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Food Microbiology 27 (2010) 101–108

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Food Microbiology

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Amplified Fragment Length Polymorphism and Multi-Locus SequenceTyping for high-resolution genotyping of Listeria monocytogenesfrom foods and the environment

Antonio Parisi a,*, Laura Latorre a, Giovanni Normanno b, Angela Miccolupo a,Rosa Fraccalvieri a, Vanessa Lorusso b, Gianfranco Santagada a

a Experimental Zooprophylactic Institute of Apulia and Basilicata, Foggia, Italyb Department of Health and Animal Welfare, Faculty of Veterinary Medicine, Valenzano (BA), Italy

a r t i c l e i n f o

Article history:Received 29 June 2009Received in revised form31 August 2009Accepted 3 September 2009Available online 10 September 2009

Keywords:Listeria monocytogenesMolecular typingDNA fingerprintingAmplified Fragment Length PolymorphismMulti-Locus Sequence Typing

* Corresponding author at: Istituto Zooprofilatticodella Basilicata, V. Chiancolla n.1, 70017 Putigna0804057858; fax: þ39 0804057753.

E-mail address: parisi.izs@tin.it (A. Parisi).

0740-0020/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.fm.2009.09.001

a b s t r a c t

Standardized tools for typing Listeria monocytogenes isolates are required in epidemiological surveysinvestigating food-borne disease outbreaks and in the food-processing environment to identify thesources of contamination and routes by which the organisms are spread. In this survey Amplified FragmentLength Polymorphism (AFLP) and Multi-Locus Sequence Typing (MLST) have been used to study 103L. monocytogenes isolates from food and environmental sources. A total of 62 AFLP types and 66 MLSTSequence Types were identified. AFLP and MLST produced similar results in terms of discriminating power.The Discrimination Index calculated for the two techniques was 0.976 for AFLP and 0.972 for MLST.These values were appreciably higher compared to serotyping (0.739). A good congruence was observedbetween AFLP and MLST. The present study demonstrated that AFLP and MLST subtyping are suitable toolsfor studying the epidemiology of L. monocytogenes. The great advantage of MLST over AFLP and othermolecular typing methods based on fragment fingerprinting lies in the unambiguity of sequence datawhile AFLP is less costly and highly processive. In conclusion the two methods can be perfectly integratedfor high-resolution genotyping of L. monocytogenes.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Listeria monocytogenes is the causative agent of listeriosis,a severe food-borne disease associated with a high case fatality rate(Farber and Peterkin,1991). L. monocytogenes can cause both invasiveand non-invasive infections. Invasive listeriosis is a severe diseasemainly associated with groups of people specifically at risk (unborninfants, neonates, immunocompromised individuals), whereas mildnon-invasive infections can also occur in healthy persons (Crum,2002). Infection by L. monocytogenes in pregnant woman may causeabortion and stillbirth (Farber and Peterkin, 1991).

L. monocytogenes is widely present in nature and may be foundin the soil, cultivated and uncultivated fields, forests, and in sewageand aquatic environments (Low and Donachie, 1997; Weis andSeeliger, 1975). Healthy carriage of L. monocytogenes has also beendescribed in a variety of animal species as well as in humans (Low

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and Donachie, 1997). The association of L. monocytogenes withseveral large food-borne disease outbreaks suggests that contam-inated foods, including meat, dairy, vegetable and fish products,may be the primary source of the organism.

Control of food-borne bacterial pathogens requires the identifi-cation of their sources and routes of transmission. Source tracking ofL. monocytogenes has proved to be difficult as it is ubiquitous in theenvironment and also because cases are generally sporadic andoutbreaks are uncommon. Standardized tools for the identificationand typing of L. monocytogenes isolates are required in epidemio-logical surveys investigating food-borne disease outbreaks (i.e.when comparing clinical and food isolates) and in the food-pro-cessing environment to identify the sources of contaminationand routes by which the organisms are spread. Serotyping as well asphage typing can be used as a routine methods, but additionalbacterial typing techniques are often applied to further discriminateisolates. Recently, several molecular methods such as Multi-LocusEnzyme Electrophoresis (MLEE) (Fenlon et al., 1995; Harvey andGilmour, 1994), ribotyping (Aarnisalo et al., 2003; Borucki et al.,2004; De Cesare et al., 2001; Lukinmaa et al., 2004; Manfreda et al.,2005), plasmid profiling (Fistrovici and Collins-Thompson, 1990),

Table 1List of the oligonucleotides used in the Amplified Fragment Length Polymorphismprocedure.

