Biojournal of Science and Technology (BJST)

A Scholarly Journal for Biological Publications

Biojournal of Science and Technology
Volume 1, ISSN:2410-9754, Article ID: m140002

Research Article

In silico miRNA Target Identification within the Human Peroxisome Proliferator -Activated Receptor Gamma (PPARG) Gene

Sudip Paul*, Moumoni Saha, Kazi Saiful Islam, Md. Yeashin Gazi, Sohel Ahmed

Jahangirnagar University, Savar, Dhaka 1342, Bangladesh

Date of Acceptance: Wednesday, August 13, 2014
Date of Published: Thursday, September 25, 2014

Address corresponds to
Sudip Paul, Dept. of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh. Mob: 01674389745. e-mail: sudippaul.bcmb@gmail.com

Acedemic Editor: Dr Mohammad Nazmul Ahsan

To cite this article
Sudip Paul*, Moumoni Saha, Kazi Saiful Islam, Md. Yeashin Gazi, Sohel Ahmed.In silico miRNA Target Identification within the Human Peroxisome Proliferator -Activated Receptor Gamma (PPARG) Gene.Biojournal of Science and Technology.Vol:1,2014

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ABSTRACT

MicroRNAs (miRNAs), an abundant class of 21-25 nucleotides long non-coding RNAs, regulate eukaryotic gene expression and therefore implicated in a wide range of biological processes. The miRNA- 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′ UTR, CDS and 3′ UTR) of human PPARG gene were investigated using miRwalk database. We found 26, 52 and 85 different miRNA target sites within the 5′ 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.

INTRODUCTION
MicroRNAs (miRNAs) are a broad class of naturally occurring small non-coding RNAs of about 21-25 nucleotides in length and found in plants, animals and some viruses. The main functions of miRNAs are to down-regulate gene expression in translational repression, cleavage of messenger RNA (mRNA) and in a variety of other biological processes. Each miRNA is partially or completely complementary to one or more mRNAs (Friedman et al. 2009, Landgraf et al. 2007). 
Transcription of miRNAs occurs through RNA polymerase II9 and subsequent processing is mediated by the nuclear ribonuclease III (RNase III) enzyme Drosha to form precursor miRNAs (70–100 nucleotides). Following transportation to the cytoplasm by exportin 5, a further cleavage occurs via another RNase III enzyme, Dicer, to form the mature miRNA (He and Hannon 2004, Zeng and Cullen 2006). 

miRNAs modulate both physiological and pathological pathways by post-transcriptionally inhibiting the expression of a plethora of target genes. miRNAs deregulate gene expression mostly by imperfect binding to complementary sites within transcript sequences and suppresses their translation, stimulate their de-adenylation and degradation or induce their cleavage (Bartel 2004, Perron and Provost 2008).

The decisive regulatory functions exhibited by the miRNA are found to be associated with a wide variety of human diseases such as cancer, heart diseases, metabolic disorders, neurodegenerative disorders etc. as reviewed by Srinivasan et al. (Srinivasan et al 2013). Therefore, microRNAs displaying deregulated expression in the context of specific diseases are of particular interest as therapeutic targets especially if they can be shown to coordinate such disease networks. 

Peroxisome proliferators-activated receptor gamma (PPARγ or PPARG) encoded by the PPARG gene in humans belongs to the nuclear hormone receptor superfamily of ligand-activated transcription factors and originally has been characterized to be important for adipogenesis and glucose metabolism. There are two isoforms described (PPARG 1 and -2) (Vidal-Puig A. J. et al. 1997). PPARG has been associated with various diseases including obesity, diabetes mellitus, atherosclerosis, and cancer. PPARG agonists have been used in the treatment of hyperlipidaemia and hyperglycemia (Li et al. 2008). PPARG is important to shape an anti-inflammatory macrophage phenotype and appears crucial for dampening inflammation (Rosen et al. 1999). miRNAs have been reported to destabilize PPARG mRNA which can lead to impaired PPARG abundance (Schoonjans et al. 1996, Vidal-Puig A. et al. 1996). Therefore, miRNA target site identification within the PPARG gene is quite important in studying PPARG gene regulation. 

There are a number of miRNA target prediction algorithms exploiting different approaches have been recently developed, and many methods of experimental validation have been premeditated. However, it is difficult to predict miRNA targets within the animal genomes due to its partial complementation to their target mRNA (Martin et al. 2007). For this shortcoming, the interactions of miRNA with their mRNA counterparts are complex and poorly understood. In the study in silico based miRNA targets identification within the human PPARG gene was performed. 

