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 .

Staphylococcus aureus is an important human pathogen and represents a growing public health burden owing to the emergence and spread of antibiotic-resistant clones, particularly within the hospital environment. Despite this, basic questions about the evolution and population biology of the species, particularly with regard to the extent and impact of homologous recombination, remain unanswered. We address these issues through an analysis of sequence data obtained from the characterization by multilocus sequence typing (MLST) of 334 isolates of S . aureus , recovered from a well-defined population, over a limited time span. We find no significant differences in the distribution of multilocus genotypes between strains isolated from carriers and those from patients with invasive disease; there is, therefore, no evidence from MLST data, which index variation within the stable “core” genome, for the existence of hypervirulent clones of this pathogen. Examination of the sequence changes at MLST loci during clonal diversification shows that point mutations give rise to new alleles at least 15-fold more frequently than does recombination. This contrasts with the naturally transformable species Neisseria meningitidis and Streptococcus pneumoniae , in which alleles change between 5- and 10-fold more frequently by recombination than by mutation. However, phylogenetic analysis suggests that homologous recombination does contribute toward the evolution of this species over the long term. Finally, we note a striking excess of nonsynonymous substitutions in comparisons between isolates belonging to the same clonal complex compared to isolates belonging to different clonal complexes, suggesting that the removal of deleterious mutations by purifying selection may be relatively slow.

Generation time varies widely across organisms and is an important factor in the life cycle, life history and evolution of organisms. Although the doubling time (DT) has been estimated for many bacteria in the laboratory, it is nearly impossible to directly measure it in the natural environment. However, an estimate can be obtained by measuring the rate at which bacteria accumulate mutations per year in the wild and the rate at which they mutate per generation in the laboratory. If we assume the mutation rate per generation is the same in the wild and in the laboratory, and that all mutations in the wild are neutral, an assumption that we show is not very important, then an estimate of the DT can be obtained by dividing the latter by the former. We estimate the DT for five species of bacteria for which we have both an accumulation and a mutation rate estimate. We also infer the distribution of DTs across all bacteria from the distribution of the accumulation and mutation rates. Both analyses suggest that DTs for bacteria in the wild are substantially greater than those in the laboratory, that they vary by orders of magnitude between different species of bacteria and that a substantial fraction of bacteria double very slowly in the wild.

Background: Cataloguing the distribution of genes within natural bacterial populations is essential for understanding evolutionary processes and the genetic basis of adaptation. Advances in whole genome sequencing technologies have led to a vast expansion in the amount of bacterial genomes deposited in public databases. There is a pressing need for software solutions which are able to cluster, catalogue and characterise genes, or other features, in increasingly large genomic datasets. Results: Here we present a pangenomics toolbox, PIRATE (Pangenome Iterative Refinement and Threshold Evaluation), which identifies and classifies orthologous gene families in bacterial pangenomes over a wide range of sequence similarity thresholds. PIRATE builds upon recent scalable software developments to allow for the rapid interrogation of thousands of isolates. PIRATE clusters genes (or other annotated features) over a wide range of amino acid or nucleotide identity thresholds and uses the clustering information to rapidly identify paralogous gene families and putative fission/fusion events. Furthermore, PIRATE orders the pangenome using a directed graph, provides a measure of allelic variation, and estimates sequence divergence for each gene family. Conclusions: We demonstrate that PIRATE scales linearly with both number of samples and computation resources, allowing for analysis of large genomic datasets, and compares favorably to other popular tools. PIRATE provides a robust framework for analysing bacterial pangenomes, from largely clonal to panmictic species.

▪ Abstract The population structures of bacterial species are complex and often controversial. To a large extent, this is due to uncertainty about the frequency and impact of recombination in bacteria. The existence of clones within bacterial populations, and of linkage disequilibrium between alleles at different loci, is often cited as evidence for low rates of recombination. However, clones and linkage disequilibrium are almost inevitable in species that divide by binary fission and can be present in populations where recombination is frequent. In recent years, it has become possible to directly compare rates of recombination in different species. These studies indicate that in many bacterial species, including Neisseria meningitidis, Streptococcus pneumoniae, and Staphylococcus aureus, evolutionary change at neutral (housekeeping) loci is more likely to occur by recombination than mutation and can result in the elimination of any deep-rooted phylogenetic signal. In such species, the long-term evolution of the population is dominated by recombination, but this does not occur at a sufficiently high frequency to prevent the emergence of adaptive clones, although these are relatively short-lived and rapidly diversify.

Some of the most common infectious diseases are caused by bacteria that naturally colonise humans asymptomatically. Combating these opportunistic pathogens requires an understanding of the traits that differentiate infecting strains from harmless relatives. Staphylococcus epidermidis is carried asymptomatically on the skin and mucous membranes of virtually all humans but is a major cause of nosocomial infection associated with invasive procedures. Here we address the underlying evolutionary mechanisms of opportunistic pathogenicity by combining pangenome-wide association studies and laboratory microbiology to compare S. epidermidis from bloodstream and wound infections and asymptomatic carriage. We identify 61 genes containing infection-associated genetic elements (k-mers) that correlate with in vitro variation in known pathogenicity traits (biofilm formation, cell toxicity, interleukin-8 production, methicillin resistance). Horizontal gene transfer spreads these elements, allowing divergent clones to cause infection. Finally, Random Forest model prediction of disease status (carriage vs. infection) identifies pathogenicity elements in 415 S. epidermidis isolates with 80% accuracy, demonstrating the potential for identifying risk genotypes pre-operatively.