Identification and Characterization of Novel micrornas and Their Targets in Cucumis melo L.: An in silico Approach

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1 Focus Sci. Feb 2016, Volume 2, Issue 1 DOI: /focsci Submitted: Accepted: Keywords: MicroRNAs Expressed Sequence Tags GSSs Comparative Genomics psrnatarget Server Focus on Sciences Research Article Identification and Characterization of Novel micrornas and Their Targets in Cucumis melo L.: An in silico Approach Sreejita Chakraborty 1,2, Karam Jayanandi Devi 1, Bibhas Deb 2, Ravi Rajwanshi 1, * 1 Department of Biotechnology, Assam University, Silchar, India 2 Bioinformatics Centre, Gurucharan College, Silchar, India * Corresponding author: Ravi Rajwanshi, Department of Biotechnology, Assam University, Silchar, Assam , India. Tel: rrajwanshi@ gmail.com Abstract Introduction: MicroRNAs are small non coding entities that play crucial regulatory role in plants as well as animals by altering their gene expression either by wrecking or blocking the of translation of the homologous mrnas. It is an accepted fact that the plant mirnas are conserved in nature and hence comparative genomics approach can be employed to identify novel mirnas from the expressed sequence tags (ESTs) and genome survey sequences (GSSs). Methods: In the present study, ESTs and GSSs of Cucumis melo were subjected to BLASTn against non redundant mirnas from mirbase. BLASTX was performed to remove non coding sequences. The remaining sequences were further subjected to MFOLD to predict the secondary structure and MFEI values. psrnatarget server was employed to predict the targets for newly identified mirnas which were functionally annotated using GO and KEGG. MEGA version 6 was used to generate phylogenetic tree. Results: The present study identified 15 novel mirnas from Cucumis melo ESTs and GSSs having average MFEI value of A total of 178 targets were predicted. GO analysis revealed a maximum of 8 and 3 mirnas involved in response to abiotic and biotic, respectively while 1 mirna was found to be involved in both the type of es. Conclusion: The identified targets were observed to have a role in plant growth and development, metabolism, senescence, disease resistance and various other responses. The findings will further contribute towards understanding of the mirnas function and their regulatory mechanisms in melon. INTRODUCTION The discovery of first plant micrornas (mirnas) from Arabidopsis thaliana way back in 2002 played a vital role in the arena of biological research as it paved a way for better and improved understanding of important regulatory roles in plants [1]. MicroRNAs are endogenously encoded small RNAs (usually about 22 nucleotides) that have the ability to post transcriptionally regulate gene expression [2]. The vast array of biological es in plants such as developmental es, metabolic activities, and defense responses to various es are regulated by mirnas [3]. For the discovery of novel mirnas, four different methods namely genetic screening, direct cloning, computational approach based on whole genome sequences, expressed sequence tags (ESTs) and genome survey sequences (GSSs) analysis can be used. The ESTs and GSSs analysis is very useful for predicting novel mirnas, particularly of those mirnas at low abundance or unavailability of whole genome sequence [1]. One of the most widely cultivated fruits amongst the family Cucurbitaceae is Cucumis melo L., commonly known as melon. The genome of melon comprises of 12 chromosomes and because of the vast morphological, physiological and biochemical diversity within the species, it is now being used as a model to understand various biological es including color, flavor and texture during fruit development [4]. However, despite the importance of melon genome as a model, not much work has been performed. Till date, only 74 mirnas belonging to different families in melon were identified and deposited in the mirna registry database [5] and hence there was a scope to identify more mirnas. Therefore, in the present study novel mirnas were identified from melon ESTs and GSSs by computational method and the targets for the same were identified, thereby highlighting its dynamic role in various physiological and metabolic es.

