Legionella pneumophila is an environmental bacterium and the causative agent of Legionnaires’ disease. Previous genomic studies have shown that recombination accounts for a high proportion (>96%) of diversity within several major disease-associated sequence types (STs) of L. pneumophila. This suggests that recombination represents a potentially important force shaping adaptation and virulence. Despite this, little is known about the biological effects of recombination in L. pneumophila, particularly with regards to homologous recombination (whereby genes are replaced with alternative allelic variants). Using newly available population genomic data, we have disentangled events arising from homologous and non-homologous recombination in six major disease-associated STs of L. pneumophila (subsp. pneumophila), and subsequently performed a detailed characterisation of the dynamics and impact of homologous recombination. We identified genomic “hotspots” of homologous recombination that include regions containing outer membrane proteins, the lipopolysaccharide (LPS) region and Dot/Icm effectors, which provide interesting clues to the selection pressures faced by L. pneumophila. Inference of the origin of the recombined regions showed that isolates have most frequently imported DNA from isolates belonging to their own clade, but also occasionally from other major clades of the same subspecies. This supports the hypothesis that the possibility for horizontal exchange of new adaptations between major clades of the subspecies may have been a critical factor in the recent emergence of several clinically important STs from diverse genomic backgrounds. However, acquisition of recombined regions from another subspecies, L. pneumophila subsp. fraseri, was rarely observed, suggesting the existence of a recombination barrier and/or the possibility of ongoing speciation between the two subspecies. Finally, we suggest that multi-fragment recombination may occur in L. pneumophila, whereby multiple non-contiguous segments that originate from the same molecule of donor DNA are imported into a recipient genome during a single episode of recombination.
While all bacteria reproduce clonally, some also import DNA from other organisms into their chromosomes through processes such as recombination or horizontal gene transfer. The imported DNA can either replace a homologous segment of the genome (homologous recombination) or comprise novel genes that are new to the recipient genome (non-homologous recombination). The former results in the replacement of genes with alternative allelic variants and requires the DNA to be highly similar, and possibly identical, at both ends of the fragment [1]. For this reason, homologous recombination usually occurs between closely related bacteria. The importance of recombination in bacterial evolution first became clear through the analysis of multi-locus sequence typing (MLST) data, which showed that phylogenetic trees constructed from individual MLST genes were often incongruent [2]. These analyses also predicted that the rate of homologous recombination varies considerably between different species [3]. There are a number of hypotheses regarding why bacteria engage in homologous recombination [4]. One explanation is that recombination is used as a mechanism by which DNA damage can be repaired using foreign DNA as a template [5]. Another is that it is a side effect of DNA uptake for use as an energy source or for DNA synthesis from nucleotide precursors [6]. Third, the ability of recombination events to remove deleterious mutations and rapidly introduce combinations of advantageous mutations could mean it increases the efficiency of natural selection and is selectively maintained [7]. Finally, a recent study has also suggested that bacteria use recombination to delete selfish mobile genetic elements from their genomes [8]. In recent years, the availability of whole genome sequence (WGS) data from multiple closely related bacterial isolates has enabled homologous recombination to be studied in great detail in species such as Streptococcus pneumoniae [9,10], Chlamydia trachomatis [11] and Neisseria meningitidis [12]. These studies have confirmed that homologous recombination plays an important role in the evolution and adaptation of important bacterial pathogens, for example by facilitating vaccine escape [9] and antibiotic resistance [10] in S. pneumoniae. Legionella pneumophila is an environmental bacterium that parasitizes and replicates inside protozoa in freshwater and soil habitats [13]. It also now colonises man-made water systems from which humans can become infected via inhalation of contaminated aerosols [14]. Infection can cause Legionnaires’ disease, a serious and potentially fatal pneumonia [15]. L. pneumophila was first reported to have a clonal population