful criterion for distinguishing miRNAsfrom other types of coding or non-coding RNAs. In our results,

Table 1List of the newly identified miRNAs in tomato

New miRNAs miRNA sequences Gene ID Gene source Locat

156a ugaaagauagagcagugagcac 84260136 GSS 5′156b ugacagaagagagagagcac 72526205 GSS 5′157a uugacagaagauagagagcac 117704089 EST 5′157b uugacagaagauagagagcac 84273338 GSS 5′157c uugacagaagauagagagcac 84236627 GSS 5′159 uuuggauugaagggagcucua 116645971 EST 3′160 ugccuggcuccuuguaugcca 72471353 GSS 5′162 ucgauaaaccucugcauccag 72276781 GSS 3′167a ugaagcugccagcaugaucua 117695324 EST 5′167b uaaagcugccagcaugaucugg 72348884 GSS 5′168 ucgcuuggugcaggucgggac 9505413 EST 5′169 Uagccaaaaaugacuugccag 84234106 GSS 5′172a agaaucuugaugaugcugcau 117691535 EST 3′172b agaaucuugaugaugcugcau 72400226 GSS 3′399a ugccaaaggagaguugcccua 117723706 EST 3′399b ugccaagggagaauugcccua 72479804 GSS 3′403 cuagauucacgcacaagcucg 72312989 GSS 3′437 aaaguuagagaaguuugaaau 84192580 GSS 5′830 ugacuauuaugagaagaagug 84049570 GSS 3′869.1 auugguuuaauuuugguguug 117684944 EST 5′1030 Aucugcaugugcaccugcacc 84291882 GSS 5′

NM: number of mismatch; LM: length of mature miRNAs; LP: length of precursor;

the newly identified plant pre-miRNAs had a high MFEI (0.70–2.02) (Table 1), with an average of about 1.01; this is significantlyhigher than that for tRNAs (0.64), rRNAs (0.59), and mRNAs(0.62–0.66) (Zhang et al., 2006c). The 21 conserved tomatomiRNAs belong to 14 miRNA families. miR156/157 wasrepresented by five members; miR167, miR172 and miR399 bytwo members; and all other miRNA families including miR159,miR437 and miR1030 by only one member. miR156/157,miR168, miR169, miR172, and miR399 had more than 14homologs of miRNAs in other plant species, while in contrast,others showed a low level of conservation orwere non-conserved.For example, miR830 and miR869.1 matched only theircounterparts in A. thaliana, while miR1030 was found only inP. patens. Interesting, although mosses are one of the mostancient land plants present among the Earth's flora (Liang et al.,2004), previously identified miRNAs in P. patens showed fewhomologs in dicots or monocots during our filtering process ofrepeat sequences. This discrepancy may be due to two non-mutually exclusive factors. First, the low or non-conservedmiRNAs may play important roles in the development of morespecies-specific characteristics. Second, many likely arose in therecent evolutionary past, with some miRNA families predatingthe divergence of vascular plants and mosses.

In a recent investigation, it was suggested that about 1% ofgenes predicted using a computational strategyweremiRNAs (Laiet al., 2003). The numbers identified in our study and other studies,on the other hand, were very small. One explanation for thisdiscrepancy is that the molecular characteristics of miRNAs andpre-miRNAs resulted in a low content in the EST and GSSdatabases. First, the majority of pre-miRNAs are usually veryshort (approximately 100 nt), and thus, processed rapidly in thecell, and second, some have a low level of expression. ManymiRNAs or pre-miRNAs may therefore have a low probability ofdetection. As more sequences become publicly available in the

ion NM (nt) LM (nt) LP (nt) A+U (%) ΔG (kal/mol) MFEIs

3 22 85 60.00 −22.56 0.710 20 355 49.56 −95.70 0.700 21 100 64.00 −43.40 1.200 21 84 60.71 −39.10 1.180 21 83 61.45 −40.20 1.260 21 178 62.92 −73.61 1.110 21 80 53.75 −33.30 0.900 21 98 53.06 −33.70 0.730 21 73 64.38 −27.30 1.052 22 237 75.53 −60.90 1.051 21 145 45.52 −59.80 0.763 21 68 51.47 −27.50 0.830 21 106 66.04 −39.80 1.110 21 135 72.59 −74.80 2.020 21 71 57.75 −30.24 1.031 21 72 58.33 −31.30 1.042 21 76 60.52 −28.90 0.963 21 54 83.33 −8.30 1.303 21 115 71.30 −22.50 0.742 21 61 63.93 −15.40 0.702 21 139 58.27 −35.40 0.73

ΔG: folding free energies; MFEIs: minimal folding free energy indexes.

