Journal of Horticultural Science & Biotechnology (2013) 88 (6) 735–742
Mining of miRNAs in pomegranate (Punica granatum L.) by pyrosequencing of part of the genome By C. KANUPRIYA1, V. RADHIKA1 and K. V. RAVISHANKAR* Division of Biotechnology, Indian Institute of Horticultural Research, Hessaraghatta Lake Post, Bangalore-560089, India (e-mail:
[email protected]) (Accepted 12 July 2013) SUMMARY Plant microRNAs (miRNAs) are short, non-coding, conserved RNAs that play an important role in the transcriptional and post-transcriptional regulation of gene expression. Five miRNAs were identified in pomegranate (Punica granatum L.) using a bioinformatics approach on 7,361 contigs from partial genome sequence data (19 Mbp) generated using Roche 454 GS FLX Titanium pyrosequencing technology. Thirty-five potential mRNA targets were identified by homology searches of all five identified pomegranate miRNAs against the Arabidopsis thaliana mRNA dataset using psRNAtarget software (http://plantgrn.noble.org/psRNATarget/). The miRNA targets identified were then subjected to Gene Ontology analysis. We found that most mRNA targets were constitutively expressed genes involved in different molecular functions, biological processes, and cellular components. Experimental validation of these computationally-identified miRNAs has the potential to elucidate miRNA-based gene regulation and evolution in a non-model crop such as pomegranate.
M
icroRNAs (miRNAs) are short, single-stranded, non-coding RNAs, approx. 21 nucleotides (nt) in length, that mediate post-transcriptional and transcriptional gene silencing (Jin and Zhu, 2010). Since the discovery of the first miRNA in Caenorhabditis elegans (Lee et al., 1993), many thousands of miRNAs have been identified in various organisms including humans, flies, worms, and plants. The complete genome sequences of Arabidopsis, rice, eucalyptus, Populus, apple, and many other species, have advanced our knowledge of the mechanisms and functions of miRNAs. Plant miRNAs are known to play an important role in the control of development. For example, miR156 was shown to control flowering-time in Arabidopsis by targeting the SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) gene family (Wang et al., 2008), hormone signalling (miR160 is involved in regulating auxin signalling; Mallory et al., 2005), cell differentiation and proliferation (miR159 regulates leaf development; Comai and Zhang, 2012), as well as plant responses to biotic and abiotic stresses (members of the miR169 family have been reported to respond to salinity and drought stress; Zhao et al., 2009). Most identified miRNAs and their targets have been predicted in plants where the complete genome sequence is available (e.g., A. thaliana and rice). At present, no experimental or computational information exists on miRNAs or their targets in pomegranate (Punica granatum L.). The genesis of miRNAs in plants is a complex, multistep process involving several complex enzymes (reviewed by Bartel, 2004). Transcription of miRNA genes is catalysed by RNA polymerase II in the cell nucleus (Lee et al., 2004). Following transcription, these *Author for correspondence. 1 These authors contributed equally to this work.
single-stranded RNAs (with internal stem-loop structures) are recognised by Dicer-Like1 (DL1; Voinnet, 2009) for sequential cleavage, converting the primary microRNAs (pri-miRNAs) into precursor microRNAs (pre-miRNAs) and, finally, into an miRNA:miRNA* duplex (Bartel, 2004). The export of this duplex to the cytoplasm is mediated by a protein called HASTY (Bollman, 2003). Methyl groups are then added to the 3'-ends of the RNA duplex (which is unwound) and the miRNA (but not the miRNA*) is preferentially incorporated into the RNA-induced silencing complex (RISC; Bartel, 2004). The miRNA then guides the RISC to the cleavage site on the target mRNA to induce its cleavage and subsequent degradation (Bartel, 2004; Griffiths et al., 2006). The identification of miRNAs in plants has been achieved using two approaches: a bioinformatics (prediction/computational) approach, and a direct cloning and sequencing (experimental) approach. The computational approach is useful for organisms in which extensive DNA/genome sequence information is available, and identifies conserved miRNAs. Many miRNA families are evolutionarily conserved across the major lineages of plants, including mosses, gymnosperms, monocots, and dicots (Zhang et al., 2006). In a study by Sunkar and Jagdeeswaran (2008), five miRNA families (miR319, miR156/157, miR169, miR165/166, and miR394) were found in 51, 45, 41, 40, and 40 diverse plant species, respectively. Computational programmes such as MiRAlign (Wang et al., 2005), MiRFinder (Huang et al., 2007), and MiRTour (Milev et al., 2011) have been developed to identify known miRNA homologues in organisms whose genome sequences are available. Recent advances in high-throughput sequencing also provide a costeffective and rapid method for discovering conserved
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Conserved miRNAs in pomegranate
miRNAs in non-model species such as pomegranate (P. granatum L.). Pomegranate (P. granatum L.; 2n = 18) is a diploid, perennial, woody plant in the family Lytheraceae. The genome size of pomegranate has been estimated to be 704 Mbp, approx. six-times greater than that of A. thaliana (Bennette and Leitch, 2010). Pomegranate is believed to have originated in Iran (Simmonds, 1976; Levin, 1994). From there, it spread to the Mediterranean Basin, India, China, and Afghanistan through ancient trade routes. It is one of the oldest known edible fruits (Damania, 2005) and is highly-prized for its nutritional and medicinal properties (Noda et al., 2002). In recent years, demand for pomegranate has increased due to its high content of anti-oxidants and other beneficial phytochemicals in the fruit and peel. Although efforts have been made to develop superior cultivars by conventional breeding, data on genomic resources such as sequence information and expressed sequence tags are limited. In the present study, we used partial genome sequence data (19.7 Mbp) obtained by Roche 454 pyrosequencing (Roche Life Sciences, Branford, CT, USA) to identify putative miRNAs. Pyrosequencing technology for quantitative analysis of DNA sequences is based on sequencing-by-synthesis. Roche 454 sequencing uses a large-scale parallel pyrosequencing system capable of sequencing approx. 400 – 600 Mbp of DNA in 10 h on an FLX Genome Sequencer using GS FLX Titanium-series reagents (Voelkerding et al., 2009). We also predicted potential miRNA target genes in the A. thaliana genome that included not only transcription factors, but also genes involved in a wide range of physiological and biochemical processes.
