In-silico identification of microRNAs potentially

In-silico identification of microRNAs potentially targeting the PGC1α gene that regulates bovine mitochondrial biogenesis Sigit Prastowo, Nuzul Widyas, Adi Ratriyanto, and Md Mahmodul Hasan Sohel

Citation: AIP Conference Proceedings 2014, 020019 (2018); doi: 10.1063/1.5054423 View online: https://doi.org/10.1063/1.5054423 View Table of Contents: http://aip.scitation.org/toc/apc/2014/1 Published by the American Institute of Physics

In-silico identification of microRNAs potentially targeting the PGC1α gene that regulates bovine mitochondrial biogenesis Sigit Prastowo1, a), Nuzul Widyas1, Adi Ratriyanto1, Md Mahmodul Hasan Sohel2 2

1 Department of Animal Science, Universitas Sebelas Maret, Surakarta Indonesia Department of Animal Science, Faculty of Agriculture; Genome and Stem Cell Centre, Erciyes University, Kayseri 38039, Turkey

a)

Corresponding author: [email protected]

Abstract. Post-transcriptional gene regulation in eukaryotic cells is mediated through several molecules including noncoding RNAs. Among the noncoding RNAs, microRNAs (miRNAs) are the master regulator of gene expression and therefore control various biological processes in living cells. Mitochondrial biogenesis is a biological process by which cell increases the number of the active mitochondrion to produce more ATP. The presence of peroxisome proliferator-activated receptor γ coactivator-1alpha (PGC1α) protein determines mitochondrial biogenesis to enhance cellular energy production in cells. Using in-silico approach, this study aims to identify the miRNAs that could potentially target PGC1α gene to regulate mitochondrial biogenesis. For this, four miRNA target prediction databases were employed to identify the potential miRNAs which may target PGC1α gene. The prediction results showed that a total of 27, 47, 77 and 79 miRNAs were predicted by MirTarget2, PicTar, TargetScan, and miRanda, respectively. Following the clustering process, we found seven miRNAs were identified as potential candidates by all four databases. Out of seven miRNAs, three miRNAs namely miR323, miR-222, and miR-137 were found to have 100% sequence similarity between human (hsa) and bovine (bta) miRNAs. miRNA binding site analysis showed that miR-323, miR-222, and miR-137 have five, two and three seeding regions, respectively, in the PGC1α 3’UTR. Our study identified three miRNAs that have multiple binding sites at the PGC1α 3ÚTR and could potentially modulate mitochondrial biogenesis. However, a validation study experiment needs to be performed to validate the interaction of these miRNAs with PGC1α gene.

INTRODUCTION Peroxisome proliferator-activated receptor-J (gamma) coactivator-1 α (alpha) (PPARGC1A), also known as PGC1α, is a gene which play a central role in mitochondrial biogenesis [1,2]. Therefore, mitochondrial activity such as cellular energy production and reactive oxygen species (ROS) scavenging were largely depended on the presence of this protein. A previous study has demonstrated that there is a positive interaction between PGC1α and AMPKA1 gene in regulating mitochondrial activity and lipid metabolism in bovine embryos [3]. Low expression of PGC1α tends to reduce the mitochondrial activity and AMPKA1 expression that could lead to the higher ROS as well as higher lipid accumulation. Moreover, a specific link between ROS, oxidative stress, antioxidant genes and mitochondrial activity has been demonstrated in bovine embryos [4]. Therefore, restoring/increasing the mitochondrial activity through enhancing mitochondrial biogenesis protein function could be a logical way in the treatment of specific diseases linked to mitochondrial metabolism such as diabetic [5], obesity [6], cancer [7] and probably could control the aging process [8]. In this regard, the role of miRNAs, master regulators of gene function, cannot be overruled. miRNAs are tiny (18-22 nt long), noncoding RNA molecules that post-transcriptionally regulate the expression of genes [9]. The gene expression regulation is accomplished by an imperfect base-pairing with the target mRNAs and

International Conference on Science and Applied Science (ICSAS) 2018 AIP Conf. Proc. 2014, 020019-1–020019-5; https://doi.org/10.1063/1.5054423 Published by AIP Publishing. 978-0-7354-1730-4/$30.00

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subsequently inducing the mRNA destabilization or translational repression [10]. miRNAs are comprised of 1-5% of the mammalian genome and bioinformatics analysis revealed that more than 60% mammalian genes could potentially target by a single miRNA [11]. Both cellular and extracellular miRNAs are found to be involved in regulating gene expression in mammalian cells or tissues [12,13]. Because of broader targeting abilities, miRNAs are found to be involved in most of the biological processes and cellular pathways [14]. However, the miRNAs that may potentially target PGC1α transcript and regulate the mitochondrial biogenesis in bovine is currently unknown. Therefore, the current study aims to identify the candidate miRNAs using in-silico approach which may modulate mitochondrial biogenesis through PGC1α gene expression in bovine.

