Open Access

Lineage specific recombination rates and microevolution in Listeria monocytogenes

  • Henk C den Bakker1Email author,
  • Xavier Didelot2,
  • Esther D Fortes1,
  • Kendra K Nightingale3 and
  • Martin Wiedmann1
BMC Evolutionary Biology20088:277

DOI: 10.1186/1471-2148-8-277

Received: 08 April 2008

Accepted: 08 October 2008

Published: 08 October 2008

Abstract

Background

The bacterium Listeria monocytogenes is a saprotroph as well as an opportunistic human foodborne pathogen, which has previously been shown to consist of at least two widespread lineages (termed lineages I and II) and an uncommon lineage (lineage III). While some L. monocytogenes strains show evidence for considerable diversification by homologous recombination, our understanding of the contribution of recombination to L. monocytogenes evolution is still limited. We therefore used STRUCTURE and ClonalFrame, two programs that model the effect of recombination, to make inferences about the population structure and different aspects of the recombination process in L. monocytogenes. Analyses were performed using sequences for seven loci (including the house-keeping genes gap, prs, purM and ribC, the stress response gene sigB, and the virulence genes actA and inlA) for 195 L. monocytogenes isolates.

Results

Sequence analyses with ClonalFrame and the Sawyer's test showed that recombination is more prevalent in lineage II than lineage I and is most frequent in two house-keeping genes (ribC and purM) and the two virulence genes (actA and inlA). The relative occurrence of recombination versus point mutation is about six times higher in lineage II than in lineage I, which causes a higher genetic variability in lineage II. Unlike lineage I, lineage II represents a genetically heterogeneous population with a relatively high proportion (30% average) of genetic material imported from external sources. Phylograms, constructed with correcting for recombination, as well as Tajima's D data suggest that both lineages I and II have suffered a population bottleneck.

Conclusion

Our study shows that evolutionary lineages within a single bacterial species can differ considerably in the relative contributions of recombination to genetic diversification. Accounting for recombination in phylogenetic studies is critical, and new evolutionary models that account for the possibility of changes in the rate of recombination would be required. While previous studies suggested that only L. monocytogenes lineage I has experienced a recent bottleneck, our analyses clearly show that lineage II experienced a bottleneck at about the same time, which was subsequently obscured by abundant homologous recombination after the lineage II bottleneck. While lineage I and lineage II should be considered separate species from an evolutionary viewpoint, maintaining single species name may be warranted since both lineages cause the same type of human disease.

Background

Listeria monocytogenes is a common bacterium that can be found in a large number of different natural and man-made habitats. While it is well adapted to a saprotrophic lifestyle, it can cause severe invasive disease in humans and a wide range of animals with manifestations including septicemia, encephalitis and late-term abortion. Human listeriosis represents a foodborne infection that predominantly affects the elderly, immunocompromised individuals, and pregnant women [1]. Based on most molecular subtyping methods, L. monocytogenes isolates can be subdivided into three main lineages, termed lineages I, II, and III [2]. Lineage I strains are significantly overrepresented among human listeriosis cases [3], while lineage II strains have a higher prevalence among isolates from environmental samples, foods, and animal listeriosis cases [2, 4]. Lineage III strains are rare and mainly associated with animals [5]. Recent evidence suggests that isolates originally classified into lineage III represent a polyphyletic group [6] that includes at least three different lineages (lineage IIIA, IIIB and IIIC), which all contain serotype 4a, 4c, and atypical serotype 4b isolates.

Recombination plays an important role in the evolution of most bacterial species [79] and has often been presented as the equivalent of sex in eukaryotes. It constitutes a means for the rapid introduction of new genetic material into the genome, thus providing a much more rapid mode of evolution than point mutations [10, 11]. Abundant recombination has also been associated with increased pathogenicity of selected bacterial strains [9]. Recombination poses a number of challenges for the evolutionary biologist as it blurs species boundaries and hampers proper inference reconstruction of the evolution of recombining organisms. For example, a comparative genomic study of Escherichia coli and Salmonella enterica [12] demonstrated that certain regions of the genome can be involved in inter-species recombination, even tens of millions of years after divergence. Recombination does not only violate the assumption of strict bifurcation of most phylogenetic algorithms [13], but also makes it difficult to infer the mutation rate and the age of the most recent common ancestor of groups of recombining organisms [14].

The goal of this study was to improve our understanding of the evolution and population structure of L. monocytogenes, with a particular focus on the role of recombination in lineage I and II. As previous studies [2, 15, 16] have suggested that there is a considerable amount of homologous recombination in the genome of at least some L. monocytogenes, we used STRUCTURE [17] and ClonalFrame [14], two programs that model the effect of recombination in genetic diversification, to draw inferences about the population structure and different aspects of the recombination process in L. monocytogenes.

Methods

L. monocytogenesisolates

Our study used multilocus sequence typing data for 195 L. monocytogenes isolates. A total of 184 isolates had been obtained from human clinical cases (n = 60), foods (n = 30) and animal clinical cases (n = 30) (as previously described by Nightingale et al. [2]) as well as natural and urban environments (n = 43) [4] and ruminants without clinical symptoms and farm environments (n = 21) [18]. All of these isolates were obtained from sources within New York State between 1999 and 2003. DNA sequences for all but the 21 isolates from ruminants without clinical symptoms and farm environments have previously been reported [18]. All isolates had previously been characterized by automated ribotyping and classified into lineages I, II, and III based on ribotype data. Because only four lineage III isolates were found among these 184 isolates we also included sequence data for 11 additional lineage III isolates representing lineage IIIA, IIIB and IIIC, for a total of 195 isolates (see Additional file 1 for extra information). Only sigB and actA sequence data had previously been reported for these 11 lineage III isolates [6]. Glycerol stocks of the isolates stored at -80°C were revived on BHI-agar plates and liquid cultures obtained from single colonies were used to make lysates.

Genes Sequenced and MLST analysis

Partial DNA sequences of seven genes including four housekeeping genes (gap, prs, purM and ribC), two virulence genes (inlA and actA), and one stress response gene (sigB) were used for our analyses; a detailed description of the function of these genes and their position on the chromosome can be found in Nightingale et al. [2]. Sequencing of these seven loci for isolates without previously reported sequence information was performed as detailed by Nightingale et al. [2].

