Strain variability of the behavior of foodborne bacterial pathogens: a review.

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

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International Journal of Food Microbiology journal homepage: www.elsevier.com/locate/ijfoodmicro

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

a r t i c l e

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Article history: Received 22 February 2013 Received in revised form 23 September 2013 Accepted 24 September 2013 Available online 2 October 2013 Keywords: Strain variability Foodborne pathogens Virulence Growth Inactivation Biofilm formation

a b s t r a c t Differences in phenotypic responses among strains of the same microbial species constitute an important source of variability in microbiological studies, and as such they need to be assessed, characterized and taken into account. This review provides a compilation of available research data on the strain variability of four basic behavioral 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 and Salmonella enterica. The implications of strain variability for food safety challenge studies and microbial risk assessment are discussed also. The information provided indicates that the variability among strains of foodborne bacterial pathogens with respect to their behavior can be significant and should not be overlooked. However, in order for the mechanisms underlying the observed strain variability to be elucidated and understood, phenotypic variability data, such as those reviewed here, should be evaluated in conjunction with corresponding findings of studies assessing the molecular/physiological basis of this variability. © 2013 Elsevier B.V. All rights reserved.

Contents

1. 2. 3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . Virulence variability . . . . . . . . . . . . . . . . . . . . Growth variability . . . . . . . . . . . . . . . . . . . . . Inactivation variability . . . . . . . . . . . . . . . . . . . 4.1. Acid inactivation . . . . . . . . . . . . . . . . . . . 4.2. Heat inactivation . . . . . . . . . . . . . . . . . . . 4.3. Inactivation by non-thermal processing technologies . . 5. Biofilm formation variability . . . . . . . . . . . . . . . . . 6. Significance of strain variability for food safety challenge studies 7. Significance of strain variability for microbial risk assessment . . 8. Concluding remarks . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction This review provides a compilation of available research data on the strain variability of the behavior of foodborne bacterial pathogens. The implications of the strain variability of foodborne pathogens' behavior for food safety challenge studies and microbial risk assessment also are discussed. ⁎ Corresponding author. Tel./fax: +30 2310991647. E-mail address: [email protected] (K.P. Koutsoumanis). 0168-1605/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ijfoodmicro.2013.09.016

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Before beginning to review experimental data on bacterial strain variability, it is important to provide a working definition of a bacterial strain. The term “strain” refers to an isolate or a group of isolates that can be distinguished from other isolates of the same bacterial species (Table 1). The process of differentiating bacterial isolates beyond the species level is referred to as “strain typing” or “subtyping” and is based on genotypic (i.e., the genetic information dictating a particular trait) or phenotypic (i.e., visible, expressed traits influenced both by the genotype and environmental factors) characteristics (Wiedmann, 2002). Subtyping methods for bacteria can be separated into conventional methods (e.g.,

A. Lianou, K.P. Koutsoumanis / International Journal of Food Microbiology 167 (2013) 310–321

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Table 1 Definitions of important bacterial subtyping terms. Term

Definition

Reference

Isolate Strain

Pure culture of bacteria, presumably derived from a single organism Isolate or group of isolates that can be distinguished from other isolates of the same species by phenotypic and/or genotypic characteristics Genetically related isolates that are indistinguishable from each other by genetic tests or that are so similar they are presumed to have directly descended from a common ancestor Group of isolates distinguished from others by the antigens expressed on the cell surface as determined by surface structures including lipopolysaccharides, lipotechoic acids, membrane proteins, and extracellular organelles such as flagella and fimbriae Group of isolates sharing particular sets of virulence factors directing them through a particular pathogenesis process

Wiedmann (2002) Wiedmann (2002)

Clonal group Serotype

Pathotype

serotyping and phage typing) and molecular methods (e.g., multilocus enzyme electrophoresis, ribotyping, pulsed-field gel electrophoresis and DNA-sequencing methods) (Graves et al., 2007). Depending on the intended application, criteria for the selection of a subtyping method include the discriminatory power and reproducibility of the method, its applicability and ease of use, as well as the cost involved (Wiedmann, 2002). With the present review discussing strain variability, in addition to “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 the same microbial species, referred to as “strain variability”, constitute an important source of variability in microbiological studies (Whiting and Golden, 2002). This means that research findings referring to a certain microbial strain cannot be extended to other strains of the same species. Thus, information regarding the strain variability of phenotypic responses of foodborne pathogens under various environmental conditions is expected to be valuable for the purpose of strain selection in food safety studies. Furthermore, as suggested by subtyping data, different strains of foodborne pathogens are differently associated with human disease and such differences can be attributed, among others, to the hardy nature of certain strains enabling them to survive and proliferate in food-related environments, or to their increased virulence towards humans (Sauders et al., 2004; Velge et al., 2005; Wiedmann, 2002). Hence, strain variability data can also facilitate the assessment of the relationships among various characteristics of foodborne pathogens including their virulence, distribution and epidemiology. Finally, as frequently commented by various researchers, the intra-species variability of microbial behavior may have an important impact on the accuracy of microbial risk assessment outcomes, and, therefore, should be systematically assessed and accounted for in the framework of such approaches (Coleman et al., 2003; Delignette-Muller and Rosso, 2000; Pouillot and Lubran, 2011). The strain variability data reviewed herein refer to four basic behavioral aspects of foodborne pathogens including: (i) virulence; (ii) growth; (iii) inactivation; and (iv) biofilm formation. A particular emphasis with regard to the information provided in this review is placed on Listeria monocytogenes, the foodborne pathogen with the most abundant published literature on strain variability overall, and Salmonella enterica. Recently published research data collected in our laboratory and referring to the phenotypic strain variability of S. enterica are briefly presented and discussed. 2. Virulence variability The virulence potential of intracellular pathogenic bacteria has been traditionally evaluated via the use of tissue culture (in vitro) and/or animal (in vivo) models. Commonly used tissue culture models include the human intestinal epithelial cell line Caco-2 as well as mammalian epithelial and macrophage cell lines (Kathariou, 2002; Poli et al., 2012; Velge and Roche, 2010). Animal models that have been used for the purpose of virulence characterization and comparison of bacterial pathogens' strains include guinea pigs, chick embryos, rodents, birds and large farm animals, with the most extensively used model, however, being