structure based on multi-locus enzyme electrophoresis (MLEE) analysis [16]. However, the three primary mechanisms of bacterial recombination (conjugation, transduction and transformation) have since all been described in L. pneumophila [17–19], and thus it was unsurprising when later studies reported its occurrence. Indeed, an early genomic study of the first sequenced genomes of L. pneumophila showed that recombination events are frequent and suggested that it can involve large chromosomal fragments of over 200kb [20]. More recently, larger genomic studies have demonstrated that >95% of single nucleotide polymorphisms (SNPs) detected within some lineages have been imported via recombination [21,22]. The occurrence of recombination within L. pneumophila populations has also led to the existence of multiple genetic subtypes within single outbreaks [21,23]. However, despite its major role in L. pneumophila evolution, the relative frequency and biological effects of recombination, such as its impact on virulence or adaptation of L. pneumophila to new niches, remain poorly understood. Here, we disentangled events arising from homologous and non-homologous recombination in six major disease-associated sequence types (STs) of L. pneumophila, and subsequently performed a detailed characterisation of the dynamics and biological impact of homologous recombination on L. pneumophila evolution. Our findings provide novel insights into the selection pressures of L. pneumophila and the dynamics of genomic flux within the species.
To investigate the relative contribution of homologous recombination to diversity in each of six major disease-associated lineages of L. pneumophila (STs 1,23,37,42,62 and 578), sequence reads from isolates (n = 291) (S1 Table) were first mapped to a reference genome of the same ST (Table 1). Isolates belonging to STs that have previously been shown to be derived from the ST1 lineage were also included with ST1 isolates [22]. Gubbins was used to detect recombined regions in each of the six genome alignments [24]. This tool uses increased SNP density on branches of a phylogenetic tree as a marker, and is well suited to these six lineages that contain low background diversity. However, it should be noted that recombination between highly similar isolates may be missed, potentially leading to an underestimation of the recombination rate. In our previous study using four of these alignments with the same, or largely the same, isolates [22], this programme showed high concordance with another recombination detection tool, BRATNextGen [25]. Detection of recombined regions using BRATNextGen is based on sequence similarity rather than SNP density, and thus the high concordance between the two different approaches provides confidence in our predicted regions. As previously reported [21,22], over 96% of SNPs in STs 1,23,37,62 and 578 were predicted to be derived from recombination events. Furthermore, 99% of SNPs in the ST42 lineage were found in recombined regions in the present study. Thus, in all six lineages the proportion of SNPs derived from recombination is higher than that reported for the highly recombinogenic S. pneumoniae PMEN1 lineage (88%) [9] and between N. meningitidis ST60 strains (94. 25%) [12]. The number of vertically inherited SNPs that remained after the removal of recombined regions in each of the six L. pneumophila lineages ranged from 94 (ST42) to 1,006 (ST1) (Table 1). Any regions detected by Gubbins that overlapped with either predicted mobile genetic elements (MGEs) or repeat regions (S2 Table) were subsequently excluded, in order to determine the sole contribution of homologous recombination to L. pneumophila diversity (Table 2). We found that between 33% (ST62) and 80% (ST578) of all SNPs were predicted to be in regions derived from homologous recombination events (Fig 1A). However, the average length of each individual genome affected by this process varied between just 1. 2% (ST42/578) and 3. 9% (ST1) (Table 2). It should be noted that the number of SNPs predicted to be from homologous recombination might be slightly over-estimated (and the number of de novo mutations slightly under-estimated) since de novo mutations may have occurred on top of recombined regions. However, the error should be no more than 1. 2–3. 9%, in proportion with the average length of genome affected by homologous recombination events. Furthermore, detectability of homologous recombination events could also be affected by lineage diversity (i. e. events may be more difficult to detect on longer tree branches where background SNP density is higher). However, because the number of SNPs associated with recombining regions is much higher compared with the background vertically inherited SNPs in all lineages, we think that any effect will be minimal. In each of the six lineages, the relative number of homologous recombination events to vertically inherited mutations (ρ/θ ratio) was calculated per branch for each phylogenetic tree (S1 Fig) and also for each lineage as a whole. The overall ρ/θ ratio for each lineage ranged from 0. 