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Research Article

Biotyping of Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates from France and Algeria Using MALDI-TOF MS

  • Meryem Berrazeg,

    Affiliations: Aix-Marseille Université, Unité de Recherche en Maladies Infectieuses et Tropicales Emergentes (URMITE), UM63, CNRS 7278, IRD 198, Inserm 1095, IHU Méditerranée Infection, Faculté de Médecine et de Pharmacie, Marseille, France, Laboratoire Antibiotiques, Antifongiques: Physico-Chimie, Synthèse et Activité Biologiques, Faculté des Sciences de la Nature, de la Vie, de la Terre et de l’Univers, Université Abou Bekr Belkaid, Tlemcen, Algérie

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  • Seydina M. Diene,

    Affiliation: Aix-Marseille Université, Unité de Recherche en Maladies Infectieuses et Tropicales Emergentes (URMITE), UM63, CNRS 7278, IRD 198, Inserm 1095, IHU Méditerranée Infection, Faculté de Médecine et de Pharmacie, Marseille, France

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  • Mourad Drissi,

    Affiliation: Laboratoire Antibiotiques, Antifongiques: Physico-Chimie, Synthèse et Activité Biologiques, Faculté des Sciences de la Nature, de la Vie, de la Terre et de l’Univers, Université Abou Bekr Belkaid, Tlemcen, Algérie

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  • Marie Kempf,

    Affiliations: Aix-Marseille Université, Unité de Recherche en Maladies Infectieuses et Tropicales Emergentes (URMITE), UM63, CNRS 7278, IRD 198, Inserm 1095, IHU Méditerranée Infection, Faculté de Médecine et de Pharmacie, Marseille, France, Laboratoire de Bactériologie, Institut de Biologie en santé – PBH, CHU, Angers, France

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  • Hervé Richet,

    Affiliation: Aix-Marseille Université, Unité de Recherche en Maladies Infectieuses et Tropicales Emergentes (URMITE), UM63, CNRS 7278, IRD 198, Inserm 1095, IHU Méditerranée Infection, Faculté de Médecine et de Pharmacie, Marseille, France

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  • Luce Landraud,

    Affiliation: Laboratoire de Bactériologie, Institut de Biologie en santé – PBH, CHU, Angers, France

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  • Jean-Marc Rolain mail

    jean-marc.rolain@univ-amu.fr

    Affiliation: Aix-Marseille Université, Unité de Recherche en Maladies Infectieuses et Tropicales Emergentes (URMITE), UM63, CNRS 7278, IRD 198, Inserm 1095, IHU Méditerranée Infection, Faculté de Médecine et de Pharmacie, Marseille, France

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  • Published: April 19, 2013
  • DOI: 10.1371/journal.pone.0061428

Abstract

Background

Klebsiella pneumoniae is one of the most important pathogens responsible for nosocomial outbreaks worldwide. Epidemiological analyses are useful in determining the extent of an outbreak and in elucidating the sources and the spread of infections. The aim of this study was to investigate the epidemiological spread of K. pneumoniae strains using a MALDI-TOF MS approach.

Methods

Five hundred and thirty-five strains of K. pneumoniae were collected between January 2008 and March 2011 from hospitals in France and Algeria and were identified using MALDI-TOF. Antibiotic resistance patterns were investigated. Clinical and epidemiological data were recorded in an Excel file, including clustering obtained from the MSP dendrogram, and were analyzed using PASW Statistics software.

Results

Antibiotic susceptibility and phenotypic tests of the 535 isolates showed the presence of six resistance profiles distributed unequally between the two countries. The MSP dendrogram revealed five distinct clusters according to an arbitrary cut-off at the distance level of 500. Data mining analysis of the five clusters showed that K. pneumoniae strains isolated in Algerian hospitals were significantly associated with respiratory infections and the ESBL phenotype, whereas those from French hospitals were significantly associated with urinary tract infections and the wild-type phenotype.

Conclusions

MALDI-TOF was found to be a promising tool to identify and differentiate between K. pneumoniae strains according to their phenotypic properties and their epidemiological distribution. This is the first time that MALDI-TOF has been used as a rapid tool for typing K. pneumoniae clinical isolates.

Introduction

Klebsiella pneumoniae are ubiquitous in nature and have two common habitats; one is the environment, including surface water, sewage, soils and plants [1], and the other is mammalian mucosal surfaces [2]. In humans, K. pneumoniae can be present in the intestinal tract, nasopharynx, and on the skin [3]. It is one of the most common Gram-negative bacteria encountered by clinicians worldwide as a cause of infections in humans [4] and is responsible for outbreaks due to the propagation of different clones associated with opportunistic infections in individuals with impaired immune defenses, such as diabetics, alcoholics and hospitalized patients with indwelling devices [3]. In the hospital environment, the principal reservoirs for K. pneumoniae transmission are blood products, contaminated medical equipment, the gastrointestinal and respiratory tracts of patients and the hands of hospital personnel [5]. The hospital-acquired infections caused by this organism mainly include pneumonia, septicemia, urinary tract infections and soft tissue infections [6].

