Prediction and analysis of human-herpes simplex virus type 1 protein-protein interactions by integrating multiple methods

doi:10.1007/s40484-020-0222-5

Quant. Biol.

2020, Vol. 8

Issue (4) : 312-324 https://doi.org/10.1007/s40484-020-0222-5

RESEARCH ARTICLE

Prediction and analysis of human-herpes simplex virus type 1 protein-protein interactions by integrating multiple methods

Xianyi Lian¹, Xiaodi Yang¹, Jiqi Shao², Fujun Hou³, Shiping Yang⁴(

), Dongli Pan³, Ziding Zhang¹(

)

¹. State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
². National Demonstration Center for Experimental Biological Sciences Education, College of Biological Sciences, China Agricultural University, Beijing 100193, China
³. Department of Medical Microbiology and Parasitology, and Department of Infectious Diseases of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
⁴. State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China

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Abstract

Background: Herpes simplex virus type 1 (HSV-1) is a ubiquitous infectious pathogen that widely affects human health. To decipher the complicated human-HSV-1 interactions, a comprehensive protein-protein interaction (PPI) network between human and HSV-1 is highly demanded.

Methods: To complement the experimental identification of human-HSV-1 PPIs, an integrative strategy to predict proteome-wide PPIs between human and HSV-1 was developed. For each human-HSV-1 protein pair, four popular PPI inference methods, including interolog mapping, the domain-domain interaction-based method, the domain-motif interaction-based method, and the machine learning-based method, were optimally implemented to generate four interaction probability scores, which were further integrated into a final probability score.

Results: As a result, a comprehensive high-confidence PPI network between human and HSV-1 was established, covering 10,432 interactions between 4,546 human proteins and 72 HSV-1 proteins. Functional and network analyses of the HSV-1 targeting proteins in the context of human interactome can recapitulate the known knowledge regarding the HSV-1 replication cycle, supporting the overall reliability of the predicted PPI network. Considering that HSV-1 infections are implicated in encephalitis and neurodegenerative diseases, we focused on exploring the biological significance of the brain-specific human-HSV-1 PPIs. In particular, the predicted interactions between HSV-1 proteins and Alzheimer’s-disease-related proteins were intensively investigated.

Conclusion: The current work can provide testable hypotheses to assist in the mechanistic understanding of the human-HSV-1 relationship and the anti-HSV-1 pharmaceutical target discovery. To make the predicted PPI network and the datasets freely accessible to the scientific community, a user-friendly database browser was released at http://www.zzdlab.com/HintHSV/index.php.

Keywords human-virus interaction protein-protein interaction prediction herpes simplex virus type 1 Alzheimer’s disease

Corresponding Author(s): Shiping Yang,Ziding Zhang

Just Accepted Date: 30 September 2020 Online First Date: 07 December 2020 Issue Date: 24 December 2020

Cite this article:

Xianyi Lian,Xiaodi Yang,Jiqi Shao, et al. Prediction and analysis of human-herpes simplex virus type 1 protein-protein interactions by integrating multiple methods[J]. Quant. Biol., 2020, 8(4): 312-324.

URL:

https://academic.hep.com.cn/qb/EN/10.1007/s40484-020-0222-5
https://academic.hep.com.cn/qb/EN/Y2020/V8/I4/312

Fig.1 Workflow for the prediction of human-HSV-1 PPIs.

Fig.2 Overlaps of the predicted PPIs among the four individual methods.

Fig.3 The number of human proteins predicted to interact with HSV-1 proteins.

Fig.4 Degree and betweenness centrality of human target proteins and non-target proteins.

Fig.5 Enriched GO terms of the brain-specific human proteins predicted to interact with HSV-1 proteins in the biological processes (A) and cellular component (B) categories.

Fig.6 Association between human-HSV-1 PPIs and the AD.

