Research Article

In silico identification and target prediction of micrornas in Sesame (sesamum indicum L.) expressed sequence tags

Received: April 17, 2018
Accepted: April 23, 2018
Published: April 28, 2018
Genet.Mol.Res. 17(2): gmr16039911
DOI: 10.4238/gmr16039911

Abstract

Sesame (Sesamum indicum L.), a member of the Pedaliaceae family, is one of the oldest oilseed crops. For its high oil content, it is known as the “queen of oilseeds”. MicroRNAs (miRNAs) represent a class of endogenous non-coding small RNAs that play important roles in multiple biological processes by degrading targeted mRNAs or repressing mRNA translation. Thousands of miRNAs have been identified in many plant species by computational methods, whereas there is no report of miRNAs in S. indicum till date. In present study, previously known plant miRNAs were BLASTed against the Expressed Sequence Tag (EST) database of Sesame genes. The aligned miRNA hits were further aligned to protein database and BLASTX was carried out to remove protein coding primary miRNAs. The non-coding precursor miRNAs were subjected to online MFold server in order to predict their secondary structures. After applying the filtering criteria, a total of 12 potential miRNAs belonging to 6 miRNAs families were detected. 203 unique miRNAs: target pairs were predicted online by psRNATarget web server. Most of the targets were found to encode transcription factors or enzymes that participate in the regulation of development, growth, metabolism, and other physiological processes and stress response.

Introduction

Sesame (Sesamum indicum L.), a member of the Pedaliaceae, is a diploid (2n = 26) dicotyledon and one of the oldest oil seed crops. The Sesame seeds have been considered as the “queen of oilseeds” for its high oil content (55–58%). It is cultivated mainly in the tropical and subtropical regions of Asia, Africa and Southern America. India is the major producer of Sesame seeds followed by Sudan, China, Myanmar and Tanzania. These top five countries account for 80% of world production. Sesame seeds are important source of oil (44-58%), protein (18-25%), and carbohydrates (13.5%). (Cheung et al., 2007) (Anila kumar et al., 2010).

MicroRNAs (miRNAs) are endogenous ~22 nucleotides-long RNAs that play important regulatory roles in animals and plants by targeting messenger RNAs (mRNAs), for cleavage or translational repression. miRNAs comprise one of the most abundant classes of gene regulatory molecules in multicellular organisms. (Jonas and Izaurralde, 2015). In plants, miRNAs control crucial biological processes like flowering, polarity, nutrient homeostasis, phase-change, biotic and abiotic stress responses by influencing the output of many protein-coding genes (Dugas and Bartel, 2004). All miRNA precursors have a well-predicted stem loop hairpin structure, and this fold-back hairpin structure has a low free energy. Many miRNAs are evolutionarily conserved. Different computational miRNA finding strategies have been developed based on these characteristics of miRNAs (Zhang et al., 2005).

There are two major approaches for identification of miRNAs in plants: experimental approaches and computational approaches. Computational approaches are faster and more affordable than the experimental approaches. Expressed Sequence Tags (ESTs) provide a powerful tool for identification of miRNAs that are conserved among various plant species. These methods are used to study the conservation and evolution of miRNAs among different species. The miRNA identification and target prediction by computational methods and recent developments have increased the speed of generating new strategies making the future of miRNA target prediction promising. (Zhang et al., 2006).

The present study was carried out to investigate the information about Sesame miRNAs by predicting miRNAs from Sesame est database and predicting their secondary structures using computational approach. the potential targets for predicted mirnas were also predicted using computational methods.

Material and Methods

Sequence and computational requirements

A total of 44905 Expressed Sequence Tag (EST) of S. indicum, deposited at Genbank database of the National Center for Biotechnology Information (NCBI) was downloaded. Protein coding sequences of S. indicum were downloaded from Protein database of NCBI (http://www.ncbi.nlm.nih.gov/).

A total of 8220 plant miRNA sequences were retrieved from the previously deposited miRNA from the publically available miRNA database miRBase, version 21. (http://www.mirbase.org/) (Griffith-Jones et al., 2008).

