Our research
Antibiotic resistance, bacteriocins, biofilms, quorum sensing, social interactions
Antibiotic resistance, bacteriocins, biofilms, quorum sensing, social interactions
What are we interested in?
Our focus is on understanding microbial interactions and social behaviors and the implications for virulence, disease and antimicrobial resistance (AMR). Our main study organism is the antibiotic resistant superbug Pseudomonas aeruginosa (Diggle & Whiteley 2020). The CDC has identified P. aeruginosa as a 'serious threat' in healthcare settings. It is also the key pathogen in cystic fibrosis lungs and is commonly isolated from non-healing diabetic wounds.
Our focus is on understanding microbial interactions and social behaviors and the implications for virulence, disease and antimicrobial resistance (AMR). Our main study organism is the antibiotic resistant superbug Pseudomonas aeruginosa (Diggle & Whiteley 2020). The CDC has identified P. aeruginosa as a 'serious threat' in healthcare settings. It is also the key pathogen in cystic fibrosis lungs and is commonly isolated from non-healing diabetic wounds.
Biofilms and infection
- P. aeruginosa assembles different aggregate types due to an interplay between environmental polymer concentration and cell surface hydrophobicity (Azimi et al. 2021).
- Heterogeneity in evolving P. aeruginosa biofilms impacts on community functions including antibiotic resistance (Darch et al. 2015; Azimi et al. 2020), quorum sensing (Azimi et al. 2020) and R-pyocin susceptibility (Mei et al. 2021).
- R-pyocins help to shape P. aeruginosa strain interactions in biofilms (Oluyombo et al. 2019; Mei et al. 2021).
- Virulence can evolve in different directions during infection of chronic wounds (Vanderwoude et al. 2020).
- We have developed an ex vivo pig lung model for studying bacterial virulence and biofilm formation in spatially structured tissue and bronchioles (Harrison et al. 2014; Harrison & Diggle 2016).
- A 1000 year old recipe from an Anglo Saxon 'Leechbook' is effective at killing MRSA biofilms (Harrison et al. 2015).
Quorum sensing and social behaviors
- We have comprehensively reviewed bacterial quorum sensing (Diggle et al. 2007a; Diggle et al. 2007b; Schuster et al. 2013; Whiteley et al. 2017; Azimi et al. 2020).
- We have discussed how social evolution theory applies to microbes (West et al. 2006; West et al. 2007; Diggle 2010; West et al. 2012).
- Density is an important component of QS (Darch et al. 2012), and bacteria can resolve social and physical uncertainty using multiple QS signals (Cornforth et al. 2014; Gurney et al. 2020).
- QS is a cooperative social behavior, which can be exploited by cheats in vitro, in vivo and in biofilms (Diggle et al. 2007; Rumbaugh et al. 2009; Wilder et al. 2011; Popat et al. 2012; Rumbaugh et al. 2012; Pollitt et al. 2014; Mund et al. 2017; Harrison et al. 2017).
- A solution to microbial cheating is cooperation between relatives (kin selection) (Diggle et al. 2007; Rumbaugh et al. 2012; Popat et al. 2015).
- Some social traits can influence the social evolution of others (Popat et al. 2017) and some traits (polysaccharides), are non-cheatable (Irie et al. 2017). Cheating is dependent upon the environmental conditions (Harrison et al. 2017).