m140002 (PDF)

Biojournal of Science and Technology
Research Article
In silico miRNA Target Identification within the Human
Peroxisome Proliferator -Activated
Activated Receptor Gamma (PPARG)
Gene

Sudip Paul*, Moumoni Saha, Kazi Saiful Islam, Md. Yeashin Gazi, Sohel Ahmed
Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
*Corresponding author
Sudip Paul, Dept. of Biochemistry and Molecular Biology,
Jahangirnagar University, Savar, Dhaka 1342,
Bangladesh. Mob: 01674389745.
e-mail: sudippaul.bcmb@gmail.com

Published: 25-09-2014
Biojournal of Science and Technology Vol.1:2014
Academic Editor: Dr. Mohammad Nazmul Ahsan

Received: 17-07-2014
2014
Accepted: 13-08-2014
2014
Article no: m140002

This is an Open Access article distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0
http://creativecommons.org/licenses/by/4.0 ), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

Abstract
MicroRNAs (miRNAs),
), an abundant class of 21
21-25 nucleotides long non-coding
coding RNAs, regulate
eukaryotic gene expression and therefore implicated in a wide range of biological processes. The miRNAmiRNA
related genetic alterations are possibly more implicated in human diseases than currently appreciated.
miRNA target prediction using bioinformatics tools is often the first line approach in studying gene
regulation. Such predictions will help in setting search priorities for experimental validation of gene
controlling mechanisms. But finding a functional miRNA target in the human genome yet remains a
challenging task. In the present study, miRNA target sites within the complete sequences (5′
(5 UTR, CDS
and 3′ UTR) of human PPARG gene were investigated using miRwalk database. We found 26,
26 52 and 85
different miRNA target sites within the 55′ UTR, CDS and 3′ UTR regions of the gene, respectively. This
computational approach will subsequently allow better in vitro confirmation of the miRNA regulatory
networks in cellular systems.

Keywords: microRNA, In silico
silico, target site, PPARG, miRWalk

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Vol:1, 2014

INTRODUCTION

(Srinivasan et al 2013). Therefore, microRNAs

MicroRNAs (miRNAs) are a broad class of

displaying deregulated expression in the context of

naturally occurring small non-coding RNAs of

specific diseases are of particular interest as

about 21-25 nucleotides in length and found in

therapeutic targets especially if they can be shown

plants, animals and some viruses. The main

to coordinate such disease networks.

functions of miRNAs are to down-regulate gene
expression in translational repression, cleavage of

Peroxisome

proliferators-activated

messenger RNA (mRNA) and in a variety of other

gamma (PPARγ or PPARG) encoded by the

biological processes. Each miRNA is partially or

PPARG gene in humans belongs to the nuclear

completely complementary to one or more mRNAs

hormone receptor superfamily of ligand-activated

(Friedman et al. 2009, Landgraf et al. 2007).

transcription factors and originally has been

Transcription of miRNAs occurs through RNA

characterized to be important for adipogenesis and

polymerase II9 and subsequent processing is

glucose metabolism. There are two isoforms

mediated by the nuclear ribonuclease III (RNase

described (PPARG 1 and -2) (Vidal-Puig A. J. et al.

III) enzyme Drosha to form precursor miRNAs

1997). PPARG has been associated with various

(70–100 nucleotides). Following transportation to

diseases including obesity, diabetes mellitus,

the cytoplasm by exportin 5, a further cleavage

atherosclerosis, and cancer. PPARG agonists have

occurs via another RNase III enzyme, Dicer, to

been

form the mature miRNA (He and Hannon 2004,

of hyperlipidaemia and hyperglycemia (Li et al.

Zeng and Cullen 2006).

2008).

used
PPARG

in
is

the

important

receptor

treatment
to

shape

an

anti-inflammatory macrophage phenotype and
miRNAs

modulate

both

physiological

and

appears crucial for dampening inflammation

pathological pathways by post-transcriptionally

(Rosen et al. 1999). miRNAs have been reported to

inhibiting the expression of a plethora of target

destabilize PPARG mRNA which can lead to

genes. miRNAs deregulate gene expression mostly

impaired PPARG abundance (Schoonjans et al.

by imperfect binding to complementary sites

1996, Vidal-Puig A. et al. 1996). Therefore,

within transcript sequences and suppresses their

miRNA target site identification within the PPARG

translation, stimulate their de-adenylation and

gene is quite important in studying PPARG gene

degradation or induce their cleavage (Bartel 2004,

regulation.