Increased K. pneumoniae infections are also associated with an increase in multidrug-resistant (MDR) strains, especially those producing extended-spectrum beta-lactamases (ESBLs) [7] associated with the prior use of antibiotics, particularly the cephalosporins [8]. Furthermore, several carbapenemase-encoding genes have been described in K. pneumoniae species, including class A beta-lactamase KPC, class B beta-lactamases NDM, IMP and VIM, and class D beta-lactamase OXA-48 [9]. The hospital epidemiology of these infections is often complex because multiple clonal strains causing focal outbreaks may co-exist with sporadic strains that also have a reservoir in the community [10]. Infections caused by multidrug-resistant K. pneumoniae strains have been associated with adverse clinical outcomes, including increased mortality, prolonged hospital stays and increased economic costs [11].

Therefore, epidemiological typing is useful in determining the extent of an outbreak and in investigating the sources, the reservoir and the spread of bacterial infections. Various methods, including protein profiling by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and DNA profiling by multilocus sequence typing (MLST), restriction fragment length polymorphism (RFLP) and pulsed field gel electrophoresis (PFGE), have been used for the epidemiological typing of K. pneumoniae isolates [12][14]. However, most of these methodologies are time consuming, laborious, require special skills and are unsuitable for use in routine clinical laboratories [15], [16]. In recent years, several reports have shown the feasibility of using matrix-assisted laser desorption ionization (MALDI)-time of flight (TOF) mass spectrometry (MS) to rapidly identify microorganisms [17]. There are only a few studies that have evaluated this method as a rapid tool to classify bacterial species at the strain level [18], [19]. However, there are some recent examples of the use of MALDI-TOF MS for the rapid identification and typing of a limited number of clinical strains, such as Streptococcus pyogenes [20] or Klebsiella pneumoniae [19].

Here, we report the evaluation of MALDI-TOF MS as a rapid and powerful tool for determining the epidemiological distribution of a large series of K. pneumoniae clinical strains of different origins from patients with various infectious syndromes and the correlation between the pathotypes, geographic locations and clonalities of these strains using MALDI-TOF MS and data-mining approaches.

Results

Clinical Data

The mean age of the infected patients was 53 years and was similar when comparing patients hospitalized in Algerian hospitals (53 years: range 12 days to 84 years) and those from French hospitals (53.1 years: range 1 day to 97 years). The male/female ratio was 1/1. Among the 535 isolates, 172 (32.1%) were retrieved from intensive care units (ICUs), 160 (29.9%) from infectious diseases wards, 40 (7.5%) from surgical wards, 38 (7.1%) from emergency wards, 22 (4.1%) from pediatric wards, 21 (3.9%) from trauma wards, 21 (3.9%) from neurology wards, 16 (3.0%) from internal medicine wards, 14 (2.6%) from nephrology wards, 14 (2.6%) from gastroenterology wards, and 17 (3.2%) from gynecology, cardiology, or geriatric medicine wards. K. pneumoniae strains were isolated from various clinical samples that originated as follows: 189 (35.3%) from urine, 113 (21.1%) from tracheal aspirate, 73 (13.6%) from pus, 67 (12.5%) from blood culture, 36 (6.7%) from rectal swab and 57 (10.6%) from other different clinical specimens.

Bacterial Identification

Using MALDI-TOF MS, all K. pneumoniae strains (100%) were identified with score values >1.9 using the Bruker Biotyper software.

Antibacterial Susceptibility Testing of Klebsiella pneumoniae Isolates

As shown in Table 1, the overall resistances (i.e., resistant+intermediate percentage (R+I%)) to various antibiotics were as follows: ampicillin, amoxicillin and ticarcillin (100%), amoxicillin/clavulanic acid (55.5%), cefalotin (53.8%), cefoxitin (7.7%), ceftazidime (51.2%), cefotaxime and ceftriaxone (51.0%), gentamicin (48.2%), tobramycin (72.4%), amikacin (26.4%), ciprofloxacin (45.1%), and trimethoprim/sulfamethoxazole (45.7%). All isolates were sensitive to imipenem and colistin. The K. pneumoniae strains from Algeria had significantly higher percentages of resistance to antibiotics compared to those from France (p<0.0001). The prevalence of resistance to third generation cephalosporin in Algeria was 88.7% compared with France (26.5%).