2 Methods Reference Sequence Set of mirnas To search potential mirnas, previously known 5939 mirnas of the plant species were obtained from mirna Registry Database (Release 19.0, [5]. These mirnas were defined as a reference set of mirna sequences. To avoid the redundant or overlapping mir- NAs, the repeated sequences of mirnas within the above species were removed using BLAST tool and the remaining sequences were used as query sequences for BLAST search. Melon ESTs and GSSs Melon ESTs and GSSs were downloaded from GenBank nucleotide databases at the National Center for Biotechnology Information (NCBI). Currently, ESTs and GSSs are available in the NCBI database. Local databases for standalone BLAST were constructed separately for melon ESTs and GSSs by using the makeblastdb application [6]. Bioinformatics Tools Prediction of Potential C. melo mirnas mirna reference sequences were subjected to BLAST search for C. melo mirna homologs against EST and GSS databases. The initial BLAST search was performed with the BLAST program downloaded from the NCBI ftp site (ftp://ftp.ncbi.nih.gov/), by adjusting the BLAST parameter settings as: expect values at 1e-3; low complexity was chosen as the sequence filter; the number of descriptions and alignments was raised to 1,000. The default word-match size between the query and database sequences was 7. RNA sequences having length 18 nt without any gap and mismatches in the range of 0-2 were chosen to be the potential mirna candidates. The selected ESTs and GSSs were BLASTed against each other to remove redundant sequences. The filtered sequences were subjected to BLASTX against the non-redundant database for removing the coding sequences. The secondary structures of the remaining EST and GSS sequences were identified using a web-based computational program, MFOLD ( (Version 3.1). The criteria adopted for screening the candidates of potential mirnas or pre-mirnas was according to Zhang and colleagues [7] (Fig. 1a). ΔG values (kcal/mol) of stem-loop structures generated by MFOLD program [8] were applied to calculate their negative minimal free energies (MFEs), which is directly correlated with the sequence length. AMFE is defined as the MFE of a 100 nucleotide length. AMFE = MFE Length of precursor sequence (LP) X 100 The minimal folding free energy index (MFEI) for each sequence was calculated as: MFEI = AMFE (G+C)% All the MFOLD outputs were recorded in a tabular format that included EST or GSS IDs, length of precursor, (G+C) %, (A+U) %, MFE, AFME, MFEI values (Table 1). Prediction of mirna Targets To predict the mirna targets, homology algorithm strategy was applied and Arabidopsis was used as a reference system for finding the targets of the candidate mirnas. The predicted mirnas of C. melo were used as query against the melon ESTs as well as the model plant Arabidopsis thaliana DFCI gene index (AGI) and Arabidopsis information resource (TAIR) transcript removed mirna genes by using psrnatarget (a Plant small RNA Target Analysis Server) ( org/psrnatarget/) with default parameters (Fig. 1b). The criteria followed were: (1) range of central mismatch for translational inhibition was considered between 9 11 nucleotides, (2) maximum expectation value was set at 3, (3) maximum mismatches at the complementary site was 4 without any gaps and, (4) multiplicity of target sites was 2. Pathway Analysis of mirna Target Genes The functions of mirna target genes were assigned by performing BLASTX search against Non Redundant (NR) database of NCBI with an e-value of 1e 10. The best hits were used to validate the target genes function and metabolic pathway regulated by mirnas. Gene Ontology (GO) terms were assigned to the target genes. Finally, the metabolic pathways and their networks regulated by the potential mirnas were searched through KAAS (KEGG Automatic Annotation Server) ( [9]. Phylogenetic Analysis of Newly Identified mirnas To search the homologous sequences of the identified mir- NAs against the entire known mirna families, standalone BLAST tool was employed. The criteria involved allows a maximum mismatch of 3 and e-value of < The precursor sequences of the homologous mirnas were identified and collected from mirbase. The collected precursor mir- NAs sequences along with the precursors of newly identified mirnas were aligned in ClustalW and subsequently used for phylogenetic analysis in MEGA version 6 [10]. Results Homology search based computational method was used for predicting novel mirnas in melon and the publically available ESTs and GSSs database was used to identify a total of 15 potential mirnas (11 from ESTs and 4 from GSSs) with 178 targets in C. melo. The sequence characteristics of the newly identified mirnas are listed in Table 1. 2