Perron and Provost 2008).
There are a number of miRNA target prediction
The decisive regulatory functions exhibited by the

algorithms exploiting different approaches have

miRNA are found to be associated with a wide

been recently developed, and many methods of

variety of human diseases such as cancer, heart

experimental validation have been premeditated.

diseases, metabolic disorders, neurodegenerative

However, it is difficult to predict miRNA targets

disorders etc. as reviewed by Srinivasan et al.

within the animal genomes due to its partial

@2014, GNP

Biojournal of Science and Technology

Pa g e |1

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Vol:1, 2014

complementation to their target mRNA (Martin et

several web-based or non web-based computer

al. 2007). For this shortcoming, the interactions of

software programs for predicting miRNAs and

miRNA with their mRNA counterparts are

their targets have been developed in order to

complex and poorly understood. In the study in

predict

silico based miRNA targets identification within

validation. Even though many computational

the human PPARG gene was performed.

methods for the identification of miRNA may have

targets

for

follow

up

experimental

their own limitations, there is no other option now

METHODS

other than to use computational methods for

The miRWalk, a comprehensive database of

miRNA predictions. The next step in miRNA

miRNA from human, mouse and rat was used to

research is to identify and experimentally validate

identify miRNA target sites within the human

their mRNA targets.

PPARG gene based on a comparison of identified

All computer-based miRNA target prediction

miRNA binding sites with the 8 established

programs are based on specific parameters where

miRNA-target prediction programs i.e. RNA22,

slight variation results for the same target input.

miRanda, miRDB, TargetScan, RNA- hybrid,

Such weakness of single in silico studies can be

PITA, PICTAR, and Diana-microT (Dweep et al.

partially compensated by predicting targets using

2011). The miRWalk algorithm identifies the

multiple

programs.

longest

dynamic

programming

consecutive

complementary

between

Scoring
(John

methods
et

al.

using
2004,

miRNA and gene sequences. miRWalk was used

Kiriakidou et al. 2004, Lewis et al. 2003) and a

for investigating predicted targets of microRNAs

complementarily-based strategy (Lewis et al. 2003,

in the complete sequences (5′ UTR, CDS and 3′

Rajewsky and Socci 2004) are generally preferred

UTR) of PPARG gene in the human genome.

to rank the prediction results. These approaches

Default parameters were used regarding minimum

have been quite successful for a few top ranked

seed length (7) and p value (0.05).

results. miRNAs targets calculated from multiple
prediction methods significantly improved target

RESULTS AND DISCUSSION

prediction accuracy. Therefore, 8 key programs

Because of the several limitations associated with

were used in the present study to optimize our

genetic screening and experimental approaches for

search and to unravel miRNA target sequences of

discovering founding members of miRNAs such as

the PPARG gene cluster with high accuracy.

low efficiency, time consuming and high cost,
Table 1. Predicted miRNA sequences within the 5′-untranslated region (5′-UTR) of human PPARG gene
miRNA
hsa-miR-181a-2*
hsa-miR-345
hsa-miR-181a-2*
@2014, GNP

Stem Loop ID
hsa-mir-181a-2
hsa-mir-345
hsa-mir-181a-2

Seed Length

Start

Position

End

10
9
9

120
75
119

1
2
2

111
67
111

Biojournal of Science and Technology

P value
0.0003
0.0010
0.0010
Pa g e |2

ISSN 2410-9754

Vol:1, 2014

hsa-miR-607
hsa-mir-607
hsa-miR-423-3p
hsa-mir-423
hsa-miR-922
hsa-mir-922
hsa-miR-1226
hsa-mir-1226
hsa-miR-345
hsa-mir-345
hsa-miR-1226
hsa-mir-1226
hsa-miR-1282
hsa-mir-1282
hsa-miR-298
hsa-mir-298
hsa-miR-192
hsa-mir-192
hsa-miR-423-3p
hsa-mir-423
hsa-miR-580
hsa-mir-580
hsa-miR-377*
hsa-mir-377
hsa-miR-624*
hsa-mir-624
hsa-miR-329
hsa-mir-329-1
hsa-miR-329
hsa-mir-329-2
hsa-miR-299-5p
hsa-mir-299
hsa-miR-634
hsa-mir-634
hsa-miR-522
hsa-mir-522
hsa-miR-548k
hsa-mir-548k
hsa-miR-1224-3p
hsa-mir-1224
hsa-miR-1300
hsa-mir-1300
hsa-miR-559
hsa-mir-559
hsa-miR-362-3p
hsa-mir-362
miRNA: microRNA; hsa: Homo sapiens

