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Elworth, R.

Publications and source records attributed to Elworth, R..

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The essential role of hypermutation in rapid adaptation to antibiotic stress

A common outcome of antibiotic exposure in patients and in vitro is the evolution of a hypermutator phenotype that enables rapid adaptation by pathogens. While hypermutation is a robust mechanism for rapid adaptation, it requires trade-offs between the adaptive mutations and the more common \"hitchhiker\" mutations that accumulate from the increased mutation rate. Using quantitative experimental evolution, we examined the role of hypermutation in driving adaptation of Pseudomonas aeruginosa to colistin. Metagenomic deep sequencing revealed 2,657 mutations at > 5% frequency in 1,197 genes and 761 mutations in 29 end point isolates. By combining genomic information, phylogenetic analyses, and statistical tests, we showed that evolutionary trajectories leading to resistance could be reliably discerned. In addition to known alleles such as pmrB, hypermutation allowed identification of additional adaptive alleles with epistatic relationships. Although hypermutation provided a short-term fitness benefit, it was detrimental to overall fitness. Alarmingly, a small fraction of the colistin adapted population remained colistin susceptible and escaped hypermutation. In a clinical population, such cells could play a role in re-establishing infection upon withdrawal of colistin. We present here a framework for evaluating the complex evolutionary trajectories of hypermutators that applies to both current and emerging pathogen populations.\n\nImportance\n\nBacteria can increase mutation rates in response to stress as an evolutionary strategy to avoid extinction. However, the complex mutational landscape of hypermutators makes it difficult to distinguish truly adaptive mutations from hitchhikers that follow similar evolutionary trajectories. We provide a framework for evaluating the complex evolutionary trajectories of hypermutators that can be applied to both current and emerging pathogen populations. Using Pseudomonas aeruginosa evolving to colistin as a model system, we examine the essential role of hypermutation in the evolution of resistance. Additionally, our results highlight the presence of a subset of cells that survive and remain susceptible during colistin exposure which can serve as a reservoir for re-infection upon withdrawal of the drug in clinical infections. This study provides a broad understanding of hypermutation during adaptation and describes a series of analyses that will be useful in identifying adaptive mutations in well annotated and novel bacterial mutator populations.

evolutionary biology

DGEN: A Test Statistic for Detection of General Introgression Scenarios

When two species hybridize, one outcome is the integration of genetic material from one species into the genome of the other, a process known as introgression. Detecting introgression in genomic data is a very important question in evolutionary biology. However, given that hybridization occurs between closely related species, a compli-cating factor for introgression detection is the presence of incomplete lineage sorting, or ILS. The D-statistic, famously referred to as the \"ABBA-BABA\" test, was pro-posed for introgression detection in the presence of ILS in data sets that consist of four genomes. More recently, DFOIL--a set of statistics--was introduced to extend the D-statistic to data sets of five genomes.\n\nThe major contribution of this paper is demonstrating that the invariants underly-ing both the D-statistic and DFOIL can be derived automatically from the probability mass functions of gene tree topologies under the null species tree model and alterna-tive phylogenetic network model. Computational requirements aside, this automatic derivation provides a way to generalize these statistics to data sets of any size and with any scenarios of introgression. We demonstrate the accuracy of the general statistic, which we call DGEN, on simulated data sets with varying rates of introgression, and apply it to an empirical data set of mosquito genomes.\n\nWe have implemented DGEN and made it available, both as a graphical user interface tool and as a command-line tool, as part of the freely available, open-source software package ALPHA (https://github.com/chilleo/ALPHA).

evolutionary biology