3 Figure 1: Steps Followed to Search Potential (a) mirnas and, (b) Targets in Melon ESTs and GSSs Table 1: Details of Newly Identified mirnas from Melon ESTs and GSSs MFEI AMFE MFE (- G kcal/mole) (A+U)% (G+C)% LP LM Strand Loc EST/ GSS ID Length of reference mirna Homologous mirna Mature sequence (5-3 direction) mirna cme-mir2673a UCUUCCUCUUCCUCUUCC mtr-mir2673a 18/22 JG Plus cme-mir6438b ACACAGAAUAGGGAAU- GAAAU ptc-mir6438b 18/22 JG Minus cme-mir5157a-5p CUUUUUAAUAGCUACAAC osa-mir5157a-5p 18/24 JG Plus cme-mir414 UCAUCUUCAUCAUCAU- ath-mir414 19/21 JG Plus CGU cme-mir5291c UUGAUGGAUGGAUUG- mtr-mir5291c 20/24 JG Plus GAUGG cme-mir396c UCAAGAAGGUCGUGGAAA mtr-mir396c 18/21 AM Plus cme-mir5200 GUAGAUACUCUCUAAG- bdi-mir /21 JG Plus GCUU cme-mir2275c GAAUCUGGAGGAAAA- osa-mir2275c 20/23 JG Minus CAAAC cme-mir5293 GAAGAAGAAGAAGGAAG- mtr-mir /24 JG Plus GAAGAAG cme-mir5086 AUUGGUGGAAGAAUUG- tae- mir /21 EB Plus GUA cme-mir5286b AACUGGAGGCAAGGGA- mtr-mir5286b 20/24 JG Plus CAGG cme-mir5772 AGAAUGUGAGUUAGAGU- gma-mir /24 HN Plus GA cme-mir6111-5p CAUGCAUGGU- cca-mir6111-3p 19/22 HN Plus GAUAUAAAU cme-mir5021 GAGAAGAAGAAGAAGAAAA ath-mir /20 HN Plus cme-mir2055 UUUCCUUGGGAAGGUG- GUU osa- mir /21 HN Plus LM, Length of mature mirna; Loc, Location; LP, Length of precursor sequence; ESTs, Expressed Sequence Tags; GSSs, Genome Survey Sequences; MFE, Minimal Free Energies; MFEI, Minimal Folding Free Energy Index. 3

4 Mature mirna sequences are usually present on the stem arm of the secondary stem-loop hairpin structure (Fig. 2). The nucleotide length of newly identified mirnas varied from 18 to 25nt (Fig. 3a), while the nucleotide length of pre-mirna ranged from 43 to 436 nt (Fig. 3b). Out of 15 mirnas identified, 8 were found to be located on the 5 arm of the stem-loop hairpin structure, while the rest 7 resided on the 3 arm. Thirteen mirnas were found to be on plus strand while the remaining two were found to be located on the minus strand. The A+U% was present in the range of 40 to 70% and the value of G+C% was present within a limit of 30 to 70%. All the newly identified melon mirna precursors have negative MFE, ranging from -8.7 to and calculated MFEI value ranged from 0.62 to 1.38 (Fig. 3c). Figure 2: Predicted Stem-loop Hairpin Secondary Structures of Newly Identified Potential mirnas in Melon that Are Generated by MFOLD. The mature mirnas located in either 5 or 3 of the secondary stem loop hairpin structure is highlighted in green. The length of the precursor may be slightly shorter or longer than the one shown in the figures.(a) cme-mir6438b (b) cme-mir396c (c) cme-mir414 (d) cme-mir2673a (e) cme- mir5291c (f) cme-mir6111-5p (g) cme-mir5021 (h) cme-mir5772 (i) cme-mir5200 (j) cme-mir5086 (k) cme-mir5157a (l) cme-mir5286 (m) cme-mir5293 (n) cme-mir2275c (o) cme-mir2055 4

5 The plant mirnas and their targets have a perfect or near perfect complementarity which was considered as important criteria for searching the 178 targets of the newly identified 15 melon mirnas using psrnatarget tool (Table 2). Identification of the targets of the predicted mirnas provides an insight into the biological functions of mirnas in plant development (Fig. 4a and Fig. 4b). It was observed that the number of targets per mirna varied and most mirnas have multiple targets such as mir5293 has 23 target genes followed by mir2673a having 18 target genes and mir396c which has 15 target genes identified from AGI, TAIR and melon ESTs. The other mirnas such as mir5291c, mir5200, mir6438b, mir5286, mir5157a-5p, mir414, mir5086 and mir2275c had lesser number of target genes. The remaining four mir- NAs were identified from GSSs in which mir5021 had 31 gene targets that was significantly higher than the other mirnas identified from GSSs. The mirnas namely cmemir6438b, cme-mir5200 and cme-mir5286 from ESTs did not show any result in psrnatarget probably due to the limited gene