03 (ST37) to 0. 27 (ST23), indicating that recombination events have occurred less frequently than vertically inherited mutations in all six lineages, despite bringing in between 20. 8 (ST37) and 93. 8 (ST23) times as many SNPs (Table 2). A similar ρ/θ ratio of 0. 124 was reported in previous analysis of 25 diverse L. pneumophila genomes as inferred by an alternative recombination detection algorithm, ClonalFrame [29]. The distribution of per-branch ρ/θ ratios also differ significantly between lineages (Kruskal-Wallis test, p<0. 05), highlighting different rates of recombination in the six major disease-associated STs. These differences could indicate variation in the biological niches of these different lineages, about which very little is currently understood, and/or the availability of recombination opportunities. To determine the relative impact of vertically inherited mutations and homologous recombination events on the coding sequence, the types of changes caused by the two processes were analysed (Fig 1B). Vertically inherited mutations have resulted in approximately twice as many non-synonymous SNPs than synonymous SNPs, a result that is expected by chance when mutations occur at random in the genome and before selection has time to act on all but the most deleterious mutations. Interestingly though, the results are reversed for homologous recombination events, which resulted mostly in synonymous mutations. However, this observation is not unexpected given that variants in sequences that are horizontally transferred between different lineages will have been subjected to a longer period of evolution and selection, which has purged harmful, non-synonymous mutations. The same phenomenon has also been observed in a previous study by Castillo-Ramirez et al. (2011) [30]. Furthermore, fewer SNPs that result in a stop codon were brought in by homologous recombination events than by vertically inherited mutations, which can also be explained by this process. The lengths of the recombined regions have an approximately exponential distribution (rate of decay = 7. 52 x 10−5 bp-1), with the majority of events being small (<10,000bp) and large events occurring relatively infrequently (Fig 1C). The median recombination fragment length in each of the six lineages ranged from 5,613bp (ST578) to 12,757bp (ST37), while the largest predicted region is 94,790bp (ST37). Large recombination segments have also been found in other species, such as Clostridium difficile [31], Streptococcus agalactiae [32] and Streptococcus pneumoniae [33]. In the latter, a similar distribution of fragment sizes as the one described here for L. pneumophila was also reported, suggesting that transformation is optimised for exchanging short sequences rather than large features such as complete operons [33]. This scenario could be favoured as it allows for larger numbers of potentially advantageous allele combinations to be tested. Next, we determined whether there are any genomic regions that are associated with a higher number of homologous recombination events, which could reveal genes that are under diversifying selection pressure. We thus calculated the number of events predicted by Gubbins that overlap with each gene with respect to the reference genomes of the six disease-associated STs. A total of 32 hotspot regions were defined (see Materials & Methods), including at least one in all six disease-associated STs (S3 Table). A total of 10 hotspot regions were identified in the ST1 lineage and, remarkably, one region contained genes that are predicted to have been involved in up to 27 recombination events (Fig 2A). By contrast, in the other five STs, the highest number of events affecting genes ranged from 2 (ST37/ST578) to 4 (ST42/ST62). We acknowledge that the number of recombination events detected per gene, particularly in hotspot regions, could be slightly underestimated due to the possible occurrence of overlapping or nested recombined regions imported on the same branch of the phylogenetic tree. Gubbins is likely to predict these as single rather than multiple events, and genomic regions with a higher number of recombination events could be disproportionately affected. Nevertheless, the identification of hotspot regions provides good evidence that the effect of recombination in L. pneumophila is to increase the genetic diversity available for natural selection to work on, and that this diversifying selection acts non-randomly on the genome. To predict the origin of the homologous recombination regions, 536 L. pneumophila genomes were first divided into clusters using hierBAPS [47], which were mapped onto a phylogenetic tree (Fig 3). The genomes comprise those belonging to isolates from the six major disease-associated STs (n = 291) (S1 Table) and others from a large, species-wide collection (n = 245) (S5 Table). Eight BAPS clusters were identified, seven of which comprised isolates from the L. pneumophila pneumophila subspecies (BAPS clusters 1–6,8), and one with isolates from L. pneumophila fraseri (BAPS cluster 7). Of the 318 homologous recombination events greater than 500bp predicted in the six major disease-associated lineages, potential donors were predicted for 292 (91. 