Hind III adapters 50-CTCGTAGACTGCGTAAA-30

50-AGCTGGTACGCAGTC-30

Hha I adapters 50-GACGATGAGTCCTGATCG-30

50-ATCAGGACTCATCG-30

Hind III pre-selective 50-GACTGCGTACCAGCTT-30

Hha I pre-selective 50-GATGAGTCCTGATCGC-30

Hind III selective 50FAM-GACTGCGTACCAGCTTA-30

Hha I selective 50-GATGAGTCCTGATCGCA-30

A. Parisi et al. / Food Microbiology 27 (2010) 101–108102

Random Amplified Polymorphic DNA (RAPD) (Cocolin et al., 2005),Pulsed Field Gel Electrophoresis (PFGE) (Aarnisalo et al., 2003; Autioet al., 2003; Brosch et al., 1996; Fistrovici and Collins-Thompson,1990; Graves and Swaminathan, 2001; Lukinmaa et al., 2004;Margolles et al., 1998), Amplified Fragment Length Polymorphism(AFLP) (Aarts et al.,1999; Autio et al., 2003; Guerra et al., 2002; Keto-Timonen et al., 2003; Mikasova et al., 2005; Ripabelli et al., 2000;Vogel et al., 2004), Multi-Locus Sequence Typing (MLST) (Reva-zishvili et al., 2004; Salcedo et al., 2003), Multi-Virulence-LocusSequence Typing (MVLST) (Zhang et al., 2004), and Multi-LocusVariable number of tandem repeat Analysis (MLVA) (Murphy et al.,2007) have been used to characterize L. monocytogenes isolates.

AFLP is a procedure which combines the power of PCR with theinformativeness of restriction enzyme analysis. It is a useful methodfor microbial genotyping (Savelkoul et al., 1999; Vos et al., 1995).Briefly, genomic DNA is fragmented using two restriction enzymes.All fragments obtained are ligated to double-stranded oligonucleo-tide adapters complementary to the sequence of restriction sites.After ligation, fragments are amplified by two PCR amplificationswith primers complementary to the adapters and accurately sizedby capillary electrophoresis.

AFLP fingerprinting has been shown to have potential for identi-fication and for high-resolution differentiation of genetically relatedbacterial isolates (Duim et al., 2001). In previous studies AFLP provedto be a highly reproducible method with good discrimination capa-bilities providing better results than other methods (serotyping, RAPD,PFGE, PCR-REA) for L. monocytogenes genotyping (Aarts et al., 1999;Keto-Timonen et al., 2003; Mikasova et al., 2005; Vogel et al., 2004).

Multi-Locus Sequence Typing (MLST) is a sequence-based typingmethod which analyses the nucleotide variations in seven lociselected among the housekeeping genes spread in the bacterialchromosome. Used recently for typing meningococci (Maiden et al.,1998), several MLST schemes have been rapidly developed formany other bacterial pathogens (www.mlst.net), including L. mon-ocytogenes (Salcedo et al., 2003). The different sequences areassigned as alleles and the alleles at the seven loci provide an allelicprofile or sequence type (ST). MLST is currently one of the mostrobust tools for investigating the global epidemiology of microbialpopulations (Enright and Spratt, 1999).

In this study we compared an AFLP fingerprinting procedureand MLST to characterize 103 L. monocytogenes isolates from foodsand environmental sources.

