International Journal of Food Microbiology 167 (2013) 310–321

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International Journal of Food Microbiology

j ourna l homepage: www.e lsev ie r .com/ locate / i j foodmicro

Review

Strain variability of the behavior of foodborne bacterial pathogens:A review

Alexandra Lianou, Konstantinos P. Koutsoumanis ⁎Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece

⁎ Corresponding author. Tel./fax: +30 2310991647.E-mail address: kkoutsou@agro.auth.gr (K.P. Koutsoum

0168-1605/$ – see front matter © 2013 Elsevier B.V. All rhttp://dx.doi.org/10.1016/j.ijfoodmicro.2013.09.016

a b s t r a c t

a r t i c l e i n f o

Article history:Received 22 February 2013Received in revised form 23 September 2013Accepted 24 September 2013Available online 2 October 2013

Keywords:Strain variabilityFoodborne pathogensVirulenceGrowthInactivationBiofilm formation

Differences in phenotypic responses among strains of the samemicrobial species constitute an important sourceof variability in microbiological studies, and as such they need to be assessed, characterized and taken intoaccount. This review provides a compilation of available research data on the strain variability of four basic be-havioral aspects of foodborne bacterial pathogens including: (i) virulence; (ii) growth; (iii) inactivation; and(iv) biofilm formation. A particular emphasis is placed on the foodborne pathogens Listeria monocytogenes andSalmonella enterica. The implications of strain variability for food safety challenge studies and microbial risk as-sessment are discussed also. The information provided indicates that the variability among strains of foodbornebacterial pathogens with respect to their behavior can be significant and should not be overlooked. However, inorder for themechanisms underlying the observed strain variability to be elucidated and understood, phenotypicvariability data, such as those reviewed here, should be evaluated in conjunction with corresponding findings ofstudies assessing the molecular/physiological basis of this variability.

© 2013 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3102. Virulence variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3113. Growth variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3124. Inactivation variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313

4.1. Acid inactivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3134.2. Heat inactivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3144.3. Inactivation by non-thermal processing technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315

5. Biofilm formation variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3156. Significance of strain variability for food safety challenge studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3177. Significance of strain variability for microbial risk assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3178. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318

1. Introduction

This review provides a compilation of available research data on thestrain variability of the behavior of foodborne bacterial pathogens. Theimplications of the strain variability of foodborne pathogens' behaviorfor food safety challenge studies and microbial risk assessment alsoare discussed.

anis).

ights reserved.

Before beginning to review experimental data on bacterial strain var-iability, it is important to provide a working definition of a bacterialstrain. The term “strain” refers to an isolate or a group of isolates thatcan be distinguished from other isolates of the same bacterial species(Table 1). The process of differentiating bacterial isolates beyond the spe-cies level is referred to as “strain typing” or “subtyping” and is based ongenotypic (i.e., the genetic informationdictating a particular trait) or phe-notypic (i.e., visible, expressed traits influencedboth by the genotype andenvironmental factors) characteristics (Wiedmann, 2002). Subtypingmethods for bacteria can be separated into conventional methods (e.g.,

Table 1Definitions of important bacterial subtyping terms.

Term Definition Reference

Isolate Pure culture of bacteria, presumably derived from a single organism Wiedmann (2002)Strain Isolate or group of isolates that can be distinguished from other isolates of the same species by phenotypic and/or

genotypic characteristicsWiedmann (2002)

Clonal group Genetically related isolates that are indistinguishable from each other by genetic tests or that are so similar theyare presumed to have directly descended from a common ancestor

Wiedmann (2002)

Serotype Group of isolates distinguished from others by the antigens expressed on the cell surface as determined by surfacestructures including lipopolysaccharides, lipotechoic acids, membrane proteins, and extracellular organelles such asflagella and fimbriae

Graves et al. (2007)

Pathotype Group of isolates sharing particular sets of virulence factors directing them through a particular pathogenesis process Marrs et al. (2005)

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serotyping and phage typing) and molecular methods (e.g., multilocusenzyme electrophoresis, ribotyping, pulsed-field gel electrophoresis andDNA-sequencing methods) (Graves et al., 2007). Depending on theintended application, criteria for the selection of a subtyping method in-clude the discriminatory power and reproducibility of themethod, its ap-plicability and ease of use, as well as the cost involved (Wiedmann,2002). With the present review discussing strain variability, in additionto “strain”, it is important that other common terms such as “isolate”,“clonal group”, “serotype” and “pathotype” are also defined (Table 1).

The inherent differences among identically treated strains of thesame microbial species, referred to as “strain variability”, constitute animportant source of variability in microbiological studies (Whiting andGolden, 2002). This means that research findings referring to a certainmicrobial strain cannot be extended to other strains of the same species.Thus, information regarding the strain variability of phenotypic re-sponses of foodborne pathogens under various environmental condi-tions is expected to be valuable for the purpose of strain selection infood safety studies. Furthermore, as suggested by subtyping data, differ-ent strains of foodborne pathogens are differently associated withhuman disease and such differences can be attributed, among others,to the hardy nature of certain strains enabling them to survive and pro-liferate in food-related environments, or to their increased virulence to-wards humans (Sauders et al., 2004; Velge et al., 2005; Wiedmann,2002). Hence, strain variability data can also facilitate the assessmentof the relationships among various characteristics of foodborne patho-gens including their virulence, distribution and epidemiology. Finally,as frequently commented by various researchers, the intra-species var-iability of microbial behavior may have an important impact on the ac-curacy of microbial risk assessment outcomes, and, therefore, should besystematically assessed and accounted for in the framework of such ap-proaches (Coleman et al., 2003; Delignette-Muller and Rosso, 2000;Pouillot and Lubran, 2011).

The strain variability data reviewed herein refer to four basicbehavioral aspects of foodborne pathogens including: (i) virulence;(ii) growth; (iii) inactivation; and (iv) biofilm formation. A particularemphasis with regard to the information provided in this review isplaced on Listeria monocytogenes, the foodborne pathogen with themost abundant published literature on strain variability overall, andSalmonella enterica. Recently published research data collected in ourlaboratory and referring to the phenotypic strain variability of S. entericaare briefly presented and discussed.

2. Virulence variability

The virulence potential of intracellular pathogenic bacteria has beentraditionally evaluated via the use of tissue culture (in vitro) and/or ani-mal (in vivo) models. Commonly used tissue culture models include thehuman intestinal epithelial cell line Caco-2 aswell asmammalian epithe-lial and macrophage cell lines (Kathariou, 2002; Poli et al., 2012; Velgeand Roche, 2010). Animal models that have been used for the purposeof virulence characterization and comparison of bacterial pathogens'strains include guinea pigs, chick embryos, rodents, birds and largefarm animals, with the most extensively used model, however, being

the murine model (Hébrard et al., 2011; Humphrey et al., 1996, 1998;Kathariou, 2002; Velge and Roche, 2010). The emergence of sophisticat-ed imaging and molecular genetic tools has further facilitated the studyof the events underlying the disease-causing ability of pathogenic bacte-ria (Cordwell et al., 2001;Hébrard et al., 2011),while, the combined eval-uation of molecular subtyping and food survey (i.e., prevalences infoods) data is also expected to be useful in attributing risk of disease tocertain foodborne pathogens' strains or subtypes (Chen et al., 2006).