METHODS 
The miRWalk, a comprehensive database of miRNA from human, mouse and rat was used to identify miRNA target sites within the human PPARG gene based on a comparison of identified miRNA binding sites with the 8 established miRNA-target prediction programs i.e. RNA22, miRanda, miRDB, TargetScan, RNA- hybrid, PITA, PICTAR, and Diana-microT (Dweep et al. 2011). The miRWalk algorithm identifies the longest consecutive complementary between miRNA and gene sequences. miRWalk was used for investigating predicted targets of microRNAs in the complete sequences (5′ UTR, CDS and 3′ UTR) of PPARG gene in the human genome. Default parameters were used regarding minimum seed length (7) and p value (0.05). 

RESULTS AND DISCUSSION
Because of the several limitations associated with genetic screening and experimental approaches for discovering founding members of miRNAs such as low efficiency, time consuming and high cost, several web-based or non web-based computer software programs for predicting miRNAs and their targets have been developed in order to predict targets for follow up experimental validation. Even though many computational methods for the identification of miRNA may have their own limitations, there is no other option now other than to use computational methods for miRNA predictions. The next step in miRNA research is to identify and experimentally validate their mRNA targets. 
All computer-based miRNA target prediction programs are based on specific parameters where slight variation results for the same target input. Such weakness of single in silico studies can be partially compensated by predicting targets using multiple programs. Scoring methods using dynamic programming (John et al. 2004, Kiriakidou et al. 2004, Lewis et al. 2003) and a complementarily-based strategy (Lewis et al. 2003, Rajewsky and Socci 2004) are generally preferred to rank the prediction results. These approaches have been quite successful for a few top ranked results. miRNAs targets calculated from multiple prediction methods significantly improved target prediction accuracy. Therefore, 8 key programs were used in the present study to optimize our search and to unravel miRNA target sequences of the PPARG gene cluster with high accuracy. 

Table 1. Predicted miRNA sequences within the 5′-untranslated region (5′-UTR) of human PPARG gene

miRNA Stem Loop ID Seed Length Start Position End P value
hsa-miR-181a-2* hsa-mir-181a-2 10 120 1 111 0.0003
hsa-miR-345 hsa-mir-345 9 75 2 67 0.0010
hsa-miR-181a-2* hsa-mir-181a-2 9 119 2 111 0.0010
hsa-miR-607 hsa-mir-607 8 205 2 198 0.0042
hsa-miR-423-3p hsa-mir-423 8 95 1 88 0.0042
hsa-miR-922 hsa-mir-922 8 149 2 142 0.0042
hsa-miR-1226 hsa-mir-1226 8 153 1 146 0.0042
hsa-miR-345 hsa-mir-345 8 264 1 257 0.0042
hsa-miR-1226 hsa-mir-1226 7 152 2 146 0.0166
hsa-miR-1282 hsa-mir-1282 7 256 1 250 0.0166
hsa-miR-298 hsa-mir-298 7 181 1 175 0.0166
hsa-miR-192 hsa-mir-192 7 116 1 110 0.0166
hsa-miR-423-3p hsa-mir-423 7 94 2 88 0.0166
hsa-miR-580 hsa-mir-580 7 252 1 246 0.0166
hsa-miR-377* hsa-mir-377 7 145 1 139 0.0166
hsa-miR-624* hsa-mir-624 7 32 2 26 0.0166
hsa-miR-329 hsa-mir-329-1 7 20 2 14 0.0166
hsa-miR-329 hsa-mir-329-2 7 20 2 14 0.0166
hsa-miR-299-5p hsa-mir-299 7 224 1 218 0.0166
hsa-miR-634 hsa-mir-634 7 151 2 145 0.0166
hsa-miR-522 hsa-mir-522 7 247 1 241 0.0166
hsa-miR-548k hsa-mir-548k 7 34 2 28 0.0166
hsa-miR-1224-3p hsa-mir-1224 7 15 2 9 0.0166
hsa-miR-1300 hsa-mir-1300 7 252 1 246 0.0166
hsa-miR-559 hsa-mir-559 7 35 1 29 0.0166
hsa-miR-362-3p hsa-mir-362 7 20 2 14 0.0166

miRNA: microRNA; hsa: Homo sapiens

 