8%) (see Materials & Methods). Many of the hits were almost perfect matches with 122 (41. 8%) of the fragments having over 99. 9% nucleotide identity, and 155 (53. 1%) having hits that covered the full length of the recombination fragment (S6 Fig). The number of homologous recombination events in each of the six major disease-associated lineages that were predicted to be derived from each of the eight BAPS clusters were calculated and visualised in a heat plot (Fig 4A). Any events with equally good hits (i. e. with the same nucleotide similarity and fragment length covered) to isolates in more than one BAPS cluster were discarded for this analysis (“No donor assigned”). The heat plot illustrates that, in five of the six STs, recombination donors most often belonged to the same BAPS cluster as the recipient. This is an expected finding since homologous recombination requires high, or even perfect, sequence similarity between the donor and recipient at both ends of the recombination fragment [1], a scenario which is more likely between closely-related bacteria. The exception is ST37 in which the highest number of recombination fragments is derived from BAPS cluster 4, although its own cluster (BAPS cluster 3) accounted for the second highest number. However, all STs, with the exception of ST578, are also predicted to have acquired recombination fragments from clusters other than their own, demonstrating the occurrence of homologous recombination between major clusters of the L. pneumophila pneumophila subspecies. This result is confirmed by the fastGEAR analysis of the prominent ST1 hotspot region, which demonstrates the sharing of alleles between different BAPS clusters (S4 Fig). Overall, the finding suggests that different clades have at least partially shared the same ecological niche and perhaps even the same individual host cells in which recombination may have occurred. Importantly, this freedom of genomic exchange has provided potential opportunities for new adaptations to be shared freely amongst different clusters, which we hypothesise has been an important factor in the recent emergence of multiple major disease-associated STs from diverse genomic backgrounds [22]. Interestingly, some BAPS clusters act frequently as donors to other clusters (e. g. BAPS clusters 4 and 5), while others hardly donate except to isolates of their own cluster (e. g. BAPS clusters 2 and 3) (Fig 4A). Similar patterns whereby different lineages donate and receive DNA at different rates have also been observed in other species such as S. pneumoniae [10], C. trachomatis [11] and E. coli [48]. Furthermore, just two events (one each in ST23 and ST62) are derived from the L. pneumophila fraseri subspecies (BAPS cluster 7). Given that this lineage shares less than 95% nucleotide identity with the L. p. pneumophila subspecies, this was not an unexpected finding, given the high level of similarity required for homologous recombination. It could be that these two subspecies have gradually diverged due to differing ecologies, and that eventually they may become different species that are fully incapable of exchange via homologous recombination. For all homologous recombination events detected in the six STs, the nucleotide identity between the imported fragment and the recipient genome that was replaced by the fragment was calculated (Fig 4B). This was to investigate the divergence levels between recombining bacteria, but it also provided a means of verifying our predictions of the recombination donors. This analysis showed that 70% of homologous recombination events occurred between closely related isolates with >98% nucleotide similarity in the affected region, which agrees with our previous finding that most fragments are derived from the same BAPS cluster as the recipient. Interestingly, two peaks can be observed at ~98% identity and ~99. 