Prediction of miRNAs

Redundancy among the miRNAs and ESTs was excluded manually. The known plant miRNA sequences were subjected to the BLAST search for Sesame homologs of miRNAs against EST database. Local BLAST search was performed with BioEdit version 7.0.5. (Hall, 1999). The expectation value was kept at 0.001 and rest of the parameters were kept at default. The results were transferred to separate data sheets. The miRNAs having more than 3nt mismatches, less than 90% identity and less than 18 alignment length were removed.

Protein BLAST

As plant miRNAs are unlikely to be located in protein coding genes, the protein blast search was performed by BLASTX of BioEdit (Hall, 1999). All the protein coding sequences from aligned miRNAs were removed and the remaining sequences were most likely to be potential miRNAs.

Prediction of secondary structure

After inspection of all hit sequences, a new database was formed from the aligned miRNA sequences, sequence with 100 nucleotides upstream and downstream of the first and last residues of the mature miRNA were extracted. These sequences were treated as precursor microRNAs (pre-miRNAs). The secondary structures were predicted online using RNA folding form of The Mfold Web Server (Zuker, 2003). (https://unafold.rna.albany.edu/?q=mfold/RNA-Folding-Form).

The secondary structures were checked to meet the criteria by Zhang et al., (2005). If it met all of them, the sequence was selected and the 5’ and 3’ ends were determined.

i. Pre-miRNA sequence can fold into an appropriate stem-loop hairpin secondary structure.

ii. It contained ~22 nt mature miRNA sequence within one arm of the hairpin.

iii. Minimal folding free energy MFE≦-20 kcal/mol.

iv. 30–70% A+U content

v. Predicted mature miRNAs had no more than six mismatches with the opposite miRNA* sequence in the other arm

vi. Maximum size of 3 nucleotides for a bulge in the miRNA sequence.

vii. No loop or break in miRNA sequences was allowed.

All the potential miRNA sequences were renamed for Sesamum indicum according to miRBase nomenclature guideline (Griffiths-Jones et al., 2006).

miRNA target prediction

Based on the newly identified potential miRNA sequences, their potential targets were predicted using online server psRNATarget against nucleotide sequences of S. indicum as target transcript sequences. (http://plantgrn.noble.org/psRNATarget/) (Dai and Zhao, 2011).

Results

Prediction of miRNA

A comparative genomic approach was carried out for initial prediction of miRNAs. The hits having <90% similarity and <18 nucleotides length were removed and 132 hits were remained. These miRNA sequences were further subjected to protein BLASTX. 5 EST sequences were found to be protein coding. So, these were eliminated and the remaining 127 pre-miRNA sequences were analyzed for prediction of secondary structures.

Secondary structure prediction

127 pre-miRNA sequences were carried on for secondary structure prediction by M-Fold software. The miRNAs were assessed for their proper folding structure, Minimum Folding Free Energy (MFE), number of nucleotides etc. The predicted structures are shown in Figure 1. The other outputs related to structures are depicted in Table 1 and Table 2.

geneticsmr-in-silico-identification-Pre-miRNA-sequences

Figure 1: Pre-miRNA sequences carried on for secondary structure prediction by M-Fold software

Family miRNA Sequence Length Location %A+U MFE  (Kcal/mol) MFEi
156/157 sin-miR156f uugacagaagagagagagcaca 22 5' 54.55 66.5 1.46
sin-miR156h ugacagaagagagagagca 19 5' 52.63 66.5 1.4
sin-miR156j ugacagaagagagagagcac 20 5' 50 66.5 1.33
sin-miR156k ugacagaagagagagagcaca 21 5' 52.38 68.23 1.43
sin-miR157a-5p uugacagaagagagagagcac 21 5' 57.14 77.1 1.8
396 sin-miR396a-5p uuccacagcuuucuugaacug 21 5' 57.14 72.8 1.7
529 sin-miR529 agaagagagagagcacagc 19 5' 42.11 66.5 1.15
sin-miR529a gaagagagagagcacagccc 21 5' 42.86 74.68 1.31
sin-miR529d agaagagagagagcacagccc 19 5' 47.37 66.5 1.26
5658 sin-miR5658 augaugaugaugaugauga 19 5' 68.42 30 0.95
8030 sin-miR8030-3p aaaccaaaucaacccaaau 19 3' 68.42 39.73 1.26
8138 sin-miR8138 uaaagaugguaacaaagg 18 3' 66.67 43.15 1.29