MATERIALS AND METHODS Plant material and pyrosequencing Genomic DNA (100 ng) was extracted by a modified CTAB method (Ravishankar et al., 2000) from young leaves (2 g) of the pomegranate variety ‘Ganesh’ and used for partial sequencing, using Roche 454 GS FLX Titanium pyrosequencing technology (Roche Life Sciences). This work was carried out at the Eurofins Genomics Facility, Whitefield, Bangalore, India. A ‘Ganesh’ pomegranate genomic DNA library was clonally amplified by emulsion polymerase chain reaction (emPCR) using the Titanium Lib-L LV emPCR Kit (Roche Life Sciences) according to the manufacturer’s manual. The amount of DNA/genomic library to be used for emPCR amplification was determined by the emPCR Method Manual-Lib-L SV (Roche Life Sciences). DNA library capture was performed using one molecule of the library per DNAcapture bead. After emPCR amplification, the reactions were collected, the emulsions were broken, and beads containing the clonally-amplified DNA were enriched following the manufacturer’s protocol (Roche Life Sciences). Enriched DNA beads were counted using a CASY Model DT cell counter (Roche Life Sciences) and were deposited into the wells of the GS FLX Titanium Pico-Titer Plate, fitted with a two-region gasket, as indicated by the manufacturer (Roche Life Sciences).
Image analysis and signal processing were performed using GS Run Browser Version 2.3 software (Roche Life Sciences). A total of 19.7 Mbp were obtained, with an average contig length of approx. 332 bp. Prediction of novel miRNAs using the miRTour tool Novel miRNAs were predicted from the partial genome sequence data. Contigs containing the predicted miRNAs, are shown as Supplementary Figure 1 (available on-line at www.jhortscib.com) using the miRTour tool (Milev et al., 2011; http://bio2server.bioinfo.uni-plovdiv.bg/miRTour/) with the default parameters. The contigs file in the FASTA format was uploaded onto the user interface of miRTour. miRTour maps the mature miRNA sequences of a plant onto sequences provided by the user, and filters out contigs with miRNA hits by applying BLASTX with an E value of 10–6 using Arabidopsis and Oryza protein sequences (Milev et al., 2011). Putative pre-miRNAs were extracted from the contigs that contained matching, known miRNAs. New pre-miRNA and candidate miRNAs were selected on the basis of the following criteria: a minimum free-energy index (MFEI) > 0.7 for the predicted pre-miRNA secondary structure; the number of mismatches in the miRNA:miRNA* to be less than five; and the number of consecutive mismatches in the miRNA:miRNA* duplex to be less than four. The outputs from miRTour consisted of putative pre-miRNA and mature miRNA sequences, homology alignments of the newly-identified miRNA precursors to known plant miRNAs, MFEI values, A+U percentage contents, and predicted target sequences for the new, mature miRNAs. Secondary structure predictions Secondary structure predictions for probable premiRNA sequences used Mfold software (Zuker, 2003; http://mfold.rna.albany.edu/?q=mfold/RNA-FoldingForm). This programme determines the optimum secondary structure of an RNA based on the free-energy contributions of various secondary structure motifs using the following four criteria: (i) the RNA sequence should fold into a complete stem-loop hairpin; (ii) the mature 19 – 21 nt miRNA sequence should be on one arm of the hairpin structure; (iii) the predicted secondary structure should have ≤ –18 kcal mol–1 free-energy; and (iv) the A+U content should be in the range of 30 – 70% (Ambros et al., 2003; Zhang et al., 2006). Identification of the targets of the predicted miRNAs To understand the biological functions of the predicted miRNAs in pomegranate, the targets of these miRNAs were identified in the A. thaliana TAIR10 cDNA library using psRNATarget (Dai and Zao, 2011). The newly-identified miRNAs were loaded onto the “User-submitted small RNAs/preloaded transcripts” interface. A. thaliana transcripts (TAIR Version 10) were selected as the pre-loaded transcript/genomic library for a target search with the default parameters. The maximum expectation should be 3, the length of complementarity scoring should be 20 hsp size (“hsp size” denotes the length of the region in which the tool will score complementarity between the miRNA and the target transcript), target accessibility should be 25, the flanking length around the target site for accessibility
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C. KANUPRIYA, V. RADHIKA and K. V. RAVISHANKAR analysis should be 17 nt upstream and 13 nt downstream, and the range of central mismatches leading to translational inhibition should be 9 – 11 nt. Annotation of target genes To classify and understand the functions of the putative miRNA target genes, biological processes, cellular components, and molecular function genes were obtained in the Gene Ontology database using InterProScan (Zdobnov and Apweiler, 2001; http://www.ebi.ac.uk/Tools/pfa/iprscan/). InterProScan compares given protein sequences against the protein signatures in the PROSITE (Hofmann et al., 1999), PRINTS (Attwood et al., 2000), Pfam (Bateman et al., 2000), ProDom (Corpet et al., 1999), and SMART (Schultz et al., 2000) databases.