Assignment of sequence types (STs) and allelic types of the individual genes was performed using DNAsp 4.10.9 [19] as detailed by Nightingale et al. [2]. Previously reported STs and allelic types were numbered to be consistent with those reported by Nightingale et al. [2]; STs and allelic types not previously encountered were given new identification numbers. Isolate information and all sequence data used in this study are available in Pathogen Tracker http://www.pathogentracker.net. Sequences and alignments have also been deposited in Genbank (actA accession nr EU497055 to EU497238 and EU847029 to EU847039, gap accession nr EU520659 to EU520842 and EU847040 to EU847050, inlA accession nr EU520843 to EU521026 and EU847051 to EU847061, prs accession nr EU521395 to EU521578 and DQ347729 to DQ347720, purM accession nr EU525662 to EU525845 and DQ347741 to DQ347732, ribC accession nr EU521027 to EU521210 and DQ347753 to DQ347744, sigB accession nr EU521211 to EU521394 and EU847062 to EU847073).

Descriptive analysis of sequence data

DNAsp 4.10.9 [19] was used to calculate the average pair-wise nucleotide difference per site (π), the average pairwise nucleotide difference per sequence (k), the number of polymorphic sites, the number of mutations, the number of alleles, the GC content, Tajima's D (test for neutrality of the data [20]), the number of synonymous and non-synonymous mutations, and the rate of non-synonymous to synonymous changes with a Jukes-Cantor correction. All calculations were performed for all isolates, and separately for the isolates belonging to lineages I and II. Separate analyses were not performed for the lineage III isolates due to the small number of isolates.

Phylogenetic analysis

Phylogenetic relationships between the individual sequence types were inferred using ClonalFrame v1.1 [14]. This software is based on a model of genetic diversification that accounts for the way recombination occurs in bacterial populations. This enables the inference of phylogenetic relationships based on sequence data of multiple MLST loci, even if they are partly incongruent due to recombination. Besides the genealogy of the sample, ClonalFrame also infers when and where recombination events took place in the evolutionary history of the sample and estimates population-wide evolutionary parameters (e.g., mutation rate; recombination rate). Sequence data of the 92 unique STs identified among the 195 isolates were input into ClonalFrame and default values were used for all options. Five independent ClonalFrame runs were performed, each consisting of 300,000 iterations. The first 100,000 iterations in each run were discarded, and the phylogeny and additional model parameters were sampled every 100 generations in the last 200,000 iterations; thus, each run produced a sample of size 2,000 from the posterior. The convergence of the Markov Chain Monte Carlo (MCMC) in the different runs was judged satisfactory based on the Gelman-Rubin test [21] as implemented in the ClonalFrame GUI, and using Tracer v1.4 (available from A. Rambaut and A. J. Drummond at http://beast.bio.ed.ac.uk/). The samples from the five different runs were then concatenated for further analysis, resulting in a sample of size 10,000 from the posterior. The genealogies for our sample population were summarized in two complementary ways: a 95% majority rule consensus tree [22] constructed using the ClonalFrame GUI, and a consensus network built using Splitstree [23].

Recombination and recombination rate

Sawyer's test of recombination was performed using GENECONV version 1.81a [24]. Analyses were performed using separate alignments for each of the seven loci; alignments were constructed using one sequence for each unique allelic type. The default settings for GENECONV were used and the number of recombinant events was inferred from the output as described in Nightingale et al. [2].

Recombination events were also assessed using ClonalFrame v1.1, assuming that a posterior probability of import above 95% is conclusive evidence for the occurrence of a recombination event. The recombination rates within lineages I and II were also inferred using ClonalFrame v1.1. For this purpose ClonalFrame was run independently using separate data sets that only included STs that grouped into lineage I and only the STs that grouped into lineage II. Because these datasets are smaller than the combined dataset for all lineages, we did not attempt to jointly infer the values of the mutation rate (θ) and the mean tract length of imported sequence fragments (δ). Instead, we set δ equal to the mean value inferred by the analyses of the whole dataset (δ = 122 bp), and we used fixed mutation rates (θ) for the individual lineages (i.e., θ = 91 for lineage I and θ = 60 for lineage II corresponding to the average value of θ obtained from single runs with 300,000 iterations). Two complementary measures of the recombination rate were calculated: ρ/θ which measures the relative frequency of occurrence of recombination and mutation in the history of the lineage [25], and r/m which measures the relative impact of recombination and mutation in the genetic diversification of the lineage [26].

To assess the influence of recombination on the inference of the phylogeny and particularly the branch lengths, five individual ClonalFrame analyses (100,000 burn-in iterations plus 200,000 sampling iterations) were performed without allowing for recombination (i.e., the recombination rate ρ was set equal to zero).

Population History

To infer the population history of lineage I and lineage II we used the external/internal branch length ratio test [27] as implemented in the ClonalFrame GUI. This test calculates the ratio of the sum of the external branches (the ones that connect a leaf of the tree) to the sum of the internal branches for each tree in the posterior. The distribution of these ratios is than compared to the distribution of the external/internal branch length ratio as expected under the coalescent model [28]. If the distribution of this ratio is significantly larger than expected this means that the genealogy is unexpectedly 'star-like', which is consistent with a recent expansion of the effective population size, either due to a population bottleneck or a selective sweep.

STRUCTURE analysis

To infer the ancestry of the lineage I and lineage II STs in our dataset, we performed an analysis using the linkage model of the program STRUCTURE [17, 29]. This software assumes that the observed data is derived from K ancestral subpopulations. In the linkage model, the observed sequences are assumed to be made of blocks, each of which is inherited from one of the ancestral subpopulations. The program therefore infers for each site of each sequence its posterior probability of deriving from any of the K ancestral subpopulations, and by averaging these probabilities over all sites, we get the average proportion of genetic material derived from each ancestral subpopulation by each individual. To estimate K (the number of ancestral subpopulations), the probability of observing the data given a certain value of K (Pr(X|K)) was calculated for values of K ranging from 1 to 10 (three replicates per value of K). These values were estimated based on relatively short runs of STRUCTURE (20,000 burn-in iterations and 40,000 sampling iterations). STRUCTURE calculates a heuristic estimate of Pr(X|K) after each individual analysis. The value of K that maximized Pr(X|K) was then used for five longer runs (20,000 burn-in iterations and 100,000 sampling iterations).

Results

Descriptive analysis of sequence data

The 195 isolates characterized were classified into 92 unique sequence types (STs; see Additional file 1 for all STs and allelic types) whereas a previous analysis of 120 L. monocytogenes isolates from human and animal clinical cases and foods (Nightingale et al. [2]) only revealed 52 STs. The addition of sequences for 75 isolates from other sources thus resulted in a considerably more diverse data set for our population genetics analyses. The increase in STs is mainly caused by an increase in lineage II isolates; the 45 additional lineage II isolates contributed 26 new STs.

