Computational identification of conserved microRNAs and functional annotation of their target genes in Citrus limon

South African Journal of Botany 130 (2020) 109
116

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South African Journal of Botany journal homepage: www.elsevier.com/locate/sajb

Computational identification of conserved microRNAs and functional annotation of their target genes in Citrus limon Identification of microRNA in Citrus limon Suraj Roy, Durbba Nath, Prosenjit Paul, Supriyo Chakraborty* Department of Biotechnology, Assam University, Silchar 788011, Assam, India

A R T I C L E

I N F O

Article History: Received 9 August 2019 Revised 23 October 2019 Accepted 11 December 2019 Available online xxx Edited by J Van Staden Keywords: Citrus limon microRNA Expressed sequence tags (ESTs) Target genes Functional annotation

A B S T R A C T

Citrus limon (lemon) is an important fruit crop of Rutaceae family with high nutritive value. The microRNAs (miRNAs) are a class of small RNAs, approximately 22 nucleotides long. They are evolutionary conserved and function at post-transcriptional level to regulate the expressivity of genes in both plants and animals. They have crucial roles in different biological and metabolic processes like growth and development, pathogen defense, abiotic and biotic stress response in plants. Computational analysis of Citrus limon expressed sequence tags (ESTs) resulted in the identification of one conserved microRNA (clm-miR5658). The secondary structure of newly identified microRNA was generated based on minimal folding energy (MFE=
50.60 kcal/ mol) and minimal folding energy index (MFEI= 0.81). Phylogenetic analysis of miR5658 family revealed a close association of clm-miR5658 with bra-miR5658. Further, a total of 14 potential target genes of the putative miRNA were identified and functionally annotated. The findings of this study will contribute to understand the role of miRNAs in lemon and expected to lay foundation for further research on lemon miRNAs. © 2019 SAAB. Published by Elsevier B.V. All rights reserved.

1. Introduction India is the world’s largest producer of Citrus limon, commonly known as lemon which is an important fruit crop worldwide with strong nutritive and commercial value. It belongs to the genus Citrus of the Rutaceae family which comprises of some other important plants like Citrus sinensis (sweet orange) and Citrus reticulata (tangerine and mandarin). Citrus fruits and juices play a significant role in balancing nutritional deficiencies as they are good source of vitamin C and show antioxidant properties. Lemon contains natural phenolic compounds like flavonoids which reduce the chances of cancer, heart
lez-Molina et al., disease and urinary disorder in humans (Gonza 2010). Lemon is one of the important health promoting fruits, and rich in minerals, vitamins, essential oils, dietary fibre and carotenoids which are well known for their nutritional and medicinal properties. It consists of natural bioactive compounds that have potential as ani
lez-Molina mal feed, manufactured foods and health care (Gonza et al., 2010). The lemon fruit is oval in shape with an aromatic rind and prominent bulge like nipple at the end, yellow in colour when ripe and green when immature. It originated in tropical and subtropical southeast Asia (Janati et al., 2012). Lemon possesses analgesic and anti-inflammatory effects in pharmacological profile (Del Rıo et al., 2004); lemon is traditionally used in

* Corresponding author. E-mail address: [email protected] (S. Chakraborty). https://doi.org/10.1016/j.sajb.2019.12.009 0254-6299/© 2019 SAAB. Published by Elsevier B.V. All rights reserved.

skin care as it helps in lightening of the skin; mixture of honey and lemon juice contributes to weight loss, lemon juice with olive oil cures stones of gall bladder and kidney, regular consumption of lemon juice helps reduce uric acid in the body, works as liver stimulant and provides relief from heartburns and irritable bowel syndrome. Sequence-specific regulatory RNAs of 20
30 nucleotides guide the nucleic acid-based processes in eukaryotic organisms. Based on differences in biogenesis and precursors, in plants, small RNAs are classified as microRNAs (miRNAs) or small interfering RNAs (siRNAs) (Won et al., 2014). Of late, research on microRNAs (miRNAs) has acquired momentum due to their impact in growth and development in plants (Chuck et al., 2009; Kidner and Martienssen, 2005). They are single stranded, highly conserved endogenous class of non-protein coding RNAs formed from hairpin RNA precursors with length varying from 18 to 23 nucleotides and they control gene expression by binding to the complementary mRNA sequences resulting in mRNA cleavage or translational repression in plants (Axtell et al., 2007; Schwab et al., 2005; Zhang et al., 2006). In plants, different miRNAs direct genes which are responsible for growth and development processes such as leaf development, flowering, auxin signalling, phase transition (Jones-Rhoades et al., 2006) and plant responses to biotic and abiotic stresses. The first miRNA in plants was reported in 2003 from Arabidopsis thaliana (Reinhart et al., 2002). The gene encoding miRNA (MIR genes), that resides in both introns and exons, is transcribed by RNA polymerase II enzyme in the nucleus to produce a primary transcript microRNA (pri-miRNA)

