Open access peer-reviewed chapter

Prediction of Ebolavirus Genomes Encoded MicroRNA-Like Small RNAs Using Bioinformatics Approaches

Written By

Yue Teng, Zhe Xu, Jin Yuan, Xiaoping An, Jiangman Song and Dan Feng

Submitted: September 15th, 2015 Reviewed: March 8th, 2016 Published: August 10th, 2016

DOI: 10.5772/62944

From the Edited Volume

Ebola

Edited by Crtomir Podlipnik

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Abstract

Recent findings revealed that certain viruses encoded microRNA-like small RNAs using the RNA interference machinery in the host cells. However, the function of these virus-encoded microRNA-like small RNAs remained unclear, and whether these microRNA-like small RNAs were involved in the replication of the virus and viral infection was still disputable. In this chapter, the negative-sense RNA genome of Ebola virus (EBOV) was scanned using bioinformatics tools to predict the EBOV-encoded microRNA-like small RNAs. Then, the potential influence of viral microRNA-like small RNAs on the viral immune evasion, host cellular signaling pathway, and epigenetic regulation of antiviral defense mechanism were also detected by the reconstructed regulatory network of target genes associated with viral encoded microRNA-like small RNAs. In this analysis, EBOV-encoded microRNA-like small RNAs were proposed to inhibit the host immune response factors, probably facilitating the evasion of EBOV from the host defense mechanisms. In conclusion, systematic investigation of microRNA-like small RNAs in EBOV genome may shed light on the underlying molecular mechanisms of the pathological process of Ebola virus disease (EVD).

Keywords

  • Ebolavirus
  • virus-encoded miRNAs
  • microRNAs
  • bioinformatics
  • NF-kB
  • TNF

1. Introduction

Zaire Ebola virus (ZEBOV) has the highest case-fatality rate with an average of approximately 83% over the past 27 years [1]. Its first outbreak took place on August 26, 1976, in Yambuku [2], and the virus was also responsible for the 2014 West Africa outbreak, which was the largest EBOV outbreak in record [36]. Moreover, neither antiviral drugs nor effective treatment was available for EBOV or Ebola virus disease (EVD) at that time [7, 8]. MicroRNAs originate from a wide variety of primary transcripts (pri-miRNAs) that are generated by RNA polymerase II (pol II) in all eukaryotes [9] or by RNA polymerase III (pol III) in some viruses [10]. The cleavage of pri-miRNAs releases a RNA hairpin intermediate (~70 nt) containing a characteristic 2 nt 3’ overhang, named a premature miRNA (pre-miRNA), which is further processed to generate the 21~23 nt mature miRNA from its arm of ~70 nt imperfect stem-loop structure [11, 12].

Since microRNAs have been discovered and their role in gene expression regulation was established, it has been hypothesized that viruses could encode microRNA-like small RNAs as well, and these virus-encoded microRNA-like small RNAs were proposed to play important regulatory roles in viral immune evasion and systemic pathogenesis [1315]. The size of viral encoded microRNA-like RNAs has a significant advantage given the tight constraints on viral genome size, which is also small enough to escape from the triggered host immune pathway. It was found that viral encoded microRNA-like small RNAs could downregulate the expression of host immune defense gene, resulting in increased viral replication or evasion from host immune surveillance [16, 17]. Until now, more than 60 viral microRNA-like small RNAs have been identified [1824], most of which came from Herpes virues [25]. Only a small part of such RNAs was detected within Retrovirus, Adenovirus, and polyomavirus families [2628].

Bioinformatics-driven prediction was an effective method to identify viral encoded microRNA-like small RNAs [21, 22]. In this study, the microRNA prediction program, VMir, was applied to scan the viral genomes for the presence of stem-loop structures in the pri- and pre-miRNAs and identify potential candidate stretches capable to form stable secondary stem-loop structures. Afterward, putative mature microRNA-like small RNAs were validated using MatureBayes [29]. The systemic prediction of the potential EBOV-encoded microRNA-like small RNAs along with their target genes on the genome-wide scale helps to further assess the function of microRNAs during viral infection and virus-host interactions in the EVD pathogenesis.