The genetic diversity of the seven loci sequenced ranged from π = 0.0044 (gap) to π = 0.0669 (purM) with the number of allelic types ranging from 18 (gap) to 50 (inlA) (Table 1). When the different parameters were calculated separately for isolates in lineages I and II, the genetic diversity in each lineage was typically considerably lower as compared to the overall genetic diversity (e.g., for actA πall = 0.0558, while πlinI = 0.0058 and πlinII = 0.0064); the only exception being gap (Table 1). One locus (ribC) showed a significant positive value for Tajima's D (Table 1) when the data for all lineages were analyzed, suggesting a divided population structure. On the other hand, prs showed a significantly negative value of Tajima's D in lineage II (-2.02, P < 0.05), which is indicative of a bottleneck or selective sweep [30, 31]. For lineage II, Tajima's D was also negative for sigB (-1.69), however this value was not significant (P > 0.05).
Table 1

Descriptive analysis of nucleotide sequence data

All Isolates

(n = 195)

Length of sequenced region (in bp)

No. of Poly-morphisms

No. of Mutations

No. of Alleles

GC content

π/site b

k c

Tajima's D d

Syn. e

Nonsyn. e

dN/dS f

actA

561

138

156

46

38.40

0.0593

33.26

0.78

59

70

0.32

gap

569

16

16

18

40.00

0.0044

2.50

-0.22

13

3

0.02

inlA

771

105

109

50

40.90

0.0264

18.90

0.04

64

44

0.10

prs

633

57

58

21

40.90

0.0209

13.25

1.01

58

0

n/a

purM

714

161

185

49

42.00

0.0669

47.74

1.61

133

26

0.05

ribC

639

121

131

34

39.60

0.0632

40.41

2.51

117

14

0.02

sigB

666

75

82

22

38.70

0.0251

16.63

0.57

76

3

0.02

Lineage I

(n = 87)

           

actA

561

15

15

15

38.90

0.0058

3.24

0.25

6

9

1.06

gap

569

2

2

3

40.10

0.0009

0.54

0.55

2

0

n/a

inlA

771

17

17

12

41.00

0.0043

3.07

-0.26

6

11

0.32

prs

633

8

8

5

40.70

0.0018

1.14

-0.70

8

0

n/a

purM

714

10

10

13

42.40

0.0034

2.41

0.56

7

3

0.02

ribC

639

18

18

8

39.00

0.0061

3.91

0.27

15

3

0.06

sigB

666

9

9

8

38.60

0.0035

2.30

0.73

9

0

n/a

Lineage II

(n = 93)

           

actA

561

19

20

17

37.90

0.0064

3.58

-0.25

11

9

0.42

gap

569

11

11

12

39.80

0.0050

2.85

0.85

8

3

0.04

inlA

771

54

54

25

41.30

0.0227

17.72

2.04

34

20

0.12

prs

633

18

18

4

41.10

0.0017

1.05

-2.02

18

0

n/a

purM

714

128

140

22

41.60

0.0603

43.05

1.91

108

26

0.08

ribC

639

86

88

16

40.20

0.0417

26.66

1.80

80

8

0.02

sigB

666

27

27

4

38.60

0.0035

2.35

-1.69

26

1

0.01

a The total of 195 includes 15 lineage III isolates, which were not analyzed separately.

b Average pairwise nucleotide difference per site.

c Average pairwise nucleotide differences per sequence.

d Tajima's D values significantly different from 0 (indicating deviation from standard neutral model) are marked in bold type.

e Syn., number of synonymous mutations; Nonsyn., number of nonsynonymous mutations.

f The dN/dS ratio was determined only when some nonsynonymous mutations were observed.

Phylogenetic analysis

The consensus network tree based on the combined output of all five ClonalFrame runs (10,000 trees) shows that the clades representing lineage I and lineage II receive significant support (>95% posterior probability) (Figure 1). The lineage IIIA and IIIB isolates each form separate significantly supported clades (>95% posterior probability). The analysis is inconclusive about the monophyly of lineage III or the phylogenetic placement of lineage IIIC. Within lineage I, significant support was found for a subdivision between STs 4, 13, 46 and 48 and the remainder of the lineage I isolates. Lineage II contains several significantly supported clades of closely related STs.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-8-277/MediaObjects/12862_2008_Article_846_Fig1_HTML.jpg
Figure 1

Unrooted consensus network based on 10,000 phylograms obtained from ClonalFrame analyses of the individual sequence types. Genealogical inference was performed for all 92 unique STs using ClonalFrame as described in the Methods. A consensus network was built using Splitstree [23]. Reticulate relationships were found in at least 20% of the trees and indicate phylogenetic uncertainty. Branches supported by a posterior probability of more than 95% are colored in red. Leaves are labeled with ST designations.

STRUCTURE analysis

Multiple STRUCTURE runs with different values of K (the number of ancestral subpopulations) showed that the probability of the data given K was maximal at K = 4. For simplicity we will call the ancestral populations with the highest proportion of lineage I, II, IIIA and IIIB isolates the 'ancestral' populations of the respective lineages [32]. Lineage I forms a genetically homogeneous group with an average of 93% of genetic material descended from the ancestral lineage I population (Figure 2). In contrast, only an average of 74% of the genetic material found in lineage II isolates is descended from the ancestral lineage II population (Figure 2), whereas 20% of it came from the ancestral lineage IIIA population. In particular, 14 STs in lineage II had an exceptionally low proportion (between 50 and 66%) of genetic material from the ancestral lineage II population. In these STs, up to 47% of the material came from the ancestral lineage III population. Due to the small number of lineage III isolates in our dataset (n = 15), we did not explore the population structure of lineages IIIA, B, and C. Rather, lineage III isolates were only included in the STRUCTURE analyses to estimate the proportion of lineage I and II genetic material that was obtained from the ancestral, lineage III populations.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-8-277/MediaObjects/12862_2008_Article_846_Fig2_HTML.jpg
Figure 2

Mixture of ancestry of the different STs as inferred by STRUCTURE. Proportions of ancestry from ancestral lineage I (purple), ancestral lineage II (green), ancestral lineage IIIA (red) and ancestral IIIB subpopulations (yellow) as inferred by STRUCTURE assuming K = 4 ancestral subpopulations. The asterisk marks a lineage IIIC isolate. Each vertical line represents an individual sequence type and is colored according to the inferred proportion of single nucleotide alleles that were derived from one of the ancestral subpopulations. This bar plot was created with the DISTRUCT software [52].

Population history

The internal/external branch length ratio test showed that both recombination corrected genealogies of lineage I and lineage II exhibit a significantly higher internal/external branch length ratio (lineage I: 1.50, P = 0.005; lineage II: 1.64, P = 0.00002) than expected under the coalescent model (see Figure 3). This clearly indicates that the contemporary population in both lineages experienced a recent expansion of the effective population size, which is consistent with a population bottleneck.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-8-277/MediaObjects/12862_2008_Article_846_Fig3_HTML.jpg
Figure 3

Distribution of the Interior/exterior branch length ratio of trees resulting from ClonalFrame analysis of lineage I (A) and II (B) as compared to trees simulated under the coalescent model. Both lineages show a higher internal/external branch length ratio (lineage I 1.50, P = 0.005; lineage II 1.64, P = 0.00002) than expected under the coalescent model, which is indicative of a bottleneck event during the population history.