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(Zhang et al., 2006). The pri-miRNA contains a cap and a poly A tail which are processed into stem-loop structure by RNase III enzyme, Dicer-like 1 (DCL 1), Serrate (SE) and Hyponastic leaves (HYL) in plants to form precursor microRNA (pre-miRNA). Pre-miRNAs are further processed by DCL1, SE and HYL to generate miRNA/miRNA* duplex with subsequent addition of a methyl group to the 30 end overhang of the duplex by Hua Enhancer (HEN1) and are exported to cytoplasm through HASTYV. Mature miRNA duplex (miRNA/miRNA*) is degraded into a single strand (guide strand) which is integrated into an argonaute (AGO) containing RISC (RNA induced silencing complex) and guides RISC to bind to the complement target mRNA for post-transcriptional modulation of gene expression either by target mRNA degradation or translational inhibition (Lin et al., 2005). Identification of miRNAs in a plant would improve our understanding of basic biological processes like growth and pattern development in plant, role of miRNAs in disease development as well as the measures to enhance crop yield and pest management. Two methods are commonly followed in identification of miRNAs: experimental analysis and computational approach. Identification of miRNAs by experimental analysis is a difficult and expensive approach compared to the computational identification which is quick and easily affordable; however, such analysis needs to be verified by experiments to determine the functionality of such predicted sequences. Many studies reported that computational approaches like EST based homology search for miRNA identification have some advantages over other methods because most of these miRNAs identified are usually validated by high throughput deep sequencing (Kwak et al., 2009). Many studies concluded that evolutionarily miRNA sequences are highly conserved in the entire plant kingdom, thus homologybased searches are performed to predict and identify conserved miRNAs and their targets genes in plant species by in silico methods (Yin et al., 2008; Zhang et al., 2005). When the complete genome sequence is not available in public databases, ESTs (expressed sequence tags) and GSS (genome survey sequences) could be used as alternative sources to predict the miRNA candidates. Identification of miRNA targets is a crucial step for functional annotation of targeted transcripts. Multiple miRNAs may govern the action of a single gene or single miRNA may target multiple genes (Dehury et al., 2013). Thus, the functional analysis of miRNAs is based on target recognition. In plant species, miRNAs are involved in cell differentiation, organ development, hormone signalling, genome maintenance and integrity, responses to biotic and abiotic stress (drought, heat, cold, oxidative, salinity, nutrient deficiency, chilling, hypoxia, UV-B radiation and bacterial pathogenesis, fungal, viral infection), DNA methylation and diverse physiological processes. The present study was done to identify new miRNA candidates in Citrus limon that are well conserved between different species in the plant kingdom and to predict the possible target genes from the coding sequence information and the known ESTs of Citrus limon which could provide useful information about the key functionalities of these miRNAs in maturation and development. 2. Materials and methods 2.1. Retrieval of mature miRNA sequences, ESTs and cds All previously reported miRNA mature sequences from the green plants clade- Viridiplantae were retrieved from miRBase Registry database (Registry 22.1, October 2018; https://www.mirbase.org/) (Griffiths-Jones et al., 2006). The mature miRNA sequences were strictly filtered and redundancy was removed to avoid overlapping. A total of 1505 ESTs (as of June, 2019) from EST database and 56 complete coding sequences of Citrus limon from Nucleotide database of NCBI (https://www.ncbi.nlm.nih.gov) were retrieved and used as experimental data for the prediction of conserved miRNAs and its potential targets.

