Multilocus sequence analysis of Porphyromonas gingivalis indicates frequent recombination

Andreas Koehler1, Helge Karch2, Thomas Beikler3, Thomas. F. Flemmig3, Sebastian Suerbaum1,{dagger} and Herbert Schmidt1,{ddagger}

1 Institut für Hygiene und Mikrobiologie der Bayerischen Julius-Maximilians-Universität, 97080 Würzburg, Germany
2 Institut für Hygiene, 48149 Münster, Germany
3 Poliklinik für Parodontologie der Westfälischen Wilhelms-Universität, 48149 Münster, Germany

Correspondence
Herbert Schmidt
Herbert.Schmidt{at}mailbox.tu-dresden.de


   ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
In this study, the genetic relationship of 19 Porphyromonas gingivalis isolates from patients with periodontitis was investigated by multilocus sequence analysis. Internal 400–600 bp DNA fragments of the 10 chromosomal genes ef-tu, ftsQ, hagB, gpdxJ, pepO, mcmA, dnaK, recA, pga and nah were amplified by PCR and sequenced. No two isolates were identical at all 10 loci. Phylogenetic analyses indicated a panmictic population structure of P. gingivalis. Split decomposition analysis, calculation of homoplasy ratios and analyses of clustered polymorphisms all indicate that recombination plays a major role in creating the genetic heterogeneity of P. gingivalis. A standardized index of association of 0·0898 indicates that the P. gingivalis genes analysed are close to linkage equilibrium.


Abbreviations: , standardized index of association

The nucleotide sequences of the fragments of all Porphyromonas gingivalis genes and their alleles (190 sequences) reported in this paper have been entered into the EMBL/GenBank databases and have received continuous accession numbers from AJ555632 to AJ555821.

{dagger}Present address: Institut für Medizinische Mikrobiologie und Krankenhaushygiene, Medizinische Hochschule Hannover, Carl-Neuberg-Straße 1, 30625 Hannover, Germany.

{ddagger}Present address: Institut für Medizinische Mikrobiologie und Hygiene, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany.


   INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Porphyromonas gingivalis is widely considered as a major pathogen in the development of destructive periodontal diseases such as chronic or aggressive periodontitis. P. gingivalis is able to express a number of pathogenicity factors, including adhesins, LPS, capsule, collagenase and other proteases (Kato et al. 1992; Kuramitsu et al., 1995; Lamont & Jenkinson, 1998). P. gingivalis adheres to epithelial cells, red blood cells, collagen complexes and other bacteria, and has furthermore been found to invade host cells, e.g. epithelial cells (Cutler et al., 1995). Differences in the progression rate of an infection may be due to the different virulence of strains within a species. Therefore, typing methods have been applied for a number of bacterial species to better define pathogenic clones. DNA-based typing methods, such as restriction endonuclease analysis (REA), restriction fragment length polymorphism (RFLP), arbitrarily primed polymerase chain reaction (AP-PCR) and amplified fragment length polymorphism (AFLP) have been used for the characterization of P. gingivalis and provided evidence for significant intraspecific heterogeneity (Chen & Slots, 1994; Loos et al., 1990, 1993; Menard et al., 1992, 1994; Ozmeric et al., 2000).

By AP-PCR, 73 P. gingivalis strains could be divided into 23 or 45 genotypes, depending on the primers used. Using REA, it has been demonstrated that a single type generally colonized individuals and strains of different volunteers were genetically different (Loos et al., 1993; Menard et al., 1994). Analysis of 100 P. gingivalis strains from humans and animals revealed 78 different multilocus enzyme electrophoresis (MLEE) types (Loos et al., 1993). Using ribotyping, 25 isolates from six subjects could be divided into six clonal types, each of which was unique for every subject (Socransky & Martin, 1992). The consensus of these studies is that there is considerable heterogeneity among P. gingivalis isolates but the intra-individual heterogeneity is low. A couple of studies have been performed to relate clonal types with increased pathogenicity. In none of these studies could a clear association with a given clonal type and the periodontal status be made.

To better understand intraspecies variation, we performed multilocus sequence analysis with 10 chromosomal genes. This method, introduced in 1998, has since been used for phylogenetic analysis of many other bacterial pathogens such as Neisseria meningitidis, Campylobacter jejuni, Helicobacter pylori and Escherichia coli (Maiden et al., 1998; Suerbaum et al., 1998, 2001; Suerbaum, 2000; Falush et al., 2003). Molecular analysis of housekeeping genes has led to a better understanding of the mode of genetic variation within a bacterial species. Here, we apply this method in a slightly modified form to the periodontal pathogen P. gingivalis.


