American Journal of Plant Sciences, 2010, 1, 77-86
doi:10.4236/ajps.2010.12010 Published Online December 2010 (
Copyright © 2010 SciRes. AJPS
Computational Identification of Conserved
microRNAs and Their Targets in Tea
(Camellia sinensis)
Akan Das1, Tapan Kumar Mondal1,2*
1Biotechnology Laboratory, Faculty of Horticulture, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, West Bengal, India; 2National
Research Center of DNA Fingerprinting, National Bureau of Plant Genetic Resources, New Delhi, India.
Email: *
Received November 2nd, 2010; revised November 15th, 2010; accepted November 22nd, 2010.
MicroRNAs (miRNAs) are a class of ~22 nucleotides long non coding RNA molecules which play an important role in
gene regulation at the post transcriptional level. The conserved nature of miRNAs provides the basis of new miRNA
identification throug h homology search. In an a ttempt to iden tify new conserved miRNAs in tea , previously kno wn plant
miRNAs were used for searching their homolog in a tea Expressed Sequence Tags and full length nucleotide sequence
database. The sequences showing homolog no more than four mismatches were predicted for their fold back structures
and passed through a series of filtration criteria, finally led us to identify 13 conserved miRNAs in tea belonging to 9
miRNA families. A total of 37 potential target genes in Arabidopsis were identified subsequently for 7 miRNA families
based on their sequence complementarity which encode transcription factors (8%), enzymes (30%) and transporters
(14%) as well as other proteins involved in physiological and metabolic processes (48%). Overall, our findings will
accelerate the way for further researches of miRNAs and their functions in tea.
Keywords: Camellia sinensis, Computational Identification, Expressed Sequence Tags, microRNA, Targets
1. Introduction
MicroRNAs (miRNAs) are short (~22 nt), endogenous
non coding RNAs that play an important role in many
biological processes [1]. They are generated from long
precursor molecules which can fold into hairpin second-
dary structures [2]. Mature miRNAs bind to the comple-
mentary sites on target mRNAs and repress post tran-
scriptional gene expression in both animals and plants [3,
4]. In plants, miRNAs are involved in diverse aspects of
plant growth and development such as leaf morphology
and polarity, root formation, transition from juvenile to
adult vegetative phase and vegetative to flowering phase,
flowering time, floral organ identity and reproduction [5,
6]. They are also found to be involved in response to
pathogen invasion [7], hormone signaling [8,9], envi-
ronmental stress [10,11] and promotion of anti-viral de-
fence [12].
Expressed Sequence Tags (ESTs) are complementary
DNA (cDNA) sequences, usually 200-500 bp in length
that represents the expressed portions of genes. Therefore,
ESTs can be used in gene identification, expression pro-
filing and polymorphism analysis [13]. The EST se-
quencing projects have been enormously successful in
the framework of many genome projects. The EST se-
quences are being used intensely as a source of informa-
tion for the discovery of new genes whose functions can
tentatively be deduced from their sequence and verified
experimentally. Recently, in silico identification of
miRNAs in various plant species have been done by EST
analysis [14-17]. The biogenesis of miRNAs suggests
that it is possible to find new miRNAs by homology
searching of known miRNAs in ESTs. Hence, EST ana-
lysis makes it possible for studying conserved miRNAs
and their functions in species whose genome sequences
have not been well known [10,18,19].
There are both experimental and computational ap-
proaches for the investigation of plant miRNAs. The
computational approach has been proved to be faster,
affordable and more effective. Most of the miRNAs in
miRBase [20] have been contributed through computa-
tional approach only. The computational approach has
been developed on the principle of looking conserved
sequences between different species which can fold into
Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)
Copyright © 2010 SciRes. AJPS
hairpin secondary structures [10]. In recent years, a
number of programs and bioinformatics tools have been
developed and used successfully for the identification
and analysis of miRNAs and their targets [21,22].
