Microbiology Papers in Press. Published May 8, 2015 as doi:10.1099/mic.0.000107
Microbiology Experimental evolution can unravel the complex causes of natural selection in clinical infections --Manuscript Draft-Manuscript Number:
MIC-D-15-00173
Full Title:
Experimental evolution can unravel the complex causes of natural selection in clinical infections
Short Title:
Experimental evolution can unravel the complex causes of natural selection in clinical infections
Article Type:
SGM Prize Lecture
Section/Category:
SGM Prize Lecture
Keywords:
evolution; clinical; microbial
Corresponding Author:
Michael Brockhurst Department of Biology, University of York UNITED KINGDOM
First Author:
Michael A Brockhurst
Order of Authors:
Michael A Brockhurst
Manuscript Region of Origin:
UNITED KINGDOM
Abstract:
It is increasingly clear that rapid evolutionary dynamics are an important process in microbial ecology. Experimental evolution, wherein microbial evolution is observed in real-time, has revealed many instances of appreciable evolutionary change occurring on very short timescales of a few days or weeks in response to a variety of biotic and abiotic selection pressures. From clinical infections, including the chronic bacterial lung infections associated with cystic fibrosis that form a focus of my research, there is now abundant evidence suggesting that rapid evolution by infecting microbes contributes to host adaptation, treatment failure and worsening patient prognosis. However, disentangling the drivers of natural selection in complex infection environments is extremely challenging and limits our understanding of the selective pressures acting upon microbes in infections. Controlled evolution experiments can make a vital contribution to this by determining the causal links between predicted drivers of natural selection and the evolutionary responses of microbes. Integration of experimental evolution into studies of clinical infections is a key next step towards a better understanding of the causes and consequences of rapid microbial evolution in infections and discovering how these evolutionary processes might be influenced to improve patient health.
Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation
Manuscript Including References (Word document) Click here to download Manuscript Including References (Word document): Fleming Essay.docx
Experimental evolution can unravel the complex causes of natural selection in clinical infections
Michael A. Brockhurst Department of Biology, University of York, York, YO10 5DD
ABSTRACT It is increasingly clear that rapid evolutionary dynamics are an important process in microbial ecology. Experimental evolution, wherein microbial evolution is observed in real-time, has revealed many instances of appreciable evolutionary change occurring on very short timescales of a few days or weeks in response to a variety of biotic and abiotic selection pressures. From clinical infections, including the chronic bacterial lung infections associated with cystic fibrosis that form a focus of my research, there is now abundant evidence suggesting that rapid evolution by infecting microbes contributes to host adaptation, treatment failure and worsening patient prognosis. However, disentangling the drivers of natural selection in complex infection environments is extremely challenging and limits our understanding of the selective pressures acting upon microbes in infections. Controlled evolution experiments can make a vital contribution to this by determining the causal links between predicted drivers of natural selection and the evolutionary responses of microbes. Integration of experimental evolution into studies of clinical infections is a key next step towards a better understanding of the causes and consequences of rapid microbial evolution in infections and discovering how these evolutionary processes might be influenced to improve patient health.
The experimental evolution advantage The large population sizes and short generation times of microbes can potentiate fast evolutionary responses to selection. This simple observation underpins the logic of microbial experimental evolution, wherein this ability to observe evolution happening in real-time has allowed evolutionary biologists to test a wide variety of the predictions of evolutionary theory (Barrick & Lenski, 2013; Buckling et al., 2009; Elena & Lenski, 2003). Unlike observational approaches to the study of
evolution, experimental evolution allows unequivocal determination of the causal relationship between the selective force applied and the evolutionary response because of the precise control over experimental conditions, treatments and the genetic composition of the starting populations (Buckling et al., 2009). Moreover, due to the ability for most microbes to be stored cryogenically, it is possible to directly compare the traits of evolved lineages to their evolutionary ancestor (or intermediates) and even to estimate the relative fitness of evolved lineages against the ancestor using head-to-head competition assays (Elena & Lenski, 2003).
Experimental evolution is commonly used to examine the tempo and mode of evolutionary adaptation to abiotic environments or stressors. Such studies typically reveal a diminishing rate of increasing fitness over time, with the rate of adaptation slowing as populations approach the optimum phenotype (Barrick et al., 2009; Buckling et al., 2009). Studies of biotic selection, and in particular biotic conflicts between bacteria and phages, reveal contrasting evolutionary dynamics (Brockhurst & Koskella, 2013; Koskella & Brockhurst, 2014). Here, recurrent coevolutionary cycles of adaptation and counter-adaptation between bacterium and phage mean that the selective force acting on each species is itself evolving and therefore continual evolutionary change is required simply to maintain current levels of fitness; an evolutionary concept known as the Red Queen hypothesis (Brockhurst et al., 2014; Van Valen, 1973). Experiments comparing lytic phage populations evolving against either genetically fixed or evolving populations of Pseudomonas fluorescens showed that allowing coevolution more than doubled the rate of molecular evolution in the phage (Paterson et al., 2010). Similarly, P. fluorescens coevolving with lytic phage gained 10times more non-synonymous substitutions than control populations evolving without phage, and there was little overlap in the sets of mutations selected under these treatments (Scanlan et al., 2015). Indeed, coevolving appears to limit the capacity for abiotic adaptation in both the bacterium (Scanlan et al., 2015) and phage (Zhang & Buckling, 2011).
Using carefully designed factorial experiments it is also possible to determine the relative importance of multiple sources of natural selection. For example, in P. fluorescens populations, phages reduce the evolution of diversity in bacterial colony morphology in spatially structured
environments, but increase diversity in spatially unstructured environments (Brockhurst et al., 2004). Thus the effects of biotic and abiotic selection interact: Although competition in spatially structured environments selects for high diversity in the absence of phages, phage-killing weakens competition and thus weakens diversifying selection (Brockhurst et al., 2004; Buckling & Rainey, 2003). Conversely, in spatially unstructured environments that would not support diversity without phages, phages select for multiple, resistant colony morphologies thus increasing diversity (Brockhurst et al., 2004; Brockhurst, 2007). Using experimental evolution therefore we can disentangle the causes of natural selection operating in multidimensional selective environments to understand their effects on microbial evolutionary responses.
