Molecular methods for serovar determination of Salmonella.

http://informahealthcare.com/mby ISSN: 1040-841X (print), 1549-7828 (electronic) Crit Rev Microbiol, Early Online: 1–17 ! 2013 Informa Healthcare USA, Inc. DOI: 10.3109/1040841X.2013.837862

REVIEW

Molecular methods for serovar determination of Salmonella Chunlei Shi1,2, Pranjal Singh1, Matthew Louis Ranieri1, Martin Wiedmann1, and Andrea Isabel Moreno Switt1

Critical Reviews in Microbiology Downloaded from informahealthcare.com by JHU John Hopkins University on 04/13/14 For personal use only.

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Department of Food Science, Cornell University, Ithaca, NY, USA and 2MOST-USDA Joint Research Center for Food Safety and Bor Luh Food Safety Center, Department of Food Science and Technology, Shanghai Jiao Tong University, Shanghai, China Abstract

Keywords

Salmonella is a diverse foodborne pathogen, which has more than 2600 recognized serovars. Classification of Salmonella isolates into serovars is essential for surveillance and epidemiological investigations; however, determination of Salmonella serovars, by traditional serotyping, has some important limitations (e.g. labor intensive, time consuming). To overcome these limitations, multiple methods have been investigated to develop molecular serotyping schemes. Currently, molecular methods to predict Salmonella serovars include (i) molecular subtyping methods (e.g. PFGE, MLST), (ii) classification using serovar-specific genomic markers and (iii) direct methods, which identify genes encoding antigens or biosynthesis of antigens used for serotyping. Here, we reviewed reported methodologies for Salmonella molecular serotyping and determined the ‘‘serovar-prediction accuracy’’, as the percentage of isolates for which the serovar was correctly classified by a given method. Serovar-prediction accuracy ranged from 0 to 100%, 51 to 100% and 33 to 100% for molecular subtyping, serovar-specific genomic markers and direct methods, respectively. Major limitations of available schemes are errors in predicting closely related serovars (e.g. Typhimurium and 4,5,12:i:-), and polyphyletic serovars (e.g. Newport, Saintpaul). The high diversity of Salmonella serovars represents a considerable challenge for molecular serotyping approaches. With the recent improvement in sequencing technologies, full genome sequencing could be developed into a promising molecular approach to serotype Salmonella.

Molecular serotyping, salmonella serovar determination, Salmonella subtyping

Introduction Salmonella is one of the most prominent foodborne pathogens in the developing and developed world (Tauxe, 2002). In the USA alone, an estimated 1.03 million cases of domestically acquired foodborne salmonellosis occur each year (Scallan et al., 2011). Salmonella is transmitted by consumption of contaminated food or by contact with infected animals (Hoelzer et al., 2011). Importantly, a number of food products have been associated with foodborne salmonellosis, including eggs, dairy, vegetables and processed foods (Greig & Ravel, 2009). In addition, a number of animals (e.g. reptiles, chicken and other young birds) (Chiodini & Sundberg, 1981; Hoelzer et al., 2011; Wiedmann & Nightingale, 2009) have been found to carry this pathogen and a number outbreaks have been linked with exposure to these animals (Hoelzer et al., 2011; Loharikar et al., 2012). Salmonellosis symptoms vary from self-limiting diarrhea that usually lasts for 12 to 72 hours to long-lasting, disabling effects, such as reactive arthritis (Saarinen et al., 2002) and systemic disease, particularly in susceptible individuals

Address for correspondence: Andrea Isabel Moreno Switt, Department of Food Science, Cornell University, Ithaca 14853, USA. E-mail: [email protected]

History Received 15 May 2013 Revised 1 August 2013 Accepted 21 August 2013 Published online 4 November 2013

(e.g. infants, immune-compromised individuals) (Feasey et al., 2012; Monack, 2012). While Salmonella is considered a public health concern, the food industry is impacted by salmonellosis as well, as recalls frequently result in major economic losses (GMA, 2010). As Salmonella transmission and routes of entry in the food chain can be complex and diverse (contamination can occur in one or more steps of food production with ingredients potentially sourced from throughout the world), tools that can differentiate Salmonella beyond the species level (e.g. to serovars) are essential to facilitate improved control of this pathogen (Olaimat & Holley, 2012). The Salmonella genus has two species named ‘‘enterica’’ and ‘‘bongori’’. The species S. enterica contains six subspecies. The six subspecies of S. enterica are enterica (subspecies I), salamae (subspecies II), arizonae (subspecies IIIa), diarizonae (subspecies IIIb), houtenae (subspecies IV) and indica (subspecies VI) (Brenner et al., 2000; Tindall et al., 2005). Recently, subspecies VII was recognized as a new group (Boyd et al., 1996; McQuiston et al., 2008; Porwollik et al., 2002). Importantly, S. enterica subspecies enterica is the most clinically significant group causing 99% of salmonellosis cases (Hadjinicolaou et al., 2009). Subspecies are further divided into serogroups and serovars (Grimont & Weill, 2007). The diversity of Salmonella is represented by more than 2600 serovars (e.g. Typhimurium, Montevideo, Agona) (Grimont & Weill, 2007; Guibourdenche et al., 2010).