8
8
8
8
8
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7

205
95
149
153
264
152
256
181
116
94
252
145
32
20
20
224
151
247
34
15
252
35
20

2
1
2
1
1
2
1
1
1
2
1
1
2
2
2
1
2
1
2
2
1
1
2

198
88
142
146
257
146
250
175
110
88
246
139
26
14
14
218
145
241
28
9
246
29
14

0.0042
0.0042
0.0042
0.0042
0.0042
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166
0.0166

Table 2. Predicted miRNA sequences within the coding sequence (CDS) of human PPARG gene
miRNA
hsa-miR-367
hsa-miR-1224-5p
hsa-miR-101
hsa-miR-371-5p
hsa-miR-654-5p
hsa-miR-25
hsa-miR-101
hsa-miR-545
hsa-miR-1224-5p
hsa-miR-923
hsa-miR-92a
hsa-miR-92a
hsa-let-7c*
hsa-miR-142-5p
hsa-miR-181c
hsa-miR-1234
@2014, GNP

Stem Loop ID
hsa-mir-367
hsa-mir-1224
hsa-mir-101-1
hsa-mir-371
hsa-mir-654
hsa-mir-25
hsa-mir-101-2
hsa-mir-545
hsa-mir-1224
hsa-mir-923
hsa-mir-92a-1
hsa-mir-92a-2
hsa-let-7c
hsa-mir-142
hsa-mir-181c
hsa-mir-1234

Seed Length
10
10
9
9
9
9
9
9
9
9
9
9
8
8
8
8

Start

Position

507
1562
769
1382
314
507
769
1478
1561
904
507
507
1224
1366
607
840

2
1
2
1
1
2
2
1
2
1
2
2
2
1
2
1

Biojournal of Science and Technology

End
498
1553
761
1374
306
499
761
1470
1553
896
499
499
1217
1359
600
833

P value
0.0014
0.0014
0.0055
0.0055
0.0055
0.0055
0.0055
0.0055
0.0055
0.0055
0.0055
0.0055
0.0216
0.0216
0.0216
0.0216

Pa g e |3

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Vol:1, 2014

hsa-miR-152
hsa-mir-152
hsa-miR-513b
hsa-mir-513b
hsa-miR-1243
hsa-mir-1243
hsa-miR-199a-3p
hsa-mir-199a-2
hsa-miR-578
hsa-mir-578
hsa-miR-1205
hsa-mir-1205
hsa-miR-206
hsa-mir-206
hsa-miR-1825
hsa-mir-1825
hsa-miR-199a-3p
hsa-mir-199a-1
hsa-miR-371-5p
hsa-mir-371
hsa-miR-541
hsa-mir-541
hsa-miR-199b-3p
hsa-mir-199b
hsa-miR-1207-3p
hsa-mir-1207
hsa-miR-1
hsa-mir-1-1
hsa-miR-1270
hsa-mir-1270
hsa-miR-181a
hsa-mir-181a-1
hsa-miR-1207-3p
hsa-mir-1207
hsa-miR-654-5p
hsa-mir-654
hsa-miR-885-5p
hsa-mir-885
hsa-miR-1
hsa-mir-1-2
hsa-miR-629*
hsa-mir-629
hsa-miR-328
hsa-mir-328
hsa-miR-33b
hsa-mir-33b
hsa-miR-545
hsa-mir-545
hsa-miR-148b
hsa-mir-148b
hsa-miR-589
hsa-mir-589
hsa-miR-545
hsa-mir-545
hsa-miR-453
hsa-mir-453
hsa-miR-33a
hsa-mir-33a
hsa-miR-635
hsa-mir-635
hsa-miR-181a
hsa-mir-181a-2
hsa-miR-92b
hsa-mir-92b
hsa-miR-923
hsa-mir-923
hsa-miR-130a*
hsa-mir-130a
hsa-miR-592
hsa-mir-592
hsa-miR-485-3p
hsa-mir-485
miRNA: microRNA; hsa: Homo sapiens

