^^^^^^^^^^^^ ЭКСПЕРИМЕНТАЛЬНЫЕ
СТАТЬИ
581.1
COMPUTATIONAL IDENTIFICATION OF miRNA GENES AND THEIR TARGETS IN MULBERRY1
© 2014 Y. Huang*, Q. Zou**, Z. B. Wang*
*Animal Science and Technology College, Henan University of Science and Technology, Luoyang City,
Henan Province, P.R. China **School of Information Science and Technology of Xiamen University, Xiamen City, Fujian Province, P.R. China
Received August 7, 2013
MicroRNAs (miRNA) are a class of tiny non protein coding and regulatory RNA molecules about 18 to 26 nt in length. miRNA regulate gene expression via the degradation or translational inhibition of their target mRNAs. Nucleotide sequences of miRNAs are highly conserved among various organisms; this forms the key feature behind the identification of miRNAs in plant species by homology alignment. So far, little is known about miRNA in mulberry (Morus alba L.) species. In our study, a computational method was used for detection of mulberry miRNAs. A total of six miRNAs were identified. The six miRNAs may regulate twenty-two potential targets, which are predicted to encode transcription factors that regulate plant development, signaling, and metabolism. To validate the prediction of miRNAs in mulberry, a RT-PCR experimental method was performed and five of these miRNAs were found to be expressed.
Keywords: Morus alba — microRNA — computational prediction — target genes
ФИЗИОЛОГИЯ РАСТЕНИЙ, 2014, том 61, № 4, с. 574-579
УДК
DOI: 10.7868/S0015330314040101
INTRODUCTION
MicroRNAs (miRNAs) are a recently discovered class of small (18—26 nt), non-coding, endogenous, single-stranded RNAs. The miRNAs function by binding to protein translation elongation factors and thereby suppressing mRNA translation and by facilitating mRNA degradation via a RNA-induced silencing complex (RISC) [1, 2]. Growing evidence has demonstrated that miRNAs execute multipurpose functions in growth organogenesis, transgene suppression, signaling pathway, environmental stresses, disease development, and defense against the invading viruses as well as in the control plant development: leaf organ morphogenesis and polarity, fruit development, flowering timing, metabolism, and stress responses [3—6]. Many miRNAs are evolutionarily conserved from species to species within the same kingdom, and miRNA genes in one species may exist as orthologs in other species [7, 8]. The conserved nature of these miRNAs becomes a logical approach for the identifi-
1 This text was submitted by the authors in English.
Abbreviations'. RISC — RNA-induced silencing complex; RT-PCR — reverse transcription polymerase chain reaction; ESTs — expressed sequence tags; GSSs — genomic survey sequences; NCBI — National Center for Biotechnology Information.
Corresponding author. Yong Huang. Animal Science and Technology College, Henan University of Science and Technology, Luoyang City, Henan Province, 471003 P.R. China; e-mail. huangyong1979111@163.com
cation of new orthologues though bioinformatics strategies based on the highly conserved sequences in mature miRNAs as well as long hairpin structures in precursors [9, 10]. Computational identification of miRNAs from expressed sequence tags (ESTs), genomic survey sequence (GSS), and nucleotide databases is a faster and reliable approach for identification of miRNAs, which are usually not easily detected by direct cloning because of their low expression levels and/or spatiotemporal expression [11, 12]. Currently, some computational programs have been successfully developed and have accurately predicted miRNA genes in Arabidopsis, rice, and other plant species [13—16].
Mulberry, grown as a perennial tree or shrub, is an economically important plant used for sericulture, as it is the sole food plant for the domesticated silkworm, Bombyx mori [17, 18]. With the development of plant genomics, quite a large number of EST, GSS, and nucleotides of mulberry have been released. So, these data became an excellent source of experimental material for the elucidation of gene expression and the prediction of mulberry miRNAs. To date, a large number of miRNAs have been reported for many species, but none for mulberry. As according to the microRNA Registry Database (http://www.mirbase.org/; v. 20, released June 2013), there is no miRNA repository for the mulberry.
In this paper, a computational analysis method was used for detection of mulberry miRNAs. A total of six
Fig. 1. Pipeline for the computational prediction of homologous miRNAs in mulberry.
potential miRNAs, belonging to five different miRNA families, were for the first time identified in mulberry. Further, their targets were also predicted and got 22 potential target genes. Most of these target genes were predicted to encode proteins that play key role in mulberry growth, development, metabolism, and stress responses. Our results will provide useful information for future investigating biological functions of miRNAs in mulberry.
MATERIALS AND METHODS
Sequence data. All available plant mature miRNAs were obtained from miRBase (http://www.mirbase.org/) on June, 2013. Most ofthese were identified or verified by experiments, and others were computationally predicted as their close homolog. Repeated sequences of miRNAs were removed and the remaining sequences were used as a reference of miRNAs. The 8640 nucleotides, 4600 ESTs, 98 GSSs of mulberry sequences were obtained from NCBI GenBank nucleotide databases (http://www.ncbi.nlm.nih.gov/).