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Traditional serotyping of Salmonella has been used for decades worldwide, and has been crucial for foodborne disease surveillance and outbreak investigations (Wattiau et al., 2011; Winokur, 2003). Unfortunately, traditional serotyping has a number of limitations; it is laborious, slow and can be imprecise (McQuiston et al., 2011). Consequently, several molecular methods to classify Salmonella serovars have been developed to complement or replace this methodology (Foley et al., 2007; Wattiau et al., 2011; Wiedmann & Nightingale, 2009). To compare molecular methods that can be used to predict Salmonella serovars, we identified here the percentage of isolates for which the serovar was correctly classified by a given method, which we defined as the ‘‘serovar-prediction accuracy’’. While this provides a basis for comparison between methods and studies, the serovarprediction accuracy depends considerably on the isolates included in a study and the values reported here need to be treated with considerable caution.

Traditional Salmonella serotyping The White-Kauffmann-Le minor scheme is the traditional method used for designation of Salmonella serovars (DeraTomaszewska, 2012; Guibourdenche et al., 2010). In this phenotype-based approach, surface antigens are detected by agglutination of bacterial cells using antisera (Schrader et al., 2008). According to this scheme, a serovar is determined on the basis of somatic (O), flagellar (H) and capsular (Vi) antigens present on the surface of Salmonella (Brenner et al., 2000). The somatic antigen, which is a polysaccharide present in Salmonella lipopolysaccharide (LPS) (Reeves et al., 1996; Schnaitman & Klena, 1993), specifies Salmonella serogroups and together with the flagellar antigen reactions, is used to determine a serovar (Schrader et al., 2008). The WhiteKauffmann-Le minor scheme recognizes 46 different somatic serogroups and 114 different flagellar antigens that, in several combinations, result in over 2600 different serovars (Guibourdenche et al., 2010). There are two flagellar antigens in salmonellae, designated phase I H-antigen (H1) and phase II H-antigen (H2) (McQuiston et al., 2004, 2011). The expression of flagellar antigens H1 and H2 is coordinated via a phase variation mechanism (Bonifield & Hughes, 2003; Silverman et al., 1979; Yamamoto & Kutsukake, 2006), therefore, serovars which express two flagellin types are called diphasic; those with only one flagellar antigen type are designated monophasic (e.g. serovar 4,5,12:i:-) (McQuiston et al., 2011). Three of the Salmonella enterica subspecies (i.e. subspecies IIIa, IV, VII), in addition to S. bongori, lack the H2-antigen, and thus are considered monophasic (McQuiston et al., 2008). In rare cases, Salmonella are triphasic, expressing a third flagellar antigen (e.g. serovars Rubislaw and Salinatis) (Old et al., 1999; Smith & Selander, 1991). In some serovars (e.g. Typhi, Dublin), a capsular antigen or Vi is present acting as a virulence-associated factor (Jansen et al., 2011). For traditional serotyping of Salmonella, a laboratory requires more than 250 different typing antisera as well as 350 different antigens for preparation and quality control of the antisera (Fitzgerald et al., 2006; McQuiston et al., 2004). In addition, for less common antigens, commercial antisera

Crit Rev Microbiol, Early Online: 1–17

are typically unavailable, or their quality is highly variable (McQuiston et al., 2004). Traditional serotyping is labor intensive, requires a minimum of 3 days for a single isolate, and in some cases can take longer depending upon the complexity of the serovar (Kim et al., 2006). Other limitations of traditional serotyping include a possible loss of expression of an antigen required for serotyping. For example, rough strains do not express the O-antigen and do not react with O-antisera. Additionally, mucoid strains produce a capsule around the bacteria which blocks detection of O-antigens, and non-motile strains do not express flagellar antigens (Fitzgerald et al., 2007). In addition, traditional serotyping is a time consuming process requiring trained technicians. All the above has increased the scientific interest in developing a reliable molecular approach for serotyping or serovar prediction of Salmonella, which could be correlated with the White-Kauffmann-Le minor scheme. This scheme has been used for about 70 years, and a considerable amount of data continues to be generated using this universal serotyping language.

