A Proposed Essential Gene Discovery Pipeline: A Campylobacter jejuni Case Study

  • Mark Reuter
  • Duncan J. H. Gaskin
  • Aline MetrisEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1279)


Genes required for an organism’s growth and survival are termed essential and represent potential intervention targets. Following in the footsteps of the genomics era, the “next-gen” genomic era provides vast amounts of genetic information. Sequencing of a representative bacterial pathogen genome has been superseded by sequencing of whole strain collections, whether from environmental or clinical sources (Harris et al., Science 327:469–474, 2010; Lewis et al., J Hosp Infect 75:37–41, 2010; Beres et al., Proc Natl Acad Sci U S A 107:4371–4376, 2010; Qi et al., PLoS Pathog 5:e1000580, 2009; He et al., Proc Natl Acad Sci U S A 107:7527–7532, 2010; Barrick et al., Nature 461:1243–1247, 2009; Sheppard et al., Mol Ecol 22:1051–1064, 2013). However, the challenge of using this information to gain biological insight remains. Nonetheless, this information, in combination with experimental data from the literature, can serve as the framework for gaining a better understanding of an organism’s biology. Generic metabolic pathways have long been known, and a number of websites (e.g., KEGG and BioCyc) attempt to map information from genome annotation to metabolic pathways (Kanehisa et al., Nucleic Acids Res 40:D109–D114, 2010; Karp et al., Nucleic Acids Res 33:6083–6089, 2005). Extending this analysis to incorporate metabolic flux models further allows in silico prediction of potential essential genes. Such efforts are of value, either to highlight novel generic antimicrobials or to seek novel treatments for non-paradigm organisms. Such in silico approaches are attractive as they can highlight pathways and genes that would otherwise only be identified by costly and time-consuming laboratory methods.

Key words

Campylobacter jejuni Essential genes Metabolic network Flux balance analysis Transposon mutagenesis analysis Network analysis 



The authors wish to thanks members of both the Computational Microbiology Research Group and Campylobacter Research Group at IFR for helpful discussions. We gratefully acknowledge the support of the Biotechnology and Biological Sciences Research Council (BBSRC) via the BBSRC Institute Strategic Program (IFR/08/3 and BB/J004529/1).