Tea is one of the most important non-alcoholic bever-
age drinks worldwide and gaining further popularity as
an important ‘health drink’. Despite the limited genome
resources of tea (Camellia sinensis), published EST and
full length nucleotide sequences in GenBank (http://www. has provided the scope to get
more genetic information. In this study, new miRNAs
were mined in local tea sequence database for the pur-
pose of understanding their roles in regulating growth
and development, metabolism and other physiological
processes in tea.
2. Methods
2.1. Collection of Reference miRNAs, Full
Length Nucleotides and EST Sequences
All available plant miRNAs and their fold back sequences
were obtained from miRBase (
on May, 2010. The homolog miRNAs were eliminated
and the rest were defined as reference for searching tea
miRNAs. Tea nucleotide and EST sequences (14819 as
on May, 2010) were downloaded from NCBI’s nucleo-
tide and dbEST database (http://www.ncbi.
All redundant and poor quality sequences were elimi-
nated and created a local nucleotide database.
2.2. Potential miRNAs and Their Precursors
The procedure for searching conserved tea miRNA ho-
mologues is summarized in Figure 1. The reference se-
quences were used as a query for homology search against
our local tea nucleotide sequence database at e-value
threshold < 0.01 using BLAST + 2.2.22 program [23].
The target sequences with no more than four mismatches
were considered for secondary structure prediction using
Mfold v 3.2 [24]. The precursor sequences were searched
at 50 nucleotides upstream or downstream from the loca-
tion of mature miRNAs with an increament of 10 nucleo-
tides. While selecting a RNA sequence as a candidate
miRNA precursor, following criteria were used accord-
ing to Zhang et al. [1] with minor modifications as: 1) a
RNA sequence can fold into an appropriate stemloop
hairpin secondary structure, 2) a mature miRNA se-
quence site in one arm of the hairpin structure, 3) miRNAs
Collect all available tea ESTs and genes
Exclude redundant and poor quality ESTs
Create a local nucleotide database
Collect all known plant miRNAs
Exclude homolog miRNAs
Sequences 4 mismatches
Total unique hits = 22
Extract pre-miRNAs and predict secondary
structures using Mfold
Finally select tea miRNAs = 13
Predict target using miRU against Arabidopsis
G -18kcal/mol
Bulge sige 7
Mature miRNAs in the stem regions
Figure 1. An overview of different steps involved in miRNA and their target prediction.
Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)
Copyright © 2010 SciRes. AJPS
had less than seven mismatches with the opposite miRNA
sequence in the other arm, 4) no loop or break in miRNA
sequences, 5) predicted secondary structures had higher
negative energy MFEs (–18 kcal/mol), and iv) 40-70%
A + U contents.
2.3. Prediction of Targets
As for tea, since only few gene sequences available, we
used Arabidopsis as a reference system for finding the
targets of the candidate miRNAs. The predicted tea
miRNAs were used as query against the Arabidopsis
thaliana DFCI gene index (AGI) release 13 using miRU
( following the
criteria as 1) maximum expectation value 3; 2) multiplic-
ity of target sites 2; 3) range of central mismatch for
translational inhibition 9-11 nucleotide; 4) maximum
mismatches at the complementary site 4 without any
2.4. Phylogenetic Analysis
Due to the conserved nature of small RNAs, orthologue
discovery can be done through bioinformatics analysis.
We analysed tea small RNA conservation with their
orthologues. A homology search of candidate tea miRNAs
was done against all plant miRNAs using NCBI stand-
alone BLAST [23] allowing maximum of 3 mismatches
and e-value <0.001. The corresponding precursor se-
quences of homolog small RNA’s were identified and
collected (Appendix). The collected sequences of diverse
plant species were aligned with homolog tea miRNA
using Clustal W [25].