RESULTS AND DISCUSSION Identification of conserved miRNAs in pomegranate The basis for computational identification of putative miRNAs is the conserved nature of mature miRNA sequences, and the predicted secondary structure of the sequence surrounding the miRNA (Ambros et al., 2003). We used 7,361 contigs, generated from partial genome sequence data, as the input for miRTour to identify conserved miRNAs. This resulted in the identification of five miRNAs in pomegranate (Table I). Our survey found two miRNAs to be conserved more frequently, with homologues in Citrus sinensis, Zea mays, Glycine max, Picea abies, Ricinus communis, Vitis vinifera, Oryza sativa, Sorghum bicolor, Physcomitrella patens (a moss), and Aquilegia caerulea. The three remaining miRNAs each had a single homologue in A. thaliana, A. lyrata, and P. patens respectively. Zhang et al. (2006) classified miRNAs as being highly, moderately, or slightly conserved, based on the number of plants in which each miRNA family had been predicted. Accordingly, miRNA miR172 was considered
to be highly-conserved in plants and was shown to target mRNAs encoding APETALA2-like transcription factors in A. thaliana (Chen, 2004). miRBase contains 17 sequences in the miR535 family. These have been reported in O. sativa, P. patens, and fruit crops such as Malus domestica, Theobroma cacao, and V. vinifera. The function of the miR535 family has not been verified. In Vitis, it is reported to be up-regulated during berry maturation (Mica et al., 2009). The other three miRNA families (miR825, miR865 and miR1049) have been reported in A. lyrata, A. thaliana, and P. patens respectively, but their molecular functions are not known. Sequence characteristics of the predicted miRNAs The lengths of the newly-predicted miRNAs varied from 20 – 21 nt (Table I), while the lengths of the premiRNA stem-loops varied from 137 – 222 nt, with an average of 180 nt. The length distributions of miRNA and pre-miRNA sequences were similar to previous reports in other plant species (Bhardwaj et al., 2010; Zhang et al., 2006). Each mature miRNA was located on the stem-arm of the secondary stem-loop hairpin structure in the putative pre-miRNA. In our study, the location of the mature miRNA varied. We found that, of the five miRNAs identified, three were located on the 5’arm of the stem-loop hairpin structure, while two resided on the 3’-arm (Figure 1). The minimum folding energy (MFE) is an important feature of pre-miRNAs, which, unlike other non-coding RNAs, have lower MFE values than random sequences (Wang et al, 2005; Jiang et al., 2007). MFE values were found to be negative for all five newly-identified miRNAs in pomegranate, and ranged from -34.8 kcal mol–1 to –82.3 kcal mol–1. We also calculated the MFEI values of the pre-miRNAs which were significantly higher (0.77 – 0.95) than for tRNAs (0.64), rRNAs (0.59), and mRNAs (0.62 – 0.66; Zhang et al, 2006). The nucleotide content of a pre-miRNA has an
TABLE I Five predicted miRNAs from pomegranate Pre-miRNA length (nt)
(A+U) content (%)
Predicted mature sequence (5'➝3')
pgm-miR172
UAUUCUCAUGAUGAUGCUGC
20
miR172
171
–52.9
0.88
65
pgm-miR535
CUGACAGCGAGAGAGAGCACA
21
miR535
137
–61.2
0.86
50
pgm-miR825 pgm-miR865 pgm-miR1049
UAUUUGAGAAGGUGCAUGAUC UUUUUGCAUCAAAUUUAUCCC CCUCUCUUAGCCAAAAUAUCU
21 21 21
miR825 miR865 miR1049
172 200 222
–34.8 –82.3 –65.8
0.83 0.95 0.77
75 57 61
#
miRNA miRNA length (nt) family
MFE#(kcal mol-1) MFEI#
Predicted miRNA
Plant homologues csi-miR172a, gma-miR172f, zma-miR172d, zma-miR172a, zma-miR172b, zma-miR172c, sbi-miR172f, sbi-miR172d, sbi-miR172a, sbi-miR172c ppt-miR535a, ppt-miR535d, ppt-miR535c, ppt-miR535b, vvi-miR535a, vvi-miR535c, vvi-miR535b, aqc-miR535, csi-miR535, pab-miR535, rco-miR535, osa-miR535 aly-miR825 ath-miR865-3p ppt-miR1049
MFE, minimum free energy; MFEI, minimum free energy index, where MFEI = [(MFE/length of the pre-miRNA sequence) 100] / (G + C)%.
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Conserved miRNAs in pomegranate
pgm-miR172 (MFE§ = -50.5 kcal mol-1)
pgm-miR535 (MFE = -61.2 kcal mol-1)
pgm-miR825 (MFE = -34.8 kcal mol-1)
pgm-miR865 (MFE = -82.3 kcal mol-1)
pgm-miR1049 (MFE = -65.8 kcal mol-1)
FIG. 1 Secondary structures of the five newly-predicted miRNAs (Panels A – E) from Punica granatum L. using Mfold software (Zuker, 2003). Secondary structures were predicted using the RNA Folding Form tool in the Mfold web-server (http://mfold.rna.albany.edu). MFE; minimum folding energy (in kcal mol–1).