While it is feasible to conduct very longterm evolution experiments with microbes lasting many 1000s of generations, a great many studies report appreciable evolutionary responses to selection over much shorter timescales of 10s or 100s of generations, that is over durations of several days or weeks, such that ecological and evolutionary timescales can be considered convergent (Buckling et al., 2009). Remarkably, bacteria are capable of evolving new functions over a weekend, as shown in a recent study of P. fluorescens where engineered immotile strains reevolved flagellar motility in less than 96 hours. Starvation on solid agar imposed strong selection for motility in the immotile bacteria which responded by evolutionary rewiring of their nitrogen gene regulatory network, switching its specificity towards regulation of flagellar genes instead, thereby allowing the cells to regain the ability to build flagella and swim towards nutrients (Taylor et al., 2015). My own experimental evolution studies of bacterial populations reveal the potential for similarly rapid evolutionary gain or loss of a wide variety of ecologically important traits including colony morphology (Brockhurst et al., 2004; Brockhurst et al., 2005; Brockhurst et al., 2007b; Vogwill et al., 2011), biofilm production (Brockhurst et al., 2007a), resistance to drugs (Habets & Brockhurst, 2012) or phages (Brockhurst et al., 2003; Brockhurst et al., 2005), motility (Brockhurst et al., 2005; Taylor et al., 2015), and loss of social secretions like siderophores (Brockhurst et al., 2008).
Rapid microbial evolution in the clinic
Many of these same bacterial traits are also known to evolve rapidly in clinical infections, and this evolution may in some cases contribute to important changes in patient prognosis or the response of the infection to treatment (e.g., (Fitzgerald, 2014; Folkesson et al., 2012)). The evolutionary dynamics of chronic bacterial lung infections associated with cystic fibrosis caused have been particularly well-described (Folkesson et al., 2012). Cystic fibrosis (CF) is a genetic disorder common among caucasians and affecting ~10,000 people in the UK (http://www.cysticfibrosis.org.uk). Patients produce thickened mucus which makes them prone to lifelong lung infections which cause morbidity and mortality. The predominant cause of chronic lung infection in CF is the opportunistic bacterial pathogen Pseudomonas aeruginosa (Williams & Davies, 2012), which can be acquired from the environment although transmissible strains capable of spreading between patients have become increasingly common (Fothergill et al., 2012). Once established in the CF lung, P. aeruginosa can persist for decades due to the poor efficacy of antibiotic treatment against these infections, offering substantial opportunity for evolutionary adaptation (Folkesson et al., 2012).
The CF lung is likely to offer a challenging environment for colonising P. aeruginosa comprising attack by the host immune system and constant exposure to antibiotics, competition with cocolonising microbes, infection by phages, as well as osmotic, oxidative and nitrosative stresses (Folkesson et al., 2012; Harrison, 2007). In response, P. aeruginosa populations rapidly adapt and diversify. Lineages accumulate mutations over time (Smith et al., 2006; Yang et al., 2011), sometimes at highly variable rates between lineages due to the frequent evolution of hypermutability in CF lung infections (Mena et al., 2008; Oliver et al., 2000). Across patients, independently evolving P. aeruginosa lineages appear to converge upon a suite of phenotypic adaptations to life in the CF lung (Folkesson et al., 2012; Marvig et al., 2015). These adaptations often include the evolution of mucoidy (Govan & Deretic, 1996), altered motility/attachment (Mahenthiralingam et al., 1994), gain of multiple antibiotic resistances (Lopez-Causape et al., 2013), and altered social behaviours and secretions including signals, siderophores and toxins (Fothergill et al., 2007; Jiricny et al., 2014). In common with many experimental evolution studies (Barrick & Lenski, 2013), evolutionary adaptation to the CF lung is often underpinned by regulatory
mutations affecting the expression of (many) other genes (Folkesson et al., 2012; Hoffman et al., 2009).
Despite a signature of parallel evolution between patients, an important feature of P. aeruginosa populations infecting CF lungs is that they frequently exhibit exceptionally high levels of intra-host diversity and rapid turnover (Ashish et al., 2013; Fothergill et al., 2010; Mowat et al., 2011; Workentine et al., 2013). While diversity at some traits may be relatively neutral with respect to patient health, for other traits the observed diversity is likely to have important clinical consequences. For example, high within-population diversity in antibiotic resistance can make it hard to diagnose which drug to prescribe (Mowat et al., 2011; Workentine et al., 2013), whereas changes over time in the frequency of overproducers of the toxin pyocyanin correlates with exacerbations in patient symptoms (Mowat et al., 2011). Taking a population-level perspective has also revealed coexistence of highly genetically diverged lineages within CF patients and sharing of lineages among multiple patients, indicating on-going patient-to-patient transmission within the cohort of patients attending the clinic (Williams et al., 2015).
Unravelling complex clinical selective environments While we have a good understanding therefore of how P. aeruginosa populations adapt to life in the CF lung it is very challenging to link these evolutionary responses to particular selective causes. Causal arguments made in the literature linking, for example, loss of motility structures to immune selection, although interesting, are mere speculation: While it is possible that host immunity selects against motility structures, it is equally possible that motility is simply not useful in this environment, or that phages may select for motility loss, or that all of these factors play a role. This gap in knowledge limits the scope for designing treatment interventions that alter the selective environment in infections to prevent (or promote) particular trajectories of pathogen evolution (e.g., to limit the evolution of drug resistance). How then can we begin to unravel this multidimensional selective environment to understand why bacterial populations evolve as they do in clinical infections? One answer is to use experimental evolution to empirically test the evolutionary hypotheses that emerge from analyses of clinical data, allowing predicted causal links between
evolutionary responses and selective causes to be validated. Integrating experimental evolution into clinical investigations would ideally become an iterative process whereby the findings from each approach would inspire further investigation (Figure 1).
