Biotechnological

Communication

Biosci. Biotech. Res. Comm. 9(3): 481-488 (2016)

Identi cation of novel micro RNAs and their targets in

Cocos nucifera

–A Bioinformatics approach

A.T. Vivek

and Faisal Moossa

Department of Biotechnology, University of Calicut, Calicut University PO, Malappuram 673 635, India

Bioinformatics Infrastructure Facility, Department of Biotechnology, University of Calicut, Calicut University

PO, Malappuram 673 635, India

ABSTRACT

MicroRNAs are endogenous, non-translated, small RNAs of ~21 nucleotides that are processed from stem-loop

regions of long transcripts. MicroRNA is responsible for regulation of gene expression of diverse aspects of plant

development at the post-transcriptional level. Coconut is one of the major perennial crop, that has wide commercial

importance. The identi cation of novel miRNAs from non-sequenced genome of plants can be done by comparative

genomics approach using expressed sequence tags (ESTs) following  ltering criteria based on structural features.In

an attempt to identify potential miRNAs from Cocos nucifera,1008 ESTs were employed for homology based search

to reported viridiplantae miRNAs. The candidate miRNAs were used for secondary structure prediction that led to

the identi cation of one novel miRNA from mir2673 family. The gene targets predicted miRNA shows crucial role in

regulation of auxin signaling pathway, transcription factors, abiotic stress, retrotransposons etc. The outcomes of this

study will considerably enhance the scope to understand the role of miRNAs in C.nucifera.

KEY WORDS:

COCOS NUCIFERA L

. ,EXPRESSED SEQUENCE TAGS, MICRORNA, MICRORNA TARGETS, RNA SECONDARY STRUCTURE.

481

ARTICLE INFORMATION:

*Corresponding Author: faisalmuhammed38@gmail.com

Received 6

Sep, 2016

Accepted after revision 29

Sep, 2016

BBRC Print ISSN: 0974-6455

Online ISSN: 2321-4007

Thomson Reuters ISI ESC and Crossref Indexed Journal

NAAS Journal Score 2015: 3.48 Cosmos IF : 4.006

reserved.

Online Contents Available at: http//www.bbrc.in/

INTRODUCTION

MicroRNAs are a set of endogenous non-coding RNAs

of ~21 nucleotides in length, which has vital role in

the transcriptional and post-transcriptional regulation

of gene expression (Zhang et al., 2006). These act as

an important regulator in various processes of develop-

ment, cell signaling and stress conditions (Millar et al.,

2005;Sunkar et al., 2006; Khraiwesh et al., 2012). The

expression of single gene may be controlled by multiple

miRNAs and single miRNA can have multiple gene tar-

gets (Dehury et al., 2013).

The plant miRNA genes are present in both introns

and exons which are transcribed to pre-miRNA(primary

miRNA) transcripts and there exists capped structures

as well polyA tail that are processed to form stem loop

structures. The mature miRNAs are produced from these

pre-miRNA transcripts by DCL1 (Dicer like-1) and endo-

482 IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Vivek and Faisal Moossa

nuclease III-like enzyme,is exported via HASTY 5 to the

cytosol. The target gene expression is regulated by RNA-

induced silencing complex (RISC) containing mature

miRNA incorporated into it (Bartel., 2004). In plants, the

 rst miRNA was reported in Arabidopsis thaliana

,fol-

lowing which several thousands of novel miRNAs has

been discovered in plants by computational and experi-

mental techniques(Reinhart et al., 2002; Grif ths-Jones

and Sam., 2006; Zhang et al., 2016; Akter et al., 2014).

The miRNAs in plants represent a high degree

of perfect or nearly perfect complementary to their

targets(Rhoades et al., 2002). This highly conservative

character of plant miRNAs promises in screening of

homolog sequences of miRNAs from nucleotide archives

of different plant kingdoms using in silico techniques

based on homology (Das et al., 2010). Many conserved

miRNAs have been screened in various model and non-

model plants by using the genomic data and homology

based computational techniques, as exempli ed in the

study which resulted in identi cation of 682 miRNAs in

155 diverse plant species (Sunkar et al., 2008). Compu-

tational predictions of miRNAs has also been reported

in many of the non-model crops such as garlic, ginger,

cannabis and basil (Panda et al., 2014; Singh et al.,

2014; Das et al., 2015 and Singh et al., 2016).