2.2. Prediction of conserved miRNAs in Citrus limon The pipeline used to search conserved miRNAs in Citrus limon is depicted in Fig. 1. The filtered sequence data were used as BLAST search queries against the lemon EST database. The BLAST search was performed using Bioedit software (Hall et al., 2011) that is integrated with BLAST 2.2.10+ programme (Altschul et al., 1997). The BLAST parameters were calibrated with an expected cut-off value of 10; a low-complexity sequence filter; 1000 descriptions and alignments and other automatically adjusted parameters. The BLAST results were assessed and the EST sequences which had only 0
3 nucleotide (nt) mismatches relative to the query miRNA sequences were chosen manually. We considered only those predicted mature miRNA sequences that were not less than 18 nt. 2.3. Prediction of secondary structures The secondary structures of the candidate miRNAs from EST sequences were generated using the widely used software Mfold 4.7 (Zuker, 2003) which is a web-based publically available application (http://unafold.rna.albany.edu/?q=mfold/RNA-Folding-Form). The parameters used in prediction of the secondary structures were: a folding temperature set at 37 °C, 1 M NaCl ionic conditions with no divalent ions; and grid lines in the energy dot plots, and the remaining parameters were kept as default. The precursor sequences of the candidate miRNAs were selected at 100 nucleotides upstream and downstream from the position of the mature miRNA sequence in the selected EST with the help of sliding window method. Several criteria were used to consider EST sequences as potential miRNAs and pre-miRNAs in C. limon as described by Wang et al., (2011) i.e. (i) It should appropriately fold into stem-loop hairpin secondary structure, (ii) The mature miRNA sequence should be positioned in one arm of the secondary structure, (iii) In the secondary structure, the predicted miRNA and its opposite sequence in the other arm (miRNA*) should have fewer than six mismatches (iv) Loop or break is not allowed in miRNA sequences, and (v) The selected secondary structure must have higher negative minimal folding free energy (
18 kcal/mol) and minimal folding free energy index (MFEI) values. One of the important characteristics for secondary structure prediction of miRNA is minimal folding free energy which represents the negative folding free energies (ΔG in kcal/mol); lower MFE denotes thermodynamically more stable secondary structure. The minimal free energies (MFEs) and minimal free energy index (MFEIs) of miRNA precursor sequences should be highly negative than all other types of coding or non-coding RNAs e.g. rRNA, tRNA. The MFEI should be more than 0.85 and the total A+U% should be in the range of 30
70% (Zhang et al., 2006). Adjusted minimal free folding energy (AMFE) is the MFE of a 100-nucleotide, both AMFE and MFEI values were calculated using the following equations as described by Zhang et al., in previous reports. AMFE ¼ ðMFE=Length of precursor sequenceÞ  100 MFEI ¼ AMFE=ðG þ CÞ% 2.4. Phylogenetic and conservation analysis of the predicted miRNAs in Citrus limon Mature miRNA sequences, homologous to the predicted miRNA, from miRBase database and published literature were collected for phylogenetic analysis (Table S2). Multiple sequence alignment (MSA) and phylogenentic tree construction were carried out using MEGA X software to show the evolutionary relationship of the potential miRNA with other members of the same family (Kumar et al., 2018). MSA of the collected miRNAs along with the identified lemon miRNAs was done using ClustalW tool embedded in MEGA X. Phylogenetic tree was generated by employing maximum likelihood

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Fig. 1. Pipeline of the entire work in the prediction of miRNA in lemon.

statistical method based on the Tamura-Nei model (Tamura and Nei, 1993) with 1000 boot-strapped replicates for the test of phylogeny (Felsenstein, 1985) and other relevant parameters were set as default. The conservation analysis of the identified miRNA was performed using WebLogo (Crooks et al., 2004). 2.5. Computational prediction of miRNA targets In this study, the newly detected miRNAs that target protein coding genes were predicted by using the widely used web tool psRNATarget (http://plantgrn.noble.org/psRNATarget/analysis?function=3) which uses the newly predicted conserved mature miRNA candidates as query sequences against the available complete coding sequences of Citrus limon as target transcripts for analysis. The default parameters of scoring scheme V2 2017 release were considered for the prediction of Citrus limon miRNA targets (Table S1). 2.6. Functional annotation of miRNA target genes and their pathway analysis The functional roles of the target genes and their metabolic pathways were annotated by Gene Ontology (GO) and Kyoto Encyclopedia

of Genes and Genomes (KEGG). It was executed using the free soft€ tz, 2008; Conesa et al., 2005; ware Blast2GO basic (Conesa and Go € tz et al., 2008). The target genes were aligned against non-redunGo dant (nr) database and annotated by BLASTX-fast with an e-value of 1.0E-3 and the number of blast hits was set to 20. The functional category GO terms were annotated for the predicted target genes, according to the best hits generated in the mapping of Gene Ontology terms with default parameters. The target genes participating in metabolic pathways were analyzed by searching the KEGG database (Kanehisa and Goto, 2000). 3. Results and discussion 3.1. Prediction of conserved miRNAs in Citrus limon Within the plant kingdom, plant miRNAs exhibit high degree of sequence conservation (Axtell and Bartel, 2005), therefore miRNA of one species can be identified with the help of known plant miRNAs from all species which are evolutionarily conserved as they share the same set of miRNA homologs between them (Fahlgren et al., 2007). As described in materials and methods, all previously reported plant mature miRNAs of Viridiplantae clade (S1) were downloaded and