   METHODS
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Bacterial strains and growth conditions.
P. gingivalis isolates used in this study were from earlier investigations of plaque derived from patients with chronic(C) or aggressive(A) periodontitis in Würzburg (d165A, Eli33A, Eli47C, Eli55C, Eli60C, Eli80C, Eli85C, Eli88C and Eli90A) and Freiburg (Fr7A, Fr14A, Fr19A, Fr22A, Fr48A, GDR8A, GDR11C, GDR17C and PP118/10A), Germany (Wittstock et al., 1996, 2000; Wittstock, 1999). All isolates were from different individuals and were epidemiologically unrelated. P. gingivalis reference strain ATCC 53977 was kindly provided by H. Kuramitsu, Department of Oral Biology, State University of New York, Buffalo, USA. Isolates were stored at -80 °C in cryotubes containing 2 ml Rosenov broth, supplemented with 8 % dimethylsulfoxide (Wittstock, 1999). For propagation, bacteria were thawed, streaked onto HCB plates (Sifin) and incubated anaerobically (Anaerocult; Merck) for 7–10 days at 37 °C. For preparing HCB plates, 10 g peptone l-1, 5 g sodium chloride l-1, 5 g yeast extract l-1, 5 g meat extract l-1, 2 g glucose l-1, 0,3 g cysteine hydrochloride l-1 and 18 g agar l-1 were mixed, adjusted to pH 7·4, autoclaved and then supplemented with 5 mg haemin l-1, 1 mg vitamin K l-1 and 10 % (w/v) defibrinated sheep red blood cells.

PCR.
A single bacterial colony was suspended in 50 µl of a 0·7 % (w/v) sodium chloride solution and this suspension was subsequently diluted 1 : 100, again in 0·7 % sodium chloride. Five microlitres of this dilution were used as template for PCRs. PCRs were performed in MicroAmp reaction tubes (Applied Biosystems) in a GeneAmp System 9600 thermal cycler (Applied Biosystems) in a total volume of 50 µl, consisting of 5 µl bacterial suspension, 30 pmol each primer, 200 µM each dNTP, 5 µl 10-fold-concentrated Taq DNA polymerase synthesis buffer, 3 µl 25 mM MgCl2 stock solution and 2 units AmpliTaq DNA polymerase (Applied Biosystems). PCR primers and conditions are described in Table 1. Analysis of PCR products was performed by horizontal agarose gel electrophoresis in 0·7–2 % (w/v) agarose gels in Tris/Borate/EDTA (TBE) buffer. Gels were stained for 10 min in a solution of 1 µg ethidium bromide ml-1, washed for 10 min in deionized water and analysed and photographed under UV light (312 nm).


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Table 1. PCR primers and conditions used in this study

 
Purification of PCR products and nucleotide sequencing.
To obtain pure DNA for sequencing, the QIAquick PCR Purification Kit (Qiagen) was used according to the manufacturer's instructions.

PCR products were sequenced from both strands as described previously (Wittstock et al., 2000), using the same primers as applied for PCRs. Four microlitres of the purified PCR product were used as template for sequencing.

Selection and processing of target genes.
The 10 chromosomal P. gingivalis genes ef-tu (accession no. AB035462), ftsQ (accession no. D84504; Ansai et al., 1995), hagB (accession no. Z35494; Progulske-Fox et al., 1995), gpdxJ (accession no. X97228), pepO (accession no. AB010440; Awano et al., 1999), mcmA (accession no. L30136; Jackson et al., 1995), dnaK (AB015879; Yoshida et al., 1999), recA (accession no. AF064682; Fletcher et al., 1997), pga (accession no. X95938) and nah (accession no. X78979) were selected for the investigation described here. Since the genome sequence of P. gingivalis had not been published when this study was performed, we used genes that have been published in the framework of the Porphyromonas gingivalis genome project (http://www.pgingivalis.org/). We primarily selected housekeeping genes and other essential genes. We amplified internal fragments of approximately 500 bp of those genes (Table 1) and sequenced both strands. From these sequences we were able to use fragments between 310 and 420 bp for analyses.

