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Host-Pathogen Interactions pp 97–112Cite as

Systems Biology Modeling to Study Pathogen–Host Interactions

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1734))

Abstract

Pathogen–host interactions (PHIs) underlie the process of infection. The systems biology view of the whole PHI system is superior to the investigation of the pathogen or host separately in understanding the infection mechanisms. Especially, the identification of host-oriented drug targets for the next-generation anti-infection therapeutics requires the properties of the host factors targeted by pathogens. Here, we provide an outline of computational analysis of PHI networks, focusing on the properties of the pathogen-targeted host proteins. We also provide information about the available PHI data and the related Web-based resources.

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References

  1. Durmuş Tekir S, Ülgen K (2013) Systems biology of pathogen-host interaction: networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era. Biotechnol J 8:85–96

    Article  PubMed  Google Scholar 

  2. Bunnik EM, Le Roch KG (2013) An introduction to functional genomics and systems biology. Adv Wound Care 2:490–498

    Article  Google Scholar 

  3. Dix A, Vlaic S, Guthke R, Linde J (2016) Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers. Clin Microbiol Infect 22:600–606

    Article  CAS  PubMed  Google Scholar 

  4. Durmuş S, Çakır T, Özgür A, Guthke R (2015) A review on computational systems biology of pathogen-host interactions. Front Microbiol 6:1–19

    Google Scholar 

  5. de Chassey B, Meyniel-Schicklin L, Vonderscher J et al (2014) Virus-host interactomics: new insights and opportunities for antiviral drug discovery. Genome Med 6:115

    Article  PubMed  PubMed Central  Google Scholar 

  6. CC W, Chen BS (2016) A systems biology approach to the coordination of defensive and offensive molecular mechanisms in the innate and adaptive host-pathogen interaction networks. PLoS One 11:e0149303

    Article  Google Scholar 

  7. Forst CV (2006) Host – pathogen systems biology. Drug Discov Today 11:220–227

    Article  CAS  PubMed  Google Scholar 

  8. Sen R, Nayak L, De RK (2016) A review on host–pathogen interactions: classification and prediction. Eur J Clin Microbiol Infect Dis 35:1581–1599

    Article  CAS  PubMed  Google Scholar 

  9. Lengeling A, Pfeffer K, Balling R (2001) The battle of two genomes: genetics of bacterial host/pathogen interactions in mice. Mamm Genome 12:261–271

    Article  CAS  PubMed  Google Scholar 

  10. Stebbins CE (2005) Structural microbiology at the pathogen-host interface. Cell Microbiol 7:1227–1236

    Article  CAS  PubMed  Google Scholar 

  11. Korkin D, Thieu T, Joshi S, Warren S (2006) Mining host-pathogen interactions. In: Yang N-S (ed) Systems and computational biology – molecular and cellular experimental systems. InTech, Rijeka, pp 163–184

    Google Scholar 

  12. Davis FP, Barkan DT, Eswar N et al (2007) Host pathogen protein interactions predicted by comparative modeling. Protein Sci 16:2585–2596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dyer MD, Murali TM, Sobral BW (2007) Computational prediction of host-pathogen protein-protein interactions. Bioinformatics 23:i159. https://doi.org/10.1093/bioinformatics/btm208

    Article  CAS  PubMed  Google Scholar 

  14. Krishnadev O, Srinivasan N (2008) A data integration approach to predict host-pathogen protein-protein interactions: application to recognize protein interactions between human and a malarial parasite. In Silico Biol 8:235–250

    CAS  PubMed  Google Scholar 

  15. Lee S-A, Chan C, Tsai C-H et al (2008) Ortholog-based protein-protein interaction prediction and its application to inter-species interactions. BMC Bioinformatics 9(Suppl 12):S11

    Article  PubMed  PubMed Central  Google Scholar 

  16. Tastan O, Qi Y, Carbonell JG, Klein-Seetharaman J (2009) Prediction of interactions between HIV-1 and human proteins by information integration. Pac Symp Biocomput 2009:516–527

    Google Scholar 

  17. Evans P, Dampier W, Ungar L, Tozeren A (2009) Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs. BMC Med Genomics 2:27

    Article  PubMed  PubMed Central  Google Scholar 

  18. Doolittle JM, Gomez SM (2010) Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens. Virol J 7:82

    Article  PubMed  PubMed Central  Google Scholar 

  19. Novkovic M, Onder L, Cupovic J et al (2016) Topological small-world organization of the fibroblastic reticular cell network determines lymph node functionality. PLoS Biol 14:1–20

    Article  Google Scholar 

  20. Ramakrishnan G, Srinivasan N, Padmapriya P, Natarajan V (2015) Homology-based prediction of potential protein – protein interactions between human erythrocytes and plasmodium falciparum. Bioinform Biol Insight 9:195–206

    Article  Google Scholar 

  21. Nourani E, Khunjush F, Durmuş S (2015) Computational approaches for prediction of pathogen-host protein-protein interactions. Front Microbiol 6:1–10

    Article  Google Scholar 

  22. Calderwood MA, Venkatesan K, Xing L et al (2007) Epstein-Barr virus and virus human protein interaction maps. Proc Natl Acad Sci U S A 104:7606–7611

