Skip to main content
SpringerLink
Log in

Yersinia pestis in the Age of Big Data

  • Chapter
  • First Online:
Yersinia pestis: Retrospective and Perspective

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 918))

Abstract

As omics-driven technologies developed rapidly, genomics, transcriptomics, proteomics, metabolomics and other omics-based data have been accumulated in unprecedented speed. Omics-driven big data in biology have changed our way of research. “Big science” has promoted our understanding of biology in a holistic overview that is impossibly achieved by traditional hypothesis-driven research. In this chapter, we gave an overview of omics-driven research on Y. pestis, provided a way of thinking on Yersinia pestis research in the age of big data, and made some suggestions to integrate omics-based data for systems understanding of Y. pestis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. McKinsey Global Institute. Big data: the next frontier for innovation, competition, and productivity. McKinsey & Company; 2011.

    Google Scholar 

  2. Gartner: IT glossary. http://www.gartner.com/it-glossary/big-data/. 2013.

  3. Kalyvas JR, Overly MR: Big data: a business and legal guide. Taylor & Francis Group, LLC 2015.

    Google Scholar 

  4. Schouten P. Big data in health care. Healthc Financ Manage. 2013;67(2):40–2.

    PubMed  Google Scholar 

  5. Pennisi E. How will big pictures emerge from a sea of biological data? Science. 2005;309(5731):94.

    Article  CAS  PubMed  Google Scholar 

  6. Howe D, Costanzo M, Fey P, Gojobori T, Hannick L, Hide W, Hill DP, Kania R, Schaeffer M, St Pierre S, et al. Big data: the future of biocuration. Nature. 2008;455(7209):47–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jeong K, Jung E, Park DK. Trend of wireless u-health. IEEE, ISCIT. 2009;2009:829–33.

    Google Scholar 

  8. Steinbrook R. Personally controlled online health data–the next big thing in medical care? N Engl J Med. 2008;358(16):1653–6.

    Article  CAS  PubMed  Google Scholar 

  9. Hood L. Systems biology and p4 medicine: past, present, and future. Rambam Maimonides Med J. 2013;4(2):e0012.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hay SI, George DB, Moyes CL, Brownstein JS. Big data opportunities for global infectious disease surveillance. PLoS Med. 2013;10(4):e1001413.

    Article  PubMed  PubMed Central  Google Scholar 

  11. de la Barrera CA, Reyes-Teran G. Influenza: forecast for a pandemic. Arch Med Res. 2005;36(6):628–36.

    Article  PubMed  Google Scholar 

  12. Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M. Real-time influenza forecasts during the 2012–2013 season. Nat Commun. 2013;4:2837.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012–4.

    Article  CAS  PubMed  Google Scholar 

  14. Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses. 2014;8(3):309–16.

    Article  PubMed  Google Scholar 

  15. Shaman J, Karspeck A. Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci U S A. 2012;109(50):20425–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ben-Ari T, Neerinckx S, Gage KL, Kreppel K, Laudisoit A, Leirs H, Stenseth NC. Plague and climate: scales matter. PLoS Pathog. 2011;7(9):e1002160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yang R, Du Z, Han Y, Zhou L, Song Y, Zhou D, Cui Y. Omics strategies for revealing Yersinia pestis virulence. Front Cell Infect Microbiol. 2012;2:157.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Committee for Science and Technology Challenges to U.S. National Security Interests: Report of a Workshop on Big Data. National Academies Press; 2012.

    Google Scholar 

  19. National Research Council. Frontiers in massive data analysis. Washington, DC: The National Academies Press. This PDF is available from The National Academies Press at http://www.napedu/catalogphp?record_id=18374. 2013.

  20. Singh OV, Nagaraj NS. Transcriptomics, proteomics and interactomics: unique approaches to track the insights of bioremediation. Brief Funct Genomic Proteomic. 2006;4(4):355–62.

