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http://dx.doi.org/10.7853/kjvs.2019.42.2.73

Computational approaches for prediction of protein-protein interaction between Foot-and-mouth disease virus and Sus scrofa based on RNA-Seq  

Park, Tamina (Department of Predictive Toxicology, Korea Institute of Toxicology)
Kang, Myung-gyun (Department of Predictive Toxicology, Korea Institute of Toxicology)
Nah, Jinju (Animal and Plant Quarantine Agency)
Ryoo, Soyoon (Animal and Plant Quarantine Agency)
Wee, Sunghwan (Animal and Plant Quarantine Agency)
Baek, Seung-hwa (Department of Predictive Toxicology, Korea Institute of Toxicology)
Ku, Bokkyung (Animal and Plant Quarantine Agency)
Oh, Yeonsu (Department of Veterinary Pathology, College of Veterinary Medicine & Institute of Veterinary Science, Kangwon National University)
Cho, Ho-seong (College of Veterinary Medicine and Bio-safety Research Institute, Chonbuk National University)
Park, Daeui (Department of Predictive Toxicology, Korea Institute of Toxicology)
Publication Information
Korean Journal of Veterinary Service / v.42, no.2, 2019 , pp. 73-83 More about this Journal
Abstract
Foot-and-Mouth Disease (FMD) is a highly contagious trans-boundary viral disease caused by FMD virus, which causes huge economic losses. FMDV infects cloven hoofed (two-toed) mammals such as cattle, sheep, goats, pigs and various wildlife species. To control the FMDV, it is necessary to understand the life cycle and the pathogenesis of FMDV in host. Especially, the protein-protein interaction between FMDV and host will help to understand the survival cycle of viruses in host cell and establish new therapeutic strategies. However, the computational approach for protein-protein interaction between FMDV and pig hosts have not been applied to studies of the onset mechanism of FMDV. In the present work, we have performed the prediction of the pig's proteins which interact with FMDV based on RNA-Seq data, protein sequence, and structure information. After identifying the virus-host interaction, we looked for meaningful pathways and anticipated changes in the host caused by infection with FMDV. A total of 78 proteins of pig were predicted as interacting with FMDV. The 156 interactions include 94 interactions predicted by sequence-based method and the 62 interactions predicted by structure-based method using domain information. The protein interaction network contained integrin as well as STYK1, VTCN1, IDO1, CDH3, SLA-DQB1, FER, and FGFR2 which were related to the up-regulation of inflammation and the down-regulation of cell adhesion and host defense systems such as macrophage and leukocytes. These results provide clues to the knowledge and mechanism of how FMDV affects the host cell.
Keywords
FMDV; Sus scrofa; RNA-Seq; Virus-host interaction; Protein-protein interaction;
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1 Fox NK, Brenner SE. & Chandonia JM. 2014. SCOPe: Structural Classification of Proteins-extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Research, 42(D1), D304-D309. doi:10.1093/nar/gkt1240   DOI
2 Franzosa EA. & Xia Y. 2011. Structural principles within the human-virus protein-protein interaction network. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1101440108   DOI
3 Gao Y, Sun SQ. & Guo HC. 2016. Biological function of Foot-and-mouth disease virus non-structural proteins and non-coding elements. Virology Journal, 13(1), 107. doi:10.1186/s12985-016-0561-z   DOI
4 Gladue DP, O'Donnell V, Baker-Branstetter R, Holinka LG, Pacheco JM, Fernandez-Sainz I. et al. 2012. Foot-and-mouth disease virus nonstructural protein 2C interacts with Beclin1, modulating virus replication. Journal of virology, 86(22), 12080-90. doi:10.1128/JVI.01610-12   DOI
5 Han SC, Guo HC, Sun SQ, Jin Y, Wei YQ, Feng X, et al. 2016. Productive Entry of Foot-and-Mouth Disease Virus via Macropinocytosis Independent of Phosphatidylinositol 3-Kinase. Scientific Reports, 6(1), 19294. doi:10.1038/srep19294   DOI
6 Itzhaki Z. 2011. Domain-domain interactions underlying herpesvirus-human protein-protein interaction networks. PLoS ONE. doi:10.1371/journal.pone.0021724   DOI
7 Jackson T, Clark S, Berryman S, Burman A, Cambier S, Mu D. et al. 2004. Integrin alphavbeta8 functions as a receptor for foot-and-mouth disease virus: role of the beta-chain cytodomain in integrin-mediated infection. Journal of virology, 78(9), 4533-40. doi:10.1128/JVI.78.9.4533-4540.2004   DOI