Exciting advances in developing more realistic lab models of CF infection, such as in vitro artificial sputum media (ASM) mimicking lung sputum (Sriramulu, 2013), ex-vivo lung tissue (Harrison et al., 2014) and in vivo mouse chronic lung infection (Fothergill et al., 2014), are allowing clinically relevant experimental evolution studies to be designed and performed. Although most existing studies have been relatively short-duration, they suggest enormous potential for experimental evolution to be a valuable tool in advancing our understanding of the selective drivers of real-time evolution in infections. For example, experimental evolution of P. aeruginosa in ASM recapitulates aspects of bacterial adaptation to the CF lung, including mutations associated with living in a biofilm (Wong et al., 2012). Exposure to antibiotics in ASM reveals both resistance evolution via stereotypical genetic mechanisms, compensatory evolution to reduce costs of resistance (Wong et al., 2012), and the evolution of greater phenotypic diversity in response to sub-inhibitory doses of some clinically relevant drugs (Wright et al., 2013). Evolution experiments in an in vivo mouse model of chronic lung infection demonstrate that P. aeruginosa establishment in the upper respiratory tract is necessary for invasion of the lung (Fothergill et al., 2014), as hypothesised in CF (Hansen et al., 2012). Evolved bacteria were better able to establish lung infections than the ancestor, suggesting evolutionary adaptation to the host environment (Fothergill et al., 2014). Mutations detected in multiple evolved clones are likely to have caused enhanced resistance to host immune peptides (Fothergill et al., 2014), and similar mutations have been observed in P. aeruginosa isolated from human CF lung infections (Moskowitz et al., 2012).
Concluding remarks Current data on rapid microbial evolution in infections is largely descriptive, providing understanding of what evolutionary changes occur, but little insight into why they occur. Better understanding of the causal link between microbial evolutionary responses and the selective causes can be gained through carefully designed experimental evolution studies, which allow the
multidimensional clinical selective environment to be unravelled. This is a vital step towards development of new treatment strategies that manipulate the in vivo selective environment to reduce selection for undesirable microbial traits, such as drug resistance, and thereby alter pathogen evolutionary trajectories towards better outcomes for patient health. Achieving this will require cross-disciplinary dialogue and multidisciplinary collaboration between evolutionary biologists, microbiologists, sociologists and clinicians.
Acknowledgements I am grateful to the SGM for the award of the Fleming Prize 2015. Thanks to all of my collaborators past and present, especially all of the postgraduates and postdocs whose hard work and creativity has been essential to the science.
Bibliography Ashish, A., Paterson, S., Mowat, E., Fothergill, J. L., Walshaw, M. J. & Winstanley, C. (2013). Extensive diversification is a common feature of Pseudomonas aeruginosa populations during respiratory infections in cystic fibrosis. Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society 12, 790-793.
Barrick, J. E., Yu, D. S., Yoon, S. H., Jeong, H., Oh, T. K., Schneider, D., Lenski, R. E. & Kim, J. F. (2009). Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature 461, 1243-U1274.
Barrick, J. E. & Lenski, R. E. (2013). Genome dynamics during experimental evolution. Nature reviews Genetics 14, 827-839.
Brockhurst, M. A., Morgan, A. D., Rainey, P. B. & Buckling, A. (2003). Population mixing accelerates coevolution. Ecol Lett 6, 975-979.
Brockhurst, M. A., Rainey, P. B. & Buckling, A. (2004). The effect of spatial heterogeneity and parasites on the evolution of host diversity. P Roy Soc Lond B Bio 271, 107-111.
Brockhurst, M. A., Buckling, A. & Rainey, P. B. (2005). The effect of a bacteriophage on diversification of the opportunistic bacterial pathogen, Pseudomonas aeruginosa. P R Soc B 272, 1385-1391.
Brockhurst, M. A. (2007). Ecology: Death and destruction determine diversity. Curr Biol 17, R512R514.
Brockhurst, M. A., Buckling, A. & Gardner, A. (2007a). Cooperation peaks at intermediate disturbance. Curr Biol 17, 761-765.
Brockhurst, M. A., Colegrave, N., Hodgson, D. J. & Buckling, A. (2007b). Niche Occupation Limits Adaptive Radiation in Experimental Microcosms. Plos One 2, -.
Brockhurst, M. A., Buckling, A., Racey, D. & Gardner, A. (2008). Resource supply and the evolution of public-goods cooperation in bacteria. Bmc Biol 6, -.
Brockhurst, M. A. & Koskella, B. (2013). Experimental coevolution of species interactions. Trends Ecol Evol In Press.
Brockhurst, M. A., Chapman, T., King, K. C., Mank, J. E., Paterson, S. & Hurst, G. D. (2014). Running with the Red Queen: the role of biotic conflicts in evolution. Proceedings Biological sciences / The Royal Society 281.
Buckling, A. & Rainey, P. B. (2003). The role of parasites in sympatric and allopatric host diversification (vol 420, pg 496, 2002). Nature 421, 294-294.
Buckling, A., Maclean, R. C., Brockhurst, M. A. & Colegrave, N. (2009). The Beagle in a bottle. Nature 457, 824-829.
Elena, S. F. & Lenski, R. E. (2003). Evolution experiments with microorganisms: The dynamics and genetic bases of adaptation. Nat Rev Genet 4, 457-469.
Fitzgerald, J. R. (2014). Evolution of Staphylococcus aureus during human colonization and infection. Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases 21, 542-547.
Folkesson, A., Jelsbak, L., Yang, L., Johansen, H. K., Ciofu, O., Hoiby, N. & Molin, S. (2012). Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective. Nature reviews Microbiology 10, 841-851.
Fothergill, J. L., Panagea, S., Hart, C. A., Walshaw, M. J., Pitt, T. L. & Winstanley, C. (2007). Widespread pyocyanin over-production among isolates of a cystic fibrosis epidemic strain. BMC microbiology 7, 45.
Fothergill, J. L., Mowat, E., Ledson, M. J., Walshaw, M. J. & Winstanley, C. (2010). Fluctuations in phenotypes and genotypes within populations of Pseudomonas aeruginosa in the cystic fibrosis lung during pulmonary exacerbations. Journal of medical microbiology 59, 472-481.
Fothergill, J. L., Walshaw, M. J. & Winstanley, C. (2012). Transmissible strains of Pseudomonas aeruginosa in cystic fibrosis lung infections. The European respiratory journal 40, 227-238.
Fothergill, J. L., Neill, D. R., Loman, N., Winstanley, C. & Kadioglu, A. (2014). Pseudomonas aeruginosa adaptation in the nasopharyngeal reservoir leads to migration and persistence in the lungs. Nature communications 5, 4780.
Govan, J. R. & Deretic, V. (1996). Microbial pathogenesis in cystic fibrosis: mucoid Pseudomonas aeruginosa and Burkholderia cepacia. Microbiological reviews 60, 539-574.
Habets, M. G. J. L. & Brockhurst, M. A. (2012). Therapeutic antimicrobial peptides may compromise natural immunity. Biol Letters 8, 416-418.