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non redundant sequences were selected to avoid overlapping and used as reference miRNA query sequences to identify the conserved miRNA in C. limon from known ESTs (1505) (S2) available at NCBI. In this study, EST based homology search approach was followed for prediction of lemon miRNAs; the main theory behind this approach is sequence similarity. The lemon EST sequences that perfectly matched with reference miRNAs having less than 3 mismatches were taken for further screening and sequence filtering. After BLASTn and BLASTx analysis, non-coding sequences were selected as source for potential miRNA and subjected to secondary structure prediction by using MFOLD. The miRNA precursors were inspected and selected manually using the criteria described in Section 2.3, after analysing all the results only one potential miRNA was identified which fulfilled all the criteria described in previous computational miRNA prediction reports. The normal frequency for identifying miRNA from EST is 0.01% (1 in 10,000 ESTs) (Zhang et al., 2006) but in this study it was approximately 0.066%. The length of the actual precursor sequences of the predicted miRNA in lemon might vary than what is presented here. The name of the newly identified miRNA was assigned in a similar pattern as miRBase (Griffiths-Jones, 2006), the mature sequences designated as “miR” with prefix “clm” for Citrus limon, this nomenclature was followed to name the newly predicted miRNA as “clm-miR5658”. The predicted miRNA represented miR5658 family and it was reported in Arabidopsis thaliana as well. The length of the newly identified mature miRNA and precursor sequence were 21 nt and 172 nt respectively which is in agreement with the previous reports that precursor sequences of miRNA range from 60
400 nucleotides in plants (Zhang et al., 2006). The precursor sequence of the identified miRNA was screened and evaluated for its A+U content which was 63.95% supporting previous results (Zhang et al., 2006). The MFE (minimal folding energy) value of the newly identified miRNA was
50.60 kcal/mol, MFEI calculated for the predicted miRNA was 0.81 which supports the criterion in previous results of Yin et al. that if the predicted MFEI value is greater than 0.67 then there is a probability for miRNA precursor (Yin et al., 2008) and the AMFE (adjusted minimal folding energy) value calculated was
29.41 kcal/mol. The mature miRNA was located at 50 in the predicted stem-loop structure of clm-miR5658 (Fig. 2) (Table 1). 3.2. Phylogenetic and conservation analysis of the newly predicted clmmiRNA In case of both precursor and mature sequences, plant miRNAs remain conserved among different remotely related species (Rodriguez et al., 2004; Zhang et al., 2006) and show high degree of sequence similarities between them. Thus, homology search of the identified clmmiRNA and comparison with remaining members of the same miRNA family would reveal its phylogenetic relationships. Only a single member (ath-miR5658) belonging to miR5658 family was reported in miRBase release 22.1. Thus, to obtain sufficient data for phylogenetic analysis, the predicted miRNAs of the miR5658 family from different plant species were collected from published reports and aligned with identified lemon miRNAs (S8). The evolutionary history was inferred by using the maximum likelihood method and Tamura-Nei model in MEGA X. The phylogenetic tree was generated for miR5658 family members along with the identified clm-miR5658 (Fig. 3). In the phylogenetic tree, the identified lemon miRNA showed high sequence similarity with other members of the same family and was found very closely related to bra-miR5658 from Brassica rapa, with which it clustered together in the same branch with small evolutionary distance. The miRNA members belonging to the same family usually showed variation of nucleotides at few positions, which might help the miRNAs to target different mRNAs. This study showed variation in evolution rate in the tree and provided evidence that in plant kingdom different miRNAs might evolve at different rates. The conservation analysis of lemon-miR5658 was performed in WebLogo and it was observed that the identified miR5658 members from different plant species shared highly conserved mature

Fig. 2. Predicted stem-loop secondary structure of the lemon miRNA with its mature miRNA sequence highlighted in red color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

sequences at each position of nucleotide (Fig. 4). This study supports the notion that mature miRNA sequences are preserved in distantly related plant species (Barozai et al., 2012; Barozai et al., 2008; Zhang et al., 2006). 3.3. Prediction of targets of the newly identified conserved clm-miRNAs Earlier studies demonstrated that plant miRNAs bind to their targets with perfect complementarity or nearly perfect sequence complementarity (Schwab et al., 2005) which allows the prediction of

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DC891889

Length of the EST sequence Coordinates of miRNA precursor Length of the precursor Mature miRNA coordinates Length of the mature miRNA miRNA family MFE (ΔG) MFEI AMFE Nucleotide mismatch Number of nucleotides in pre-miRNA sequence (A+T/U)% (G+C)% Precursor miRNA sequence

575 230
401

Mature miRNA sequence MiRNA* sequence Location of the miRNA Homolog mature miRNA name Homolog mature miRNA sequence

172nt 231
251 21nt miR5658
50.60 kcal/mol 0.81
29.41 kcal/mol 2 A=54, T/U=56, G=43, C=19 63.95% 36.04% AATGATGAGGATGATGATGAAGAAGGCAGTGGCGAGGAGGATGATGATGAATAGAAGTTGAAGGAAGAAATTAGCATAGTCATGTTAGGCTGTGACAATGTTTCAAACAACATATGTTAATGCT TTGTGCTTCCATGTTAATTTTTTTGGATGCTT TCATCATCTTCATCAT AUGAUGAGGAUGAUGAUGAAG UACUACUUCUACUACU 50 ath-miR5658 (Arabidopsis thaliana) AUGAUGAUGAUGAUGAUGAAA

MFEI: minimal folding free energy index, ΔG: folding free energies, AMFE: adjusted minimal folding free energy.

miRNA targets. The prediction of targets of the conserved miRNA clm-miR5658 in Citrus limon would help us gain understanding of the functions and roles of these miRNA in a gene regulatory network. The miRNAs work at post-transcriptional level to regulate genes either by attenuating protein translation or by degrading target transcripts. For this study, the psRNATarget tool was employed to search for miRNA targets in C. limon. The complete coding sequences of C.