Cocos nucifera.L. (2n=2x=32), a member of Arecaceae

family , is one of the commercially important palms.

Being a perennial tree, it is cultivated widely across the

world for its versatile utilities uses from food to cosmetics

(Govaerts., 2003). Since the available genetic resources

are less, comprehensive analysis on the available data

in online repositories has to be exercised. At present, no

miRNA has been predicted using the available ESTs for

C.nucifera. Hence, in this study, an in silico approach

for screening of potential miRNAs was performed using

homolog search of Viridiplantae miRNAs (miRBase)

against the expressed sequence tags. The elimination of

coding sequences and use of non-coding ESTs for second-

ary structure of the precursor miRNA was predicted using

MFOLD which led to the identi cation of novel miRNA.

Finally, the predicted novel miRNA was used to  nd the

target genes from Arabidopsis thaliana transcripts.

MATERIAL AND METHODS

The reported 8,496 miRNAs belonging to Viridiplan-

tae were downloaded from miRBase (Released 21: June,

2014) and clustered by CD- HIT-EST, with threshold value

of 100 (Li et al., 2006). From the clustered sequences of

3777, non-redundant miRNAs were selected which in

turn used as reference miRNAs for  nding the homologs

in C. nucifera to create a local nucleotide sequence

database. Publically available, 1008 ESTs were down-

loaded from EST database, NCBI (www.ncbi.nlm.nih.

gov/dbEST/) to represent query sequences for miRNA

prediction.

The alignment tool NCBI Blast+ was used for con-

served miRNA prediction of Cocos nucifera ESTs by

making local database of miRNA after retrieving the

representative sequences of miRNA (Cock et al., 2015).

The secondary structures of pre-miRNAs were performed

through online version of MFOLD (Zuker and Michael.,

2003). The putative target genes for the predicted miRNA

of coconut were identi ed using plant small RNA psR-

NATarget Web Server (Dai et al., 2011). The circos plot

was drawn using online Circoletto tool(Darzentas et al.,

2010).

The EST sequences were taken to perform blastn

search against locally setup Viridiplantae miRNA

database downloaded from miRBase. The blast search

was performed with following parameters: (a) e-value

threshold<0.01 (b) mismatches=0-2. The obtained hits

with less than 3 nucleotide mismatches and without gap

were selected to extract the precursor sequences (pre-

miRNA). The method of a sliding window of 100 nt in

size from ~80nt upstream and ~80nt nucleotide down-

stream of the mature miRNA were set to pick the best

miRNA precursors (Singh and Nagaraju., 2008).

The secondary structures were predicted using web

based MFOLD tool with following criteria:

(a) linear RNA sequence

(b) folding temperature  xed at 37

ions

(d) percent sub-optimality number of 5

(e) maximum interior/bulge loop size of 30

(f) energy dot plot was turned on and the other

parameters were set as default.

After prediction of the secondary structure of the

precursor sequence of potential miRNA homologs, the

following criteria were setup to select potential miRNA

as described by Zhang in 2005 (Zhang et al., 2005):

(a) A minimal length of the pre-miRNA: 60 nt. (b) The

pre-miRNA must be into appropriate stem loop hairpin

secondary structure. (c) The position of mature miRNA

sequence should be in one arm of the hairpin structure.

(d) The mature miRNA sequence and its opposite miRNA

strand should not have more than 6 nt mismatches. (e)

The total A+U % should be in the range of 30-70%. (f)

should have higher minimal folding free energy index

(MFEI) and negative minimal folding free energy (MFE)

to distinguish the miRNA secondary from other small

RNAs. (f) The MFEI and adjusted MFE(AMFE) was calcu-

lated using the following equations:

MFEI =[(AMFE/(G+C)% ]