Hansen, S. K., Rau, M. H., Johansen, H. K. & other authors (2012). Evolution and diversification of Pseudomonas aeruginosa in the paranasal sinuses of cystic fibrosis children have implications for chronic lung infection. The ISME journal 6, 31-45.
Harrison, F. (2007). Microbial ecology of the cystic fibrosis lung. Microbiology 153, 917-923.
Harrison, F., Muruli, A., Higgins, S. & Diggle, S. P. (2014). Development of an ex vivo porcine lung model for studying growth, virulence, and signaling of Pseudomonas aeruginosa. Infection and immunity 82, 3312-3323.
Hoffman, L. R., Kulasekara, H. D., Emerson, J., Houston, L. S., Burns, J. L., Ramsey, B. W. & Miller, S. I. (2009). Pseudomonas aeruginosa lasR mutants are associated with cystic fibrosis lung disease progression. Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society 8, 66-70.
Jiricny, N., Molin, S., Foster, K. & other authors (2014). Loss of social behaviours in populations of Pseudomonas aeruginosa infecting lungs of patients with cystic fibrosis. PloS one 9, e83124.
Koskella, B. & Brockhurst, M. A. (2014). Bacteria-phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS microbiology reviews 38, 916-931.
Lopez-Causape, C., Rojo-Molinero, E., Mulet, X., Cabot, G., Moya, B., Figuerola, J., Togores, B., Perez, J. L. & Oliver, A. (2013). Clonal dissemination, emergence of mutator lineages and antibiotic resistance evolution in Pseudomonas aeruginosa cystic fibrosis chronic lung infection. PloS one 8, e71001.
Mahenthiralingam, E., Campbell, M. E. & Speert, D. P. (1994). Nonmotility and phagocytic resistance of Pseudomonas aeruginosa isolates from chronically colonized patients with cystic fibrosis. Infection and immunity 62, 596-605.
Marvig, R. L., Sommer, L. M., Molin, S. & Johansen, H. K. (2015). Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis. Nature genetics 47, 57-64.
Mena, A., Smith, E. E., Burns, J. L., Speert, D. P., Moskowitz, S. M., Perez, J. L. & Oliver, A. (2008). Genetic adaptation of Pseudomonas aeruginosa to the airways of cystic fibrosis patients is catalyzed by hypermutation. Journal of bacteriology 190, 7910-7917.
Moskowitz, S. M., Brannon, M. K., Dasgupta, N. & other authors (2012). PmrB mutations promote polymyxin resistance of Pseudomonas aeruginosa isolated from colistin-treated cystic fibrosis patients. Antimicrobial agents and chemotherapy 56, 1019-1030.
Mowat, E., Paterson, S., Fothergill, J. L., Wright, E. A., Ledson, M. J., Walshaw, M. J., Brockhurst, M. A. & Winstanley, C. (2011). Pseudomonas aeruginosa Population Diversity and Turnover in Cystic Fibrosis Chronic Infections. Am J Resp Crit Care 183, 1674-1679.
Oliver, A., Canton, R., Campo, P., Baquero, F. & Blazquez, J. (2000). High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288, 1251-1254.
Paterson, S., Vogwill, T., Buckling, A. & other authors (2010). Antagonistic coevolution accelerates molecular evolution. Nature 464, 275-278.
Scanlan, P. D., Hall, A. R., Blackshields, G., Friman, V. P., Davis, M. R., Jr., Goldberg, J. B. & Buckling, A. (2015). Coevolution with Bacteriophages Drives Genome-Wide Host Evolution and Constrains the Acquisition of Abiotic-Beneficial Mutations. Molecular biology and evolution.
Smith, E. E., Buckley, D. G., Wu, Z. & other authors (2006). Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients. Proceedings of the National Academy of Sciences of the United States of America 103, 8487-8492.
Sriramulu, D. (2013). Evolution and impact of bacterial drug resistance in the context of cystic fibrosis disease and nosocomial settings. Microbiology insights 6, 29-36.
Taylor, T. B., Mulley, G., Dills, A. H. & other authors (2015). Evolutionary resurrection of flagellar motility via rewiring of the nitrogen regulation system. Science 347, 1014-1017.
Van Valen, L. (1973). A new evolutionary law. Evolutionary Theory 1, 1-30.
Vogwill, T., Fenton, A. & Brockhurst, M. A. (2011). Coevolving parasites enhance the diversitydecreasing effect of dispersal. Biol Letters 7, 578-580.
Williams, D., Evans, B., Haldenby, S., Walshaw, M. J., Brockhurst, M. A., Winstanley, C. & Paterson, S. (2015). Divergent, Coexisting Pseudomonas aeruginosa Lineages in Chronic Cystic Fibrosis Lung Infections. American journal of respiratory and critical care medicine 191, 775-785.
Williams, H. D. & Davies, J. C. (2012). Basic science for the chest physician: Pseudomonas aeruginosa and the cystic fibrosis airway. Thorax 67, 465-467.
Wong, A., Rodrigue, N. & Kassen, R. (2012). Genomics of adaptation during experimental evolution of the opportunistic pathogen Pseudomonas aeruginosa. PLoS genetics 8, e1002928.
Workentine, M. L., Sibley, C. D., Glezerson, B., Purighalla, S., Norgaard-Gron, J. C., Parkins, M. D., Rabin, H. R. & Surette, M. G. (2013). Phenotypic heterogeneity of Pseudomonas aeruginosa populations in a cystic fibrosis patient. PloS one 8, e60225.
Wright, E. A., Fothergill, J. L., Paterson, S., Brockhurst, M. A. & Winstanley, C. (2013). Subinhibitory concentrations of some antibiotics can drive diversification of Pseudomonas aeruginosa populations in artificial sputum medium. Bmc Microbiol 13, 170.
Yang, L., Jelsbak, L., Marvig, R. L. & other authors (2011). Evolutionary dynamics of bacteria in a human host environment. Proceedings of the National Academy of Sciences of the United States of America 108, 7481-7486.
Zhang, Q. G. & Buckling, A. (2011). Antagonistic coevolution limits population persistence of a virus in a thermally deteriorating environment. Ecol Lett 14, 282-288.