5–100% identity. These levels of divergence correspond to the nucleotide similarity observed between isolates belonging to different clusters or the same cluster, respectively (Fig 4C), and thus they represent recombination between and within clusters. It is also interesting to note that the distribution of pairwise nucleotide similarities of genomes from different clusters has a major peak around ~98% (Fig 4C), which aligns with previous findings that homologous recombination tends make clusters equidistant from each other [49,50]. Recombination hotspot regions were next re-analysed to investigate whether the hotspots were driven by recombination events from the same or different BAPS clusters. The analysis focused on the ST1 lineage, which was previously found to contain the highest number of recombination events and the most prominent hotspots. The most notable hotspot region (hotspot 6), which was found to contain genes involved in up to 27 recombination events, was found to be driven mostly by recombination regions derived from the same BAPS cluster to which ST1 belongs (BAPS cluster 2) (S7 Fig). However, a small number of recombination events that are predicted to be from BAPS cluster 5 were also observed in this region. While the analysis of this region using fastGEAR is not directly comparable to the results using Gubbins, it does also suggest that the recombined regions have been imported from both the same and different BAPS clusters (S4 Fig). Meanwhile, while some of the recombination events affecting the LPS locus (hotspot 3) could not be assigned a donor, others were derived from BAPS clusters 1,2 and 5, suggesting that high diversity in this region may be especially important. Hotspot 4 appears to be driven by recombination events from BAPS clusters 5,6 and 8 and contains no events derived from BAPS cluster 2 (to which ST1 belongs). However, the small number of events with predicted donors in most of these hotspots limits the conclusions that can be made. Finally, the homologous recombination events that were predicted within the ST1 lineage were mapped onto the phylogenetic tree together with information regarding their predicted origin (Fig 5). This was to search for evidence of multi-fragment recombination, a process in which multiple non-contiguous segments that originate from the same molecule of DNA are imported into a recipient genome in a single episode of recombination. This process is well documented in S. pneumoniae [33,51,52]. Since the recombining fragments are non-contiguous, Gubbins will detect these as separate events although the events should be predicted to have occurred on the same branch and have the same predicted donor. Indeed, we found some evidence for the occurrence of this process in L. pneumophila, since many events with the same predicted donor, down to the BAPS cluster level and even the individual isolate level, are co-localised on branches (Fig 5). For example, 8 recombinant regions distributed throughout the chromosome that occurred on the terminal branch leading to ST1_28 are predicted to have originated from BAPS cluster 4, and more specifically, a strain (or multiple strains) closely related to EUL 25 (ST44) (S8 Fig). Furthermore, some of these imported regions also share very similar SNP densities with respect to EUL 25 (i. e. 5 events have SNP densities from 0–0. 06% and 3 events have SNP densities from 0. 28–0. 33%), reinforcing the possibility that some of these recombining fragments could have been acquired from the same donor in the same event. However, it could also be that the recombining isolates have shared a common niche for a prolonged period of time, and that multiple independent recombination events have occurred during this time. Thus, further experimental studies will be required to confirm the occurrence of this process in L. pneumophila. In summary, this study has demonstrated a major role for homologous recombination in shaping the population structure and evolution of L. pneumophila, and provided detailed insights into recombination dynamics within the species. We predict that homologous recombination has played a critical role in the emergence of this environmental bacterium as an important human pathogen and suggest that future studies are required to further delineate the role of homologous recombination in the virulence and adaptation of L. pneumophila to modern, man-made environments.
L. pneumophila isolates belonging to six major disease-associated lineages are primarily used in this study (n = 291), all of which have been previously sequenced [21,22,27,53–55]. These include 81 ST1 (or ST1-derived), 42 ST23,72 ST37,15 ST42,35 ST62, and 46 ST578 isolates (S1 Table). A further 245 L. pneumophila isolates, which belong to a range of STs, were also used in the inference of recombination donors (S5 Table). WGS data from all but five of these isolates have been published [20–22,26–28,55–60]. Importantly, these include a set of genomes that were selected for sequencing using sequence-based typing (SBT) data, analogous to MLST, with the aim of encompassing as much of the species diversity as possible [55]. Accession