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*1, Moumoni Saha1, Kazi Saiful Islam2, Md. Yeashin Gazi1, Sohel Ahmed1

Date of Acceptance: 13-08-2014
Published in Online: 2014/09/25

1 Jahangirnagar University, Savar, Dhaka 1342, Bangladesh 2 Dept. of Biochemistry and Molecular Biology, Dhaka-1000, Bangladesh
Address Correspond to:
Sudip Paul Dept. of Biochemistry and Molecular Biology Jahangirnagar University, Savar, Dhaka 1342, Bangladesh. Mob: 01674389745. e-mail: [email protected]

Academic editor: Dr. Mohammad Nazmul Ahsan

To Cite This Article:
Sudip Paul, M.S., 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 Techno. 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.

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Content Section

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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