Wiedmann (2002) Graves et al. (2007)

Marrs et al. (2005)

the murine model (Hébrard et al., 2011; Humphrey et al., 1996, 1998; Kathariou, 2002; Velge and Roche, 2010). The emergence of sophisticated imaging and molecular genetic tools has further facilitated the study of the events underlying the disease-causing ability of pathogenic bacteria (Cordwell et al., 2001; Hébrard et al., 2011), while, the combined evaluation of molecular subtyping and food survey (i.e., prevalences in foods) data is also expected to be useful in attributing risk of disease to certain foodborne pathogens' strains or subtypes (Chen et al., 2006). L. monocytogenes has been traditionally regarded as pathogenic at the species level, with a generally accepted belief that all isolates of the organism should be considered as potentially virulent and capable of causing disease (McLauchlin et al., 2004; Rocourt et al., 2000). Nonetheless, as demonstrated by experimental data collected over the last decade, L. monocytogenes exhibits a significant serotype/strain variation in virulence and pathogenicity, with many epidemic strains being highly infective and sometimes deadly, while others (especially food and environmental isolates) being significantly less virulent (Velge and Roche, 2010). Jacquet et al. (2002) reported extensive virulence heterogeneity, as illustrated by distinct differences in proteins essential for infection expressed by strains of different serotypes, origins, or strains causing different forms of listeriosis. Extensive virulence variation among L. monocytogenes strains has been also demonstrated using experimental animal models (Brosch et al., 1993; Buncic et al., 2001; Conner et al., 1989), and source- or subtype-related differences have been indicated in some cases (Avery and Buncic, 1997; Barbour et al., 2001; Buncic et al., 2001; Jensen et al., 2008; Nørrung and Andersen, 2000). For instance, Nørrung and Andersen (2000) reported that strains belonging to electrophoretic types 2 and 4 were less virulent for chick embryos than strains of other electrophoretic types, and strains from clinical cases were more virulent than strains from foods. Similarly, when the behavior of four L. monocytogenes strains was evaluated and compared using selected virulence models, clinical strains appeared to have a higher virulence potential than fish processing plant persistent strains (Jensen et al., 2008). Moreover, according to the findings of Buncic et al. (2001), serotype 4b strains, as a group, tended to have higher pathogenicity for chick embryos, when transferred from cold storage (4 °C) to body temperature (37 °C), than the group of serotype 1/2a strains. Indeed, of the 13 L. monocytogenes serotypes, only three (1/2a, 1/2b and 4b) are responsible for ca. 96% of human infections, and serotype 4b has been primarily associated with large listeriosis outbreaks whereas serotype 1/2a has been mainly linked to sporadic cases (Velge and Roche, 2010). Similarly, L. monocytogenes isolates belonging to the phylogenetic lineage I are more common among human listeriosis cases, while isolates from lineages II and III have been mainly associated with sporadic human and animal listeriosis, respectively. These observations, in conjunction with cytopathogenicity and dose–response data, are considered to be indications of differences in pathogenic potential among clonal groups of L. monocytogenes (Chen et al., 2006; Gray et al., 2004; Jeffers et al., 2001; Mereghetti et al., 2004; Wiedmann et al., 1997). Nevertheless, so far no clear direct relationship has been observed between the abovementioned subgroups and the virulence of L. monocytogenes strains (Velge and Roche, 2010). Although the 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 expression may be different among different strains, or certain strains may possess additional, currently unidentified, virulence determinants (Kathariou, 2002). Studies investigating the molecular basis underlying the differences in pathogenic potential among L. monocytogenes strains have revealed the existence of serotype-specific sequences in the genome of serotype 4b strains, most likely reflecting evolutionary differentiation (Doumith et al., 2004; Lei et al., 2001). With regard to S. enterica, the species includes more than 2500 serologically distinct types, or serotypes, which, despite their genetic relatedness, vary significantly in their epidemiology (Fierer and Guiney, 2001; Velge et al., 2005). Such serotype-related differences in the epidemiology of the organism are assumed to be driven by a corresponding diversity in the virulence traits underlying the different clinical outcomes of salmonellosis (Fierer and Guiney, 2001). In addition to serotype-specific differences, there are research data indicating the existence of considerable virulence variability among S. enterica strains belonging to the same serotype. In a study undertaken by Humphrey et al. (1996), two S. enterica serotype Enteritidis PT4 isolates, differing in their inherent resistances to heat, acid, H2O2 and their ability to survive on surfaces, were used to infect mice, day-old chicks or lay hens. The investigators observed that between the two isolates studied, the acid-, heat-, H2O2- and surface-tolerant isolate was more virulent in mice and more invasive in laying hens (particularly in reproductive tissue cells) than the other (Humphrey et al., 1996). The principal objective of a subsequent investigation was the identification, if any, of phenotypic traits (i.e., tolerance to certain hostile environments) with potential value as pathogenicity markers in S. Enteritidis PT4. It was concluded that differences in in vitro acid-, heat- or H2O2-tolerance had no effect on the ability of the isolates to multiply in the spleens of orally infected mice; among the traits tested, only the ability to survive on surfaces could serve as a potential marker of pathogenicity (Humphrey et al., 1998). Using microarray analysis Zou et al. (2011) assayed the virulence gene profiles in S. enterica isolates from food and/or food animal environments, and reported a considerable variability among the strains tested which, 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 pathogenesis of S. enterica. Salmonella pathogenicity islands (SPI) contribute to host cell invasion and intracellular pathogenesis; at present, 12 SPI have been described with their distribution in S. enterica serotypes having the potential to be markedly different (Hensel, 2004). Information pertinent to the pathogenesis and virulence strategies of S. enterica has been reviewed in several publications (Andrews-Polymenis et al., 2010; Bäumler et al., 2011; Bueno et al., 2007; Gordon, 2011). The mechanisms underlying the virulence diversity of S. enterica strains have constituted the objective of extensive research, with research data indicating that genetic variations, or polymorphisms, are particularly prominent in two general classes of loci: (i) genes encoding surface structures such as lipopolysaccharides, flagella and fimbriae; and (ii) specific virulence genes encoding factors that modify host cell physiology or protect the pathogen from the antimicrobial systems of the host (Fierer and Guiney, 2001). The surface structures not only affect the virulence of bacteria, but also constitute key targets of the host immune system, resulting in selective pressure to generate genetic polymorphisms coding for antigenic diversity. In addition, specific virulence determinants may be clustered together on polymorphic pathogenicity islands or located on transmissible genetic elements (e.g., plasmids or phages). Such arrangements facilitate the modular transmission of genes involved in pathogenesis and, thus, increase the diversity in virulence phenotypes among strains (Fierer and Guiney, 2001). Indeed, according to recent research findings, novel mobile genetic elements involved in gene dissemination linked to S. enterica virulence have been identified and characterized, and the abundance of such elements may facilitate the emergence of strains with novel combinations of pathogenic traits (Moreno Switt et al., 2012).