Overview of molecular approaches to Salmonella serotyping A number of different molecular approaches for Salmonella subtyping, especially DNA based methods, have been developed (Wattiau et al., 2011; Wiedmann, 2002). Molecular subtyping methods have many advantages over traditional methods, such as an increased discriminatory power, better standardization and better reproducibility (Herrera-Leoń et al., 2007). However, many of these techniques are still novel and research is needed to improve, optimize and validate them. Here, we conceptually separated molecular serotyping approaches into the following three categories: (i) methods that could predict serovars based on molecular subtype (e.g. PFGE, ribotyping, rep-PCR), (ii) methods based on serovar-specific genomic markers and (iii) direct methods that target genes encoding antigens. Most of the initial DNA based subtyping methods for foodborne pathogens were based on the generation of banding patterns, from either genomic or plasmid DNA (Hartmann & West, 1997; Wachsmuth et al., 1991). These patterns are generated after restriction digestion or from PCR amplified DNA fragments (Nair et al., 2000; Ribot et al., 2006). DNA sequencing-based subtyping methods include, for example, multilocus sequence typing (MLST), which classifies Salmonella subtypes according to allelic profiles of selected housekeeping genes (Achtman et al., 2012; Enright & Spratt, 1999). Direct methods are based on PCR, sequencing or probes that target the genes encoding the somatic (O) and flagellar (H1 and H2) antigens (Braun et al., 2012). Finally, whole genome sequencing of multiple serovars has allowed the identification of serovar-specific genomic markers, which have also been used to predict Salmonella serovars (Arrach et al., 2008; Huehn & Malorny, 2009; Malorny et al., 2007). While subtyping methods and genomic markers based methods predict serovars based on molecular subtype, direct methods allow for a direct comparison of obtained results with traditional serotyping.

DOI: 10.3109/1040841X.2013.837862

Molecular methods for serovar determination of Salmonella

Prediction of serovars with banding pattern-based molecular subtyping methods

Giannatale et al., 2008; Jeoffreys et al., 2001; Ribot et al., 2006), further characterization of diverse isolate sets is necessary to identify the specific limitations encountered when PFGE is used to predict Salmonella serovars. In addition, a robust PFGE pattern database, of the most prevalent Salmonella serovars, such as PulseNet, is essential to interrogate isolates for Salmonella serotyping (Zou et al., 2012). Limitations with PFGE-based serovar prediction include: (i) multiple serovars can have identical PFGE types, because they recently emerged from a common ancestor, such as S. Typhimurium versus S. 4,5,12:i:- (Guerra et al., 2000; Hoelzer et al., 2010; Soyer et al., 2009; Wiedmann & Nightingale, 2009) or (ii) a single serovar can show high level of PFGE diversity, particularly serovars that are polyphyletic (e.g. Newport, Saintpaul (Achtman et al., 2012; Alcaine et al., 2006; Harbottle et al., 2006; Porwollik et al., 2004; Sukhnanand et al., 2005)). Importantly, polyphyletic serovars only represent a challenge if representatives of all phylogenetic groups representing a specific serovar are not included in the database used for serovar prediction. Unfortunately, the serovars mentioned above include some of the most common Salmonella serovars associated with human and animal cases (Hoelzer et al., 2011; CDC, 2009); hence errors in serovar prediction could have important public health implications.

Banding pattern based molecular subtyping methods target the bacterial chromosome to classify bacteria into subtypes. Cluster analysis can be used to identify groups of similar subtypes that typically represent the same serovar (Wattiau et al., 2011). Hence by analyzing the subtype of an individual isolate, one can predict a serovar (Achtman et al., 2012; Gaul et al., 2007).


Pulsed field gel electrophoresis Pulsed field gel electrophoresis (PFGE) is a restriction fragment length polymorphism based method. Enzymerestricted bacterial DNA is separated, with alternating electric fields, into various fragments representing sizes up to 2000 kb (Schwartz & Cantor, 1984; Singh et al., 2006). In the past decade, several researchers have examined the potential of PFGE to predict Salmonella serovars (Gaul et al., 2007; Ke´rouanton et al., 2007; Nde et al., 2006; Weigel et al., 2004; Zou et al., 2010) (see Table 1). These studies have analyzed from 46 to 1128 isolates, representing from 6 to 40 different Salmonella serovars. Nde et al. (2006) compared PFGE patterns for 80 Salmonella isolates, representing a limited number (n ¼ 6) of serovars. A total of 79/80 isolates were clustered into distinct clades representing a single serovar based on their XbaI PFGE patterns. The exception was one S. Montevideo isolate, which clustered together with three S. Senftenberg isolates (Nde et al., 2006) (Suppl. Table 1). For this study we calculated an overall serovar-prediction accuracy of 99%. Even though the serovar-prediction accuracy was high, in this study the number of serovars tested was too low to be conclusive. In a separate study, PFGE patterns for 674 Salmonella isolates representing 12 serovars were clustered and most of the serovars fell into distinct groups (573/674). Exceptions included S. Typhimurium var. Copenhagen, S. 4,[5],12:i:-, and S. Typhimurium, which were clustered in the same group; and S. Putten and S. Agona, which also clustered together (Gaul et al., 2007). The overall serovarprediction accuracy in this study was calculated as 85% (Table 1). The study involving the greatest number of isolates (n ¼ 1128), representing 31 serovars, was conducted by Ke´rouanton et al. (2007); in this study, the overall serovar prediction accuracy was 97% (1088/1128). Incorrectly predicted serovars included S. Paratyphi B, S. Give, S. Saintpaul, S. Agona, S. Montevideo, and S. Newport (Ke´rouanton et al., 2007). Recently, 46 isolates representing 40 serovars were analyzed by PFGE, yielding a serovar-prediction accuracy of 75%. Serovars 4,5,12:i:-, Saintpaul and Typhimurium var. Copenhagen matched with serovar Typhimurium; in addition, for eight isolates PFGE patterns were found to be different from all existing isolate patterns in the database (Ranieri et al., 2013). Overall, the serovar-prediction accuracy for PFGE ranged from 75% to 99%; however, most studies did not include adequate serovar diversity (e.g. isolates representing both common and rare serovars from multiple sources and locations) (Table 1). While PFGE has been a useful method to subtype Salmonella and for outbreak investigations (e.g. Di