A query of tea small RNAs against known miRNA
families (miRBase, release 15,
allowed us to identify 3 previously reported large fami-
lies. The precursor sequences of three known family
members were selected along with respective precursor
sequences of tea (Appendix). Then, the maximum likely-
hood trees were constructed for each family based on
Tamura-Nei model [26] with default bootstrap values
using MEGA 4.0 [27] to illustrate the evolutionary rela-
tionships among the members of the family.
2.5. Nomenclature of miRNAs
The predicted miRNAs were named in accordance with
miRBase [28]. The mature sequences are designated ‘miR’,
and the precursor hairpins are labeled as ‘mir’ with the
prefix ‘csi’ for C. sinensis, ‘cas’ for C. assamica and
‘cja’ for C. japonica. In the cases where distinct precur-
sor sequences have identical miRNAs with different
mismatch pattern, they were named as csi-mir-1-a and
3. Results
3.1. Prediction of miRNAs
A total of 14,819 sequences containing 2,023 full length
nucleotides and 12,796 ESTs were obtained from Gen-
Bank. Out of these, 22 sequences had less than five mis-
matches with previously known plant miRNAs. After
carefully evaluating the hairpin structures using the crite-
ria mentioned in the method, 13 small RNAs were finally
identified from different species of Camellia namely
sinensis, japonica and assamica. Details of the predicted
miRNAs such as source sequences, location in the source
sequences, length of precursor sequences and their
minimum folding free energies and A+ U content are
tabulated below (Table 1). A total of 9 miRNAs were
Table 1. Details of the predicted miRNAs in tea.
New MiRNAs NS Gene ID Strand SP EPMEMature MiRNAs E Value P L A + U
(%) MFE
csi-miR 408 EST 206583693 3’ 137 11718/21CUGCACUGCCUCUUCCCUGAG 0.001 336 45.24-120.1
csi-miR1171 EST 171355265 5’ 286 30822/23UGGAGUGGAGUGAAGUGGAGUGG 3E-04 181 56.98-45.97
csi-miR414a EST 206583641 3’ 757 63718/21UCUUCCUCAUCAUCAUCUUCU 0.001 663 57.32-63.18
csi-miR414d EST 284026209 3’ 186 16620/21UCAUCGUCAUCGUCAUCAUCU 0.004 193 61.14-37.72
csi-miR414f EST 212378632 5’ 122 14220/21UCAUCAUCAUCAUCAUCUUCA 6E-05 68 57.35-18.5
cas-miR1122 FL 214011104 5’ 214 23720/24UACUCCCUCCGUCCCAAAAUAAUG 6E-05 294 69.83-91.23
csi-miR414g EST 51453040 3’ 474 45418/21CCUUCCUCAUCAUCAUCGUCC 0.001 70 45.71-25.2
csi-miRf10132-akr EST 51453383 3’ 58 3425/25GCGAGCUUCUCGAAGAUGUCGUUGA 9E-08 200 49.00-69.5
cja-miR2910 FL 1777723 5’ 1262 128221/21UAGUUGGUGGAGCGAUUUGUC 1E-05 301 49.83-91.0
csi-miR2914 FL 34787361 5’ 345 36722/23UAUGGUGGUGACGGGUGACGGAG 5E-06 65 49.23-20.9
cas-miRf10185-akr EST 221071827 3’ 232 21217/21GAAAGGGGAAAACAUUGUAGC 0.004 139 48.92-51.1
cas-miR11590-akr EST 212379609 3’ 113 9417/20UUUUGGUGUGCCUUCAACCU 0.003 75 53.33-23.8
csi-miR414h EST 295345415 3’ 79 5817/21UCAUCCUCAUCAUCGUCAGAA 0.004 644 55.36-86.83
NS = Nucleotide Source, FL = Full-Length, SP = Start Point, EP = End Point, ME = Match Extent, PL = Pre-miRNA Length, MEF = Minimal Free Energy
Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)
Copyright © 2010 SciRes. AJPS
predicted from ESTs whereas 4 were from full length
nucleotide sequences. Five of them were located in the
direct strand and the rest were in indirect strand. The
newly identified precursor miRNAs have minimum
folding free energies (mfe) ranging from –186.83 to
–18.5 kcal/mol, with an average of about –72.69
kcal/mol and the A + U content were ranges from 45.24
to 69.83% with an average of 53.79%. The length of the
precursors ranges from 65 to 663 nt with an average of
248 nt and mature sequences ranges from 20 to 25 nt.