2. Methods

2.1. Bacterial isolates, identification, serotyping and DNA isolation

A total of 103 L. monocytogenes isolates, from different envi-ronmental and food sources, having no epidemiological correlation,were analyzed. The isolates were collected during two differentsurveys, one regarding the occurrence of L. monocytogenes in a totalof 5788 samples of different foods marketed in two Southern Italianregions (Apulia and Basilicata) over a 12 year period (1993–2004)(Latorre et al., 2007), and the other focussing on the occurrence ofListeria spp. in the rural and urban environments (data not pub-lished). In addition, isolates deriving from monitoring activities inmanufacturing plants were included. The isolates were groupedinto nine categories based on the source of isolation: backwater(n ¼ 3), cured fish (n ¼ 4), dairy products (n ¼ 19), environmentalswabs (n ¼ 9), mammalian stools (n ¼ 5), meat products (53),ready-to-eat products (n ¼ 5), soil (n ¼ 3), vegetables (n ¼ 2). Allisolates were identified using morphological and biochemicalcharacteristics including: gram stain, mobility at 25 �C on motilityagar, catalase and oxidase tests, fermentation of xylose and rham-nose, hemolysis test on sheep blood agar plates, CAMP test against

Staphylococcus aureus and Rhodococcus equi, and, finally, using theAPI Listeria (BioMerieux, Marcy l’Etoile – France). Serotyping wasdone using DENKA-SEIKEN kit (Tokio, Japan) as previouslydescribed (Ueda et al., 2002).

Genomic DNA was extracted from each isolate using GFX genomicblood DNA purification kit (Amersham, Piscataway, NJ, USA). DNAconcentrations were determined spectrophotometrically by UV-spectrophotometer – DU 640 (Beckman, Fullerton, CA, USA) andadjusted to 10 ng/ml.

2.2. Amplified Fragment Length Polymorphism analysis

A preliminary screening compared two AFLP procedures usingdifferent enzyme combinations to analyze ten L. monocytogenesisolates belonging to four different serotypes: 1/2a (n ¼ 4), 1/2b(n ¼ 3), 1/2c (n ¼ 2), 4b/4e (n ¼ 1). An AFLP analysis commercial kitwas used for the enzymes Eco RI and Mse I following the manu-facturer’s instructions (AFLP� Microbial Fingerprinting – AppliedBiosystem, Foster City, CA, USA). The selective primer combination,(Eco RI þ A) and (Mse I þ C), was chosen as previously described(Aarts et al., 1999). An in-home standardized protocol (describedbelow) was used for Hind III and Hha I. All the four possibleselective primer combinations, using (Hind III þ A) or (Hind III þ T)for the first enzyme, and (Hha I þ A) or (Hha I þ T) for the second,were tested. Based on the results obtained from the preliminarytrials (number and distribution of the peaks, efficacy of PCRamplification and cost of single test) (data not shown) the followingprotocol was selected.

Briefly, 20 ng of chromosomal DNA was digested with 5 U ofHind III and 5 U of Hha I (New England Biolabs, Ipswich, MA, USA)and simultaneously ligated to 4 pmol Hind III and 40 pmol Hha Iadapters (Table 1) in a total volume of 14 ml containing 0.5 ml of1 mg/ml bovine serum albumin, 1 ml of 0.5 M NaCl, 1 U T4 DNAligase (New England Biolabs), 1.4 ml 10� T4 DNA ligase buffer withATP (New England Biolabs). After incubation at 37 �C for 2 h, themix were diluited with 186 ml of double distilled water. A pre-selective PCR was carried out in a total volume of 20 ml containing4 ml of diluited restriction ligation mix, 5 pmol of Hind III pre-selective primer, 20 pmol of Hha I pre-selective primer (Table 1),0.2 mM of each dNTPs, 1 U HotMaster Taq (Eppendorf, Hamburg,Germany), 2 ml of 10� HotMaster Taq Buffer (Eppendorf). Pre-selective amplification was performed for 20 cycles with thefollowing cycle profile: initial denaturation step 2 min at 94 �C, 20 sat 94 �C, 30 s at 56 �C and a 2 min extension step at 72 �C. The PCRproducts were diluited with 180 ml of Milli Q grade water and 2 mlwere subjected to a new PCR step in a total volume of 20 ml con-taining 2.5 pmol of fluorescently labelled FAM-Hind IIIþ A selectiveprimer, 10 pmol of Hha I þ A selective primer (Table 1), 0.2 mM ofeach dNTP’s, 1 U HotMaster Taq (Eppendorf), 2 ml of 10� HotMasterTaq Buffer (Eppendorf). After an initial denaturation step of 2 min at94 �C, selective amplification was performed for 30 cycles with thefollowing cycle profile: 20 s at 94 �C, 30 s at 66 �C for the first cycle,a touchdown protocol was applied in the next nine cycles, the

A. Parisi et al. / Food Microbiology 27 (2010) 101–108 103

temperature was decreased by 1 �C at each cycle, finally in the last20 cycles the annealing temperature was 56 �C, and a 2 minextension step at 72 �C. This was followed by a final extension at60 �C for 30 min. The PCR was carried out on a 9700 GeneAmp PCRsystem (Applied Biosystem). DNA from L. monocytogenes ATCC19115, as an internal control and double distilled water as a nega-tive control were included in every AFLP session.

