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J Bacteriol. May 2009; 191(9): 2934–2943.
Published online Feb 27, 2009. doi:  10.1128/JB.00892-08
PMCID: PMC2681804

A Stenotrophomonas maltophilia Multilocus Sequence Typing Scheme for Inferring Population Structure[down-pointing small open triangle]


Stenotrophomonas maltophilia is an opportunistic, highly resistant, and ubiquitous pathogen. Strains have been assigned to genogroups using amplified fragment length polymorphism. Hence, isolates of environmental and clinical origin predominate in different groups. A multilocus sequence typing (MLST) scheme was developed using a highly diverse selection of 70 strains of various ecological origins from seven countries on all continents including strains of the 10 previously defined genogroups. Sequence data were assigned to 54 sequence types (ST) based on seven loci. Indices of association for all isolates and clinical isolates of 2.498 and 2.562 indicated a significant linkage disequilibrium, as well as high congruence of tree topologies from different loci. Potential recombination events were detected in one-sixth of all ST. Calculation of the mean divergence between and within predicted clusters confirmed previously defined groups and revealed five additional groups. Consideration of the different ecological origins showed that 18 out of 31 respiratory tract isolates, including 12 out of 19 isolates from cystic fibrosis (CF) patients, belonged to genogroup 6. In contrast, 16 invasive strains isolated from blood cultures were distributed among nine different genogroups. Three genogroups contained isolates of strictly environmental origin that also featured high sequence distances to other genogroups, including the S. maltophilia type strain. On the basis of this MLST scheme, isolates can be assigned to the genogroups of this species in order to further scrutinize the population structure of this species and to unravel the uneven distribution of environmental and clinical isolates obtained from infected, colonized, or CF patients.

Stenotrophomonas maltophilia is ubiquitous in nature. It has, for instance, been isolated from the rhizosphere of various plants and animals (14, 27, 37). Due to its tolerance against cadmium and its ability to degrade xenobiotic compounds, it has been proposed for remediation of contaminated ground (9, 39). Increasingly, it is being isolated from immunosuppressed individuals and intensive care and cystic fibrosis (CF) patients and has been shown to be resistant to many antimicrobial agents (16, 17, 69). However, the role of this opportunistic pathogen as an innocent bystander or causative agent often remains unclear (30), and little is known about its virulence factors (20, 33).

Recently, novel Stenotrophomonas species were described: Stenotrophomonas nitritireducens sp. nov. (24), Stenotrophomonas acidaminiphila (3), Stenotrophomonas rhizophila (73), and Stenotrophomonas africana sp. nov. (21). However, the latter is a synonym of S. maltophilia (10).

Using amplified fragment length polymorphism (AFLP) fingerprinting and DNA-DNA hybridizations, remarkable diversity has been shown to exist among S. maltophilia isolates recovered from both the environment and human clinical samples. This species can be subdivided into 10 AFLP genomic groups (35) that comprise to various extents both clinical and environmental isolates. Similarly, different genomic groups of the genus Stenotrophomonas can be distinguished using restriction fragment length polymorphisms (RFLP) in the gyrB gene (11). Surprisingly, 36 out of 40 isolates from CF patients are grouped in just two clusters. However, no such differences were seen in other investigations using pulsed-field gel electrophoresis (PFGE) after DraI digestion, molecular typing by BOX-PCR, or temperature-gradient gel electrophoresis of 16S rRNA PCR fragments (7). Later DNA sequence analyses of the 16S rRNA revealed three clusters, with grouping of the strains according to their sources of isolation and signature sequences in the region V1, which distinguishes clinical from environmental isolates (44).

The objective of this study was to develop a multilocus sequence typing (MLST) scheme on the basis of a diverse strain collection comprising isolates from different ecological origins, continents, and DNA hybridization groups (35). We then employed this scheme to start initial analyses of the population structure of this species.

(This study was conducted by S. Kaiser in partial fulfillment of the requirements for a diploma thesis in biology from the Faculty of Biology, University of Freiburg, Freiburg, Germany, 2007.)


Stenotrophomonas spp. culture collection.