numbers or references for all genomes are provided in S1 Table and S5 Table. Sequence reads from isolates belonging to each of the six disease-associated STs (1,23,37,42,62 and 578) were mapped to a reference genome of the same ST to enable each lineage to be studied at a high resolution. The complete genomes of Paris [26] and Alcoy [28] were used for ST1 and ST578, and reference genomes previously generated using a Pacific Biosciences (PacBio) RSII sequencer were used for STs 23 (EUL 28), 37 (EUL 165), 42 (EUL 120) and 62 (H044120014) [27]. All six reference genomes were annotated using an in-house pipeline at the Wellcome Trust Sanger Institute (WTSI), which uses Prokka [61]. The four annotated reference genomes obtained using PacBio sequencing are available from the European Nucleotide Archive under the accession numbers GCA_900119755. 1 (EUL 28), GCA_900119775. 1 (EUL 165), GCA_900119785. 1 (EUL 120) and GCA_900119765. 1 (H044120014). Repetitive regions over 100bp were detected in the six reference genomes using repeat-match from MUMmer v3. 0 [62] (S2 Table). All processing and sequencing of genomic DNA from the five newly sequenced isolates was performed by the core sequencing facility at the WTSI. Paired end libraries were created as described previously [63] and samples were sequenced using the Illumina HiSeq platform and paired-end reads of 100 bases. Sequence reads of all isolates belonging to the six major disease-associated STs under study were mapped to the appropriate reference genome of the same ST using SMALT v0. 7. 4 (available at: http: //www. sanger. ac. uk/science/tools/smalt-0). All isolates used in the study (n = 536) were also mapped to the Paris (ST1) reference genome [26] in order to study the species-wide phylogenetic structure. An in-house pipeline at the WTSI was used to call bases and identify SNPs as previously described [64]. All assemblies were produced from the Illumina data using a pipeline developed by the Pathogen Informatics team at the WTSI. This firstly uses Velvet Optimiser (https: //github. com/tseemann/VelvetOptimiser) to determine the optimal kmer size before using Velvet to produce the assembly [65]. The assembly was further improved using SSPACE [66] to scaffold the contigs of the assembly and GapFiller [67] to close gaps of 1 or more nucleotides. Recombined regions were detected in the alignments of the six disease-associated STs using Gubbins [24]. Phylogenetic trees of these lineages were generated using RAxML v7. 0. 4 [68], firstly using all SNPs to later allow ancestral sequence reconstruction, and secondly using only the vertically inherited SNPs (i. e. excluding SNPs in recombined regions). A phylogenetic tree of the total 536 isolates was constructed using all the detected SNPs, as the high diversity of the whole collection renders recombination detection very difficult. In all cases, the GTR+GAMMA method for among site rate variation was used and 100 bootstrap replicates were performed to assess support for nodes. The alignment of all 536 genomes against the Paris reference genome was also used to group the isolates into clusters using hierBAPS [47]. The annotation files from each of the six reference genomes were parsed to detect regions annotated as “integrase”, “transposase”, “recombinase”, “phage”, “lvrA”, “csrA”, “HTX”, “helix-turn-helix”, “xre”, “conjugal”, “conjugation”, “tra”, “trb”, “vir” and “mobile”. Both the published annotation files of the Paris (ST1) and Alcoy (ST578) complete genomes and those generated using the in-house pipeline at the WTSI were used. However, the new annotations were only considered when the original one was a “hypothetical protein” in order to respect experimentally proven annotations. Plots showing the mapping coverage of each isolate against the corresponding reference genome were also evaluated. Regions over 8kb with no coverage and that did not match repetitive regions were considered as potential mobile regions. Other software to detect mobile genetic elements (MGEs) was also used including AlienHunter [69] and Island Viewer, the latter of which incorporates IslandPick, IslandPath-DIMOB and SIGI-HMM [70]. However, these results were discarded due to major incongruences between them. Finally, manual curation of all predicted MGEs was performed using Artemis v15. 0. 