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. de Chassey B, Navratil V, Tafforeau L et al (2008) Hepatitis C virus infection protein network. Mol Syst Biol 4:230

    Article  PubMed  PubMed Central  Google Scholar 

  24. Forsman A, Rüetschi U, Ekholm J, Rymo L (2008) Identification of intracellular proteins associated with the EBV-encoded nuclear antigen 5 using an efficient TAP procedure and FT-ICR mass spectrometry. J Proteome Res 7:2309–2319

    Article  CAS  PubMed  Google Scholar 

  25. Gautier VW, Gu L, O’Donoghue N et al (2009) In vitro nuclear interactome of the HIV-1 Tat protein. Retrovirology 6:47

    Article  PubMed  PubMed Central  Google Scholar 

  26. Shapira SD, Gat-viks I, Shum BOV et al (2009) A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 139:1255–1267

    Article  PubMed  PubMed Central  Google Scholar 

  27. Dyer MD, Neff C, Dufford M et al (2010) The Human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis. PLoS One 5:e12089

    Article  PubMed  PubMed Central  Google Scholar 

  28. Tripathi LP, Kataoka C, Taguwa S et al (2010) Network based analysis of hepatitis C virus core and NS4B protein interactions. Mol Biosyst 6:2539–2553

    Article  CAS  PubMed  Google Scholar 

  29. Khadka S, Vangeloff AD, Zhang C et al (2011) A physical interaction network of dengue virus and human proteins. Mol Cell Proteomics 10:1–16

    Article  Google Scholar 

  30. Komarova AV, Combredet C, Meyniel-Schicklin L et al (2011) Proteomic analysis of virus-host interactions in an infectious context using recombinant viruses. Mol Cell Proteomics 10:M110.007443

    Article  PubMed  PubMed Central  Google Scholar 

  31. Yang H, Ke Y, Wang J et al (2011) Insight into bacterial virulence mechanisms against host immune response via the yersinia pestis-human protein-protein interaction network. Infect Immun 79:4413–4424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jäger S, Cimermancic P, Gulbahce N et al (2012) Global landscape of HIV-human protein complexes. Nature 481:365–370

    Article  Google Scholar 

  33. Pichlmair A, Kandasamy K, Alvisi G et al (2012) Viral immune modulators perturb the human molecular network by common and unique strategies. Nature 487:486–490

    Article  CAS  PubMed  Google Scholar 

  34. Wu W, Tran KC, Teng MN et al (2012) The interactome of the human respiratory syncytial virus NS1 protein highlights multiple effects on host cell biology. J Virol 86:7777–7789

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Dolan PT, Zhang C, Khadka S et al (2013) Identification and comparative analysis of hepatitis C virus-host cell protein interactions. Mol Biosyst 9:3199–3209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ngo HTT, Pham LV, Kim J-W et al (2013) Modulation of mitogen-activated protein kinase-activated protein kinase 3 by hepatitis C virus core protein. J Virol 87:5718–5731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kwofie SK, Schaefer U, Sundararajan VS et al (2011) HCVpro: Hepatitis C virus protein interaction database. Infect Genet Evol 11:1971–1977

    Article  CAS  PubMed  Google Scholar 

  38. Fu W, Sanders-Beer BE, Katz KS et al (2009) Human immunodeficiency virus type 1, human protein interaction database at NCBI. Nucleic Acids Res 37:417–422

    Article  Google Scholar 

  39. Bleves S, Dunger I, Walter MC et al (2014) HoPaCI-DB: Host-Pseudomonas and Coxiella interaction database. Nucleic Acids Res 42:671–676

    Article  Google Scholar 

  40. Kumar R, Nanduri B (2010) HPIDB - a unified resource for host-pathogen interactions. BMC Bioinformatics 11(Suppl. 6):S16

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wattam AR, Abraham D, Dalay O et al (2014) PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res 42:581–591

    Article  Google Scholar 

  42. Urban M, Pant R, Raghunath A et al (2015) The Pathogen-Host Interactions database (PHI-base): additions and future developments. Nucleic Acids Res 43:D645–D655

    Article  CAS  PubMed  Google Scholar 

  43. Durmuş Tekir S, Çakır T, Ardıç E et al (2013) PHISTO: pathogen-host interaction search tool. Bioinformatics 29:1357–1358

    Article  PubMed  Google Scholar 

  44. Vialás V, Nogales-Cadenas R, Nombela C et al (2009) Proteopathogen, a protein database for studying Candida albicans - host interaction. Proteomics 9:4664–4668

    Article  PubMed  Google Scholar 

  45. Navratil V, De chassey B, Meyniel L et al (2009) VirHostNet: a knowledge base for the management and the analysis of proteome-wide virus-host interaction networks. Nucleic Acids Res 37:661–668

    Article  Google Scholar 

  46. Calderone A, Licata L, Cesareni G (2015) VirusMentha: a new resource for virus-host protein interactions. Nucleic Acids Res 43:D588–D592

