Literature DB >> 22584005

Computational identification of microRNAs in Anatid herpesvirus 1 genome.

Jun Xiang1, Anchun Cheng, Mingshu Wang, Shunchuan Zhang, Dekang Zhu, Renyong Jia, Shun Chen, Yi Zhou, Xiaoyu Wang, Xiaoyue Chen.   

Abstract

BACKGROUND: MicroRNAs (miRNAs) are a group of short (~22 nt) noncoding RNAs that specifically regulate gene expression at the post-transcriptional level. miRNA precursors (pre-miRNAs), which are imperfect stem loop structures of ~70 nt, are processed into mature miRNAs by cellular RNases III. To date, thousands of miRNAs have been identified in different organisms. Several viruses have been reported to encode miRNAs.
FINDINGS: Here, we extended the analysis of miRNA-encoding potential to the Anatid herpesvirus 1 (AHV-1). Using computational approaches, we found that AHV-1 putatively encodes 12 mature miRNAs. We then compared the 12 mature miRNAs candidates with the all known miRNAs of the herpesvirus family. Interestingly, the "seed sequences" (nt 2 to 8) of 2 miRNAs were predicted to have the high conservation in position and/or sequence with the 2 miRNAs of Marek's disease virus type 1 (MDV-1). Additionally, we searched the targets from viral mRNAs.
CONCLUSIONS: Using computational approaches, we found that AHV-1 putatively encodes 12 mature miRNAs and 2 miRNAs have the high conservation with the 2 miRNAs of MDV-1. The result suggested that AHV-1 and MDV-1 should have closed evolutionary relation, which provides a valuable evidence of classification of AHV-1. Additionally, seven viral gene targets were found, which suggested that AHV-1 miRNAs could affect its own gene expression.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 22584005      PMCID: PMC3422165          DOI: 10.1186/1743-422X-9-93

Source DB:  PubMed          Journal:  Virol J        ISSN: 1743-422X            Impact factor:   4.099


Findings

MicroRNAs (miRNAs) are noncoding small RNA molecules with important regulatory functions in expression of target genes [1,2]. The miRNAs are about 19 to 25 nucleotides (nt) long. They are firstly transcribed as long primary miRNAs, which are processed into 60–70 nt miRNA precursor (pre-miRNA) by nuclear RNase III Drosha [3]. Pre-miRNA is then exported to the cytoplasm by the export factor Exportin 5 and further cleaved into ∼ 22 nt duplexes [4]. Mature miRNAs regulate protein-coding gene expression via the RNA silencing machinery, typically by forming imperfect duplexes with target messenger RNAs (mRNAs). To date, thousands of miRNAs have been identified in different organisms [5]. The discovery of miRNAs encoded by DNA viruses suggests that viruses have evolved to exploit RNA silencing for regulation of the expression of their own genes, host genes, or both [6]. Most viral miRNAs (vmiRNAs) have been identified by cDNA cloning of small RNAs from virus-infected cells [7-10], whereas others have been identified following computational prediction and hybridization analysis [10-12]. Experimental screening of vmiRNAs via high-throughput sequencing of large numbers of cDNA clones from infected cells is technically challenging, time consuming and could be incomplete, given that viral gene expression can have highly constrained tissue-, time-, and replication state-specific patterns [12]. Of the vmiRNAs identified so far, most are encoded by viruses in the herpesvirus family, containing α [13-16], β [17], γ subfamily [8,9,18]. AHV-1, an unassigned virus in the family Herpesviridae, can induce duck viral enteritis in waterfowl of the family Anatidae. To query whether this strategy is also employed by AHV-1 or not, we have analyzed putative miRNA-encoding capacity of AHV-1. The AHV-1 miRNA prediction was performed using the complete genome sequence of AHV-1 strain CHv (JQ647509) [19]. The genome size is 162,175 nt. Figure 1 shows a flowchart of the computational prediction process. Briefly, the viral genome was scanned for hairpin-structured miRNA precursors using VMir Analyzer program [20,21]. 197 sequences with potential hairpin-like structures were extracted as candidate miRNA precursors. Then candidates within or antisense to protein-coding regions were removed according to the NCBI genome annotations. 50 precursors were further identified using MiPred program (http://www.bioinf.seu.edu.cn/miRNA/) and the sequences with lower minimum free energy (equivalent or below −25 kcal.mol-1) were remained, subsequently, the remained 24 real pre-microRNA sequences were conducted BLASTn searching against itself to remove repeated sequences. Finally, 16 sequences were selected as miRNA precursors candidates. At the last step, the mature sequences were predicted by Bayes-SVM-MiRNA web server v1.0 (http://wotan.wistar.upenn. edu/BayesSVMmiRNAfind/). After that, 12 mature sequences were predicted with 21nt in length (Table 1) and the secondary structure of pre-miRNAs were shown in Figure 2.
Figure 1

Flowchart of the AHV-1 miRNA prediction procedure.

