Our research
Biofilms, quorum sensing, social evolution

What are we interested in? 
Our focus is on understanding microbial interactions and social behaviors and the implications for virulence, disease and antimicrobial resistance. Our main study organism is the antibiotic resistant superbug Pseudomonas aeruginosa. 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

• We have demonstrated that heterogeneity in evolving Pseudomonas aeruginosa populations can impact on community functions including antibiotic resistance (Azimi et al. 2020).
• We have shown the bacteriocins known as R-pyocins help to shape Pseudomonas aeruginosa cystic fibrosis strain interactions in biofilms (Oluyombo et al. 2019).
• ​We have shown that phenotypic and genotypic diversity in Pseudomonas aeruginosa cystic fibrosis infections is driven in part by recombination and that this has implications for antibiotic resistance and susceptibility testing (Darch et al. 2015).
• 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). This model has been used in combination with three dimensional imaging, to visualize antimicrobials in biofilms growing in tissue (Davies et al. 2017). We have also developed a simple and high throughput mung bean infection model which allows researchers to test the virulence of P. aeruginosa strains (Garge et al. 2018).
• We have discussed whether in vitro experiments provide relevant information about biofilms and chronic disease (Roberts et al. 2015).
• We have shown that preformed aggregates of cells are important for biofilm formation (Melaugh et al. 2016; Kragh et al. 2016).
• We have found that a 1000 year old recipe from an Anglo Saxon 'Leechbook' is effective at killing MRSA biofilms. It is the combination of ingredients that is important for its activity and suggests that Anglo Saxon doctors may have been conducting empirical research against disease (Harrison et al. 2015).

​
 Quorum sensing and social behaviors

• ​We have comprehensively reviewed quorum sensing (Whiteley et al. 2017; Schuster et al. 2013; Diggle et al. 2007), and explained why quorum sensing between bacterial cells may not always represent true signaling (Diggle et al. 2007).
• We have discussed how social evolution theory applies to microbes and the implications of this for virulence (West et al. 2006; West et al. 2007; Diggle 2010; Thomas & Diggle 2019). We have explained why quorum sensing and diffusion sensing should not be viewed as competing hypotheses (West et al. 2012). 
• We have shown that QS is a cooperative social behavior, which can be exploited by cheats in vitro (Diggle et al. 2007; Wilder et al. 2011; Mund et al. 2017), in vivo (Rumbaugh et al. 2009; Pollitt et al. 2014) and in biofilms (Popat et al. 2012), and that potential solutions to microbial cheating are cooperation between relatives (kin selection) (Diggle et al. 2007; Rumbaugh et al. 2012; Popat et al. 2015) and predation (Friman et al. 2013).
• We demonstrated that cooperation and cheating has an impact on the evolution of virulence (Rumbaugh et al. 2009; Rumbaugh et a. 2012; Pollitt et al. 2014), and discussed ways that social cheating can be exploited as a medical intervention strategy (Brown et al. 2009), and whether we will be able to produce evolution free drugs (Allen et al. 2014).
• ​We have shown that some social traits can influence the social evolution of others (Popat et al. 2017). We have also shown that some social traits (polysaccharides), are non-cheatable, with most of the benefits going to the producing cell (Irie et al. 2017), and that cheating is dependent upon the environmental conditions (Harrison et al. 2017). 
• ​We have shown that density is an important component of QS, something which was assumed in the literature but had not previously been experimentally tested (Darch et al. 2012), and that bacteria can resolve social and physical uncertainty using multiple signals (Cornforth et al. 2014; Gurney et al. 2020). ​