The newly predicted two tea miRNA (cja-miR2910,
csi-miRf10132-akr) sequences were perfectly (100%)
matched with the corresponding homologues of populus
and rice, whereas the remaining 11 mature miRNA se-
quences differ by 1 to 4 nucleotides from their homo-
logues. All the mature miRNAs were found in the stem
portion of the hairpin structures (Figure 2) containing
less than 7 mismatches in the other arm without break or
loop inside the se- quences. It was found that tea miRNA
(csi-miR408) has been conserved with diverse plant spe-
cies (Figure 3) from monocotyledonous plants such as
rice, maize to dicotyledonous plants such as populous.
Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)
Copyright © 2010 SciRes. AJPS
Figure 2. Predicted hairpin secondary structures of pre-miRNAs. MiRNAs are highlighted (red color) in the stem portion.
Figure 3. Conservation of tea miRNA (csi-miR408) with diverse plant species. Conserved portion is highlighted. Abbreviated
names are given in full in appendix.
Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)
Copyright © 2010 SciRes. AJPS
3.2. Phylogenetic Analysis
The newly identified tea miRNAs belong to 9 miRNA
families including three known independent large miRNA
families (mir 408, mir414 and mir1122). There are one
tea miRNA namely csi-miR 408 and cas-miR1122 in
each family of mir-408 and mir-1122, respectively.
However, five members of family mir408 were found in
tea (csi-miR414a, csi-miR414d, csi-miR414f, csi-miR414g,
and csi-miR414h). The comparison of the predicted
miRNA precursor sequences with other members in the
same family showed that most members could be found
to have a high degree of sequence similarity with others.
The phylogenetic trees among the members of each fam-
ily illustrated the evolutionary relationships of tea
miRNAs (Figure 4).
3.3. Target Prediction
A total of 37 potential targets were identified for the 7
predicted miRNA families which include 11 miRNAs
based on their perfect or nearly perfect complementarity
with their target sequences in Arabidopsis (Table 2,
Figure 5). For all the miRNAs, single binding site was
found in the targets without any gaps in the complement-
tary region and expectation value ranges from 0 to 3.
These potential miRNA targets were belonged to a num-
ber of gene families that involved in different biological
functions such as regulation of cell cycle, metal ion
transportation, starch metabolic processes etc. There
were 8% of genes encoding transcription factors, 30% of
Figure 4. Phylogenetic relationships among the miRNA
family members of (a) miRNA414 (b) miRNA1122 (c)
genes encoding different enzymes and 14% of genes en-
coding transporters as well as 48% of genes encoding
various proteins of physiological and metabolic proc-
esses (Table 2). The miRNA family ‘miR414’ showed
the highest 30 numbers of independent target genes fol-
lowed by ‘miR408’ family with 2 numbers of target
genes. The rest miRNA families were with single target
genes in Arabidopsis (Table 2). The ‘miR1171’ and
‘miR1122’ miRNA family members did not bind to any
target sequences within our filtration criteria.
4. Discussion
With the availability of sequence resources in public da-
tabases, computer based miRNA identification methods
have been focused more and more in the recent years due
to its advantages of low cost and high efficiency. Se-
quence and structure homologies are the main theory
behind the computer-based approach for miRNAs pre-
diction. At present, four kinds of databases namely ge-
nome, GSS, EST and nucleotide are mainly used for
plant miRNA mining. Considering the unavailability of
genome and genomic survey sequences of tea, both EST
and nucleotide databases were mined for miRNA identi-
fication. The number and sorts of miRNAs predicted in
tea supported the fact that software-based approach is
feasible and effective [3,10,14].