L. monocytogenes has been traditionally regarded as pathogenic atthe species level, with a generally accepted belief that all isolates ofthe organism should be considered as potentially virulent and capableof causing disease (McLauchlin et al., 2004; Rocourt et al., 2000). None-theless, as demonstrated by experimental data collected over the lastdecade, L. monocytogenes exhibits a significant serotype/strain variationin virulence and pathogenicity, with many epidemic strains beinghighly infective and sometimes deadly, while others (especially foodand environmental isolates) being significantly less virulent (Velgeand Roche, 2010). Jacquet et al. (2002) reported extensive virulenceheterogeneity, as illustrated by distinct differences in proteins essentialfor infection expressed by strains of different serotypes, origins, orstrains causing different forms of listeriosis. Extensive virulence varia-tion among L. monocytogenes strains has been also demonstratedusing experimental animal models (Brosch et al., 1993; Buncic et al.,2001; Conner et al., 1989), and source- or subtype-related differenceshave been indicated in some cases (Avery and Buncic, 1997; Barbouret al., 2001; Buncic et al., 2001; Jensen et al., 2008; Nørrung andAndersen, 2000). For instance, Nørrung and Andersen (2000) reportedthat strains belonging to electrophoretic types 2 and 4were less virulentfor chick embryos than strains of other electrophoretic types, andstrains from clinical cases were more virulent than strains from foods.Similarly, when the behavior of four L. monocytogenes strains was eval-uated and compared using selected virulencemodels, clinical strains ap-peared to have a higher virulence potential than fish processing plantpersistent strains (Jensen et al., 2008). Moreover, according to the find-ings of Buncic et al. (2001), serotype 4b strains, as a group, tended tohave higher pathogenicity for chick embryos, when transferred fromcold storage (4°C) to body temperature (37°C), than the group of sero-type 1/2a strains. Indeed, of the 13 L. monocytogenes serotypes, onlythree (1/2a, 1/2b and 4b) are responsible for ca. 96% of human infec-tions, and serotype 4b has been primarily associated with large listerio-sis outbreakswhereas serotype 1/2a has beenmainly linked to sporadiccases (Velge and Roche, 2010). Similarly, L. monocytogenes isolates be-longing to the phylogenetic lineage I are more common amonghuman listeriosis cases, while isolates from lineages II and III havebeen mainly associated with sporadic human and animal listeriosis, re-spectively. These observations, in conjunction with cytopathogenicityand dose–response data, are considered to be indications of differencesin pathogenic potential among clonal groups of L. monocytogenes (Chenet al., 2006; Gray et al., 2004; Jeffers et al., 2001; Mereghetti et al., 2004;Wiedmann et al., 1997). Nevertheless, so far no clear direct relationshiphas been observed between the abovementioned subgroups and thevirulence of L. monocytogenes strains (Velge and Roche, 2010). Althoughthe key virulence factors identified to date are present in all strains of

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the organism (Vázquez-Boland et al., 2001), the regulation of their ex-pression may be different among different strains, or certain strainsmay possess additional, currently unidentified, virulence determinants(Kathariou, 2002). Studies investigating the molecular basis underlyingthe differences in pathogenic potential among L. monocytogenes strainshave revealed the existence of serotype-specific sequences in the ge-nome of serotype 4b strains, most likely reflecting evolutionary differ-entiation (Doumith et al., 2004; Lei et al., 2001).

With regard to S. enterica, the species includes more than 2500 sero-logically distinct types, or serotypes, which, despite their genetic relat-edness, vary significantly in their epidemiology (Fierer and Guiney,2001; Velge et al., 2005). Such serotype-related differences in the epide-miology of the organism are assumed to be driven by a correspondingdiversity in the virulence traits underlying the different clinical out-comes of salmonellosis (Fierer and Guiney, 2001). In addition toserotype-specific differences, there are research data indicating the ex-istence of considerable virulence variability among S. enterica strains be-longing to the same serotype. In a study undertaken by Humphrey et al.(1996), two S. enterica serotype Enteritidis PT4 isolates, differing in theirinherent resistances to heat, acid, H2O2 and their ability to survive onsurfaces, were used to infectmice, day-old chicks or lay hens. The inves-tigators observed that between the two isolates studied, the acid-, heat-,H2O2- and surface-tolerant isolate was more virulent in mice and moreinvasive in laying hens (particularly in reproductive tissue cells) thanthe other (Humphrey et al., 1996). The principal objective of a subse-quent investigation was the identification, if any, of phenotypic traits(i.e., tolerance to certain hostile environments) with potential value aspathogenicitymarkers in S. Enteritidis PT4. It was concluded that differ-ences in in vitro acid-, heat- or H2O2-tolerance had no effect on the abil-ity of the isolates to multiply in the spleens of orally infected mice;among the traits tested, only the ability to survive on surfaces couldserve as a potential marker of pathogenicity (Humphrey et al., 1998).Using microarray analysis Zou et al. (2011) assayed the virulence geneprofiles in S. enterica isolates from food and/or food animal environ-ments, and reported a considerable variability among the strains testedwhich, however, was independent of serotype.

Virulence genes are usually located on pathogenicity islands (i.e.,distinct genetic chromosomal loci) and play a crucial role in the patho-genesis of S. enterica. Salmonella pathogenicity islands (SPI) contributeto host cell invasion and intracellular pathogenesis; at present, 12 SPIhave been described with their distribution in S. enterica serotypes hav-ing the potential to be markedly different (Hensel, 2004). Informationpertinent to the pathogenesis and virulence strategies of S. entericahas been reviewed in several publications (Andrews-Polymenis et al.,2010; Bäumler et al., 2011; Bueno et al., 2007; Gordon, 2011). Themechanisms underlying the virulence diversity of S. enterica strainshave constituted the objective of extensive research, with researchdata indicating that genetic variations, or polymorphisms, are particu-larly prominent in two general classes of loci: (i) genes encoding surfacestructures such as lipopolysaccharides, flagella and fimbriae; and(ii) specific virulence genes encoding factors that modify host cell phys-iology or protect the pathogen from the antimicrobial systems of thehost (Fierer and Guiney, 2001). The surface structures not only affectthe virulence of bacteria, but also constitute key targets of the hostimmune system, resulting in selective pressure to generate geneticpolymorphisms coding for antigenic diversity. In addition, specificvirulence determinants may be clustered together on polymorphicpathogenicity islands or located on transmissible genetic elements(e.g., plasmids or phages). Such arrangements facilitate the modulartransmission of genes involved in pathogenesis and, thus, increase thediversity in virulence phenotypes among strains (Fierer and Guiney,2001). Indeed, according to recent research findings, novel mobile ge-netic elements involved in gene dissemination linked to S. enterica viru-lence have been identified and characterized, and the abundance ofsuch elements may facilitate the emergence of strains with novel com-binations of pathogenic traits (Moreno Switt et al., 2012).

