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

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

Abstract

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 Sequencing of a representative bacterial pathogen genome has been superseded by sequencing of whole strain collections, whether from environmental or clinical sources (Harris et al., Science 327:469–474, 2010; Lewis et al., J Hosp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 

Notes

Acknowledgement

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).

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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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