Sixty-seven different S. maltophilia isolates from five different collections were selected with the aim of covering as far as possible the full genetic breadth of this species with only a limited number of analyzed strains (Table (Table1).1). Apart from S. maltophilia ATCC 13637T (named 886_pat), type strains of three other species were included: S. africana ATCC 700475T, S. nitritireducens ATCC BAA 12T, and S. acidaminiphila ATCC 700916T. The strains were designated with a number and a suffix indicating species other than S. maltophilia and origin or sample type including patient (pat), conjunctivitis (conj), sputum, tracheal secretion (ts), respiratory tract specimen from CF patients, blood culture (bc), pus, cerebrospinal fluid (CSF), outbreaks (out) in intensive care units (ICU), hospital environment (hosp env), and environmental origin unrelated to a health care setting (env) (a complete list is given with Table Table11).

Properties of Stenotrophomonas spp. strains ordered by their genomic groups

First, 12 clinical strains were received from five different hospitals participating in the German project Surveillance of Antimicrobial Use and Antimicrobial Resistance in Intensive Care Units. Ten of these were pairs of isolates that had been isolated during different outbreaks lasting for at least 77 to 308 days and in each case had identical AFLPs (data not shown) (64).

Second, 19 strains were isolated from epidemiologically nonassociated CF patients in England (n = 13) and Germany (n = 6) and were obtained from T. Pitt, Laboratory of Hospital Infection, Health Protection Agency, Colindale, London, Great Britain, and from M. Hogardt at the German Reference Center for Cystic Fibrosis, Max von Pettenkofer-Institute, Munich.

Third, 13 strains were obtained from the Belgian Co-ordinated Collections of Microorganisms, Ghent University, including the type strains of the four species and one representative from each of the 10 genogroups (Laboratorium voor Microbiologie no. 958, 10853, 10871, 10873, 10874, 10879, 10991, 11089, 11108, 11114), as defined by L. Hauben et al. (35).

Fourth, 14 strains were provided by N. Foster, The University of Western Australia, Crawley, Australia, as a collection with different gyrB RFLPs analyzed as described previously (25).

Finally, 12 isolates of strictly environmental origin were obtained from G. Berg, Department of Environmental Biotechnology at the Graz University of Technology, Graz, Austria. These stemmed either from the rhizosphere of plants or from the sea and had no apparent anthropogenic origin.

In addition, data from the K279a and R551-3 genome sequencing projects were included for the purpose of sequence analysis in silico. These sequence data were produced by the S. maltophilia sequencing group at the Sanger Institute (12) and can be obtained at ftp://ftp.sanger.ac.uk/pub/pathogens/sma/ (release date, 10 Dec 2004) and from the U.S. Department of Energy Joint Genome Institute (http://genome.jgi-psf.org/finished_microbes/stema/stema.info.html).

Culture of isolates and preparation of DNA.

Bacterial strains were maintained at −70°C in defibrinated horse blood and cultured on 5% Columbia sheep blood agar. Species identification was confirmed biochemically by using the API 20 NE or Vitek Classic system (both from bioMérieux, Nürtingen, Germany) and 5′ end sequencing of the 16S rRNA gene of all strictly environmental isolates (54). Purified DNA was prepared by means of a Qiagen Blood Kit (Qiagen, Hilden, Germany).

Locus selection.

Several potential loci were identified using markers already successfully employed in MLST schemes developed for other species such as Pseudomonas aeruginosa, Pseudomonas syringae, Burkholderia pseudomallei, Burkholderia cepacia, and Acinteobacter baumannii (5, 6, 13, 29, 59). The available sequence data were used for BLAST analysis with data from the K279a genome sequencing project (2). These sequence data were produced by the S. maltophilia sequencing group at the Sanger Institute (12).

The entire putative coding sequences of the housekeeping genes were identified by use of the Artemis genome viewer and annotation tool (56). The seven genes finally selected for use with the MLST scheme were atpD, gapA, guaA, mutM, nuoD, ppsA, and recA.

Amplification and sequencing of loci.

Using the primer3 software tool (55), PCR primers were designed for the loci based on different regions of the putative coding sequences, which, as work progressed, revealed themselves to be comparatively conserved. The primer sequences are shown in Table Table2.2. Practical annealing temperatures (Tanns) of primer pairs were determined on a gradient cycler (FlexCycler; Analytik Jena, Jena, Germany). They were used both for sequencing and amplification.

