Advertisement

Network Biology Approaches to Identify Molecular and Systems-Level Differences Between Salmonella Pathovars

  • Marton Olbei
  • Robert A. Kingsley
  • Tamas KorcsmarosEmail author
  • Padhmanand Sudhakar
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1918)

Abstract

The field of systems biology endeavors to map, study, and simulate cellular systems and their underlying mechanisms. The internal mechanisms of biological systems can be represented with networks comprising nodes and edges. Nodes denote the constituents of the biological system whereas edges indicate the relationships among them. Likewise, every layer of cellular organization can be represented by networks. Multilayered networks capture interactions between various network types, such as transcriptional regulatory networks, protein–protein interaction networks, and metabolic networks from the same biological system. This property makes multilayered networks representative of the system while its internal mechanisms are investigated. However, there are not many multilayered networks containing integrated data for nonmodel organisms including the bacterial pathogens Salmonella. Here, we outline the steps to create such an integrated network database, through the example of SalmoNet, the first integrated multilayered data resource for multiple strains belonging to distinct Salmonella serovars.

Key words

Systems biology Multilayered networks Network reconstruction framework Pathogen Salmonella 

Notes

Acknowledgments

The authors would like to acknowledge all the contributors of the SalmoNet resource as well as the helpful discussions from the members and visitors of the Baranyi, Korcsmaros, and Kingsley groups. This work was supported by a fellowship to T.K. in computational biology at the Earlham Institute (Norwich, UK) in partnership with the Quadram Institute (Norwich, UK), and strategically supported by the Biotechnological and Biosciences Research Council, UK grants (BB/J004529/1, BB/P016774/1, and BB/CSP17270/1). This work was also supported by the BBSRC Norwich Research Park Biosciences Doctoral Training Partnership grant number BB/M011216/1.