Fig. 3. Phylogenetic tree of the identified mature miRNA of Citrus limon along with members of miR5658 family (lemon-miRNA is labelled with a black triangle ~).

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limon were retrieved from NCBI and 14 potential targets were predicted for the miRNA clm-miR5658. The predicted targets of the miRNAs showed both translational and cleavage type of inhibition. It was observed that clm-miR5658 targets multiple genes in C. limon (KY085897, NC_034690, MH898447, MH898448, MH898449, MH898450, MH885874, MH885873, MH885875, MH885876, FJ174793, AB813876, AB813877, JX171141), these findings justified that an individual miRNA could target multiple genes belonging to different metabolic pathways and cellular processes. Most of the predicted miRNA target genes were transcription factors, while other target genes showed catalytic activities and a few protein targets were implicated in different metabolic processes. In plants, many genes associated with transcription process are miRNA targets (Guo et al., 2005). In this study, clm-miR5658 targeted R2R3-MYB family transcription factors which belong to one of the huge families of transcription factors responsible for plant development, stress response and metabolism (Dubos et al., 2010). Many R2R3-MYB genes were found in the tissues of different citrus species from genome-wide studies which revealed high presence of this MYB domain genes in citrus family (Xie et al., 2014), thus this protein target might regulate the developmental patterns in C. limon (Table 2). In this study, we found that clm-miR5658 targeted the gene (FJ174793) encoding chlorophyllase enzyme that regulates chlorophyll metabolism in plants (Holden, 1961). Moreover, clm-miR5658 also targeted the genes involved in geranyltransferase activities (AB813876, AB813877) and hydrolysis process (JX171141). Most of the plant miRNA targets were reported to be associated with defense response (Gan et al., 2008). Since coumarin derivatives commonly found in the Rutaceae family play important role in plant defense (Munakata et al., 2012), so umbelliferone 8-geranyltransferase, a coumarin derivative, might be involved in C. limon pathogen defense response (Table 2). Moyle et al. (2017) validated the computationally predicted rice miR529b target transcripts identified on the basis of psRNATarget tool through transient co-agro infiltration dual luciferase (LUC) assay. The cloning procedure was carried out with pGrDL_SPb reporter plasmid in the host plant i.e. Nicotiana benthamiana. The highest scoring five transcripts were used for cloning along with miRNA529b. Subsequently, firefly LUC repression effect was observed when five putative target sequences were present in the vector. Among the five predicted targets, the highest scoring target OsSPL14 gene (a SBP-box gene family member) showed perfect complementarity with miR529b except at G:U wobble pairings alongwith the lowest e value of 0.5. Thus, it exhibited highest repression mediated by the miR529b. The genes OsHXT1;4 and OsFBX292 of rice showed comparatively lower scores and moderate repression as well (Moyle et al., 2017). Again, it was reported that psRNATarget 2017 release was able to generate high precision results without elevating the total number of predictions. Arabidopsis benchmark dataset included 147 strictly validated miRNA target sequences. The dataset was utilised to investigate the performance of psRNATarget. Out of 147 target sequences, 143 validated interactions between miRNA and mRNA were predicted by psRNATarget release 2017. Similar study was

Fig. 4. Nucleotide conservation analysis of lemon-miR5658 and other members of miR5658 family.

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Table 2 Potential targets of the identified conserved miRNA clm-miR5658 in lemon. miRNA name

Target Acc. No.

clm-miR5658 KY085897, NC_034690 MH898447, MH898448, MH898449, MH898450, MH885874, MH885873, MH885875, MH885876 FJ174793 AB813876, AB813877 JX171141

Target sites Targeted protein

Inhibition type Target gene name

1 1

NADH-plastoquinone oxidoreductase subunit 3 Cleavage R2R3-MYB family transcription factor Translation

ndhC PH4

1 1 1

Chlorophyllase umbelliferone 8-geranyltransferase beta-amylase

Chlase1 Cl-PT1a, Cl-PT1b BAM1

Cleavage Translation Cleavage

Fig. 5. Functional distribution of target transcripts regulated by identified clm-miR5658; (a) Biological process (b) Molecular function (c) Cellular component. The values within parentheses indicate number and percentage of transcripts involved in each activity and their localisation.