8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8

1405
661
456
393
446
1087
436
1407
393
1381
314
393
1538
436
870
607
887
313
351
436
1051
1308
1403
1477
1405
1295
1388
1512
1403
1376
607
507
903
1485
292
934

2
1
2
1
2
2
1
1
1
2
1
1
1
1
1
2
1
2
1
1
2
2
1
2
2
1
2
1
1
1
2
2
2
1
2
1

1398
654
449
386
439
1080
429
1400
386
1374
307
386
1531
429
863
600
880
306
344
429
1044
1301
1396
1470
1398
1288
1381
1505
1396
1369
600
500
896
1478
285
927

0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216
0.0216

Table 3. Predicted miRNA sequences within the 3′-untranslated region (3′-UTR) of human PPARG gene
miRNA

Stem Loop ID

Seed Length

hsa-miR-559
hsa-miR-511
hsa-miR-548d-5p

hsa-mir-559
hsa-mir-511-1
hsa-mir-548d-2

9
8
8

@2014, GNP

Start
1879
1863
1880

Position
2
1
1

Biojournal of Science and Technology

End
1871
1856
1873

P value
0.0008
0.0032
0.0032
Pa g e |4

ISSN 2410-9754
hsa-miR-24
hsa-miR-548i
hsa-miR-511
hsa-miR-548c-5p
hsa-miR-513a-3p
hsa-miR-548n
hsa-miR-24
hsa-miR-449a
hsa-miR-548i
hsa-miR-511
hsa-miR-545*
hsa-miR-548h
hsa-miR-548b-5p
hsa-miR-548j
hsa-miR-27b
hsa-miR-548i
hsa-miR-27a
hsa-miR-511
hsa-miR-34a
hsa-miR-548h
hsa-miR-338-5p
hsa-miR-548i
hsa-miR-548h
hsa-miR-548d-5p
hsa-miR-454
hsa-miR-548a-5p
hsa-miR-513a-3p
hsa-miR-548h
hsa-miR-548a-5p
hsa-miR-513a-3p
hsa-miR-1243
hsa-miR-576-5p
hsa-miR-548h
hsa-miR-511
hsa-miR-513a-5p
hsa-miR-548d-5p
hsa-miR-891b
hsa-miR-24
hsa-miR-449b
hsa-miR-548i
hsa-miR-511
hsa-miR-548c-5p
hsa-miR-7
hsa-miR-513a-3p
hsa-miR-889
@2014, GNP

Vol:1, 2014
hsa-mir-24-1
hsa-mir-548i-1
hsa-mir-511-1
hsa-mir-548c
hsa-mir-513a-2
hsa-mir-548n
hsa-mir-24-2
hsa-mir-449a
hsa-mir-548i-2
hsa-mir-511-2
hsa-mir-545
hsa-mir-548h-1
hsa-mir-548b
hsa-mir-548j
hsa-mir-27b
hsa-mir-548i-3
hsa-mir-27a
hsa-mir-511-2
hsa-mir-34a
hsa-mir-548h-2
hsa-mir-338
hsa-mir-548i-4
hsa-mir-548h-3
hsa-mir-548d-1
hsa-mir-454
hsa-mir-548a-3
hsa-mir-513a-1
hsa-mir-548h-4
hsa-mir-548a-3
hsa-mir-513a-1
hsa-mir-1243
hsa-mir-576
hsa-mir-548h-4
hsa-mir-511-1
hsa-mir-513a-2
hsa-mir-548d-2
hsa-mir-891b
hsa-mir-24-1
hsa-mir-449b
hsa-mir-548i-1
hsa-mir-511-1
hsa-mir-548c
hsa-mir-7-1
hsa-mir-513a-2
hsa-mir-889

