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      Pseudomonas aeruginosa Evolutionary Adaptation and Diversification in Cystic Fibrosis Chronic Lung Infections

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          Abstract

          Pseudomonas aeruginosa populations undergo a characteristic evolutionary adaptation during chronic infection of the cystic fibrosis (CF) lung, including reduced production of virulence factors, transition to a biofilm-associated lifestyle, and evolution of high-level antibiotic resistance. Populations of P. aeruginosa in chronic CF lung infections typically exhibit high phenotypic diversity, including for clinically important traits such as antibiotic resistance and toxin production, and this diversity is dynamic over time, making accurate diagnosis and treatment challenging. Population genomics studies reveal extensive genetic diversity within patients, including for transmissible strains the coexistence of highly divergent lineages acquired by patient-to-patient transmission. The inherent spatial structure and spatial heterogeneity of selection in the CF lung appears to play a key role in driving P. aeruginosa diversification.

          Trends

          During chronic lung infections of CF patients common genetic adaptations occur in P. aeruginosa, such as conversion to mucoidy, loss of virulence factors, and resistance to antibiotics.

          Although pathoadaptive mutations in regulatory proteins are common, the actual regulators affected vary between populations.

          P. aeruginosa populations in CF lungs exhibit high levels of phenotypic diversity.

          Fine-scale population genomics approaches reveal that divergent sublineages can coexist, with evidence for regional isolation in the spatially structured and heterogeneous lung environment.

          Experimental evolution is beginning to provide insights into the selective drivers of evolution in P. aeruginosa infection, including the role of social interactions.

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          Most cited references 71

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          eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data.

          The introduction of multilocus sequence typing (MLST) for the precise characterization of isolates of bacterial pathogens has had a marked impact on both routine epidemiological surveillance and microbial population biology. In both fields, a key prerequisite for exploiting this resource is the ability to discern the relatedness and patterns of evolutionary descent among isolates with similar genotypes. Traditional clustering techniques, such as dendrograms, provide a very poor representation of recent evolutionary events, as they attempt to reconstruct relationships in the absence of a realistic model of the way in which bacterial clones emerge and diversify to form clonal complexes. An increasingly popular approach, called BURST, has been used as an alternative, but present implementations are unable to cope with very large data sets and offer crude graphical outputs. Here we present a new implementation of this algorithm, eBURST, which divides an MLST data set of any size into groups of related isolates and clonal complexes, predicts the founding (ancestral) genotype of each clonal complex, and computes the bootstrap support for the assignment. The most parsimonious patterns of descent of all isolates in each clonal complex from the predicted founder(s) are then displayed. The advantages of eBURST for exploring patterns of evolutionary descent are demonstrated with a number of examples, including the simple Spain(23F)-1 clonal complex of Streptococcus pneumoniae, "population snapshots" of the entire S. pneumoniae and Staphylococcus aureus MLST databases, and the more complicated clonal complexes observed for Campylobacter jejuni and Neisseria meningitidis.
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            Microbiological effects of sublethal levels of antibiotics.

            The widespread use of antibiotics results in the generation of antibiotic concentration gradients in humans, livestock and the environment. Thus, bacteria are frequently exposed to non-lethal (that is, subinhibitory) concentrations of drugs, and recent evidence suggests that this is likely to have an important role in the evolution of antibiotic resistance. In this Review, we discuss the ecology of antibiotics and the ability of subinhibitory concentrations to select for bacterial resistance. We also consider the effects of low-level drug exposure on bacterial physiology, including the generation of genetic and phenotypic variability, as well as the ability of antibiotics to function as signalling molecules. Together, these effects accelerate the emergence and spread of antibiotic-resistant bacteria among humans and animals.
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              Social evolution theory for microorganisms.

              Microorganisms communicate and cooperate to perform a wide range of multicellular behaviours, such as dispersal, nutrient acquisition, biofilm formation and quorum sensing. Microbiologists are rapidly gaining a greater understanding of the molecular mechanisms involved in these behaviours, and the underlying genetic regulation. Such behaviours are also interesting from the perspective of social evolution - why do microorganisms engage in these behaviours given that cooperative individuals can be exploited by selfish cheaters, who gain the benefit of cooperation without paying their share of the cost? There is great potential for interdisciplinary research in this fledgling field of sociomicrobiology, but a limiting factor is the lack of effective communication of social evolution theory to microbiologists. Here, we provide a conceptual overview of the different mechanisms through which cooperative behaviours can be stabilized, emphasizing the aspects most relevant to microorganisms, the novel problems that microorganisms pose and the new insights that can be gained from applying evolutionary theory to microorganisms.
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                Author and article information

                Contributors
                Journal
                Trends Microbiol
                Trends Microbiol
                Trends in Microbiology
                Elsevier Trends Journals
                0966-842X
                1878-4380
                1 May 2016
                May 2016
                : 24
                : 5
                : 327-337
                Affiliations
                [1 ]Department of Clinical Infection, Microbiology and Immunology, Institute of Infection and Global Health, Ronald Ross Building, University of Liverpool, 8 West Derby Street, Liverpool, L69 7BE, UK
                [2 ]Department of Biology, University of York, Wentworth Way, York, YO10 5DD, UK
                Author notes
                Article
                S0966-842X(16)00021-4
                10.1016/j.tim.2016.01.008
                4854172
                26946977
                © 2016 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                Categories
                Review
                Special Issue: Microbial Endurance

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