In addition to L. monocytogenes and S. enterica, strain variability in virulence potential has been also documented for other foodborne pathogens including enteropathogenic and enterohemorrhagic Escherichia coli (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 growth kinetic behavior among L. monocytogenes strains has been demonstrated at several instances, with the first reports dating back to the late 1980s (Junttila et al., 1988; Rosenow and Marth, 1987; Walker et al., 1990). Barbosa et al. (1994) compared 39 L. monocytogenes strains with respect to their growth potential at 4, 10 and 37 °C, and their results demonstrated a highly strain-dependent growth behavior of the pathogen as evaluated based on the estimated values of lag phase, exponential growth rate and generation time. Growth differences among four strains of the organism were also documented in vacuum-packaged ground beef of normal or high pH stored at 4°C (Barbosa et al., 1995). Avery and Buncic (1997) reported that clinical L. monocytogenes isolates exhibited on average a shorter lag phase compared to meat isolates in culture broth at 37 °C, a difference which was even more evident when cultures were previously stored at 4 °C under starvation. When the growth of 58 L. monocytogenes strains was evaluated 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 variability of the estimated lag phase among the strains was extensive under all the tested conditions (Begot et al., 1997). The findings of subsequent investigations were similar with regard to the important intra-species variability 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 growth behavior in culture broth (pH 6.5 and 0.1 M lactate) at 5 or 35 °C, and reported considerable strain and, in some cases, intra-lineage variation; at 5 °C, the estimated lag phase values ranged from 0.9 to 4.83 days and growth rate values from 0.33 to 0.59 log units per day. Similarly, as reported by Uyttendaele et al. (2004), the response of L. monocytogenes to suboptimal growth conditions in culture broth (at different combinations of temperature, pH, aw, and NaCl and sodium lactate concentrations) was shown to be strain-dependent, while strain variation was also observed when growth of selected strains was evaluated in modified broth simulating conditions associated with cooked ham or pâté. In general, as supported by many research findings, the growth variability among strains of L. monocytogenes appears to increase at growth conditions, and particularly temperatures, away from the optimum for this organism (Barbosa et al., 1994; Begot et al., 1997; De Jesús and Whiting, 2003; Lebert et al., 1998; Lianou et al., 2006). With reference to S. enterica, the available data on growth differences among strains of the organism are relatively few. Fehlhaber and Krüger (1998) assessed the growth of 45 S. Enteritidis food isolates in culture broth over a temperature range from 7 to 42 °C and reported considerable strain-specific differences in the estimated generation times. Furthermore, and in agreement with corresponding findings for L. monocytogenes, these researchers observed that generation time variability increased as temperature moved away from the optimal range, with variation coefficients tending to rise as temperature fell (Fehlhaber and Krüger, 1998). In a recent study undertaken by DíezGarcía et al. (2012), the growth kinetic behavior of a total of 69 S. enterica strains belonging to 10 serotypes was evaluated, and it was observed that the values of the growth parameters (i.e., lag phase and growth rate) varied among the tested serotypes. In a study undertaken in our laboratory, aimed at the evaluation of the growth variability among S. enterica strains as affected by the growth environment, the kinetic behavior of 60 isolates of the pathogen (belonging to various serotypes) was assessed at 37 °C in tryptone soy broth (TSB) of different


pH values (4.3–7.0) and NaCl concentrations (0.5–6.0%) (Lianou and Koutsoumanis, 2011a). It was observed that the variability of the estimated maximum specific growth rate (μmax) values among the tested strains was 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 to 1.67 at pH 5.0, from 0.78 to 1.37 at pH 4.5, and from 0.68 to 1.22 at pH 4.3 (Fig. 1a). In TSB of pH 7.0, the mean μmax (h−1) ranged from 1.51 to 2.13 at 0.5% NaCl, from 0.96 to 1.39 at 3.5% NaCl, from 0.74 to 1.10 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 conditions became more stressful both in terms of pH and NaCl (Lianou and Koutsoumanis, 2011a). With the exception of the abovementioned studies, the rest of the available research data regarding the growth behavior of S. enterica refer to a limited number of strains and have not indicated considerable strain variability (Juneja et al., 2003; Membré et al., 2005; Oscar, 2000). Additional foodborne pathogens for which strain-dependent differences in growth behavior have been documented include E. coli O157: H7 (Nauta and Dufrenne, 1999; Palumbo et al., 1995; Whiting and Golden, 2002) and S. aureus (Dengremont and Membré, 1995; Lindqvist, 2006). In a study carried out to assess the growth behavior of 17 E. coli O157:H7 strains in culture broth of pH 5.3 and 1.5% NaCl at 15 °C, extensive strain variability was demonstrated in the estimated growth kinetic parameters, with the lag phase varying from 13.7 to 55.6 h and the exponential growth rate varying from 0.055 to 0.106 log/h (Whiting and Golden, 2002). Likewise, Lindqvist (2006) reported important growth variability among 34 S. aureus strains, with the coefficient of variation of growth parameters being up to six times larger among strains than within strains. In addition to the strain variability of the growth behavior demonstrated by the findings of the abovementioned studies, a corresponding variability is also expected to be observed in the growth ability of foodborne pathogens (i.e., variability of the growth/no growth boundaries among different strains). Nevertheless, the available research data 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 inactivation behavior of L. monocytogenes under low-pH conditions are numerous. The screening of 30 L. monocytogenes strains of both clinical and food origin revealed extensive inter-strain variability with respect to their response to acid stress, as imposed by exposure to pH 2.5 (using HCl as the acidulant) for 2 h in culture broth. Furthermore, a potential association between acid resistance and source of strain isolation

a 2.5

µmax (h-1)