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Table 1. Origin and repartition of strains used in this study.

doi:10.1371/journal.pone.0061428.t001

Six resistance phenotypes were found based on susceptibility to beta-lactams (Table 2). These comprised 240 (44.8%) wild-type, 7 (1.3%) inhibitor-resistant TEM penicillinase, 11 (2.0%) high-level penicillinase, 3 (0.6%) cephalosporinase, 240 (44.8%) extended-spectrum beta-lactamase (ESBL), and 34 (6.3%) ESBL associated with cephalosporinase. The difference in antibiotic resistance rate was significant between Algerian and French strains (p<0.0001). Overall, the percentage of non-ESBL-producing strains was higher in France (73.1%) than in Algeria (11.3%), while that of ESBL-producing strains was higher in Algeria (88.6%) compared with France (26.8%).

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Table 2. Antibiotic susceptibility testing results of Klebsiella pneumoniae strains.

doi:10.1371/journal.pone.0061428.t002

Data Mining Analysis of Klebsiella pneumoniae MSP Dendrogram

The MSP dendrogram revealed five distinct clusters according to an arbitrary cut-off at the distance level of 500 (Figure 1). Data mining analysis of the five clusters using PASW 17.0 software showed that K. pneumoniae strains isolated in Algerian hospitals (Tlemcen, Sidi Bel Abbes, Oran, Annaba) were significantly associated with respiratory tract infections and ESBL phenotype in the fifth cluster (p<0.0001), whereas K. pneumoniae strains isolated in Marseille hospitals were significantly associated with urinary tract infections and wild type phenotype in the first, the second and the fourth clusters (p<0.0001). K. pneumoniae strains isolated in Angers hospitals were associated with urinary tract infections and wild type phenotype in the fourth cluster (p<0.0001). Conversely, K. pneumoniae strains isolated in Nice hospital were associated with blood cultures and pus samples and wild type phenotype in the third cluster (p<0.0001) (Table 3). All the details of the distribution of K. pneumoniae strains into the five clusters according to the dendrogram are given in supplementary Table S1. Interestingly, clustering the strains according to the arbitrary distance levels of 180 and 100 significantly clustered strains from the same hospital into the same cluster. At the distance level of 180, 34 distinct clusters were identified, and at the distance level of 100, 52 distinct clusters were identified (Table S2). For example, at the distance level of 100, one cluster containing 15 strains showed that all of them were from Marseille hospital, a second cluster contained three strains (all from Nice hospital), and a third cluster contained ten strains (eight out of ten were from Angers hospital (p<0.0001)).

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Figure 1. Geographic location of hospitals implicated in this study.

1: Tlemcen, 2: Sidi Bel Abbes, 3: Oran, 4: Annaba, 5: Marseille, 6: Nice, 7: Angers.

doi:10.1371/journal.pone.0061428.g001
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Table 3. Antibacterial resistance phenotypes of Klebsiella pneumoniae strains.

doi:10.1371/journal.pone.0061428.t003

The monthly distribution of K. pneumoniae strains for the January 2008 - March 2011 period was determined by PASW 17.0 software according to clusters. Strains from Angers and Algerian hospitals were significantly correlated with winter (January-March) (p<0.0001), whereas, strains from Marseille hospital were associated with spring (April-June) (p<0.0001). No seasonal variation was associated with strains from Nice hospital.

Multilocus Sequence Typing

The MLST allelic profile of all strains distributed in the five clusters is presented in Table 4. From the 28 tested strains, we found 24 different sequence types (ST) (Figure 2). No relationship was observed between ST and the clinical and epidemiological data.

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Figure 2. Mean spectra projection (MSP) dendrogram of Klebsiella pneumoniae strains generated by BIOTYPER software (version 2; Bruker Daltonics).

ESBL: Extended-spectrum beta-lactamase, WT: wild type.

doi:10.1371/journal.pone.0061428.g002
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Table 4. Data mining analysis of Klebsiella pneumoniae MSP dendrogram.

doi:10.1371/journal.pone.0061428.t004

Discussion

K. pneumoniae is an important pathogen with a complex pan-genome responsible for serious nosocomial infections, especially in intensive care units (ICUs) and in wards for surgery, emergency, neurology, pediatrics, and neonatology [5]. More concerning has been the emergence and increase in the isolation rate of ESBL-producing K. pneumoniae worldwide [4], which frequently possess resistance factors to other classes of antibiotics, notably aminoglycosides, fluoroquinolones and trimethoprim/sulfamethoxazole [21]. In our study, we confirmed that there is an increase in the number of multidrug-resistant K. pneumoniae strains with a considerable variation between the two countries studied. The antibiotic resistance rate in France (26.8%) was lower than that in Algeria (88.6%). In comparing our results with those of the Mystic study (1997–2003) [7] and another surveillance trial study (2004–2009) [22], we notice a worldwide north-south gradient evolution of ESBL production rate in K. pneumoniae strains with 12.3–12.8% in North America, 16.7% in Northern Europe, 24.4% in Southern Europe, 33.8% in the Middle East, 28.2–35.6% in Asia-Pacific, 45.5–51.9% in South America and 54.9% in Africa.