    Article  CAS  PubMed  Google Scholar 

  21. Yang Y, Xie B, Yan J. Application of next-generation sequencing technology in forensic science. Genomic Proteomic Bioinforma. 2014;12(5):190–7.

    Article  Google Scholar 

  22. Roberts RJ, Carneiro MO, Schatz MC. The advantages of SMRT sequencing. Genome Biol. 2013;14(7):405.

    Article  PubMed  Google Scholar 

  23. Chin CS, Alexander DH, Marks P, Klammer AA, Drake J, Heiner C, Clum A, Copeland A, Huddleston J, Eichler EE, et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat Methods. 2013;10(6):563–9.

    Article  CAS  PubMed  Google Scholar 

  24. Feng Y, Zhang Y, Ying C, Wang D, Du C. Nanopore-based fourth-generation DNA sequencing technology. Genomic Proteomic Bioinforma. 2015;13(1):4–16.

    Article  Google Scholar 

  25. Eisenstein M. Oxford Nanopore announcement sets sequencing sector abuzz. Nat Biotechnol. 2012;30(4):295–6.

    Article  CAS  PubMed  Google Scholar 

  26. Burgess DJ. Technology: bead capture for single-cell transcriptomics. Nat Rev Genet. 2015;16(4):195.

    Article  CAS  PubMed  Google Scholar 

  27. Li N, Xu Z, Zhai L, Li Y, Fan F, Zheng J, Xu P, He F. Rapid development of proteomics in China: from the perspective of the Human Liver Proteome Project and technology development. Sci China Life Sci. 2014;57(12):1162–71.

    Article  CAS  PubMed  Google Scholar 

  28. Castro CC, Martins RC, Teixeira JA, Silva Ferreira AC. Application of a high-throughput process analytical technology metabolomics pipeline to Port wine forced ageing process. Food Chem. 2014;143:384–91.

    Article  CAS  PubMed  Google Scholar 

  29. Noto A, Dessi A, Puddu M, Mussap M, Fanos V. Metabolomics technology and their application to the study of the viral infection. J Matern Fetal Neonatal Med. 2014;27 Suppl 2:53–7.

    Article  CAS  PubMed  Google Scholar 

  30. Feng S, Zhou L, Huang C, Xie K, Nice EC. Interactomics: toward protein function and regulation. Expert Rev Proteomic. 2015;12(1):37–60.

    Article  CAS  Google Scholar 

  31. Uetz P, Dong YA, Zeretzke C, Atzler C, Baiker A, Berger B, Rajagopala SV, Roupelieva M, Rose D, Fossum E, et al. Herpesviral protein networks and their interaction with the human proteome. Science. 2006;311(5758):239–42.

    Article  CAS  PubMed  Google Scholar 

  32. Mendez-Rios J, Uetz P. Global approaches to study protein-protein interactions among viruses and hosts. Future Microbiol. 2010;5(2):289–301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Simonis N, Rual JF, Lemmens I, Boxus M, Hirozane-Kishikawa T, Gatot JS, Dricot A, Hao T, Vertommen D, Legros S, et al. Host-pathogen interactome mapping for HTLV-1 and −2 retroviruses. Retrovirology. 2012;9:26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yang H, Ke Y, Wang J, Tan Y, Myeni SK, Li D, Shi Q, Yan Y, Chen H, Guo Z, et al. Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network. Infect Immun. 2011;79(11):4413–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yang L, Li M, Shan Y, Shen S, Bai Y, Liu H. Recent advances in lipidomics for disease research. J Sep Sci. 2015.

    Google Scholar 

  36. Vaz FM, Pras-Raves M, Bootsma AH, van Kampen AH. Principles and practice of lipidomics. J Inherit Metab Dis. 2015;38(1):41–52.