C. KANUPRIYA, V. RADHIKA and K. V. RAVISHANKAR important function. G and C (which form three hydrogen bonds) contribute to the formation and stabilisation of secondary stem-loop hairpin structures. Zhang et al. (2006) reported that pre-miRNAs contained higher A+U contents than other RNAs. Gupta et al. (2010) found that miRNAs with a higher A+U content bound more strongly to certain proteins. The A+U contents of the five pre-miRNA sequences from pomegranate ranged from 50 – 75%, as observed in cotton (63.0 ± 11.8%; Wang et al., 2012). Prediction of miRNA targets in A. thaliana To understand the biological functions of the five predicted miRNAs from pomegranate, we used psRNAtarget software (http://plantgrn.noble.org/ psRNATarget/) to search for putative target genes using the default parameters. It has been shown that a given miRNA can have multiple mRNA targets (Bonnet et al., 2004). Consistent with this, in A. thaliana we found 35 target genes for the five miRNAs of pomegranate. The detailed BLASTX annotation results against the nonredundant protein database are shown in Table II. All 35 annotated targets regulated by the miRNAs of pomegranate were then subjected to GO analysis to investigate their gene ontology. In agreement with a previous report stating that conserved miRNAs target transcription factors (Chuck et al., 2009), our analysis revealed that pgm-miR172 bound to mRNAs for four nascent-polypeptideassociated complex (NAC) domain-containing proteins and that pgm-miR825 bound to mRNAs encoding four homeodomain-like superfamily proteins involved in the
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regulation of transcription. pgm-miR535, bound to an mRNA for a cleavage and polyadenylation specificity factor (CPSF), an important multi-subunit component of the 3'-end processing apparatus of mRNAs in eukaryotes. pgm-miR825 bound to 11 different mRNAs involved in F-box and associated interaction domaincontaining proteins such as dihydrolipoamide succinyltransferase, and general regulatory factor 11. Both pgm-miR865 and pgm-miR1049 bound to five different mRNAs for calcinurin B-like protein (CBL)interacting protein kinase 20, which is known to mediate plant responses to a variety of external stresses (Albrecht et al., 2003), Rho guanyl-nucleotide exchange factor 2, /-hydrolase superfamily proteins, transposable element genes, and multi-functional protein 2 (MFP-2). All 35 mRNA targets (genes) in A. thaliana regulated by the five annotated miRNAs identified in pomegranate were then subjected to GO analysis to investigate their ontology (Du et al., 2010). We found that 29 of the 35 target genes (mRNAs) were involved in 49 different molecular functions, 25 of the genes were involved in 43 biological processes, and 34 of the genes influenced 17 cellular components (Table III).
CONCLUSIONS We have used next-generation sequencing technology to generate partial genome sequence data (19 Mbp) for pomegranate to identify five new miRNAs using a bioinformatics approach. We identified 35 putative target genes (mRNAs) in A. thaliana. It will be important to characterise these five miRNAs experimentally and to
TABLE II Details of the miRNA targets identified in A. thaliana and their functions obtained from psRNATarget§ (a plant small RNA target analysis server) Predicted miRNA
Predicted target gene ID#
Target gene description
pgm-miR172
AT3G10490.1 AT3G10490.2 AT5G58540.2 AT3G10480.2 AT3G10480.1 AT5G58540.1 AT5G58540.3 AT3G10480.3 AT4G14210.1 AT4G14210.2 AT2G30950.1 AT1G49320.1 AT5G38640.1 AT3G11960.1 AT3G47150.1 AT4G26910.3 AT4G26910.2 AT4G26910.1 AT5G61090.1 AT1G13450.1 AT1G13450.2 AT1G13450.3 AT2G03220.1 AT1G34760.2 AT1G34760.1 AT5G36780.1 AT5G36690.1 AT5G45820.1 AT4G13540.1 AT3G31375.1 AT1G01700.1 AT1G19190.1 AT2G10760.1 AT5G49465.1 AT3G06860.1
Nascent polypeptide-associated complex (NAC) domain containing protein 52 NAC domain containing protein 52 Protein kinase superfamily protein NAC domain containing protein 50 NAC domain containing protein 50 Protein kinase superfamily protein Protein kinase superfamily protein NAC domain containing protein 50 Phytoene desaturase 3 Phytoene desaturase 3 FtsH extracellular protease family Unknown seed protein like 1 NagB/RpiA/CoA transferase-like superfamily protein Cleavage and polyadenylation specificity factor (CPSF) A subunit protein F-box and associated interaction domains-containing protein Dihydrolipoamide succinyltransferase Dihydrolipoamide succinyltransferase Dihydrolipoamide succinyltransferase Polynucleotidyl transferase, ribonuclease H-like superfamily protein Homeodomain-like superfamily protein Homeodomain-like superfamily protein Homeodomain-like superfamily protein Fucosyltransferase 1 General regulatory factor 11 General regulatory factor 11 AT5G36780 and AT5G36690 represent identical copies. AT5G36690 and AT5G36780 represent identical copies. CBL-interacting protein kinase 20 Unknown protein; involved in N-terminal protein myristoylation; Transposable element gene Rho guanyl-nucleotide exchange factor 2 /-Hydrolases superfamily protein Transposable element gene Transposable element gene Multifunctional protein 2
pgm-miR535 pgm-miR825
pgm-miR865
pgm-miR1049
# §
Accession numbers of A. thaliana sequences in The Arabidopsis Information Resource (TAIR) database (http://www.arabidopsis.org/). (http://www.plantgrn.nobel.org/psRNATarget/; Dai and Zhao, 2011).