The high infection occurrence of K. pneumoniae, both ESBL-producing and non-producing strains, in Algerian and French hospitals pushed us to examine the epidemiology of these strains, which are considered to have a complex pan-genome containing plastic genome repertoires that differentiate strains according to their geographical locations, pathotypes, ecotypes and resistance phenotypes [23]. MALDI-TOF MS was successfully used as a tool for biotyping because we found specific clusters that were significantly associated with particular phenotypes from different clinical and geographical sources and from different seasons. This result is seemingly supported by Trevino et al., with a series of only 13 clinical isolates of K. pneumoniae [24]. By increasing the number of strains collected, our dendrogram became more refined, not only by country but also by hospital. This result could be of clinical importance for unknown pathogenic isolates, whose geographical sources could be detected rapidly using MALDI-TOF MS.

It is recognized that the distribution of bacteria may be related to geographical patterns, such as climatic zones and movement of human populations, and can provide information about pathogen evolution and transmission [25], [26]. The spatial distribution of bacterial pathogens can be considered at different levels; in a hospital setting, this may indicate nosocomial transmission, but in a community setting, the simultaneous appearance of an identical phenotype in widely dispersed locations may be a warning of an outbreak [15]. Our data also suggested that rates of K. pneumoniae infections varied seasonally and were significantly associated with periods of isolation that were due to changes in temperature and humidity. Several characteristics of this species that were previously described support our findings, which show that temperature and dew point were both linearly predictive of increasing rates of K. pneumoniae clinical isolates [27], [28]. Furthermore, most of the MALDI-TOF MS spectra are composed of well-conserved proteins with housekeeping functions that are minimally affected by environmental conditions and thus are considered to be optimal for the proteomic typing of bacteria, which is considerably different from genetic typing [29], [30].

By comparing the clusters obtained from the MSP dendrogram (Figure 1) with the ST distribution in the minimal spanning tree (MST) of K. pneumoniae strains (Figure 2), we noted that no correlation was observed for these two types of analysis. Moreover, no relationship was observed between ST, clinical and epidemiological data. These results suggest that the two approaches, comprising the MSP and MLST analyses, highlight two different bacterial aspects. Indeed, MLST analysis based on the conserved bacterial genes (the house-keeping genes) classifies bacteria according to their core genome, which represents less than 10% of the genome [31], while 90% of the genome is composed of “accessory genes” (mobile genetic elements) that can be lost or acquired by lateral gene transfer (LGT), and that is mainly responsible for the bacterial phenotype [23], [32]. Therefore, this core genome does not represent the majority of expressed proteins, in contrast to the MSP dendrogram analysis, which is based on the functional and expressed proteins of whole cells that is more representative of the global phenotype [33]. Therefore, these two approaches could be complementary to the extent that the MST approach, based on the analysis of the core genome, allows us to classify bacteria according to their conserved genes independent of their “accessory genes,” which can affect phenotypes and bacterial classification.

Many researchers have used molecular methods, particularly PFGE and MLST, to distinguish K. pneumoniae clinical isolates in order to understand transmission patterns and to aid in the management of these infections [34]. In 2008, a comparative study between these two methods found that PFGE is appropriate to discriminate among epidemiologically unrelated strains and appears more suitable for short-term epidemiology, while MLST is appropriate for strain phylogeny and large-scale epidemiology [35]. Furthermore, these molecular methods are costly and time-consuming to obtain results and introduce delays when attempting to limit the spread of K. pneumoniae clones [36]. For instance, only a few hours are required to obtain results by MALDI-TOF MS, whereas several days are necessary to collect MLST data [37]. In addition, the cost of MALDI-TOF MS instrumentation is comparable to that of a sequencing machine, but running costs and consumables are considerably lower than for these methods [38]. Barbuddhe et al. have used MALDI-TOF MS to accurately identify different Listeria species and correctly classified all L. monocytogenes serotypes in agreement with PFGE [38], and recently Wang et al. identified and classified Streptococcus pyogenes into clusters with MALDI-TOF MS [20].

Thus, MALDI-TOF MS combined with a statistical classification strategy is appropriate for studies of local epidemiology and global population structure when compared to a local database from clinical strains. It is a powerful epidemiological method and is sufficiently reproducible and sensitive enough to rapidly survey the evolution of existing or emerging phenotypes with reduced financial and human costs [39].

Conclusion

We believe that our preliminary results should be expanded and confirmed with more strains obtained from different countries. To the best of our knowledge, this is the first study that analyzes the epidemiology of a large series of K. pneumoniae clinical isolates using MALDI-TOF MS application. We suggest the creation of a local database that is updated regularly to survey for the presence of abnormal phenotypes at the strain level. If a particular phenotype is detected, a real time genome sequencing approach could then be performed to investigate the origin and specific features of the strain. We believe that this methodology could be used routinely in clinical microbiology laboratories as a surveillance tool for hospital epidemiology studies to prevent outbreaks and dissemination of pathogens in hospital settings.