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2. Methods

2.1. EBOV whole genome sequences and alignment

The full-length genome sequences of EBOV were retrieved from the genome browser at Ebola virus resource (http://www.ncbi.nlm.nih.gov/genome/viruses/variation/ebola/) and UCSC Ebola portal (https://genome.ucsc.edu/ebolaPortal/). MAFFT Multiple Sequence Alignment Software Version 7 were applied for the alignment of the EBOV genomes [30].

2.2. Bioinformatics prediction of the EBOV genome-encoded microRNA-like small RNAs

Briefly, the viral genome was scanned for stem-loop structures of miRNA precursor (pre-miRNA) using VMir [31] with default parameter settings (http://www.hpi-hamburg.de/research/departments-and-research-groups/antiviral-defense-mechanism/software-down- load.html). The putative pre-miRNAs with VMir score ≥150 and a window count ≥ 35 were retained. Then, MiPred software [32] was applied to check all of the putative miRNA precursors, and precursors with confidence equal to or greater than 70% were further analyzed. Subsequently, mature miRNA sequences were predicted from the putative pre-miRNA stem-loops. Finally, the MatureBayes tool [29] was used to extend the prediction coverage of the mature miRNAs under default parameter settings.

2.3. Prediction of the target genes and signaling pathway analysis

Target genes of predicted EBOV-encoded microRNA-like small RNAs in the human genome were predicted using TargetScan [33]. Putative targets within the viral genome were predicted using TargetScan Perl script. The signaling pathways collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) [3436] PATHWAY databases were applied in the pathway analysis.

Figure 1.

The predicted EBOV-encoded pre-miRNAs and microRNA-like small RNAs. The MiPred algorithm was used to identify genuine pre-miRNAs, and the MatureBayes tool was used to predict the mature miRNA sequences. (A) The secondary structures of the four EBOV pre-miRNAs. (B) The tertiary structures of the EBOV-encoded microRNA-like small RNAs.

2.4. Constructing gene regulation network

The genetic regulation network was constructed based on systematic integration of various datasets. Transcription factors related with the target genes of EBOV-encoded microRNA-like small RNAs were selected from Transcriptional Regulatory Element Database (TRED) [3739]. The integrated regulatory network of target genes with transcription factors was constructed using Cytoscape software (http://cytoscape.org/).

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3. Key findings regarding the bioinformatics prediction of EBOV genome-encoded microRNA-like small RNAs

3.1. Predicted precursor and mature EBOV genome-encoded microRNA-like small RNAs

The released full-length genome sequences of the retrieved EBOV strains were aligned and then scanned for miRNA precursor (pre-miRNA) using VMir software. Afterward, the putative pre-miRNAs with VMir score ≥150 and a window count ≥35 were selected for further assessment. Within the EBOV genome, four putative microRNA precursors, EBOV-pre-miRNA-1, EBOV-pre-miRNA-2, EBOV-pre-miRNA-3, and EBOV-pre-miRNA-4 were predicted (Figure 1A). The mature miRNA sequences were predicted from the putative pre-miRNA stem loops. Seven different mature EBOV miRNA candidates, including EBOV-miR-1-5p, EBOV-miR-1-3p, EBOV-miR-2-5p, EBOV-miR-2-3p, EBOV-miR-3-5p, EBOV-miR-3-3p, EBOV-miR-4-5p, and EBOV-miR-4-3p were resolved using MatureBayes tool (Figure 1B).