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performed with rice benchmark dataset and high quality result was generated (Dai et al., 2018). These findings stand as good evidences for reliability of psRNATarget tool in predicting the genes as miRNA targets. 3.4. Functional annotation of clm-miR5658 target genes based on GO classification and KEGG pathways To better understand different functions and pathways of clmmiR5658 target genes, these genes were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes KEGG database for functional annotation. GO terms were assigned to the target genes, on the basis of three major categories viz. biological processes, molecular function and cellular component (Ashburner et al., 2000; Consortium, 2004). As shown in Fig. 5(a), out of 33% of total identified target transcripts of clm-miR5658, KY085897 and NC_034690 encoding the protein ‘NADH-plastoquinone oxidoreductase subunit 30 were involved in biological processes like photosynthetic light reaction (GO:0019684) and oxidation
reduction process (GO:0055114). In Fig. 5(b), KY085897 and NC_034690 that accounted for 11% of the total target transcripts were involved in molecular functions like NADH dehydrogenase (ubiquinone activity) (GO:0008137), quinone binding (GO:0048038) and found to localize in plasma membrane (GO:0005886), chloroplast thylakoid membrane (GO:0009535) and integral component of membrane (GO0016021). Fig. 5(c) showed that 11% of the total transcripts (KY085897 and NC_034690) were found in chloroplast thylakoid membrane. Eight target transcripts of clmmiR5658 (MH898447, MH898448, MH898449, MH898450, MH885874, MH885873, MH885875, MH885876) belonged to MYB family of transcription factors and involved in DNA binding (GO:0003677) as molecular function and found to be located in nucleus (GO:0005634). Other protein targets of clm-miR5658 like chlorophyllase (FJ174793) and beta-amylase (JX17114) play important roles in biological processes like chlorophyll catabolic process (GO:0015996) and polysaccharide catabolic process (GO:0000272) in plants. Gene ontology classification for functional annotation of target genes followed by KEGG mapping

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for particular targets based on enzyme code showed that the protein targets participated in different metabolic pathways in plants such as oxidative phosphorylation (ec00190), porphyrin and chlorophyll metabolism (ec00860), starch and sucrose metabolism (ec00500), biosynthesis of secondary metabolites (ec01110), drug metabolism-other enzymes (ec00983) and metabolic pathways (ec01100). These pathways were regulated by clm-miR5658 by targeting different proteins. These findings would help acquire more information for understanding the functions and pathways of clm-miR5658 regulated target genes (Tables 3 and 4).

4. Conclusion In transcriptomics research, the study on miRNA is one of the leading topics now-a-days. Usually the miRNA functions at posttranscriptional stage and has a great impact on the overall gene regulatory network. In this study, one conserved miRNA in lemon i.e. clm-miR5658 was identified by employing EST based homology method out of 1505 ESTs available for lemon in NCBI database and its potential target genes were also predicted. The predicted secondary structure of clm-miR5658 may provide useful information on the location of the miRNA during experimental investigation. The functions of the anticipated target genes mostly involve transcription factors, genes associated with development processes and metabolism in lemon. In general, the miRNAs are associated with the regulation of growth and development in plants, therefore further research may be focussed on lemon miRNAs for better understanding of their regulatory network. This study is based on computational approach which can assist in designing of experiments and the findings need to be validated through experimental analysis like deep-sequencing. The findings from this study might shed light on the structural associations of the identified miRNA and to interpret its functions and regulatory mechanisms in lemon. It is an in silico based identification of conserved miRNA in lemon, whose entire genome is not yet sequenced and annotated.

Table 3 GO terms and functional annotations of the clm-miR5658 target genes. clm-miRNA

Target Accession No.

Biological Process

Molecular Function

Cellular component

Photosynthesis light reaction (GO:0019684), Oxidation-reduction process (GO:0055114)

NADH dehydrogenase (ubiquinone activity) (GO:0008137), Quinone binding (GO:0048038)

MH898447, MH898448, MH898449, MH898450, MH885874, MH885873, MH885875. MH885876 FJ174793

no hits found

DNA binding (GO:0003677)

plasma membrane (GO:0005886), chloroplast thylakoid membrane (GO:0009535), Integral component of membrane (GO0016021 Nucleus (GO:0005634)

Chlorophyll catabolic process (GO:0015996)

AB813876, AB813877

no hits found

Chlorophyllase activity (GO:0047746), Pheophytinase b activity (GO:0102293) Phenyltransferase activity (GO:0004659)

JX171141

Polysaccharide catabolic process (GO:0000272)

clm-miR5658 KY085897, NC_034690

Amylopectin maltohydrolase activity (GO:0102229), beta-amylase activity (GO:0016161)

Chloroplast(GO:0009507)

Integral component of membrane (GO:0016021), Chloroplast membrane (GO:0031969) no hits found

Table 4 Pathway annotation of the clm-miR5658 targets in KEGG database. clm-miRNA

Target accession no.