Table 1

Sequences and genomic positions of putative AHV-1 miRNAs

No.Predicted mature miRNAsequence (5’ to 3’)Position,orientationLocation
1
CUCCCUUGCUUUGACAUGUCC
26325-26345, -
Within intergenic region between UL44 and UL45
2
UCGUUGGGCGGUUUCUUCGUG
72302-72322, -
Within intergenic region between UL26 and UL27
3
UUCAAACGGAGGCGUUGUGCG
72512-72532, +
Within intergenic region between UL26 and UL27
4
UUUCUGGGACCUCACCGCGGA
79262-79282, +
Within intergenic region between UL22 and UL23
5
UAAGAACUGCUGGUACCUUGC
112559-112579, +
Within intergenic region between UL4 and UL5
6
CAACGGAUGAACGUCGGCGCG
112720-112740, -
Within intergenic region between UL4 and UL5
7
CAUGGGAACAUUUAACACCCC
123128-123148, +
Within intergenic region between ICP22 and LORF2
8
UGGAUGGUUUGGAGACAGCUG
125173-125193, +
Within intergenic region between ICP4 and ICP22
9
UAUGUUUUGCCCGGGCAAAUG
132907-132927, +
Within intergenic region between US1 and ICP4
10
AAAUCUGGCGUUCGCACUCUG
134522-134542, -
Within intergenic region between US1 and ICP4
11
AUUUCGGAGUGCGAAUAUGUG
134586-134606, -
Within intergenic region between US1 and ICP4
12GGUAGGUUGUUUGGAGAUUGC160321-160341, +Within unique short terminal repeat region
Figure 2

Secondary structure predictions of AHV-1 pre-miRNAs. The putative mature miRNAs sequence were shown in red. A-L in order named AHV-1-pre-miR-1 to AHV-1-pre-miR-12.

Flowchart of the AHV-1 miRNA prediction procedure. Sequences and genomic positions of putative AHV-1 miRNAs Secondary structure predictions of AHV-1 pre-miRNAs. The putative mature miRNAs sequence were shown in red. A-L in order named AHV-1-pre-miR-1 to AHV-1-pre-miR-12. In order to investigate whether the AHV-1 miRNAs predicted are conserved in other herpesviruses, each of the 12 putative mature AHV-1 miRNA candidates was compared with the all known miRNAs of the herpesvirus family independently in database (http://www.sanger.ac.uk/Software/Rfam/mirna/). Interestingly, the “seed sequences” (nt 2 to 8) of 2 miRNAs were predicted to have the high conservation with the 2 miRNAs of MDV-1 (Table 2). The genomic positions of the 2 miRNAs encoded by MDV-1 are proximal to the latencyassociated transcript region. The vmiRNAs have generally been reported to lack sequence conservation across different viral species [10], with the exception of the primate polyomaviruses [11]. But among closed viral species, they could showed conservation in position and/or sequence. Jurak et al identified 16 and 17 miRNAs expressed by herpes simplex viruses 1 and 2 (HSV-1 and −2), respectively. The genomic positions of most miRNAs encoded by these two viruses are within or proximal to the latency associated transcript region. Nine miRNAs are conserved in position and/or sequence, particularly in the seed region, between these two viruses [16]. Additional, Waidner et al reported the genome locations, but not microRNA sequences, are conserved among four avian herpesviruses, infectious laryngotracheitis virus (ILTV) and herpesvirus of turkeys (HVT), as well as Marek's disease viruses (MDV-1 and MDV-2). Most are clustered in the repeats flanking the unique long region (I/TRL), except in ILTV which lacks these repeats [14]. So the result suggested that AHV-1 and MDV-1 should have closed evolutionary relation, which provides a valuable evidence of classification of AHV-1. Meanwhile, the prevalence of microRNAs in the genomic repeat regions suggests that the latent infection in herpesviruses could be relevant to function of microRNA.
Table 2

miRNA homologs expressed by AHV-1 and MDV-1

NamemiRNA sequenceaNo. of identical nt/totalin seed region (nt positions 2–8)
AHV-1-pre-miR-7
-CAUGGGAACAUU-UAACACCCCGCAUGGAAACGUCCUGGGAAA- -* * * * * †* * * † * † * † † *
5/7
MDV-1-miR-M13
 