  1. 1.
    Harris SR, Feil EJ, Holden MT, Quai MA, Nickerson EK, Chantratita N, Gardete S, Tavares A, Day N, Lindsay JA, Edgeworth JD, de Lencastre H, Parkhill J, Peacock SJ, Bentley SD (2010) Evolution of MRSA during hospital transmission and intercontinental spread. Science 327:469–474PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Lewis T, Loman NJ, Bingle L, Jumaa P, Weinstock GM, Mortiboy D, Pallen MJ (2010) High-throughput whole-genome sequencing to dissect the epidemiology of Acinetobacter baumannii isolates from a hospital outbreak. J Hosp Infect 75:37–41PubMedCrossRefGoogle Scholar
  3. 3.
    Beres SB, Carroll RK, Shea PR, Sitkiewicz I, Martinez-Gutierrez JC, Low DE, McGeer A, Willey BM, Green K, Tyrrell GJ, Goldman TD, Feldgarden M, Birren BW, Fofanov Y, Boos J, Wheaton WD, Honisch C, Musser JM (2010) Molecular complexity of successive bacterial epidemics deconvoluted by comparative pathogenomics. Proc Natl Acad Sci U S A 107:4371–4376PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Qi W, Kaser M, Roltgen K, Yeboah-Manu D, Pluschke G (2009) Genomic diversity and evolution of Mycobacterium ulcerans revealed by next-generation sequencing. PLoS Pathog 5:e1000580PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    He M, Sebaihia M, Lawley TD, Stabler RA, Dawson LF, Martin MJ, Holt KE, Seth-Smith HM, Quail MA, Rance R, Brooks K, Churcher C, Harris D, Bentley SD, Burrows C, Clark L, Corton C, Murray V, Rose G, Thurston S, van Tonder A, Walker D, Wren BW, Dougan G, Parkhill J (2010) Evolutionary dynamics of Clostridium difficile over short and long time scales. Proc Natl Acad Sci U S A 107:7527–7532PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Barrick JE, Yu DS, Yoon SH, Jeong H, Oh TK, Schneider D, Lenski RE, Kim JF (2009) Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature 461:1243–1247PubMedCrossRefGoogle Scholar
  7. 7.
    Sheppard SK, Didelot X, Jolley KA, Darling AE, Pascoe B, Meric G, Kelly DJ, Cody A, Colles FM, Strachan NJ, Ogden ID, Forbes K, French NP, Carter P, Miller WG, McCarthy ND, Owen R, Litrup E, Egholm M, Affourtit JP, Bentley SD, Parkhill J, Maiden MC, Falush D (2013) Progressive genome-wide introgression in agricultural Campylobacter coli. Mol Ecol 22:1051–1064PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Karp PD, Ouzounis CA, Moore-Kochlacs C, Goldovsky L, Kaipa P, Ahren D, Tsoka S, Darzentas N, Kunin V, Lopez-Bigas N (2005) Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res 33:6083–6089PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Metris A, Reuter M, Gaskin DJ, Baranyi J, van Vliet AH (2011) In vivo and in silico determination of essential genes of Campylobacter jejuni. BMC Genomics 12:535PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotech 14:491–496PubMedCrossRefGoogle Scholar
  12. 12.
    Joyce AR, Palsson BO (2008) Predicting gene essentiality using genome-scale in silico models. Methods Mol Biol 416:433–457PubMedCrossRefGoogle Scholar
  13. 13.
    Bochner BR (2009) Global phenotypic characterization of bacteria. FEMS Microbiol Rev 33:191–205PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Parkhill J, Wren BW, Mungall K, Ketley JM, Churcher C, Basham D, Chillingworth T, Davies RM, Feltwell T, Holroyd S, Jagels K, Karlyshev AV, Moule S, Pallen MJ, Penn CW, Quail MA, Rajandream MA, Rutherford KM, van Vliet AH, Whitehead S, Barrell BG (2000) The genome sequence of the food-borne pathogen Campylobacter jejuni reveals hypervariable sequences. Nature 403:665–668PubMedCrossRefGoogle Scholar
  15. 15.
    Tomb JF, White O, Kerlavage AR, Clayton RA, Sutton GG, Fleischmann RD, Ketchum KA, Klenk HP, Gill S, Dougherty BA, Nelson K, Quackenbush J, Zhou L, Kirkness EF, Peterson S, Loftus B, Richardson D, Dodson R, Khalak HG, Glodek A, McKenney K, Fitzegerald LM, Lee N, Adams MD, Hickey EK, Berg DE, Gocayne JD, Utterback TR, Peterson JD, Kelley JM, Cotton MD, Weidman JM, Fujii C, Bowman C, Watthey L, Wallin E, Hayes WS, Borodovsky M, Karp PD, Smith HO, Fraser CM, Venter JC (1997) The complete genome sequence of the gastric pathogen Helicobacter pylori. Nature 388:539–547PubMedCrossRefGoogle Scholar
  16. 16.
    Thiele I, Vo TD, Price ND, Palsson BO (2005) Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants. J Bacteriol 187:5818–5830PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BO (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3:121PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Kelly DJ (2008) Complexity and versality in the physiology and metabolism of Campylobacter jejuni. In: Nachamkin I, Szymanski CM, Blaser MJ (eds) Campylobacter, 3rd edn. American Society for Microbiology, Washington, DC, p 41Google Scholar
  19. 19.
    Line JE, Hiett KL, Guard-Bouldin J, Seal BS (2010) Differential carbon source utilization by Campylobacter jejuni 11168 in response to growth temperature variation. J Microbiol Methods 80:198–202PubMedCrossRefGoogle Scholar