Computational prediction of miRNAs. Software BLAST-2.2.20 was downloaded from NCBI GenBank. The mature sequences of all known plant miRNAs were subjected to BLAST search in the mulberry (Morus alba L.) sequence databases using BLAST 2.2.20. The steps of our prediction procedure were similar to those in our earlier work [19]. The workflow of the search for potential miRNAs in mulberry is shown in fig. 1. Three criteria were used to predict the mulberry miRNAs and pre-miRNAs: (1) pre-miRNAs sequence can fold into an appropriate hairpin secondary structure that contains the about 22-nt mature miRNA sequence within one arm of the hairpin; (2) miRNA precursors with secondary structures had higher negative minimal free energies (MFEs) and minimal free energy index (MFEIs) than other various types of RNAs by RNA-fold prediction; and (3) the predicted mature miRNA should have an A+U content of 30— 80% [20].
Prediction of miRNA targets. The mRNA database of the mulberry was downloaded from NCBI database. Previous study has shown that most known plant miRNAs bind to the protein-coding region of their mRNA tar-
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Table 1. Sequences of stem—loop RT primers, forward primers, and reverse primers
miRNA ID Primer Sequence
miR828 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGGAAT
Forward GCGTCTTGCTCAAATGAGT
Reverse GTGCAGGGTCCGAGGT
miR838 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACGAGGCA
Forward GCGCCTTTCTTCTACTTCT
Reverse GTGCAGGGTCCGAGGT
miR1854 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCAAAAT
Forward GCCGATTGGAAATTTGTAG
Reverse GTGCAGGGTCCGAGGT
miR5021 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTTTTCT
Forward GTGCTGAAGAAGAAGAAGA
Reverse GTGCAGGGTCCGAGGT
miR5021a RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCTTCT
Forward CGCGTGGAAGAAGAAGA
Reverse GTGCAGGGTCCGAGGT
miR5538 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACGCAGCA
Forward GCCGACTGAACTCAATCACT
Reverse GTGCAGGGTCCGAGGT
18S rRNA Forward GGAAGTAAAAGTCGTAACA
Reverse TCCTCCGCTTATTGATATGC
Table 2. The six novel identified miRNAs in mulberry
miRNA Source Mature sequence (5' to 3') Side NM, nt LP, nt
828 JX193499 UCUUGCUCAAAUGAGUAUUCCA 5' 1 128
838 JN026302 CUUUCUUCUACUUCUUGCCUC 5' 3 77
1854 JK706164 AUUGGAAAUUUGUAGAUUUUG 5' 3 160
5021 FX177388 GAAGAAGAAGAAGAAGAAAA 5' 2 108
5021a FX179860 GUGGAAGAAGAAGAAGAAGA 3' 3 70
5538 DQ226511 ACUGAACUCAAUCACUUGCUGC 3' 0 102
NM — number of mismatch; LP — length of precursor.
gets with perfect or nearly-perfect sequence complementarity and degrade the target mRNA. So, the targets were screened according to these criteria: the number of mismatches should be less than three, the minimal free energy of the pairing between miRNA and its target mRNA was lower than —28.2 kcal/mol [21]. After the removal of the repeated sequences, the potential target genes were aligned by BLAST program against protein databases to predict their function.
RT-PCR assay. Plant materials were obtained from the National Mulberry Gene Bank of the Sericultural Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Zhenjiang, Jiangsu Province, China. The mulberry small RNA samples from young leaves were isolated using mirVana™ miRNA isola-
tion kit ("Ambion, Inc.", United States) according to the manufacturer's primers according to criteria mentioned previously in [22, 23]. The stem—loop RT primers and gene-specific primers are listed in table 1. The DNA fragments were directly subcloned into pMD18-T vector ("TaKaRa", Japan) and sequenced. The analysis of mature miRNA expression by RT-PCR was carried out with the method described previously [12, 15]. The cDNAs were diluted 10 times to perform PCR for expression confirmation and expression pattern analysis. PCRs were performed in 20 ^L of the mixture containing 1 ^L of cDNA, 0.5 ^M forward and reverse primers, 10x PCR buffer, 0.25 ^M each of dNTPs ("TaKaRa") and 2U of Taq polymerase ("TaKaRa") under the following parameters: 94°C for
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Fig. 2. RT-PCR validation of mulberry miRNAs.
M - 20-bp ladder maker; 1 - miRNA828; 2 - miRNA1854; 3 - miRNA5021; 4 - miRNA5021a; 5 - miRNA5538; 6 -miRNA838.
30 s, 55°C for 30 s, and 72°C for 30 s. The PCR products were detected by electrophoresis with 3% agarose gel containing ethidium bromide and photographed under UV light. The expression of 18S rRNA gene was performed for cDNA normalization. Molecular sizes of the amplified fr
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