3.2. Bioinformatics analysis of the genetic regulation network in the target genes of EBOV genome-encoded microRNA-like small RNAs

Target genes of the predicted mature microRNA-like small RNAs were searched within TargetScan, and the potential target genes in host were identified (Table S1, the list of potential target genes of EBOV-encoded microRNA-like small RNAs). KEGG pathway enrichment analysis was performed using the DAVID bioinformatics tool for these target genes. The results showed that the target genes were closely related on function and were involved in multiple canonical pathways, such as NF-kB activation by viruses, role of protein kinase (PKR) in interferon induction and antiviral response, induction of apoptosis by HIV1, B cell-activating factor signaling, and role of PI3K/AKT signaling in the pathogenesis of influenza, which were important in human immune response to virus infection (Table 1).

Canonical pathwaysp-ValueRatioMolecules
AMPK signaling1.49E+002.26E-02PDRK1, FASN, ADRA2B, RRKAB2
Angiopoietin signaling4.47E-011.54E-02NFKBIE
April mediated signaling6.43E-012.63E-02NFKBIE
ATM signaling4.81E-011.69E-02MRE11A
B cell activating factor signaling6.24E-012.5E-02NFKBIE
B cell receptor signaling4.92E-011.17E-02PDPK1, NFKBIE
CD27 signaling in lymphocytes5.34E-011.96E-02NFKBIE
CD28 signaling in t helper cells7.5E-011.77E-02PDPK1, NFKBIE
CD40 signaling4.53E-011.56E-02NFKBIE
bf2 signaling4.89E-011.16E-02PDPK1, EIF2AK4
ErbB signaling3.58E-011.18E-02PDPK1
ErbB2-ErbB3 signaling5E-011.79E-02PDPK1
ErbB4 signaling4.87E-011.72E-02PDPK1
Erythropoietin signaling1.12E+002.99E-02PDPK1, NFKBIE
HGF signaling2.95E-019.62E-03ELF3
HIF1a signaling3.07E-011E-02MMP25
IGF-1 signaling1.55E+003.09E-02GRB10, PDPK1, SOCS4
IL-1 signaling8.99E-012.2E-02GNAT1, NFKBIE
IL-10 signaling4.32E-011.47E-02NFKBIE
IL-17A signaling in airway cells4.53E-011.56E-02NFKBIE
IL-17A signaling in fibroblasts6.75E-012.86E-02NFKBIE
il-6 signaling2.63E-018.62E-03NFKBIE
Induction of apoptosis by HIV11.22E+003.39E-02NFKBIE, RIPK1
Insulin receptor signaling6.69E-011.56E-02GRB10, PDPK1
JAK/Stat signaling4.12E-011.39E-02SOCS4
Lymphotoxin β receptor signaling5.13E-011.85E-02PDPK1
MIF regulation of innate immunity6.14E-012.44E-02NFKBIE
mTOR signaling4.57E-011.1E-02PDPK1, PRKAB2
NF-KB activation by viruses1.06E+002.74E-02NFKBIE, RIPK1
NF-KB signaling4.99E-011.18E-02NFKBIE, RIPK1
NGF signaling2.89E-019.43E-03PDPK1
P53 signaling3.13E-011.02E-02CCND2
PI3K signaling in B lymphocytes6.94E-011.63E-02PDPK1, NFKBIE
PI3K/AKT signaling7.05E-011.65E-02PDPK1, NFKBIE
PKCθ signaling in T lymphocytes2.71E-018.85E-03NFKBIE
PPARa/RXRa activation9.95E-011.82E-02FASN, NFKBIE, PRKAB2
Regulation of IL-2 expression in activated
and anergic T lymphocytes
3.86E-011.28E-02NFKBIE
Role of IL-17A in arthritis5.13E-011.85E-02NFKBIE
Role of NFAT in regulation of the immune
response
5.1E-011.2E-02GNAT1, NFKBIE
Role of PI3K/AKT signaling in the
pathogenesis of influenza
4.75E-011.67E-02NFKBIE
Role of PKR in interferon induction and
antiviral response
6.24E-012.5E-02NFKBIE
STAT3 pathway4.08E-011.37E-02SOCS4
TNFR1 signaling1.4E+004.26E-02NFKBIE, RIPK1
TNFR2 signaling7.62E-013.57E-02NFKBIE
AMPK signaling1.49E+002.26E-02PDPK1, FASN, ADRA2B, PRKAB2
Angiopoietin signaling4.47E-011.54E-02NFKBIE
April mediated signaling6.43E-012.63E-02NFKBIE
ATM signaling4.81E-011.69E-02MRE11A
B cell activating factor signaling6.24E-012.5E-02NFKBIE
B cell receptor signaling4.92E-011.17E-02PDPK1, NFKBIE
CD27 signaling in lymphocytes5.34E-011.96E-02NFKBIE
CD28 signaling in T helper cells7.5E-011.77E-02PDPK1, NFKBIE
CD40 signaling4.53E-011.56E-02NFKBIE
EIF2 signaling4.89E-011.16E-02PDPK1, EIF2AK4
ErbB signaling3.58E-011.18E-02PDPK1
ErbB2-ErbB3 signaling5E-011.79E-02PDPK1
ErbB4 signaling4.87E-011.72E-02PDPK1
Erythropoietin signaling1.12E+002.99E-02PDPK1, NFKBIE
HGF signaling2.95E-019.62E-03ELF3
HIF1a signaling3.07E-011E-02MMP25
IGF-1 signaling1.55E+003.09E-02GRB10, PDPK1, SOCS4
IL-1 signaling8.99E-012.2E-02GNAT1, NFKBIE
IL-10 signaling4.32E-011.47E-02NFKBIE
IL-17A signaling in airway cells4.53E-011.56E-02NFKBIE
IL-17A signaling in fibroblasts6.75E-012.86E-02NFKBIE
IL-6 signaling2.63E-018.62E-03NFKBIE
Induction of apoptosis by HIV11.22E+003.39E-02NFKBIE, RIPK1