Recombination events

The majority of the 17 recombination events found by the Sawyer's test was found in the house-keeping genes purM (4 events) and ribC (8 events) (Table 2). The average tract length of recombination imports inferred by the Sawyer's test was 302 bp for inlA, 114 bp for purM and 192 for ribC. ClonalFrame also inferred a high number of recombination events in purM (13 events) and ribC (10 events), but in contrast to the Sawyer's test it inferred a high number of recombination events in the virulence genes actA (11 events, only one event according to the Sawyer's test) and inlA (16 events, only three events according to the Sawyer's test) (Table 3). The number of events inferred by ClonalFrame may be higher because it detects recombination events in a phylogenetic framework, which allows the detection of recombination events involving the entire length of the locus as well as events involving only parts of the locus. Closer examination of the results of both tests revealed that the majority of the recombination events in actA involved an 80 bp region on the 5'-end of the locus. The average tract length of recombination imports (δ) for all loci inferred from the combined ClonalFrame runs was 126 bp with a 95% credibility interval of 100 to 154 bp. While ClonalFrame may have a tendency to underestimate the tract length [14], this result is in fairly good agreement with the short lengths of recombinant fragments inferred by Sawyer's test.
Table 2

Summary of recombination analysis using Sawyers test (GENECONV)

 

Global inner recombination a

Global inner events

   

Within lineages

Between lineages

 

Sim. P value b

Fragments c (events d )

I

II

III

I/II

I/III

II/III

multiple e

actA

0.0031*

19 (1 event)

0

0

0

0

0

1

0

gap

0.8650

0 (0 events)

0

0

0

0

0

0

0

inlA

<0.0001*

26 (3 events)

0

1

0

0

0

1

1 (II/II and I/II)

prs

0.0043*

3(1 event)

0

0

1

0

0

0

0

purM

<0.0001*

141 (4 events)

0

3

0

0

0

0

1 (II/II and II/III)

ribC

<0.0001*

68 (8 events)

1

2

0

2

0

0

3 (II/II and II/III)

sigB

0.0735

0 (0 events)

0

0

0

0

0

0

0

total

 

226 (17 events)

1

6

1

2

0

2

5

a"Global inner recombination" denotes recombination between ancestors of sequences in alignment; no significant support was found for recombination events involving an ancestor outside the alignment or events obscured by subsequent mutations (i.e., "Global outer fragments").

b Sim. P value, simulated P value. P values indicating statistically significant (P < 0.05) evidence for recombination events are marked with asterisks.

c Number of segments of alignment sufficiently similar to imply recombination.

d Group of fragments linked to the same 5' and/or 3' breakpoints were classified as a single recombination event (as described by Nightingale et al., 2005).

e "Multiple" refers to global inner recombination events that involved significant fragments between and within lineages.

Table 3

Recombination events inferred by ClonalFrame1

 

No. of recombination events in

Locus

Lineage I

Lineage II

Lineage III

Total

actA

0

9

2

11

gap

0

0

0

0

inlA

0

16

0

16

prs

2

3

1

6

purM

1

12

0

13

ribC

2

8

0

10

sigB

0

1

0

1

Total

5

49

3

57

1 Only recombination events with a posterior probability of 95% or higher were counted

Overall effect of recombination

To compare the rate of recombination between lineage I and II, ClonalFrame analyses were run separately on isolates representing each lineage (Table 4). We found a mean value for ρ (the recombination rate times two) of 11.4 (with credibility interval [2.2–21.8]) for lineage I, and of 42.6 (C.I. [29.3–56.3]) for lineage II. The relative frequency of occurrence of recombination versus mutation (ρ/θ) was 0.13 (C.I. [0.03–0.23]) for lineage I, and 0.71 (C.I. [0.49–0.94]) for lineage II. The relative effect of recombination versus point mutation (r/m) was 0.66 (C.I. [0.24–1.19]) for lineage I and 4.42 (C.I. [3.04–5.89]) for lineage II. Both the frequency and effect of recombination was therefore estimated to be about six times higher in lineage II than in lineage I, and the non-overlapping of the credibility intervals between lineage I and II strains for all parameters indicated that these differences are statistically significant.
Table 4

Recombination rates inferred by ClonalFrame analysis.

  

ρa

r/mb

ρ/θc

Lineage I

Run 1

10.75 (3.25–22.83)

0.64 (0.27–1.28)

0.12 (0.04–0.25)

 

Run 2

11.48 (4.27–22.77)

0.69 (0.29–1.27)

0.13 (0.05–0.25)

 

Run 3

10.86 (2.59–21.00)

0.64 (0.26–1.21)

0.12 (0.03–0.23)

 

Run 4

12.32 (3.35–27.06)

0.67 (0.27–1.25)

0.14 (0.04–0.30)

 

Run 5

12.03 (4.19–22.55)

0.69 (0.28–1.26)

0.13 (0.05–0.25)

 

All runs combined

11.4 (2.17–21.76)

0.66 (0.24–1.19)

0.13 (0.03–0.23)

Lineage II

Run 1

41.47 (30.32–56.30)

4.30 (3.17–5.76)

0.69 (0.51–0.94)

 

Run 2

42.07 (29.89–56.13)

4.24 (3.00–5.79)

0.70 (0.50–0.94)

 

Run 3

43.28 (31.86–58.83)

4.61 (3.35–6.17)

0.72 (0.53–0.95)

 

Run 4

41.87 (29.93–55.59)

4.35 (3.12–5.86)

0.70 (0.50–0.93)

 

Run 5

42.46 (28.77–61.04)

4.43 (3.02–6.26)

0.71 (0.48–1.02)

 

All runs combined

42.65 (29.27–56.30)

4.42 (3.04–5.89)

0.71 (0.49–0.94)

Mean value of the parameters with the 95% credibility interval given in brackets.

a Recombination rate

b Relative impact of recombination as compared to point mutation in the genetic diversification of the lineage

c Relative frequency of occurrence of recombination as compared to point mutation in the history of the lineage

The influence of recombination on the phylogenetic inference of L. monocytogenes was clearly demonstrated by comparison of ClonalFrame results with and without correction for recombination (Figure 4). The phylogram based on the analysis without correction for recombination (Figure 4a) suggested a time to the most recent common ancestor (TMRCA) of lineage I that is clearly smaller than the TMRCA of lineage II, whereas a phylogram based on the analysis with correction for recombination (Figure 4b) revealed that the TMRCA of lineages I and II are similar. This indicates that lineage I and II appeared at approximately the same time, and that the higher genetic variability of lineage II can be attributed to its higher recombination rate.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-8-277/MediaObjects/12862_2008_Article_846_Fig4_HTML.jpg
Figure 4

Phylogenies inferred by ClonalFrame without (A) and with (B) correction for recombination. The phylogram (A) shows a 50% majority-rule consensus tree based on ClonalFrame output (see Methods section) for all 92 unique STs and ignoring the role of recombination. The phylogram (B) is the same, but recombination was taken into account in the model of genetic diversification. The rulers indicate the time in coalescent units. Dashed grey lines show the estimated time to the most recent common ancestors of lineage I and II.