2.0

313

was proposed, as none of the clinical isolates demonstrated a significant acid sensitivity (Dykes and Moorhead, 2000). Important variation in acid resistance, evaluated in broth acidified to 3.5 using lactic acid, among strains of the pathogen was also reported by Francis and O'Beirne (2005). Liu et al. (2005) tested the acid tolerance of six L. monocytogenes strains of known virulence (three virulent and three avirulent strains) to pH values ranging from 2.0 to 5.0 (using HCl), and reported that, although strain differences were observed, both virulent and avirulent strains were able to tolerate pH values of 3.0 or lower. In a subsequent study assessing, among others, the acid resistance variation in culture broth (pH 3.0 with lactic acid) among 25 L. monocytogenes strains of various serotypes and origins, extensive strain variation was demonstrated with the estimated acid death rates ranging from 0.012 to 0.134 log CFU/ml/min (Lianou et al., 2006). According to the findings of Lundén et al. (2008), great differences in acid tolerance (exposure to pH 2.4 for 2 h in broth acidified with HCl) were observed among 40 strains of L. monocytogenes, while a potential association between the pathogen's acid tolerance and its persistence also was indicated. Acid resistance differences among L. monocytogenes strains have been associated with respective strain differences at the molecular level, as well as with cell membrane features that determine the pathways of H+ influx and efflux across the membrane (Cotter et al., 2005; Olier et al., 2004; Phan-Thanh et al., 2000). With reference to S. enterica, most of the published research data regarding the strain variability of its acid resistance have been presented in the form of side observations, in the context of investigations not specifically 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 are some studies reporting acid inactivation differences among multiple strains of the pathogen. De Jonge et al. (2003) evaluated both the logand stationary-phase acid tolerance response (ATR) of several S. enterica serotype Typhimurium strains, both DT104 and non-DT104 isolates, and reported significant variation among the tested strains regarding their ability to survive extreme low-pH (using HCl) environments. Similarly in a subsequent study, Berk et al. (2005) reported a considerable strain variability when assessing the survival profiles of acid-adapted cultures (i.e., growth overnight at pH 5.0 prior to use in acid challenge experiments) of 37 S. Typhimurium strains during exposure to pH 2.5 for 2h in broth acidified using HCl. To our knowledge, the research studies reporting on the inherent and pH-independent acid resistance variability among multiple S. enterica strains are scarce. One such study is that carried out by Jørgensen et al. (2000) who investigated the stress resistance of 38 strains of S. Typhimurium DT104, grown to stationary phase in nutrient broth (i.e., a medium without added glucose), and demonstrated the existence of significant differences among the strains regarding 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

Q1

2.5

Min Median

2.0

Max Q3

1.5

1.5

1.0

1.0

0.5

0.5 0.0

0.0 4.3

4.5

5.0

pH

5.5

0.5

3.5

4.5

6.0

%NaCl

Fig. 1. Boxplots of the maximum specific growth rate (μmax) values of 60 Salmonella enterica strains in tryptone soy broth of different pH values (0.5% NaCl) (a) and NaCl concentrations (pH 7.0) (b); data from Lianou and Koutsoumanis (2011a).

314


a

b

0.25

0.40 0.35 0.30

Probability

Probability

0.20 0.15 0.10

0.25 0.20 0.15 0.10

0.05

0.05 0.00 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0

kacid (h-1)

0.00 0.4

0.6

0.7

0.9

1.0

1.2

1.3

1.5

1.6

kheat (min-1)

Fig. 2. Probability distributions of the inactivation rate (k) values of 60 Salmonella enterica strains in tryptone soy broth without dextrose acidified to pH 3.0 (a) or heated at 57 °C (b); data from Lianou and Koutsoumanis (2013).

