Developing insights into the mechanisms of evolution of bacterial pathogens from whole-genome sequences

Abstract

Evolution of bacterial pathogen populations has been detected in a variety of ways including phenotypic tests, such as metabolic activity, reaction to antisera and drug resistance and genotypic tests that measure variation in chromosome structure, repetitive loci and individual gene sequences. While informative, these methods only capture a small subset of the total variation and, therefore, have limited resolution. Advances in sequencing technologies have made it feasible to capture whole-genome sequence variation for each sample under study, providing the potential to detect all changes at all positions in the genome from single nucleotide changes to large-scale insertions and deletions. In this review, we focus on recent work that has applied this powerful new approach and summarize some of the advances that this has brought in our understanding of the details of how bacterial pathogens evolve.

Figure 1. Mapping and assembly with sequencing reads

Both mapping to a high-quality reference and de novo assembly of sequencing reads can provide us with evolutionary information. However, the resolution of this information and their possible applications vary. Mapping enables the rapid identification of high-quality SNPs, which can be used to build phylogenies for both evolutionary and epidemiological inference. However, de novo assembly is required to study larger variants, such as insertion/deletions, mobile elements and rearrangements. SNP: Single-nucleotide polymorphism.

Figure 2. Detecting single nucleotide variation and large-scale insertions and deletions from read mapping

An example looking at Chlamydia trachomatis chromosome and plasmid sequences. The pale blue bar highlights a deletion site observed from read data which has significant implications for the effective typing of this pathogen (see [99] for further details). (A) Depth of coverage for reads from a test genome mapped against a reference genome sequence represented by the orange bar (chromosome) and brown bar (plasmid). The depth of coverage steps up in the plasmid region due to its higher copy number per cell relative to the chromosome. Towards the right of the plasmid region the plot drops to zero, indicating a deletion in the test sample relative to the reference. (B) Stack view of the mapped reads showing those that are exactly equivalent in sequence and length to each other (green), those that are unique in sequence and length (blue) and positions where the reads disagree with the reference (red). Sporadic red marks likely represent errors, while true variants are indicated by vertical red lines where all reads mapped to that position indicate the variant. As with (A), the deletion region shows no mapped reads. (C) Insertion/deletion size can be estimated by plotting read pair inferred size on a log scale. The (log) mapped insert size is plotted on the y-axis to show that, where there is an insertion/deletion, the mapped insert size is increased relative to the mean insert size, which is the true fragment length. In essence, by mapping to a reference that does not have the deletion, we artificially introduce sequence length between the reads, thus increasing the mapped insert size. This reveals the presence of the deletion by the increase in inferred size and drop in coverage. The inferred insert size calculated from this subset of reads is far bigger than the normal size range of read pairs in this region. In addition, the absence of lines linking paired reads within the normal size range across this region is indicative of deletion. Image provided courtesy of S Harris and T Carver (Wellcome Trust Sanger Institute, UK).