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Professor Chris Bayliss

Supervisor Details


Contact Details

Professor Chris Bayliss

Department of Genetics and Genome Biology, University of Leicester

Research Interests

Hypermutable DNA sequences enable bacterial pathogens and commensals to colonise and persist in host organisms. A common mechanism involves mutations in tandem DNA repeat tracts (microsatellites). Professor Bayliss’ group focuses on the hypermutable DNA sequences of three bacterial pathogens:

  • Neisseria meningitidis (meningitis and septicaemia).
  • Haemophilus influenzae (meningitis, COPD and otitis media).
  • Campylobacter jejuni (foodborne gastroenteritis).

One key research area is understanding the mutability of these repetitive DNA tracts. Mutations in these repeats switch gene expression ‘on’ and ‘off’ in a process called phase variation. The group uses mutants and reporter constructs to identify cis- and trans-acting factors that control mutability of these tracts.

The group’s other key research area is using bioinformatics molecular biology, epidemiology and modelling to understand how repetitive DNA contributes to host colonisation and disease processes. Their wide range of findings include:

  • Identifying phase-variable genes (the phasome) in genomes of pathogens.
  • Observing genetic changes during asymptomatic carriage of meningococci in university students.
  • Identifying potential determinants of disease.

Professor Bayliss also has an interest in vaccines for preventing meningitis and uptake of these vaccines by university students.

Scientific Inspiration

Professor Richard Moxon – he was my mentor at Oxford University and is the founder of the contingency loci field and bacterial conjugate vaccines.

Project Details

Professor Bayliss is the primary supervisor on the below project:

Impact of Hypermutability and Restriction-Modification by Campylobacter jejuni, a foodborne pathogen, on Bacteriophage Control Measures

Secondary Supervisor(s): Professor Martha Clokie

University of Registration: University of Leicester

BBSRC Research Themes:

Apply here!

Deadline: 23 May, 2024

Project Outline

Campylobacter jejuni is a major foodborne pathogen responsible for thousands of cases gastroenteritis every year. The major source of infections by this bacterial pathogen is contaminated poultry products. C. jejuni is a commensal of birds and can spread rapidly within poultry flocks. Multiple approaches have so far failed to significantly reduce the infection burden within poultry farms and products. Bacteriophage treatments have been mooted as one potential approach but is usually disregarded due to high levels of bacterial resistance to infection.

One feature of C. jejuni biology is the presence of hypermutable sequences within the coding regions of surface-determinants. High frequency mutations in these sequences are responsible for rapid and reversible switches in expression of these antigens – referred to as phase variation. These switches are partly responsible for the phage resistance as the receptors for the phages can be switched off. As with many bacteria, C. jejuni also encodes multiple restriction-modification (RM) systems that are known to contribute to phage resistance. Campylobacter-specific phages have adjusted to the variability in receptor availability and RM systems by diversifying to target multiple receptors and developing resistance mechanisms (e.g. exclusion of RM restriction sites from genomes).

This project aims to assess the extent of phage resistance in C. jejuni that occurs due to hypermutability and RM systems. The key objectives are:- 1) to screen a panel of C. jejuni strains and phages to determine whether hypermutability and RM systems are major determinants of phage resistance; 2) to construct and test mutants in hypermutable genes and RM systems for altered phage resistance; 3) to co-evolve C. jejuni and mixtures of phages to determine the dynamics and mechanisms of phage resistance; 4) to perform in silico models to predict whether combinatorial phage therapy can overcome RM- or PV-driven resistance.

The methods will include growth of bacterial pathogens, propagation and testing of phage infections, construction of mutants in bacterial genes, PCR-based assays for detecting mutations in hypermutable sequences and in silico modelling of the co-evolution of phages and bacteria.


Phage exposure causes dynamic shifts in the expression states of specific phase-variable genes of Campylobacter jejuni.
Aidley J, Sørensen MCH, Bayliss CD, Brøndsted L.Microbiology (Reading). 2017 Jun;163(6):911-919. doi: 10.1099/mic.0.000470. Epub 2017 Jun 8.PMID: 28597819 

Phase variation of a Type IIG restriction-modification enzyme alters site-specific methylation patterns and gene expression in Campylobacter jejuni strain NCTC11168.
Anjum A, Brathwaite KJ, Aidley J, Connerton PL, Cummings NJ, Parkhill J, Connerton I, Bayliss CD.Nucleic Acids Res. 2016 Jun 2;44(10):4581-94. doi: 10.1093/nar/gkw019. Epub 2016 Jan 18.PMID: 26786317

How does feedback from phage infections influence the evolution of phase variation in Campylobacter?
Sandhu SK, Bayliss CD, Morozov AY.PLoS Comput Biol. 2021 Jun 14;17(6):e1009067. doi: 10.1371/journal.pcbi.1009067. eCollection 2021 Jun.PMID: 34125841

Phage-Resistant Phase-Variant Sub-populations Mediate Herd Immunity Against Bacteriophage Invasion of Bacterial Meta-Populations.
Turkington CJR, Morozov A, Clokie MRJ, Bayliss CD.Front Microbiol. 2019 Jul 5;10:1473. doi: 10.3389/fmicb.2019.01473. eCollection 2019.PMID: 31333609

A bacteriophage cocktail delivered in feed significantly reduced Salmonellacolonization in challenged broiler chickens.
Thanki AM, Hooton S, Whenham N, Salter MG, Bedford MR, O'Neill HVM, Clokie MRJ.Emerg Microbes Infect. 2023 Dec;12(1):2217947. doi: 10.1080/22221751.2023.2217947.PMID: 37224439


  • Growth of bacterial pathogens;
  • propagation of phages;
  • screening bacterial strain collections for phage infectivity;
  • bioinformatics of bacterial genomes;
  • construction of mutants in bacterial genes;
  • PCR-based assays for detecting mutations in hypermutable sequences;
  • in silico modelling in Python;
  • statistical testing in R.