In order to test AFLP reproducibility, the genomic DNA of fiveisolates was extracted in duplicate and each DNA preparation wassubjected to AFLP analysis for three times. Six runs for each isolatewere separately analyzed as described below.

2.3. AFLP banding patterns analysis

The selective-PCR products of each isolate were separated on anABI PRISM 3130 genetic analyzer (Applied Biosystem) with GenescanLIZ 500 (Applied Biosystem) as the internal size standard. Fragmentssize was determined with the Genemapper 4.0 software (AppliedBiosystem). The AFLP fragments detected in the 75–500 bp size rangewere considered for numerical analysis. Genemapper-processed datafiles containing bacterial AFLP profiles were imported into Bionu-merics 5.0 software (Applied Maths, Saint-Martens-Latem, Belgium).Normalized AFLP profiles were compared using the Dice correlationcoefficient and clustered by the Unweighted Pair Group Method withArithmetic mean (UPGMA).

Two cut-off similarity values were fixed: isolates showing anAFLP Similarity Level (S.L.) >90% were grouped in the same ‘‘AFLPgroup’’ that was identified by a number, while an S.L.>95% groupedthe isolates in the same ‘‘AFLP type’’ assuming that they wereclosely related genetically. The different AFLP types were identifiedby an alphanumerical code consisting of the number of the relativegroup and a letter (1A, 1B, 1C etc.).

2.4. Multi-Locus Sequence Typing

The PCR amplifications were performed using previousdescribed primers (Salcedo et al., 2003) with the exception of theldh gene for which the primers suggested from a modified MLSTscheme (www.pasteur.fr/mlst) were used (Ragon et al., 2008).

PCRs were carried out in 25 ml reaction volumes containing10 pmol of each primer, 0.2 mM of each dNTP’s, 1 U HotMaster Taq(Eppendorf), 2.5 ml of 10� HotMaster Taq Buffer (Eppendorf). Thereaction conditions were an initial denaturation at 94 �C for 2 min,followed by 25 cycles at 94 �C for 3000, 52 �C for 3000 and 72 �Cfor 1 min. The PCR products were purified using Montage� PCR filterunits (Millipore, Milan, Italy). Sequence reactions were carried outusing BigDye 3.1 Ready reaction mix (Applied Biosystems) accord-ing to the manufacturer’s instructions. The sequenced productswere separated with a 3130 Genetic Analyzer (Applied Biosystems).Sequences were imported and assembled with the Bionumerics5.0 software (Applied Maths). Alleles and ST were assigned bysubmitting the DNA sequences to the Listeria MLST database at thePasteur Institute France (www.pasteur.fr/mlst). Sequence TypeAnalysis and Recombinational Tests software (S.T.A.R.T. ver.2;http://pubmlst.org/software/analysis/start2) was used to calculatethe G þ C content and perform recombination and selection (dn/ds)tests. Sawyer’s tests were relied on to provide statistical evidence ofrecombinational exchanges of the sequences analyzed (Sawyer,1989). Linkage analysis was carried out by using the index of asso-ciation (IA) as defined previously (Jolley et al., 2001). A comparisonof ST’s was performed by the minimum spanning tree algorithm inthe Bionumerics 5.0 software (Applied Maths). Strains were grou-ped into clonal complexes, defined as groups of profiles differing byno more than one gene from at least one other profile of the group(Feil, 2004) using the entire Listeria MLST database.

2.5. Statistical analysis

Simpson’s index of diversity (DI) was calculated as describedelsewhere (Hunter and Gaston, 1988) to compare the discrimina-tory power of serotyping, AFLP and MLST. The same index was usedto assess the variability of the sequence of each MLST locus.