Nucleus Mitochondrion, membrane Mitochondrion, membrane Mitochondrion, membrane Nucleus Intracellular nucleus Intracellular nucleus Intracellular nucleus Golgi apparatus membrane Cytoplasm Cytoplasm Nucleous Nucleus Cytosol
Seed development Cellular metabolic process, negative regulation of translational initiation, regulation of catalytic activity Cotyledon development, hormone-mediated signalling pathway, negative regulation of response to stimulus, nuclear-transcribed mRNA catabolic process, protein glycosylation, sugar mediated signaling pathway Biological process unknown Metabolic process, systemic acquired resistance, tricarboxylic acid cycle Metabolic process, systemic acquired resistance, tricarboxylic acid cycle Metabolic process, systemic acquired resistance, tricarboxylic acid cycle Biological process unknown Regulation of transcription Regulation of transcription Regulation of transcription Plant-type cell wall biogenesis, xyloglucan biosynthetic process Biological process unknown Biological process unknown Biological process unknown Biological process unknown Signal transduction, protein phosphorylation
Molecular function unknown
GTP binding guanyl-nucleotide exchange factor activity, translation initiation factor activity Nucleic acid binding
AT1G49320.1
AT5G38640.1
pgm-miR865
AT3G47150.1 AT4G26910.3 AT4G26910.2 AT4G26910.1 AT5G61090.1 AT1G13450.1 AT1G13450.2 AT1G13450.3 AT2G03220.1 AT1G34760.2
pgm-miR825
AT5G36780.1 AT5G36690.1 AT5G45820.1
AT1G34760.1
AT3G11960.1
pgm-miR535
AT2G30950.1
AT4G14210.2
Function unknown Zinc ion binding, acyltransferase activity Zinc ion binding, acyltransferase activity Zinc ion binding, acyltransferase activity Nucleic acid binding Sequence-specific DNA binding transcription factor activity Sequence-specific DNA binding transcription factor activity Sequence-specific DNA binding transcription factor activity Fucosyltransferase activity, transferring glycosyl groups ATPase binding, amino acid binding, protein domain specific binding, protein phosphorylated amino acid binding ATPase binding, amino acid binding, protein domain specific binding, protein phosphorylated amino acid binding Metabolic function unknown Metabolic function unknown Protein kinase activity, protein serine/threonine kinase activity, ATP binding, transferase activity, transferring phosphorus-containing groups
Multicellular organismal development, regulation of transcription Carotenoid biosynthetic process, oxidation-reduction process, protein autophosphorylation, regulation of photon transport Carotenoid biosynthetic process, oxidation-reduction process, protein autophosphorylation, regulation of photon transport Seedling development
Sequence-specific DNA binding transcription factor activity Oxidoreductase activity acting on paired donors with incorporation or reduction of molecular oxygen, phytoene dehydrogenase activity Oxidoreductase activity acting on paired donors with incorporation or reduction of molecular oxygen, phytoene dehydrogenase activity Metalloendopeptidase activity, ATP binding, nucleosidetriphosphatase activity, nucleotide binding, zinc ion binding
AT5G58540.3
AT3G10480.2 AT3G10480.1 AT5G58540.1
AT3G10480.3 AT4G14210.1
Cellular compartment
Multicellular organismal development, pollen development Multicellular organismal development, pollen development Glucuronoxylan metabolic process, plant-type cell wall biogenesis, protein phosphorylation, xylan biosynthetic process Multicellular organismal development, regulation of transcription Multicellular organismal development, regulation of transcription Glucuronoxylan metabolic process, plant-type cell wall biogenesis, protein phosphorylation, xylan biosynthetic process Glucuronoxylan metabolic process, plant-type cell wall biogenesis, protein phosphorylation, xylan biosynthetic process
pgm-miR172
Expressed in: 7 plant structures; expressed during: four leaves visible, anthesis, petal differentiation and expansion stage Nucleus Chloroplast, chloroplast envelope, chloroplast thylakoid, plastid Chloroplast, chloroplast envelope, chloroplast thylakoid, plastid Chloroplast thylakoid membrane, membrane, thylakoid, chloroplast thylakoid, chloroplast, chloroplast envelope, thylakoid lumen Extracellular region, protein storage vacuole Eukaryotic translation initiation factor 2B complex Nucleus
Nucleus Nucleus Plasma membrane
Nucleolus, nucleus Nucleolus, nucleus Nucleus
Biological processes
Molecular functions
Sequence-specific DNA binding transcription factor activity Sequence-specific DNA binding transcription factor activity ATP binding, kinase activity, protein kinase activity, transferase activity, transferring phosphorus-containing groups Sequence-specific DNA binding transcription factor activity Sequence-specific DNA binding transcription factor activity ATP binding, kinase activity, protein kinase activity, transferase activity, transferring phosphorus-containing groups ATP binding, kinase activity, protein kinase activity, transferase activity, transferring phosphorus-containing groups
Target
AT3G10490.1 AT3G10490.2 AT5G58540.2
miRNA
TABLE III Annotation of the 35 identified miRNA target genes in A. thaliana for the five predicted miRNAs from pomegranate
740 Conserved miRNAs in pomegranate
Nucleus Nucleus #
AT1G01700.1 AT1G19190.1 pgm-miR1049
Accession numbers of A. thaliana sequences in The Arabidopsis Information Resource (TAIR) database (http://www.arabidopsis.org/).