The idenfied new miRNAs were belonged to 9 fami-
lies where miR414 family has 5 members and the rests
have single member in each. This familial distribution of
miRNAs was also observed in Arabidopsis, rice and maize
Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)
Copyright © 2010 SciRes. AJPS
Table 2. Potential target genes of the identified miRNA families.
sites Targeted proteins Targets involved in EV* Gene IDs
miR408 1 Cyclin-dependent protein kinase regulation of cell cycle 3.0 TC284764
1 Copper ion binding protein metal ion transport 3.0 TC300377
miR414 1 RAN GTPase activating protein 2 cytokinesis 1.0 TC283936
1 50S ribosomal protein L21 translation process 1.0 TC290468
1 26S proteasome AAA-ATPase subunit RPT5a proteasomal protein catabolic process 1.0 TC304323
1 SEC12p-like transporter ER to golgi vesicle mediated transpor 1.5 NP225634
1 Nucleotide binding protein nucleotide binding 2.0 TC280880
1 MYB transcription factor regulation of circadian rythm 2.0 TC283178
1 Aldose 1-epimerase carbohydrate metabolic process 0.5 TC285263
1 Phosphatase 2C-like protein protein amino acid dephosphorylation 0.5 TC285483
1 Phosphatidylinositol phosphatase inositol or phosphatidylinositol activity 0.5 TC312518
1 Starch branching enzyme class II starch metabolic process 1.0 AA586097
1 Reproductive meristem protein 1 regulation of transcription 1.0 TC284340
1 Calcium ion binding protein calcium ion binding 1.0 TC299015
1 Emb protein RNA processing 1.0 TC294192
1 Zinc ion binding protein regulation of transcription 1.0 TC299076
1 Calmodulin-4 calcium ion binding 1.5 TC294389
1 Translation initiation factor 3 subunit 8 translation initiation 0 TC280751
1 SMC3 protein chromosome segregation process 0 TC281006
1 MLO-like protein 3 cell death 0 TC293173
1 Ubiquitin conjugating enzyme proteolysis 0 TC299050
1 Ubiquitin conjugating enzyme ubiquitin dependant protein catabolic process 0.5 CA781750
1 Zinc finger protein regulation of transcription 0.5 NP030706
1 Plastid protein protein targetting to chloroplast 0.5 TC290688
1 Ubiquitin thiolesterase ubiquitin dependant protein catabolic process 1.5,
1.5 TC305774
1 Methionyl-tRNA synthetase methionyl-tRNA aminoacylation 1.5 TC282196
1 Metal ion binding protein metal ion binding 1.5 TC305801
1 Sfc4 protein xylem or phloem pattern formation 2.0 TC280894
1 Transcription factor regulation of transcription 2.0,
1 Synaptosomal-associated protein SNAP25 vesicle mediated transport 2.5 TC293303
1 ATP binding protein amino acid phosphorylation 2.5 TC299397
1 ADP-ribosylation factor-like protein intracellular protein transport 3.0 TC289959
miRf10132 1 Histone H2B like protein nucleosome assembly 1.5,
miR2910 1 Extracellular matrix structural constituent matrix organisation 0 TC310823,
miR2914 1 Glutamate semialdehyde dehydrogenase glutamate metabolism 2.0 TC287905
miRf10185 1 Carboxylic ester hydrolase hydrolase activity 3.0,
miR11590 1 FRIGIDA protein regulation of flower development 2.0 TC309547
*EV=Expectation value
Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)
Copyright © 2010 SciRes. AJPS
Figure 5. Predicted miRNA targets (black in colour) and their complementary sites with miRNAs (red in colour).