Pathways

Pathway ID

clm-miR5658

KY085897, NC_034690 FJ174793

Oxidative phosphorylation, metabolic pathways Porphyrin and chlorophyll metabolism, metabolic pathways, biosynthesis of secondary metabolites, drug metabolism-other enzymes Starch and sucrose metabolism, metabolic pathways

map00190, map01100 map00860, map01100, map01110, map00983

JX171141

map00500, map01100

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Declaration of competing interest The authors declare that there are no conflicts of interest in this work. Acknowledgment The authors are thankful to Assam University, Silchar, Assam, India for providing the necessary lab facilities for this study. Supplementary materials Supplementary material associated with this article can be found in the online version at doi:10.1016/j.sajb.2019.12.009. References €ffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J., Altschul, S.F., Madden, T.L., Scha 1997. Gapped blast and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25, 3389–3402. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., 2000. Gene ontology: tool for the unification of biology. Nature Genetics 25, 25. Axtell, M.J., Bartel, D.P., 2005. Antiquity of microRNAs and their targets in land plants. The Plant Cell 17, 1658–1673. Axtell, M.J., Snyder, J.A., Bartel, D.P., 2007. Common functions for diverse small RNAs of land plants. The Plant Cell 19, 1750–1769. Barozai, M.Y.K., Baloch, I.A., Din, M., 2012. Identification of micrornas and their targets in helianthus. Molecular Biology Reports 39, 2523–2532. Barozai, M.Y.K., Irfan, M., Yousaf, R., Ali, I., Qaisar, U., Maqbool, A., Zahoor, M., Rashid, B., Hussnain, T., Riazuddin, S., 2008. Identification of micro-RNAs in cotton. Plant Physiology and Biochemistry 46, 739–751. Chuck, G., Candela, H., Hake, S., 2009. Big impacts by small RNAs in plant development. Current Opinion in Plant Biology 12, 81–86. € tz, S., 2008. Blast2GO: a comprehensive suite for functional analysis in Conesa, A., Go plant genomics. International Journal of Plant Genomics 2008, 2008. €
mez, J.M., Terol, J., Talo
n, M., Robles, M., 2005. Blast2GO: a Conesa, A., Gotz, S., García-Go universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. Consortium, G.O., 2004. The gene ontology (GO) database and informatics resource. Nucleic Acids Research 32, D258–D261. Crooks, G.E., Hon, G., Chandonia, J.-.M., Brenner, S.E., 2004. WebLogo: a sequence logo generator. Genome Research 14, 1188–1190. Dai, X., Zhuang, Z., Zhao, P.X., 2018. psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Research 46, W49–W54. Dehury, B., Panda, D., Sahu, J., Sahu, M., Sarma, K., Barooah, M., Sen, P., Modi, M.K., 2013. In silico identification and characterization of conserved miRNAs and their target genes in sweet potato (Ipomoea batatas L.) expressed sequence tags (ESTs). Plant Signaling & Behavior 8, e26543.
mez, P., Porras, I., Garcıa-Lido
n, A., Ortun ~ o, A., 2004. Citrus limon: Del Rıo, J., Fuster, M., Go a source of flavonoids of pharmaceutical interest. Food Chemistry 84, 457–461. Dubos, C., Stracke, R., Grotewold, E., Weisshaar, B., Martin, C., Lepiniec, L., 2010. MYB transcription factors in arabidopsis. Trends in Plant Science 15, 573–581. Fahlgren, N., Howell, M.D., Kasschau, K.D., Chapman, E.J., Sullivan, C.M., Cumbie, J.S., Givan, S.A., Law, T.F., Grant, S.R., Dangl, J.L., 2007. High-throughput sequencing of arabidopsis microRNAs: evidence for frequent birth and death of mirna genes. Plos One 2, e219. Felsenstein, J., 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791. Gan, Q.-h., Chi, X.-y., Qin, S., 2008. Roles of microRNA in plant defense and virus offense interaction. Plant Cell Reports 27, 1571–1579. Gonz
alez-Molina, E., Domínguez-Perles, R., Moreno, D., García-Viguera, C., 2010. Natural bioactive compounds of citrus limon for food and health. Journal of Pharmaceutical and Biomedical Analysis 51, 327–345.