1	C. D. Conrady, , D. A. Drevets, and , D. J. J. Carr (2010) Herpes simplex type I (HSV-1) infection of the nervous system: is an immune response a good thing? J. Neuroimmunol., 220, 1–9 https://doi.org/10.1016/j.jneuroim.2009.09.013 pmid: 19819030
2	, R. Piacentini, G. De Chiara, , , D. D. Li Puma, , C. Ripoli, M. E. Marcocci, , , E. Garaci, , A. T. Palamara and C. Grassi, (2014) HSV-1 and Alzheimer’s disease: more than a hypothesis. Front. Pharmacol., 5, 97 https://doi.org/10.3389/fphar.2014.00097 pmid: 24847267
3	, D. Watanabe (2010) Medical application of herpes simplex virus. J. Dermatol. Sci., 57, 75–82 https://doi.org/10.1016/j.jdermsci.2009.10.014 pmid: 19939634
4	, G. De Chiara, R. Piacentini, , , M. Fabiani, , A. Mastrodonato, M. E. Marcocci, , , D. Limongi, , G. Napoletani, V. Protto, , , P. Coluccio, , I. Celestino, et al. (2019) Recurrent herpes simplex virus-1 infection induces hallmarks of neurodegeneration and cognitive deficits in mice. PLoS Pathog., 15, e1007617 https://doi.org/10.1371/journal.ppat.1007617 pmid: 30870531
5	, K. J. Looker, A. S. Magaret, , , M. T. May, , K. M. E. Turner, P. Vickerman, , , S. L. Gottlieb and , L. M. Newman (2015) Global and regional estimates of prevalent and incident herpes simplex virus type 1 infections in 2012. PLoS One, 10, e0140765 https://doi.org/10.1371/journal.pone.0140765 pmid: 26510007
6	, P. Ashford, A. Hernandez, , , T. M. Greco, , A. Buch, B. Sodeik, , , I. M. Cristea, , K. Grünewald, A. Shepherd, and , M. Topf (2016) HVint: a strategy for identifying novel protein-protein interactions in herpes simplex virus type 1. Mol. Cell. Proteomics, 15, 2939–2953 https://doi.org/10.1074/mcp.M116.058552 pmid: 27384951
7	, R. F. Itzhaki (2018) Corroboration of a major role for herpes simplex virus type 1 in Alzheimer’s disease. Front. Aging Neurosci., 10, 324 https://doi.org/10.3389/fnagi.2018.00324 pmid: 30405395
8	, I. Steiner (2011) Herpes simplex virus encephalitis: new infection or reactivation? Curr. Opin. Neurol., 24, 268–274 https://doi.org/10.1097/WCO.0b013e328346be6f pmid: 21483260
9	, R. F. Itzhaki, W. R. Lin, , , D. Shang, G. K. Wilcock, , , B. Faragher and , G. A. Jamieson (1997) Herpes simplex virus type 1 in brain and risk of Alzheimer’s disease. Lancet, 349, 241–244 https://doi.org/10.1016/S0140-6736(96)10149-5 pmid: 9014911
10	, A. M. Agelidis and D. Shukla, (2015) Cell entry mechanisms of HSV: what we have learned in recent years. Future Virol., 10, 1145–1154 https://doi.org/10.2217/fvl.15.85 pmid: 27066105
11	, A. Orvedahl, D. Alexander, , , Z. Tallóczy, , Q. Sun, Y. Wei, , , W. Zhang, D. Burns, , , D. A. Leib and , B. Levine (2007) HSV-1 ICP34.5 confers neurovirulence by targeting the Beclin 1 autophagy protein. Cell Host Microbe, 1, 23–35 https://doi.org/10.1016/j.chom.2006.12.001 pmid: 18005679
12	, M. C. Smith, C. Boutell, and , D. J. Davido (2011) HSV-1 ICP0: paving the way for viral replication. Future Virol., 6, 421–429 https://doi.org/10.2217/fvl.11.24 pmid: 21765858
13	, K. F. Bryant, Z. Yan, , , D. H. Dreyfus and , D. M. Knipe (2012) Identification of a divalent metal cation binding site in herpes simplex virus 1 (HSV-1) ICP8 required for HSV replication. J. Virol., 86, 6825–6834 https://doi.org/10.1128/JVI.00374-12 pmid: 22491472
14	, S. E. Dremel and N. A. DeLuca, (2019) Herpes simplex viral nucleoprotein creates a competitive transcriptional environment facilitating robust viral transcription and host shut off. eLife, 8, e51109 https://doi.org/10.7554/eLife.51109 pmid: 31638576