Figure Legend Figure 1 | Schematic of the iterative loop of investigation, from the lab, to the clinic, and back again
Figure Click here to download Figure: fleming fig 1-1.tiff
Microbiology Experimental evolution can unravel the complex causes of natural selection in clinical infections --Manuscript Draft-Manuscript Number:
MIC-D-15-00173
Full Title:
Experimental evolution can unravel the complex causes of natural selection in clinical infections
Short Title:
Experimental evolution can unravel the complex causes of natural selection in clinical infections
Article Type:
SGM Prize Lecture
Section/Category:
SGM Prize Lecture
Keywords:
evolution; clinical; microbial
Corresponding Author:
Michael Brockhurst Department of Biology, University of York UNITED KINGDOM
First Author:
Michael A Brockhurst
Order of Authors:
Michael A Brockhurst
Manuscript Region of Origin:
UNITED KINGDOM
Abstract:
It is increasingly clear that rapid evolutionary dynamics are an important process in microbial ecology. Experimental evolution, wherein microbial evolution is observed in real-time, has revealed many instances of appreciable evolutionary change occurring on very short timescales of a few days or weeks in response to a variety of biotic and abiotic selection pressures. From clinical infections, including the chronic bacterial lung infections associated with cystic fibrosis that form a focus of my research, there is now abundant evidence suggesting that rapid evolution by infecting microbes contributes to host adaptation, treatment failure and worsening patient prognosis. However, disentangling the drivers of natural selection in complex infection environments is extremely challenging and limits our understanding of the selective pressures acting upon microbes in infections. Controlled evolution experiments can make a vital contribution to this by determining the causal links between predicted drivers of natural selection and the evolutionary responses of microbes. Integration of experimental evolution into studies of clinical infections is a key next step towards a better understanding of the causes and consequences of rapid microbial evolution in infections and discovering how these evolutionary processes might be influenced to improve patient health.
Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation
Manuscript Including References (Word document) Click here to download Manuscript Including References (Word document): Fleming Essay.docx
Experimental evolution can unravel the complex causes of natural selection in clinical infections
Michael A. Brockhurst Department of Biology, University of York, York, YO10 5DD
ABSTRACT It is increasingly clear that rapid evolutionary dynamics are an important process in microbial ecology. Experimental evolution, wherein microbial evolution is observed in real-time, has revealed many instances of appreciable evolutionary change occurring on very short timescales of a few days or weeks in response to a variety of biotic and abiotic selection pressures. From clinical infections, including the chronic bacterial lung infections associated with cystic fibrosis that form a focus of my research, there is now abundant evidence suggesting that rapid evolution by infecting microbes contributes to host adaptation, treatment failure and worsening patient prognosis. However, disentangling the drivers of natural selection in complex infection environments is extremely challenging and limits our understanding of the selective pressures acting upon microbes in infections. Controlled evolution experiments can make a vital contribution to this by determining the causal links between predicted drivers of natural selection and the evolutionary responses of microbes. Integration of experimental evolution into studies of clinical infections is a key next step towards a better understanding of the causes and consequences of rapid microbial evolution in infections and discovering how these evolutionary processes might be influenced to improve patient health.
The experimental evolution advantage The large population sizes and short generation times of microbes can potentiate fast evolutionary responses to selection. This simple observation underpins the logic of microbial experimental evolution, wherein this ability to observe evolution happening in real-time has allowed evolutionary biologists to test a wide variety of the predictions of evolutionary theory (Barrick & Lenski, 2013; Buckling et al., 2009; Elena & Lenski, 2003). Unlike observational approaches to the study of
evolution, experimental evolution allows unequivocal determination of the causal relationship between the selective force applied and the evolutionary response because of the precise control over experimental conditions, treatments and the genetic composition of the starting populations (Buckling et al., 2009). Moreover, due to the ability for most microbes to be stored cryogenically, it is possible to directly compare the traits of evolved lineages to their evolutionary ancestor (or intermediates) and even to estimate the relative fitness of evolved lineages against the ancestor using head-to-head competition assays (Elena & Lenski, 2003).
Experimental evolution is commonly used to examine the tempo and mode of evolutionary adaptation to abiotic environments or stressors. Such studies typically reveal a diminishing rate of increasing fitness over time, with the rate of adaptation slowing as populations approach the optimum phenotype (Barrick et al., 2009; Buckling et al., 2009). Studies of biotic selection, and in particular biotic conflicts between bacteria and phages, reveal contrasting evolutionary dynamics (Brockhurst & Koskella, 2013; Koskella & Brockhurst, 2014). Here, recurrent coevolutionary cycles of adaptation and counter-adaptation between bacterium and phage mean that the selective force acting on each species is itself evolving and therefore continual evolutionary change is required simply to maintain current levels of fitness; an evolutionary concept known as the Red Queen hypothesis (Brockhurst et al., 2014; Van Valen, 1973). Experiments comparing lytic phage populations evolving against either genetically fixed or evolving populations of Pseudomonas fluorescens showed that allowing coevolution more than doubled the rate of molecular evolution in the phage (Paterson et al., 2010). Similarly, P. fluorescens coevolving with lytic phage gained 10times more non-synonymous substitutions than control populations evolving without phage, and there was little overlap in the sets of mutations selected under these treatments (Scanlan et al., 2015). Indeed, coevolving appears to limit the capacity for abiotic adaptation in both the bacterium (Scanlan et al., 2015) and phage (Zhang & Buckling, 2011).
Using carefully designed factorial experiments it is also possible to determine the relative importance of multiple sources of natural selection. For example, in P. fluorescens populations, phages reduce the evolution of diversity in bacterial colony morphology in spatially structured
environments, but increase diversity in spatially unstructured environments (Brockhurst et al., 2004). Thus the effects of biotic and abiotic selection interact: Although competition in spatially structured environments selects for high diversity in the absence of phages, phage-killing weakens competition and thus weakens diversifying selection (Brockhurst et al., 2004; Buckling & Rainey, 2003). Conversely, in spatially unstructured environments that would not support diversity without phages, phages select for multiple, resistant colony morphologies thus increasing diversity (Brockhurst et al., 2004; Brockhurst, 2007). Using experimental evolution therefore we can disentangle the causes of natural selection operating in multidimensional selective environments to understand their effects on microbial evolutionary responses.