€tz, S., García-Go
mez, J.M., Terol, J., Williams, T.D., Nagaraj, S.H., Nueda, M.J., Go
n, M., Dopazo, J., Conesa, A., 2008. High-throughput functional Robles, M., Talo annotation and data mining with the blast2go suite. Nucleic Acids Research 36, 3420–3435. Griffiths-Jones, S., 2006. miRBase: the microRNA sequence database. MicroRNA Protocols. Springer, pp. 129–138. Griffiths-Jones, S., Grocock, R.J., Van Dongen, S., Bateman, A., Enright, A.J., 2006. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Research 34, D140–D144. Guo, H.-S., Xie, Q., Fei, J.-F., Chua, N.-H., 2005. MicroRNA directs mRNA cleavage of the transcription factor NAC1 to downregulate auxin signals for arabidopsis lateral root development. The Plant Cell 17, 1376–1386. Hall, T., Biosciences, I., Carlsbad, C., 2011. BioEdit: an important software for molecular biology. GERF Bulletin of Bioscience 2, 60–61. Holden, M., 1961. The breakdown of chlorophyll by chlorophyllase. Biochemical Journal 78, 359. Janati, S.S.F., Beheshti, H.R., Feizy, J., Fahim, N.K., 2012. Chemical composition of lemon (Citrus limon) and peels its considerations as animal food. GIDA: Journal of Food 37, 267–271. Jones-Rhoades, M.W., Bartel, D.P., Bartel, B., 2006. MicroRNAs and their regulatory roles in plants. Annual Review of Plant Biology 57, 19–53. Kanehisa, M., Goto, S., 2000. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research 28, 27–30. Kidner, C.A., Martienssen, R.A., 2005. The developmental role of microRNA in plants. Current Opinion in Plant Biology 8, 38–44. Kumar, S., Stecher, G., Li, M., Knyaz, C., Tamura, K., 2018. MEGA X: molecular evolutionary genetics analysis across computing platforms. Molecular Biology and Evolution 35, 1547–1549. Kwak, P.B., Wang, Q.Q., Chen, X.S., Qiu, C.X., Yang, Z.M., 2009. Enrichment of a set of microRNAs during the cotton fiber development. Bmc Genomics 10, 457. Lin, S.-L., Chang, D., Ying, S.-Y., 2005. Asymmetry of intronic pre-miRNA structures in functional risc assembly. Gene 356, 32–38. Moyle, R.L., Carvalhais, L.C., Pretorius, L.S., Nowak, E., Subramaniam, G., Dalton-Morgan, J., Schenk, P.M., 2017. An optimized transient dual luciferase assay for quantifying microrna directed repression of targeted sequences. Frontiers in Plant Science 8, 1631. Munakata, R., Inoue, T., Koeduka, T., Sasaki, K., Tsurumaru, Y., Sugiyama, A., Uto, Y., Hori, H., Azuma, J.-i., Yazaki, K., 2012. Characterization of coumarin-specific prenyltransferase activities in citrus limon peel. Bioscience, Biotechnology, and Biochemistry 76, 1389–1393. Reinhart, B.J., Weinstein, E.G., Rhoades, M.W., Bartel, B., Bartel, D.P., 2002. MicroRNAs in plants. Genes & Development 16, 1616–1626. Rodriguez, A., Griffiths-Jones, S., Ashurst, J.L., Bradley, A., 2004. Identification of mammalian microRNA host genes and transcription units. Genome Research 14, 1902–1910. Schwab, R., Palatnik, J.F., Riester, M., Schommer, C., Schmid, M., Weigel, D., 2005. Specific effects of microRNAs on the plant transcriptome. Developmental Cell 8, 517–527. Tamura, K., Nei, M., 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial dna in humans and chimpanzees. Molecular Biology and Evolution 10, 512–526. Wang, L., Liu, H., Li, D., Chen, H., 2011. Identification and characterization of maize microRNAs involved in the very early stage of seed germination. BMC Genomics 12, 154. Won, S.Y., Yumul, R.E., Chen, X., 2014. Small RNAs in plants. In: Howell, S.H. (Ed.), Molecular Biology. Springer New York, New York, NY, pp. 95–127. Xie, R., Li, Y., He, S., Zheng, Y., Yi, S., Lv, Q., Deng, L., 2014. Genome-wide analysis of citrus R2R3MYB genes and their spatiotemporal expression under stresses and hormone treatments. Plos One 9, e113971. Yin, Z., Li, C., Han, X., Shen, F., 2008. Identification of conserved microRNAs and their target genes in tomato (Lycopersicon esculentum). Gene 414, 60–66. Zhang, B., Pan, X., Cannon, C.H., Cobb, G.P., Anderson, T.A., 2006. Conservation and divergence of plant microRNA genes. The Plant Journal 46, 243–259. Zhang, B., Pan, X., Cobb, G.P., Anderson, T.A., 2006. Plant microRNA: a small regulatory molecule with big impact. Developmental Biology 289, 3–16. Zhang, B., Pan, X., Cox, S., Cobb, G., Anderson, T., 2006. Evidence that miRNAs are different from other RNAs. Cellular and Molecular Life Sciences CMLS 63, 246–254. Zhang, B.H., Pan, X.P., Wang, Q.L., George, P.C., Anderson, T.A., 2005. Identification and characterization of new plant microRNAs using est analysis. Cell Research 15, 336. Zuker, M., 2003. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research 31, 3406–3415.