REFERENCES
Biological process unknown Metabolic process
Intracellular, nucleus DNA binding transcription coactivator activity, zinc ion binding AT1G04020.1
AT4G13540.1 AT3G31375.1 AT3G47690.1 AT1G04020.2
741
assign their targets in order to understand their functions and mechanisms of action in the regulatory network of the plant. A significant number of novel miRNAs still remain to be identified and characterised in this important and ancient fruit crop.
Metabolic function unknown Metabolic function unknown Microtubule binding DNA methylation, DNA repair, DNA replication, DNA-dependent DNA replication, cell cycle process, cell proliferation, chromatin modification, double-strand break repair via homologous recombination, gene silencing by RNA, histone H3-K9 methylation, leaf development, leaf morphogenesis, pollen development, regulation of DNA replication, regulation of cell cycle, regulation of meristem structural organization, somatic cell DNA recombination DNA methylation, DNA repair, DNA replication, DNA-dependent DNA replication, cell cycle process, cell proliferation, chromatin modification, double-strand break repair via homologous recombination, gene silencing by RNA, histone H3-K9 methylation, leaf development, leaf morphogenesis, pollen development, regulation of DNA replication, regulation of cell cycle, regulation of meristem structural organization, somatic cell DNA recombination Rho guanyl-nucleotide exchange factor activity Hydrolase activity
N-terminal protein myristoylation Biological process unknown Biological process unknown DNA binding transcription coactivator activity, zinc ion binding
Cytoplasm Unknown Cytoplasm Intracellular, nucleus
C. KANUPRIYA, V. RADHIKA and K. V. RAVISHANKAR
ALBRECHT, V., WEINL, S., BLAZEVIC, D., ANGELO, C. D., BATISTIC, O., KOLUKISAOGLU, U., BOCK, R., SCHULZ, B., HARTER, K. and KUDLA, J. (2003).The calcium sensor CBL1 integrates plant responses to abiotic stresses. Plant Journal, 36, 457–470. AMBROS, V., BARTEL, B., BARTEL, D. P., BURGE, C. B., CARRINGTON, J. C., CHEN, X., DREYFUSS, G., EDDY, S. R., GRIFFITHS-JONES, S., MARSHALL, M., MATZKE, M., RUVKUN, G. and TUSCHL, T. (2003). A uniform system for microRNA annotation. RNA, 9, 277–279. ATTWOOD, T. K., CRONING, M. D. R., FLOWER, D. R., LEWIS, A. P., MABEY, J. E., SCORDIS, P., SELLEY, J. N. and WRIGHT, W. (2000). PRINTS-S: the database formerly known as PRINTS. Nucleic Acids Research, 28, 225–227. BARTEL, D. P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116, 281–297. BATEMAN, A., BIRNEY, E., CERRUTI, L., DURBIN, R., ETWILLER, L., EDDY, S. R., GRIFFITHS-JONES, S., HOWE, K. L., MARSHALL, M. and SONNHAMMER, E. L. (2002). The pfam protein families database. Nucleic Acids Research, 30, 276–280. BENNETT, M. D. and LEITCH, I. J. (2010). Angiosperm DNA C-values database. (Release 7.0 December. 2010). http://www.kew.org/ cvalues/ BHARDWAJ, J., MOHAMMAD, H. and YADAV, S. (2010). Computational identification of microRNAs and their targets from the expressed sequence tags of horsegram (Macrotyloma uniflorum (Lam.) Verdc.). Journal of Structural and Functional Genomics, 11, 233–240. BOLLMAN, K. M., AUKERMAN, M. J., PARK, M. Y., HUNTER, C., BERARDINI, T. Z. and POETHIG, R. S. (2003). HASTY, the Arabidopsis ortholog of exportin 5/MSN5, regulates phase change and morphogenesis. Development, 130, 1493–1504. BONNET, E., WUYTS, J., ROUZE, P. and VAN DE PEER, Y. (2004). Detection of 91 potential conserved plant microRNAs in Arabidopsis thaliana and Oryza sativa identifies important target genes. Proceedings of the National Academy of Sciences of the USA, 101, 11511–11516. CHEN, X. (2004). A microRNA as a translational repressor of APETALA2 in Arabidopsis flower development. Science, 303, 2022–2025. CHUCK, G., CANDELA, H. and HAKE, S. (2009). Big impacts by small RNAs in plant development. Current Opinion in Plant Biology, 12, 81–86. COMAI, L. and ZHANG, B. (2012). MicroRNAs: key gene regulators with versatile functions. Plant Molecular Biology, 12, 81–86. CORPET, F., SERVANT, F., GOUZY, J. and KAHN, D. (2000). ProDom and ProDom-CG: tools for protein domain analysis and whole genome comparisons. Nucleic Acids Research, 28, 267–269. DAI, X. and ZHAO, P. X. (2011). psRNATarget: a plant small RNA target analysis server. Nucleic Acids Research, 39, 1–5. DAMANIA, A. B. (2005). The Pomegranate: Its Origin, Folklore, and Efficacious Medicinal Properties. (Nene, Y.L., Ed.). Agriculture Heritage of Asia. Proceedings of the International Conference of the Asian Agri History Foundation, Secundarabad, India. 175–183. DU, Z., ZHOU, X., LING, Y., ZHANG, Z. and SU, Z. (2010). AgriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Research, 38, 64–70. GRIFFITHS-JONES, S., GROCOCK, R. J., VAN DONGEN, S., BATEMAN, A. and ENRIGHT, A. J. (2006). miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Research, 34, 140–144.
