Genomic plasticity and rapid host switching can promote the evolution of generalism: a case study in the zoonotic pathogen Campylobacter

Dan J Woodcock, Peter Krusche, Norval J C Strachan, Ken J Forbes, Frederick M Cohan, Guillaume Méric, Samuel K Sheppard* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
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Horizontal gene transfer accelerates bacterial adaptation to novel environments, allowing selection to act on genes that have evolved in multiple genetic backgrounds. This can lead to ecological specialization. However, little is known about how zoonotic bacteria maintain the ability to colonize multiple hosts whilst competing with specialists in the same niche. Here we develop a stochastic evolutionary model and show how genetic transfer of host segregating alleles, distributed as predicted for niche specifying genes, and the opportunity for host transition could interact to promote the emergence of host generalist lineages of the zoonotic bacterium Campylobacter. Using a modelling approach we show that increasing levels of homologous recombination enhance the efficiency with which selection can fix combinations of beneficial alleles, speeding adaptation. We then show how these predictions change in a multi-host system, with low levels of recombination, consistent with real r/m estimates, increasing the standing variation in the population, allowing a more effective response to changes in the selective landscape. Our analysis explains how observed gradients of host specialism and generalism can evolve in a multihost system through the transfer of ecologically important loci among coexisting strains.

Original languageEnglish
Article number9650
Number of pages13
JournalScientific Reports
Early online date29 Aug 2017
Publication statusPublished - 29 Aug 2017

Bibliographical note

This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) grant BB/I02464X/1, the Medical Research Council (MRC) grants MR/M501608/1 and MR/L015080/1, and the Wellcome Trust grant 088786/C/09/Z. GM was supported by a NISCHR Health Research Fellowship (HF-14–13).


  • computational models
  • population genetics


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