assessed in our laboratory, the observed strain variability of the inactivation behavior of the pathogen was extensive (Fig. 2a). The population reductions observed after 4h of exposure to pH 3.0 (in TSB without dextrose acidified with HCl) ranged from 0.84 to 5.75 log CFU/ml, while the estimated inactivation rate (kacid) values ranged from 0.47 to 3.25 h−1 (Lianou and Koutsoumanis, 2013). With regard to the molecular explanation of strain variability of acid inactivation, point or larger mutations in certain genes (e.g., rpoS) have been suggested as a potential explanation for the very different stress responses exhibited by different serotypes or strains of S. enterica (Jørgensen et al., 2000; Humphrey, 2004). It has been proposed that, in addition to gene mutations, acid sensitivity may also be associated with reduced level of expression of RpoS-dependent genes, with the latter being potentially attributed to mutations affecting the translational processing of the RpoS protein or, alternatively, to protein instability (Jørgensen et al., 2000). Given the high scientific interest in elucidating the molecular mechanisms underlying the acid stress responses of S. enterica, gene characterization and determination of the role of RpoS in gene expression constitute objectives of ongoing research (Jennings et al., 2011). Beyond their value in ascertaining the role of specific genes in the acid and other stress resistance phenotypes of S. enterica, such research data are expected to also be useful in explaining the important strain variability of the stress responses of this organism. Strain differences in acid inactivation have been also frequently reported 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-dependent stationary phase ATR of nine strains of enterohemorrhagic E. coli in the presence of various acidulants, their findings demonstrated significant survival variability among the tested strains. Membrane modification via the synthesis of cyclopropane fatty acids has been recognized as a phenomenon of major importance in the acid resistance of E. coli. On the basis that the synthesis of cyclopropane fatty acids is, at least in part, under the transcriptional control of RpoS, it has been proposed that the strain-dependency of this organism's acid survival can be explained to some extent by the partially or totally defective rpoS alleles carried by many strains (Chang and Cronan, 1999). 4.2. Heat inactivation Strain variability in thermal resistance has been well documented in several research studies for L. monocytogenes, with some strains being 2.5 to 3 times more heat resistant than others (Doyle et al., 2001). Early research data on differences in heat resistance among L. monocytogenes strains were reported by Golden et al. (1988), who studied four strains of the organism and estimated decimal reduction times (D-values) at 56 °C in tryptose phosphate broth ranging from 5.7 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 (ranging from 6.5 to 26min) between the least and the most heat resistant strain. In addition to strain variability, the findings of some investigations also indicated that the heat resistance of the pathogen may also vary among serotypes (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 81 L. monocytogenes strains and observed that serotype 4b isolates, on average, survived post-cold storage heat treatment better than serotype 1/2a isolates. Nonetheless, according to the findings of another study investigating the heat resistance of 25 strains of the pathogen belonging to various serotypes, serotype 4b isolates appeared to have significantly lower heat resistance (i.e., higher death rates) as a group than did isolates representing all other serotypes combined (Lianou et al., 2006). Another study highlighting the great differences in heat tolerance among L. monocytogenes strains was that conducted by Lundén et al. (2008), who reported a 3-log difference in the surviving populations of 40 strains of the organism after a 40-min heat challenge (55°C) in culture broth. Strain variation has been also demonstrated in several heat inactivation studies of L. monocytogenes in different types of foods, including 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 resulting in a wide range of thermal lethality determinations (Doyle and Mazzotta, 2000). In a study comparing the D-values of foodborne pathogens collected from the literature, the most D-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 of S. enterica is strain-dependent, with some strains of the organism being innately more heat resistant than others (Doyle and Mazzotta, 2000). The investigation undertaken by Ng et al. (1969), involving S. enterica cultures belonging to 75 different serotypes, is, most likely, the oldest study carried out on a large number of strains and serotypes of this pathogen and demonstrating the strain-dependent character of its thermal resistance. Such an observation was further substantiated by the findings of subsequent investigations assessing the behavior of a small or large number of strains of the organism, and undertaken both 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 belonging to different serotypes in culture broth at 58 °C, and reported D-values ranging from 0.79 to 2.67 min. Similarly, considerable D-value variability among S. enterica strains was observed by Juneja et al. (2001) who assessed the heat resistance of 35 strains of the organism in chicken


broth at 58 °C. Nonetheless, given that the S. enterica cultures used in most of the aforementioned studies were grown in media containing glucose, cross-protection phenomena (i.e., enhanced heat resistance as a result of acid adaptation) cannot be excluded. Research data regarding the strain variability of the inherent heat resistance of S. enterica were first provided by Humphrey et al. (1995), who examined the heat (52 °C) inactivation kinetics of stationary-phase cultures of S. Enteritidis PT4 and demonstrated that different isolates can have significantly different survival profiles. The observations made by us in a study evaluating the inherent heat resistance of S. enterica were similar to the above, with the thermal survival of the organism appearing to be strain-specific (Fig. 2b). The populations of 60 strains of the pathogen upon completion of 20-min heat challenge trials at 57°C (in TSB without dextrose) were reduced by 1.96–6.52 log CFU/ml, and the estimated inactivation rate (kheat) values ranged from 0.42 to 1.33 min−1 (Lianou and Koutsoumanis, 2013). A S. enterica strain notorious for its resistance to thermal treatments is S. enterica serotype Seftenberg 775 W (Mañas et al., 2003; Murphy et al., 1999; Ng et al., 1969). This particular strain was first mentioned by Winter et al. (1946) as a H2S-negative strain of S. Seftenberg capable of surviving almost 5 min of heating at 60°C in liquid egg, while in a subsequent investigation it was evaluated as the most heat resistant strain out of 269 salmonellae tested in culture medium (Ng et al., 1969). Due to its outstanding thermal resistance, and although not associated with human disease, S. Seftenberg 775 W has been frequently used as a test organism for the evaluation and validation of thermal processes (Doyle and Mazzotta, 2000). Nonetheless, as supported by research findings, the heat resistance exhibited by the specific strain is exceptional, and should not be regarded as typical of the serotype Seftenberg and by no means of the S. enterica species (Lianou and Koutsoumanis, 2013; Ng et al., 1969; Van Asselt and Zwietering, 2006). Serotype-related differences among S. enterica strains with regard to their heat inactivation patterns have been indicated in some cases. For instance, S. Enteritidis has been proven to be more heat resistant than S. Typhimurium in most experiments with eggs, but this has not always been true in culture media (Doyle and Mazzotta, 2000; Lianou and Koutsoumanis, 2013). Furthermore, given that potential trends related to S. enterica serotypes have been frequently indicated in the context of studies involving a limited number of strains (Juneja et al., 2003; Stopforth et al., 2008), such observations need to be ascertained through the assessment of the survival profiles of multiple strains of the pathogen. Considerable intra-species variability in survival under lethal heat conditions 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 past 40 years as food processing alternatives capable of prolonging the microbial shelf life of foods while at the same time avoiding some of the unfavorable quality changes that thermal processes often cause (e.g., protein denaturation, non-enzymatic browning and loss of vitamins and volatile compounds) (Corbo et al., 2009). Strain differences have also been reported with regard to the inactivation behavior of foodborne pathogens under the influence of such alternative processing technologies including primarily physical processes such as highpressure processing as well as electromagnetic processes such as irradiation and pulsed electric fields. Simpson and Gilmour (1997) exposed three L. monocytogenes strains to a range of pressures (300 to 540 MPa) in phosphatebuffered saline and observed a wide variation in their resistance to high pressure, an observation that was also made in a series of model food systems. Benito et al. (1999) reported wide differences in the resistance of six E. coli O157 strains to high hydrostatic pressure, with the most pressure-resistant strains being more resistant to mild heat