8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7

1725
1880
1863
1880
1790
1880
1725
1731
1880
1863
1793
1880
1880
1880
1797
1880
1797
1863
1731
1880
1852
1880
1880
1880
1757
1880
1790
1880
1879
1789
1751
1828
1879
1862
1797
1879
1754
1724
1730
1879
1862
1879
1748
1789
1888

1
1
1
1
1
2
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
2
2
1
2
1
2
2
2
2
2
1
2
1

Biojournal of Science and Technology

1718
1873
1856
1873
1783
1873
1718
1724
1873
1856
1786
1873
1873
1873
1790
1873
1790
1856
1724
1873
1845
1873
1873
1873
1750
1873
1783
1873
1873
1783
1745
1822
1873
1856
1791
1873
1748
1718
1724
1873
1856
1873
1742
1783
1882

0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0032
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
Pa g e |5

ISSN 2410-9754
hsa-miR-586
hsa-mir-586
hsa-miR-24
hsa-mir-24-2
hsa-miR-128
hsa-mir-128-2
hsa-miR-7
hsa-mir-7-2
hsa-miR-340
hsa-mir-340
hsa-miR-449a
hsa-mir-449a
hsa-miR-548i
hsa-mir-548i-2
hsa-miR-511
hsa-mir-511-2
hsa-miR-7
hsa-mir-7-3
hsa-miR-548h
hsa-mir-548h-1
hsa-miR-656
hsa-mir-656
hsa-miR-301b
hsa-mir-301b
hsa-miR-548b-5p
hsa-mir-548b
hsa-miR-548j
hsa-mir-548j
hsa-miR-34c-5p
hsa-mir-34c
hsa-miR-27b
hsa-mir-27b
hsa-miR-548i
hsa-mir-548i-3
hsa-miR-27a
hsa-mir-27a
hsa-miR-511
hsa-mir-511-2
hsa-miR-548k
hsa-mir-548k
hsa-miR-34a
hsa-mir-34a
hsa-miR-548h
hsa-mir-548h-2
hsa-miR-128
hsa-mir-128-1
hsa-miR-590-3p
hsa-mir-590
hsa-miR-301a
hsa-mir-301a
hsa-miR-338-5p
hsa-mir-338
hsa-miR-409-3p
hsa-mir-409
hsa-miR-548i
hsa-mir-548i-4
hsa-miR-513a-5p
hsa-mir-513a-1
hsa-miR-130b
hsa-mir-130b
hsa-miR-335*
hsa-mir-335
hsa-miR-548h
hsa-mir-548h-3
hsa-miR-130a
hsa-mir-130a
hsa-miR-1279
hsa-mir-1279
hsa-miR-548l
hsa-mir-548l
hsa-miR-548d-5p
hsa-mir-548d-1
hsa-miR-454
hsa-mir-454
miRNA: microRNA; hsa: Homo sapiens

Vol:1, 2014
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7

1847
1724
1796
1748
1857
1730
1879
1862
1748
1879
1886
1756
1879
1879
1730
1796
1879
1796
1862
1880
1730
1879
1796
1894
1756
1851
1736
1879
1797
1756
1800
1879
1756
1832
1880
1879
1756

1
2
1
1
1
2
2
2
1
2
1
2
2
2
2
2
2
2
2
1
2
2
1
1
2
2
2
2
1
2
1
2
2
1
1
2
2

1841
1718
1790
1742
1851
1724
1873
1856
1742
1873
1880
1750
1873
1873
1724
1790
1873
1790
1856
1874
1724
1873
1790
1888
1750
1845
1730
1873
1791
1750
1794
1873
1750
1826
1874
1873
1750

0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128
0.0128

Using miRWalk, number of potential target sites

region) of PPARG in the human genome. The

for miRNAs were identified within the sequences

functional regions of the PPARG gene cluster as

of 5′-UTR (5′-untranslated region), CDS (coding

possible sites for miRNA targeting were further

DNA sequence) and 3′ UTR (3′- untranslated

analyzed. A unique target pattern was pointed

@2014, GNP

Biojournal of Science and Technology

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ISSN 2410-9754

Vol:1, 2014

within the genomic sequences representing the 5′

regulation

UTR, CDS and 3′ UTR of PPARG gene. Specific

potentials.

and

their

expected

therapeutic

sequences within 5′ UTR, CDS and 3′ UTR of
human PPARG gene along with seed sequences, its

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