3. Results

3.1. Amplified Fragment Length Polymorphism analysis

The isolates belonged to different serotypes: 1/2a (n ¼ 44), 1/2b(n¼ 18),1/2c (n¼ 17), 3a (n¼ 5), 3b (n¼ 2), 4b/4e (n¼ 16), 4c (n¼ 1).The AFLP patterns of the 103 L. monocytogenes isolates presentedevenly distributed bands in the range of interest (75–500 bp). TheAFLP analysis grouped the L. monocytogenes isolates into two majorclusters (I and II) that diverged at a S.L. of 55% (Fig. 1). The number ofbands ranged approximately from 40 to 60 with cluster II profileshaving more complex patterns with a greater number of bands.Cluster I (S.L. ¼ 78%) included 37 isolates subdivided into 13 AFLPgroups and 26 AFLP types whereas cluster II (S.L. ¼ 78%) included66 isolates arranged in 15 AFLP groups and 36 AFLP types. All theisolates of serotypes 1/2b, 3b, 4b/4e and 4/c fell into cluster I,whereas cluster II included the isolates of serotypes 1/2a, 1/2c and3a. The AFLP results correlated with the serotyping results. SeveralAFLP groups included isolates of a single serotype even if morethan one AFLP group was identified for all the serotypes with theexception of 1/2c isolates. A different genetic variability wasobserved for each serotype, while all the 1/2c isolates were clusteredin 9 AFLP types belonging to the same AFLP group, 25 AFLP types in14 different AFLP groups were identified for the 1/2a isolates (n¼ 44)(Fig.1). In the reproducibility test, the different runs of three separateanalyses of two DNA preparations for each isolate resulted in iden-tical banding profiles (S.L. ranged from 96 to 100%) (data not shown).

3.2. Multi-Locus Sequence Typing

On the whole MLST identified 66 different allelic profiles (STs),52 (78.8%) of which were represented by only one isolate. ST 9(n ¼ 12; 11.6%) and ST 121 (n ¼ 10; 9.7%) were the most commonprofiles (Fig. 1).

Compared to the previously published MLST scheme (Salcedoet al., 2003) the analyzed region of each locus was slightly shorterexcept for the ldh gene which was extended on the 50 side toimprove isolate discrimination (Ragon et al., 2008). The number ofunique identified alleles ranged from 9 for lhkA to 18 for cat anddapE. The number and the percentages of polymorphic nucleotidesites, GC content, the proportion of nucleotide alterations thatchanged the amino acid sequence (nonsynonymous substitution,dn) and the silent changes (synonymous substitution, ds) (dn/ds), aswell as the DI for each allele are illustrated in the Table 2. The lhkAgene showed a poor discriminatory power as two out of the nineidentified alleles (no. 1, no. 5) were common to 89 of the 103isolates (86.4%). The D.I. of lhkA was remarkably lower (0.571) thanthat of other loci which ranged from 0.814 to 0.880. Sawyer’s testsrevealed statistical evidence of recombinational exchanges for abcZ(SSCF P ¼ 0.022) and dapE (SSCF P ¼ 0.002).

Significant linkage disequilibrium was detected either when thecomplete ST dataset (IA¼ 2.1669) or separate data from each cluster(cluster I: IA ¼ 2.0257; cluster II: IA ¼ 2.249) were analyzed.

A minimum spanning tree of ST data identified 8 main ClonalComplexes (CC) that were indicated by the number of the probableST founder: CC1, CC2, CC3, CC8, CC9, CC29, CC121 and CC182 (Fig. 2).In lineage I three clonal complexes were identified, CC1 and CC2including 4b/4e isolates and the only 4c isolate (45-P) and CC3

A. Parisi et al. / Food Microbiology 27 (2010) 101–108104

Table 2Characteristics of MLST loci.

Locus Amplicon size No. alleles No. (%) polymorphic nucleotide sites % G þ C dn/ds D.I.

abcZ 537 13 31 (5.8) 37.61 0.0173 0.860bglA 399 14 24 (6.0) 40.57 0.0219 0.830cat 486 18 36 (7.4) 41.11 0.0366 0.883dapE 462 18 45 (9.7) 42.89 0.0572 0.880dat 471 11 61 (12.9) 36.55 0.0479 0.814ldh 453 12 19 (4.2) 43.49 0.0000 0.832lhkA 480 9 20 (4.2) 37.19 0.0385 0.571

A. Parisi et al. / Food Microbiology 27 (2010) 101–108 105

comprising isolates of serotype 1/2b and 3b. In lineage II five clonalcomplexes were identified, CC8, CC29, CC121 and CC182, whichincluded isolates of serotypes 1/2a and 3a, and CC9 that comprisedall the 1/2c isolates.