Table 2Potential targets of the identified miRNAs in tomato

miRNA Targeted protein Target function Target genes

156/157 Squamosa promoterbinding protein(SBP)/ SBPL

Transcription factor BG630869 BI927983BI931517 BI928213BI927982 AW933950

Tospovirusresistance protein

Stress response BG125997

Lateral suppressorprotein

Transcription factor BF097000

AUX/IAA protein Auxin-responsivefactor

BE436147

DNA (cytosine-5)-methyltransferase

Cell growth BI925272

160 Auxin-responsivefactor

Auxin-responsivefactor

BP880486

Serine/threonineprotein kinase

Transcription factor AW038480

167 Auxin-responsivefactor

Auxin-responsivefactor

BI925927 BP902363BE434602 CD003107DB715336 AW735838AW223741

168 Putative phosphatase Transcription factor DB697961 DB706552BF097936

Sucrose transporter Transcription factor BE461110mRNA bindingprotein precursor

Transcription factor BE462736

172 Ethylene-responsiveelement bindingprotein

Transcription factor BM413247 AI486618BI208808 BM410833BM408872

Putative protease/hydrolase

Transcription factor AI484737

Phospholipase family Transcription factor BP893968 BI935838Insulin degradingenzyme

Metabolism BG627220

Polyphenol oxidase Metabolism AI774952Eukaryotic translationinitiation factor

Transcription factor BE436427 BE432648

869.1 Threonine deaminase Transcription factor BI935333 BI9353116BI933914 BI933284BI933270 BI932869BI932857 BI935963BG134948 BM413156BG127250 BG129951BG126331 BG126303BG123821

Ethylene-responsivenuclear protein

Transcription factor BG626979

P69C protein Transcription factor BI932424Subtilisin-like protease Metabolism BG421482 BI935794NBS-LRR resistanceprotein-like protein

Transcription factor BG132455 BM410377

64 Z. Yin et al. / Gene 414 (2008) 60–66

repositories of the EST and GSS databases, and as more miRNAsare identified in plants, we believe that the likelihood of suc-cessfully determining miRNAs in these databases will increase.

3.2. The diversity and multiplicity of miRNA targets in tomato

Gaining insight into the miRNA targets will help us tounderstand the range ofmiRNAexpression regulation and tomorecoherently describe the functional importance of miRNAs.Evolutionarily conserved sites seem to be functional, and thereforethe conserved sequence can serve as a filter to define likely targetregions (Ioshikhes et al., 2007; Lall et al., 2006). Using the newlyidentified miRNA sequences as BLAST search queries, 57 targetgenes were predicted in the tomato mRNA database using a set ofstrict criteria (Table 2). According to the information provided byNCBI, the identified mRNA targets could be separated intoseveral groups. The largest group contained targets thought toencode transcription factors, which are known to be involvedmainly in plant growth and developmental patterning. This isprobably a general characteristic of plant miRNAs that tends to becomplementary to their regulatory targets (Qiu et al., 2007). Thenext group contained targets encoding a range of different proteinsimplicated in various metabolic processes, while another groupwas involved in functions such as hormone responses, stressdefense and signaling. Interestingly, a miRNA can be comple-mentary to more than one regulatory target (Table 2); for example,10 sequences were detected as targets of miR156/157, and ofthese, 6 were found to be Squamosa promoter Binding Proteins(SBP). Whether these different miRNAs have the same authenticcomplementary sites to their targets depends on whether themiRNA complementary sites are within the context of a domainstrongly conserved among familymembers (Rhoades et al., 2002).

The well-conserved tomato miRNAs seem to have retainedhomologous target interactions and performed analogous mole-cular functions amongst divergent plant species (Table 2).Representative examples are as follows. miR156/157 waspredicted as the target of SBP or SBP-like proteins (Table 2), aplant-specific family of transcription factors involved in earlyflower development and vegetative phase changes (Achard et al.,2004). SPL3, SPL4 and SPL5 (SPL3/4/5) are closely relatedmembers of the SBP-like family in A. thaliana (Cardon et al.,1999). The expression of juvenile vegetative traits and delayedflowering is regulated by an increase in the expression of SPL3/4/5, which occurs as a result of a decrease in the level of miR156(Wu and Poethig, 2006). Some studies have suggested that SPL3/4/5 are all cleaved in the middle of the miR156 target site locatedwithin their 3′UTR and 5′RLM-RACE (Schwab et al., 2005,Wuand Poethig, 2006). However, Gandikota et al. (2007) demon-strated that SPL3 prevents early flowering by translationalinhibition in seedlings. This functional miRNA response elementemerges also in the mRNA of the SBP-box genes in moss (Rieseet al., 2007), suggesting that SBP-box gene family members aredependently regulated by the ancient origin of miRNA.

The phytohormone auxin plays critical roles during plantgrowth, many of which are mediated by members of the auxinresponse transcription factor (ARF) family (Guilfoyle et al.,1998). Recent studies have shown that miR160 is complementary

to ARF10, ARF16 and ARF17 (Megraw et al., 2006; Rhoadeset al., 2002; Wang et al., 2005), while miR167 is complementaryto ARF6 and ARF8 (Wu et al., 2006). Our results suggested thatmiR160 andmiR167 and the targetARFs are conserved in tomato(Table 2). Increased levels of auxin accelerate proteolysis of Aux/IAA proteins, which allows ARF proteins to homodimerize andimpose their regulatory functions on early auxin-response geneexpression (Dharmasiri and Estelle, 2002; Liscum and Reed,2002). Intriguingly, bothmiR160 andmiR167 regulate ARFs, butthey have different complementary sites and unrelated sequences.In A. thaliana, miR167 was found only to cause transcript

65Z. Yin et al. / Gene 414 (2008) 60–66

degradation of ARF8 but not ARF6 (Ru et al., 2006); this wasexplained by the fact that the fewer base-pairs between ARF6 andmiR167may result in inefficient cleavage of theARF6 transcripts.Mallory et al. (2005) suggested that miR160 and miR167coordinatelymodulate GH3-likemRNA expression by regulatingexpression of repressing and activating ARF proteins encoded byARF17 and ARF8. Thus, in the future, it will be fascinating toexperimentally determine the subtle correlations among miR160,miR167and ARFs.