    Article  CAS  PubMed  Google Scholar 

  37. Sturino J, Zorych I, Mallick B, Pokusaeva K, Chang YY, Carroll RJ, Bliznuyk N. Statistical methods for comparative phenomics using high-throughput phenotype microarrays. J Biostat. 2010;6(1):Article 29.

    Google Scholar 

  38. Viti C, Decorosi F, Marchi E, Galardini M, Giovannetti L. High-throughput phenomics. Methods Mol Biol. 2015;1231:99–123.

    Article  CAS  PubMed  Google Scholar 

  39. Schrimpe-Rutledge AC, Jones MB, Chauhan S, Purvine SO, Sanford JA, Monroe ME, Brewer HM, Payne SH, Ansong C, Frank BC, et al. Comparative omics-driven genome annotation refinement: application across Yersiniae. PLoS ONE. 2012;7(3):e33903.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ansong C, Schrimpe-Rutledge AC, Mitchell HD, Chauhan S, Jones MB, Kim YM, McAteer K, Deatherage Kaiser BL, Dubois JL, Brewer HM, et al. A multi-omic systems approach to elucidating Yersinia virulence mechanisms. Mol BioSyst. 2013;9(1):44–54.

    Article  CAS  PubMed  Google Scholar 

  41. Ansong C, Deatherage BL, Hyduke D, Schmidt B, McDermott JE, Jones MB, Chauhan S, Charusanti P, Kim YM, Nakayasu ES, et al. Studying Salmonellae and Yersiniae host-pathogen interactions using integrated ‘omics and modeling. Curr Top Microbiol Immunol. 2013;363:21–41.

    CAS  PubMed  Google Scholar 

  42. Johnson SL, Daligault HE, Davenport KW, Jaissle J, Frey KG, Ladner JT, Broomall SM, Bishop-Lilly KA, Bruce DC, Coyne SR et al. Thirty-two complete genome assemblies of nine Yersinia species, including Y. pestis, Y. pseudotuberculosis, and Y. enterocolitica. Genome announcements. 2015;3(2).

    Google Scholar 

  43. Zhgenti E, Johnson SL, Davenport KW, Chanturia G, Daligault HE, Chain PS, Nikolich MP. Genome assemblies for 11 Yersinia pestis strains isolated in the caucasus region. Genome announcements. 2015;3(5).

    Google Scholar 

  44. Du Z, Yang H, Tan Y, Tian G, Zhang Q, Cui Y, Yanfeng Y, Wu X, Chen Z, Cao S, et al. Transcriptomic response to Yersinia pestis: RIG-I like receptor signaling response is detrimental to the host against plague. J Genet Genomics. 2014;41(7):379–96.

    Article  PubMed  Google Scholar 

  45. Das R, Dhokalia A, Huang XZ, Hammamieh R, Chakraborty N, Lindler LE, Jett M. Study of proinflammatory responses induced by Yersinia pestis in human monocytes using cDNA arrays. Genes Immun. 2007;8(4):308–19.

    Article  CAS  PubMed  Google Scholar 

  46. Rogers JV, Choi YW, Giannunzio LF, Sabourin PJ, Bornman DM, Blosser EG, Sabourin CL. Transcriptional responses in spleens from mice exposed to Yersinia pestis CO92. Microb Pathog. 2007;43(2–3):67–77.

    Article  CAS  PubMed  Google Scholar 

  47. Galindo CL, Moen ST, Kozlova EV, Sha J, Garner HR, Agar SL, Chopra AK. Comparative analyses of transcriptional profiles in mouse organs using a pneumonic plague model after infection with wild-type Yersinia pestis CO92 and its Braun lipoprotein mutant. Comp Funct Genomics. 2009;2009:914762.