AMFE=[MFE÷ length of precursor miRNA] X100

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS 483

Vivek and Faisal Moossa

Table 1: List of coding and non-coding

ESTs

ID Coding/

Non-coding

Coding

potential

score

573332413 coding 2.81173

573332205 noncoding -1.34675

573332140 coding 0.197992

573332096 noncoding -1.39801

573332024 noncoding -0.82414

573332021 noncoding -1.21493

573331999 noncoding -1.05615

573329619 noncoding -1.26066

573329529 noncoding -1.23343

573329506 coding 0.746432

323150074 noncoding -1.08997

323150034 noncoding -1.39945

The perfect complementarity or near complemen-

tarity permits the identi cation of miRNA targets. The

C. nucifera miRNA targets were identi ed through

homolog search by subjecting mature miRNA sequence

as query against Arabidopsis thaliana transcript library

with removed miRNA gene set(ftp://ftp.arabidopsis.

org/home/tair/Genes/TAIR10_genome_release/TAIR10_

blastsets/TAIR10_cdna_20101214_updated) using psR-

NAtarget webserver. The following parameters were

employed in prediction of miRNA targets :

(a) No.of target genes for each small RNA:10

(b) Maximum mismatch at complementary site:≤2

without any gaps

(d) Target accessibility-allowed maximum energy

to un-pair the target site (UPE): 25

(e) Range of central mismatch leading to transla-

tion inhibition: 9–11 nt

(f) Flanking length around the target accessibility

analysis: 17 bp upstream and 13 bp in down-

stream length of complementarity score: 20

The entire work ow for the prediction of coconut

miRNAs is illustrated in Fig.1

RESULTS AND DISCUSSION

The conserved microRNAs in plants among different

species including monocot and dicot are involved in

various biological activities (Yang et al.,2007). The novel

miRNA in C.nucifera was predicted using homology

based in silico approach. The known miRNA from mir-

Base and the available C.nucifera ESTs were performed

BLAST to obtain a total of 13 homologs after elimina-

tion of repeated sequences. Thereafter, 13 ESTs were

Table 2: Details of the predicted miRNA

Length of EST sequence 1008

Length of precursor miRNA 99nt

Length of mature miRNA 19nt

Precursor miRNA coordinates 74-172

Mature miRNA coordinates 135-153

Family of miRNA mir 2673

MFE 49.70 -Kcal/mol

MFEI 0.80

AMFE 50.2020 -Kcal/mol

Nucleotide mismatch 1

Number of each nucleotides

in pre-miRNA sequences

A-14

T/U-23

G-36

C-26

(A+T/U)% 37.38%

(G+C)% 62.62 %

Precursor miRNA sequence

GGGUUUUUGGGUCUCGCUCCCUUCUU

CUGCCCUUCGCCUCUCGGCGUAGGUCA

GGUGAGGCGAAGACGAAGAGGAAGAG

GAGGAGCCCUCGCCGUGUGG

Mature miRNA sequence GAAGACGAAGAGGAAGAGG

Star miRNA sequence CUUCCCGUCCUUCUUCCC

484 IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Vivek and Faisal Moossa

FIGURE 1. Work ow of Coconut miRNA prediction

FIGURE 2. The stem loop structure of the predicted potential pre-miRNA miRNA in C.nucifera with its

mature miRNA sequence (highlighted in blue)

screened for coding and non-coding sequences, which

resulted in 9 non-coding miRNAs, out of which strong

non-coding sequences were taken based on the basis of

coding potential score (Ref. Table 1).

After circumspectly considering nucleotide BLAST

and CPC tool results, we were able to identify the nine

unique EST sequences that showed homology with the

known the miRNAs in mirBase.These unique ESTs were

subjected for secondary structure prediction and the

results attained were investigated for appropriate pre-

cursor miRNA and its respective stem-loop structure.