Primers, Tanns used for amplification, and positions in the amplicons used for assigning allelic types

The PCR conditions were as follows: initial activation of the Taq DNA polymerase (Ampli Taq Gold; Applied Biosystems, Darmstadt, Germany) for 9 min at 95°C, followed by 30 cycles of 20 s of denaturation at 94°C, annealing for 1 min at the appropriate Tann, and extension for 50 s at 72°C (Table (Table2).2). The program ended with a 5-min fill-in step at 72°C. Two separately generated amplicons for forward and reverse sequencing were purified from unincorporated nucleotides using exonuclease I-phosphatase (USB, Staufen, Germany) according to the manufacturer's protocol. The purified template was quantified by using a Nanodrop ND-1000 spectral photometer (Peqlab Biotechnologie GmbH, Erlangen, Germany). The sequencing reaction was performed with 20 ng DNA and the BigDye Terminator Ready Reaction Mix (version 1.1; Applied Biosystems). Cycle sequencing with standard conditions was used for primers with a Tann of >60°C. In the case of lower Tann values, denaturation for 10 s at 96°C was followed by annealing at Tann for 10 s and subsequent elongation for 4 min at 60°C. Unincorporated dye terminators were removed by precipitation with absolute ethanol. The air-dried reaction product was resuspended in 20 μl of Hi-Di formamide and loaded, separated, and detected on an ABI Prism 310 genetic analyzer using POP-6 polymer and a 61-cm genetic analysis capillary (Applied Biosystems).

Allele and sequence type (ST) assignment.

The sequencing files were assembled from the resultant chromatograms with the Staden suite (version 1.7.0) of computer programs (66, 67). The database can be accessed at http://pubmlst.org/smaltophilia/.

Phylogenetic analysis.

For statistical analysis of allele profiles and sequence data, START2 was employed to calculate GC content, frequencies of alleles, number of variable sites, and the ratio of nonsynonymous to synonymous substitutions (dN/dS) (36). The index of association (IA) was calculated (63), and significance was proven by an observed variance greater than the maximum variance in 1,000 random trials (P < 0.001) (http://linux.mlst.net/link_dis/index.htm).

BURST (based upon related sequence types) analysis was done with the tool provided at the pubmlst site mentioned above with a group definition of profile match at four loci to any other member of the group.

Cluster analysis by the neighbor-joining (NJ) method with HKY85 model of DNA substitution and 200 bootstrap replications were done employing PAUP, version 4.0 b 10 (68). The value of similarity was calculated as 1 minus the corresponding distance value. For proof of separate sequence similarity clusters, the k parameter, which is the ratio of the between-group divergence to the mean of the within-group divergence levels (50), was calculated as described previously (8). A ratio above 2 indicates that the groups can be considered to be separate sequence similarity clusters (8).

The tree presentation of phylogenetic data was obtained with TreeView, version 1.6.6 (48), with the corresponding sequence data of Xanthomonas campestris pathovar campestris Xcc 8004 (GenBank accession number CP000050) for rooting as an outgroup (53).

Pearson correlations of DNA HKY85 distance matrices were tested for significance by use of the Mantel's test implemented in PopTools, version 3.0.6 (http://www.cse.csiro.au/poptools), according to a described algorithm (40).

The test of congruence was performed to compare the topology of the NJ trees from different loci as described previously (22, 57). The trees were constructed by use of the HKY85 model of DNA substitution, implemented in PAUP (23, 34). Trees were optimized for the transition-to-transversion ratio, alpha parameter, and branch lengths. For each gene, the log likelihood score (−ln L) and the log likelihood differences (Δ−ln L) between the corresponding values of the remaining six other loci were computed after optimization of values for the transition-to-transversion ratio, alpha parameter, and branch lengths. Two hundred random trees were computed for each gene and optimized again as described above. The Δ−ln L values between the random trees and the NJ tree of each gene were calculated. Trees revealing a Δ−ln L above the second lowest value of the 200 random trees, i.e., within the 99th percentile, were considered to be noncongruent.