Table 2. Predicted miRNA sequences within the coding sequence (CDS) of human PPARG gene

miRNA Stem Loop ID Seed Length Start Position End P value
hsa-miR-367 hsa-mir-367 10 507 2 498 0.0014
hsa-miR-1224-5p hsa-mir-1224 10 1562 1 1553 0.0014
hsa-miR-101 hsa-mir-101-1 9 769 2 761 0.0055
hsa-miR-371-5p hsa-mir-371 9 1382 1 1374 0.0055
hsa-miR-654-5p hsa-mir-654 9 314 1 306 0.0055
hsa-miR-25 hsa-mir-25 9 507 2 499 0.0055
hsa-miR-101 hsa-mir-101-2 9 769 2 761 0.0055
hsa-miR-545 hsa-mir-545 9 1478 1 1470 0.0055
hsa-miR-1224-5p hsa-mir-1224 9 1561 2 1553 0.0055
hsa-miR-923 hsa-mir-923 9 904 1 896 0.0055
hsa-miR-92a hsa-mir-92a-1 9 507 2 499 0.0055
hsa-miR-92a hsa-mir-92a-2 9 507 2 499 0.0055
hsa-let-7c* hsa-let-7c 8 1224 2 1217 0.0216
hsa-miR-142-5p hsa-mir-142 8 1366 1 1359 0.0216
hsa-miR-181c hsa-mir-181c 8 607 2 600 0.0216
hsa-miR-1234 hsa-mir-1234 8 840 1 833 0.0216
hsa-miR-152 hsa-mir-152 8 1405 2 1398 0.0216
hsa-miR-513b hsa-mir-513b 8 661 1 654 0.0216
hsa-miR-1243 hsa-mir-1243 8 456 2 449 0.0216
hsa-miR-199a-3p hsa-mir-199a-2 8 393 1 386 0.0216
hsa-miR-578 hsa-mir-578 8 446 2 439 0.0216
hsa-miR-1205 hsa-mir-1205 8 1087 2 1080 0.0216
hsa-miR-206 hsa-mir-206 8 436 1 429 0.0216
hsa-miR-1825 hsa-mir-1825 8 1407 1 1400 0.0216
hsa-miR-199a-3p hsa-mir-199a-1 8 393 1 386 0.0216
hsa-miR-371-5p hsa-mir-371 8 1381 2 1374 0.0216
hsa-miR-541 hsa-mir-541 8 314 1 307 0.0216
hsa-miR-199b-3p hsa-mir-199b 8 393 1 386 0.0216
hsa-miR-1207-3p hsa-mir-1207 8 1538 1 1531 0.0216
hsa-miR-1 hsa-mir-1-1 8 436 1 429 0.0216
hsa-miR-1270 hsa-mir-1270 8 870 1 863 0.0216
hsa-miR-181a hsa-mir-181a-1 8 607 2 600 0.0216
hsa-miR-1207-3p hsa-mir-1207 8 887 1 880 0.0216
hsa-miR-654-5p hsa-mir-654 8 313 2 306 0.0216
hsa-miR-885-5p hsa-mir-885 8 351 1 344 0.0216
hsa-miR-1 hsa-mir-1-2 8 436 1 429 0.0216
hsa-miR-629* hsa-mir-629 8 1051 2 1044 0.0216
hsa-miR-328 hsa-mir-328 8 1308 2 1301 0.0216
hsa-miR-33b hsa-mir-33b 8 1403 1 1396 0.0216
hsa-miR-545 hsa-mir-545 8 1477 2 1470 0.0216
hsa-miR-148b hsa-mir-148b 8 1405 2 1398 0.0216
hsa-miR-589 hsa-mir-589 8 1295 1 1288 0.0216
hsa-miR-545 hsa-mir-545 8 1388 2 1381 0.0216
hsa-miR-453 hsa-mir-453 8 1512 1 1505 0.0216
hsa-miR-33a hsa-mir-33a 8 1403 1 1396 0.0216
hsa-miR-635 hsa-mir-635 8 1376 1 1369 0.0216
hsa-miR-181a hsa-mir-181a-2 8 607 2 600 0.0216
hsa-miR-92b hsa-mir-92b 8 507 2 500 0.0216
hsa-miR-923 hsa-mir-923 8 903 2 896 0.0216
hsa-miR-130a* hsa-mir-130a 8 1485 1 1478 0.0216
hsa-miR-592 hsa-mir-592 8 292 2 285 0.0216
hsa-miR-485-3p hsa-mir-485 8 934 1 927 0.0216

miRNA: microRNA; hsa: Homo sapiens

 