15	, S. Yang, C. Fu, , , X. Lian, X. Dong, and , Z. Zhang (2019) Understanding human-virus protein-protein interactions using a human protein complex-based analysis framework. mSystems, 4, e00303–e00318 https://doi.org/10.1128/mSystems.00303-18 pmid: 30984872
16	, E. Nourani, F. Khunjush, and , S. Durmuş (2016) Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data. Mol. Biosyst., 12, 1976–1986 https://doi.org/10.1039/C6MB00065G pmid: 27072625
17	, F.-E. Eid, M. ElHefnawi, and , L. S. Heath (2016) DeNovo: virus-host sequence-based protein-protein interaction prediction. Bioinformatics, 32, 1144–1150 https://doi.org/10.1093/bioinformatics/btv737 pmid: 26677965
18	, Y. Qi, O. Tastan, , , J. G. Carbonell, , J. Klein-Seetharaman and J. Weston, (2010) Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins. Bioinformatics, 26, i645–i652 https://doi.org/10.1093/bioinformatics/btq394 pmid: 20823334
19	, Y. Zhou, Y. S. Zhou, , , F. He, J. Song, and , Z. Zhang (2012) Can simple codon pair usage predict protein-protein interaction? Mol. Biosyst., 8, 1396–1404 https://doi.org/10.1039/c2mb05427b pmid: 22392100
20	, Y. Guo, L. Yu, , , Z. Wen and M. Li, (2008) Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res., 36, 3025–3030 https://doi.org/10.1093/nar/gkn159 pmid: 18390576
21	, M. Kotlyar, C. Pastrello, , , F. Pivetta, , A. Lo Sardo, C. Cumbaa, , , H. Li, , T. Naranian, Y. Niu, , , Z. Ding, F. Vafaee, , et al. (2015) In silico prediction of physical protein interactions and characterization of interactome orphans. Nat. Methods, 12, 79–84 https://doi.org/10.1038/nmeth.3178 pmid: 25402006
22	, Z.-G. Li, F. He, , , Z. Zhang and Y.-L. Peng, (2012) Prediction of protein-protein interactions between Ralstonia solanacearum and Arabidopsis thaliana. Amino Acids, 42, 2363–2371 https://doi.org/10.1007/s00726-011-0978-z pmid: 21786137
23	S. Schleker, J. Garcia-Garcia, , J. Klein-Seetharaman, and , B. Oliva (2012) Prediction and comparison of Salmonella-human and Salmonella-Arabidopsis interactomes. Chem. Biodivers., 9, 991–1018 https://doi.org/10.1002/cbdv.201100392 pmid: 22589098
24	, P. Evans, W. Dampier, , , L. Ungar and , A. Tozeren (2009) Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs. BMC Med. Genomics, 2, 27 https://doi.org/10.1186/1755-8794-2-27 pmid: 19450270
25	, S. A. Lee, C. H. Chan, , , C. H. Tsai, J. M. Lai, , , F. S. Wang, , C. Y. Kao and C. Y. Huang, (2008) Ortholog-based protein-protein interaction prediction and its application to inter-species interactions. BMC Bioinformatics, 9, S11 https://doi.org/10.1186/1471-2105-9-S12-S11 pmid: 19091010
26	, Z. Itzhaki (2011) Domain-domain interactions underlying herpesvirus-human protein-protein interaction networks. PLoS One, 6, e21724 https://doi.org/10.1371/journal.pone.0021724 pmid: 21760902
27	, E. A. Franzosa and Y. Xia, (2011) Structural principles within the human-virus protein-protein interaction network. Proc. Natl. Acad. Sci. USA, 108, 10538–10543 https://doi.org/10.1073/pnas.1101440108 pmid: 21680884
28	, T. Hagai, A. Azia, , , M. M. Babu and R. Andino, (2014) Use of host-like peptide motifs in viral proteins is a prevalent strategy in host-virus interactions. Cell Reports, 7, 1729–1739 https://doi.org/10.1016/j.celrep.2014.04.052 pmid: 24882001
29	, S. Yang, H. Li, , , H. He, Y. Zhou, and , Z. Zhang (2019) Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods. Brief. Bioinform., 20, 274–287 https://doi.org/10.1093/bib/bbx123 pmid: 29028906
30	, C. von Mering, L. J. Jensen, , , B. Snel, , S. D. Hooper, M. Krupp, , , M. Foglierini, , N. Jouffre, M. A. Huynen, and , P. Bork (2005) STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res., 33, D433–D437 https://doi.org/10.1093/nar/gki005 pmid: 15608232