Horizontal gene transfer (HGT) was investigated by use of multiple approaches to limit the risk of identifying false recombination events or overlooking the occurrence of true recombinations (51, 52). Calculation of recombination tests was performed with the RDP3Beta26 program (43) by applying the following algorithms: RDP (for recombination detection program) (41), Geneconv (47), Chimera (52), MaxChi (62), BootScan (42), and SiScan (28). The concatenated data (atpD, gapA, guaA, mutM, nuoD, ppsA, and recA) of all STs were imported into RDP in Fasta format. The following settings were used for all of the methods: (i) sequences were linear, (ii) sequences in the alignment were screened in triplets, and (iii) statistical significance was set to a P value of 0.001 with a Bonferoni correction for multiple comparisons. In Geneconv, the parameter GSCALE was set to 1. In MaxChi and Chimera, a sliding window was used, and the number of permutations was 1,000. Only recombination events detected by more than two methods were considered further.

Nucleotide sequence accession numbers.

The GenBank accession numbers for the sequences reported in this study are as follows: for atpD, EU983582 to EU983651; for gapA, EU983652 to EU983721; for guaA, EU983722 to EU983791; for mutM, EU983792 to EU983861; for nuoD, EU983862 to EU983931; for ppsA, EU983932 to EU984001; and for recA, EU984002 to EU984071.


Delineation of Stenotrophomonas spp.

Analysis of the HKY85-corrected distance matrix for the concatenated sequences from all seven loci revealed an average similarity (expressed as the percentage of 1 − the distance value) of 95.9% (± 0.03% standard error [SE]) for all S. maltophilia strains. The average similarity for the S. africana type strain in comparison to all S. maltophilia strains was higher at 96.3% (± 0.21% SE). In contrast, comparison of all S. maltophilia strains with the S. nitritireducens type strain and the S. acidaminiphila type strain revealed a lower similarity of 89.9% (± 0.06%) and 89.7% (± 0.05%). Thus, in all subsequent analyses, the S. africana strain was considered to be a member of the S. maltophilia species.

Allelic variation in S. maltophilia.

Sequence data analysis of all 70 S. maltophilia strains revealed 54 STs (Table (Table1).1). Comparison of isolates from five different outbreaks showed the stability of the allelic profile over at least 77 to 308 days (Table (Table1).1). It should be noted that outbreak isolates from two ICUs located in different German states belonged to the same ST 29. In the following analysis, data from all five subsequent outbreak isolates were omitted. Analysis of the data of the 54 STs revealed that the number of allele types ranged from 38 for mutM to 53 for guaA (Table (Table3).3). The percentages of variable sites ranged considerably from 11.9% for atpD to 37.0% for mutM. The Simpson's index of diversity was always ≥0.971, which is indicative of a highly discriminatory typing method. The low dN/dS ratios indicate the absence of a strong positive selection pressure at these loci and the suitability of these loci for population genetic studies.

Analysis of the seven loci in the S. maltophilia STs detected

Linkage disequilibrium.

In order to assess the clonality of S. maltophilia, the IA was calculated for different groups of isolates. This measure was significantly different from zero, which indicates a linkage disequilibrium, if all 65 strains or all 48 clinical isolates were considered (Table (Table4).4). The IA differed from zero in regard of all 54 STs, all 41 STs of clinical isolates, or all 17 environmental isolates but did not reach statistical significance in these analyses because the maximum variance in 1,000 random trials exceeded the observed variance.

IA calculated for different groups of S. maltophiliaa

Congruence of the different loci.

In order to compare the sequence similarities in all seven loci, HKY85 matrices were calculated and compared by Pearson correlation (see Table S1 in the supplemental material). Randomization of pairwise correlated matrices employing Mantel's test revealed significant correlation coefficients in all cases, i.e., above the 95.50% percentile of 1,000 permutations, except for the comparison of mutM and guaA. In all possible combinations, the mutM matrix displayed the lowest correlation coefficients, followed by values of the guaA matrix. The only exception was ppsA, which had the second-lowest coefficient in correlation with nuoD. This is one indication of marked differences in the topology of the mutM and guaA trees compared with the five other loci. Trees of the seven loci of all 54 STs can be depicted (see Figure S1 in the supplemental material).