While it is feasible to conduct very longterm evolution experiments with microbes lasting many 1000s of generations, a great many studies report appreciable evolutionary responses to selection over much shorter timescales of 10s or 100s of generations, that is over durations of several days or weeks, such that ecological and evolutionary timescales can be considered convergent (Buckling et al., 2009). Remarkably, bacteria are capable of evolving new functions over a weekend, as shown in a recent study of P. fluorescens where engineered immotile strains reevolved flagellar motility in less than 96 hours. Starvation on solid agar imposed strong selection for motility in the immotile bacteria which responded by evolutionary rewiring of their nitrogen gene regulatory network, switching its specificity towards regulation of flagellar genes instead, thereby allowing the cells to regain the ability to build flagella and swim towards nutrients (Taylor et al., 2015). My own experimental evolution studies of bacterial populations reveal the potential for similarly rapid evolutionary gain or loss of a wide variety of ecologically important traits including colony morphology (Brockhurst et al., 2004; Brockhurst et al., 2005; Brockhurst et al., 2007b; Vogwill et al., 2011), biofilm production (Brockhurst et al., 2007a), resistance to drugs (Habets & Brockhurst, 2012) or phages (Brockhurst et al., 2003; Brockhurst et al., 2005), motility (Brockhurst et al., 2005; Taylor et al., 2015), and loss of social secretions like siderophores (Brockhurst et al., 2008).
Rapid microbial evolution in the clinic
Many of these same bacterial traits are also known to evolve rapidly in clinical infections, and this evolution may in some cases contribute to important changes in patient prognosis or the response of the infection to treatment (e.g., (Fitzgerald, 2014; Folkesson et al., 2012)). The evolutionary dynamics of chronic bacterial lung infections associated with cystic fibrosis caused have been particularly well-described (Folkesson et al., 2012). Cystic fibrosis (CF) is a genetic disorder common among caucasians and affecting ~10,000 people in the UK (http://www.cysticfibrosis.org.uk). Patients produce thickened mucus which makes them prone to lifelong lung infections which cause morbidity and mortality. The predominant cause of chronic lung infection in CF is the opportunistic bacterial pathogen Pseudomonas aeruginosa (Williams & Davies, 2012), which can be acquired from the environment although transmissible strains capable of spreading between patients have become increasingly common (Fothergill et al., 2012). Once established in the CF lung, P. aeruginosa can persist for decades due to the poor efficacy of antibiotic treatment against these infections, offering substantial opportunity for evolutionary adaptation (Folkesson et al., 2012).
The CF lung is likely to offer a challenging environment for colonising P. aeruginosa comprising attack by the host immune system and constant exposure to antibiotics, competition with cocolonising microbes, infection by phages, as well as osmotic, oxidative and nitrosative stresses (Folkesson et al., 2012; Harrison, 2007). In response, P. aeruginosa populations rapidly adapt and diversify. Lineages accumulate mutations over time (Smith et al., 2006; Yang et al., 2011), sometimes at highly variable rates between lineages due to the frequent evolution of hypermutability in CF lung infections (Mena et al., 2008; Oliver et al., 2000). Across patients, independently evolving P. aeruginosa lineages appear to converge upon a suite of phenotypic adaptations to life in the CF lung (Folkesson et al., 2012; Marvig et al., 2015). These adaptations often include the evolution of mucoidy (Govan & Deretic, 1996), altered motility/attachment (Mahenthiralingam et al., 1994), gain of multiple antibiotic resistances (Lopez-Causape et al., 2013), and altered social behaviours and secretions including signals, siderophores and toxins (Fothergill et al., 2007; Jiricny et al., 2014). In common with many experimental evolution studies (Barrick & Lenski, 2013), evolutionary adaptation to the CF lung is often underpinned by regulatory
mutations affecting the expression of (many) other genes (Folkesson et al., 2012; Hoffman et al., 2009).
Despite a signature of parallel evolution between patients, an important feature of P. aeruginosa populations infecting CF lungs is that they frequently exhibit exceptionally high levels of intra-host diversity and rapid turnover (Ashish et al., 2013; Fothergill et al., 2010; Mowat et al., 2011; Workentine et al., 2013). While diversity at some traits may be relatively neutral with respect to patient health, for other traits the observed diversity is likely to have important clinical consequences. For example, high within-population diversity in antibiotic resistance can make it hard to diagnose which drug to prescribe (Mowat et al., 2011; Workentine et al., 2013), whereas changes over time in the frequency of overproducers of the toxin pyocyanin correlates with exacerbations in patient symptoms (Mowat et al., 2011). Taking a population-level perspective has also revealed coexistence of highly genetically diverged lineages within CF patients and sharing of lineages among multiple patients, indicating on-going patient-to-patient transmission within the cohort of patients attending the clinic (Williams et al., 2015).
Unravelling complex clinical selective environments While we have a good understanding therefore of how P. aeruginosa populations adapt to life in the CF lung it is very challenging to link these evolutionary responses to particular selective causes. Causal arguments made in the literature linking, for example, loss of motility structures to immune selection, although interesting, are mere speculation: While it is possible that host immunity selects against motility structures, it is equally possible that motility is simply not useful in this environment, or that phages may select for motility loss, or that all of these factors play a role. This gap in knowledge limits the scope for designing treatment interventions that alter the selective environment in infections to prevent (or promote) particular trajectories of pathogen evolution (e.g., to limit the evolution of drug resistance). How then can we begin to unravel this multidimensional selective environment to understand why bacterial populations evolve as they do in clinical infections? One answer is to use experimental evolution to empirically test the evolutionary hypotheses that emerge from analyses of clinical data, allowing predicted causal links between
evolutionary responses and selective causes to be validated. Integrating experimental evolution into clinical investigations would ideally become an iterative process whereby the findings from each approach would inspire further investigation (Figure 1).