3.3. Comparison between AFLP and MLST

AFLP and MLST produced similar results in terms of discrimi-nating power. The D.I. calculated for the two techniques was 0.976for AFLP and 0.972 for MLST. These values were appreciably higherthan the D.I. for serotyping (0.739). On the whole we observeda good agreement between the AFLP and MLST results, especiallywhen comparing AFLP groups and MLST clonal complexes (Fig. 1).

Among the main clonal complexes CC9 included all the isolatesbelonging to the AFLP group 14 (S.L. ¼ 90.7), CC121 comprised 21isolates belonging to two AFLP groups (AFLP group: 3, 5; S.L.¼ 85.6)similarly to CC182 which included 4 isolates (AFLP group: 1, 32;S.L.¼ 89.5). In many other cases AFLP and MLST coincided, althoughwith some exceptions such as isolate 384 that exhibited a slightlydifferent AFLP pattern from the isolate 243 of the same ST (no. 49).

4. Discussion

It is known that once L. monocytogenes contaminates a food-processing plant it can survive there for a long time if the temper-ature is low and the organism is protected by the food components(Palumbo and Williams, 1990). Thus, typing L. monocytogenes isimportant in epidemiological studies to investigate food-bornedisease outbreaks (i.e. to compare clinical and food isolates) and inthe food-processing environment to identify the source of contam-ination and the routes by which the organism spreads. L. mono-cytogenes typing is also necessary to understand the epidemiology ofthe organism in both animals and humans.

Serotyping has classically been used for the subtyping ofL. monocytogenes, based on somatic (O) and flagellar (H) antigens.L. monocytogenes isolates are divided into 13 serotypes (Seeliger andHohne, 1979) but over 95% of isolates in human listeriosis and infoods belong to serotypes 1/2a, 1/2b and 4b. This undermines theusefulness of serotyping in epidemiological investigations (Schon-berg et al., 1996). A molecular typing method with a high discrim-inatory power could overcome this problem. Recently severalfingerprinting techniques have been compared for typing environ-mental and clinical L. monocytogenes isolates (Kerouanton et al.,1998; Vogel et al., 2004). Among the fingerprinting typing methodsdescribed in the literature, PFGE, RAPD and AFLP have a comparablediscriminatory power (Vogel et al., 2004). RAPD is rapid and inex-pensive but it has a low inter-laboratory reproducibility (Wernarset al., 1996) and PFGE is highly standardized but labour-intensive(Brosch et al., 1996; Graves and Swaminathan, 2001).

Fig. 1. Amplified Fragment Length Polymorphism of the 103 L. monocytogenes isolates. Thecluster analysis method with the UPGMA (unweighted pair group method with arithmetic midentification), Source of isolation, (BKW: backwater; CUF: cured fish; ENS: environmental sweat; SOI: soil; VGT: vegetables) Serotype, AFLP type, ST and Clonal Complex are illustrate fo

AFLP presents several advantages in terms of reproducibility andthroughput compared to other fingerprinting techniques. Theuse of capillary electrophoresis with an automatic sequencer forthe accurate sizing of fragments and a specific software for datamanaging and rapid analysis of fingerprinting profiles makes thismethod a valuable tool for the characterization of microbial pop-ulations. AFLP is a flexible method in that the enzyme combinationsand primer selectivity help define the optimal protocol for typingdifferent genomes. Some studies regarding AFLP typing of L. mon-ocytogenes with one restriction enzyme and gel based electropho-resis reported a low discriminatory power (Guerra et al., 2002;Ripabelli et al., 2000) or produced few band profiles (Guerra et al.,2002; Vogel et al., 2001); other protocols using different enzymecombinations: Eco RI/Mse I (Aarts et al., 1999; Mikasova et al.,2005), Eco RI/Bam HI (Vogel et al., 2001), Hind III/HpyCH4 IV (Keto-Timonen et al., 2003; Vogel et al., 2001), obtained more complexpatterns. The enzymes Hind III and Hha I have been used forCampylobacter spp. AFLP typing (Duim et al., 2001). In the presentstudy we used these enzymes and a suitable selective primercombination which gave optimal banding patterns for typingL. monocytogenes and produced better results in terms of bandsamplification and distribution than did an AFLP commercial kitusing the Eco RI and Mse I enzyme combination. Fingerprintingpatterns presented an adequate number of fragments, approxi-mately from 40 to 60, that were evenly distributed in the range ofinterest (75–500 bp).