Materials and Methods

Bacterial Strains

A total of 535 non redundant clinical strains of K. pneumoniae, isolated from different clinical samples, were collected during a period of 39 weeks, between January 2008 and March 2011. All of these were collected from hospitals in France and Algeria (Figure 3): Marseille hospitals, France (n = 170 strains); Angers hospital, France (n = 100 strains); Nice hospital, France (n = 54 strains), Tlemcen hospital, Algeria (n = 73 strains); Oran hospital, Algeria (n = 92 strains); Sidi Bel Abbes hospital, Algeria (n = 28 strains) and Annaba hospital, Algeria (n = 18 strains). The clinical sources of the different strains are noted in Table 5.

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Figure 3. Minimal spanning tree (MST) of K. pneumoniae isolates, showing relationships between STs, compared with clusters obtained from the dendrogram generated by BIOTYPER software.

doi:10.1371/journal.pone.0061428.g003
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Table 5. MLST allelic profile of Klebsiella pneumoniae clinical isolates distributed in the five clusters.

doi:10.1371/journal.pone.0061428.t005

Klebsiella pneumoniae Identification using MALDI-TOF MS

Isolates were plated on Trypticase Soy Agar (BioMerieux) and incubated for 24 h at 37°C. One single colony from each isolate was deposited on a MALDI-TOF MTP 384 target plate (Bruker Daltonics, Bremen, Germany) in four replicates to minimize random effects. Two microliters of matrix solution (saturated α-cyano-4-hydroxycinnamic acid, 50% acetonitrile, 2.5% trifluoroacetic acid) were then added and allowed to co-crystallize with the sample. Analysis was performed in a MALDI-TOF MS spectrometer (337 nm) (Autoflex; Bruker Daltonics) with FLEX control software (Bruker Daltonics). Ions were accelerated in the positive ion mode with an accelerating voltage of 20 kV. The pulsed extraction of ions was optimized for 1000 Da. The software employed, Bruker Biotyper 2.0 (Bruker Daltonics), automatically acquired spectra with fuzzy control of the laser intensity and analyzed them by standard pattern matching against the spectra of 2881 species used as reference data. After comparing the unknown spectra with all reference spectra in the database, the log scores were ranked. Values of >1.9 were required for secure identification at the species level, and values between 1.9 and 1.7 were required for secure identification at the genus level.

Clustering of MALDI-TOF Spectra

A consensus spectrum was produced, and an MSP dendrogram was constructed using the correlation distance measure with the average linkage algorithm setting of the Biotyper 2.0 software. Clusters were then detailed and analyzed according to arbitrary distance levels at 500, 180 and 100.

Antibiotic Susceptibility and Synergy Testing

Antibiotic susceptibility testing was performed using the disk diffusion method on Mueller-Hinton medium as per the guidelines of the French Society of Microbiology (www.sfm.asso.fr). E. coli ATCC 25922 was used as a quality control strain. The antimicrobial disks tested were as follows: ampicillin (10 µg), amoxicillin (25 µg), amoxicillin/clavulanic acid (20/10 µg), ticarcillin (75 µg), cefalotin (30 µg), cefoxitin (30 µg), ceftazidime (30 µg), cefotaxime (30 µg), ceftriaxone (30 µg), imipenem (10 µg), gentamicin (15 µg), amikacin (30 µg), tobramycin (10 µg), ciprofloxacin (5 µg), trimethoprim/sulfamethoxazole (1,25/23,75 µg), and colistin (50 µg). ESBL production was detected using the method of combined antibiotic disks as previously described [40].

Antibiotic Resistance Phenotypic Classification of K. pneumoniae Strains based on Beta-Lactamine Compounds

We considered a wild type phenotype as a strain that confer resistance to aminopenicillins, carboxypenicillins and to ureidopenicillins. The high level penicillinase phenotype was presented by a high penicillinase activity responsible for resistance to aminopenicillins and their inhibitors, to carboxypenicillins, to ureidopenicillins, and to first generation cephalosporins (1 GC). The inhibitor-resistant TEM penicillinase phenotype included resistance to aminopenicillins, carboxypenicillins, and ureidopenicillins. It was distinguished by resistance to aminopenicillins and carbocxypenicillins associated to the beta-lactam inhibitors. 1 GC generally retain their activity. The cephalosporinase phenotype corresponded to a marked resistance to penicillins, 1 GC, 2 GC, and to at least one 3 GC. The extended spectrum B-lactamase phenotype includes resistance to penicillins and cephalosporins except cephamycins. The resistance to 3 GC and 4 GC was more or less pronounced depending on the enzymes and the strains.