    Article  PubMed  Google Scholar 

  48. Comer JE, Sturdevant DE, Carmody AB, Virtaneva K, Gardner D, Long D, Rosenke R, Porcella SF, Hinnebusch BJ. Transcriptomic and innate immune responses to Yersinia pestis in the lymph node during bubonic plague. Infect Immun. 2010;78(12):5086–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Liu H, Wang H, Qiu J, Wang X, Guo Z, Qiu Y, Zhou D, Han Y, Du Z, Li C, et al. Transcriptional profiling of a mice plague model: insights into interaction between Yersinia pestis and its host. J Basic Microbiol. 2009;49(1):92–9.

    Article  CAS  PubMed  Google Scholar 

  50. Chromy BA, Choi MW, Murphy GA, Gonzales AD, Corzett CH, Chang BC, Fitch JP, McCutchen-Maloney SL. Proteomic characterization of Yersinia pestis virulence. J Bacteriol. 2005;187(23):8172–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhou L, Ying W, Han Y, Chen M, Yan Y, Li L, Zhu Z, Zheng Z, Jia W, Yang R, et al. A proteome reference map and virulence factors analysis of Yersinia pestis 91001. J Proteome. 2012;75(3):894–907.

    Article  CAS  Google Scholar 

  52. Zhou S, Deng W, Anantharaman TS, Lim A, Dimalanta ET, Wang J, Wu T, Chunhong T, Creighton R, Kile A, et al. A whole-genome shotgun optical map of Yersinia pestis strain KIM. Appl Environ Microbiol. 2002;68(12):6321–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yen YT, Bhattacharya M, Stathopoulos C. Genome-wide in silico mapping of the secretome in pathogenic Yersinia pestis KIM. FEMS Microbiol Lett. 2008;279(1):56–63.

    Article  CAS  PubMed  Google Scholar 

  54. Pieper R, Huang ST, Robinson JM, Clark DJ, Alami H, Parmar PP, Perry RD, Fleischmann RD, Peterson SN. Temperature and growth phase influence the outer-membrane proteome and the expression of a type VI secretion system in Yersinia pestis. Microbiology. 2009;155(Pt 2):498–512.

    Article  CAS  PubMed  Google Scholar 

  55. Nozadze M, Zhgenti E, Meparishvili M, Tsverava L, Kiguradze T, Chanturia G, Babuadze G, Kekelidze M, Bakanidze L, Shutkova T, et al. Comparative proteomic studies of Yersinia pestis strains isolated from natural foci in the republic of Georgia. Front Public Health. 2015;3:239.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Zhang CG, Gonzales AD, Choi MW, Chromy BA, Fitch JP, McCutchen-Maloney SL. Subcellular proteomic analysis of host-pathogen interactions using human monocytes exposed to Yersinia pestis and Yersinia pseudotuberculosis. Proteomics. 2005;5(7):1877–88.

    Article  CAS  PubMed  Google Scholar 

  57. Pieper R, Huang ST, Clark DJ, Robinson JM, Parmar PP, Alami H, Bunai CL, Perry RD, Fleischmann RD, Peterson SN. Characterizing the dynamic nature of the Yersinia pestis periplasmic proteome in response to nutrient exhaustion and temperature change. Proteomics. 2008;8(7):1442–58.

    Article  CAS  PubMed  Google Scholar 

  58. Pieper R, Huang ST, Parmar PP, Clark DJ, Alami H, Fleischmann RD, Perry RD, Peterson SN. Proteomic analysis of iron acquisition, metabolic and regulatory responses of Yersinia pestis to iron starvation. BMC Microbiol. 2010;10:30.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Chromy BA, Perkins J, Heidbrink JL, Gonzales AD, Murphy GA, Fitch JP, McCutchen-Maloney SL. Proteomic characterization of host response to Yersinia pestis and near neighbors. Biochem Biophys Res Commun. 2004;320(2):474–9.

    Article  CAS  PubMed  Google Scholar 

  60. Li B, Tan Y, Guo J, Cui B, Wang Z, Wang H, Zhou L, Guo Z, Zhu Z, Du Z, et al. Use of protein microarray to identify gene expression changes of Yersinia pestis at different temperatures. Can J Microbiol. 2011;57(4):287–94.