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Ribotyping Ribotyping is another subtyping method based on restriction fragment length polymorphism. It can categorize Salmonella isolates into groups designed as ribotypes (Esteban et al., 1993), which can correspond to specific serovars. Conventional ribotyping involves cleavage of bacterial genomic DNA with a restriction endonuclease, having conserved cleavage sites inside and outside the rRNA operon; this is followed by hybridization of electrophoretically separated genomic DNA fragments with a ribosomal operon probe (Bouchet et al., 2008). Importantly, high reproducibility has been demonstrated with the Riboprinter Microbial Characterization System (Dupont Qualicon, USA) (Ito et al., 2003). In seven studies (Bailey et al., 2002; Capita et al., 2007; De Cesare et al., 2001; Esteban et al., 1993; Oscar, 1998; Rodriguez et al., 2006; Ranieri et al., 2013) where ribotyping was used to predict Salmonella serovars, the overall serovarprediction accuracy ranged from 39% to 100% (Table 1 and Suppl. Table 1). The results varied depending on the serovar diversity tested; in these studies from 2 to 40 serovars were tested. In most cases, when limited serovars were involved, the serovar-prediction accuracy of ribotyping was 100% (De Cesare et al., 2001; Esteban et al., 1993). For example, EcoRI and PvuII ribotyping of 112 Salmonella isolates, representing only S. Enteritidis (n ¼ 71) and S. Typhimurium (n ¼ 41), predicted the serovar of 100% of the isolates (De Cesare et al., 2001). Similarly, another study predicted the serovars of all isolates, which represented only three serovars (i.e. S. Reading, S. Typhimurium and S. Senftenberg) (Esteban et al., 1993). However, in studies with more serovar diversity (i.e. more than nine serovars), the serovar-prediction accuracy

No. of serovars tested

Isolate sources

Methods that Predict Serovars Based on Molecular Subtypes PFGE 80 6 Turkey processing plant 68 10 Swine farms 674 12 Swine 866 8 Food animals, production facilities, and clinical samples 1128 31 Food, animals, humans, natural environment, and processing plants 46 40 Human and cattle Ribotyping 1 112 2 (SE, ST) Broiler and turkey farms, and poultry meat-based foods 121 3 Animals 60 9 Chicken carcasses 91 14 Beef and dairy cattle operations, swine production facilities, and poultry farms 117 22 Broiler chicken processor 259 32 Poultry feces, carcass rinses, scald water, drag swabs 46 40 Human and cattle RAPD-PCR 1 42 2 (SE, ST) Humans and food 112 2 (SE, ST)1 Broiler, hen, and turkey farms and poultry meat-based foods 235 12 Human sporadic and outbreak strains, food and water isolates 85 22 Sewage effluent and surface water 128 43 Patients and lab stock collections Rep-PCR 70 2 (SE, ST)1 Different avian species samples 54 4 Turkey processing plants 68 10 Swine farms 70 15 Clinic patients for humans 44 21 Poultry 155 21 Poultry 89 22 Sewage effluent and surface water 65 49 Poultry meat and feces 2 133 80 Human feces, meat, eggs, food animals, reptiles, water, farm environment and equipment 46 40 Human and cattle PCR-RFLP 30 7 Cantaloupe and chile pepper farms 41 10 Human feces, human, meat, beef, snake, reference collection 112 52 Poultry farms AFLP 30 15 Reference collection 110 25 Human or veterinary source 78 62 Poultry

No. of isolates tested

Table 1. Comparison of different molecular serotyping methods for Salmonella spp.