Statistical Analysis

Clinical and epidemiological data were recorded in an Excel file (Microsoft, Redmond, WA, USA), including the clustering obtained using the MSP dendrogram generated by the Biotyper software (version 2; Bruker Daltonics), and were analyzed using PASW Statistics software version 17.0 (SPSS Inc., Chicago, IL, USA). Dependent variable series were analyzed using Expert Modeler, which automatically generates the best-fitting model. The chi-square analysis was used also to compare proportions using the same software, and P values <0.05 were considered to be statistically significant. Statistical analyses were conducted using Epi Info version 6 (Centers for Disease Control and Prevention, Atlanta, GA, USA).

Multilocus Sequence Typing

Randomly, 28 strains distributed in the five clusters were selected to perform the full MLST as described previously using seven housekeeping genes, including gapA, infB, mdh, pgi, phoE, rpoB and tonB [34]. The MLST database for K. pneumoniae can be found at http://www.pasteur.fr. Based on the allelic profiles, the evolutionary relationship between isolates was assessed by the minimal spanning tree (MST) algorithm in Bionumerics (Applied Maths, NV St-Martens-Latem, Belgium). A stringent definition of 6/7 shared alleles was used to define clonal complexes (single locus variants only).

Supporting Information

Table S1.

Distribution of Klebsiella pneumoniae strains according to the dendrogram generated by BIOTYPER software (version 2, Bruker Daltonics) at the distance level of 500.

doi:10.1371/journal.pone.0061428.s001

(DOCX)

Table S2.

Comparison of the clusters obtained according to different cut-offs at 500, 180 and 100 of the dendrogram generated by BIOTYPER software (version 2, Bruker Daltonics).

doi:10.1371/journal.pone.0061428.s002

(DOCX)

Author Contributions

Conceived and designed the experiments: JMR LL MD MK. Performed the experiments: MB SMD HR. Analyzed the data: MB SMD MD MK HR LL JMR. Contributed reagents/materials/analysis tools: MB SMD HR LL. Wrote the paper: MB SMD HR JMR.