In addition to L. monocytogenes and S. enterica, strain variability invirulence potential has been also documented for other foodborne path-ogens including enteropathogenic and enterohemorrhagic Escherichiacoli (Baker et al., 1997; Contreras et al., 2010; Sonntag et al., 2004),Staphylococcus aureus (Spanu et al., 2012) and Campylobacter jejuni(Poli et al., 2012).

3. Growth variability

The variability of the growthkinetic behavior among L.monocytogenesstrains has been demonstrated at several instances, with thefirst reportsdating back to the late 1980s (Junttila et al., 1988; Rosenow and Marth,1987; Walker et al., 1990). Barbosa et al. (1994) compared 39L. monocytogenes strains with respect to their growth potential at 4, 10and 37 °C, and their results demonstrated a highly strain-dependentgrowth behavior of the pathogen as evaluated based on the estimatedvalues of lag phase, exponential growth rate and generation time.Growth differences among four strains of the organism were also docu-mented in vacuum-packaged ground beef of normal or high pH stored at4°C (Barbosa et al., 1995). Avery and Buncic (1997) reported that clinicalL. monocytogenes isolates exhibited on average a shorter lag phasecompared to meat isolates in culture broth at 37 °C, a difference whichwas even more evident when cultures were previously stored at 4 °Cunder starvation. When the growth of 58 L. monocytogenes strains wasevaluated in meat broth under different combinations of temperature(10 or 37 °C), pH (5.6 or 7.0) and aw (0.960 or 1.00), the observed vari-ability of the estimated lag phase among the strainswas extensive underall the tested conditions (Begot et al., 1997). The findings of subsequentinvestigations were similar with regard to the important intra-speciesvariability characterizing the growth behavior of L. monocytogenes(Buncic et al., 2001; De Jesús and Whiting, 2003; Lianou et al., 2006;Uyttendaele et al., 2004). For instance, De Jesús and Whiting (2003)characterized 21 L. monocytogenes strains with respect to their growthbehavior in culture broth (pH 6.5 and 0.1M lactate) at 5 or 35°C, and re-ported considerable strain and, in some cases, intra-lineage variation; at5 °C, the estimated lag phase values ranged from 0.9 to 4.83 days andgrowth rate values from 0.33 to 0.59 log units per day. Similarly, as re-ported by Uyttendaele et al. (2004), the response of L. monocytogenesto suboptimal growth conditions in culture broth (at different combina-tions of temperature, pH, aw, and NaCl and sodium lactate concentra-tions) was shown to be strain-dependent, while strain variation wasalso observed when growth of selected strains was evaluated in modi-fied broth simulating conditions associated with cooked ham or pâté.In general, as supported bymany research findings, the growth variabil-ity among strains of L. monocytogenes appears to increase at growth con-ditions, and particularly temperatures, away from the optimum for thisorganism (Barbosa et al., 1994; Begot et al., 1997; De Jesús andWhiting,2003; Lebert et al., 1998; Lianou et al., 2006).

With reference to S. enterica, the available data on growth differ-ences among strains of the organism are relatively few. Fehlhaber andKrüger (1998) assessed the growth of 45 S. Enteritidis food isolates inculture broth over a temperature range from 7 to 42 °C and reportedconsiderable strain-specific differences in the estimated generationtimes. Furthermore, and in agreement with corresponding findingsfor L. monocytogenes, these researchers observed that generation timevariability increased as temperature moved away from the optimalrange, with variation coefficients tending to rise as temperature fell(Fehlhaber and Krüger, 1998). In a recent study undertaken by Díez-García et al. (2012), the growth kinetic behavior of a total of 69S. enterica strains belonging to 10 serotypes was evaluated, and it wasobserved that the values of the growth parameters (i.e., lag phase andgrowth rate) varied among the tested serotypes. In a study undertakenin our laboratory, aimed at the evaluation of the growth variabilityamong S. enterica strains as affected by the growth environment, thekinetic behavior of 60 isolates of the pathogen (belonging to various se-rotypes) was assessed at 37 °C in tryptone soy broth (TSB) of different

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pH values (4.3–7.0) and NaCl concentrations (0.5–6.0%) (Lianou andKoutsoumanis, 2011a). It was observed that the variability of the esti-mated maximum specific growth rate (μmax) values among the testedstrainswas important and greater than that observed within the strains(i.e., among replicates). More specifically, in TSB containing 0.5% NaCl,the mean μmax (h−1) ranged from 1.27 to 1.95 at pH 5.5, from 1.02 to1.67 at pH 5.0, from 0.78 to 1.37 at pH 4.5, and from 0.68 to 1.22 atpH 4.3 (Fig. 1a). In TSB of pH 7.0, the mean μmax (h−1) ranged from1.51 to 2.13 at 0.5% NaCl, from 0.96 to 1.39 at 3.5% NaCl, from 0.74 to1.10 at 4.5% NaCl, and from 0.12 to 0.59 at 6.0%NaCl (Fig. 1b).Moreover,it was observed that strain variability increased as the growth condi-tions became more stressful both in terms of pH and NaCl (Lianou andKoutsoumanis, 2011a). With the exception of the abovementionedstudies, the rest of the available research data regarding the growth be-havior of S. enterica refer to a limited number of strains and have not in-dicated considerable strain variability (Juneja et al., 2003;Membré et al.,2005; Oscar, 2000).

Additional foodborne pathogens for which strain-dependent differ-ences in growth behavior have been documented include E. coli O157:H7 (Nauta and Dufrenne, 1999; Palumbo et al., 1995; Whiting andGolden, 2002) and S. aureus (Dengremont and Membré, 1995;Lindqvist, 2006). In a study carried out to assess the growth behaviorof 17 E. coli O157:H7 strains in culture broth of pH 5.3 and 1.5% NaClat 15°C, extensive strain variability was demonstrated in the estimatedgrowth kinetic parameters, with the lag phase varying from 13.7 to55.6 h and the exponential growth rate varying from 0.055 to0.106 log/h (Whiting and Golden, 2002). Likewise, Lindqvist (2006) re-ported important growth variability among34 S. aureus strains, with thecoefficient of variation of growthparameters beingup to six times largeramong strains than within strains.

In addition to the strain variability of the growth behavior demon-strated by the findings of the abovementioned studies, a correspondingvariability is also expected to be observed in the growth ability offoodborne pathogens (i.e., variability of the growth/no growth bound-aries among different strains). Nevertheless, the available researchdata with regard to the extent of such variability are very limited.