[29]. This may be an indicative of dominant nature of
miR414 family in miRNA-mediated gene regulation in
tea. The miRNAs were found diverse in nature such as
location of mature miRNA sequences and length of pre-
cursor sequences. The average length of precursor se-
quence was 248 nucleotides; however a majority of them
(62%) have 65-200 nucleotides. This finding is similar to
other plants where the length of precursors varied in con-
trast to consistent miRNA length of animal miRNAs
(70-80 nt) [30,31]. In tea miRNAs, diversity was also
observed within the members of same family which was
also found in maize [1]. The identified precursor
miRNAs were fold into hairpin secondary structures us-
ing minimum free energies, with an average –72.69
kcal/mol which was lower than the values of Arabidopsis
thaliana precursor miRNAs and much lower than the
folding free energies of tRNA (–27.5 kcal /mol) and
rRNA (–33 kcal/ mol) [32].
Out of 13 newly identified miRNAs, ten were from
ESTs. There are several reports on miRNA identification
from ESTs in various plant species [1,33]. The source
sequences of miRNAs show a link between miRNAs and
their tissues, organs, developmental stages or expression
to which it belongs. On that basis, it was recognized that
csi-miR414f, cas-miRf10185 and cas-miR11590-akr might
be expressed in root and the rests were in leaf tissues.
Moreover, csi-miR1171 and csi-miR414d were found in
leaf tissue under the stress of winter dormancy and pest
infestation, respectively. Three miRNAs namely cas-
miR1122, cja-miR2910 and csi-miR2914 were identified
from the full length nucleotides of RNA polymerase
second largest subunit (intron 23) and 18S ribosomal
subunit, respectively. Plant miRNAs are highly con-
served among distantly related plant species, both in
terms of primary and mature miRNAs [4]. This finding is
also supported by our results, the identified miRNA was
Computational Identification of Conserved microRNAs and Their Targets in Tea (Camellia sinensis)
Copyright © 2010 SciRes. AJPS
found conserved in diverse plant species from mono-
cotyledonous to dicotyledonous plants. These results
suggested that different miRNAs might have evolved at
different rates not only within the same plant species, but
also in different ones.
The miRNA target gene identification is an important
step for understanding the role of miRNAs in gene regu-
latory networks. Our prediction of target genes for the tea
miRNAs revealed that more than one gene was regulated
by individual miRNA. This result was similar to the re-
cent findings in other plant species [1,10] which sug-
gested that miRNA research should be focused on net-
works rather than individual connections between miRNA
and strongly predicted targets. MiRNAs may directly
target transcription factors which affect plant growth and
development, and also specific genes which control me-
tabolism [4]. In this study, we identified a total of 37
potential targets for the 7 identified miRNA families in
tea. The identified target genes appeared to be associated
with diverse biological functions. There were genes en-
coding transcription factors such as MYB, translation
intiation factor such as TIF3, important proteosome de-
grading pathway enzyme such as ubiquitin conjugating
enzyme, different ion transporters such as copper ion
binding protein, carbohydrate metabolism related enzyme
such as aldose 1-epimerase, glutamate metabolism related
enzymes such Glutamate semialdehyde dehydrogenase,
important protein for nucleosome assembly such as his-
tone as well as ribosomal proteins. In an earlier report, it
was found as 20% transcription factors and 53% proteins
related to diverse physiological processes, however their
investigation was limited to only four miRNAs [34].
Overall, these findings made us clear that tea miRNAs
targeted both trancription factors as well as specific
This findings of miRNAs in tea will pave the way for
understanding the function and processing of tea small
RNAs in future. Moreover, it shows a path for the pre-
diction and analysis of miRNAs to those species whose
genomes are not available through bioinformatics tools.
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Abbreviations used: BLAST, basic local alignment
search tool; dbEST, database of expressed sequence tags;
ESTs, expressed sequence tags; mRNA, messenger RNA;
miRNA, microRNA; mfe, minimum free energy; nt, nu-
cleotide; NCBI, national center for biotechnological in-