(Millemann et al., 1996) (Anderson et al., 2010) (Weigel et al., 2004) (Johnson et al., 2001) (Wise et al., 2009) (Chenu et al., 2012) (Burr et al., 1998) (Van Lith & Aarts, 1994) (Rasschaert et al., 2005) (Ranieri et al., 2013)

(Peters & Threlfall, 2001) (Torpdahl et al., 2005) (Aarts et al., 1998)

(63/70) (54/54) (61/68) (67/70) (28/44) (143/155) (0/89) (65/65) (123/133) (30/46)

90% 100% 90% 96 % 64% 92% 0% 100% 92% 65%

(Rizzi et al., 2005) (De Cesare et al., 2001) (Soto et al., 1999) (Burr et al., 1998) (Shangkuan & Lin, 1998)

100% (30/30) 96% (106/110) 100% (78/78)

(0/42) (112/112) (235/235) (0/85) (116/128)

0% 100% 100% 0 91%

(De Cesare et al., 2001) (Esteban et al., 1993) (Capita et al., 2007) (Rodriguez et al., 2006) (Oscar, 1998) (Bailey et al., 2002) (Ranieri et al., 2013)

(Gallegos-Robles et al., 2008) (Nair et al., 2002) (Hong et al., 2003)

(112/112) (121/121) (43/60) (68/91) (45/117) (188/259) (34/46)

100% 100% 72% 75% 39% 73% 74%

(Nde et al., 2006) (Weigel et al., 2004) (Gaul et al., 2007) (Zou et al., 2010) (Ke´rouanton et al., 2007) (Ranieri et al., 2013)

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100% (30/30) 2% (1/41) 71% (37/52)

(79/80) (57/68) (573/674) (832/866) (1088/1128) (35/46)

99% 84% 85% 96% 97% 75%

Serovar-prediction accuracy5


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SE ¼ Salmonella Enteritidis, ST ¼ Salmonella Typhimurium. Five untypeable isolates were not added in the calculations of serovar-prediction accuracy. 3 Only the blinded set of isolates (isolate serovar data were unknown for the evaluation) were included in the calculations in this table. 4 SG ¼ serogroups. In this study only serogroup information was available. 5 Percent of isolates for which the serovar was correctly classified with a given method.

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MLST 25 7 Chickens 66 12 Cattle, birds, horses and other mammals 110 25 Human and veterinary source 152 33 Reference collection 4257 554 Reference collection 46 40 Human and cattle CRISPR typing 171 10 Human, environment and food 744 130 Human and food products Genomic marker based methods 30 2 (SE, ST)1 Reference collections, poultry feces and food 63 2 (SE, ST)1 Pig feces 77 3 Veterinary, environmental, food, and clinical sources 475 7 Prefectural livestock hygiene samples from different farms and geographical regions in Japan 41 23 Humans, pigs or chickens 273 30 Humans 853 34 Eggs, poultry and meat 1423 42 Clinical sources, feedlot cattle feces, turkey, swine, horse, and reptiles 754 58 Food-producing animals 751 58 Humans 97 37 Common in European Union for humans 60 26 Chicken 24 12 Not provided Direct methods PCR and PCR-sequencing based methods targeting O and H antigen alleles 45 21 Reference collections 267 Blinded3 Reference collections 400 Blinded3 Reference collections 500 30 Reference collections 85 37 Reference collections 239 43 Animals and human 116 109 Human and cattle Probe based methods targeting O and H antigen alleles 200 10 SG4 Reference collections 500 100 CDC collection 16 16 Reference collections 1003 58 Reference collections 1053 43 Reference collections (23/25) (65/66) (108/110) (152/152) (3753/4257) (42/46)

(30/30) (62/63) (77/77) (475/475) (37/41) (271/273) (62/85) (135/142) (714/754) (639/751) (49/97) (39/60) (24/24) (15/45) (255/267) (386/400) (423/500) (49/85) (129/239) (96/109) (189/200) (402/500) (12/16) (87/100) (93/105)

100% 98% 100% 100% 90% 99% 73% 95% 95% 85% 51% 65% 100% 33% 96% 97% 85% 58% 54% 88% 95% 80% 75% 87% 89%

78% (133/171) 98% (730/744)

92% 99% 98% 100% 88% 91%


(Fitzgerald et al., 2007) (McQuiston et al., 2011) (Yoshida et al., 2007) (Franklin et al., 2011) (Braun et al., 2012)

(Hirose et al., 2002) (Munõz et al., 2010) (Cardona-Castro et al., 2009) (Herrera-Leoń et al., 2007) (Rajtak et al., 2011) (Hong et al., 2008) (Ranieri et al., 2013)

(Liu et al., 2012) (Lee et al., 2009) (Alvarez et al., 2004) (Akiba et al., 2011) (Aarts et al., 2011) (Kim et al., 2006) (Mertes et al., 2010) (Peterson et al., 2010) (Wattiau et al., 2008) (Leader et al., 2009) (Rajtak et al., 2011) (O’Regan et al., 2008) (Arrach et al., 2008)