Table 1: The characteristics of predicted miRNAs including family, sequences, length, location of miRNAs in precursor

miRNA Targets Inhibition Target Description
sin-miR156f XM_011073548.2 Cleavage PREDICTED:  Sesamumindicum  1-deoxy-D-xylulose  5-phosphate  reductoisomerase,  chloroplastic  (LOC105157212),  mRNA
sin-miR156f XM_011082778.2 Cleavage PREDICTED:  Sesamumindicum  protein  TPX2  (LOC105164177),  transcript  variant  X1,  mRNA
sin-miR156f XM_011082779.2 Cleavage PREDICTED:  Sesamumindicum  protein  TPX2  (LOC105164177),  transcript  variant  X2,  mRNA
sin-miR156h XM_011073548.2 Translation PREDICTED:  Sesamumindicum  1-deoxy-D-xylulose  5-phosphate  reductoisomerase,  chloroplastic  (LOC105157212),  mRNA
sin-miR156h XM_011101719.2 Cleavage PREDICTED:  Sesamumindicum  classical  arabinogalactan  protein  4-like  (LOC105178272),  mRNA
sin-miR156j XM_011098026.2 Translation PREDICTED:  Sesamumindicumsquamosa  promoter-binding  protein  1  (LOC105175555),  mRNA
sin-miR157a-5p XM_020695985.1 Cleavage PREDICTED:  Sesamumindicumsquamosa  promoter-binding-like  protein  12  (LOC105168427),  transcript  variant  X1,  mRNA
sin-miR396a-5p XM_011079770.2 Cleavage PREDICTED:  Sesamumindicum  growth-regulating  factor  1  (LOC105161914),  mRNA
sin-miR529a XM_011093891.2 Cleavage PREDICTED:  Sesamumindicum  ATP-citrate  synthase  alpha  chain  protein  2  (LOC105172457),  mRNA
sin-miR529a XM_020695985.1 Cleavage PREDICTED:  Sesamumindicumsquamosa  promoter-binding-like  protein  12  (LOC105168427),  transcript  variant  X1,  mRNA
sin-miR5658 XM_020692989.1 Cleavage PREDICTED:  Sesamumindicum  ABC  transporter  B  family  member  19-like  (LOC105158093),  mRNA
sin-miR5658 XM_011073794.2 Cleavage PREDICTED:  Sesamumindicum  alpha-galactosidase  (LOC105157383),  mRNA
sin-miR5658 XM_011077608.2 Cleavage PREDICTED:  Sesamumindicumauxin-responsive  protein  SAUR32  (LOC105160291),  mRNA
sin-miR5658 XM_011099043.2 Cleavage PREDICTED:  Sesamumindicum  BAG  family  molecular  chaperone  regulator  1-like  (LOC105176294),  mRNA
sin-miR5658 XM_011076179.2 Cleavage PREDICTED:  SesamumindicumBEL1-like  homeodomain  protein  4  (LOC105159201),  mRNA
sin-miR5658 XM_011092565.2 Cleavage PREDICTED:  Sesamumindicumbrefeldin  A-inhibited  guanine  nucleotide-exchange  protein  2  (LOC105171444),  mRNA
sin-miR5658 XM_011085319.2 Cleavage PREDICTED:  SesamumindicumbZIP  transcription  factor  44  (LOC105166092),  mRNA
sin-miR5658 XM_011080551.2 Cleavage PREDICTED:  SesamumindicumbZIP  transcription  factor  TGA10  (LOC105162512),  mRNA
sin-miR5658 XM_011081773.2 Cleavage PREDICTED:  Sesamumindicum  casein  kinase  1-like  protein  10  (LOC105163434),  mRNA
sin-miR5658 XM_011072757.2 Cleavage PREDICTED:  Sesamumindicumcyclin-D1-1  (LOC105156585),  mRNA
sin-miR8030-3p XM_011074620.2 Translation PREDICTED:  Sesamumindicum  lysine-specific  demethylaseREF6  (LOC105158021),  transcript  variant  X1,  mRNA
sin-miR8030-3p XM_011096846.2 Cleavage PREDICTED:  Sesamumindicum  probable  WRKY  transcription  factor  70  (LOC105174673),  mRNA
sin-miR8030-3p XM_011094724.2 Cleavage PREDICTED:  Sesamumindicum  transcription  factor  bHLH13-like  (LOC105173075),  transcript  variant  X1,  mRNA

Table 2: miRNA and their predicted targets with their description

All MFEs were expressed as negative kcal/mol. Adjusted MFE (AMFE) represented the MFE of 100 nucleotides. It was calculated by following equation.