4. Inactivation variability

4.1. Acid inactivation

The studies reporting on considerable strain variability of the inacti-vation behavior of L. monocytogenes under low-pH conditions are nu-merous. The screening of 30 L. monocytogenes strains of both clinicaland food origin revealed extensive inter-strain variability with respectto their response to acid stress, as imposed by exposure to pH 2.5(using HCl as the acidulant) for 2h in culture broth. Furthermore, a po-tential association between acid resistance and source of strain isolation

a

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4.3 4.5 5.0 5.5

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µm

ax (

h-1)

Q1

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Fig. 1. Boxplots of the maximum specific growth rate (μmax) values of 60 Salmonella enterica st(pH 7.0) (b); data from Lianou and Koutsoumanis (2011a).

was proposed, as none of the clinical isolates demonstrated a significantacid sensitivity (Dykes and Moorhead, 2000). Important variation inacid resistance, evaluated in broth acidified to 3.5 using lactic acid,among strains of the pathogen was also reported by Francis andO'Beirne (2005). Liu et al. (2005) tested the acid tolerance of sixL. monocytogenes strains of known virulence (three virulent and threeavirulent strains) to pH values ranging from 2.0 to 5.0 (using HCl), andreported that, although strain differences were observed, both virulentand avirulent strains were able to tolerate pH values of 3.0 or lower.In a subsequent study assessing, among others, the acid resistancevariation in culture broth (pH 3.0 with lactic acid) among 25L. monocytogenes strains of various serotypes and origins, extensivestrain variation was demonstrated with the estimated acid death ratesranging from 0.012 to 0.134 log CFU/ml/min (Lianou et al., 2006). Ac-cording to the findings of Lundén et al. (2008), great differences inacid tolerance (exposure to pH 2.4 for 2 h in broth acidified with HCl)were observed among 40 strains of L. monocytogenes, while a potentialassociation between the pathogen's acid tolerance and its persistencealso was indicated. Acid resistance differences among L. monocytogenesstrains have been associated with respective strain differences at themolecular level, as well as with cell membrane features that determinethe pathways of H+ influx and efflux across themembrane (Cotter et al.,2005; Olier et al., 2004; Phan-Thanh et al., 2000).

With reference to S. enterica, most of the published research data re-garding the strain variability of its acid resistance have been presentedin the formof side observations, in the context of investigations not spe-cifically designed to assess the strain variability of acid inactivation and,therefore, using a limited number of strains (Bacon et al., 2003;Humphrey et al., 1995; Samelis et al., 2003). Nevertheless, there aresome studies reporting acid inactivation differences among multiplestrains of the pathogen. De Jonge et al. (2003) evaluated both the log-and stationary-phase acid tolerance response (ATR) of several S. entericaserotype Typhimurium strains, both DT104 and non-DT104 isolates,and reported significant variation among the tested strains regardingtheir ability to survive extreme low-pH (using HCl) environments. Sim-ilarly in a subsequent study, Berk et al. (2005) reported a considerablestrain variability when assessing the survival profiles of acid-adaptedcultures (i.e., growth overnight at pH 5.0 prior to use in acid challengeexperiments) of 37 S. Typhimurium strains during exposure to pH 2.5for 2h in broth acidified usingHCl. To our knowledge, the research stud-ies reporting on the inherent and pH-independent acid resistance vari-ability among multiple S. enterica strains are scarce. One such study isthat carried out by Jørgensen et al. (2000) who investigated the stressresistance of 38 strains of S. Typhimurium DT104, grown to stationaryphase in nutrient broth (i.e., a medium without added glucose), anddemonstrated the existence of significant differences among the strainsregarding their ability to survive exposure to low pH (pH 2.8 with HCl).When the inherent acid resistance of 60 S. enterica strains was recently

b

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Fig. 2. Probability distributions of the inactivation rate (k) values of 60 Salmonella enterica strains in tryptone soy brothwithout dextrose acidified to pH 3.0 (a) or heated at 57°C (b); datafrom Lianou and Koutsoumanis (2013).

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assessed in our laboratory, the observed strain variability of the inacti-vation behavior of the pathogenwas extensive (Fig. 2a). The populationreductions observed after 4h of exposure to pH 3.0 (in TSBwithout dex-trose acidified with HCl) ranged from 0.84 to 5.75log CFU/ml, while theestimated inactivation rate (kacid) values ranged from 0.47 to 3.25 h−1

(Lianou and Koutsoumanis, 2013). With regard to the molecular expla-nation of strain variability of acid inactivation, point or larger mutationsin certain genes (e.g., rpoS) have been suggested as a potential explana-tion for the very different stress responses exhibited by different sero-types or strains of S. enterica (Jørgensen et al., 2000; Humphrey,2004). It has been proposed that, in addition to gene mutations, acidsensitivity may also be associated with reduced level of expression ofRpoS-dependent genes, with the latter being potentially attributed tomutations affecting the translational processing of the RpoS protein or,alternatively, to protein instability (Jørgensen et al., 2000). Given thehigh scientific interest in elucidating themolecular mechanisms under-lying the acid stress responses of S. enterica, gene characterization anddetermination of the role of RpoS in gene expression constitute objec-tives of ongoing research (Jennings et al., 2011). Beyond their value inascertaining the role of specific genes in the acid and other stress resis-tance phenotypes of S. enterica, such research data are expected to alsobe useful in explaining the important strain variability of the stress re-sponses of this organism.

Strain differences in acid inactivation have been also frequently re-ported for E. coli (Arnold and Kaspar, 1995; Benito et al., 1999;Buchanan and Edelson, 1999; Miller and Kaspar, 1994; Samelis et al.,2003). When Buchanan and Edelson (1999) studied the pH-dependentstationary phase ATR of nine strains of enterohemorrhagic E. coli in thepresence of various acidulants, their findings demonstrated significantsurvival variability among the tested strains. Membrane modificationvia the synthesis of cyclopropane fatty acids has been recognized as aphenomenon of major importance in the acid resistance of E. coli. Onthe basis that the synthesis of cyclopropane fatty acids is, at least inpart, under the transcriptional control of RpoS, it has been proposedthat the strain-dependency of this organism's acid survival can be ex-plained to some extent by the partially or totally defective rpoS allelescarried by many strains (Chang and Cronan, 1999).

4.2. Heat inactivation

Strain variability in thermal resistance has beenwell documented inseveral research studies for L. monocytogenes, with some strains being2.5 to 3 times more heat resistant than others (Doyle et al., 2001).Early research data on differences in heat resistance amongL. monocytogenes strains were reported by Golden et al. (1988), whostudied four strains of the organism and estimated decimal reductiontimes (D-values) at 56 °C in tryptose phosphate broth ranging from5.7 to 16 min. Mackey et al. (1990), in a subsequent study, assessed

the heat inactivation of 29 L. monocytogenes strains at 57 °C in broth,and reported a four-fold difference in the estimated D-values (rangingfrom6.5 to 26min) between the least and themost heat resistant strain.In addition to strain variability, the findings of some investigations alsoindicated that the heat resistance of the pathogenmay also vary amongserotypes (Buncic et al., 2001; Francis and O'Beirne, 2005; Sörqvist,1994) or genetic lineages (De Jesús and Whiting, 2003). For example,Buncic et al. (2001) investigated the thermal inactivation of 81L. monocytogenes strains and observed that serotype 4b isolates, onaverage, survived post-cold storage heat treatment better than serotype1/2a isolates. Nonetheless, according to the findings of another study in-vestigating the heat resistance of 25 strains of the pathogen belongingto various serotypes, serotype 4b isolates appeared to have significantlylower heat resistance (i.e., higher death rates) as a group than did iso-lates representing all other serotypes combined (Lianou et al., 2006).Another study highlighting the great differences in heat toleranceamong L. monocytogenes strains was that conducted by Lundén et al.(2008), who reported a 3-log difference in the surviving populationsof 40 strains of the organism after a 40-min heat challenge (55°C) in cul-ture broth. Strain variation has been also demonstrated in several heatinactivation studies of L. monocytogenes in different types of foods, in-cluding meat or meat products, milk, eggs, seafood and vegetables(Ben Embarek and Huss, 1993; Beuchat et al., 1986; Bhaduri et al.,1991; Bradshaw et al., 1985; Foegeding and Leasor, 1990; Gaze et al.,1989; Kim et al., 1994).