References

  1. 1.
    Majowicz SE, Musto J, Scallan E et al (2010) The global burden of nontyphoidal salmonella gastroenteritis. Clin Infect Dis 50:882–889. https://doi.org/10.1086/650733CrossRefPubMedGoogle Scholar
  2. 2.
    Guirguis GF, Patel K, Gittens-Williams L et al (2017) Salmonella enterica serotype typhi bacteremia complicating pregnancy in the third trimester. Case Rep Obstet Gynecol 2017:4018096. https://doi.org/10.1155/2017/4018096CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Mohanty S, Gaind R, Paglietti B et al (2010) Bacteraemia with pleural effusions complicating typhoid fever caused by high-level ciprofloxacin-resistant salmonella enterica serotype Typhi. Ann Trop Paediatr 30:233–240. https://doi.org/10.1179/146532810X12786388978760CrossRefPubMedGoogle Scholar
  4. 4.
    Métris A, Sudhakar P, Fazekas D et al (2017) SalmoNet, an integrated network of ten salmonella enterica strains reveals common and distinct pathways to host adaptation. NPJ Syst Biol Appl 3:31. https://doi.org/10.1038/s41540-017-0034-zCrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Chen H, Sharp BM (2004) Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinformatics 5:147. https://doi.org/10.1186/1471-2105-5-147CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Hoffmann R, Valencia A (2004) A gene network for navigating the literature. Nat Genet 36:664. https://doi.org/10.1038/ng0704-664CrossRefPubMedGoogle Scholar
  7. 7.
    Kerrien S, Aranda B, Breuza L et al (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40:D841–D846. https://doi.org/10.1093/nar/gkr1088CrossRefPubMedGoogle Scholar
  8. 8.
    Mosca R, Céol A, Aloy P (2013) Interactome3D: adding structural details to protein networks. Nat Methods 10:47–53. https://doi.org/10.1038/nmeth.2289CrossRefPubMedGoogle Scholar
  9. 9.
    Kreimer A, Borenstein E, Gophna U, Ruppin E (2008) The evolution of modularity in bacterial metabolic networks. Proc Natl Acad Sci U S A 105:6976–6981. https://doi.org/10.1073/pnas.0712149105CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Thiele I, Hyduke DR, Steeb B et al (2011) A community effort towards a knowledge-base and mathematical model of the human pathogen salmonella Typhimurium LT2. BMC Syst Biol 5:8. https://doi.org/10.1186/1752-0509-5-8CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Chelliah V, Juty N, Ajmera I et al (2015) BioModels: ten-year anniversary. Nucleic Acids Res 43:D542–D548. https://doi.org/10.1093/nar/gku1181CrossRefPubMedGoogle Scholar
  12. 12.
    O’Brien KP, Remm M, Sonnhammer ELL (2005) Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res 33:D476–D480. https://doi.org/10.1093/nar/gki107CrossRefPubMedGoogle Scholar
  13. 13.
    Nichio BTL, Marchaukoski JN, Raittz RT (2017) New tools in orthology analysis: a brief review of promising perspectives. Front Genet 8:165. https://doi.org/10.3389/fgene.2017.00165CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Yu H, Luscombe NM, Lu HX et al (2004) Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome Res 14:1107–1118. https://doi.org/10.1101/gr.1774904CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Kiliç S, White ER, Sagitova DM et al (2014) CollecTF: a database of experimentally validated transcription factor-binding sites in bacteria. Nucleic Acids Res 42:D156–D160. https://doi.org/10.1093/nar/gkt1123CrossRefPubMedGoogle Scholar
  16. 16.
    Gama-Castro S, Salgado H, Santos-Zavaleta A et al (2016) RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res 44:D133–D143. https://doi.org/10.1093/nar/gkv1156CrossRefPubMedGoogle Scholar
  17. 17.
    Grote A, Klein J, Retter I et al (2009) PRODORIC (release 2009): a database and tool platform for the analysis of gene regulation in prokaryotes. Nucleic Acids Res 37:D61–D65. https://doi.org/10.1093/nar/gkn837CrossRefPubMedGoogle Scholar
  18. 18.
    Bailey TL, Boden M, Buske FA et al (2009) MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37:W202–W208. https://doi.org/10.1093/nar/gkp335CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Medina-Rivera A, Defrance M, Sand O et al (2015) RSAT 2015: regulatory sequence analysis tools. Nucleic Acids Res 43:W50–W56. https://doi.org/10.1093/nar/gkv362CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Hertz GZ, Stormo GD (1999) Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics 15:563–577CrossRefGoogle Scholar
  21. 21.
    Medina-Rivera A, Abreu-Goodger C, Thomas-Chollier M et al (2011) Theoretical and empirical quality assessment of transcription factor-binding motifs. Nucleic Acids Res 39:808–824. https://doi.org/10.1093/nar/gkq710CrossRefPubMedGoogle Scholar
  22. 22.
    Haycocks JRJ, Grainger DC (2016) Unusually situated binding sites for bacterial transcription factors can have hidden functionality. PLoS One 11:e0157016. https://doi.org/10.1371/journal.pone.0157016CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Turatsinze J-V, Thomas-Chollier M, Defrance M, van Helden J (2008) Using RSAT to scan genome sequences for transcription factor binding sites and cis-regulatory modules. Nat Protoc 3:1578–1588. https://doi.org/10.1038/nprot.2008.97CrossRefPubMedGoogle Scholar
  24. 24.
    Nelson AC, Wardle FC (2013) Conserved non-coding elements and cis regulation: actions speak louder than words. Development 140:1385–1395. https://doi.org/10.1242/dev.084459CrossRefPubMedGoogle Scholar
  25. 25.
    Sierro N, Makita Y, de Hoon M, Nakai K (2008) DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information. Nucleic Acids Res 36:D93–D96. https://doi.org/10.1093/nar/gkm910CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Marton Olbei
    • 1
    • 2
  • Robert A. Kingsley
    • 1
  • Tamas Korcsmaros
    • 1
    • 2
    Email author
  • Padhmanand Sudhakar
    • 1
    • 2
  1. 1.Quadram Institute Bioscience, Norwich Research ParkNorwichUK
  2. 2.Earlham Institute, Norwich Research ParkNorwichUK

Personalised recommendations