sequences available in case of melon. Figure 3: The Distributions Identified in Melon ESTs and GSSs (a) Length of Mature mirnas (b) Length of Precursor mirnas (c) MFEI Table 2: Targets for the Newly Identified mirnas from Melon ESTs, AGI and TAIR mirna Target Acc ID Targeted Protein Cellular Component cme-mi- R2673a Plant hormone signal transduction cmemir2275c AT2G molybdate transporter1 AT5G SNF1-related kinase 2.5 JG transcription initiation factor TFIID subunit 10 AT5G Potential natural antisense gene GO Annotation Biological Process Ion transport DNA template transcription, initiation, Regulation of transcription GO: Integral component of membrane GO: Cell Molecular Function GO: Molybdate ion transmembrane transporter GO: Sulfate transmembrane transporter ATP Binding GO: Kinase GO: GO: Transcription factor, sequence specific DNA binding ATP Binding GO: Kinase KEGG Basal transcription factor 5

6 AT2G Secretory carrier membrane (SCAMP) family JG ubiquitin-conjugating enzyme E2 cme-mir5293 AT1G Argonaute family AT3G AT2G Topoisomerase II UDP-D-glucuronate 4-epimerase 4 JG homeobox knotted-1-like 7 AM AT2G malate dehydrogenase [NADP] cmemir5291c oxophytodienoate-reductase 3 Transport Ubiquitin-dependent catabolic Gene silencing Gene silencing Metabolic Metabolic Organ development Metabolic, redox state Signal transduction AT4G MAP kinase 2 Stress response Biosynthetic JG cysteine ase inhibitor A cme-mir5200 AT2G RNA-dependent RNA polymerase family cmemir6438b Stress response Gene silencing by RNA Signaling pathway AT5G FUMARASE 2 Metabolic pathway cme-mir396c AT2G glutamate synthase 2 Biosynthetic GO: Integral component of membrane GO: Plasma membrane GO: Ribonucleo complex GO: Synaptonemal complex GO: Golgi apparatus GO: Chloroplast GO: Mitochondrion GO: Thyllakoid GO: Peroxisome GO: Cell GO: Chloroplast GO: Cytosol GO: Endoplasmic reticulum GO: Extracellular region GO: Mitochondrion GO: Chloroplast GO: Plastid GO: Transmembrane transporter GO: Acid-amino acid ligase GO: Ubiquitin- transferase GO: RNA binding ATP Binding GO: DNA binding GO: DNA Topoisomerase GO: Catalytic GO: DNA binding GO: Transcription factor, sequence specific DNA binding GO: Malate dehydogenase GO: NAD(P)+ transhydrogenase GO: oxophytodienoate-reductase GO: FMN binding ATP Binding GO: Kinase GO: MAP Kinase GO: GO: Cobalt ion binding GO: Cysteine-type endopeptidase GO: GO: RNA binding GO: RNA-dependent RNA polymerase GO: Fumarate hydratase GO: GO: glutamate synthase Ubiquitin mediated proteolysis, Protein ing in endoplasmic reticulum pathway Starch and sucrose metabolism Amino acid and nucleotide sugar metabolism Pyruvate metabolism Carbon fixation in photosynthetic organisms Carbon metabolism Alpha-linolenic acid metabolism Metabolic pathway Citrate cycle (TCA cycle) Pyruvate metabolism Biosynthesis of secondary metabolites Carbon metabolism Cyanoamino acid metabolism Starch and sucrose metabolism Phenylpropanoid biosynthesis Biosynthesis of secondary metabolites 6

7 AT3G beta glucosidase 19 AT2G WRKY DNA-binding 25 JG GTP-binding Carbohydrate metabolic GO: Chloroplast GO: Endoplasmic reticulum lumen GO: Cell GO: beta glucosidase GO: GO: Ribosome binding Glyoxylate and dicarboxylic metabolism Nitrogen metabolism Plant pathogen interaction cme-mi- R5157a-5p AT1G C-8,7 sterol isomerase JG cyclin-dependent kinase F-4-like isoform X2 cme-mir5086 AT5G glycerol-3-phosphate acyltransferase JK cysteine-rich repeat secretory 38-like Signal transduction Metabolic Gene silencing Positive regulation of translation Metabolic GO: cme-mir414 AT5G Lon protease 2 Protein ing Plant growth and development AT5G U3 ribonucleo (Utp) family JG high mobility group B 1 cme-mir5286 AT3G Disease resistance (TIR-NBS- LRR class) family cme-mir2055 AT3G monodehydroascorbate reductase AT4G indole-3-acetic acid inducible 29 JG N-lysine methyltransferase METTL21A JG S ribosomal L8 Regulation of flower development rrna ing GO: Chromatin assembly or disassembly Signal transduction Biosynthetic Signaling pathway Methylation Translation cme-mir5772 AT3G dicer-like 2 Cell-cell signaling, Chromatin silencing GO: Endoplasmic reticulum GO: Plasma membrane GO: Cell wall GO: Extracellular region GO: Vacoule GO: Organelle lumen GO: Peroxisome GO: Cell GO: Cytosol GO: Chromatin GO: Cytosol GO: Plasma membrane GO: Vacoule GO: Ribosome GO: C-8,7 sterol isomerase ATP binding GO: Kinase GO: Protein serine/threonine kinase glycerol-3-phosphate acyltransferase ATP Binding Steroid biosynthesis Biosynthesis of secondary metabolites Glycerolipid metabolism Glycerophospholipid metabolism Ribosome biogenesis in eukaryotes GO: Chromatin binding GO: DNA binding GO: Structural constituent of chromatin GO: Transcription factor, sequence specific DNA binding GO: ADP binding GO: monodehydroascorbate reductase GO: FAD binding GO: Protein dimerization GO: Methyltransferase GO: Ribosome binding GO: Structural constituent of ribosome GO: Covalent chromatin modification Base excision repair Ascorbate and aldarate metabolism Plant hormone signal transduction 7

8 cmemir6111-5p JG JG AT1G JG polygalacturonase inhibitor-like galactinol synthase 2-like NagB/ RpiA/CoA transferase-like superfamily nascent polypeptide-associated complex subunit alpha-like 1 cme-mir5021 AT5G zeaxanthin epoxidase (ZEP) (ABA1) Amino acid transport Cell communication Signal transduction Carbohydrate biosynthetic Translation initiation salt Protein import into nucleus Biosynthetic AT3G NAD kinase 1 Biosynthetic JG ethylene-responsive transcription factor ERF105 JG carbonic anhydrase 2 isoform X2 GO: Cell division Ethylene biosynthetic Carbon utilization MAPK cascade Pentose phosphate shunt Photosynthesis Protein targeting to membrane GO: Golgi bodies GO: Plasmodesma GO: Cytosol GO: Extracellular region GO: Cell wall GO: Cytosol GO: Golgi bodies GO: Plasmodesma GO: Chloroplast GO: Apoplast GO: Membrane GO: Plasma membrane GO: Chloroplast stroma GO: Thyllakoid GO: polygalacturonase inhibitor GO: Inositol 3-alpha-galactosyl transferase GO: Metal ion binding GO: Translation initiation factor GO: zeaxanthin epoxidase ATP binding GO: NADH kinase GO: DNA binding GO: GO: Transcription factor, sequence specific DNA binding GO: Carbonic dehydratase GO: Zinc ion binding RNA transport Carotenoid biosynthesis Biosynthesis of secondary metabolites Nicotinate and nicotinamide metabolism ESTs, Expressed Sequence Tags; GSSs, Genome Survey Sequences; AGI, A. thaliana DFCI Gene Index; TAIR, The Arabidopsis Information Resource; GO, Gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genome Pathway analysis revealed that most of the predicted targets are metabolism associated. Among these, 9 mirnas (mir396c, mir5157a-5p, mir5086 mir5293, mir6438b, mir5291c, mir5772, mir2055 and mir5021) target genes were found to be involved in metabolic pathways. Four mir- NAs (mir396c, mir5157a-5p, mir5086 and mir6438b) were also found to be involved in targeting the genes associated with biosynthesis of secondary metabolites. Another interesting feature observed was that most of the predicted target genes code for transcription factors that may have important role to play in plant growth and development (Table 2). Remaining mirna target genes are involved in a broad spectrum of biological functions, such as kinase (mir2673a, mir5293, mir5291c, mir1521b, mir2055 and mir5021), transport (mir2673a, mir5293, mir396c). The other targets identified included cellulose 8 synthase, zinc finger family, nucleosome assembly, endomembrane, arginine trna transferases, and helicases. For understanding the mirna function, the target gene set was subjected to GO term analysis which helps to characterize the mirna-gene regulatory network in terms of molecular function, biological es and cellular component. Present study revealed the involvement of 103 target genes in a wide range of molecular functions. mir2673a, mir2275c, mir5291c, mir5200, mir6438b, mir396c, mir2055, mir5772, mir6111-5p and mir5021 have been observed to induce response in melon which includes response to broad spectrum abiotic es like salt, cold and oxidative. Even some mirnas like mir2275c, mir5291c and mir396c were involved in biotic. KEGG analysis was carried out to get a deeper insight into the

9 Figure 4: Pie Chart Depicting the Functions of Newly Identified mirna Target Genes from (a) AGI and TAIR; (b) Melon ESTs Figure 5: Phylogenetic Analysis of Pre-miRNA Sequences in (a) mir396c and (b) mir5021 associated metabolic and biosynthetic pathways identified during GO enrichment analysis. KEGG analysis showed that thirteen mirnas of the identified fifteen mirnas were involved in thirty two different metabolic pathways. It was noted that the mirnas namely mir-5021, mir5772, mir2055 and mir6111-5p when used as query against melon ESTs though had targets but as per KEGG analysis none of the targets were involved in any biological pathway. 9