    Article  CAS  PubMed  Google Scholar 

  47. Dyer MD, Murali TM, Sobral BW (2008) The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog 4:e32

    Article  PubMed  PubMed Central  Google Scholar 

  48. Singh I, Tastan O, Klein-Seetharaman J (2010) Comparison of virus interactions with human signal transduction pathways. In: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, Niagara Falls, NY, 17–24

    Google Scholar 

  49. Durmuş S, Ülgen K (2016) Comparative interactomics for virus-human protein-protein interactions: DNA viruses versus RNA viruses. FEBS Open Bio 7:96–107

    Article  Google Scholar 

  50. Tekir SD, Çakır T, Ülgen KÖ (2012) Infection strategies of bacterial and viral pathogens through pathogen-human protein-protein interactions. Front Microbiol 3:1–11

    Google Scholar 

  51. Gene Ontology Consortium (2010) The Gene Ontology in 2010 : extensions and refinements. Nucleic Acids Res 38:331–335

    Article  Google Scholar 

  52. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Du Z, Zhou X, Ling Y et al (2010) agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res 38:64–70

    Article  Google Scholar 

  54. Reimand J, Arak T, Adler P et al (2016) g:Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res 44:W83–W89

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Carbon S, Ireland A, Mungall CJ et al (2009) AmiGO: online access to ontology and annotation data. Bioinformatics 25:288–289

    Article  CAS  PubMed  Google Scholar 

  56. Maere S, Heymans K, Kuiper M (2005) BiNGO : a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks. Bioinformatics 21:3448–3449

    Article  CAS  PubMed  Google Scholar 

  57. Zheng Q, Wang X (2008) GOEAST : a web-based software toolkit for Gene Ontology enrichment analysis. Nucleic Acids Res 36:358–363

    Article  Google Scholar 

  58. Huang DW, Sherman BT, Lempicki RA (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57

    Article  Google Scholar 

  59. Eden E, Navon R, Steinfeld I et al (2009) GOrilla : a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 7:1–7

    Google Scholar 

  60. Falcon SÃ, Gentleman R (2007) Using GOstats to test gene lists for GO term association. Bioinformatics 23:257–258

    Article  CAS  PubMed  Google Scholar 

  61. Beißbarth T, Speed TP (2004) GOstat : find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20:1464–1465

    Article  PubMed  Google Scholar 

  62. Bauer S, Grossmann S, Vingron M, Robinson PN (2008) Ontologizer 2.0 - a multifunctional tool for GO term enrichment analysis and data exploration. Bioinformatics 24:1650–1651

    Article  CAS  PubMed  Google Scholar 

  63. Xu Q, Shi Y, Lu Q et al (2008) GORouter: an RDF model for providing semantic query and inference services for Gene Ontology and its associations. BMC Bioinformatics 9(Suppl 1):S6

    Article  PubMed  PubMed Central  Google Scholar 

  64. Na D, Son H, Gsponer J (2014) Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity. BMC Genomics 15:1091

    Article  PubMed  PubMed Central  Google Scholar 

  65. Gene Ontology Consortium. GO slim and subset guide. http://geneontology.org/page/go-slim-and-subset-guide.

    Google Scholar 

  66. Biswas M, O’Rourke JF, Camon E et al (2002) Applications of InterPro in protein annotation and genome analysis. Brief Bioinform 3:285–295

    Article  CAS  PubMed  Google Scholar 

  67. Camon E, Magrane M, Barrell D et al (2003) The Gene Ontology Annotation (GOA) Project:implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Res 13:662–672

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8:e1002375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Robinson MD, Grigull J, Mohammad N, Hughes TR (2002) FunSpec: a web-based cluster interpreter for yeast. BMC Bioinformatics 3:35

    Article  PubMed  PubMed Central  Google Scholar 

  70. Xie C, Mao X, Huang J et al (2011) KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res 39:316–322

    Article  Google Scholar 

  71. Mi H, Muruganujan A, Thomas PD (2013) PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res 41:377–386

    Article  Google Scholar 

  72. Tian L, Greenberg SA, Kong SW et al (2005) Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci U S A 102:13544–13549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wang J, Duncan D, Shi Z, Zhang B (2013) WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res 41:77–83

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey

    Müberra Fatma Cesur & Saliha Durmuş

Authors
  1. Müberra Fatma Cesur
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  2. Saliha Durmuş
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Correspondence to Saliha Durmuş .

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Editors and Affiliations

  1. Departamento de Biología Molecular e Ingeniería Bioquímica, Centro Andaluz de Biología del Desarrollo, CSIC/Universidad Pablo de, Olavide/Junta de Andalucía, Seville, Spain

    Carlos Medina

  2. Department of Microbiology, University of Seville, Sevilla, Sevilla, Spain

    Francisco Javier López-Baena

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Cesur, M.F., Durmuş, S. (2018). Systems Biology Modeling to Study Pathogen–Host Interactions. In: Medina, C., López-Baena, F. (eds) Host-Pathogen Interactions. Methods in Molecular Biology, vol 1734. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7604-1_10

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  • DOI: https://doi.org/10.1007/978-1-4939-7604-1_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7603-4

  • Online ISBN: 978-1-4939-7604-1

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