Table 1.

Key canonical pathway analysis of the potential mature EBOV miRNA target genes.

Figure 2.

Bioinformatics analysis of the genetic regulatory network of target genes of EBOV-encoded microRNA-like small RNAs (A and B). The key regulation network of the potential target genes of EBOV-encoded microRNA-like small RNAs.

Based on the gene regulation network (GRN) analysis (Figure S1), it was found that target genes, FASN, RUNX1T1, and ELF3, were important immune and inflammation response factors and actively interacted with transcription regulator, such as KLF2 and NF-kB in host cells (Figure 2A) [40, 41]. They were also the key co-regulator of TNF complex in human immune system (Figure 2B) [42], implying that the EBOV might inhibit the infection response of immune system by affecting the related signaling pathway using noncoding RNA. Furthermore, it was speculated that the mature EBOV-encoded microRNA-like small RNAs might induce large-scale epigenetic modification in host genome to downregulate the expression of epigenetic factor, such as histone h3, HDAC5, JARID2, and SMARCA4, resulting in the inactivation of immune signaling and immune system with the antiviral response (Figure 2A and 2B) [4045].

3.3. Potential EBOV genome-encoded microRNA-like small RNAs associated with the Immune response-related pathways

Figure 3.

The RIG1I like receptor pathway associated with the potential target genes of EBOV-encoded microRNA1-like small RNAs. The target genes of EBOV-encoded microRNA1-like small RNAs, NF1kB, and RIPK, were involved in the RIG1I-like receptor pathway to trigger IFN signaling pathway with the antiviral response.