References

  1. 1. Kumar A, Chakraborti S, Joshi P, Chakrabarti P, Chakraborty R (2011) A multiple antibiotic and serum resistant oligotrophic strain, Klebsiella pneumoniae MB45 having novel dfrA30, is sensitive to ZnO QDs. Ann Clin Microbiol Antimicrob 10: 19 1476-0711-10-19 [pii];10.1186/1476-0711-10-19 [doi].
  2. 2. Brisse S, Duijkeren E (2005) Identification and antimicrobial susceptibility of 100 Klebsiella animal clinical isolates. Vet Microbiol 105: 307–312 S0378-1135(04)00436-5 [pii];10.1016/j.vetmic.2004.11.010 [doi].
  3. 3. EARS-Net (2010) Antimicrobial resistance surveillance in Europe. Annual report of the European Antimicrobial Resistance Surveillance Network (EARS-Net) 2009.
  4. 4. Khan E, Ejaz M, Zafar A, Jabeen K, Shakoor S, et al. (2010) Increased isolation of ESBL producing Klebsiella pneumoniae with emergence of carbapenem resistant isolates in Pakistan: report from a tertiary care hospital. J Pak Med Assoc 60: 186–190.
  5. 5. Podschun R, Ullmann U (1998) Klebsiella spp. as nosocomial pathogens: epidemiology, taxonomy, typing methods, and pathogenicity factors. Clin Microbiol Rev 11: 589–603.
  6. 6. Decre D, Verdet C, Emirian A, Le GT, Petit JC, et al. (2011) Emerging severe and fatal infections due to Klebsiella pneumoniae in two university hospitals in France. J Clin Microbiol 49: 3012–3014 JCM.00676-11 [pii];10.1128/JCM.00676-11 [doi].
  7. 7. Turner PJ (2005) Extended-spectrum beta-lactamases. Clin Infect Dis 41 Suppl 4S273–S275 CID36165 [pii];10.1086/430789 [doi].
  8. 8. Mosqueda-Gomez JL, Montano-Loza A, Rolon AL, Cervantes C, Bobadilla-del-Valle JM, et al. (2008) Molecular epidemiology and risk factors of bloodstream infections caused by extended-spectrum beta-lactamase-producing Klebsiella pneumoniae A case-control study. Int J Infect Dis 12: 653–659 S1201-9712(08)00084-2 [pii];10.1016/j.ijid.2008.03.008 [doi].
  9. 9. Nordmann P, Carrer A (2010) [Carbapenemases in enterobacteriaceae]. Arch Pediatr 17 Suppl 4S154–S162 S0929-693X(10)70918-0 [pii];10.1016/S0929-693X(10)70918-0 [doi].
  10. 10. Falagas ME, Karageorgopoulos DE (2009) Extended-spectrum beta-lactamase-producing organisms. J Hosp Infect 73: 345–354 S0195-6701(09)00177-7 [pii];10.1016/j.jhin.2009.02.021 [doi].
  11. 11. Schwaber MJ, Carmeli Y (2007) Mortality and delay in effective therapy associated with extended-spectrum beta-lactamase production in Enterobacteriaceae bacteraemia: a systematic review and meta-analysis. J Antimicrob Chemother 60: 913–920 dkm318 [pii];10.1093/jac/dkm318 [doi].
  12. 12. Malik A, Hasani SE, Shahid M, Khan HM, Ahmad AJ (2003) Nosocomial Klebsiella infection in neonates in a tertiary care hospital: protein profile by SDS-page and klebocin typing as epidemiological markers. Indian J Med Microbiol 21: 82–86.
  13. 13. de Melo ME, Cabral AB, Maciel MA, da Silveira VM, de Souza Lopes AC (2011) Phylogenetic groups among Klebsiella pneumoniae isolates from Brazil: relationship with antimicrobial resistance and origin. Curr Microbiol 62: 1596–1601 10.1007/s00284-011-9903-7 [doi].
  14. 14. Siu LK, Fung CP, Chang FY, Lee N, Yeh KM, et al. (2011) Molecular Typing and Virulence Analysis of Serotype K1 Klebsiella pneumoniae Strains Isolated from Liver Abscess Patients and Stool Samples from Noninfectious Subjects in Hong Kong, Singapore, and Taiwan. J Clin Microbiol 49: 3761–3765 JCM.00977-11 [pii];10.1128/JCM.00977-11 [doi].
  15. 15. Baker S, Hanage WP, Holt KE (2010) Navigating the future of bacterial molecular epidemiology. Curr Opin Microbiol 13: 640–645 S1369-5274(10)00116-5 [pii];10.1016/j.mib.2010.08.002 [doi].
  16. 16. Barbuddhe SB, Maier T, Schwarz G, Kostrzewa M, Hof H, et al. (2008) Rapid identification and typing of listeria species by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Appl Environ Microbiol 74: 5402–5407 AEM.02689-07 [pii];10.1128/AEM.02689-07 [doi].
  17. 17. Sauer S, Kliem M (2010) Mass spectrometry tools for the classification and identification of bacteria. Nat Rev Microbiol 8: 74–82 nrmicro2243 [pii];10.1038/nrmicro2243 [doi].
  18. 18. Seng P, Rolain JM, Fournier PE, La Scola B, Drancourt M, et al. (2010) MALDI-TOF-mass spectrometry applications in clinical microbiology. Future Microbiol 5: 1733–1754 10.2217/fmb.10.127 [doi].
  19. 19. Murray PR (2010) Matrix-assisted laser desorption ionization time-of-flight mass spectrometry: usefulness for taxonomy and epidemiology. Clin Microbiol Infect 16: 1626–1630 CLM3364 [pii];10.1111/j.1469-0691.2010.03364.x [doi].
  20. 20. Wang J, Zhou N, Xu B, Hao H, Kang L, et al. (2012) Identification and Cluster Analysis of Streptococcus pyogenes by MALDI-TOF Mass Spectrometry. PLoS One 7: e47152 10.1371/journal.pone.0047152 [doi];PONE-D-12-13659 [pii].
  21. 21. Briales A, Rodriguez-Martinez JM, Velasco C, de Alba PD, Rodriguez-Bano J et al. (2012) Prevalence of plasmid-mediated quinolone resistance determinants qnr and aac(6′)-Ib-cr in Escherichia coli and Klebsiella pneumoniae producing extended-spectrum beta-lactamases in Spain. Int J Antimicrob Agents. S0924-8579(12)00028-3 [pii];10.1016/j.ijantimicag.2011.12.009 [doi].