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compared to other strains. Their findings also suggested that the pressure resistance differences may be related to the differential susceptibility of the tested strains to membrane damage. According to the findings of Alpas et al. (1999), the viability loss (in log cycles) following pressurization (in peptone solution) at 345 MPa at 25 °C ranged from 0.9 to 3.5 among nine L. monocytogenes strains, 0.7 to 7.8 among seven S. aureus strains, 2.8 to 5.6 among six E. coli O157:H7 strains, and 5.5 to 8.3 among six S. enterica strains; nonetheless, both the strain and species differences were greatly reduced when pressurization was applied at 50 °C. When the pressure resistance of nine L. monocytogenes strains and one L. innocua strain in tryptose broth was investigated, the variability observed among strains was significant, with the decrease in log CFU/ml during the pressure treatment ranging from 1.4 to 4.3 at 400 MPa and from 3.9 to more than 8.0 at 500 MPa (Tay et al., 2003). In a subsequent study, the effect of high pressure on the log reduction of six E. coli O157:H7 strains and five S. enterica serotypes was investigated in TSB, sterile distilled water and fruit juices, and considerable strain variability was observed in some cases (Whitney et al., 2007). Chen et al. (2009) screened 30 L. monocytogenes strains for their pressure tolerance phenotype in TSB with yeast extract at 400 MPa, and reported reductions ranging from 1.9 to 7.1 log CFU/ml. No correlation was, however, observed between pressure tolerance and other stress (e.g., heat or acid) tolerances. Among four S. enterica strains belonging to different serotypes evaluated in a subsequent study, the strain belonging to serotype Braenderup was found to be the most pressure resistant (Maitland et al., 2011). Finally, as demonstrated by the results of a recent study, treatment of 19 strains of C. jejuni at 300 MPa, performed in minced poultry meat, also revealed an extensive intraspecies 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 and E. coli O157:H7. Niemira et al. (2003) observed significant variability among six S. enterica strains individually inoculated into orange juice concentrate with regard to their response to freezing (−20 °C) in combination with irradiation (0.5–2.0 kGy), with the exact response being dose-dependent. When cultures of 24 S. enterica strains suspended in phosphate buffer were subjected to gamma radiation at doses up to 1 kGy, the estimated D-values varied from 0.18 to 0.36 kGy (Niemira et al., 2006). With reference to additional to the abovementioned non-thermal processing technologies, Rodríguez-Calleja et al. (2006) reported that no considerable resistance variation was observed among 15 S. aureus strains subjected to pulsed electric field or ultrasound 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 affected by environmental factors (e.g., aw of the medium) and associated with the sigma factor RpoS (Somolinos et al., 2008). Lastly, as supported by the findings of Saldaña et al. (2009), who investigated the effect of pulsed electric fields against different strains of L. monocytogenes, S. aureus, E. coli and S. Typhimurium, both the resistance and sublethal injury of each organism depended strongly on the strain tested. 5. Biofilm formation variability L. monocytogenes strain variability data have been presented in various studies assessing the adherence and biofilm-forming ability of the organism on different surfaces. Norwood and Gilmour (1999) investigated the adherence of 111 L. monocytogenes strains on stainless steel coupons, and reported important inter-strain variation which appeared to be associated with the strains' serotype and persistence in the food processing environments; strains belonging to serotype 1/2c and persistent strains were found to adhere in significantly greater numbers than strains of other serotypes (i.e., 1/2a and 4b) and sporadic strains, respectively. In another study, when 13 strains of the pathogen were used to assess their biofilm-forming ability on glass surfaces, it was also noticed that, regardless of their planktonic growth behavior, they varied


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 polystyrene microtiter plates, and we observed that the strain variability of biofilm formation appeared to increase as the environmental conditions became less favorable for the organism (Lianou and Koutsoumanis, 2012). In addition, the prevailing environmental conditions also had a considerable impact on the S. enterica biofilm formation per se, with the exact influence of each parameter, however, depending on the tested strain. Among the evaluated conditions, most of the S. enterica strains were clustered as forming their highest amount of biofilm at pH 5.5 (35 strains; 58.3%), at 0.5% NaCl (29 strains; 48.3%) and at 25 °C (32 strains; 53.3%) (Fig. 3). Although no relationship between the biofilm-forming ability of the S. enterica strains and their serotype could be established based on our findings (Lianou and Koutsoumanis, 2012), differences among serotypes of the pathogen have been reported in some cases. For example, research data have frequently supported the greater ability of S. enterica serotype Agona to form biofilms compared to other non-typhoidal S. enterica serotypes (Bridier et al., 2010; DíezGarcía et al., 2012; Vestby et al., 2009). In addition to S. enterica and L. monocytogenes, research data also demonstrate the potentially extensive strain variation of the biofilmforming 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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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 to form biofilms, although no considerable differences were detected among different serotypes, significant differences were observed between phylogenetic divisions of the pathogen: Division II strains (i.e., strains belonging to serotypes 1/2a and 1/2c) formed more biofilm than Division I strains (i.e., strains belonging to serotypes 1/2b and 4b). Moreover, these researchers reported that persistent strains (isolated from bulk milk samples) showed increased biofilm formation relative to non-persistent strains, and that exopolysaccharide production correlated 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. monocytogenes strains as a function of environmental conditions, environmental persistence status and strain origin and serotype. Their results clearly demonstrated a high inter-strain variation in biofilm formation, while grouping the isolates by serotypes revealed, in most cases, significantly greater biofilm production among serotype 1/2a strains. As illustrated by the findings of the abovementioned studies, despite the considerable observed strain variability, a definite association of certain subtypes of L. monocytogenes with biofilm formation cannot be established. For instance, as demonstrated by the results of the studies undertaken by Norwood and Gilmour (1999), Borucki et al. (2003) and Nilsson et al. (2011), strains belonging to serotype 1/2a may or may not be better biofilm-formers than strains belonging to other serotypes depending on the study conditions or design. Although both organisms are well known for their ability to form biofilms, S. enterica appears to be a stronger biofilm producer than L. monocytogenes (Stepanović et al., 2004), while the environmental conditions (e.g., nutrient content of the medium) favoring biofilm development also seem to be different for the two pathogens (Stepanović et al., 2004). The strain-dependent character of the biofilm-forming ability of S. enterica has been well documented. Stepanović et al. (2004) investigated the biofilm production by 122 Salmonella strains in different types of broths using a plastic microtiter plate test, and concluded that the nutrient content of the medium significantly influenced the quantity of produced biofilm, with the latter effect, however, depending on the tested strain. Oliveira et al. (2006) assessed the adhesion ability of four S. Enteritidis isolates to different materials (polyethylene, polypropylene and granite) commonly used in kitchens, and observed that the different extents of adhesion exhibited by the pathogen could not be explained in terms of surface hydrophobicity and roughness of the materials tested; hence, their main conclusion was that the adhesion of the pathogen was strongly straindependent, despite the similar degree of hydrophobicity displayed by all the strains assayed. Similar were the findings of a subsequent study comparing the adhesion of the same four S. Enteritidis isolates to stainless steel, where the physico-chemical properties of the strains (i.e., elemental composition and cell surface hydrophobicity) could not account for their differential adhesion ability, and it was, therefore, suggested that other factors (e.g., differential production of polysaccharides) should also be considered when trying to interpret the strain variability of the pathogen's biofilm-forming behavior (Oliveira et al., 2007). Agarwal et al. (2011) evaluated the biofilm-forming ability of 151 S. enterica strains belonging to 69 serotypes using a microtiter plate assay; these researchers reported that the majority of the tested strains (87 strains) were moderate biofilm producers, 34 and 29 strains were weak and strong biofilm producers, respectively, while one strain did not produce any biofilm. Nevertheless, neither the serotype nor the source of the tested isolates appeared to affect their ability to form biofilms (Agarwal et al., 2011). Díez-García et al. (2012) assessed the ability of 69 S. enterica strains to develop biofilms on polystyrene micro-well plates, with the tested strains being classified as weak (35 strains), moderate (22 strains) or strong (12 strains) biofilm producers. In a recent study carried out in our laboratory, we evaluated the single