0 [71] (S2 Table). In each of the six lineages, any recombined regions predicted by Gubbins that overlap with either repetitive regions or putative MGEs in the reference genome were discarded for the majority of the analysis in this study, leaving only putative homologous recombination regions. An in-house script was used to calculate the number of times each gene and each base had been involved in a homologous recombination event. Recombination “hotspots” were defined as genes with a recombination frequency above the 95th percentile observed in that particular ST and that have been involved in at least two events. Based on these criteria, the minimum number of recombination events that a gene must have been involved in to be considered within a hotspot region was four events in the ST1 lineage and two events in the remaining five STs. FastGEAR [34] was run on 54 individual gene alignments, comprising all 536 strains included in the study, which were extracted from the alignment of all genomes against the Paris reference. These genes span the prominent ST1 hotspot (lpp1761-lpp1794) and also include 10 flanking loci on either side. The software infers the population structure of individual alignments, allowing detection of lineages in an alignment and “ancestral” and “recent” recombinations between them. The results were compared to those from Gubbins in terms of the number of recombination events predicted in each gene and the sharing of alleles among the different predicted lineages. Notably, if a recombination spans the entire length of an alignment, fastGEAR will detect this as another lineage in the alignment, rather than a recombination. Therefore, to make recombination counts between fastGEAR and Gubbins comparable, we used the estimated phylogeny and post-processed fastGEAR output by identifying branches in the tree where the population structure changed, and interpreted these as recombinations (these can be seen as “blocks” with a colour different from yellow in S3B Fig and S4 Fig). The scripts used to make this calculation and to produce S3B Fig and S4 Fig can be found in https: //users. ics. aalto. fi/~pemartti/fastGEAR/. Homologous recombination regions were extracted from the ancestral sequences inferred from the nodes of the six phylogenetic trees, constructed prior to recombination removal, using PAML 4 [72]. Specifically, ancestral recombination sequences were extracted from the node downstream of the phylogenetic tree branch on which the recombination event was predicted to have occurred. A custom genome BLAST database (BLAST v2. 2. 30+) [73] was constructed using de novo assemblies and/or complete genomes from all 536 L. pneumophila isolates used in this study. The reconstructed recombined regions were used as query sequences in BLAST searches against the custom genome database and the NCBI non-redundant nucleotide database. The resulting hits were filtered to remove those against isolates that are descended from the branch in which the recombination event was detected. Of the remaining hits, the BAPS cluster containing the isolate with the highest bit score was considered as the potential donor, provided that the hit covered at least 50% of the recombination fragment length and had a minimum of 99% nucleotide identity. Recombination fragments with no hits that met these thresholds were not assigned a donor cluster (“No donor predicted”). Only recombined regions greater than 500bp were used in this analysis, firstly because they were deemed more likely to be a “true” event, and secondly because small regions would likely have high similarity to many genomes.
Legionella pneumophila is an environmental bacterium that causes Legionnaires’ disease, a serious and potentially fatal pneumonia. Previous studies have shown that members of this species undergo a process called recombination, whereby DNA is imported from another bacterial cell into the recipient genome. The imported DNA can either replace an equivalent segment of the genome (homologous recombination) or can comprise novel genes that are new to the recipient genome (non-homologous recombination). Whilst recombination plays an undoubtedly important role in L. pneumophila evolution, accounting for more than 96% of the diversity observed within some lineages, little is known about its biological impact. In this study, we performed a detailed characterisation of the dynamics and effect of homologous recombination on L. pneumophila evolution in six clinically important lineages of L. pneumophila. We identified “hotspot” regions of the genome in which an excess of homologous recombination events was observed, which provided important clues to the selection pressures faced by L. pneumophila. By determining the donors of the recombined regions, we also revealed that recombination has occurred most frequently between isolates from the same clade, but also occurred between isolates from different major clades. This demonstrates the possibility of new adaptations arising in one lineage and being transferred to another distantly related lineage, which we predict has been an important factor in the emergence of several major disease-causing strains from diverse genomic backgrounds.
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Dynamics and impact of homologous recombination on the evolution of Legionella pneumophila