(Liu et al., 2011) (Fabre et al., 2012)

(Liu et al., 2010) (Sukhnanand et al., 2005) (Torpdahl et al., 2005) (Ben-Darif et al., 2010) (Achtman et al., 2012) (Ranieri et al., 2013)

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was dramatically reduced (Table 1). Ribotyping of 60 Salmonella isolates, representing nine serovars, resulted in an overall prediction-accuracy of 72% (43/60) (Capita et al., 2007). In this particular study, the authors reported four clusters composed of multiple serovars; specifically, (i) S. Paratyphi B clustered with S. Enteritidis; (ii) S. Newport, S. Enteritidis, S. Paratyphi B, and S. Infantis clustered together; (iii) S. Newport clustered into an S. Enteritidis group; and (iv) S. Infantis, S. Typhimurium, S. Enteritidis, S. Virchow, and S. Derby clustered together. The lowest serovar-prediction accuracy of only 39% (45/117) was obtained in one study conducted with 117 Salmonella isolates, representing 22 serovars (Oscar, 1998). This low serovar-prediction accuracy could be attributed to serovars with multiple ribotypes, such as S. Worthington (4 ribotypes), S. Hadar (4 ribotypes), S. Mbandaka (3 ribotypes), and S. Senftenberg (3 ribotypes). A number of ribotypes also contained multiple serovars, such as one ribotype composed of S. Typhimurium, S. Brandenburg, S. Cerro, and S. Heidelberg (Oscar, 1998) (Suppl. Table 1). Ribotyping usually generates relatively few bands (typically 5–10 bands) (Bouchet et al., 2008), and while this can facilitate pattern analysis (Bailey et al., 2002; Foley et al., 2007), a reduced number of bands introduces some shortcomings, such as lower discrimination power and consequently incorrect serovar prediction. This is more relevant for those isolates representing closely related serovars (e.g. serovars Typhimurium versus 4,5,12:i:- (Bailey et al., 2002; Guerra et al., 2000). In addition, the PvuII Riboprinter pattern database only contains 227 of the 2600 currently recognized Salmonella serovars (Guibourdenche et al., 2010), limiting the ability of serovar prediction when this database is used. Random amplified polymorphic DNA-PCR Subtyping with random amplified polymorphic DNA-PCR (RAPD-PCR) is based on random DNA amplification of the bacterial genome (Ellsworth et al., 1993; Holmberg & Feroze, 1996; Singh et al., 2006). RAPD-PCR uses arbitrary primers of approximately 10 mers, which amplify the DNA under flexible PCR conditions (Williams et al., 1990). The discriminatory power of this method is greatly affected by the choice of primers and PCR conditions, which also makes this method difficult to reproduce (Smith et al., 2011). There are only few studies that used RAPD-PCR to predict Salmonella serovars; however, the results are rather contradictory and serovar-prediction accuracy ranged from 0 to 100% (Burr et al., 1998; De Cesare et al., 2001; Rizzi et al., 2005; Shangkuan & Lin, 1998; Soto et al., 1999) (Table 1), which may reflect the typically poor reproducibility of RAPD-PCR. In two of the studies (Burr et al., 1998; Rizzi et al., 2005) we identified a 0% serovar-prediction accuracy; in both studies RAPD-PCR produced identical patterns for different serovars (Table 1). Conversely, in three other studies RAPD-PCR was sensitive enough to predict Salmonella serovars and the RAPD-PCR serovar-prediction accuracy ranged from 91 to 100% (De Cesare et al., 2001; Shangkuan & Lin, 1998; Soto et al., 1999) (Table 1). While in some studies RAPD-PCR methods have shown high discriminatory power, a major drawback is the lack of reproducibility.