742
Conserved miRNAs in pomegranate
GUPTA, R., SONI, N., PATNAIK, P., SOOD, I., SINGH, R., RAWAL, K. and RANI, V. (2010). High AU content: a signature of upregulated miRNA in cardiac diseases. Bioinformation, 5, 132–135. HOFMANN, K., BUCHER, P., FALQUET, L. and BAIROCH, A. (1999). The PROSITE database, its status in 1999. Nucleic Acid Research, 27, 215–219. HUANG, T. H., FAN, B., ROTHSCHILD, M. F., HU, Z. L., LI, K. and ZHAO, S, H. (2007). MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans. BMC Bioinformatics, 17, 341. JIANG, P., WU, H., WANG, W., MA, W., SUN, X. and LU, Z. (2007). MiPred: classification of real and pseudo-microRNA precursors using random forest prediction model with combined features. Nucleic Acids Research, 35, 339–344. JIN, H. and ZHU, J. K. (2010). A viral suppression protein inhibits host RNA silencing by hooking up with Argonautes. Genes and Development, 24, 853–856. LEE, R. C., FEINBAUM, R. L. and AMBROS, V. (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75, 843–854. LEE, Y., KIM, M., HAN, J., YEOM, K. H., LEE, S., BAEK, S. H. and KIM, V. N. (2004). MicroRNA genes are transcribed by RNA polymerase II. European Molecular Biology Organisation Journal, 23, 4051–4060. LEVIN, G. M. (1994). Pomegranate plant genetic resources in Turkmenistan. Plant Genetic Resources Newsletter, 97, 31-36. MALLORY, A. C., BARTEL, D. P. and BARTEL, B. (2005). MicroRNAdirected regulation of Arabidopsis AUXIN RESPONSE FACTOR17 is essential for proper development and modulates expression of early auxin response genes. Plant Cell, 17, 13601375. MICA, E., PICCOLO, V., DELLEDONNE, M., FERRARINI, A., PEZZOTTI, M., CASATI, C., DEL FABBRO, C., VALLE, G., POLICRITI, A. and MORGANTE, M. (2009). High-throughput approaches reveal splicing of primary microRNA transcripts and tissue specific expression of mature microRNAs in Vitis vinifera. BMC Genomics, 10, 558. MILEV, I., YAHUBYAN, G., MINKOV, I. and BAEV, V. (2011). miRTour:Plant miRNA and target prediction tool. Bioinformation, 6, 248-249.
NODA, Y., KANEYUKI, T., MORI, A. and PACKER, L. (2002). Antioxidant activities of pomegranate fruit extract and its anthocyanidins: delphinidin, cyanidin, and pelargonidin. Journal of Agricultural and Food Chemistry, 50, 166–171. RAVISHANKAR, K. V., ANAND, L. and DINESH, M. R. (2000). Assessment of genetic relatedness among mango cultivars of India using RAPD markers. Journal of Horticultural Science & Biotechnology, 75, 198–201. SCHULTZ, J., COPLEY, R. R., DOERKS, T., PONTING, C. P. and BORK, P. (2000). SMART: a web-based tool for the study of genetically mobile domains. Nucleic Acids Research, 28, 231–234. SIMMONDS, N. W. (1976). Evolution of Crop Plants. Longmans, London, UK. 350 pp. SUNKAR, R. and JAGADEESWARAN, G. (2008). In silico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biology, 8, 37–50. VOELKERDING, K. V., DAMES, S. A. and DURTSCHI, J. D. (2009). Nextgeneration sequencing: from basic research to diagnostics. Clinical Chemistry, 55, 641–658. VOINNET, O. (2009). Origin, biogenesis and activity of plant microRNAs. Cell, 136, 669–687. WANG, X., ZHANG, J., LI, F., GU, J., HE, T., ZHANG, X. and LI, Y. (2005). MicroRNA identification based on sequence and structure alignment. Bioinformatics, 21, 3610–3614. WANG, M., WANG, Q. and WANG, B. (2012). Identification and characterization of microRNAs in Asiatic cotton (Gossypium arboreum L.). PLoS ONE, 7, e33696. ZDOBNOV, E. M. and APWEILER, R. (2001). InterProScan – an integration platform for the signature-recognition methods in InterPro. Bioinformatics, 17, 847–848. ZHANG, B. H., PAN, X. P., COX, S. B., COBB, G. P. and ANDERSON, T. A. (2006). Evidence that miRNAs are different from other RNAs. Cell and Molecular Life Science, 63, 246–254. ZHAO, B., GE, L., LIANG, R., LI, W., RUAN, K. and LIN, H. (2009). Members of miR-169 family are induced by high salinity and transiently inhibit the NF-YA transcription factor. BMC Molecular Biology, 10, 29–40. ZUKER, M. (2003). Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research, 31, 3406–3415.
