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

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

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/62944

#### **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 virusencoded microRNA-like small RNAs remained unclear, and whether these microRNAlike 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 approximate‐ ly 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

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

EBOV outbreak in record [3–6]. 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 (polII)in all eukaryotes [9] or byRNApolymerase III(polIII)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 [13–15]. 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 expres‐ sion 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 [18–24], most of which came from Herpes virues [25]. Only a small part of such RNAs was detected within Retrovirus, Adenovirus, and polyomavirus families [26–28].

Bioinformatics-driven prediction was an effective method to identify viral encoded micro‐ RNA-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 premiRNAs 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 patho‐ genesis.

#### **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 (premiRNA) using VMir [31] with default parameter settings (http://www.hpi-hamburg.de/ research/departments-and-research-groups/antiviral-defense-mechanism/software-download.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 precur‐ sors, and precursors with confidence equal to or greater than 70% were further analyzed. Subsequently, mature miRNA sequences were predicted from the putative pre-miRNA stemloops. 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**

EBOV outbreak in record [3–6]. 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 (polII)in all eukaryotes [9] or byRNApolymerase III(polIII)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 [13–15]. 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 expres‐ sion 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 [18–24], most of which came from Herpes virues [25]. Only a small part of such RNAs was detected within Retrovirus, Adenovirus, and polyomavirus families [26–28].

Bioinformatics-driven prediction was an effective method to identify viral encoded micro‐ RNA-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 premiRNAs 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 patho‐

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

**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 (premiRNA) using VMir [31] with default parameter settings (http://www.hpi-hamburg.de/ research/departments-and-research-groups/antiviral-defense-mechanism/software-down-

Software Version 7 were applied for the alignment of the EBOV genomes [30].

genesis.

86 Ebola

**2. Methods**

**2.1. EBOV whole genome sequences and alignment**

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) [34–36] 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 micro‐ RNA-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) [37– 39]. The integrated regulatory network of target genes with transcription factors was con‐ structed using Cytoscape software (http://cytoscape.org/).

### **3. Key findings regarding the bioinformatics prediction of EBOV genomeencoded 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-premiRNA-1, EBOV-pre-miRNA-2, EBOV-pre-miRNA-3, and EBOV-pre-miRNA-4 were predict‐ ed (**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, EBOVmiR-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**).


**2.4. Constructing gene regulation network**

88 Ebola

**encoded microRNA-like small RNAs**

**genome-encoded microRNA-like small RNAs**

important in human immune response to virus infection (**Table 1**).

**Canonical pathways p-Value Ratio Molecules**

Angiopoietin signaling *4.47E-01 1.54E-02 NFKBIE* April mediated signaling *6.43E-01 2.63E-02 NFKBIE* ATM signaling *4.81E-01 1.69E-02 MRE11A*

AMPK signaling *1.49E+00 2.26E-02 PDRK1, FASN, ADRA2B, RRKAB2*

structed using Cytoscape software (http://cytoscape.org/).

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) [37– 39]. The integrated regulatory network of target genes with transcription factors was con‐

**3. Key findings regarding the bioinformatics prediction of EBOV genome-**

**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-premiRNA-1, EBOV-pre-miRNA-2, EBOV-pre-miRNA-3, and EBOV-pre-miRNA-4 were predict‐ ed (**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-

**3.2. Bioinformatics analysis of the genetic regulation network in the target genes of EBOV**

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

miR-4-5p, and EBOV-miR-4-3p were resolved using MatureBayes tool (**Figure 1B**).



**Table 1.** Key canonical pathway analysis of the potential mature EBOV miRNA target genes.

Prediction of Ebolavirus Genomes Encoded MicroRNA-Like Small RNAs Using Bioinformatics Approaches http://dx.doi.org/10.5772/62944 91

**Canonical pathways p-Value Ratio Molecules** Role of IL-17A in arthritis *5.13E-01 1.85E-02 NFKBIE*

STAT3 pathway *4.08E-01 1.37E-02 SOCS4*

TNFR2 signaling *7.62E-01 3.57E-02 NFKBIE*

Angiopoietin signaling *4.47E-01 1.54E-02 NFKBIE* April mediated signaling *6.43E-01 2.63E-02 NFKBIE* ATM signaling *4.81E-01 1.69E-02 MRE11A* B cell activating factor signaling *6.24E-01 2.5E-02 NFKBIE*

CD27 signaling in lymphocytes *5.34E-01 1.96E-02 NFKBIE*

CD40 signaling *4.53E-01 1.56E-02 NFKBIE*

ErbB signaling *3.58E-01 1.18E-02 PDPK1* ErbB2-ErbB3 signaling *5E-01 1.79E-02 PDPK1* ErbB4 signaling *4.87E-01 1.72E-02 PDPK1*