Different automatic analysis algorithms of complex patterns canbe applied to compare AFLP profiles using a band-based analysissuch as the Dice coefficient method, or a curve-based analysis suchas the Pearson correlation coefficient. Unlike other studies (de Boeret al., 2000) we had better results using the Dice algorithm whenwe studied the reproducibility of our test.

The reproducibility of the AFLP method was assessed by per-forming three separate analyses of two DNA extractions for fiveisolates. Minor variations were observed in peaks size or height,and replicate AFLP patterns exhibited similarity levels ranging from96 to 100%. Isolates with AFLP patterns exhibiting >90% identitywere assumed to be related genetically and clustered in the sameAFLP group (Fig. 1), while isolates with a AFLP similarity levelgreater than 95% were grouped in the same AFLP type.

Analogously to studies that used AFLP (Aarts et al., 1999; Keto-Timonen et al., 2003; Mikasova et al., 2005; Vogel et al., 2004),PFGE (Brosch et al., 1996; Vogel et al., 2004), RAPD (Vogel et al.,2004), and ribotyping (De Cesare et al., 2001; Manfreda et al.,2005), our procedure identified two major clusters (I and II) thatdiverged at a similarity level of 55% (Fig. 1). Cluster I included 13AFLP groups and 26 AFLP types of 37 isolates belonging to theserotypes 1/2b, 3b, 4b/4e and 4c. Cluster II comprised 15 AFLPgroups and 36 AFLP types relating to 66 isolates that belonged to

dendrogram was constructed by means of AFLP data using the Dice correlation andean). Lineage I and II are identified. Allele number for each locus (MLST), Code (strainab; MAS: mammalian stools; MEP: meat products; MIP: dairy products; RTE: ready-to-r each isolate. *Double locus variant isolate.

Fig. 2. Multi-Locus Sequence Typing: the 103 Listeria monocytogenes isolates were clustered using Minimum spanning tree. Numbers within the circles denote the correspondingST. The area of the circles is related to the number of isolates within the same ST. The connection of the circles differs based on the homology of MLST loci. The eight main ClonalComplexes (CC) are shown.

A. Parisi et al. / Food Microbiology 27 (2010) 101–108106

serotypes 1/2a, 1/2c and 3a. The isolates included in this study camefrom samples collected during surveillance activities for food andenvironmental control procedures so the different distribution ofthe serotypes reflected their frequency of isolation. According toanother Italian survey, serotypes 1/2a, 1/2b, 1/2c and 4b/4e arethe most common both in environmental or food sources (Fig. 1)(Gianfranceschi et al., 2009). Even if a third genetic lineage,comprising isolates of serotype 4a and 4c, has been described(De Cesare et al., 2001; Vogel et al., 2004) a single isolates belongingto serotype 4c was included in our study and grouped in the clusterI by both the typing methods (AFLP Type 19D; CC2) (Fig. 1).

In the recent years MLST has become one the most popularmethods for bacterial typing. This is the DNA sequence-based evolu-tion of Multi-locus Enzyme Electrophoresis of which it keeps thesuitability for global epidemiology, while providing better discrimi-natory power and ensuring inter-laboratory data-sharing. MoreoverMLST data are stored in a web server that can be queried by any useranywhere in the world. Even if MLST schemes are based on thesequence of internal fragments of seven housekeeping genes they canidentify many allelic profiles. In our study, 66 STs and 8 main ClonalComplexes were identified on 103 L. monocytogenes isolates. Accord-ing to sequence data, the evolution of L. monocytogenes appears to bemainly driven by the progressive accumulation of mutations overtime. Recombinational events are rare even if Sawyer’s tests do showevidence of intragenic recombination in the abcZ and dapE loci. Unlikeother authors (Salcedo et al., 2003) a significant linkage disequilibriumwas detected either when a complete ST dataset or separate data fromeach cluster were analyzed. This is consistent with the hypothesis thatthe genetic structure of L. monocytogenes populations is basicallyclonal (Piffaretti et al., 1989).