Exciting advances in developing more realistic lab models of CF infection, such as in vitro artificial sputum media (ASM) mimicking lung sputum (Sriramulu, 2013), ex-vivo lung tissue (Harrison et al., 2014) and in vivo mouse chronic lung infection (Fothergill et al., 2014), are allowing clinically relevant experimental evolution studies to be designed and performed. Although most existing studies have been relatively short-duration, they suggest enormous potential for experimental evolution to be a valuable tool in advancing our understanding of the selective drivers of real-time evolution in infections. For example, experimental evolution of P. aeruginosa in ASM recapitulates aspects of bacterial adaptation to the CF lung, including mutations associated with living in a biofilm (Wong et al., 2012). Exposure to antibiotics in ASM reveals both resistance evolution via stereotypical genetic mechanisms, compensatory evolution to reduce costs of resistance (Wong et al., 2012), and the evolution of greater phenotypic diversity in response to sub-inhibitory doses of some clinically relevant drugs (Wright et al., 2013). Evolution experiments in an in vivo mouse model of chronic lung infection demonstrate that P. aeruginosa establishment in the upper respiratory tract is necessary for invasion of the lung (Fothergill et al., 2014), as hypothesised in CF (Hansen et al., 2012). Evolved bacteria were better able to establish lung infections than the ancestor, suggesting evolutionary adaptation to the host environment (Fothergill et al., 2014). Mutations detected in multiple evolved clones are likely to have caused enhanced resistance to host immune peptides (Fothergill et al., 2014), and similar mutations have been observed in P. aeruginosa isolated from human CF lung infections (Moskowitz et al., 2012).
Concluding remarks Current data on rapid microbial evolution in infections is largely descriptive, providing understanding of what evolutionary changes occur, but little insight into why they occur. Better understanding of the causal link between microbial evolutionary responses and the selective causes can be gained through carefully designed experimental evolution studies, which allow the
multidimensional clinical selective environment to be unravelled. This is a vital step towards development of new treatment strategies that manipulate the in vivo selective environment to reduce selection for undesirable microbial traits, such as drug resistance, and thereby alter pathogen evolutionary trajectories towards better outcomes for patient health. Achieving this will require cross-disciplinary dialogue and multidisciplinary collaboration between evolutionary biologists, microbiologists, sociologists and clinicians.
Acknowledgements I am grateful to the SGM for the award of the Fleming Prize 2015. Thanks to all of my collaborators past and present, especially all of the postgraduates and postdocs whose hard work and creativity has been essential to the science.
Bibliography Ashish, A., Paterson, S., Mowat, E., Fothergill, J. L., Walshaw, M. J. & Winstanley, C. (2013). Extensive diversification is a common feature of Pseudomonas aeruginosa populations during respiratory infections in cystic fibrosis. Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society 12, 790-793.
Barrick, J. E., Yu, D. S., Yoon, S. H., Jeong, H., Oh, T. K., Schneider, D., Lenski, R. E. & Kim, J. F. (2009). Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature 461, 1243-U1274.
Barrick, J. E. & Lenski, R. E. (2013). Genome dynamics during experimental evolution. Nature reviews Genetics 14, 827-839.
Brockhurst, M. A., Morgan, A. D., Rainey, P. B. & Buckling, A. (2003). Population mixing accelerates coevolution. Ecol Lett 6, 975-979.
Brockhurst, M. A., Rainey, P. B. & Buckling, A. (2004). The effect of spatial heterogeneity and parasites on the evolution of host diversity. P Roy Soc Lond B Bio 271, 107-111.
Brockhurst, M. A., Buckling, A. & Rainey, P. B. (2005). The effect of a bacteriophage on diversification of the opportunistic bacterial pathogen, Pseudomonas aeruginosa. P R Soc B 272, 1385-1391.
Brockhurst, M. A. (2007). Ecology: Death and destruction determine diversity. Curr Biol 17, R512R514.
Brockhurst, M. A., Buckling, A. & Gardner, A. (2007a). Cooperation peaks at intermediate disturbance. Curr Biol 17, 761-765.
Brockhurst, M. A., Colegrave, N., Hodgson, D. J. & Buckling, A. (2007b). Niche Occupation Limits Adaptive Radiation in Experimental Microcosms. Plos One 2, -.
Brockhurst, M. A., Buckling, A., Racey, D. & Gardner, A. (2008). Resource supply and the evolution of public-goods cooperation in bacteria. Bmc Biol 6, -.
Brockhurst, M. A. & Koskella, B. (2013). Experimental coevolution of species interactions. Trends Ecol Evol In Press.
Brockhurst, M. A., Chapman, T., King, K. C., Mank, J. E., Paterson, S. & Hurst, G. D. (2014). Running with the Red Queen: the role of biotic conflicts in evolution. Proceedings Biological sciences / The Royal Society 281.
Buckling, A. & Rainey, P. B. (2003). The role of parasites in sympatric and allopatric host diversification (vol 420, pg 496, 2002). Nature 421, 294-294.
Buckling, A., Maclean, R. C., Brockhurst, M. A. & Colegrave, N. (2009). The Beagle in a bottle. Nature 457, 824-829.
Elena, S. F. & Lenski, R. E. (2003). Evolution experiments with microorganisms: The dynamics and genetic bases of adaptation. Nat Rev Genet 4, 457-469.
Fitzgerald, J. R. (2014). Evolution of Staphylococcus aureus during human colonization and infection. Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases 21, 542-547.
Folkesson, A., Jelsbak, L., Yang, L., Johansen, H. K., Ciofu, O., Hoiby, N. & Molin, S. (2012). Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective. Nature reviews Microbiology 10, 841-851.
Fothergill, J. L., Panagea, S., Hart, C. A., Walshaw, M. J., Pitt, T. L. & Winstanley, C. (2007). Widespread pyocyanin over-production among isolates of a cystic fibrosis epidemic strain. BMC microbiology 7, 45.
Fothergill, J. L., Mowat, E., Ledson, M. J., Walshaw, M. J. & Winstanley, C. (2010). Fluctuations in phenotypes and genotypes within populations of Pseudomonas aeruginosa in the cystic fibrosis lung during pulmonary exacerbations. Journal of medical microbiology 59, 472-481.
Fothergill, J. L., Walshaw, M. J. & Winstanley, C. (2012). Transmissible strains of Pseudomonas aeruginosa in cystic fibrosis lung infections. The European respiratory journal 40, 227-238.
Fothergill, J. L., Neill, D. R., Loman, N., Winstanley, C. & Kadioglu, A. (2014). Pseudomonas aeruginosa adaptation in the nasopharyngeal reservoir leads to migration and persistence in the lungs. Nature communications 5, 4780.
Govan, J. R. & Deretic, V. (1996). Microbial pathogenesis in cystic fibrosis: mucoid Pseudomonas aeruginosa and Burkholderia cepacia. Microbiological reviews 60, 539-574.
Habets, M. G. J. L. & Brockhurst, M. A. (2012). Therapeutic antimicrobial peptides may compromise natural immunity. Biol Letters 8, 416-418.