10 Plant mirnas are highly conserved among the various species and hence a phylogenetic study can give an insight into the evolutionary relationship of melon mirnas with other plant species. It was observed that multiple sequence alignment of the precursor sequences of the identified melon mir- NAs with other members of the same family displayed a great level of sequence similarity as is evident in case of mir396c (Fig. 5a). It was also observed that cme-mir5021 was evolutionarily related to ath-mir5021 (Fig. 5b). The results from the present study show that plant mirnas are highly conserved among related species. mirnas whose homologous sequences were not found, showed an unrelated evolutionary relationship (Data not shown). DISCUSSION The mirnas reported in the present study have their homologs both in dicots and monocots. Among the 15 mirnas reported, few have homologs both in dicots and monocots while some are specific to either dicot or monocot. 21 mir- NA families are reported to be conserved between monocot and dicot plants [11]. Among the 21 conserved mirna families, the present study identified one member of mirna family namely mir396. Earlier studies have shown that homologs of cme-mir414 [12, 13] cme-mir5772 [14, 15] and cme-mir2055 [16, 17] are present in both dicot and monocot plants. The homologs of cme-mir6438b [18], cme-mi- R2673a [19], cme-mir5291c [20], cme-mir6111-5p [21], cme-mir5021 [22], cme-mir5286 [20], and cme-mir5293 [20] have been reported in dicot plants while the homologs of cme-mir5200 [23], cme-mir5157a [24], cme-mir5086 [25] and cme-mir2275c [26] have been reported in monocot plants. The various sequence characteristics of the identified mir- NAs were found to be consistent with previous reports [1, 19]. Structural feature filtering strategy proposes three criteria namely negative MFE, adjusted minimal fold energy (AMFE) and the MFEI to distinguish mirnas from other non-coding and coding RNAs. The mature sequences of melon mirnas have been found in the stem portion of the hairpin structures, as should be the case with respect to the structure of probable mirnas. All the newly identified melon mirna precursors have negative MFE, ranging from -8.7 to It is also hypothesized that lower the value of the MFE, the higher is the thermodynamically stable secondary structure [7]. In consistent with this fact, in the present study most sequences have been found to be having low MFE value and hence were considered as thermodynamically stable. The newly identified melon mirnas were found to have high MFEI value ranging from 0.62 to 1.38 (Fig. 3c), with an average of about 0.803, which is in accordance to the value suggested by Zhang et al. [7]. In consistent with previous studies, it was observed that most of the predicted targets were the genes coding transcription factors that are mainly involved in plant growth, developmental patterning or cell differentiation. The different mrna targets can be of different groups such as targets that are predicted to encode transcription factors, enzymes and s involved in plant metabolism, signaling molecules, defense response s, transferring groups and response associated (Fig. 4a and Fig. 4b). Two mir- NAs namely mir5021 and mir5293 were found to have the 10 maximum number of targets such as 31 and 23, respectively and importantly both of these mirnas target AGO1 which is a part of the RISC. mir5021 was predicted to target o-fucosyltransferase, alpha-beta hydrolases, homeobox1, NAD kinase, zeaxanthin epoxidase and MAP kinase as reported in Jatropha [1]. The targets of mir2673a have been found to be similar to those reported in Coffea arabica with involvement in transcriptional activation, cell signaling and transporter [19]. mir2673a reported in the present study target the MYB genes thus mediating gibberellic acid and abscisic acid signaling which is similar to previously reported mir159 targets of Arabidopsis [27]. On the contrary, mir2275c identified in the present study has been reported to target different genes involved in defense and response when compared to those