Sequence analyses.
Sequence electropherograms were analysed with the Lasergene software package (DNAStar). DNA sequences were edited and aligned with Bio Edit, version 4.8.10 (http://jwbrown.mbio.ncsu.edu/BioEdit/bioedit.html) (Hall, 1999) and converted into MEGA and NEXUS files with START [Sequence Type Analysis and Recombinational Tests; keith.jolley{at}ceid.ox.ac.uk (http://outbreak.ceid.ox.ac.uk/software.htm)]. Phylogenetic trees were compiled with MEGA 2.1 (http://www.megasoftware.net/) using the unweighted pair group method with arithmetic averages (UPGMA) (Kumar et al., 1994). Genetic distances were calculated with CLUSTAL W included in Bio Edit (Thompson et al., 1994). Split decomposition analysis (Bandelt & Dress, 1992; Huson, 1998) was performed with SPLITSTREE 2.0 (http://bibiserv.techfak.uni-bielefeld.de/splits/). Homoplasy tests were performed with HOMOPLASY (Suerbaum et al., 1998). Numerical analysis of polymorphic sites was done with DNASP (Rozas & Rozas, 1999) and graphical analysis with HAPPLOT (http://www.shigatox.net/stec/programs/happlot/). A stand-alone UNIX version of LIAN 3.1 to calculate the standardized index of association () was kindly provided by Bernhard Haubold, Lion Bioscience AG, Heidelberg, Germany (Haubold et al., 1998). The program STEPHENS (http://www.shigatox.net/stec/programs/stephens) was used to detect clustered polymorphisms in the sequences analysed.


   RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Multilocus sequence analysis of P. gingivalis
To analyse the phylogenetic relationship of 19 epidemiological unrelated P. gingivalis isolates from patients with periodontitis, we sequenced short DNA fragments of 10 chromosomal genes (Table 1). For all loci, all 19 sequences could be aligned without gaps. Each allele of the 10 genes analysed was numbered successively in an ascending manner. The number and kind of sequence changes have not been taken into account for allele designation (Maiden et al., 1998). The combinations of allele numbers for all isolates are shown in Table 2 (rows). Each unique combination of allele numbers represents one sequence type.


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Table 2. Sequence types and numbers of alleles of P. gingivalis genes used in this study

 
The allele numbers for each gene and the total number of alleles for each gene found in the set of P. gingivalis isolates are shown in Table 2. There were between 4 (nah) and 15 (gpdxJ) alleles per locus, and for most loci, there were more than 10 alleles, indicating high variability of the P. gingivalis genes. Each allelic profile/sequence type occurred only once. There were no clusters correlating with geographical origin of the isolates or disease.

The allelic numbers were used to calculate a distance matrix. This matrix was converted into NEXUS format and graphically displayed with SPLITSTREE (Fig. 1). The relationship of P. gingivalis isolates based on multilocus analysis is depicted as a star-like structure with rays of different lengths. This star phylogeny is also consistent with a recombinational population structure and is consistent with the observation that each strain carries its own allelic combination. Isolates Eli47/GDR8 und Eli55/Fr19 are more closely related than the other isolates.



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Fig. 1. Split decomposition analysis of fragments of 10 P. gingivalis genes from 19 isolates.

 
Split decomposition analysis
Sequences were analysed for evidence of recombination by split decomposition analysis. The algorithm used in this software is able to display conflicting results in the phylogenetic descent of sequences. A tree-like structure is created when the descent is clonal, but an interconnecting network or a bush-like structure will appear when recombination plays a role in the evolutionary history of P. gingivalis genes. The results are shown in Fig. 2. The graphical display of these analyses showed substantial differences in the P. gingivalis genes analysed. The SPLITSTREE graphs obtained with recA, ftsQ and hagB present as network-like structures. This indicates the presence of homoplasies, probably evolved by intragenic recombination (Fig. 2). The SPLITSTREE graphs of the other genes, such as ef-tu, mcmA, gpdxJ, nah, dnaK and pepO, display a star- or bush-like structure consisting of a single origin in the centre of the graph, from which single branches radiate. For pga, an additional decentral edge was observed. The latter let us suggest that evolution of some of the P. gingivalis genes analysed has been initiated by a couple of parallel mutations, originating from one ancestor. The differences in the structure of the SPLITSTREE graphs can be explained most logically by recombination, because this process can lead to the assembly of genes with different evolutionary history within one strain.



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Fig. 2. SPLITSTREE graph of multilocus sequence analysis of 19 P. gingivalis isolates with 10 genes. The numbering refers to the numbering (strain no.) in Table 2.