To test for congruence using the maximum-likelihood approach, an unweighted pair group method with arithmetic mean dendrogram was constructed using allelic profiles and was subsequently truncated at a linkage distance of 0.55 so that 49 lineages were included. A single strain was selected at random to obtain a set of isolates distinctly related to each other. Trees revealing log-likelihood differences below the third lowest value of the 200 random trees, i.e., not within the 99th percentile, were considered congruent. This applied to the majority of the loci except for the guaA tree relative to mutM sequences and vice versa (see Table S2 in the supplemental material).

HGT detection.

The output of the HGT analyses performed using the RDP3 package is summarized in Table S3 in the supplemental material. The occurrence of a potential HGT event was accepted only if validated by at least three distinct methods and sustained by strong statistical support. This approach revealed four events, involving 9 of 54 STs. Three gene fragments (guaA, mutM, and nuoD) concatenated in MLST profiles were affected by HGT events. In two HGT events, breakpoints were located within a single locus; in two other events breakpoints were limited to pairs of genes arranged consecutively. Recombinant STs were found in strains belonging to the environmental groups 5, 8, 9, and 10. An analysis of alleles involved in single HGT events revealed that mut-4 was involved in all six STs involved in events 1 and 4.

Genogroups in S. maltophilia.

The BURST analysis for clonal complexes revealed three groups of triple locus variants comprised of (i) STs 17 and 30 belonging to genomic group 7, (ii) STs 1, 26, and 27 belonging to genomic group 6, and (iii) STs 2 and 43 also belonging to genomic group 6. All the strains of these STs were of clinical origin.

The NJ tree presentation was chosen (Fig. (Fig.1)1) for cluster analysis of all 70 S. maltophilia strains. The 10 different genogroups defined by Hauben et al. (35) could be delineated on the basis of all seven loci. We were able to use the same numbering of genogroups by including strains that had already been assigned in that previous study. Additional groups were given alphabetical designations. None of the other isolates investigated grouped together with strain 888_env, formerly assigned to AFLP genogroup 10. Sequence divergence between different genogroups and calculated k parameters are given in Tables S4 and S5 in the supplemental material. On average, sequence divergence between different genogroups was as high as 0.048 (± 0.002 SE), with the highest values ranking in the order of genogroups 8, A, 9, and 5. All the groups could be separated by a significant ratio of the between-group divergence to the mean of the within-group divergence above 2, except for the comparison of genogroup 2 with groups 3 to 6, B, D, and E. Moreover, five additional groups became apparent, which we designated alphabetically. S. africana belonged to group 4, along with four other S. maltophilia strains. Two isolates, 470_bc and 639_CF could not be assigned to any genogroup.

FIG. 1.
NJ tree based on the concatenated data for all seven loci of the 70 S. maltophilia strains. The isolates originated from blood culture, CF patients, tracheal secretions from outbreaks, sputum, pus, conjunctivitis, environmental specimens, hospital environment, ...

Twenty-two (44%) out of 50 clinical isolates originating from patients in seven German cities and four other countries were clustered in group 6, which consisted of clinical isolates only. Twelve (63%) out of 19 strains isolated from epidemiologically nonassociated CF patients also belonged to this group. Clinical invasive isolates originating from blood cultures or CSF were distributed more evenly among nine genogroups, with just 3 (18.7%) out of 19 strains belonging to genogroup 6. The three hospital environment strains were found in three different groups (2, D, and E), which also included clinical strains. Three genogroups, though containing a limited number of isolates, were exclusively comprised of strains of environmental origin: genogroups 5 (n = 4), 8 (n = 6), and 9 (n = 3).


In the past decades, there have been several changes in the taxonomy of Stenotrophomonas spp. (49). At present, this genus is comprised of nine species, five of which, S. dokdonensis, S. humi, S. koreensis, S. rhizophila, and S. terrae, were not available for the study presented here. The delineation of S. africana from S. maltophilia has been debated (10, 21). This work also confirms that S. africana is a synonym for S. maltophilia.