8 Lester SN. & Li K. 2014. Toll-like receptors in antiviral innate immunity. Journal of molecular biology, 426(6), 1246. doi:10.1016/J.JMB.2013.11.024   DOI
9 Neff S, Sa-Carvalho D, Rieder E, Mason PW, Blystone SD, Brown EJ. & Baxt B. 1998. Foot-and-mouth disease virus virulent for cattle utilizes the integrin alpha(v)beta3 as its receptor. Journal of virology, 72(5), 3587-94. http://www.ncbi.nlm.nih.gov/pubmed/9557639. Accessed 2 January 2019   DOI
10 Liao Y, Smyth GK. & Shi W. 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923-930. doi:10.1093/bioinformatics/btt656   DOI
11 Park J, Lappe M. & Teichmann SA. 2001. Mapping protein family interactions: intramolecular and intermolecular protein family interaction repertoires in the PDB and yeast. Journal of Molecular Biology, 307(3), 929-938. doi:10.1006/JMBI.2001.4526   DOI
12 Rai MF, Tycksen ED, Sandell LJ. & Brophy RH. 2018. Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears. Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 36(1), 484-497. doi:10.1002/jor.23661   DOI
13 Robinson MD, McCarthy DJ. & Smyth GK. 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (Oxford, England), 26(1), 139-40. doi:10.1093/bioinformatics/btp616   DOI
14 Rodriguez Pulido M. & Saiz M. 2017. Molecular Mechanisms of Foot-and-Mouth Disease Virus Targeting the Host Antiviral Response. Frontiers in cellular and infection microbiology, 7, 252. doi:10.3389/fcimb.2017.00252   DOI
15 Shen J, Zhang J, Luo X, Zhu W, Yu K, Chen K, et al. 2007. Predicting protein-protein interactions based only on sequences information. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.0607879104   DOI
16 Zhang T, Chen H, Qi L, Zhang J, Wu R, Zhang Y. & Sun Y. 2018. Transcript Profiling Identifies Early Response Genes against FMDV Infection in PK-15 Cells. Viruses, 10(7), 364. doi:10.3390/v10070364   DOI
17 Walhout AJ, Sordella R, Lu X, Hartley JL, Temple GF, Brasch MA, et al. 2000. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science (New York, N.Y.), 287(5450), 116-22. doi:10.1126/SCIENCE.287.5450.116   DOI
18 Wang D, Fang L, Li P, Sun L, Fan J, Zhang Q, et al. 2011. The leader proteinase of foot-and-mouth disease virus negatively regulates the type I interferon pathway by acting as a viral deubiquitinase. Journal of virology, 85(8), 3758-66. doi:10.1128/JVI.02589-10   DOI
19 Wang Z. & Ma'ayan A. 2016. An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study. F1000Research, 5, 1574. doi:10.12688/f1000research.9110.1   DOI
20 Yu H, Luscombe NM, Lu HX, Zhu X, Xia Y, Han JDJ, et al. 2004. Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome research, 14(6), 1107-18. doi:10.1101/gr.1774904   DOI
21 Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. 2013. STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England), 29(1), 15-21. doi:10.1093/bioinformatics/bts635   DOI
22 Alexandersen S, Zhang Z, Donaldson A. & Garland AJ. 2003. The Pathogenesis and Diagnosis of Foot-and-Mouth Disease. Journal of Comparative Pathology, 129(1), 1-36. doi:10.1016/S0021-9975(03)00041-0   DOI
23 Baxt B. & Becker Y. 1990. The effect of peptides containing the arginine-glycine-aspartic acid sequence on the adsorption of foot-and-mouth disease virus to tissue culture cells. Virus genes, 4(1), 73-83. http://www.ncbi.nlm.nih.gov/pubmed/2168107. Accessed 31 December 2018   DOI
24 Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. 2009. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics (Oxford, England), 25(8), 1091-3. doi:10.1093/bioinformatics/btp101   DOI
25 Boratyn GM, Schaffer AA, Agarwala R, Altschul SF, Lipman DJ. & Madden TL. 2012. Domain enhanced lookup time accelerated BLAST. Biology direct, 7, 12. doi:10.1186/1745-6150-7-12   DOI
26 Boutet E, Lieberherr D, Tognolli M, Schneider M, Bansal P, Bridge AJ, et al. 2016. UniProtKB/Swiss-Prot, the Manually Annotated Section of the UniProt KnowledgeBase: How to Use the Entry View (pp. 23-54). Humana Press, New York, NY. doi:10.1007/978-1-4939-3167-5_2
27 Christensen LS, Normann P, Thykier-Nielsen S, Sorensen JH, Stricker K. de & Rosenorn S. 2005. Analysis of the epidemiological dynamics during the 1982-1983 epidemic of foot-and-mouth disease in Denmark based on molecular high-resolution strain identification. Journal of General Virology, 86(9), 2577-2584. doi:10.1099/vir.0.80878-0   DOI
28 Cook H, Doncheva N, Szklarczyk D, von Mering C, Jensen L, Cook HV, et al. 2018. Viruses.STRING: A Virus-Host Protein-Protein Interaction Database. Viruses, 10(10), 519. doi:10.3390/v10100519   DOI