Equation

The minimal folding free energy index (MFEI) was calculated by the equation:

Equation

The MFE ranged from -30 to -77.1 Kcal/mol. The average value was found to be -61.52 Kcal/mol. Most of the miRNAs had MFEI greater than 1. The predicted miRNAs varied from 18-22 nucleotides length with an average of 20 nucleotides. miRNAs bind more strongly to certain proteins as they have a higher(A+U) content compared to other RNAs. The %A+U contents were lying between 42.11 to 68.42% with an average value of 54.97%. miRNAs were located at both the 5’ and 3’ arm of precursor stem-loop structures. Mature miRNA sequences have been reported to be evenly located on both the arms of the stem-loop hairpin structures of potential pre-miRNAs (Gorodkin et al., 2006). In the present study, more mature miRNAs were located in the 5’ end of the pre-miRNA than were located in the 3’ end.

After applying all the filtering criteria, a total of 12 miRNAs belonging to 6 different families were predicted.

miRNA target prediction

Targets of these miRNAs were predicted by online server named psRNATarget. Gene sequences of Sesamum indicum were used as database to predict the targets. Total of 203 unique targets were predicted for all the miRNAs. Some of the miRNAs and their targets are shown in table 2. The mode of inhibition of predicted miRNAs were either cleaving the target or translation inhibition. In most of the cases, the inhibition was found to be the cleavage of targets. Only 5 out of 203 predicted targets were inhibited by translation. The highest targets were predicted for sin-miR5658 family with 64 targets and the lowest targets were predicted for sin-miR8030. The number of targets predicted for each family was irrespective of the number of members in it.

Discussion

The nature of sequences conservation across different species for most of the known miRNAs is well known. Most miRNAs have shown to be conserved among related species and homologs were even found among distantly related species. Due to the high degree of sequence identity within families of mature miRNAs of plants, the number of hits was reduced to ~90%. When compared to the number of miRNA reported in other plant species and on the basis of the principle of prediction of one mature miRNA for every 10,000 EST sequence, at least 5 mature should have been identified from Sesame . The small number miRNA families identified may be due to fewer EST sequences available for S. indicum (44,905) than that of other plant species (Axtell and Bartel., 2005).

Formation of the stem-loop hairpin secondary structure is a critical step in miRNA maturation and one of the most important characteristics of pre-miRNAs. However, a stem-loop hairpin structure is not a unique characteristic of miRNA. Other RNAs (mRNA, rRNA, and tRNA) can also form similar hairpin structures. Thus, a potential stem-loop hairpin structure containing the ~22-nt mature miRNA sequence within one arm of the hairpin is a basic precondition for predicting and annotating new miRNAs or miRNA homologs. The MFEI can be easily used to distinguish miRNA from other non-coding and coding RNAs. The MFEI is a unique criterion to designate miRNAs. When the MFEI is more than 0.85, the sequence is most likely to be miRNA.

The prediction of targets for the identified miRNAs was expected to help us for clarification of the important function and regulation of these novel miRNAs in S. indicum. Most plant miRNAs are perfectly or near-perfectly complementary with their targets (Schwab et al., 2005). For this reason, searching potential miRNA targets by blasting the mature miRNA sequences against the nucleotide or genome database is considered a reliable technique. Nevertheless, several studies have demonstrated that many target genes have 1 to 4 nucleotide mismatches with mature miRNA sequences (Xie et al., 2010).

The predicted potential targets belonged to several gene families, and had multiple biological functions. Most of the targets were transcription factors. In addition to the transcription factors, the predicted targeted genes were involved in a broad range of biological processes, such as abiotic stress response, metabolism, transportation, disease resistance, and signal transduction. It was reported by Zhang et al., (2005), that 26% of EST contigs containing miRNAs are related to different biotic or abiotic environmental stresses, suggesting that environmental stress may play an important role in miRNA gene expression in plants.