AHV-1-pre-miR-9
UAUGUUUUGCCCGGGCAAAUG-UCUGUUGUUCCGUAGUGUUCUC* * * * * * * * † * † † †
5/7
MDV-1-miR-M6-5p  

Sequences of orthologous miRNAs expressed by AHV-1 and MDV-1 aligned using ClustalW2. The seed sequences are shown in boldface. Identical nucleotides and nucleotides with conserved target binding potential are indicated with * and †, respectively.

miRNA homologs expressed by AHV-1 and MDV-1 Sequences of orthologous miRNAs expressed by AHV-1 and MDV-1 aligned using ClustalW2. The seed sequences are shown in boldface. Identical nucleotides and nucleotides with conserved target binding potential are indicated with * and †, respectively. What is the function of the vmiRNA? In order to know whether the vmiRNA could modulate its own genes expression, we checked the 3’UTR of viral mRNAs that could perfectly complement with the “seed sequence” of vmiRNA. AHV-1-pre-miR-4 was predicted to target UL29 gene (DNA replication-recombination; binds single-stranded DNA) and US5 gene (unknown function). AHV-1-pre-miR-7 was predicted to target UL16 gene (capsid maturase). AHV-1-pre-miR-9 was predicted to target UL15B gene (DNA cleavage-encapsidation), UL45 (tegument/envelope protein), and US1 gene (immediate-early and late transrepressor protein). AHV-1-pre-miR-12 was predicted to target UL45 gene and US7 gene (cell-cell spread). However, none of gene targets were found for the other vmiRNAs. Additionally, we wonder whether the putative vmiRNA could be used by AHV-1 to modulate host cell genes expression profiles. But so far genome of duck is in the process of being annotated and there is not available 3’UTR database of duck genes, so prediction can not be carried on. Here, we introduced a concept that the AHV-1 genome could reasonably encode candidate pre-miRNAs. Studies are in progress to experimentally identify the putative vmiRNAs during AHV-1 infection.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JX carried out most of the data collection, data analysis and drafted the manuscript. ACC, MSW, SCZ, DKZ, RYJ, SC, YZ, XYW and XYC helped draft the manuscript. All authors read and approved the final manuscript.
  20 in total

Review 1.  The functions of animal microRNAs.

Authors:  Victor Ambros
Journal:  Nature       Date:  2004-09-16       Impact factor: 49.962

2.  Kaposi's sarcoma-associated herpesvirus expresses an array of viral microRNAs in latently infected cells.

Authors:  Xuezhong Cai; Shihua Lu; Zhihong Zhang; Carlos M Gonzalez; Blossom Damania; Bryan R Cullen
Journal:  Proc Natl Acad Sci U S A       Date:  2005-03-30       Impact factor: 11.205

Review 3.  MicroRNAs and viral infection.

Authors:  Christopher S Sullivan; Don Ganem
Journal:  Mol Cell       Date:  2005-10-07       Impact factor: 17.970

4.  Cloning and identification of a microRNA cluster within the latency-associated region of Kaposi's sarcoma-associated herpesvirus.

Authors:  Mark A Samols; Jianhong Hu; Rebecca L Skalsky; Rolf Renne
Journal:  J Virol       Date:  2005-07       Impact factor: 5.103

5.  A combined computational and microarray-based approach identifies novel microRNAs encoded by human gamma-herpesviruses.