Discussion

The different tests and analyses we used all show that recombination is more prevalent in L. monocytogenes lineage II than in lineage I. The Bayesian inference of the relative recombination rate (ρ/θ) for both lineages shows that the relative recombination rate in lineage II is approximately six times higher than in lineage I (Table 4). Comparison of the values of ρ/θ with those that have been previously calculated for other species or groups of related species of bacteria (Table 5) shows that the recombination rate in lineage II is higher to that inferred for the Bacillus anthracis/cereus-clade [14], but still relatively low in comparison to pathogens such as Neisseria meningitidis [33]Streptococcus pneumoniae [34] or Clostridium perfringens [35]. While the higher genetic diversity among L. monocytogenes lineage II strains compared to lineage I strains has previously been reported [36, 37], one other recent study [38] found no clear differences in recombination rates between L. monocytogenes lineage I and II isolates. By contrast, our data reported here indicate that the higher diversity among lineage II strains is exclusively due to a higher recombination rate in lineage II strains, and in particular to a large number of imports from lineage IIIA.
Table 5

Relative recombination rates found in other bacteria

Taxon

ρ/θa

Reference

Bacillus (anthracis/cereus clade)

0.2–0.5

Didelot & Falush 2007b

Listeria monocytogenes Lineage I

0.03–0.23

this studyb

Listeria monocytogenes Lineage II

0.49–0.94

this studyb

Streptococcus pneumoniae

2.1

Fraser et al. 2005c

Staphylococcus aureus

0.11

Fraser et al. 2005c

Clostridium perfringens

3.2

Rooney et al. 2006c

Neisseria meningitis

1.1

Fraser et al. 2005c

a Relative recombination rate expressed as the relative frequency of occurrence of recombination as compared to point mutation in the history of the lineage

b Estimate obtained by Bayesian inference

c Estimate obtained by Maximum likelihood

While both our data reported here and the data reported by Ragon et al. [38] are consistent with regard to the relative contribution of recombination and point mutations to the diversity of lineage I strains (r/m rates for nucleotides were between 0.6 and 0.7 in both studies), our data indicate a much higher r/m ratio for lineage II strains (r/m = 4.42) as compared to the r/m rate reported for linage II strains by Ragon et al. (r/m = 0.47) [38]. These differences in the relative recombination rates for lineage I and II strains between our study and the Ragon et al. [38] study are likely related to differences in the selection of isolates and target genes for MLST between these two studies. While the study of Ragon et al. [38] is heavily biased towards human clinical isolates (75%) with limited representation of animal (7%), food (3%) and environmental isolates (3%), 69% of the isolates in our study were from sources other than human clinical cases. The higher source diversity represented among our isolates, including use of a considerable number of environmental isolates that may be more likely exposed to donors of genetic material, may explain the increased frequency of recombination among the lineage II isolates characterized here. Ragon et al. [38] also used seven housekeeping genes, which are assumed to be under negative selection and subject to less homologues recombination, while our study used four housekeeping genes as well as two genes involved in virulence (actA and inlA) and one stress response gene (sigB). Interestingly, we identified recombination events in both virulence as well as housekeeping genes, consistent with a comparative genomic study by Orsi et al. [16], who found that close to 50% of the 2267 genes found in the L. monocytogenes/L. innocua core genome show evidence for recombination and that recombination is more frequent in lineage II than lineage I strains based on evaluation of 40 randomly selected genes. While the loci used by Ragon et al. [38] are thus ideal for intraspecific phylogenetic reconstruction, due to their low recombination rate, these genes are not necessarily representative for the recombination rate throughout the whole genome. The loci used in our study may be biased towards recombining loci and therefore lead to over-estimation of recombination rates, however the nearly identical r/m rates for lineage I isolates reported here and by Ragon et al. [38], suggest that selection of loci has a limited effect on estimates of recombination rate among lineage I strains. While multiple studies (e.g., Meinersmann et al. [36], Orsi et al. [16], etc.) support that recombination is frequent among lineage II strains, clearly some genes (e.g., those selected by Ragon et al. [38]) will show limited recombination in both lineages. An unbiased estimate of the contributions of recombination to diversity among bacteria will thus require future genome wide studies, such as those reported by Orsi et al. [16], and Lefebure and Stanhope [39]. Combined with an initial genome wide study already published (e.g., Orsi et al. [16]), our study shows that phylogenetic lineages, even within a given bacterial species, can differ significantly in their recombination and mutation rates. Similar findings have previously been reported for different species within a given genus. For example Lefébure and Stanhope [39], found that a much higher percentage (37%) of genes in the Streptococcus core-genome of Streptococcus pyogenes show evidence for recombination compared to Streptococcus agalactiae (18%). These results illustrate the need to develop new inferential tools that account for the possibility of variations in the recombination rate from one lineage to another.

Mutation hotspots, short stretches of sequence that evolve at a higher mutation rate than their flanking region, have been shown to cause homoplasy that can be incorrectly identified as being caused by recombination [40]. One stretch of sequence in our dataset, the first 80 bp of the actA locus, is potentially a mutation hotspot. Both the Sawyer test and ClonalFrame identify this stretch of sequence as being involved in recombination. ClonalFrame inferred that this fragment was involved in 9 independent recombination events. It is highly unlikely that the same stretch of sequence is involved in that many import events and therefore we consider this a case of mutation hotspot related homoplasy. The first 80 bp of the actA locus contain two amino acid positions that have been proven to be under positive selection [2], which may explain why this stretch of sequence experiences an elevated mutation rate. The occurrence of this hotspot may influence the inference of the recombination rate and the average length of the recombination fragments, however its effect is limited because it only involves ca 20% of the events inferred by ClonalFrame.

Mechanisms for horizontal gene transfer in bacteria include conjugation, transduction and transformation [41]. Transduction and conjugation usually involve long imported fragments (several kb's to hundred's of kb's) [41], and are therefore unlikely to be responsible for the homologous recombination events in L. monocytogenes we observed. Considering the short size of the recombinant fragments identified, horizontal gene transfer in L. monocytogenes most likely occurs through transformation. This is supported by the observation that short import elements of only a couple of hundred bp have been observed in the naturally competent bacterium Helicobacter pylori [42]. While L. monocytogenes does not show evidence for competence under laboratory conditions generally used to induce competency [43], our observations suggest that L. monocytogenes and in particular lineage II isolates may be competent under certain environmental conditions. This hypothesis is further supported by the fact that L. monocytogenes possesses a large number of genes (e.g., comC, comEA, comEB), which, in B. subtilis, encode proteins important for transformation [43].

