Bacteria have evolved a wide diversity of mechanisms for combating antibiotics, and the spread of resistance depends on processes that occur across a broad range of biological scales ranging from individual molecules to bacterial communities. We try and embrace this diversity by working on resistance evolution from a variety of different perspectives, and the main themes of our research are outlined below. To explore these themes, we primarily use controlled in vitro experimental evolution in the opportunistic pathogenic bacterium Pseudomonas aeruginosa.
Fitness costs and compensatory evolution
Antibiotic resistance usually reduces bacterial competitive ability, and this fitness cost is thought to represent a key obstacle to the spread of resistance at an epidemiological scale. We are interested in understanding why resistance carries a cost, and when this cost will cause resistance to decline after antibiotic use is reduced.
Evolutionary consequences of intervention strategies
How should we use antibiotics? This theme explores the evolutionary consequences of different intervention strategies, such as altering the frequency and intensity of antibiotic use, and how this impacts the dynamics of resistance at a population level.
Many of the most important antibiotic resistance genes in clinical pathogens are found on plasmids, autonomously replicating circles of DNA that can jump between bacteria. We are interested in understanding why resistance genes are on plasmids, and on how plasmids can persist in bacterial populations when antibiotic use declines.
Genomic drivers of resistance
Bacteria show extensive diversity in genome content and sequence. We are interested in understanding how genomic background shapes the rate and mechanisms of resistance evolution.
Recent Key Publications
- A. San Millan, J.A Escudero*, D.Gifford*, D. Mazel and R.C MacLean. Multicopy plasmids potentiate the evolution of antibiotic resistance in bacteria. Nature Ecology and Evolution (2016) DOI:10.1038/s41559-016-0010 [Link] [Nature Microbiology Comment][Faculty of 1000][Behind the paper]
- M.Toll-Riera, A. San Millan, A. Wagner and R. C MacLean. The genomic basis of evolutionary innovation in Pseudomonas aeruginosa. PLOS Genetics (2016) doi:10.1371/journal.pgen.1006005 [Link]
- A. San Millan, M. Toll-Riera, Q. Qi and R.C. MacLean. Interactions between horizontally acquired genes create a fitness cost in Pseudomonas aeruginosa. Nature Communications (2015) 6, doi:10.1038/ncomms7845 [Link]
- Q, Qi, G. Preston and R.C. MacLean. Linking system-wide impacts of RNA polymerase mutations to the fitness cost of rifampicin resistance in Pseudomonas aeruginosa. mBio (2014) 5, doi: 10.1128/mBio.01562-14 [Link]
- A. San Millan*, R.Peña-Miller*, M. Toll-Riera, Z.Halbert, A.McLean, B.Cooper and R.C MacLean. Positive selection and compensatory adaptation interact to stabilize non-transmissible plasmids. Nature Communications (2014) 5, doi:10.1038/ncomms6208. [Link]
- T. Vogwill, M. Kojadinovic, V. Furió and R.C. MacLean. Testing the role of genetic background in parallel evolution using the comparative experimental evolution of antibiotic resistance. Molecular Biology and Evolution (2014) doi: 10.1093/molbev/msu262 [Link]
Recent reviews and opinion
- A. San Millan and R.C MacLean. Fitness Costs of Plasmids: A Limit to Plasmid Transmission. Microbiology Spectrum (2017) doi:10.1128/microbiolspec.MTBP-0016-2017 [Link]
- R.C MacLean and T. Vogwill. Limits to compensatory adaptation and the persistence of antibiotic resistance in pathogenic bacteria. Evolution, Medicine and Public Health (2015) doi:10.1093/emph/eou032 [Link]
- T. Vogwill and R. C. MacLean. The genetic basis of the fitness cost of antimicrobial resistance: a meta-analysis approach. Evolutionary Applications (2014) doi:10.1111/eva.12202 [Link]
- R.C. MacLean, C. Torres-Barceló and R. Moxon. Evaluating models of stress-induced mutagenesis in bacteria. Nature Reviews Genetics (2013) 14:221-227. [Link]
- R.C. MacLean, A. Hall, G. Perron, and A. Buckling. The population genetics of antibiotic resistance: integrating molecular mechanisms and treatment context. Nature Reviews Genetics (2010) 11:405-414. [Link]