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

miRNA Stem Loop ID Seed Length Start Position End P value
hsa-miR-559 hsa-mir-559 9 1879 2 1871 0.0008
hsa-miR-511 hsa-mir-511-1 8 1863 1 1856 0.0032
hsa-miR-548d-5p hsa-mir-548d-2 8 1880 1 1873 0.0032
hsa-miR-24 hsa-mir-24-1 8 1725 1 1718 0.0032
hsa-miR-548i hsa-mir-548i-1 8 1880 1 1873 0.0032
hsa-miR-511 hsa-mir-511-1 8 1863 1 1856 0.0032
hsa-miR-548c-5p hsa-mir-548c 8 1880 1 1873 0.0032
hsa-miR-513a-3p hsa-mir-513a-2 8 1790 1 1783 0.0032
hsa-miR-548n hsa-mir-548n 8 1880 2 1873 0.0032
hsa-miR-24 hsa-mir-24-2 8 1725 1 1718 0.0032
hsa-miR-449a hsa-mir-449a 8 1731 1 1724 0.0032
hsa-miR-548i hsa-mir-548i-2 8 1880 1 1873 0.0032
hsa-miR-511 hsa-mir-511-2 8 1863 1 1856 0.0032
hsa-miR-545* hsa-mir-545 8 1793 2 1786 0.0032
hsa-miR-548h hsa-mir-548h-1 8 1880 1 1873 0.0032
hsa-miR-548b-5p hsa-mir-548b 8 1880 1 1873 0.0032
hsa-miR-548j hsa-mir-548j 8 1880 1 1873 0.0032
hsa-miR-27b hsa-mir-27b 8 1797 1 1790 0.0032
hsa-miR-548i hsa-mir-548i-3 8 1880 1 1873 0.0032
hsa-miR-27a hsa-mir-27a 8 1797 1 1790 0.0032
hsa-miR-511 hsa-mir-511-2 8 1863 1 1856 0.0032
hsa-miR-34a hsa-mir-34a 8 1731 1 1724 0.0032
hsa-miR-548h hsa-mir-548h-2 8 1880 1 1873 0.0032
hsa-miR-338-5p hsa-mir-338 8 1852 1 1845 0.0032
hsa-miR-548i hsa-mir-548i-4 8 1880 1 1873 0.0032
hsa-miR-548h hsa-mir-548h-3 8 1880 1 1873 0.0032
hsa-miR-548d-5p hsa-mir-548d-1 8 1880 1 1873 0.0032
hsa-miR-454 hsa-mir-454 8 1757 1 1750 0.0032
hsa-miR-548a-5p hsa-mir-548a-3 8 1880 1 1873 0.0032
hsa-miR-513a-3p hsa-mir-513a-1 8 1790 1 1783 0.0032
hsa-miR-548h hsa-mir-548h-4 8 1880 1 1873 0.0032
hsa-miR-548a-5p hsa-mir-548a-3 7 1879 2 1873 0.0128
hsa-miR-513a-3p hsa-mir-513a-1 7 1789 2 1783 0.0128
hsa-miR-1243 hsa-mir-1243 7 1751 1 1745 0.0128
hsa-miR-576-5p hsa-mir-576 7 1828 1 1822 0.0128
hsa-miR-548h hsa-mir-548h-4 7 1879 2 1873 0.0128
hsa-miR-511 hsa-mir-511-1 7 1862 2 1856 0.0128
hsa-miR-513a-5p hsa-mir-513a-2 7 1797 1 1791 0.0128
hsa-miR-548d-5p hsa-mir-548d-2 7 1879 2 1873 0.0128
hsa-miR-891b hsa-mir-891b 7 1754 1 1748 0.0128
hsa-miR-24 hsa-mir-24-1 7 1724 2 1718 0.0128
hsa-miR-449b hsa-mir-449b 7 1730 2 1724 0.0128
hsa-miR-548i hsa-mir-548i-1 7 1879 2 1873 0.0128
hsa-miR-511 hsa-mir-511-1 7 1862 2 1856 0.0128
hsa-miR-548c-5p hsa-mir-548c 7 1879 2 1873 0.0128
hsa-miR-7 hsa-mir-7-1 7 1748 1 1742 0.0128
hsa-miR-513a-3p hsa-mir-513a-2 7 1789 2 1783 0.0128
hsa-miR-889 hsa-mir-889 7 1888 1 1882 0.0128
hsa-miR-586 hsa-mir-586 7 1847 1 1841 0.0128
hsa-miR-24 hsa-mir-24-2 7 1724 2 1718 0.0128
hsa-miR-128 hsa-mir-128-2 7 1796 1 1790 0.0128
hsa-miR-7 hsa-mir-7-2 7 1748 1 1742 0.0128
hsa-miR-340 hsa-mir-340 7 1857 1 1851 0.0128
hsa-miR-449a hsa-mir-449a 7 1730 2 1724 0.0128
hsa-miR-548i hsa-mir-548i-2 7 1879 2 1873 0.0128
hsa-miR-511 hsa-mir-511-2 7 1862 2 1856 0.0128
hsa-miR-7 hsa-mir-7-3 7 1748 1 1742 0.0128
hsa-miR-548h hsa-mir-548h-1 7 1879 2 1873 0.0128
hsa-miR-656 hsa-mir-656 7 1886 1 1880 0.0128
hsa-miR-301b hsa-mir-301b 7 1756 2 1750 0.0128
hsa-miR-548b-5p hsa-mir-548b 7 1879 2 1873 0.0128
hsa-miR-548j hsa-mir-548j 7 1879 2 1873 0.0128
hsa-miR-34c-5p hsa-mir-34c 7 1730 2 1724 0.0128
hsa-miR-27b hsa-mir-27b 7 1796 2 1790 0.0128
hsa-miR-548i hsa-mir-548i-3 7 1879 2 1873 0.0128
hsa-miR-27a hsa-mir-27a 7 1796 2 1790 0.0128
hsa-miR-511 hsa-mir-511-2 7 1862 2 1856 0.0128
hsa-miR-548k hsa-mir-548k 7 1880 1 1874 0.0128
hsa-miR-34a hsa-mir-34a 7 1730 2 1724 0.0128
hsa-miR-548h hsa-mir-548h-2 7 1879 2 1873 0.0128
hsa-miR-128 hsa-mir-128-1 7 1796 1 1790 0.0128
hsa-miR-590-3p hsa-mir-590 7 1894 1 1888 0.0128
hsa-miR-301a hsa-mir-301a 7 1756 2 1750 0.0128
hsa-miR-338-5p hsa-mir-338 7 1851 2 1845 0.0128
hsa-miR-409-3p hsa-mir-409 7 1736 2 1730 0.0128
hsa-miR-548i hsa-mir-548i-4 7 1879 2 1873 0.0128
hsa-miR-513a-5p hsa-mir-513a-1 7 1797 1 1791 0.0128
hsa-miR-130b hsa-mir-130b 7 1756 2 1750 0.0128
hsa-miR-335* hsa-mir-335 7 1800 1 1794 0.0128
hsa-miR-548h hsa-mir-548h-3 7 1879 2 1873 0.0128
hsa-miR-130a hsa-mir-130a 7 1756 2 1750 0.0128
hsa-miR-1279 hsa-mir-1279 7 1832 1 1826 0.0128
hsa-miR-548l hsa-mir-548l 7 1880 1 1874 0.0128
hsa-miR-548d-5p hsa-mir-548d-1 7 1879 2 1873 0.0128
hsa-miR-454 hsa-mir-454 7 1756 2 1750 0.0128