Importantly, even slight changes in reagents or reaction conditions may result in significant differences in the banding patterns produced and hence potentially incorrect serovar prediction (Ellsworth et al., 1993; Holmberg & Feroze, 1996; Lin et al., 1996; Singh et al., 2006). Repetitive element (Rep) PCR Repetitive element PCR (Rep-PCR) amplifies genomic DNA fragments using primers that target repetitive DNA elements (Ridley, 1998; Versalovic et al., 1991). For Salmonella subtyping, three sets of repetitive elements present in the genome of Enterobacteriaceae have been used (i.e. 38-bp repetitive extragenic palindromic (Rep) sequence, 126-bp enterobacterial repetitive intergenic consensus (ERIC) sequences, and 154-bp BOX sequence) (Gilson et al., 1990; Hulton et al., 1991; Martin et al., 1992). These sequences allow discrimination because they are genetically stable and vary with respect to chromosomal location and copy number between strains (Bennasar et al., 2000). Similarly to RAPDPCR, reproducibility is a problem in both Rep-PCR and ERICPCR (Rasschaert et al., 2005). However, the DiversiLabÕ system from bioMerieux Industry (Marcy-l’Etoile, France), which is a commercial product for Rep-PCR, offers improved reproducibility (Chenu et al., 2012; Healy et al., 2005). In ten studies (Anderson et al., 2010; Burr et al., 1998; Chenu et al., 2012; Johnson et al., 2001; Millemann et al., 1996; Ranieri et al., 2013; Rasschaert et al., 2005; Van Lith & Aarts, 1994; Weigel et al., 2004; Wise et al., 2009) that used Rep-PCR to predict Salmonella serovars, the serovar-prediction accuracy ranged from 64 to 100%, except for one study conducted in 1998 (Burr et al., 1998), in which the serovarprediction accuracy was 0% (Table 1). The most comprehensive study that used Rep-PCR to predict Salmonella serovars included 133 isolates representing 80 serovars (Rasschaert et al., 2005). The serovar-prediction accuracy in this study was calculated as 92% (125/133); the exceptions included: two isolates of S. Typhimurium var. Copenhagen that grouped with S. Typhimurium, two isolates of S. Infantis that grouped with S. 6,7:r:, one isolate of serovar S. Enteritidis that grouped with S. 9::, and one S. Urbana that grouped with S. Sundsvall (Suppl. Table 1). Similarly, in a recent study, using the DiversiLabÕ kit, of 155 blinded Salmonella isolates representing 21 serovars, the overall serovar-prediction accuracy was 92% (143/155); specifically, ten isolates resulted in ‘No Match’, one S. 4,12:-:- was incorrectly matched to S. Typhimurium, and one S. Sofia isolate was also incorrectly matched to S. Typhimurium (Chenu et al., 2012). As with other subtyping methods that are based on banding patterns, Rep-PCR encountered difficulty with prediction of homologous and polyphyletic serovars (Weigel et al., 2004; Wise et al., 2009). In addition, while reproducibility was a limitation in some studies (Rasschaert et al., 2005), use of a commercial standardized kit, such as the DiversiLabÕ product, appears to considerably improve reproducibility. PCR-restriction fragment length polymorphism (PCR-RFLP) PCR-restriction fragment length polymorphism (PCR-RFLP) is based on banding patterns that are obtained through


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restriction digestion of PCR-amplified DNA (Olive & Bean, 1999). In order to utilize PCR-RFLP as a subtyping method, a target sequence containing polymorphisms that can differentiate isolates up to the subspecies level must be identified (Wassenaar & Newell, 2000). For Salmonella serovar prediction, fliC and fljB, genes responsible for phase I and phase II flagellar antigens, have been used as target for PCR-RFLP (Dauga et al., 1998; Gallegos-Robles et al., 2008; Hong et al., 2008; Kilger & Grimont, 1993; Shima et al., 2004). Importantly, the discriminatory power of the method depends also upon the type of restriction endonuclease chosen (Wassenaar & Newell, 2000). Three studies have used PCR-RFLP to predict Salmonella serovars (Gallegos-Robles et al., 2008; Hong et al., 2003; Naire et al., 2002). Two of these studies used H antigen encoding genes (i.e. only fliC in one study (Gallegos-Robles et al., 2008)) and fliC and fljB in the other (Hong et al., 2003), the third study used groEL (Nair et al., 2002) (Table 1 and Suppl. Table 1). As multiple targets, not restricted to H-antigen encoding genes, can been used in a PCR-RFLP scheme, studies that used this methodology are mentioned in this section, instead of below in the section of this manuscript that discusses methods that target antigen encoding genes. Serovar-prediction accuracy among the three studies ranged from 2% for the study that used groEL PCR-RFLP to 100% for the study that used fliC PCR-RFLP. The study which characterized the greatest number of isolates (n ¼ 112), representing the most diverse set of Salmonella serovars (n ¼ 52) used fliC and fljB PCR-RFLP (Hong et al., 2003). This study showed a serovar-prediction accuracy of 71% (37/52) (Table 1). Important non-typhoidal serovars associated with human salmonellosis cases, including S. Typhimurium, S. Dublin, and S. Enteritidis, could not be differentiated with this method, because they shared the same patterns (Hong et al., 2003). While the PCR-RFLP method is technically simple to perform, it relies on variation of a very small bacterial genomic region for differentiation, which can adversely affect the discriminatory power and can cause difficulty interpreting the data produced (Olive & Bean, 1999). While fliC and fljB PCR-RFLP may be able to differentiate homologous serovars (e.g. S. 4,5,12:i:- versus S. Typhimurium), the discriminatory power of PCR-RFLP is not high enough to differentiate O-antigen negative variant serovars (e.g. S. Typhimurium var. Copenhagen versus S. Typhimurium); or serovars with highly similar antigens (e.g. S. Enteritidis versus S. Dublin) (Dauga et al., 1998; Masten & Joys, 1993). fliC and fljB PCR-RFLP patterns of 264 Salmonella serovars were established in 1998 (Dauga et al., 1998); while 112 Salmonella isolates were added in 2003 (Hong et al., 2003), no comprehensive efforts have been executed to expand this database, which limits the application of PCR-RFLP for Salmonella serotyping.