    Article  CAS  PubMed  Google Scholar 

  61. Li B, Jiang L, Song Q, Yang J, Chen Z, Guo Z, Zhou D, Du Z, Song Y, Wang J, et al. Protein microarray for profiling antibody responses to Yersinia pestis live vaccine. Infect Immun. 2005;73(6):3734–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Navid A, Almaas E. Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001. Mol BioSyst. 2009;5(4):368–75.

    Article  CAS  PubMed  Google Scholar 

  63. Charusanti P, Chauhan S, McAteer K, Lerman JA, Hyduke DR, Motin VL, Ansong C, Adkins JN, Palsson BO. An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92. BMC Syst Biol. 2011;5:163.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Navid A, Almaas E. Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach. BMC Syst Biol. 2012;6:150.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Song Y, Tong Z, Wang J, Wang L, Guo Z, Han Y, Zhang J, Pei D, Zhou D, Qin H, et al. Complete genome sequence of Yersinia pestis strain 91001, an isolate avirulent to humans. DNA Res. 2004;11(3):179–97.

    Article  CAS  PubMed  Google Scholar 

  66. Zhou D, Han Y, Song Y, Tong Z, Wang J, Guo Z, Pei D, Pang X, Zhai J, Li M, et al. DNA microarray analysis of genome dynamics in Yersinia pestis: insights into bacterial genome microevolution and niche adaptation. J Bacteriol. 2004;186(15):5138–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhou D, Tong Z, Song Y, Han Y, Pei D, Pang X, Zhai J, Li M, Cui B, Qi Z, et al. Genetics of metabolic variations between Yersinia pestis biovars and the proposal of a new biovar, microtus. J Bacteriol. 2004;186(15):5147–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Abu Kwaik Y, Bumann D. Microbial quest for food in vivo: ‘nutritional virulence’ as an emerging paradigm. Cell Microbiol. 2013;15(6):882–90.

    Article  CAS  PubMed  Google Scholar 

  69. Dyer MD, Neff C, Dufford M, Rivera CG, Shattuck D, Bassaganya-Riera J, Murali TM, Sobral BW. The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis. PLoS ONE. 2010;5(8):e12089.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Ke Y, Tan Y, Wei N, Yang F, Yang H, Cao S, Wang X, Wang J, Han Y, Bi Y, et al. Yersinia protein kinase A phosphorylates vasodilator-stimulated phosphoprotein to modify the host cytoskeleton. Cell Microbiol. 2015;17(4):473–85.

    Article  CAS  PubMed  Google Scholar 

  71. Yang H, Tan Y, Zhang T, Tang L, Wang J, Ke Y, Guo Z, Yang X, Yang R, Du Z. Identification of novel protein-protein interactions of Yersinia pestis type III secretion system by yeast two hybrid system. PLoS ONE. 2013;8(1):e54121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing Institute of Microbiology and Epidemiology, No. Dongdajie, Fengtai, Beijing, 100071, China

    Ruifu Yang

  2. Departments of Pathology and Microbiology & Immunology, University of Texas Medical Branch, Galveston, TX, 77555, USA

    Vladimir L. Motin

Authors
  1. Ruifu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladimir L. Motin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruifu Yang .

Editor information

Editors and Affiliations

  1. Beijing Institute of Microbiology and Epidemiology, Beijing, China

    Ruifu Yang

  2. State Research Center for Applied Microbiology, Moscow Region, Russia

    Andrey Anisimov

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Yang, R., Motin, V.L. (2016). Yersinia pestis in the Age of Big Data. In: Yang, R., Anisimov, A. (eds) Yersinia pestis: Retrospective and Perspective. Advances in Experimental Medicine and Biology, vol 918. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-0890-4_9

Download citation

Publish with us

Policies and ethics

Search

Navigation