Heat inactivation of S. enterica has been studied extensively resultingin a wide range of thermal lethality determinations (Doyle andMazzotta, 2000). In a study comparing the D-values of foodborne path-ogens collected from the literature, themostD-values (i.e., 1161 values)found and reported were for S. enterica (Van Asselt and Zwietering,2006). It has been well acknowledged that the heat resistance ofS. enterica is strain-dependent, with some strains of the organismbeing innately more heat resistant than others (Doyle and Mazzotta,2000). The investigation undertaken by Ng et al. (1969), involvingS. enterica cultures belonging to 75 different serotypes, is, most likely,the oldest study carried out on a large number of strains and serotypesof this pathogen and demonstrating the strain-dependent character ofits thermal resistance. Such an observation was further substantiatedby the findings of subsequent investigations assessing the behavior ofa small or large number of strains of the organism, and undertakenboth in laboratory media and food products (Alvarez et al., 2006;Humphrey et al., 1995; Juneja et al., 2001, 2003; Murphy et al., 1999;Quintavalla et al., 2001; Stopforth et al., 2008). Quintavalla et al.(2001) evaluated the heat resistance of 94 S. enterica strains belongingto different serotypes in culture broth at 58 °C, and reported D-valuesranging from 0.79 to 2.67min. Similarly, considerable D-value variabil-ity among S. enterica strains was observed by Juneja et al. (2001) whoassessed the heat resistance of 35 strains of the organism in chicken

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broth at 58 °C. Nonetheless, given that the S. enterica cultures used inmost of the aforementioned studies were grown in media containingglucose, cross-protection phenomena (i.e., enhanced heat resistance asa result of acid adaptation) cannot be excluded. Research data regardingthe strain variability of the inherent heat resistance of S. enterica werefirst provided by Humphrey et al. (1995), who examined the heat(52°C) inactivation kinetics of stationary-phase cultures of S. EnteritidisPT4 and demonstrated that different isolates can have significantlydifferent survival profiles. The observations made by us in a study eval-uating the inherent heat resistance of S. enterica were similar to theabove, with the thermal survival of the organism appearing to bestrain-specific (Fig. 2b). The populations of 60 strains of the pathogenupon completion of 20-min heat challenge trials at 57°C (in TSBwithoutdextrose) were reduced by 1.96–6.52log CFU/ml, and the estimated in-activation rate (kheat) values ranged from 0.42 to 1.33 min−1 (Lianouand Koutsoumanis, 2013).

A S. enterica strain notorious for its resistance to thermal treatmentsis S. enterica serotype Seftenberg 775 W (Mañas et al., 2003; Murphyet al., 1999; Ng et al., 1969). This particular strain was first mentionedby Winter et al. (1946) as a H2S-negative strain of S. Seftenberg capableof surviving almost 5min of heating at 60°C in liquid egg, while in a sub-sequent investigation it was evaluated as the most heat resistant strainout of 269 salmonellae tested in culture medium (Ng et al., 1969). Dueto its outstanding thermal resistance, and although not associated withhuman disease, S. Seftenberg 775W has been frequently used as a testorganism for the evaluation and validation of thermal processes (Doyleand Mazzotta, 2000). Nonetheless, as supported by research findings,the heat resistance exhibited by the specific strain is exceptional, andshould not be regarded as typical of the serotype Seftenberg and by nomeans of the S. enterica species (Lianou and Koutsoumanis, 2013; Nget al., 1969; Van Asselt and Zwietering, 2006). Serotype-related differ-ences among S. enterica strainswith regard to their heat inactivation pat-terns have been indicated in some cases. For instance, S. Enteritidis hasbeen proven to be more heat resistant than S. Typhimurium in most ex-periments with eggs, but this has not always been true in culture media(Doyle and Mazzotta, 2000; Lianou and Koutsoumanis, 2013). Further-more, given that potential trends related to S. enterica serotypes havebeen frequently indicated in the context of studies involving a limitednumber of strains (Juneja et al., 2003; Stopforth et al., 2008), such obser-vations need to be ascertained through the assessment of the survivalprofiles of multiple strains of the pathogen.

Considerable intra-species variability in survival under lethal heatconditions has been also demonstrated for E. coli (Benito et al., 1999;Duffy et al., 1999; Miller and Kaspar, 1994; Whiting and Golden,2002) and S. aureus (Batish et al., 1991; Rodríguez-Calleja et al., 2006).

4.3. Inactivation by non-thermal processing technologies

Non-thermal approaches have been studied extensively in the past40 years as food processing alternatives capable of prolonging the mi-crobial shelf life of foods while at the same time avoiding some of theunfavorable quality changes that thermal processes often cause (e.g.,protein denaturation, non-enzymatic browning and loss of vitaminsand volatile compounds) (Corbo et al., 2009). Strain differences havealso been reported with regard to the inactivation behavior offoodborne pathogens under the influence of such alternative processingtechnologies including primarily physical processes such as high-pressure processing as well as electromagnetic processes such as irradi-ation and pulsed electric fields.

Simpson and Gilmour (1997) exposed three L. monocytogenesstrains to a range of pressures (300 to 540 MPa) in phosphate-buffered saline and observed a wide variation in their resistance tohigh pressure, an observation that was also made in a series of modelfood systems. Benito et al. (1999) reportedwide differences in the resis-tance of six E. coli O157 strains to high hydrostatic pressure, with themost pressure-resistant strains being more resistant to mild heat

compared to other strains. Their findings also suggested that the pres-sure resistance differencesmay be related to the differential susceptibil-ity of the tested strains to membrane damage. According to the findingsof Alpas et al. (1999), the viability loss (in log cycles) following pressur-ization (in peptone solution) at 345MPa at 25°C ranged from 0.9 to 3.5among nine L. monocytogenes strains, 0.7 to 7.8 among seven S. aureusstrains, 2.8 to 5.6 among six E. coli O157:H7 strains, and 5.5 to 8.3among six S. enterica strains; nonetheless, both the strain and speciesdifferences were greatly reduced when pressurization was applied at50 °C. When the pressure resistance of nine L. monocytogenes strainsand one L. innocua strain in tryptose broth was investigated, the vari-ability observed among strains was significant, with the decrease inlog CFU/ml during the pressure treatment ranging from 1.4 to 4.3 at400MPa and from 3.9 to more than 8.0 at 500MPa (Tay et al., 2003).In a subsequent study, the effect of high pressure on the log reductionof six E. coli O157:H7 strains and five S. enterica serotypes was investi-gated in TSB, sterile distilled water and fruit juices, and considerablestrain variability was observed in some cases (Whitney et al., 2007).Chen et al. (2009) screened 30 L. monocytogenes strains for their pres-sure tolerance phenotype in TSB with yeast extract at 400MPa, and re-ported reductions ranging from 1.9 to 7.1 log CFU/ml. No correlationwas, however, observed between pressure tolerance and other stress(e.g., heat or acid) tolerances. Among four S. enterica strains belongingto different serotypes evaluated in a subsequent study, the strain be-longing to serotype Braenderup was found to be the most pressure re-sistant (Maitland et al., 2011). Finally, as demonstrated by the resultsof a recent study, treatment of 19 strains of C. jejuni at 300MPa, per-formed in minced poultry meat, also revealed an extensive intra-species variation in pressure resistance (Liu et al., 2012).