C. KANUPRIYA, V. RADHIKA and K. V. RAVISHANKAR
Contig sequences containing miRNAs pgm-miR172 TCTCAATTATTCATTACATTTTCACACTTTTTCTCATAATCAACAATACAATCATTACAAACCAATTAAAATCAAAAACTCAACTCAGTTTAAC TCTAAACCAAACACAACATTAGAATCTTATTTTCCTAACTCCTGAGGCAAAGCATATTATTAGAACACTTCTATTTCACCACTTCAAATACCTC CAAATATCTAGTAAGTAGTAGCTGTAACCCACAGTTATTGCAAAATCATAGACATTTCTCCATGAGAAAAATTCGGAGGACAAAATCATCATT CGAATATGAGTTGATATGATTTTTCAACTTTATTATTCTCATGATGATGCTGCACAATCCGAATTTGGCAAGGCCGAATGTCTCACAAATG ACATTTTTGCAGTCAATGAAAAAAGCAGCGGCAATTTGTACTTGAGTGGCATTTGGAGTAACATCCTCCAAACATTTTCGGAGGAAGCTTTG TTTGGTTTATGAATAATAATTCAACTTTACTCAACTCAAT
pgm-miR535 AAGTACTAGGATTTTTATGAAGTTTCTCTTTTTGTGCTTTTCATTTCTTTGTTTGAGTCTTTCTCATAGTTAGCTCATCAATAAGATGAAACC TTTTAGTTATGAGATGTAACATTTTTGCTTGCAAGGCAGGAGGACTTCCAGCAGCCATAGCAACTGTATAATAACCTTTTAATTAAAAGCTT TAGCTGATCAAAGGGTTTACATACATGTCTCAATGTCTCTTAAATAACGTTTGTGAATTTGTTCTTTCTTGCAGGGTGTGCAGACTGTTACT AACACTTTGGAGTGTCGTAATCTGACAGCGAGAGAGAGCACACTAGTCGGCATCCATGCTGGGAGACATGGCTGTGTACAAGCGTGCT CTCTTTCGTTGTCATACGATGTGCCGTCCAAGGGAAGATCCACTACTCTC
pgm-miR825 ATATTTTTACATACTAAATTCAATTTCATATTCTCACTGGAAAAAAATAATATGCCTTTCTGTATACTAAATTTTTACACTTTATAACACATGC ACCTATATCTTTACGAGTGTCTTTTTAATGTAATTTTATAAAAAAAATAAGCGCTTGCACCTTCTCATGTAAATTAAGCCTCGCACCAATTGT CAGTTTTAAAACATACATTTTCATCAAATAAATGTTGCTCTTGAAACATAAAACTAAAAGGAAAAGATTACTTATTATAGGCATAATTCTATT CTATATTTTCATTGAAAATTTTTGTATATGCTATATGCTAAACTTTTACCTAAATATAGAATCATGTTTTTCACATTTACTTTCCAAAAATTAT TCTTTATAGACTTTTAGTATTATTTGGATGTATTTGAGAAGGTGCATGATCATTTCTATGTGAAAAGTGTATGGTTATTTGTTGAAAGAT AAAAATTAAGAGACTTTTCTAAGTGTCTTTTCAATGTAACTAGTAATATTAAAAGGATATATACTAG
pgm-miR865 TTTTTTGTCAAATANATTTCATGGTTACTTTTTGCATCAAATTTATCCCGGCGTTATCTTTTCCGTCCGATATCTAACGGCCGTAATGACG TGGCATGTGGAGCGGTACTAGGCCACACTTGCTACGCGGGCGCCACGTCAGCACAACCGTTAGATATCGACGAAAAAGATAACGGCGTAAT AGATTTGAACCAAAATGTAAACCATGGGATATAGATGACAAAAAAAAACAATGGAATAAATTTGACAAAATGCGTAAACCATAAGTCATTTG GGGTAAAAACCCCTCAATAAAATGATTATTAATCAACGTAAAATAAATTAGTATAGTGTACCGCAAATAAGTTCTCATATTTCACATATAGTA TTAAAATATAAATTACTAAAATTCACCCTTTTTTATCAAAATATTATTAAAACATTTATAAATTAGTTATGTATTTTAAAATACCACCACTTTT GTCTCTGAATAATTTTTGCCCCCACATAGAC
pgm-miR1049 TCTTCTTCGGTCGTGGGACCCACGGGCTACCCAAGATGAGTCACCCCTTGCCCTAGAAGTTTTGGCTTCTTCTTCTTCAATGCAAGAAGCCA ACTTGCCCTCTCTTAGCCAAAATATCTTGGAGAGGTTGAAGACAACACTCAACTTCTCATCAATTCTCATCAATGCTCATCAATGTCT TATCTCTTCTTCATTAATGCAATGGAAGAAGATGAGACTTGATATTTTCTTAATCAAACCGAAATTTGCTAAGCATGGGGGTTAAGAGCACT TGCTTTTGCTAATTGTGTCATTAGCTTTGTCTATAAATGGCCCAAAAAGTGATTAAG SUPPLEMENTARY FIG. 1 Five contig sequences from pomegranate obtained by pyrosequencing the genome and containing the five putative miRNA sequences (highlighted in bold).