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Temperature (°C) Fig. 3. Percentage of 60 Salmonella enterica strains exhibiting their highest biofilm-forming ability 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).


frequently reported among strains of foodborne pathogens (Boddicker et al., 2002; Rode et al., 2007). For example, the variability in epithelial biofilm formation among S. Typhimurium strains, as observed by Boddicker et al. (2002), was attributed to differential expression of alleles of the fimH adhesion gene. Hence, the potentially great variability characterizing the molecular mechanisms underlying biofilm formation of a given bacterial species may, at least partially, account for the significant 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 and conducting challenge studies aimed at the assessment of the behavior of bacterial pathogens in food products or in systems simulating foodrelated environments. It has been recommended that, in order for variations in growth and survival among strains to be accounted for, multiple strains (3–5) of foodborne pathogens (individually or in combination) should be used in food safety research studies (NACMCF, 2005; Scott et al., 2005). Alternatively, challenge studies can be conducted using single strains with robust growth or inactivation characteristics as evaluated after screening a variety of strains (Scott et al., 2005). Although an inoculum of multiple strains is usually preferred, strain selection should be driven by the nature and/or objectives of the research studies conducted. For instance, while multiple strain composites of pathogens would be more appropriate in studies employing multiple stress conditions, the use of one or more single strains with certain phenotypic characteristics (e.g., unique resistance to an environmental condition or applied intervention) may be required in basic research studies assessing the mechanisms underlying these characteristics (Lianou et al., 2006; Scott et al., 2005). In any case, characterization of a variety of strains with regard to phenotypic responses is expected to be very useful, providing all the necessary information for decision making with regard to strain selection. Nonetheless, when using multiple strains, assessment of strain compatibility/interactions may be needed in order to minimize the 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 characterizing and selecting strains for use in challenge studies is the origin of their isolation and their source. Information on the behavior of foodborne pathogens' strains of various origins should be available, and isolates selected should be appropriate for the food product being challenged. More specifically, challenge studies on a specific type of food product may include isolates from a product of this type and from its processing environment, as well as clinical isolates from outbreaks related to the product to be challenged (Scott et al., 2005). With reference to their source, strains used in food safety studies can be either strains isolated (and maybe characterized) by various research groups and maintained in laboratory collections, or strains obtained from national or international culture repositories. Although in both cases strains used should be as well characterized as possible, strain variability data derived from strains obtained from national or international culture collections are expected to be of great value for challenge studies. As also commented by Scott et al. (2005), such strains collections provide researchers with standard sets of well characterized (e.g., origin, year of isolation, serotype, ribotype and pulsotype) isolates, thus allowing for data comparison among different laboratories. 7. Significance of strain variability for microbial risk assessment In general, it has been recommended that variability should be quantitatively expressed in risk estimates to the greatest scientifically achievable extent (Codex Alimentarius Commission, 2007). An assumption frequently made by food microbiologists is that strain-to-strain variation of microbial behavior is equal to or smaller than the experimental variation, and, as such, is not necessary to be determined and characterized (Whiting and Golden, 2002). Nevertheless, intra-species variability of