miRNA: microRNA; hsa: Homo sapiens

 
Using miRWalk, number of potential target sites for miRNAs were identified within the sequences of 5′-UTR (5′-untranslated region), CDS (coding DNA sequence) and 3′ UTR (3′- untranslated region) of PPARG in the human genome. The functional regions of the PPARG gene cluster as possible sites for miRNA targeting were further analyzed. A unique target pattern was pointed within the genomic sequences representing the 5′ UTR, CDS and 3′ UTR of PPARG gene. Specific sequences within 5′ UTR, CDS and 3′ UTR of human PPARG gene along with seed sequences, its location and size respectively are shown in tables 1, 2 and 3. These experimental data show that the number of miRNA target sites ranges differently in different regions of PPARG. In the 5′ UTR of the screened gene, we found 29 different miRNA target sites with different p values. Among them, the target site for miRNA-181a-2 had the lowest p value (0.003), i.e. most significant value (Table 1). In case of CDS, we obtained 52 target sites, miRNA-367 being the most significant one (p value= 0.0014) (Table 2). Finally, 85 different miRNA target sites were identified within the 3′ UTR. We found miRNA-559 be the most significant one (p= 0.0080 amongst all within this region (Table 3). The findings would help when we want to select miRNAs for studying their role in PPARG regulation in laboratory conditions.

A number of computational miRNA-target prediction algorithms have been developed due to lack of high-throughput experimental methods but these programs still lacking sensitivity and specificity. The miRWalk database provides a comprehensive atlas of putative miRNA binding site prediction from multiple algorithms and therefore attracts researchers. These existing algorithms will become more accurate with more understanding of miRNA regulatory mechanism (Dweep et al. 2013). It can thus be concluded that a combination of both computational and experimental approaches would be required to unravel the complex networks of miRNA gene regulation and their expected therapeutic potentials. 

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