Geoghegan, J. | bioRxiv Science

bioRxiv2018

Mobile genetic element-encoded hypertolerance to copper protects Staphylococcus aureus from killing by host phagocytes

Pathogens are exposed to toxic levels of copper during infection and copper tolerance may be a general virulence mechanism used by bacteria to resist host defences. In support of this, inactivation of copper-exporter genes has been found to reduce the virulence of bacterial pathogens in vivo. Here we investigate the role of copper-hypertolerance in methicillin resistant Staphylococcus aureus. We show that a copper-hypertolerance locus (copB-mco), carried on a mobile genetic element, is prevalent in a collection of invasive S. aureus strains and more widely among clonal complex 22, 30 and 398 strains. The copB and mco genes encode a copper efflux pump and a multicopper oxidase, respectively. Isogenic mutants lacking copB or mco had impaired growth in subinhibitory concentrations of copper. Transfer of a copB-mco encoding plasmid to a naive clinical isolate resulted in a gain of copper hypertolerance and enhanced bacterial survival inside primed macrophages. The copB and mco genes were upregulated within infected macrophages and their expression was dependent on the copper sensitive operon repressor CsoR. Isogenic copB and mco mutants were impaired in their ability to persist intracellularly in macrophages and were less resistant to phagocytic killing in human blood than the parent strain. The importance of copper-regulated genes in resistance to phagocytic killing was further elaborated using mutants expressing a copper-insensitive variant of CsoR. Our findings suggest that the gain of mobile genetic elements carrying copper-hypertolerance genes contributes to the evolution of virulent strains of S. aureus, better equipped to resist killing by host immune cells.

microbiology↗

bioRxiv2017

Inferring demographic parameters in bacterial genomic data using Bayesian and hybrid phylogenetic methods

BackgroundRecent developments in sequencing technologies make it possible to obtain genome sequences from a large number of isolates in a very short time. Bayesian phylogenetic approaches can take advantage of these data by simultaneously inferring the phylogenetic tree, evolutionary timescale, and demographic parameters (such as population growth rates), while naturally integrating uncertainty in all parameters. Despite their desirable properties, Bayesian approaches can be computationally intensive, hindering their use for outbreak investigations involving genome data for a large numbers of pathogen isolates. An alternative to using full Bayesian inference is to use a hybrid approach, where the phylogenetic tree and evolutionary timescale are estimated first using maximum likelihood. Under this hybrid approach, demographic parameters are inferred from estimated trees instead of the sequence data, using maximum likelihood, Bayesian inference, or approximate Bayesian computation. This can vastly reduce the computational burden, but has the disadvantage of ignoring the uncertainty in the phylogenetic tree and evolutionary timescale.\n\nResultsWe compared the performance of a fully Bayesian and a hybrid method by analysing six whole-genome SNP data sets from a range of bacteria and simulations. The estimates from the two methods were very similar, suggesting that the hybrid method is a valid alternative for very large datasets. However, we also found that congruence between these methods is contingent on the presence of strong temporal structure in the data (i.e. clocklike behaviour), which is typically verified using a date-randomisation test in a Bayesian framework. To reduce the computational burden of this Bayesian test we implemented a date-randomisation test using a rapid maximum likelihood method, which has similar performance to its Bayesian counterpart.\n\nConclusionsHybrid approaches can produce reliable inferences of evolutionary timescales and phylodynamic parameters in a fraction of the time required for fully Bayesian analyses. As such, they are a valuable alternative in outbreak studies involving a large number of isolates.

bioinformatics↗