S. maltophilia can be isolated from a wide variety of environments and geographical regions and may occupy various niches such as soil, rhizosphere, water, and food (17). S. maltophilia has emerged in many hospitals as an important nosocomial pathogen, especially in immunocompromised patients (61). Although this species was previously considered to have limited pathogenicity, reports indicate that infection with the organism is associated with significant morbidity and mortality, particularly in severely compromised patients (46). Yet the clinical importance of this opportunistic pathogen as a mere colonizer or infectious agent often remains unresolved. There is an ongoing debate about the role of this species in later stages of CF. At any rate, isolation of S. maltophilia in CF patients tends to be associated with more advanced disease (15, 30, 32). Treatment of S. maltophilia infection is also complicated by its inherent resistance to many broad-spectrum agents, including carbapenems, which to a considerable extent are mediated by multidrug efflux pumps (1) and broad-spectrum beta-lactamases (4). The emerging resistance to trimethoprim/sulfametoxazole, which is one of the few remaining treatment options, is of major concern (69). Currently, little is known about the virulence factors of S. maltophilia, such as factors for adherence to plastics, an extracellular protease, and a phage-encoded zonula occludens-like toxin (19, 33, 72). More recently, a diffusible signal factor has been described that is assumed to control the expression of virulence and antimicrobial resistance as a cell-cell signaling factor by use of a two-component regulatory system (26).

Genotyping methods have been used successfully in the molecular epidemiology of S. maltophilia. Many typing approaches have revealed that this species is of high genodiversity. Genotyping of isolates from hospitalized CF and non-CF patients by PFGE or randomly amplified polymorphic DNA PCR revealed a high degree of diversity with changes in the strains consecutively isolated from CF patients (18, 38, 70, 71). A multilocus enzyme electrophoresis scheme has been proposed for investigations of hospital epidemiology (60). However, this scheme could not be used here, partly because it is based on markers such as peptidases or elastases, which cannot be assumed to be neutral for selection, and on other markers we could not retrieve unambiguously from the genome sequence databases.

There have been several attempts to delineate the genetic groups of related strains. S. maltophilia could be subdivided into three clusters by use of 16S rRNA gene sequence signatures in the V1 (positions 73 to 97) and V6 (positions 451 to 482) domains (44). A comparative investigation of clinical isolates revealed six 16S rRNA groups based on variable positions in the positions 41 to 109 and four so-called phylogenetic groups based on the smeD-smeT intergenic sequences (31). This region is assumed to be involved in regulation of the smeDEF multidrug efflux pump genes (58). The most extensive work was done by Hauben et al., who used AFLP to assign a highly diverse strain collection of different origins to 10 genomic groups. The results were in part confirmed by 16S rRNA gene sequencing and DNA-DNA hybridization experiments (35). However, 18 out of 107 investigated strains were nongroupable. In a different approach, nine different genomic groups of the genus Stenotrophomonas could be distinguished on the basis of RFLPs in the gyrB gene (11). Surprisingly, 36 out of 40 isolates from CF patients were grouped in just two clusters, group B and group C. In summary, on the basis of RFLP, there is increasing evidence of the existence of particular subgroups of different ecological origin and clinical importance within this bacterial species (11, 35). However, the impact of a few recombination events can be sufficient to obliterate the phylogenetic signal in the gene trees of many species (22). Thus, for future investigation of isolates, this study aimed to develop an MLST scheme to cluster different genotypes in phylogenetically meaningful groups.

The scheme was developed on the basis of a restricted set of carefully selected strains. This diverse strain collection included strains from 10 previously defined genogroups, type strains, and 17 isolates of nonanthropogenic origin. Also, for the purpose of sequence analysis, data were included from two genome sequencing projects of strains K279a and R551-3 even though changes in the sequence data of the latter strain were possible before final publication. However, grouping of the environmental isolate R551-3 together with other environmental isolates in genogroup 5 allowed use of these preliminary data to appear plausible although allelic types were unique in the data set of all seven loci investigated so far.

Despite the fact that the number of isolates investigated is still limited, analysis of the individual loci revealed a high degree of diversity, reflected by a high number of allele types and variable sites, as well as by a Simpson's index of diversity of at least 0.971 in all seven sequences. However, one should bear in mind that the strains had already been selected from different collections on the basis of the maximum diversity available to us. Furthermore, in contrast to MLST or single nucleotide polymorphism (SNP) analysis of core genome genes, it is possible that RFLP-based methods, like AFLP or PFGE, which also cover the entire accessory genome, give greater insight into epidemiological associations or pathoadaptive processes by gain or loss of genomic islands and virulence factors, as has been described for the recombinant species of P. aeruginosa, for instance (45).