With regard to inactivation of foodborne pathogens by irradiation,strain variability observations have been made for S. enterica andE. coli O157:H7. Niemira et al. (2003) observed significant variabilityamong six S. enterica strains individually inoculated into orange juiceconcentrate with regard to their response to freezing (−20°C) in com-bination with irradiation (0.5–2.0 kGy), with the exact response beingdose-dependent. When cultures of 24 S. enterica strains suspended inphosphate buffer were subjected to gamma radiation at doses up to1 kGy, the estimated D-values varied from 0.18 to 0.36 kGy (Niemiraet al., 2006). With reference to additional to the abovementionednon-thermal processing technologies, Rodríguez-Calleja et al. (2006)reported that no considerable resistance variation was observedamong 15 S. aureus strains subjected to pulsed electric field or ultra-sound under pressure (manosonication). The results of another study,assessing the resistance of four E. coli strains to pulsed electric field,clearly indicated important strain variability, which appeared to be af-fected by environmental factors (e.g., aw of themedium) and associatedwith the sigma factor RpoS (Somolinos et al., 2008). Lastly, as supportedby the findings of Saldaña et al. (2009), who investigated the effect ofpulsed electric fields against different strains of L. monocytogenes,S. aureus, E. coli and S. Typhimurium, both the resistance and sublethalinjury of each organism depended strongly on the strain tested.

5. Biofilm formation variability

L. monocytogenes strain variability data have been presented in var-ious studies assessing the adherence and biofilm-forming ability of theorganism on different surfaces. Norwood and Gilmour (1999) investi-gated the adherence of 111 L. monocytogenes strains on stainless steelcoupons, and reported important inter-strain variation which appearedto be associated with the strains' serotype and persistence in the foodprocessing environments; strains belonging to serotype 1/2c andpersis-tent strains were found to adhere in significantly greater numbers thanstrains of other serotypes (i.e., 1/2a and 4b) and sporadic strains, respec-tively. In another study, when 13 strains of the pathogen were used toassess their biofilm-forming ability on glass surfaces, it was also noticedthat, regardless of their planktonic growth behavior, they varied

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Fig. 3.Percentage of 60 Salmonella enterica strains exhibiting their highest biofilm-formingability at different conditions of pH (0.5% NaCl–37 °C) (a), NaCl concentration (pH 7.0–37 °C) (b), and temperature (pH 7.0–0.5% NaCl) (c); data from Lianou and Koutsoumanis(2012).

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significantly in their ability to adhere to the surface and form biofilms(Chae and Schraft, 2000). According to the findings of Borucki et al.(2003), who screened 80 L. monocytogenes isolates for their ability toform biofilms, although no considerable differences were detectedamong different serotypes, significant differences were observed be-tween phylogenetic divisions of the pathogen: Division II strains (i.e.,strains belonging to serotypes 1/2a and 1/2c) formed more biofilmthan Division I strains (i.e., strains belonging to serotypes 1/2b and4b). Moreover, these researchers reported that persistent strains (iso-lated from bulk milk samples) showed increased biofilm formation rel-ative to non-persistent strains, and that exopolysaccharide productioncorrelated with cell adherence for high-biofilm-producing strains(Borucki et al., 2003). Using a microtiter plate assay, Nilsson et al.(2011) studied the biofilm-forming ability of 95 L. monocytogenesstrains as a function of environmental conditions, environmental persis-tence status and strain origin and serotype. Their results clearly demon-strated a high inter-strain variation in biofilm formation,while groupingthe isolates by serotypes revealed, in most cases, significantly greaterbiofilm production among serotype 1/2a strains. As illustrated by thefindings of the abovementioned studies, despite the considerable ob-served strain variability, a definite association of certain subtypes of L.monocytogenes with biofilm formation cannot be established. For in-stance, as demonstrated by the results of the studies undertaken byNorwood and Gilmour (1999), Borucki et al. (2003) and Nilsson et al.(2011), strains belonging to serotype 1/2a may or may not be betterbiofilm-formers than strains belonging to other serotypes dependingon the study conditions or design.

Although both organisms are well known for their ability to formbiofilms, S. enterica appears to be a stronger biofilm producer thanL. monocytogenes (Stepanović et al., 2004), while the environmentalconditions (e.g., nutrient content of the medium) favoring biofilmdevelopment also seem to be different for the two pathogens(Stepanović et al., 2004). The strain-dependent character of thebiofilm-forming ability of S. enterica has been well documented.Stepanović et al. (2004) investigated the biofilm production by 122Salmonella strains in different types of broths using a plastic microtiterplate test, and concluded that the nutrient content of the medium sig-nificantly influenced the quantity of produced biofilm, with the lattereffect, however, depending on the tested strain. Oliveira et al. (2006)assessed the adhesion ability of four S. Enteritidis isolates to differentmaterials (polyethylene, polypropylene and granite) commonly usedin kitchens, and observed that the different extents of adhesion ex-hibited by the pathogen could not be explained in terms of surface hy-drophobicity and roughness of the materials tested; hence, their mainconclusion was that the adhesion of the pathogen was strongly strain-dependent, despite the similar degree of hydrophobicity displayed byall the strains assayed. Similar were the findings of a subsequent studycomparing the adhesion of the same four S. Enteritidis isolates tostainless steel, where the physico-chemical properties of the strains(i.e., elemental composition and cell surface hydrophobicity) could notaccount for their differential adhesion ability, and it was, therefore, sug-gested that other factors (e.g., differential production of polysaccha-rides) should also be considered when trying to interpret the strainvariability of the pathogen's biofilm-forming behavior (Oliveira et al.,2007). Agarwal et al. (2011) evaluated the biofilm-forming ability of151 S. enterica strains belonging to 69 serotypes using a microtiterplate assay; these researchers reported that the majority of the testedstrains (87 strains) were moderate biofilm producers, 34 and 29 strainswere weak and strong biofilm producers, respectively, while one straindid not produce any biofilm. Nevertheless, neither the serotype nor thesource of the tested isolates appeared to affect their ability to formbiofilms (Agarwal et al., 2011). Díez-García et al. (2012) assessed theability of 69 S. enterica strains to develop biofilms on polystyrenemicro-well plates, with the tested strains being classified as weak (35strains), moderate (22 strains) or strong (12 strains) biofilm producers.In a recent study carried out in our laboratory, we evaluated the single