Ethylene-responsive element binding proteins (EREBPs) andAPETALA2 (AP2) are prototypic members of a plant-specificfamily of AP2/EREBP transcription factors (Nole-Wilson andKrizek, 2000; Riechmann and Meyerowitz, 1998; Theissen andSaedler, 1999). Recent studies using A. thaliana show thatmiR172 and its AP2-Like target genes regulate flowering time andfloral organ identity (Aukerman and Sakai, 2003; Chen, 2004). Inaddition, Glossy15, an AP2-like gene from maize that regulatesleaf epidermal cell identity, is also detected as a target of miR172(Lauter et al., 2005). In this research, five predicted targets ofmiR172 were found to be members of the AP2 gene family (Table2). Aukerman and Sakai (2003) suggested that the progressiveaccumulation of miR172 results in the complete absence of AP2and other AP2-like proteins; as a consequence, plants set flowersearly with disrupted specification of floral organ identity.Recently, another study demonstrated that flower developmentis regulated by at least four miRNAs: miR156, miR159, miR164andmiR172 (Gandikota et al., 2007). In fact, miR156 andmiR172are expressed in opposite temporal patterns, suggesting that themiR156 module may regulate the expression of miR172 (Will-mann and Poethig, 2007). In addition, Nilsson et al. (2007)suggested that the AP2 gene in spruce has the capacity tosubstitute an A class gene required in flower development.Additional studies are now required to determine the basis for thisregulation and the complicated regulating network.

As the above data suggest, the molecular identities of themiRNAs and their targets seem to have remained constant, pro-viding additional evidence for the real existence of thesemiRNAs.Presently, however, it is not possible to determine whether thisconservation is the result of shared ancestry or functional con-vergence from an independent origin during evolution.

Different combinations of miRNAs are expressed in differentcell types and may regulate cell-specific target genes coordi-nately (Cimmino et al., 2005). For example, Vaucheret et al.(2004) confirmed that AGO1 is controlled by miR168 inA. thaliana. However, in this study, the predicted targets ofmiR168 did not encode AGO family proteins, but ratherputative phosphatase and sucrose transporter (Table 2). Maybe,it is some small changes in the temporal, spatial or environ-mental regulation of these modules over long periods of timethat result in large different effects in developmental processesof other species. Moreover, among the detected targets ofmiR156/157, miR160, miR167, miR172 and miR869.1, therewere some atypical target genes that have yet to be validated byan experimental approach. Interestingly, serine/threonine pro-tein kinase was among the targeted proteins of miR160.Combined with the fact that transcriptional repressor ARFs allhave Pro-Ser-Thr-rich middle regions (Tiwari et al., 2004), this

confirms that the targeting propensity for genes related todifferent biological processes can be explained largely by theirprotein interaction. Thus, we believe that an investigation ofprotein connectivity would offer critical information on theinteractions between miRNAs and their target genes.

With some miRNAs, such as miR159, miR162, miR169,miR399, miR403, miR437, miR830 and miR1030, we failedto discover any target in tomato (Table 2). This could haveresulted from incomplete coverage of the mRNA in the data-bases. It is likely that a number of mRNAs could not beidentified because they are poorly expressed or highly unstable,or because their expression is restricted to times and locationssuch that isolation of sufficient amounts of RNA for cloning isimpractical. Further analysis of this therefore requires sequen-cing of miRNA complements, and testing of these hypotheseswill become possible as more is learned about target specificity.

In summary, in the present study, we presented global pre-dictions of conserved tomato miRNAs and their targets. A total of21 potential miRNAs, belonging to 14miRNA families, and 57 oftheir potential target genes were detected. Most of the pooledtargets were predicted, with functions in a variety of biologicalprocesses, including growth and developmental patterning, meta-bolic processes, hormone responses, stress defense and signaling.It should be noted, however, that this study used only a com-putational approach, which can never replace biological verifica-tion and can be used only to guide experimental design. The nextmajor steps, therefore, are to experimentally analyze the func-tional categories suggested by our computational approach, de-termine the analogous molecular functions amongst divergentplant species and further elucidate any significant correlationbetween the miRNAs and their target genes.

Acknowledgments

This research was supported by the China Key DevelopmentProject for Basic Research (973) (grant no. 2007CB116208)and the China Agricultural Commonweal Industry Project(nyhyzx07-005).

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at doi:10.1016/j.gene.2008.02.007.

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