Interestingly some similarities can be detected when comparingour results with previous published MLST data on a French collectionof L. monocytogenes isolates (Ragon et al., 2008). For example, CC9comprised all isolates of serotype 1/2c confirming the speculatedmonophyletic origin of this serotype; CC2, CC3, CC8 and CC9 wereamong the more prevalent CCs; CC1 and CC2 were dominated byisolates of serotype 4b/4e; CC3 comprised a large proportion ofserotype 1/2b. At the same time some differences have emerged – weobserved two complexes, CC121 and CC182, that represented a major

groups of isolates, and were rarely identified among the 360 studiedFrench isolates. On the other hand we did not identify any of the CC5and CC7 that were frequently identified in the French survey. Thisunderlines the differences in regional prevalence of single genotypesand the need for global studies to integrate information derived fromdifferent areas of the world. AFLP and MLST exhibited comparablediscriminatory powers – 0.976 and 0.972 respectively – that werehigher than that of serotyping (DI ¼ 0.739) and highlighted the lowefficacy of this typing method. Moreover, according to other authors(Revazishvili et al., 2004), some genetically close isolates (e.g.171 and18-P) exhibit a different serotype from other isolates of the same ST,raising doubts about the epidemiological value of serotyping.

The 1/2a, 1/2b and 4/b/4e isolates, the most common serotypesinvolved in human listeriosis (Schonberg et al., 1996), presented thegreatest genetic diversity. This finding emphasizes the need to usea powerful typing system in epidemiological investigations per-formed during outbreaks in order to accurately identify the sourcesof infection. By contrast, all the 1/2c isolates (n ¼ 17) fell withina single AFLP group or Clonal Complex (Fig. 1) thus exhibiting a lowgenetic variability as previously reported by Aarts et al. (1999).

Interestingly, some AFLP groups and MLST Clonal Complexincluded isolates from several different food or environmentalsources, thus providing evidence that certain genotypes which cancolonize diverse ecological niches and contaminate several kinds offood are widespread (Fig. 1). A certain connection with the source ofisolation was recorded for the 1/2c isolates since 14 of 17 (82.3%)were isolates from meat products. This is in agreement with thefindings of other Italian investigators (Gianfranceschi et al., 2009).

In conclusion, the present study demonstrated that AFLP with theHind III and Hha I restriction enzyme combination and MLST aresuitable tools for studying the epidemiology of L. monocytogenes. Onegreat advantage of MLST over AFLP and other molecular typingmethods based on fragment fingerprinting lies in the unambiguity ofsequence data which permits inter-laboratory comparison of results.Another great advantage is availability of the MLST database thatmakes it possible to relate isolates from all over the world using thesame methods. MLST may become the ‘‘gold standard’’ for globalepidemiology studies of bacterial pathogens. Unfortunately, despitethe standardization of the method there is still no common MLST

A. Parisi et al. / Food Microbiology 27 (2010) 101–108 107

scheme for L. monocytogenes. After the publication of the first MLSTprotocol (Salcedo et al., 2003) other loci were investigated in house-keeping or in virulence genes (Jiang et al., 2008; Revazishvili et al.,2004; Zhang et al., 2004) for their use as candidates inL. monocytogenes genotyping. However, it is rather expensive andtime-consuming compared to AFLP, which is less costly, highly proc-essive and easily enables species identification of the Listeria genus(data not shown). We suggest using AFLP to perform epidemiologicalstudies in food-processing plants or to investigate outbreaks wherea large number of isolates have to be typed and resorting to MLST fora definitive characterization of the identified AFLP types.

Acknowledgments

The authors had an equal share in the work. This research wassupported by Italian Ministry of Health – Research Project IZS PB003/06. We thank platform Genotyping of Pathogens and PublicHealth (Institut Pasteur) for coding MLST alleles and profiles. Wethank M. Natale and N. Nuzzolese for technical assistance.

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