  22. 22. Bertrand X, Dowzicky MJ (2012) Antimicrobial susceptibility among gram-negative isolates collected from intensive care units in North America, Europe, the Asia-Pacific Rim, Latin America, the Middle East, and Africa between 2004 and 2009 as part of the Tigecycline Evaluation and Surveillance Trial. Clin Ther 34: 124–137 S0149-2918(11)00775-2 [pii];10.1016/j.clinthera.2011.11.023 [doi].
  23. 23. Diene SM, Merhej V, Henry M, El FA, Roux V et al. (2012) The rhizome of the multidrug-resistant Enterobacter aerogenes genome reveals how new “killer bugs” are created because of a sympatric lifestyle. Mol Biol Evol. mss236 [pii];10.1093/molbev/mss236 [doi].
  24. 24. Trevino M, Navarro D, Barbeito G, Garcia-Riestra C, Crespo C, et al. (2011) Molecular and epidemiological analysis of nosocomial carbapenem-resistant Klebsiella spp. using repetitive extragenic palindromic-polymerase chain reaction and matrix-assisted laser desorption/ionization-time of flight. Microb Drug Resist 17: 433–442 10.1089/mdr.2010.0182 [doi].
  25. 25. Leimkugel J, Hodgson A, Forgor AA, Pfluger V, Dangy JP, et al. (2007) Clonal waves of Neisseria colonisation and disease in the African meningitis belt: eight- year longitudinal study in northern Ghana. PLoS Med 4: e101 06-PLME-RA-0377R3 [pii];10.1371/journal.pmed.0040101 [doi].
  26. 26. Vesaratchavest M, Tumapa S, Day NP, Wuthiekanun V, Chierakul W, et al. (2006) Nonrandom distribution of Burkholderia pseudomallei clones in relation to geographical location and virulence. J Clin Microbiol 44: 2553–2557 44/7/2553 [pii];10.1128/JCM.00629-06 [doi].
  27. 27. Anderson DJ, Richet H, Chen LF, Spelman DW, Hung YJ, et al. (2008) Seasonal variation in Klebsiella pneumoniae bloodstream infection on 4 continents. J Infect Dis 197: 752–756 10.1086/527486 [doi].
  28. 28. Okada S, Gordon DM (2001) Host and geographical factors influence the thermal niche of enteric bacteria isolated from native Australian mammals. Mol Ecol 10: 2499–2513. 1384 [pii].
  29. 29. Croxatto A, Prod’hom G, Greub G (2012) Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol Rev 36: 380–407 10.1111/j.1574-6976.2011.00298.x [doi].
  30. 30. Ryzhov V, Fenselau C (2001) Characterization of the protein subset desorbed by MALDI from whole bacterial cells. Anal Chem 73: 746–750.
  31. 31. Dieckmann R, Helmuth R, Erhard M, Malorny B (2008) Rapid classification and identification of salmonellae at the species and subspecies levels by whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry. Appl Environ Microbiol 74: 7767–7778 AEM.01402-08 [pii];`10.1128/AEM.01402-08 [doi].
  32. 32. Lukjancenko O, Wassenaar TM, Ussery DW (2010) Comparison of 61 sequenced Escherichia coli genomes. Microb Ecol 60: 708–720 10.1007/s00248-010-9717-3 [doi].
  33. 33. Yip TT, Hutchens TW (1992) Mapping and sequence-specific identification of phosphopeptides in unfractionated protein digest mixtures by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. FEBS Lett 308: 149–153. 0014–5793(92)81264-M [pii].
  34. 34. Diancourt L, Passet V, Verhoef J, Grimont PA, Brisse S (2005) Multilocus sequence typing of Klebsiella pneumoniae nosocomial isolates. J Clin Microbiol 43: 4178–4182 43/8/4178 [pii];10.1128/JCM.43.8.4178-4182.2005 [doi].
  35. 35. Vimont S, Mnif B, Fevre C, Brisse S (2008) Comparison of PFGE and multilocus sequence typing for analysis of Klebsiella pneumoniae isolates. J Med Microbiol 57: 1308–1310 57/10/1308 [pii];10.1099/jmm.0.2008/003798-0 [doi].
  36. 36. Fujinami Y, Kikkawa HS, Kurosaki Y, Sakurada K, Yoshino M, et al. (2011) Rapid discrimination of Legionella by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Microbiol Res 166: 77–86 S0944-5013(10)00028-5 [pii];10.1016/j.micres.2010.02.005 [doi].
  37. 37. Ilina EN, Borovskaya AD, Malakhova MM, Vereshchagin VA, Kubanova AA, et al. (2009) Direct bacterial profiling by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry for identification of pathogenic Neisseria. J Mol Diagn 11: 75–86 S1525-1578(10)60212-7 [pii];10.2353/jmoldx.2009.080079 [doi].
  38. 38. Barbuddhe SB, Maier T, Schwarz G, Kostrzewa M, Hof H, et al. (2008) Rapid identification and typing of listeria species by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Appl Environ Microbiol 74: 5402–5407 AEM.02689-07 [pii];10.1128/AEM.02689-07 [doi].
  39. 39. Drancourt M (2010) Detection of microorganisms in blood specimens using matrix-assisted laser desorption ionization time-of-flight mass spectrometry: a review. Clin Microbiol Infect 16: 1620–1625 CLM3290 [pii];10.1111/j.1469-0691.2010.03290.x [doi].
  40. 40. Jarlier V, Nicolas MH, Fournier G, Philippon A (1988) Extended broad-spectrum beta-lactamases conferring transferable resistance to newer beta-lactam agents in Enterobacteriaceae: hospital prevalence and susceptibility patterns. Rev Infect Dis 10: 867–878.