effects of pH (3.8–7.0), NaCl concentration (0.5–8.0%) and temperature(4–37°C) on the biofilm-forming ability of 60 S. enterica strains in poly-styrene microtiter plates, and we observed that the strain variabilityof biofilm formation appeared to increase as the environmentalconditions became less favorable for the organism (Lianou andKoutsoumanis, 2012). In addition, the prevailing environmental condi-tions also had a considerable impact on the S. enterica biofilm formationper se, with the exact influence of each parameter, however, dependingon the tested strain. Among the evaluated conditions, most of theS. enterica strainswere clustered as forming their highest amount of bio-film at pH 5.5 (35 strains; 58.3%), at 0.5% NaCl (29 strains; 48.3%) and at25°C (32 strains; 53.3%) (Fig. 3). Although no relationship between thebiofilm-forming ability of the S. enterica strains and their serotype couldbe established based on our findings (Lianou and Koutsoumanis, 2012),differences among serotypes of the pathogen have been reported insome cases. For example, research data have frequently supported thegreater ability of S. enterica serotype Agona to form biofilms comparedto other non-typhoidal S. enterica serotypes (Bridier et al., 2010; Díez-García et al., 2012; Vestby et al., 2009).

In addition to S. enterica and L. monocytogenes, research data alsodemonstrate the potentially extensive strain variation of the biofilm-forming abilities of S. aureus (Kwon et al., 2008; Møretrø et al., 2003;Rode et al., 2007) and E. coli (Reisner et al., 2006; Rivas et al., 2007a,2007b). Variation in biofilm-associated gene expression has been

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frequently reported among strains of foodborne pathogens (Boddickeret al., 2002; Rode et al., 2007). For example, the variability in epithelialbiofilm formation among S. Typhimurium strains, as observed byBoddicker et al. (2002), was attributed to differential expression ofalleles of the fimH adhesion gene. Hence, the potentially great variabilitycharacterizing themolecular mechanisms underlying biofilm formationof a given bacterial species may, at least partially, account for the signif-icant intra-species variability of this phenotypic response.

6. Significance of strain variability for food safety challenge studies

Strain selection is a decision of vital importance when designing andconducting challenge studies aimed at the assessment of the behavior ofbacterial pathogens in food products or in systems simulating food-related environments. It has been recommended that, in order for varia-tions in growth and survival among strains to be accounted for, multiplestrains (3–5) of foodborne pathogens (individually or in combination)should be used in food safety research studies (NACMCF, 2005; Scottet al., 2005). Alternatively, challenge studies can be conducted usingsingle strainswith robust growth or inactivation characteristics as evalu-ated after screening a variety of strains (Scott et al., 2005). Although aninoculum of multiple strains is usually preferred, strain selection shouldbe driven by the nature and/or objectives of the research studies con-ducted. For instance, while multiple strain composites of pathogenswould be more appropriate in studies employing multiple stress condi-tions, the use of one ormore single strains with certain phenotypic char-acteristics (e.g., unique resistance to an environmental condition orapplied intervention)may be required in basic research studies assessingthe mechanisms underlying these characteristics (Lianou et al., 2006;Scott et al., 2005). In any case, characterization of a variety of strainswith regard to phenotypic responses is expected to be very useful, pro-viding all the necessary information for decision making with regard tostrain selection. Nonetheless, when using multiple strains, assessmentof strain compatibility/interactions may be needed in order to minimizethe risk of biased estimates (Juneja et al., 2003; Scott et al., 2005).

In addition to the number of strains used for inoculum preparation,another parameter that should be taken into account when characteriz-ing and selecting strains for use in challenge studies is the origin of theirisolation and their source. Information on the behavior of foodbornepathogens' strains of various origins should be available, and isolates se-lected should be appropriate for the food product being challenged.More specifically, challenge studies on a specific type of food productmay include isolates from a product of this type and from its processingenvironment, as well as clinical isolates from outbreaks related to theproduct to be challenged (Scott et al., 2005). With reference to theirsource, strains used in food safety studies can be either strains isolated(and maybe characterized) by various research groups and maintainedin laboratory collections, or strains obtained from national or interna-tional culture repositories. Although in both cases strains used shouldbe as well characterized as possible, strain variability data derivedfrom strains obtained from national or international culture collectionsare expected to be of great value for challenge studies. As alsocommented by Scott et al. (2005), such strains collections provide re-searchers with standard sets of well characterized (e.g., origin, year ofisolation, serotype, ribotype and pulsotype) isolates, thus allowing fordata comparison among different laboratories.

7. Significance of strain variability for microbial risk assessment

In general, it has been recommended that variability should be quan-titatively expressed in risk estimates to the greatest scientifically achiev-able extent (Codex Alimentarius Commission, 2007). An assumptionfrequentlymade by foodmicrobiologists is that strain-to-strain variationof microbial behavior is equal to or smaller than the experimental varia-tion, and, as such, is not necessary to be determined and characterized(Whiting and Golden, 2002). Nevertheless, intra-species variability of

microbial behavior may have an important impact on the accuracy ofmicrobial risk assessment outcomes (Delignette-Muller and Rosso,2000). In order for both the “hazard characterization” and “exposure as-sessment” components ofmicrobial risk assessment to be credible, suffi-cient information on the distributions of all the parameters involved inrisk estimation is required, and both the uncertainty and variability ofeach parameter need to be distinctively taken into consideration(Anderson andHattis, 1999; Delignette-Muller et al., 2006; Lammerding,1997; Nauta, 2000; Poschet et al., 2003). Uncertainty is usually asso-ciated with imprecise measurements or lack of knowledge of the effectof factors not included in models, and may be reduced by taking addi-tionalmeasurements. On the other hand, variability is irreducible by ad-ditional measurements, because it is mainly associated with straindifferences and corresponds to what is known as “biological variability”(Anderson and Hattis, 1999; Nauta, 2002). Although approaches fortheir dissociation have been proposed (Delignette-Muller et al., 2006;Pouillot et al., 2003), such a task is generally difficult, and uncertaintyand variability are often treated alike by implicitly assuming that eitherone or the other is negligible (Nauta, 2000; Ross and McMeekin, 2003).

In many cases, for the purpose of microbial risk assessment, straineffects in microbial dose–response data are not discerned, meaningthat the approach used does not account for strain variability in patho-genicity and virulence, other than perhaps, recognizing the existence ofavirulent strains (Coleman et al., 2004). Such a default assumption (i.e.,not taking into account the inherent genetic variability among strains ofpathogenic bacteria and the existence of distinct pathotypes), common-ly practiced in microbial risk assessments, can constitute an importantsource of uncertainty in dose–response modeling (Coleman andMarks, 1998; Coleman et al., 2004). Indeed, human clinical data haveclearly demonstrated the need for the development and implementa-tion of biologically-based alternatives, capable of predicting dose–re-sponse as a function, among others, of strain virulence (Coleman et al.,2004; Oscar, 2004). Gene network identification is expected to be veryuseful towards this direction, contributing significantly to thepredictionof virulence genes and, thus, to the characterization ofmicrobial hazardsin the context of microbial risk assessment (Wassenaar et al., 2007). Ingeneral, the development of “omics technologies” (e.g., genome se-quencing, genome-wide transcriptional analysis, proteomics and meta-bolomics) and their application to key foodborne pathogens (S. enterica,C. jejuni, L. monocytogenes and E. coli O157:H7) is expected to facilitatethe assessment of strain variability and to substantiate our understand-ing of the dose–response relationship (Brul et al., 2012).