  20. 20.
    Hoffman PS, Goodman TG (1982) Respiratory physiology and energy-conservation efficiency of Campylobacter jejuni. J Bacteriol 150:319–326PubMedCentralPubMedGoogle Scholar
  21. 21.
    Leach S, Harvey P, Wali R (1997) Changes with growth rate in the membrane lipid composition of and amino acid utilization by continuous cultures of Campylobacter jejuni. J Appl Microbiol 82:631–640PubMedCrossRefGoogle Scholar
  22. 22.
    Mohammed KA, Miles RJ, Halablab MA (2004) The pattern and kinetics of substrate metabolism of Campylobacter jejuni and Campylobacter coli. Lett Appl Microbiol 39:261–266PubMedCrossRefGoogle Scholar
  23. 23.
    Westfall HN, Rollins DM, Weiss E (1986) Substrate utilization by Campylobacter jejuni and Campylobacter coli. Appl Environ Microbiol 52:700–705PubMedCentralPubMedGoogle Scholar
  24. 24.
    Parrish JR, Yu J, Liu G, Hines JA, Chan JE, Mangiola BA, Zhang H, Pacifico S, Fotouhi F, DiRita VJ, Ideker T, Andrews P, Finley RL Jr (2007) A proteome-wide protein interaction map for Campylobacter jejuni. Genome Biol 8:R130PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Stahl M, Stintzi A (2011) Identification of essential genes in C. jejuni genome highlights 5 hyper-variable plasticity regions. Funct Integr Genomics 11:241–257PubMedCrossRefGoogle Scholar
  26. 26.
    Gundogdu O, Bentley SD, Holden MT, Parkhill J, Dorrell N, Wren BW (2007) Re-annotation and re-analysis of the Campylobacter jejuni NCTC11168 genome sequence. BMC Genomics 8:162PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Arakaki AK, Tian W, Skolnick J (2006) High precision multi-genome scale reannotation of enzyme function by EFICAz. BMC Genomics 7:315PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28:977–982PubMedCrossRefGoogle Scholar
  29. 29.
    Claudel-Renard C, Chevalet C, Faraut T, Kahn D (2003) Enzyme-specific profiles for genome annotation: PRIAM. Nucleic Acids Res 31:6633–6639PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Gundogdu O, Mills DC, Elmi A, Martin MJ, Wren BW, Dorrell N (2011) The Campylobacter jejuni transcriptional regulator Cj1556 plays a role in the oxidative and aerobic (O2) stress response and is important for bacterial survival in vivo. J Bacteriol 193:4238–4249PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BO (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6:1290–1307PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Palsson BO (ed) (2006) Systems biology. Cambridge University Press, CambridgeGoogle Scholar
  33. 33.
    Satish Kumar V, Dasika MS, Maranas CD (2007) Optimization based automated curation of metabolic reconstructions. BMC Bioinform 8:212CrossRefGoogle Scholar
  34. 34.
    Bautista EJ, Zinski J, Szczepanek SM, Johnson EL, Tulman ER, Ching WM, Geary SJ, Srivastava R (2013) Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum. PLoS Comput Biol 9:e1003208PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Raghunathan A, Reed J, Shin S, Palsson BO, Daefler S (2009) Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction. BMC Syst Biol 3:38PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Jeong H, Mason SP, Barabasi AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411:41–42PubMedCrossRefGoogle Scholar
  37. 37.
    Maslov S, Sneppen K (2002) Specificity and stability in topology of protein networks. Science 296:910–913PubMedCrossRefGoogle Scholar
  38. 38.
    Yu H, Braun P, Yildirim MA, Lemmens I, Venkatesan K, Sahalie J, Hirozane-Kishikawa T, Gebreab F, Li N, Simonis N, Hao T, Rual JF, Dricot A, Vazquez A, Murray RR, Simon C, Tardivo L, Tam S, Svrzikapa N, Fan C, de Smet AS, Motyl A, Hudson ME, Park J, Xin X, Cusick ME, Moore T, Boone C, Snyder M, Roth FP, Barabasi AL, Tavernier J, Hill DE, Vidal M (2008) High-quality binary protein interaction map of the yeast interactome network. Science 322:104–110PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Gao J, Tarcea VG, Karnovsky A, Mirel BR, Weymouth TE, Beecher CW, Cavalcoli JD, Athey BD, Omenn GS, Burant CF, Jagadish HV (2010) Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks. Bioinformatics 26:971–973PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C (2008) WikiPathways: pathway editing for the people. PLoS Biol 6:e184PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39:D685–D690PubMedCentralPubMedCrossRefGoogle Scholar
  43. 43.
    Ducati RG, Basso LA, Santos DS (2007) Mycobacterial shikimate pathway enzymes as targets for drug design. Curr Drug Targets 8:423–435PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Mark Reuter
    • 1
  • Duncan J. H. Gaskin
    • 1
  • Aline Metris
    • 1
    Email author
  1. 1.Institute of Food ResearchNorwich Research ParkNorwichUK

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