While most lineage I and II strains possess the ability to cause listeriosis, lineage I, the lineage with the lowest recombination rate, appears to be more virulent for humans [44]. In particular, lineage I strains have been linked to almost all major human listeriosis outbreaks and are responsible for the majority of sporadic listeriosis cases [45], show a higher infectious dose [44], and, on average, have a higher ability to spread inside human cells [46]. In contrast, Wirth et al. [9] proposed, based on data for Escherichia coli, that epidemic and virulent bacteria face an increased selective pressure for rapid diversification in response to host immune defenses, resulting in higher recombination rates. As L. monocytogenes is an opportunistic pathogen with a wide host range as well as a saprotroph found in many different environments, we propose that the high recombination rate in lineage II is not due to selective forces involved in its virulence. Rather recombination may be critical for lineage II to successfully compete and survive in a broad range of different environments, consistent with the observation that lineage II strains are typically found at higher levels than lineage I strains (e.g., in foods [44]) and are more common than lineage I strains in natural environments.

Our data suggest that both lineages I and II have been subject to a recent expansion in population size, which is obscured in lineage II by a high level of recombination. The question is if this recent expansion started from a small founder population that survived a major extinction event (a population bottleneck) or from a limited number of strains with a higher fitness as compared to related strains in the ancestral population (selective sweep). It is difficult to differentiate between these two scenarios, since they both leave the same population genetic signal in a contemporary population. While previous studies [36, 37] proposed that only L. monocytogenes lineage I strains experienced a recent bottleneck, our analyses suggest that both lineages have been affected. Using ClonalFrame, which takes recombination into account when reconstructing a phylogeny, we showed that lineage I and II do not differ considerably in age (assuming that the mutation rate follows a molecular clock in both lineages). The external/internal branch length test shows significant support for a bottleneck scenario in lineages I and II. Additional support for a bottleneck in lineage II is found in a significant negative value of Tajima's D for prs and an almost significant negative value for sigB; a negative value of Tajima's D indicates that these loci did not evolve neutrally and is suggestive of a selective sweep or a population bottleneck [30, 31]. This signature of a bottleneck event in lineage II is not apparent among the other genes studied as most of them (i.e., actA, inlA, purM and ribC) were subject to a considerable number of recombination events in lineage II. The occurrence of a most recent common ancestor around the same time in two independent lineages supports the scenario of a population bottleneck (as opposed to a selective sweep), since it implies a common cause for the reduction in population size, such as a climatological change or an alteration of their habitat. Meinersmann et al. [36] estimated the time to the most recent common ancestor of lineage I to be between one-half and one million years ago. This coincides with the first glaciation cycles of the Pleistocene [47], which induced a rapid change both in climate and biota. A decline of available hosts and/or a strong selective pressure for adaptation to colder temperatures could therefore be the cause for the population bottlenecks in both lineages. It is tempting to speculate that the fraction of the population that survived may have been adapted to growth and survival at low temperatures (a hallmark phenotypic characteristic of L. monocytogenes [48]) and/or to specific mammalian hosts that were available at that time.

Our data also support L. monocytogenes lineage I and lineage II should be considered distinct species-like evolutionary lineages, consistent with the conclusions reached by Ragon et al. [38] in their recent study. The results of our ClonalFrame and STRUCTURE analyses support the existence of lineage I and II as separate evolutionary lineages with little genetic material exchanged between them, which is in agreement with a number of previous studies [2, 49]. The number of lineage III isolates in this study is too low to draw conclusions on the evolutionary status of this lineage. Though a well-supported subdivision was found within lineage I in the phylogenetic analysis, no evidence for a further subdivision of lineage I and lineage II could be found with the STRUCTURE analysis. The STRUCTURE analysis showed that 14 lineage II STs acquired less than 67% of their nucleotide alleles from the ancestral lineage II population. Wirth et al. [9] considered isolates with less than 2/3 overlap with one of the ancestral populations to belong to a hybrid group. The uncertainty in our phylogenetic inference (the reticulate relationships depicted in Figure 1) of the lineage II STs seems to be mainly caused by these 'hybrid' STs. Therefore lineage II could be considered a so-called fuzzy species [50], i.e., a species that has unclear boundaries because of its inherent ability to import genetic material from other species. A previous study [51] has shown that in the case of Neisseria gonorrhoeae, the apparent status as a fuzzy species was an artefact caused by sequencing of differentially amplified fragments from mixed non-viable historical cultures. In our case all sequences were obtained from viable cultures and started from single colonies in order to avoid mixed cultures. We can therefore be confident that lineage II truly represents a fuzzy species. Lineage I, on the other hand, seems to be a species with clearly defined species boundaries and few imports from an external origin.

Conclusion

Our study not only adds to an emerging body of literature that supports the importance of recombination in the evolution of bacterial populations, but also shows that even closely related bacterial populations can differ considerably with regard to the contributions of homologous recombination to diversification and evolution. Our study further illustrates the challenges that lineage specific recombination provides for inference of the phylogeny and population history of bacterial populations. While L. monocytogenes lineage I has traditionally been seen as less divergent and more clonal than lineage II, our analyses clearly show that high diversity in lineage II is due to recombination and that both lineages went through a population bottleneck. Previous suggestions that only lineage I experienced a bottleneck appear to be an artifact of the high recombination rate in lineage II. While both lineages I and II seem to represent species-like lineages, lineage I appears to be a genetically isolated population with a low recombination rate, whereas lineage II seems to have more diffuse genetic boundaries due to a relatively high frequency of imports of genetic material from other lineages. While our data supports that lineage I and lineage II each constitute species from an evolutionary perspective and while the two lineages appear to differ in their virulence [44], they both are able to cause human listeriosis. From a practical perspective, it may therefore be best not to change theses lineages to an official rank of species.

Declarations

Acknowledgements

This work was supported by USDA-CSREES Special Research Grant 2005-34459-15625 and by the USDA Cooperative State Research, Education and Extension Service through the Hatch project NYC-143451 (to MW). We would like to thank Renato H. Orsi for his helpful comments and discussions.