HGF signaling *2.95E-01 9.62E-03 ELF3* HIF1a signaling *3.07E-01 1E-02 MMP25*

IL-10 signaling *4.32E-01 1.47E-02 NFKBIE* IL-17A signaling in airway cells *4.53E-01 1.56E-02 NFKBIE* IL-17A signaling in fibroblasts *6.75E-01 2.86E-02 NFKBIE* IL-6 signaling *2.63E-01 8.62E-03 NFKBIE*

Induction of apoptosis by HIV1 *1.22E+00 3.39E-02 NFKBIE, RIPK1*

**Table 1.** Key canonical pathway analysis of the potential mature EBOV miRNA target genes.

TNFR1 signaling *1.4E+00 4.26E-02 NFKBIE, RIPK1*

B cell receptor signaling *4.92E-01 1.17E-02 PDPK1, NFKBIE*

CD28 signaling in T helper cells *7.5E-01 1.77E-02 PDPK1, NFKBIE*

EIF2 signaling *4.89E-01 1.16E-02 PDPK1, EIF2AK4*

Erythropoietin signaling *1.12E+00 2.99E-02 PDPK1, NFKBIE*

IGF-1 signaling *1.55E+00 3.09E-02 GRB10, PDPK1, SOCS4* IL-1 signaling *8.99E-01 2.2E-02 GNAT1, NFKBIE*

AMPK signaling *1.49E+00 2.26E-02 PDPK1, FASN, ADRA2B, PRKAB2*

*5.1E-01 1.2E-02 GNAT1, NFKBIE*

*4.75E-01 1.67E-02 NFKBIE*

*6.24E-01 2.5E-02 NFKBIE*

Role of NFAT in regulation of the immune

Role of PI3K/AKT signaling in the pathogenesis of influenza

Role of PKR in interferon induction and

response

90 Ebola

antiviral response

**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**) [40–45].

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

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, Paramyx‐ ovirus, 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 struc‐ tures 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

**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.

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.

#### **4. Conclusions**

in the inactivation of immune signaling and immune system with the antiviral response

**3.3. Potential EBOV genome-encoded microRNA-like small RNAs associated with the**

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, Paramyx‐ ovirus, 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 struc‐ tures 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

**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.

(**Figure 2A** and **2B**) [40–45].

92 Ebola

**Immune response-related pathways**

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 polyo‐ mavirus 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.

#### **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 genomeencoded microRNA-like small RNAs using experimental method.

#### **Acknowledgements**

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

#### **Authors' contributions**

Zhe Xu, Yuan Jin, and Xiaoping An characterized the materials, under the supervision of Yue Teng, Zhe Xu, and Dan Feng wrote the manuscript with further contributions from Jiangman Song and Yuan Jin analyzed the data. All authors reviewed the manuscript.

#### **Conflict of interest**

Competing financial interests and the authors declare no competing financial interests.

#### **Author details**

Yue Teng1\*, Zhe Xu2 , Jin Yuan1 , Xiaoping An1 , Jiangman Song3 and Dan Feng4

\*Address all correspondence to: yueteng@me.com; fddd@263.net

1 The State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China

2 Core Laboratory for Clinical Medical Research, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

3 Department of Neurology, People's Hospital, Peking University, Beijing, China

4 Division of Standard Operational Management, Institute of Hospital Management, Chi‐ nese PLA General Hospital, Beijing, China

Yue Teng and Zhe Xu contributed equally to this work.

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exactly the same among various EBOV strains. Thus, it is difficult to find microRNAs that are

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-

This work was supported by a grant from the State Key Laboratory of Pathogen and BioSe‐

Zhe Xu, Yuan Jin, and Xiaoping An characterized the materials, under the supervision of Yue Teng, Zhe Xu, and Dan Feng wrote the manuscript with further contributions from Jiangman

Competing financial interests and the authors declare no competing financial interests.

1 The State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and

2 Core Laboratory for Clinical Medical Research, Beijing Tiantan Hospital, Capital Medical

4 Division of Standard Operational Management, Institute of Hospital Management, Chi‐

3 Department of Neurology, People's Hospital, Peking University, Beijing, China

, Jiangman Song3

and Dan Feng4

Song and Yuan Jin analyzed the data. All authors reviewed the manuscript.

, Xiaoping An1

\*Address all correspondence to: yueteng@me.com; fddd@263.net

completely conserved among different viral strains due to genome mutations.

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, Jin Yuan1

**Acknowledgements**

Ebola94

**Authors' contributions**

**Conflict of interest**

**Author details**

Yue Teng1\*, Zhe Xu2

Epidemiology, Beijing, China

University, Beijing, China

nese PLA General Hospital, Beijing, China

Yue Teng and Zhe Xu contributed equally to this work.


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