STUDY METHODS Prediction of miRNAs targeted to PGC1α gene In the current study, we performed three steps as a pipeline for in-silico miRNA-mRNA study which is described in our previous study [15]. These three steps are target prediction, similarity analysis, and prediction of the binding site. Four miRNAs prediction tools (databases) were employed to predict miRNAs the target PGC1α gene namely MirTarget2 (www.mirdb.org), PicTar (http://pictar.mdc-berlin.de/), TargetScan (www.targetscan.org) and miRanda (http://microRNA.org). Thereafter, common miRNAs that were found in all four databases were selected as a candidate.

miRNA sequence similarity analysis The mature sequence of selected miRNA candidates was retrieved from miRbase (www.mirbase.org), followed by sequence similarity analysis between human (hsa) vs bovine (bta) miRNA. The major reason to perform this step is the unavailability of bovine miRNA primers. For individual primer assay, available commercial primers are for either human or for rodents. Therefore, for future miRNA profiling study using bovine sample this homologous human or rodent primers can be used.

Prediction of miRNA binding sites in PGC1α 3’UTR mRNA Finally, miRNAs which have 100% identical sequence between human vs bovine, proceeded to 3’UTR mRNA binding sites (seeding region) prediction using STarMir (http://sfold.wadsworth.org/cgi-bin/starmir.pl). In this study, FASTA sequence of bovine PGC1α 3’UTR mRNA transcript variant X1 was obtained from NCBI (https://www.ncbi.nlm.nih.gov/; Gene ID: 338446; accession XM_015471550.1).

RESULTS AND DISCUSSIONS The miRNAs which are potentially targeting PGC1α gene were determined by in silico analysis using different miRNA target prediction tools. The result showed that a total of 27, 47, 77 and 79 miRNAs were identified as a potential candidate by MirTarget2, PicTar, TargetScan, and miRanda, respectively. The results of clustering analysis of all predicted miRNAs are presented in Figure 1 and Table 1. It revealed a total of seven miRNAs were identified by all four databases after clustered using Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/). The differences in the predicted number of miRNAs by each bioinformatic databases is because of the differences in the computational algorithms feature in each database for predicting the miRNA targets. These features include Seed Region Match, Conservation, Free Energy, In-site Features and Accessibility Energy [16]. For this reason, selecting a potential candidate miRNA which is identified by all databases is a better choice to avoid computational errors that could potentially contribute by the software algorithm during identifying candidate of miRNAs. Next, we checked the sequence similarities of these seven bovine miRNAs (predicted by all four databases) with human miRNAs. All the mature sequences of both human and bovine miRNAs were obtained from the miRbase database. Sequence similarity comparison revealed that only three miRNAs have 100% identical sequence in both species (Table 2). These three miRNAs, (namely miR-323, miR-222, and miR-137) were used further for 3ÚTR binding site prediction. Subsequently, miR-23a, miR-23b, miR-150, and miR-137 were discarded from the analysis as there was no similar sequence. We performed sequence similarity study because most of the available miRNA

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primers are to detect human miRNAs. Fortunately, miRNAs are widely conserved in different mammalian species and this feature allowed us to use human miRNA primers to study same miRNAs in bovine.

FIGURE 1. Venn diagram showing the number of miRNAs predicted to be targeted PGC1α gene TABLE 1. Name and number of miRNAs that are predicted by at least three databases as potential regulatory miRNAs of PGC1α gene miRNA database Total miRNA name MirTarget2, PicTar, TargetScan, 7 hsa-miR-323-3p, hsa-miR-23b, hsa-miR-23a, miRanda hsa-miR-222, hsa-miR-150, hsa-miR-137, hsa-miR-221 MirTarget2, PicTar, miRanda

1

hsa-miR-130b

MirTarget2, TargetScan, miRanda

7

hsa-miR-579, hsa-miR-642, hsa-miR-548c-3p, hsa-miR-203, hsa-miR-487a, hsa-miR-512-3p, hsa-miR-603