Authors’ Affiliations

(1)
Department of Food Science, Cornell University
(2)
Department of Statistics, University of Warwick
(3)
Department of Animal Science, Colorado State University

References

  1. Gray M, Freitag N, Boor K: How the bacterial pathogen Listeria monocytogenes mediates the switch from environmental Dr. Jekyll to pathogenic Mr. Hyde. Infect Immun. 2006, 74 (5): 2505-2512. 10.1128/IAI.74.5.2505-2512.2006.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Nightingale K, Windham K, Wiedmann M: Evolution and molecular phylogeny of Listeria monocytogenes isolated from human and animal listeriosis cases and foods. J Bacteriol. 2005, 187 (16): 5537-5551. 10.1128/JB.187.16.5537-5551.2005.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Gray M, Zadoks R, Fortes E, Dogan B, Cai S, Chen Y, Scott V, Gombas D, Boor K, Wiedmann M: Listeria monocytogenes isolates from foods and humans form distinct but overlapping populations. Appl Environ Microbiol. 2004, 70 (10): 5833-5841. 10.1128/AEM.70.10.5833-5841.2004.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Sauders B, Durak M, Fortes E, Windham K, Schukken Y, Lembo A, Akey B, Nightingale K, Wiedmann M: Molecular characterization of Listeria monocytogenes from natural and urban environments. J Food Prot. 2006, 69 (1): 93-105.PubMedGoogle Scholar
  5. Liu D, Lawrence M, Wiedmann M, Gorski L, Mandrell R, Ainsworth A, Austin F: Listeria monocytogenes subgroups IIIA, IIIB, and IIIC delineate genetically distinct populations with varied pathogenic potential. J Clin Microbiol. 2006, 44 (11): 4229-4233. 10.1128/JCM.01032-06.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Roberts A, Nightingale K, Jeffers G, Fortes E, Kongo JM, Wiedmann M: Genetic and Phenotypic Characterization of Listeria monocytogenes lineage III. Microbiology. 2006, 152 (3): 685-693. 10.1099/mic.0.28503-0.View ArticlePubMedGoogle Scholar
  7. Fraser C, Hanage W, Spratt B: Recombination and the nature of bacterial speciation. Science. 2007, 315 (5811): 476-480. 10.1126/science.1127573.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Spratt B, Hanage W, Feil E: The relative contributions of recombination and point mutation to the diversification of bacterial clones. Curr Opin Microbiol. 2001, 4 (5): 602-606. 10.1016/S1369-5274(00)00257-5.View ArticlePubMedGoogle Scholar
  9. Wirth T, Falush D, Lan R, Colles F, Mensa P, Wieler L, Karch H, Reeves P, Maiden M, Ochman H, et al: Sex and virulence in Escherichia coli: an evolutionary perspective. Mol Microbiol. 2006, 60 (5): 1136-1151. 10.1111/j.1365-2958.2006.05172.x.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Redfield R: Do bacteria have sex?. Nat Rev Genet. 2001, 2 (8): 634-639. 10.1038/35084593.View ArticlePubMedGoogle Scholar
  11. Narra H, Ochman H: Of what use is sex to bacteria?. Curr Biol. 2006, 16 (17): R705-710. 10.1016/j.cub.2006.08.024.View ArticlePubMedGoogle Scholar
  12. Retchless A, Lawrence J: Temporal fragmentation of speciation in bacteria. Science. 2007, 317 (5841): 1093-1096. 10.1126/science.1144876.View ArticlePubMedGoogle Scholar
  13. Holmes EC, Urwin R, Maiden MC: The influence of recombination on the population structure and evolution of the human pathogen Neisseria meningitidis. Mol Biol Evol. 1999, 16 (6): 741-749.View ArticlePubMedGoogle Scholar
  14. Didelot X, Falush D: Inference of bacterial microevolution using multilocus sequence data. Genetics. 2007, 175 (3): 1251-1266. 10.1534/genetics.106.063305.PubMed CentralView ArticlePubMedGoogle Scholar
  15. Orsi RH, Ripoll DR, Yeung M, Nightingale KK, Wiedmann M: Recombination and positive selection contribute to evolution of Listeria monocytogenes inlA. Microbiology. 2007, 153: 2666-2678. 10.1099/mic.0.2007/007310-0.View ArticlePubMedGoogle Scholar
  16. Orsi RH, Sun Q, Wiedmann M: Genome-wide analyses reveal lineage specific contributions of positive selection and recombination to the evolution of Listeria monocytogenes. BMC Evol Biol. 2008, 8: 233-10.1186/1471-2148-8-233.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Pritchard J, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155 (2): 945-959.PubMed CentralPubMedGoogle Scholar
  18. Nightingale K, Schukken Y, Nightingale C, Fortes E, Ho A, Her Z, Grohn Y, McDonough P, Wiedmann M: Ecology and transmission of Listeria monocytogenes infecting ruminants and in the farm environment. Appl Environ Microbiol. 2004, 70 (8): 4458-4467. 10.1128/AEM.70.8.4458-4467.2004.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Rozas J, Sánchez-DelBarrio J, Messeguer X, Rozas R: DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics. 2003, 19 (18): 2496-2497. 10.1093/bioinformatics/btg359.View ArticlePubMedGoogle Scholar
  20. Tajima F: Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989, 123 (3): 585-595.PubMed CentralPubMedGoogle Scholar
  21. Gelman A, Rubin DB: Inference from Iterative Simulation Using Multiple Sequences. Statistical Science. 1992, 7 (4): 457-472. 10.1214/ss/1177011136.View ArticleGoogle Scholar
  22. Margush T, McMorris FR: Consensus n-Trees. Bulletin of Mathematical Biology. 1981, 43 (2): 239-244.Google Scholar
  23. Huson D, Bryant D: Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006, 23 (2): 254-267. 10.1093/molbev/msj030.View ArticlePubMedGoogle Scholar
  24. Sawyer SA: GENECONV: A computer package for the statistical detection of gene conversion. Distributed by the author, Department of mathematics, Washington University in St louis. 1999, [http://www.math.wustl.edu/~sawyer/geneconv/]Google Scholar
  25. Milkman R, Bridges M: Molecular evolution of the Escherichia coli chromosome. III. Clonal frames. Genetics. 1990, 126 (3): 505-517.PubMed CentralPubMedGoogle Scholar
  26. Guttman DS, Dykhuizen DE: Clonal divergence in Escherichia coli as a result of recombination, not mutation. Science. 1994, 266 (5189): 1380-1383. 10.1126/science.7973728.View ArticlePubMedGoogle Scholar
  27. Fiala KI, Sokal RR: Factors determining the accuracy of cladogram estimation – evaluation using computer-simulation. Evolution. 1985, 39 (3): 609-622. 10.2307/2408656.View ArticleGoogle Scholar