Highly conserved S. indicum miRNAs also had conserved miRNA target sites on specific target genes, which was also observed in previous studies of other plants (Frazier et al., 2010). For example, it has been established that the plant-specific transcription factor, squamosa promoter binding protein, is involved in regulating changes during the early flower development and vegetative phase. In addition, it has been widely accepted that this transcription factor is a conserved target of the miR156 family in plants (Yin et al., 2008). In present study, 16 different squamosa promoter binding protein-encoding genes were identified as the targets of miR156/157 in S. indicum. Other than these cationic amino acid transporter, TPX2 micro-tubule binding protein and F-box protein involved in protein ubiquitination were also predicted for miR156/157. Different types of auxin response factors (ARFs) that are known to be engaged in signal transduction were predicted. This result can be compared to other studies in Arabidopsis. NAC domain containing proteins are known to be involved in plant morphogenesis, auxin responses and root development were predicted. (Zhang et al., 2006 and Mallory et al., 2005).

miR5658 has highest predicted targets. The targets included various transcription factors, proteins involved in signal transduction, molecular chaperone regulator, growth regulators, protein ligases, WRKY transcription factor, argonaute protein, etc. The earlier reports support this result showing more numbers of targets predicted by miR5658 (Han et al., 2013; Li et al., 2014). miR5658 targeted zinc finger transcription factors are a superfamily of proteins involved in numerous activities of plant growth and development, and are also known to regulate resistance mechanisms for various biotic and abiotic stresses (Giri et al., 2011). The basic/helixloop-helix (bHLH) transcription factors that control cell proliferation and cell lineage establishment are potential targets for miR5658. miR5658 also target the mRNA of homeobox-leucine zipper transcription factor, which is reported to regulate leaf polarity and vascular differentiation via the miR165/166 family (Jung and Park, 2007).

Many studies have shown that plant miRNAs are involved in biotic and abiotic stress (Xie et al., 2010; Gao et al., 2011; Sunkar et al., 2012). In present studies it was also identified the stress-related proteins targeted by S. indicum miRNAs. In sunflower, when plant exposed to high temperatures, a WRKY TF exhibited inverse correlation with miR5658. High level of miR5658 was observed in older leaves in contrast to the distal portion where the expression was low (Giacomelli et al., 2012). In rice treated with arsenic, miR5658 was downregulated which resulted in the upregulation of its target, WRKY TF (Liu and Zhang, 2012).

miR5658 targeted the heat shock proteins that are expressed in response to heat stress, which indicates the important role of these miRNAs during heat stress. Most plant resistance genes (R genes) encode nucleotide binding site leucine-rich repeat (NBS-LRR) proteins (McHale et al., 2006). The NBS-LRR resistance protein was predicted to be the target of the S. indicum miR5658 family.

Summary and Conclusion

Numbers of microRNAs have been predicted through computational methods for various plant but for Sesame , no miRNAs have been reported till date. Availability of good set of EST database provided an opportunity to peep into gene regulation and prediction of miRNAs. Total of 12 microRNA belonging to 6 different families were predicted using computational methods. These computational methods are cost-effective and faster than other conventional methods. There is a well-established fact that a mature miRNA is derived from its precursor miRNA and its structure can be used as a search template. A pre-RNA forms a short stable extended stem-loop structure or a hairpin structure, with continuous helical pairing and a few internal bulges. There are strict filtering criteria to distinguish miRNAs from other small RNAs. So, the prediction of secondary structure proved to be the most important filtering criteria. More than 70% pre-miRNAs were not fulfilling all the filtering criteria. For 12 miRNAs, 203 non-duplicated unique targets were predicted. Most of these target genes were predicted to be involved in plant development, signal transduction, metabolic pathways, disease resistance, and environmental stress response. Thus the identification of the novel miRNAs in Sesamum indicum is foreseen to provide baseline information for further research about the biological functions and evolution of miRNAs in S. indicum

About the Authors

Corresponding Author

Halak Joshi

Department of Biotechnology, Junagadh Agricultural University, Junagadh-362001, India

Email:
[email protected]

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