On applying the  ltering criteria of secondary struc-

ture prediction, one novel miRNA was found (Fig. 2 and

Fig. 3). The miRNA represented mir 2673 family with

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS 485

Vivek and Faisal Moossa

Table 3: List of the potential targets of newly identi ed miRNA in coconut

Target Acc. e-Value Inhibition Target Desc. GO Biological

Process

GO Molecular

Function

GO Cellular

Component

AT3G25450.1 0 Cleavage Transposable element Copia-

like retrotransposon family

Not Available Not Available Not

Available

AT2G37650.1 0.5 Cleavage GRAS family transcription

factor |

regulation of

transcription, DNA-

templated, transcription,

DNA-templated

transcription

factor activity,

sequence-speci c

DNA binding

nucleus

AT1G10940.2 1 Cleavage Protein kinase superfamily,

Encodes a plant protein

kinase similar to the calcium/

calmodulin-dependent

protein kinase subfamily and

the SNF1 kinase subfamily

(SnRK2) whose activity is

activated by ionic (salt) and

non-ionic (mannitol) osmotic

stress.

intracellular signal

transduction,

primary root

development, protein

phosphorylation,

response to abscisic

acid, response to

osmotic stress, response

to salt stress

ATP binding,

kinase activity,

protein binding,

protein kinase

activity, protein

serine/threonine

kinase activity

cytosol,

membrane,

nucleus

AT1G10940.1 1 Cleavage Protein kinase superfamily,

Encodes a plant protein

kinase similar to the calcium/

calmodulin-dependent

protein kinase subfamily and

the SNF1 kinase subfamily

(SnRK2) whose activity is

activated by ionic (salt) and

non-ionic (mannitol) osmotic

stress.

intracellular signal

transduction,

primary root

development, protein

phosphorylation,

response to abscisic

acid, response to

osmotic stress, response

to salt stress

ATP binding,

kinase activity,

protein binding,

protein kinase

activity, protein

serine/threonine

kinase activity

cytosol,

membrane,

nucleus

AT5G06710.2 1 Cleavage homeobox from Arabidopsis

thaliana / Homeobox-leucine

zipper protein

regulation of

transcription, DNA-

templated, transcription,

DNA-templated

sequence-speci c

DNA binding,

transcription

factor activity,

sequence-speci c

DNA binding

nucleus

AT5G06710.1 1 Cleavage homeobox from Arabidopsis

thaliana / Homeobox-leucine

zipper protein

regulation of

transcription, DNA-

templated, transcription,

DNA-templated

sequence-speci c

DNA binding,

transcription

factor activity,

sequence-speci c

DNA binding

nucleus

AT1G21590.1 1.5 Cleavage Protein kinase protein with

adenine nucleotide alpha

hydrolases-like domain

Protein

phosphorylation,

response to stress

ATP binding,

hydrolase activity,

kinase activity,

protein serine/

threonine kinase

activity

nucleus

a precursor length of 99nt, which justi es the previ-

ous reports that miRNA precursors length varies from

60-400 nucleotides (Zhang et al., 2006). If the calculated

MFEI is greater than 0.67, then there is a possibility for

precursor miRNA (Yin et al., 2008). MFEI of the pre-

dicted coconut miRNA was found to be 0.80, which sup-

ports this criterion. The AMFE calculated for the coconut

miRNA was 50.2 -kcalmol

-1

which follows the reported

average AMFE (45.93±9.43 -kcalmol

-1

) of 513 precursor

miRNA of plants (Zhang et al., 2006) (Ref. Table 2).

So far, the predicted miRNA family is reported only

in Medicago truncatula and therefore progress has to

be made in understanding the role of this novel miRNA

(Lelandais-Brière et al., 2009). As proposed by miRBase,

the new miRNA of coconut is named as ‘cnu-miR2673’

following the miRNA nomenclature procedure (Grif ths-

Jones and Sam., 2006) .

The understanding of target gene function of miRNA

is necessary step to know its regulation. The nature of

conserved miRNAs in plants is an important factor that

Vivek and Faisal Moossa

486 IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

FIGURE 3. EST sequence containing pre-miRNA(yellow color higlighted) and mature miRNA(blue color high-

lighted)

FIGURE 4. Circos plot showing Cnu-miR2673 and its target

genes: The coloured ribbons indicate the genes targeted by

the coconut microRNA. The labels represent the target genes

 anked by green (start of the target sequence) and orange

(end of target sequence) blocks.

allows the  nding of the target genes based on their

complementarity or near complementarity of miRNAs

to their respective targets( Zhang et al., 2006).We have

found the  ve target genes for this one miRNA fam-

ily based on our  ltering criteria using the psRNAtar-

get tool using the available transcripts of Arabidopsis

thaliana. All of the putative target genes seem to inhibit

through cleavage mode by the identi ed miRNA and it

has multiple targets as shown in Table 3 and Fig. 4.

