Transformation of Natural Genetic Variation into Haemophilus Influenzae Genomes
Many bacteria are able to efficiently bind and take up double-stranded DNA fragments, and the resulting natural transformation shapes bacterial genomes, transmits antibiotic resistance, and allows escape from immune surveillance. The genomes of many competent pathogens show evidence of extensive historical recombination between lineages, but the actual recombination events have not been well characterized. We used DNA from a clinical isolate of Haemophilus influenzae to transform competent cells of a laboratory strain. To identify which of the ~40,000 polymorphic differences had recombined into the genomes of four transformed clones, their genomes and their donor and recipient parents were deep sequenced to high coverage. Each clone was found to contain ~1000 donor polymorphisms in 3–6 contiguous runs (8.1±4.5 kb in length) that collectively comprised ~1–3% of each transformed chromosome. Seven donor-specific insertions and deletions were also acquired as parts of larger donor segments, but the presence of other structural variation flanking 12 of 32 recombination breakpoints suggested that these often disrupt the progress of recombination events. This is the first genome-wide analysis of chromosomes directly transformed with DNA from a divergent genotype, connecting experimental studies of transformation with the high levels of natural genetic variation found in isolates of the same species. The ability of bacteria to acquire genetic information from their relatives—called natural competence—poses a major health risk, since recombination between pathogenic bacterial lineages can help bacteria develop resistance to antibiotics and adapt to host defenses. In this study we transformed competent cells of the human pathogen Haemophilus influenzae with genomic DNA from a divergent clinical isolate and used deep sequencing to identify the recombination events in four transformed chromosomes. The results show that transformation of single competent cells is more extensive than expected, and suggests that transformation can be used as a tool to map traits that vary between clinical isolates.