31	, S. Bandyopadhyay, S. Ray, , , A. Mukhopadhyay and , U. Maulik (2015) A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions. Brief. Bioinform., 16, 830–851 https://doi.org/10.1093/bib/bbu041 pmid: 25479794
32	, M. L. Szpara, D. Gatherer, , , A. Ochoa, , B. Greenbaum, A. Dolan, , , R. J. Bowden, , L. W. Enquist, M. Legendre, and , A. J. Davison (2014) Evolution and diversity in human herpes simplex virus genomes. J. Virol., 88, 1209–1227 https://doi.org/10.1128/JVI.01987-13 pmid: 24227835
33	, L. Meyniel-Schicklin, B. de Chassey, , , P. André and , V. Lotteau (2012) Viruses and interactomes in translation. Mol. Cell. Proteomics, 11, M111.014738
34	, M. D. Dyer, T. M. Murali, and , B. W. Sobral (2008) The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog., 4, e32 https://doi.org/10.1371/journal.ppat.0040032 pmid: 18282095
35	, N. Zhang, J. Yan, , , G. Lu, Z. Guo, , , Z. Fan, , J. Wang, , Y. Shi, , J. Qi and G. F. Gao, (2011) Binding of herpes simplex virus glycoprotein D to nectin-1 exploits host cell adhesion. Nat. Commun., 2, 577 https://doi.org/10.1038/ncomms1571 pmid: 22146396
36	, W. Azab, A. Gramatica, , , A. Herrmann and , N. Osterrieder (2015) Binding of alphaherpesvirus glycoprotein H to surface α4β1-integrins activates calcium-signaling pathways and induces phosphatidylserine exposure on the plasma membrane. MBio, 6, e01552–15 https://doi.org/10.1128/mBio.01552-15 pmid: 26489864
37	, K. Zheng, M. Chen, , , Y. Xiang, K. Ma, , , F. Jin, , X. Wang, X. Wang, , , S. Wang and Y. Wang, (2014) Inhibition of herpes simplex virus type 1 entry by chloride channel inhibitors tamoxifen and NPPB. Biochem. Biophys. Res. Commun., 446, 990–996. https://doi.org/10.1016/j.bbrc.2014.03.050 pmid: 24657267
38	, R. J. O’Brien and P. C. Wong, (2011) Amyloid precursor protein processing and Alzheimer’s disease. Annu. Rev. Neurosci., 34, 185–204 https://doi.org/10.1146/annurev-neuro-061010-113613 pmid: 21456963
39	, M. Mayer-Proschel, J. M. Hogestyn, and , D. J. Mock (2018) Contributions of neurotropic human herpesviruses herpes simplex virus 1 and human herpesvirus 6 to neurodegenerative disease pathology. Neural Regen. Res., 13, 211–221 https://doi.org/10.4103/1673-5374.226380 pmid: 29557362
40	, B. Readhead, J.-V. Haure-Mirande, , , C. C. Funk, , M. A. Richards, P. Shannon, , , V. Haroutunian, , M. Sano, W. S. Liang, , , N. D. Beckmann, , N. D. Price, et al. (2018) Multiscale analysis of independent Alzheimer’s cohorts finds disruption of molecular, genetic, and clinical networks by human herpesvirus. Neuron, 99, 64–82.e7 https://doi.org/10.1016/j.neuron.2018.05.023 pmid: 29937276
41	, K. Bourgade, H. Garneau, , , G. Giroux, , A. Y. Le Page, C. Bocti, , , G. Dupuis, , E. H. Frost and T. Fülöp, Jr. (2015) b-Amyloid peptides display protective activity against the human Alzheimer’s disease-associated herpes simplex virus-1. Biogerontology, 16, 85–98 https://doi.org/10.1007/s10522-014-9538-8. pmid: 25376108
42	The UniProt Consortium. (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res., 47, D506–D515 https://doi.org/10.1093/nar/gky1049 pmid: 30395287
43	, X. Lian, S. Yang, , , H. Li, C. Fu, and , Z. Zhang (2019) Machine-learning-based predictor of human-bacteria protein-protein interactions by incorporating comprehensive host-network properties. J. Proteome Res., 18, 2195–2205 https://doi.org/10.1021/acs.jproteome.9b00074 pmid: 30983371
44	, G. Csárdi and T. Nepusz, (2006) The igraph software package for complex network research. InterJournal Complex Syst., 1695, 1–9