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microbial behavior may have an important impact on the accuracy of microbial risk assessment outcomes (Delignette-Muller and Rosso, 2000). In order for both the “hazard characterization” and “exposure assessment” components of microbial risk assessment to be credible, sufficient information on the distributions of all the parameters involved in risk estimation is required, and both the uncertainty and variability of each parameter need to be distinctively taken into consideration (Anderson and Hattis, 1999; Delignette-Muller et al., 2006; Lammerding, 1997; Nauta, 2000; Poschet et al., 2003). Uncertainty is usually associated with imprecise measurements or lack of knowledge of the effect of factors not included in models, and may be reduced by taking additional measurements. On the other hand, variability is irreducible by additional measurements, because it is mainly associated with strain differences and corresponds to what is known as “biological variability” (Anderson and Hattis, 1999; Nauta, 2002). Although approaches for their dissociation have been proposed (Delignette-Muller et al., 2006; Pouillot et al., 2003), such a task is generally difficult, and uncertainty and variability are often treated alike by implicitly assuming that either one or the other is negligible (Nauta, 2000; Ross and McMeekin, 2003). In many cases, for the purpose of microbial risk assessment, strain effects in microbial dose–response data are not discerned, meaning that the approach used does not account for strain variability in pathogenicity and virulence, other than perhaps, recognizing the existence of avirulent strains (Coleman et al., 2004). Such a default assumption (i.e., not taking into account the inherent genetic variability among strains of pathogenic bacteria and the existence of distinct pathotypes), commonly practiced in microbial risk assessments, can constitute an important source of uncertainty in dose–response modeling (Coleman and Marks, 1998; Coleman et al., 2004). Indeed, human clinical data have clearly demonstrated the need for the development and implementation of biologically-based alternatives, capable of predicting dose–response as a function, among others, of strain virulence (Coleman et al., 2004; Oscar, 2004). Gene network identification is expected to be very useful towards this direction, contributing significantly to the prediction of virulence genes and, thus, to the characterization of microbial hazards in the context of microbial risk assessment (Wassenaar et al., 2007). In general, the development of “omics technologies” (e.g., genome sequencing, genome-wide transcriptional analysis, proteomics and metabolomics) and their application to key foodborne pathogens (S. enterica, C. jejuni, L. monocytogenes and E. coli O157:H7) is expected to facilitate the assessment of strain variability and to substantiate our understanding of the dose–response relationship (Brul et al., 2012). Depending on the foodborne pathogen of concern, the variability in growth dynamics may constitute one of the most important factors affecting the level of risk (Augustin et al., 2011; Pouillot and Lubran, 2011), and, thus, its explicit consideration in quantitative microbial risk assessment (QMRA) approaches may be of vital importance for their precision. However, given that, in addition to strain variability, many other factors may have a considerable impact on the outcome of QMRA (e.g., product characteristics, time–temperature conditions in the food supply chain, consumer behavior), their comparative evaluation and the determination of each factor's contribution to the overall uncertainty and variability is also important. Such a comparative appraisement of each factor's importance is expected to be useful for the recognition of the main effects (and the place that strain variability has among them) that need to be taken into account in QMRA approaches. Predictive modeling approaches are of great value in the quantitative assessment of food-related risks (Nauta, 2002) and considerable efforts have been made in order for various sources of variability to be expressed and taken into account (Ross and McMeekin, 2003). Deterministic models (i.e., models that provide point estimates of microbial concentrations) have been acknowledged as being incompetent to take into account biological variability, and as such, they have been questioned with regard to their value in microbial risk assessment and food 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 experimental data in quantitative microbiology (Poschet et al., 2003), the vast majority of the modeling approaches developed and implemented for the description and integration of various sources of microbial growth variability (e.g., food characteristics, initial contamination level, individual cell behaviors, biological parameters, storage conditions and food 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 development of predictive models, particularly when the latter are used for the 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 predictive models, is a subjective situation and may introduce systematic biases into QMRA and result in risk overestimation (Begot et al., 1997; Nauta and Dufrenne, 1999). Similarly, the use of mixtures of representative strains of foodborne pathogens in model development for QMRA purposes also imposes a considerable risk for biased estimates, particularly when knowledge of the interactions among the strains used is lacking (Juneja et al., 2003), as discussed previously. Therefore, an increasing interest in incorporating growth variability among strains of foodborne pathogens in predictive models has been observed during the last decade. For this purpose, secondary models incorporating intra-species variability in their biological parameters, such as cardinal values and growth parameters, are usually exploited (Couvert et al., 2010; Delignette-Muller and Rosso, 2000; Delignette-Muller et al., 2006; Koutsoumanis et al., 2010; Lianou and Koutsoumanis, 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 in stochastic modeling (Ross and McMeekin, 2003). In the context of this approach, a relative rate relationship based on the square-root model (Ratkowsky et al., 1982) for temperature was used, with a probability distribution being assigned to the growth rate at a reference temperature (μref) included in the secondary model, while the minimum growth temperature (Tmin) was assumed to be constant (Ross and McMeekin, 2003; USFDA/USDA-FSIS, 2003). Nonetheless, such an approach results in a variability which is not affected by the growth conditions, something which, as demonstrated by research findings (Lianou and Koutsoumanis, 2011a), is not true. Hence, stochastic modeling approaches aiming at describing and expressing strain variability should also explicitly take into account the effect of environmental conditions on this type of variability (Lianou and Koutsoumanis, 2011b). The increase in the availability and application of omics technologies as observed the last decade will, most likely, affect considerably the way that microbial risk assessment is carried out. By allowing for mechanistic explanations of microbial behavior to be given, these tools have the potential to fill key knowledge gaps and enrich microbial risk assessment, by providing new perspectives on strain variability and physiological uncertainty (Brul et al., 2012; Rantsiou et al., 2011). 8. Concluding remarks The information provided in this review indicates that the variability among strains of foodborne bacterial pathogens with respect to their behavior is extensive. Differences in phenotypic responses such as virulence, growth, inactivation and biofilm formation among strains of the same microbial species can be significant and should not be overlooked. In addition to strain variability per se, parameters that may have a considerable effect on this variability, such as the prevailing environmental conditions, should also be taken into account. Research data on strain variability, such as the ones reviewed above, are expected to be useful in 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 variability studies discussed in the present review, most of these studies have been carried out in culture media under laboratory conditions. Given that the generated data should be as relevant as possible to real conditions in foods and food-related environments, it is really important that more in situ and in vivo studies are conducted and that their findings are compared to those of in vitro analyses. Furthermore, due to the fact that it is often difficult to compare findings of different studies (e.g., differences in the prior history and the growth phase of the tested strains, different media etc.), studies assessing strain variability should be as self-sufficient as possible by testing multiple strains, under well described conditions, and with appropriate replicate experiments as well as control strains. With reference to QMRA approaches, since, ideally, variability and uncertainty should be separated, efforts for their dissociation should always be made if the credibility of such approaches is to be assured. Finally, in order for the mechanisms underlying the observed strain variability of the behavior of foodborne pathogens to be elucidated and understood, phenotypic variability data should be evaluated in conjunction with corresponding findings of studies assessing the molecular/physiological basis of this variability. Acknowledgments We acknowledge the action THALIS: “Biological Investigation Of the Forces that Influence the Life of pathogens having as Mission to Survive in various Lifestyles; BIOFILMS”. The action falls under the Operational Programme (OP) “Education and Lifelong Learning (EdLL)” and is cofinanced by the European Social Fund (ESF) and National Resources. References Agarwal, R.K., Singh, S., Bhilegaonkar, K.N., Singh, V.P., 2011. 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