Hansen, S. K., Rau, M. H., Johansen, H. K. & other authors (2012). Evolution and diversification of Pseudomonas aeruginosa in the paranasal sinuses of cystic fibrosis children have implications for chronic lung infection. The ISME journal 6, 31-45.
Harrison, F. (2007). Microbial ecology of the cystic fibrosis lung. Microbiology 153, 917-923.
Harrison, F., Muruli, A., Higgins, S. & Diggle, S. P. (2014). Development of an ex vivo porcine lung model for studying growth, virulence, and signaling of Pseudomonas aeruginosa. Infection and immunity 82, 3312-3323.
Hoffman, L. R., Kulasekara, H. D., Emerson, J., Houston, L. S., Burns, J. L., Ramsey, B. W. & Miller, S. I. (2009). Pseudomonas aeruginosa lasR mutants are associated with cystic fibrosis lung disease progression. Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society 8, 66-70.
Jiricny, N., Molin, S., Foster, K. & other authors (2014). Loss of social behaviours in populations of Pseudomonas aeruginosa infecting lungs of patients with cystic fibrosis. PloS one 9, e83124.
Koskella, B. & Brockhurst, M. A. (2014). Bacteria-phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS microbiology reviews 38, 916-931.
Lopez-Causape, C., Rojo-Molinero, E., Mulet, X., Cabot, G., Moya, B., Figuerola, J., Togores, B., Perez, J. L. & Oliver, A. (2013). Clonal dissemination, emergence of mutator lineages and antibiotic resistance evolution in Pseudomonas aeruginosa cystic fibrosis chronic lung infection. PloS one 8, e71001.
Mahenthiralingam, E., Campbell, M. E. & Speert, D. P. (1994). Nonmotility and phagocytic resistance of Pseudomonas aeruginosa isolates from chronically colonized patients with cystic fibrosis. Infection and immunity 62, 596-605.
Marvig, R. L., Sommer, L. M., Molin, S. & Johansen, H. K. (2015). Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis. Nature genetics 47, 57-64.
Mena, A., Smith, E. E., Burns, J. L., Speert, D. P., Moskowitz, S. M., Perez, J. L. & Oliver, A. (2008). Genetic adaptation of Pseudomonas aeruginosa to the airways of cystic fibrosis patients is catalyzed by hypermutation. Journal of bacteriology 190, 7910-7917.
Moskowitz, S. M., Brannon, M. K., Dasgupta, N. & other authors (2012). PmrB mutations promote polymyxin resistance of Pseudomonas aeruginosa isolated from colistin-treated cystic fibrosis patients. Antimicrobial agents and chemotherapy 56, 1019-1030.
Mowat, E., Paterson, S., Fothergill, J. L., Wright, E. A., Ledson, M. J., Walshaw, M. J., Brockhurst, M. A. & Winstanley, C. (2011). Pseudomonas aeruginosa Population Diversity and Turnover in Cystic Fibrosis Chronic Infections. Am J Resp Crit Care 183, 1674-1679.
Oliver, A., Canton, R., Campo, P., Baquero, F. & Blazquez, J. (2000). High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288, 1251-1254.
Paterson, S., Vogwill, T., Buckling, A. & other authors (2010). Antagonistic coevolution accelerates molecular evolution. Nature 464, 275-278.
Scanlan, P. D., Hall, A. R., Blackshields, G., Friman, V. P., Davis, M. R., Jr., Goldberg, J. B. & Buckling, A. (2015). Coevolution with Bacteriophages Drives Genome-Wide Host Evolution and Constrains the Acquisition of Abiotic-Beneficial Mutations. Molecular biology and evolution.
Smith, E. E., Buckley, D. G., Wu, Z. & other authors (2006). Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients. Proceedings of the National Academy of Sciences of the United States of America 103, 8487-8492.
Sriramulu, D. (2013). Evolution and impact of bacterial drug resistance in the context of cystic fibrosis disease and nosocomial settings. Microbiology insights 6, 29-36.
Taylor, T. B., Mulley, G., Dills, A. H. & other authors (2015). Evolutionary resurrection of flagellar motility via rewiring of the nitrogen regulation system. Science 347, 1014-1017.
Van Valen, L. (1973). A new evolutionary law. Evolutionary Theory 1, 1-30.
Vogwill, T., Fenton, A. & Brockhurst, M. A. (2011). Coevolving parasites enhance the diversitydecreasing effect of dispersal. Biol Letters 7, 578-580.
Williams, D., Evans, B., Haldenby, S., Walshaw, M. J., Brockhurst, M. A., Winstanley, C. & Paterson, S. (2015). Divergent, Coexisting Pseudomonas aeruginosa Lineages in Chronic Cystic Fibrosis Lung Infections. American journal of respiratory and critical care medicine 191, 775-785.
Williams, H. D. & Davies, J. C. (2012). Basic science for the chest physician: Pseudomonas aeruginosa and the cystic fibrosis airway. Thorax 67, 465-467.
Wong, A., Rodrigue, N. & Kassen, R. (2012). Genomics of adaptation during experimental evolution of the opportunistic pathogen Pseudomonas aeruginosa. PLoS genetics 8, e1002928.
Workentine, M. L., Sibley, C. D., Glezerson, B., Purighalla, S., Norgaard-Gron, J. C., Parkins, M. D., Rabin, H. R. & Surette, M. G. (2013). Phenotypic heterogeneity of Pseudomonas aeruginosa populations in a cystic fibrosis patient. PloS one 8, e60225.
Wright, E. A., Fothergill, J. L., Paterson, S., Brockhurst, M. A. & Winstanley, C. (2013). Subinhibitory concentrations of some antibiotics can drive diversification of Pseudomonas aeruginosa populations in artificial sputum medium. Bmc Microbiol 13, 170.
Yang, L., Jelsbak, L., Marvig, R. L. & other authors (2011). Evolutionary dynamics of bacteria in a human host environment. Proceedings of the National Academy of Sciences of the United States of America 108, 7481-7486.
Zhang, Q. G. & Buckling, A. (2011). Antagonistic coevolution limits population persistence of a virus in a thermally deteriorating environment. Ecol Lett 14, 282-288.
Figure Legend Figure 1 | Schematic of the iterative loop of investigation, from the lab, to the clinic, and back again
Figure Click here to download Figure: fleming fig 1-1.tiff