Depending on the foodborne pathogen of concern, the variability ingrowth dynamics may constitute one of the most important factors af-fecting the level of risk (Augustin et al., 2011; Pouillot and Lubran,2011), and, thus, its explicit consideration in quantitative microbial riskassessment (QMRA) approaches may be of vital importance for theirprecision. However, given that, in addition to strain variability, manyother factors may have a considerable impact on the outcome of QMRA(e.g., product characteristics, time–temperature conditions in the foodsupply chain, consumer behavior), their comparative evaluation andthe determination of each factor's contribution to the overall uncertaintyand variability is also important. Such a comparative appraisement ofeach factor's importance is expected to be useful for the recognition ofthe main effects (and the place that strain variability has among them)that need to be taken into account in QMRA approaches.

Predictivemodeling approaches are of great value in the quantitativeassessment of food-related risks (Nauta, 2002) and considerable effortshave been made in order for various sources of variability to beexpressed and taken into account (Ross and McMeekin, 2003). Deter-ministic models (i.e., models that provide point estimates of microbialconcentrations) have been acknowledged as being incompetent totake into account biological variability, and as such, they have beenquestioned with regard to their value in microbial risk assessment andfood safety management (Juneja et al., 2003; Koseki et al., 2011;Nicolaï and Van Impe, 1996; Poschet et al., 2003). With Monte Carlo

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analysis constituting a very useful tool for incorporating variation on ex-perimental data in quantitative microbiology (Poschet et al., 2003), thevast majority of the modeling approaches developed and implementedfor the description and integration of various sources of microbialgrowth variability (e.g., food characteristics, initial contamination level,individual cell behaviors, biological parameters, storage conditions andfood microflora) are stochastic (Augustin et al., 2011; Couvert et al.,2010; Delignette-Muller et al., 2006; Oscar, 2002, 2008). Nevertheless,strain selection remains an important issue when it comes to the devel-opment of predictive models, particularly when the latter are used forthe purpose of exposure assessment (Nauta and Dufrenne, 1999). A“worst case” scenario (i.e., use of strains with robust growth behavior),which is frequently embraced in predictivemodels, is a subjective situa-tion and may introduce systematic biases into QMRA and result in riskoverestimation (Begot et al., 1997;Nauta andDufrenne, 1999). Similarly,the use of mixtures of representative strains of foodborne pathogens inmodel development for QMRA purposes also imposes a considerablerisk for biased estimates, particularly when knowledge of the interac-tions among the strains used is lacking (Juneja et al., 2003), as discussedpreviously. Therefore, an increasing interest in incorporating growthvariability among strains of foodborne pathogens in predictive modelshas been observed during the last decade. For this purpose, secondarymodels incorporating intra-species variability in their biological param-eters, such as cardinal values and growth parameters, are usuallyexploited (Couvert et al., 2010; Delignette-Muller and Rosso, 2000;Delignette-Muller et al., 2006; Koutsoumanis et al., 2010; Lianou andKoutsoumanis, 2011b; Pouillot et al., 2003).

In the risk assessment of L. monocytogenes in ready-to-eat foods,undertaken by the U.S. Food and Drug Administration and the U.S.Department of Agriculture Food Safety and Inspection Service(USFDA/USDA-FSIS, 2003), an approach known as the “relative rate”approach was used to describe growth rate variability for inclusion instochastic modeling (Ross and McMeekin, 2003). In the context of thisapproach, a relative rate relationship based on the square-root model(Ratkowsky et al., 1982) for temperature was used, with a probabilitydistribution being assigned to the growth rate at a reference tempera-ture (μref) included in the secondarymodel, while theminimumgrowthtemperature (Tmin) was assumed to be constant (Ross and McMeekin,2003; USFDA/USDA-FSIS, 2003). Nonetheless, such an approach resultsin a variability which is not affected by the growth conditions, some-thing which, as demonstrated by research findings (Lianou andKoutsoumanis, 2011a), is not true. Hence, stochastic modeling ap-proaches aiming at describing and expressing strain variability shouldalso explicitly take into account the effect of environmental conditionson this type of variability (Lianou and Koutsoumanis, 2011b).

The increase in the availability and application of omics technologiesas observed the last decadewill, most likely, affect considerably thewaythat microbial risk assessment is carried out. By allowing for mechanis-tic explanations of microbial behavior to be given, these tools have thepotential to fill key knowledge gaps and enrich microbial risk assess-ment, by providing new perspectives on strain variability and physio-logical uncertainty (Brul et al., 2012; Rantsiou et al., 2011).

8. Concluding remarks

The information provided in this review indicates that the variabilityamong strains of foodborne bacterial pathogens with respect to theirbehavior is extensive. Differences in phenotypic responses such as viru-lence, growth, inactivation and biofilm formation among strains of thesamemicrobial species can be significant and should not be overlooked.In addition to strain variability per se, parameters that may have a con-siderable effect on this variability, such as the prevailing environmentalconditions, should also be taken into account. Research data on strainvariability, such as the ones reviewed above, are expected to be usefulin science-based strain selection for use in food safety challenge studies,as well as in the description and integration of this type of variability

in microbial risk assessment. However, as indicated by the strain vari-ability studies discussed in the present review, most of these studieshave been carried out in culture media under laboratory conditions.Given that the generated data should be as relevant as possible to realconditions in foods and food-related environments, it is really impor-tant that more in situ and in vivo studies are conducted and that theirfindings are compared to those of in vitro analyses. Furthermore, dueto the fact that it is often difficult to comparefindings of different studies(e.g., differences in the prior history and the growth phase of the testedstrains, different media etc.), studies assessing strain variability shouldbe as self-sufficient as possible by testing multiple strains, under welldescribed conditions, and with appropriate replicate experiments aswell as control strains. With reference to QMRA approaches, since, ide-ally, variability anduncertainty should be separated, efforts for their dis-sociation should always be made if the credibility of such approaches isto be assured. Finally, in order for the mechanisms underlying the ob-served strain variability of the behavior of foodborne pathogens to beelucidated and understood, phenotypic variability data should be evalu-ated in conjunctionwith correspondingfindings of studies assessing themolecular/physiological basis of this variability.

Acknowledgments

We acknowledge the action THALIS: “Biological Investigation Of theForces that Influence the Life of pathogens having as Mission to Survivein various Lifestyles; BIOFILMS”. The action falls under the OperationalProgramme (OP) “Education and Lifelong Learning (EdLL)” and is co-financed by the European Social Fund (ESF) and National Resources.

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