 
Calculation of homoplasy ratios
The sets of sequences were also tested by the homoplasy test, which analyses the apparent homoplasies among informative, synonymous polymorphic sites. Homoplasies occur when the same site changes twice in the ancestry of a set of sequences. Basically, a parsimonious tree is constructed for a set of sequences and the number of observed homoplasies (obsh) is calculated. The expected number of homoplasies when the population is clonal is ‘exph and the expected number of homoplasies when recombination is so frequent that the population is in linkage equilibrium is sh’. With these values, the homoplasy ratio (H) can be calculated with the following equation: H=(obsh-exph)/(sh-exph) (for more details see Suerbaum et al., 1998; Maynard Smith & Smith, 1998). The frequency of apparent homoplasies, as measured by the homoplasy ratio, is an indicator of the frequency of recombination. The homoplasy ratio can vary between 0, indicating completely clonal descent by the accumulation of mutations and 1, indicating free recombination where all sequence polymorphisms are found repeatedly in independent sequences that are in different branches of a maximal parsimony tree (apparent homoplasies). The homoplasy test requires a sufficient number of informative sites to yield interpretable results. This was only the case for two out of the 10 fragments (dnaK and gpdxJ). Homoplasy tests were repeated 10 times and median values were H=0·272 for gpdxJ and H=0·375 for dnaK. These homoplasy ratios indicate an intermediate frequency of recombination when compared to the mean values determined for other bacteria, such as H. pylori (H=0·85; 3 genes), N. meningitidis (H=0·34; 11 genes) or Borrelia burgdorferi (H=0·06; 1 gene) (Suerbaum & Achtman, 1999). Homoplasy ratios determined for H. pylori are the highest currently known and indicate free recombination activity and a panmictic population structure. Although it has been shown that intra- and interspecific recombination occur frequently in N. meningitidis, homoplasy values are lower than for H. pylori. The very low value for B. burgdorferi points to a clonal population with only low recombination activity. The homoplasy ratios measured for two P. gingivalis genes as described here were in the same order of magnitude as those of N. meningitidis and clearly suggest recombination activity, at least for these genes.

Linkage equilibrium
Two loci are in linkage equilibrium if genotype frequencies at one locus are independent of genotype frequencies at a second locus, otherwise the two loci are in linkage disequilibrium. Linkage disequilibrium can arise from physical linkage, genetic drift and selection on multilocus genotypes. Linkage equilibrium is a situation that should exist in a population undisturbed by selection, migration, etc., in which all possible combinations of linked genes should be present at equal frequency. Recombination (crossover) shifts a population towards linkage equilibrium; selection shifts a population towards linkage disequilibrium.

Using the program LIAN, we tested the null hypothesis of linkage equilibrium for the P. gingivalis dataset. Linkage equilibrium is characterized by statistical independence of alleles at all loci of multilocus data. LIAN tests for this independent assortment by computing , which is a measure of the degree of linkage in multilocus datasets. is a function of the rate of recombination and is zero when all alleles are in linkage equilibrium. That means probably that the distribution of alleles occurs independently from each other. The of the P. gingivalis genes analysed was 0·0898. This low () value is indicative of extensive recombination. For comparison, the value of Campylobacter jejuni was 0·256 (Suerbaum et al., 2001). Simple IA values, which are not standardized, have been obtained by analysis of MLEE data of Neisseria gonorrhoeae, N. meningitidis and H. influenzae and were 0·004, 1·96 and 5·4, respectively (Maynard Smith et al., 1993). Using the equation for calculation of (described in the documentation for LIAN 3.1 and by Haubold et al., 1998) and the information on the number of loci given in that paper (Maynard Smith et al., 1993), we could calculate their values (adjusted to the number of loci investigated) to 0·005, 0·14 and 0·337, respectively. The value calculated for P. gingivalis is similar to that reported for N. gonorrhoeae and therefore supports our estimation that the genes investigated in P. gingivalis are close to linkage equilibrium.

Analysis of clustered polymorphisms
To get more evidence for the influence of recombination on the observed genetic heterogeneity of P. gingivalis genes, we investigated the alleles for clustered polymorphisms, a hallmark of intragenic recombination. Visual inspection of the distribution of polymorphic sites showed multiple examples where alleles appeared to be mosaics of different clusters of polymorphic sites. The statistical significance of clusters was analysed with the test described by Stephens (1985), implemented in the program STEPHENS by T. Whittam (available at http://www.shigatox.net/stec/programs/stephens/). The program classifies each polymorphic site by how it groups (or partitions) the sequences and determines the probability that the sites supporting a given partition are clustered. For our dataset, significant clustering of polymorphic sites was detected in hagB, pga, ftsQ, dnaK and ef-tu. The results of this analysis were concordant with the clusters detected by visual inspection of graphical outputs of HAPPLOT (Fig. 3).