Additionally, NF-kB and RIPK were also involved in the RIG-I-like receptor pathway (Figure 3) [46, 47]. As shown in Figure 3, the RIG-I-like receptor pathway played a key role in antiviral response that is a sensor for viruses such as influenza A, Rhabdovirus, Flavivirus, Paramyxovirus, Epstein-Barr virus, and Filovirus [48]. The RIG-I-like receptor pathway is stimulated during RNA virus infection by the interaction between cytosolic RIG-I and viral RNA structures that contain short hairpin dsRNA and 5’ triphosphate (5’ppp) terminal structure. The EBOV might utilize the microRNA-like small RNAs to inhibit the RIG-I-like receptor pathway to evade the host defense mechanisms, or conversely to trigger apoptosis responses as a mechanism to increase viral infection [49, 50]. For viruses, effective RIG-I-mediated antiviral responses are dependent on functionally active LGP2. The dysfunction of LGP2 resulted in promoting viral replication, preventing virus-induced apoptosis, and suppressing the immune response for the invading pathogen [51]. Certain retroviruses, such as HIV-1, encode a protease that directs RIG-1 to the lysosome for degradation, and thereby evade RIG-1 mediated signaling. RIG-I and MDA-5 are involved in activating interferon (IFN) signaling pathway with the antiviral response.

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4. Conclusions

MicroRNAs are encoded by cellular or viral genomes and play an essential role in numerous cellular processes, including viral infection, viral immune evasion, and antiviral cell-mediated immune response. Most viral genome-encoded microRNA-like small RNAs have been identified by traditional cloning strategy from virus-infected cells, yet others have been identified following computational prediction. Using the VMir analyzer program, the polyomavirus simian vacuolating virus 40 (SV40) [22] and Merkel Cell virus (MCV) [13] have been found to encode microRNA-like small RNAs, suggesting that VMir analyzer program is an effective tool for searching new viral miRNA-like small RNAs [52]. Therefore, we analyzed the genome of EBOV with the VMir software and obtained four pre-miRNAs located in the coding region of viral genome, indicating that the RNA secondary structures of EBOV genome might be processed into microRNA-like small RNAs [53, 54].

Infected cells have several signaling mechanisms to sense and respond to virus infection [55], for example, cross talk between different cellular pathways to modulate the expression and antiviral function of interferon (IFNs) with RIG-I-like receptor pathway and specific gene products. RIG-I-like receptor pathway and IFNs cytokines are important regulators of innate and adaptive immune responses [56]. Besides their antiviral role, they are potent regulators of cell growth and have immunomodulatory activity. INFs were activated after virus infection, probably through viral dsRNA and other viral gene products. The most intensely studied molecule in the RIG-I-like receptor pathway is the dsRNA-activated serine/threonine protein kinase (PKR). PKR was activated in the presence of cytoplasmic dsRNA, leading to the rapid phosphorylation of eukaryotic initiation factor eIF2 and subsequent inhibition of both host and viral mRNA [57, 58].

Although the bioinformatics prediction could be inaccurate, the bioinformatics prediction was potentially more selective and effective than experimental method. The target genes of viral genome-encoded microRNA-like small RNAs would help to develop an effective treatment for the EBOV infection.

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5. Limitations

Due to the high mutation rate of reverse transcription in replication, EBOV presents numerous mutations over viral genome during host adaption, suggesting that the viral genome is not exactly the same among various EBOV strains. Thus, it is difficult to find microRNAs that are completely conserved among different viral strains due to genome mutations.

However, it is possible that some microRNA-like small RNAs are relatively conserved among diverse viral adapted hosts. Moreover, the expression pattern of viral microRNA-like small RNAs was highly unpredictable. Therefore, it might be difficult to validate the EBOV genome-encoded microRNA-like small RNAs using experimental method.

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Acknowledgments

This work was supported by a grant from the State Key Laboratory of Pathogen and BioSecurity Program (No. SKLPBS1408 and No. SKLPBS1451).

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Written By

Yue Teng, Zhe Xu, Jin Yuan, Xiaoping An, Jiangman Song and Dan Feng

Submitted: September 15th, 2015 Reviewed: March 8th, 2016 Published: August 10th, 2016