PicTar, TargetScan, miRanda

24

hsa-miR-30d, hsa-miR-219-5p, hsa-miR-30a, hsa-let-7i, hsamiR-152, hsa-miR-199a-5p, hsa-miR-320a, hsa-miR-148b, hsa-miR-148a, hsa-miR-7, hsa-miR-369-3p, hsa-miR-138, hsa-miR-136, hsa-miR-29a, hsa-miR-218, hsa-miR-98, hsa-miR-211, hsamiR-217, hsa-miR-29c, hsa-miR-204, hsa-miR-199b-5p, hsa-miR-33a, hsa-miR-29b, hsa-miR-30e

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TABLE 2. Sequence similarity between human (hsa) vs bovine (bta) miRNAs targeting PGC1α 3’UTR mRNA transcript variant X1 miRNA miR-323

miRNA name

hsa-miR-323a-3p bta-miR-323 miR-23b hsa-miR-23b-5p bta-miR-23b-5p miR-23a hsa-miR-23a-5p bta-miR-23a miR-222 hsa-miR-222-3p bta-miR-222 miR-150 hsa-miR-150-5p bta-miR-150 miR-137 hsa-miR-137 bta-miR-137 miR-221 hsa-miR-221-5p bta-miR-221 *) source: www.miRbase.com

Accession number

Sequence (5’-3’)*)

MIMAT0000755 MIMAT0009284 MIMAT0004587 MIMAT0012538 MIMAT0004496 MIMAT0003827 MIMAT0000279 MIMAT0003530 MIMAT0000451 MIMAT0003845 MIMAT0000429 MIMAT0009231 MIMAT0004568 MIMAT0003529

CACAUUACACGGUCGACCUCU CACAUUACACGGUCGACCUCU UGGGUUCCUGGCAUGCUGAUUU GGGUUCCUGGCAUGCUGAUUU GGGGUUCCUGGGGAUGGGAUUU AUCACAUUGCCAGGGAUUUCCA AGCUACAUCUGGCUACUGGGU AGCUACAUCUGGCUACUGGGU UCUCCCAACCCUUGUACCAGUG UCUCCCAACCCUUGUACCAGUGU UUAUUGCUUAAGAAUACGCGUAG UUAUUGCUUAAGAAUACGCGUAG ACCUGGCAUACAAUGUAGAUUU AGCUACAUUGUCUGCUGGGUUU

Similarity (%) 100 100 100 -

TABLE 3. The binding site position of candidate miRNAs at bovine PGC1α 3’UTR mRNA transcript variant X1 Number of Position in 3ÚTR sequence miRNA name binding site (canonical matched sites) miR-323 5 1645-1686 (7mer-8mer); 3170-3198 (6mer); 3479-3511 (8mer); 3524-3557 (8mer); 3703-3751 (6mer) miR-222 2 984-1019 (offset-6mer); 3197-3228 (8mer) miR-137 3 1367-1393 (6mer); 2392-2427 (8mer); 2619-2633 (8mer)

FIGURE 2. Predicted binding site of miR-323 (A), miR-222 (B) and miR-137 (C) binding site at bovine PGC1α 3’UTR mRNA transcript variant X1

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A single miRNA can have several binding sites to its target mRNA’s 3ÚTR and if there are more than one binding sites, it is highly likely that the miRNA strongly suppresses its target gene expression [17]. Therefore, we investigated the number of binding sites of all three selected miRNAs in the 3ÚTR of PGC1α gene and the results are presented in Table 3. Binding site prediction analysis showed that miR-323 has the highest number of binding sites while miR222 has the lowest number of binding sites 3ÚTR of PGC1α gene. Among the five binding sites miR-323 has in the 3ÚTR of PGC1α gene, three of them are 8-mer and two of them are 6-mer (Table 3). We considered a binding site as a potential seeding region when there was more than six nucleotides match between miRNA and 3ÚTR sequence (Figure 2). The bioinformatics study suggests that the miR-323 is the most potential candidate miRNA that could regulate the expression of PGC1α gene in bovine mitochondrial biogenesis. To know the exact regulatory function of selected candidate miRNAs with PGC1α, a series of profiling and validation study under in vitro condition need to be performed. This study could be a stepping stone or preliminary selection model to minimize laborious work in a wet lab experiment. Moreover, along with the improvement of miRNA target prediction database, especially its computational algorithm features, a more precise prediction which resulting different list of miRNA candidate needs to be taken into account.

CONCLUSIONS In the current study, we demonstrated that PGC1α gene could be potentially targeted by several miRNAs. Using specific investigation criteria, we found that miR-323, miR-222, and miR-137 were the potential candidates that might have a regulatory role in mitochondrial biogenesis process through its interaction with PGC1α gene. Indeed, this result could only mean in the biological function if the validation study has been done in order to know the exact function of selected miRNAs candidate.

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