45	, C. Stark, B.-J. Breitkreutz, , , T. Reguly, , L. Boucher, A. Breitkreutz, and , M. Tyers (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res., 34, D535–D539 https://doi.org/10.1093/nar/gkj109 pmid: 16381927
46	, I. Xenarios, D. W. Rice, , , L. Salwinski, , M. K. Baron, E. M. Marcotte, and , D. Eisenberg (2000) DIP: the database of interacting proteins. Nucleic Acids Res., 28, 289–291 https://doi.org/10.1093/nar/28.1.289 pmid: 10592249
47	, M. G. Ammari, C. R. Gresham, , , F. M. McCarthy and , B. Nanduri (2016) HPIDB 2.0: a curated database for host-pathogen interactions. Database (Oxford), baw103 https://doi.org/10.1093/database/baw103 pmid: 27374121
48	, H. Hermjakob, L. Montecchi-Palazzi, , , C. Lewington, , S. Mudali, S. Kerrien, , , S. Orchard, , M. Vingron, B. Roechert, , , P. Roepstorff, , A. Valencia, et al. (2004) IntAct: an open source molecular interaction database. Nucleic Acids Res., 32, D452–D455 https://doi.org/10.1093/nar/gkh052 pmid: 14681455
49	, A. R. Wattam, J. J. Davis, , , R. Assaf, , S. Boisvert, T. Brettin, , , C. Bun, , N. Conrad, E. M. Dietrich, , , T. Disz, , J. L. Gabbard, et al. (2017) Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res., 45, D535–D542 https://doi.org/10.1093/nar/gkw1017 pmid: 27899627
50	, K. Breuer, A. K. Foroushani, , , M. R. Laird, , C. Chen, A. Sribnaia, , , R. Lo, , G. L. Winsor, R. E. W. Hancock, , , F. S. L. Brinkman and , D. J. Lynn (2013) InnateDB: systems biology of innate immunity and beyond−recent updates and continuing curation. Nucleic Acids Res., 41, D1228–D1233 https://doi.org/10.1093/nar/gks1147 pmid: 23180781
51	, T. Guirimand, S. Delmotte, and , V. Navratil (2015) VirHostNet 2.0: surfing on the web of virus/host molecular interactions data. Nucleic Acids Res., 43, D583–D587 https://doi.org/10.1093/nar/gku1121 pmid: 25392406
52	, M. H. Schaefer, J. F. Fontaine, , , A. Vinayagam, , P. Porras, E. E. Wanker, and M. A. Andrade-Navarro, (2012) HIPPIE: Integrating protein interaction networks with experiment based quality scores. PLoS One, 7, e31826 https://doi.org/10.1371/journal.pone.0031826 pmid: 22348130
53	, A. Stein, A. Céol, and , P. Aloy (2011) 3did: identification and classification of domain-based interactions of known three-dimensional structure. Nucleic Acids Res., 39, D718–D723 https://doi.org/10.1093/nar/gkq962 pmid: 20965963
54	, X. Liu, Y. Huang, , , J. Liang, , S. Zhang, Y. Li, , , J. Wang, Y. Shen, , , Z. Xu and , Y. Zhao (2014) Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites. BMC Bioinformatics, 15, 393 https://doi.org/10.1186/s12859-014-0393-z pmid: 25433733
55	, S. El-Gebali, J. Mistry, , , A. Bateman, , S. R. Eddy, A. Luciani, , , S. C. Potter, , M. Qureshi, L. J. Richardson, , , G. A. Salazar, , A. Smart, et al. (2019) The Pfam protein families database in 2019. Nucleic Acids Res., 47, D427–D432 https://doi.org/10.1093/nar/gky995 pmid: 30357350
56	, S. R. Eddy (1998) Profile hidden Markov models. Bioinformatics, 14, 755–763 https://doi.org/10.1093/bioinformatics/14.9.755 pmid: 9918945
57	, S. Maere, K. Heymans, and , M. Kuiper (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics, 21, 3448–3449 https://doi.org/10.1093/bioinformatics/bti551 pmid: 15972284
58	, P. Shannon, A. Markiel, , , O. Ozier, , N. S. Baliga, J. T. Wang, , , D. Ramage, N. Amin, , , B. Schwikowski and , T. Ideker (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 13, 2498–2504 https://doi.org/10.1101/gr.1239303 pmid: 14597658

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