The predicted targets involves two transcription fac-

tors namely Homeobox leucine zipper family and GRAS

family of transcription factor. HD-ZIP (Homeobox-leu-

Vivek and Faisal Moossa

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS 487

cine zipper protein) has homeodomain (HD) and linear

zipper motif present. This class of transcription factors is

unique to plants and are involved in plant growth and

development especially in response to abscisic acid, abi-

otic stress, shade avoidance and auxin signaling (Ariel

et al., 2007; Elhiti et al., 2009). GRAS proteins act as

transcription factors, a number of which have nuclear

localization signal for localization of several other pro-

teins (Tian et al., 2004). It has important functions in

GA (Gibberilic acid) and light signaling and regulation

of root patterning (Hirsch et al., 2009) .

We have found one of the target gene for Copia retro-

transposons. These retrotransposons are  anked by long

terminal repeats (LTRs) that certain promoter and down-

stream controls elements their internal domain usually

contains the genes required for reverse transcriptase,

integrase and gag (Wilhelm and Wilhelm.,2001). The

gene that had Copia like transposons has blast match

to gag pol protein from Glycine max and previous  nd-

ings support the existence of SIRI, a copia/Ty1-like

retrotransposon element encoding a retroviral enve-

lope like protein assumed to be originated from tomato

genome through horizontal gene transfer (Cheng et al.,

2009). Two of the target genes showed the exiatence of

protein kinase out of which one encodes the calcium/

calmodulin mediated kinase super subfamily and SNF1

kinase subfamily activated during ionic and non-ionic

omotic stress. As exempli ed, CDPKs (Calcium depend-

ent protein kinases) play major roles in development

for recycling transcription to hormone levels and are

known to function in abiotic stress response and ABA

signaling through phosphorylation activity(Ishida et al.,

2008; Mori et al., 2006). On the other hand, SnRK(SNF

kinases) are expressed in nucleus during seed devel-

opment and germination in Arabidopsis. Any altera-

tion in these protein induces gene expression change,

both, in the up-regulation of ABA repressive genes and

down-regulation of ABA inducible genes. The altera-

tion of gene expression leads to loss of dormancy and

growth defects during seed development (Nakashima et

al., 2009). The other kinase protein with adenine alpha

hydrolases phosphorylates proteins fuctions in response

to stress with probable interaction with serine/threonine

to ATP.

As in plants, for every 10,000 EST sequences is

expected to contain one miRNA (Zhang et al., 2006).

But, from our study, we propose that even 1000-2000

ESTs could be utilized to mine the conserved potential

miRNAs across the non-model crops. The  ndings from

this study indicate that coconut miRNA mir 2673 pos-

sibly targets both transcription factors and individual

speci c genes. Hence, the methodology adopted in this

research will enumerate the understanding of regulatory

miRNA in coconut in much rapid pace in near future.

CONCLUSION

The quest for potential miRNA is a highlighted research

routine in the  eld of transcriptomics. However, over the

recent years the miRNAs from non-model plants have

been explored using ESTs. In this research, attempts has

been made to screen miRNAs from 1008 ESTs reported

in C.nucifera by employing, EST based homology search

method, which resulted in one novel miRNA. Its putative

function of gene targets were also catalogued. The impact

of this study will be a starting point in understanding

the miRNA biogenesis and its structure in Cocos nucifera

and to explore repertoire of small RNAs that contribute

to different biological mechanisms of the plant system.

Since the genetic information is limited, the present in

silico approach will help to improve the miRNAome in

C.nucifera through further experimental validation.

ACKNOWLEDGEMENT

The authors gratefully acknowledge the Bioinformatics

Infrastructure Facility sponsored by Department of Bio-

technology, Government of India, New Delhi. They also

acknowledge the Head and Coordinator, Department of

Biotechnology, University of Calicut, Kerala.

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