  28. Kingman JFC: The Coalescent. Stochastic Processes and their Applications. 1982, 13: 235-248. 10.1016/0304-4149(82)90011-4.View ArticleGoogle Scholar
  29. Falush D, Stephens M, Pritchard J: Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003, 164 (4): 1567-1587.PubMed CentralPubMedGoogle Scholar
  30. Simonsen K, Churchill G, Aquadro C: Properties of statistical tests of neutrality for DNA polymorphism data. Genetics. 1995, 141 (1): 413-429.PubMed CentralPubMedGoogle Scholar
  31. Nielsen R: Statistical tests of selective neutrality in the age of genomics. Heredity. 2001, 86: 641-647. 10.1046/j.1365-2540.2001.00895.x.View ArticlePubMedGoogle Scholar
  32. Falush D, Wirth T, Linz B, Pritchard J, Stephens M, Kidd M, Blaser M, Graham D, Vacher S, Perez-Perez G, et al: Traces of human migrations in Helicobacter pylori populations. Science. 2003, 299 (5612): 1582-1585. 10.1126/science.1080857.View ArticlePubMedGoogle Scholar
  33. Jolley KA, Wilson DJ, Kriz P, McVean G, Maiden MC: The influence of mutation, recombination, population history, and selection on patterns of genetic diversity in Neisseria meningitidis. Mol Biol Evol. 2005, 22 (3): 562-569. 10.1093/molbev/msi041.View ArticlePubMedGoogle Scholar
  34. Fraser C, Hanage W, Spratt B: Neutral microepidemic evolution of bacterial pathogens. Proc Natl Acad Sci USA. 2005, 102 (6): 1968-1973. 10.1073/pnas.0406993102.PubMed CentralView ArticlePubMedGoogle Scholar
  35. Rooney AP, Swezey JL, Friedman R, Hecht DW, Maddox CW: Analysis of core housekeeping and virulence genes reveals cryptic lineages of Clostridium perfringens that are associated with distinct disease presentations. Genetics. 2006, 172 (4): 2081-2092. 10.1534/genetics.105.054601.PubMed CentralView ArticlePubMedGoogle Scholar
  36. Meinersmann R, Phillips R, Wiedmann M, Berrang M: Multilocus sequence typing of Listeria monocytogenes by use of hypervariable genes reveals clonal and recombination histories of three lineages. Appl Environ Microbiol. 2004, 70 (4): 2193-2203. 10.1128/AEM.70.4.2193-2203.2004.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Ward T, Gorski L, Borucki M, Mandrell R, Hutchins J, Pupedis K: Intraspecific phylogeny and lineage group identification based on the prfA virulence gene cluster of Listeria monocytogenes. J Bacteriol. 2004, 186 (15): 4994-5002. 10.1128/JB.186.15.4994-5002.2004.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Ragon M, Wirth T, Hollandt F, Lavenir R, Lecuit M, Monnier AL, Brisse S: A New Perspective on Listeria monocytogenes Evolution. PLoS Pathog. 2008, 4 (9): e1000146-10.1371/journal.ppat.1000146.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Lefebure T, Stanhope MJ: Evolution of the core and pan-genome of Streptococcus: positive selection, recombination, and genome composition. Genome Biol. 2007, 8 (5): R71-10.1186/gb-2007-8-5-r71.PubMed CentralView ArticlePubMedGoogle Scholar
  40. Galtier N, Enard D, Radondy Y, Bazin E, Belkhir K: Mutation hot spots in mammalian mitochondrial DNA. Genome Res. 2006, 16 (2): 215-222. 10.1101/gr.4305906.PubMed CentralView ArticlePubMedGoogle Scholar
  41. Snyder L, Champness W: Molecular Genetics of Bacteria. 2003, Washington, D.C.: ASM Press, 2Google Scholar
  42. Falush D, Kraft C, Taylor N, Correa P, Fox J, Achtman M, Suerbaum S: Recombination and mutation during long-term gastric colonization by Helicobacter pylori: estimates of clock rates, recombination size, and minimal age. Proc Natl Acad Sci USA. 2001, 98 (26): 15056-15061. 10.1073/pnas.251396098.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Borezee E, Msadek T, Durant L, Berche P: Identification in Listeria monocytogenes of MecA, a homologue of the Bacillus subtilis competence regulatory protein. J Bacteriol. 2000, 182 (20): 5931-5934. 10.1128/JB.182.20.5931-5934.2000.PubMed CentralView ArticlePubMedGoogle Scholar
  44. Chen Y, Ross WH, Gray MJ, Wiedmann M, Whiting RC, Scott VN: Attributing risk to Listeria monocytogenes subgroups: dose response in relation to genetic lineages. J Food Prot. 2006, 69 (2): 335-344.PubMedGoogle Scholar
  45. Borucki M, Kim S, Call D, Smole S, Pagotto F: Selective discrimination of Listeria monocytogenes epidemic strains by a mixed-genome DNA microarray compared to discrimination by pulsed-field gel electrophoresis, ribotyping, and multilocus sequence typing. J Clin Microbiol. 2004, 42 (11): 5270-5276. 10.1128/JCM.42.11.5270-5276.2004.PubMed CentralView ArticlePubMedGoogle Scholar
  46. Wiedmann M, Bruce JL, Keating C, Johnson AE, McDonough PL, Batt CA: Ribotypes and virulence gene polymorphisms suggest three distinct Listeria monocytogenes lineages with differences in pathogenic potential. Infect Immun. 1997, 65 (7): 2707-2716.PubMed CentralPubMedGoogle Scholar
  47. Hewitt GM: The genetic legacy of the Quaternary ice ages. Nature. 2000, 405 (6789): 907-913. 10.1038/35016000.View ArticlePubMedGoogle Scholar
  48. Seeliger HPR, Jones D: Listeria. Bergey's manual of Systematic Bacteriology. Edited by: Sneath PHA. 1986, Balrimore, MD, USA: Williams & Wilkins, 2: 1235-1245.Google Scholar
  49. Rasmussen OF, Skouboe P, Dons L, Rossen L, Olsen JE: Listeria monocytogenes exists in at least three evolutionary lines: evidence from flagellin, invasive associated protein and listeriolysin O genes. Microbiology. 1995, 141 (Pt 9): 2053-2061.View ArticlePubMedGoogle Scholar
  50. Hanage WP, Fraser C, Spratt BG: Fuzzy species among recombinogenic bacteria. BMC Biol. 2005, 3: 6-10.1186/1741-7007-3-6.PubMed CentralView ArticlePubMedGoogle Scholar
  51. Bennett J, Jolley K, Sparling P, Saunders N, Hart C, Feavers I, Maiden MC: Species status of Neisseria gonorrhoeae: evolutionary and epidemiological inferences from multilocus sequence typing. BMC Biol. 2007, 5: 35-10.1186/1741-7007-5-35.PubMed CentralView ArticlePubMedGoogle Scholar
  52. Rosenberg NA: DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2004, 4: 137-138. 10.1046/j.1471-8286.2003.00566.x.View ArticleGoogle Scholar

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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