Finally, the low dN/dS ratios indicate the absence of a strong positive selection pressure at the chosen loci and their suitability for population genetic studies.

Another distinct feature is the apparent difference in the tree topology of the different loci. Whereas most had a congruent topology, mutM and guaA differed, as demonstrated by the low correlation coefficient of the computed distance matrices. Such variations have been described within the genome of a species, for instance, in Haemophilus influenzae (22).

In a first approach, we attempted to identify clonal complexes with the established BURST algorithm. Despite the restricted and highly diverse strain collection, with a relaxed definition of at least four identical loci, we could already identify three complexes, two of which originated within genogroup 6, which included the largest number of isolates. These findings were also supported by the neighboring phylogenetic tree presentation (Fig. (Fig.1)1) of the genogroup 7 strains (isolates 889_conj and 909_bc) as well as group 6 strains (isolates 645_CF, 673_CF, 441_ts_out, 681_CF, and K279a).

There were three indications for clonality in this species. First, we found a high and significant Pearson correlation between the distance matrices calculated in the different loci except for mutM and guaA. One conceivable reason for this might be the large number of variable sites in both loci. However, two other loci with a similarly large number of variable sites (recA and ppsA) revealed high correlation coefficients. This fact constrains the argument of a low correlation due to a high number of SNPs, at least as the sole explanation. Of note, mutM and guaA were the most frequently affected loci in RDP analyses.

Second, for various subsets, calculation of the IA revealed values different from zero, which tested significant in regard of all 65 copy strain-cleared isolates and all 48 clinical isolates. The significant IA of all 65 isolates in comparison to a nonsignificant IA of all 54 STs could be in line with a clonal epidemic population structure. Consideration of all 41 STs of the clinical isolates resulted in an IA of 1.216. However, this was not significant in contrast to the IA of all 48 clinical isolates because the observed variance was smaller than the maximal trial variance. This observation could also be in accordance with a clonal epidemic population structure with the successful genomic group 6 or be due to a number of tested strains simply too small to reach statistical significance. The absence of significant evidence for a linkage disequilibrium in the 17 isolates of environmental origin with an IA of 2.911 can simply be due to the low number of isolates tested.

Finally, the statistical test of congruence using the maximum-likelihood approach, which in contrast to BURST analysis does not require a large MLST data set, pointed toward a clonal population structure, again with the exception of both the mutM and guaA loci.

However, based on today's species definition of S. maltophilia, there are conceivable limitations to the high overall linkage disequilibrium calculated in this study, particularly in consideration of the extensive sequence divergence of different genogroups, e.g., 5, 8, and 9 (see Table S4 in the supplemental material). Future studies could delineate further species within the boundaries of the species designated as belonging to S. maltophilia today. In this case, fixed differences between these new species and a low interspecies recombination rate could prove to be the reason for an apparently high overall linkage disequilibrium of what are considered to be S. maltophilia species today. However, it should be noted that we were also able to show a significant high IA for the clinical isolates including genogroup 6 with the species-defining type strain and other less distantly related genogroups.

Furthermore, data were scrutinized for putative recombination events by employing different detection algorithms in the RDP suite. Our analyses identified four independent events of HGT involving one-sixth of all analyzed ST profiles. Interestingly, all of these involved isolates of genogroups 5, 8, 9, and 10 with a large DNA sequence distance to the species-defining type strain ATCC 13637T (isolate 886_pat) in genogroup 6. These findings were corroborated by at least four independent methods for detection of the HGT. Therefore, it seems rather unlikely that the occurrence of true recombinations was misidentified or overlooked. Nevertheless, future investigations of considerably larger numbers of STs might drastically improve the statistical power of detection by the suite of methods applied here (51, 52).