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Fig. 3. Polymorphisms in the alleles of all 10 genes as visualized by HAPPLOT. Vertical bars display the sequence polymorphisms detected in all alleles included in this study.

 

   DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
The rate of periodontal disease progression shows great variability between individuals and the mean annual attachment level changes represent a Gaussian frequency distribution (Baelum et al., 1997a, b). This distribution pattern of the progression rate is responsible for the prevalence of the different disease severities found in adults. Current epidemiological data indicate that many adults have mild to moderate loss of periodontal attachment at some sites in their mouth (Brown & Loe, 1993; Sheiham, 1991, 1997). However, only a relatively small proportion, i.e. approximately 10–15 % of a population suffer from severe periodontal disease (Albandar & Kingman, 1999; Baelum et al., 1988; Pilot & Barmes, 1987).

The remarkable differences in progression and severity of periodontal disease have been linked to variability in the virulence of strains within the pathogenic species. Studies in animals and humans have provided evidence that certain strains of P. gingivalis are associated with periodontal disease, whereas other strains seem less virulent (Amano et al., 1999; Baker et al., 2000; Nakagawa et al., 2002). For example, it has been shown that two FimA genotypes (Type II and Type IV) of P. gingivalis were strongly associated with periodontal disease, whereas others were weakly or not associated with periodontal disease (Amano et al., 1999, 2000). Thus, the differentiation of disease-contributing strains by variations of the fimA gene has been considered an alternative approach for risk assessment and treatment planning of P. gingivalis-related periodontal diseases.

Molecular epidemiology attempts bacterial classification on the basis of natural genetic variation. During the last decades, molecular epidemiology has been used as an adjunct and increasingly as a replacement for classical methods.

Three types of population structures are known in bacteria: clonal, panmictic and epidemic. These types are represented by Salmonella enterica, N. gonorrhoeae and N. meningitidis, respectively. In a clonal species, genetic changes would result from the sequential accumulation of mutations after descent from a common ancestor. However, only a few species are truly clonal and horizontal gene transfer has occurred even in S. enterica (Maynard Smith et al., 1993). Panmictic populations may be so variable that identical strains are only found among isolates from direct contacts. Even panmictic populations can contain clonal groupings or geographical specialization. This was not the case in our study, but the number of isolates analysed here is too low to make any conclusions about the occurrence of such clonal groupings (Achtman, 2001).

The analysis of the population structure of P. gingivalis presented here shows a substantial extent of recombination in P. gingivalis. In a recent study, Frandsen et al. (2001) analysed four housekeeping genes from 57 P. gingivalis strains. They found 41 different sequence types. In that study, the IA was calculated as 0·143 for the 41 sequence types and 0·206 when all 57 isolates were included. When we use the equation for the calculation of with the information given in the paper of Frandsen et al. (2001), which includes the number of loci investigated, for all 57 isolates is 0·068 and therefore similar to the value calculated in this paper for P. gingivalis. The authors suggested a non-clonal population structure characterized by recombination (Frandsen et al., 2001). Our study, based on 10 chromosomal gene fragments from 19 P. gingivalis isolates confirms the importance of recombination in P. gingivalis by multiple population genetic methods.

Recombination has also been described to occur during the chronic colonization of H. pylori in the gastric mucosa (Falush et al., 2001). In this study, the authors could also show that the panmictic population structure of H. pylori may result from frequent recombination during mixed colonization by unrelated strains.

Where does recombination between P. gingivalis strains occur? Periodontal pockets of patients with periodontitis may be a favourable environment for horizontal gene transfer and recombination between P. gingivalis strains. Although it has been shown that P. gingivalis is able to receive foreign DNA by transformation in vitro (Yoshimoto et al., 1993), there are only a few studies available demonstrating indirect evidence for horizontal gene transfer in vivo (Hanley et al., 1999). Frequent recombination between strains is likely to facilitate the spread of favourable traits. Future experiments on recombination of periodontal pathogens in vivo may broaden our understanding of mechanisms and meaning of genetic variation in this important group of bacteria.


   ACKNOWLEDGEMENTS
 
This work was in part supported by the Federal Ministry of Education and Research (Fö. 01KS9604/0), and the Interdisciplinary Centre of Clinical Research, Münster (IZKF Project C18). We thank Barbara Plaschke, Würzburg, for skilful technical assistance. We also thank Bernhard Haubold, Lion Bioscience AG, Heidelberg, for helpful discussions.


   REFERENCES
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
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Received 28 January 2003; revised 2 May 2003; accepted 2 June 2003.



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