previously predicted in case of Aquilegia sp. [28]. Hence it can be concluded that the same mirna may target different genes in different plant species. The present study identified the cme-mir396, the homolog of mir396 to be induced in abiotic and biotic response which is in agreement with the previous reports as reviewed by Rajwanshi et al. [3]. In rice, mir414 target OsDHB (Oryza sativa DBH) gene was experimentally validated by Macovei and Tuteja [29]. Results show that the genes of DEAD-box helicase family namely OsABP (ATP-Binding Protein), OsD- SHCT (DOB1/SK12/helY-like DEAD-box Helicase) and OsDBH (DEAD-Box Helicase) genes were upregulated and mirnas showed decrease in abundance in response to salt. It can be expected that mir414 identified in the present study might have similar role to play in C. melo. Devi et al. [30] reported high mobility group as one of the targets of mir414. In the present study also, cme-mir414 was found to target high mobility group. mir3623 and mir5021 identified in the present study were also found to target the DBH gene. The additional target genes as revealed through psrnatarget using melon EST database also revealed involvement of the genes in the various functions such as response to both biotic and abiotic, developmental as well as biosynthetic es. A significant feature observed in KEGG analysis was the involvement of many different mirnas in targeting the same pathway. Carbon metabolism is regulated by mir5021, mir5772 as well as mir6438b. Similarly biosynthesis of secondary metabolites is common to mir6438b, mir396d, mir5157, mir5086, mir396c, mir4385 and mir5021. Similarly, plant hormone signal transduction pathway is commonly regulated by mir2673a and mir2055. These results suggest that there is interplay among the various targets in order to produce a particular effect in plant. One mirna might also target different pathways as is evident from the present study. The targets of mir5021 have been found to be involved in the biosynthesis of carotenoids, secondary metabolites, and amino acids as well as Tricarboxylic acid cycle. Similarly mir5772, mir2055, mir396c, mir6438 were also found to be involved in multiple pathways. One of the major phytoconstituents of melon is terpenoids and interestingly, the newly identified mir5086 is also involved in terpenoid backbone biosynthesis. Results from the present study validate the fact that mirnas regulate a vast array of biological es and are key regulators in plants. mirna-related research is a rapid and accurate approach for the prediction of novel and conserved mirnas in both plants and animals. Though a large number of mirnas have

11 been identified in model monocot plants like rice but the scenario is not same in case of melon. Melon is an economical important plant variety with key phytoconstituents like beta-carotene, terpenoids, flavonoids, carbohydrates, fatty acids and volatile compounds which confer major medicinal properties like analgesic, antioxidant, antiulcer, anticancer, antidiabetic and hepatoprotective. Considering importance of melon, a computational approach using ESTs and GSSs and structural feature criteria was adopted to predict novel mirnas. The findings from the present study will contribute to further understanding of the mirnas function and regulatory mechanisms in melon. However, this study uses only computational approach which cannot be an alternative to the biological verification and hence the next major steps will be to experimentally validate the predicted mirnas which will aid in the better understanding of molecular mechanism of mirna-mediated post transcriptional gene silencing in C. melo. CONFLICT OF INTEREST The authors declare that no conflict of interest exists. ACKNOWLEDGEMENTS SC is grateful to Department of Biotechnology, Government of India for the infrastructural facilities to carry out a part of the present research work at Bioinformatics Centre, Gurucharan College, Silchar, Assam, India. KJD is thankful to University Grants Commission (UGC), Government of India for non-net fellowship. References 1. Vishwakarma NP, Jadeja VJ. Identification of mirna encoded by Jatropha curcas from EST and GSS. Plant Signal Behav. 2013;8:e Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. 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