Authors:  Adam Grundhoff; Christopher S Sullivan; Don Ganem
Journal:  RNA       Date:  2006-03-15       Impact factor: 4.942

6.  Human cytomegalovirus expresses novel microRNAs during productive viral infection.

Authors:  Walter Dunn; Phong Trang; Qiu Zhong; Edward Yang; Christopher van Belle; Fenyong Liu
Journal:  Cell Microbiol       Date:  2005-11       Impact factor: 3.715

7.  SV40-encoded microRNAs regulate viral gene expression and reduce susceptibility to cytotoxic T cells.

Authors:  Christopher S Sullivan; Adam T Grundhoff; Satvir Tevethia; James M Pipas; Don Ganem
Journal:  Nature       Date:  2005-06-02       Impact factor: 49.962

8.  Identification of microRNAs of the herpesvirus family.

Authors:  Sébastien Pfeffer; Alain Sewer; Mariana Lagos-Quintana; Robert Sheridan; Chris Sander; Friedrich A Grässer; Linda F van Dyk; C Kiong Ho; Stewart Shuman; Minchen Chien; James J Russo; Jingyue Ju; Glenn Randall; Brett D Lindenbach; Charles M Rice; Viviana Simon; David D Ho; Mihaela Zavolan; Thomas Tuschl
Journal:  Nat Methods       Date:  2005-02-16       Impact factor: 28.547

9.  Prediction and identification of herpes simplex virus 1-encoded microRNAs.

Authors:  Can Cui; Anthony Griffiths; Guanglin Li; Lindsey M Silva; Martha F Kramer; Terry Gaasterland; Xiu-Jie Wang; Donald M Coen
Journal:  J Virol       Date:  2006-06       Impact factor: 5.103

10.  Epstein-Barr virus microRNAs are evolutionarily conserved and differentially expressed.

Authors:  Xuezhong Cai; Alexandra Schäfer; Shihua Lu; John P Bilello; Ronald C Desrosiers; Rachel Edwards; Nancy Raab-Traub; Bryan R Cullen
Journal:  PLoS Pathog       Date:  2006-03-24       Impact factor: 6.823

View more
  5 in total

Review 1.  Programmed cell death: the battlefield between the host and alpha-herpesviruses and a potential avenue for cancer treatment.

Authors:  Chuankuo Zhao; Mingshu Wang; Anchun Cheng; Qiao Yang; Ying Wu; Dekang Zhu; Shun Chen; Mafeng Liu; XinXin Zhao; Renyong Jia; Kunfeng Sun; Xiaoyue Chen
Journal:  Oncotarget       Date:  2018-07-17

2.  Molecular characterization and antiapoptotic function analysis of the duck plague virus Us5 gene.

Authors:  Chuankuo Zhao; Tianqiong He; Yang Xu; Mingshu Wang; Anchun Cheng; XinXin Zhao; Dekang Zhu; Shun Chen; Mafeng Liu; Qiao Yang; Renyong Jia; Xiaoyue Chen; Ying Wu; Shaqiu Zhang; Yunya Liu; Yanling Yu; Ling Zhang
Journal:  Sci Rep       Date:  2019-03-19       Impact factor: 4.379

3.  The LORF5 Gene Is Non-essential for Replication but Important for Duck Plague Virus Cell-to-Cell Spread Efficiently in Host Cells.

Authors:  Bingjie Shen; Yunjiao Li; Anchun Cheng; Mingshu Wang; Ying Wu; Qiao Yang; Renyong Jia; Bin Tian; Xumin Ou; Sai Mao; Di Sun; Shaqiu Zhang; Dekang Zhu; Shun Chen; Mafeng Liu; Xin-Xin Zhao; Juan Huang; Qun Gao; Yunya Liu; Yanling Yu; Ling Zhang; Leichang Pan
Journal:  Front Microbiol       Date:  2021-12-02       Impact factor: 5.640

Review 4.  Role of virus-encoded microRNAs in Avian viral diseases.

Authors:  Yongxiu Yao; Venugopal Nair
Journal:  Viruses       Date:  2014-03-21       Impact factor: 5.048

5.  Duck Enteritis Virus VP16 Antagonizes IFN-β-Mediated Antiviral Innate Immunity.

Authors:  Yang Li; Mingshu Wang; Anchun Cheng; Renyong Jia; Qiao Yang; Shun Chen; Dekang Zhu; Mafeng Liu; Xinxin Zhao; Shaqiu Zhang; Juan Huang; Xumin Ou; Sai Mao; Yanling Yu; Ling Zhang; Yunya Liu; Leichang Pan; Bin Tian; Mujeeb Ur Rehman; Xiaoyue Chen
Journal:  J Immunol Res       Date:  2020-05-15       Impact factor: 4.818

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.