In the end, we were able to confirm previously described genogroups and identify new ones. It is important to note that genogroups defined on the basis of DNA similarities can comprise strains with entirely different allelic profiles, which are already changed by introduction of one SNP in each locus and have a remaining sequence similarity of >99%. For instance, both genogroup 6 strains 325 and 396 differed in 31 SNPs out of 3,591 nucleotides, which varied from one SNP in gapA to 11 in the guaA locus. Despite entirely different allelic profiles, the strains have a sequence similarity of 99.14%. This MLST scheme provides some further evidence that the distribution of isolates from the nonanthropogenic environment, like the rhizosphere or water, apparently differs from that of humans. Like another analysis of previously described genogroups (35) containing isolates of predominantly (>75%) or entirely clinical origin (2, 6, and 7) or environmental origin (5, 8, and 9), the study presented here also found the same genogroups comprising exclusively isolates of the corresponding different ecological origins. This is reminiscent of niche separation of different ecotypes. Yet, to some extent, the small number of isolates (<10) assigned to the particular genogroups is a clear limitation of both studies. In addition, this MLST scheme confirmed a genetic relatedness among the majority of genotypes found in isolates from CF patients (11). In the previous study based on a gyrB RFLP, 90% of all the 40 CF isolates were grouped in just two clusters. However, the delineation in the study appeared to be rather fuzzy because group B corresponded to the AFLP groups 1 and 3, and group C included the AFLP groups 2, 4, 6, and 7 as well as the type strains of S. maltophilia, S. africana, and S. rhizophilia. More precisely, our study assigned 12 out of 19 isolates from epidemiological nonassociated CF patients to genogroup 6. The remaining CF isolates belonged to five other genogroups, and one could not be assigned to any of the groups. Of note, genogroup 6 included additional respiratory tract isolates from three different hospital outbreaks and the S. maltophilia type strain ATCC 13637T. In contrast, this typing scheme could not provide evidence for particularly virulent, i.e., invasive, genogroups. Sixteen isolates from blood cultures or CSF isolates were found among eight genogroups. One might speculate that these strains gave raise to invasive infections due mainly to the impaired immune response of the host, and thus there was no need for particular virulence factors in the pathogen. The association of genogroup 6 isolates with respiratory tract specimens and CF or ICU patients may lead to the hypothesis that these strains are particularly adapted to colonization of this environment or to interacting as a specialist with particular microbial consortia prevailing in this ecological niche. Similarly, 4 out of 10 respiratory tract isolates and 2 out of 3 CF isolates have previously been described as belonging to AFLP genogroup 6 (35).

We noted a high average sequence divergence of 0.048 (± 0.016 standard deviation) between different genogroups, with a range from 0.026 to 0.088, in the calculation of k parameters to assess the significance of genogroups identified in the tree presentation. Based on published 16S rRNA gene sequence data, the percentage of sequence similarities ranged from 91.6% to 99% (mean, 96.5%) (35), which is somewhat below the species definition, which usually proposes a 16S rRNA gene sequence similarity of >97% (65). However, in the absence of characteristic phenotypes and relatively high intergroup DNA-DNA hybridization values, the authors of the previous study refrained from terming the genogroups as separate species.

In conclusion, this MLST scheme for S. maltophilia presents a discriminatory typing method with stable markers appropriate for studying the population structure. Based on DNA similarities, S. africana belongs to the species S. maltophilia. MLST data confirmed the existence of previously defined genogroups and identified an additional five new genogroups. Thus, this work provides a sequence database and a method for assigning further isolates to already defined or new genogroups and for refining population structure analyses. Initial data analyses for inferring population structure provide additional evidence for a clonal rather than a recombinant structure. However, to corroborate these findings, a greater number of isolates must be investigated with this newly established scheme, especially isolates belonging to the environmental genogroups, which are apparently just distantly related to the S. maltophilia type strain. The predominance of clinical isolates, particularly in genogroup 6, requires further elucidation, as does the strict environmental origin of isolates in genogroups 5 and 8 to 10. Further taxonomic studies are required to assess whether S. maltophilia must be separated into several distinct species.

Supplementary Material

[Supplemental material]


We thank S. Brisse, Institut Pasteur, Paris, and P. Graumann, Institute of Microbiology, University of Freiburg, for helpful comments and Deborah Lawrie-Blum for assisting with the preparation of the manuscript. We are also grateful to their colleagues and the German study group Surveillance of Antimicrobial Use and Antimicrobial Resistance in Intensive Care Units for providing isolates.

We report no conflicts of interest relevant to this article.


[down-pointing small open triangle]Published ahead of print on 27 February 2009.

Supplemental material for this article may be found at http://jb.asm.org/.


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