This is followed by two PCRs with adapter-specific primers under highly stringent conditions (Savelkoul et al., 1999; Singh et al., 2006). Finally, PCR products are separated by gel electrophoresis and the banding patterns (typically 40–200 bands) are obtained. In three studies that used AFLP to predict Salmonella serovars (Aarts et al., 1998; Peters & Threlfall, 2001; Torpdahl et al., 2005), the serovar-prediction accuracy was high (Table 1). Up to 100% of serovar-prediction accuracy was identified in two of these studies (Aarts et al., 1998; Peters & Threlfall, 2001), one analyzed 30 isolates representing 15 serovars and the other analyzed 78 isolates representing 62 serovars (Table 1). According to the authors’ findings, isolates from the same serovar had an identical AFLP profile. However, only a few isolates representing the same serovar were actually interrogated (Aarts et al., 1998; Peters & Threlfall, 2001; Torpdahl et al., 2005) (Suppl. Table 1). One of the advantages of AFLP is the fact that it can be used to generate fingerprints from DNA of any origin without prior sequence knowledge (Aarts et al., 1998). AFLP generates results within 2 days; however, reproducibility and band interpretation are major limitations (Torpdahl et al., 2005). In addition, only limited efforts have been made to optimize AFLP for Salmonella serovar prediction.

Amplified fragment length polymorphism (AFLP) In AFLP, DNA is fragmented with two restriction enzymes, one high cutting-frequency enzyme (e.g. MseI or TaqI) and one average cutting-frequency enzyme (e.g. EcoRI, PstI). Then, adapter sequences (double stranded oligonucleotides) are linked to the ends of a group of restriction fragments.

7

Limitations of serovar prediction by banding pattern based subtyping methods Two main limitations for Salmonella serovar prediction appear to be ubiquitous among all subtyping methods based on banding patterns. These limitations include (i) serovar prediction of highly homologous serovars, and (ii) serovar prediction of polyphyletic serovars. In cases where multiple serovars have identical banding patterns, these serovars are typically highly similar or have a common ancestor. For example, a mutation in the rfb cluster, which encodes the O-antigen, may result in a change in O-antigen expression; this is the case for S. Typhimurium (antigenic formula: 1,4,[5],12:i:1,2) and S. Typhimurium var. Copenhagen (antigenic formula: 1,4,12:i:1,2) (Hauser et al., 2011; Heisig et al., 1995). Another example is a deletion or insertion in the fljAB operon (Bonifield & Hughes, 2003), which may affect H2-antigen expression, as is the case of S. Typhimurium (1,4,[5],12:i:1,2) and S. 4,5,12:i:- (Laorden et al., 2010; Soyer et al., 2009). Thus, banding pattern based serovar-prediction of isolates belonging to these serovars may not be reliable. For polyphyletic serovars, as serovars S. Newport, S. Saintpaul and S. Kentucky (Alcaine et al., 2006; Harbottle et al., 2006; Sangal et al., 2010; Sukhnanand et al., 2005), multiple distinct patterns may represent the same serovar (Didelot et al., 2011; Falush et al., 2006; Octavia & Lan, 2006; Sangal et al., 2010). For polyphyletic serovars, clustering may lead to incorrect serovar prediction if isolates in the database are not representative of all clades of a given serovar. It is important to mention that the highly homologous and polyphyletic serovars mentioned above are ranked among the most common Salmonella serovars associated with human and animal salmonellosis globally (CDC, 2009; Galanis et al., 2006); therefore, an incorrect prediction could interfere with

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epidemiological investigations and surveillance of this important foodborne pathogen.

Prediction of serovars with sequence-based molecular subtyping methods


Multilocus sequence typing (MLST) MLST is a method based on the determination of nucleotide sequences of internal regions of a series of typically housekeeping genes (Achtman et al., 2012; Enright & Spratt, 1999). Importantly, as a subtyping method, MLST has two major advantages: (i) it is highly reproducible and (ii) results can easily be exchanged between laboratories, which make MLST a valuable tool for international and national surveillance (Achtman et al., 2012; Torpdahl et al., 2005). In addition, to facilitate the analysis, there are several publicly accessible MLST databases for MLST allelic profiles and allele sequences as well as software and web based data analysis tools (e.g. http://pubmlst.org/, www.pasteur.fr/mlst/, http://mlst.ucc.ie/mlst/dbs/, http://www.mlst.net/). A number of studies have investigated the ability of MLST to predict Salmonella serovars with different schemes, including 3-gene, 4-gene or 7-gene MLST (Achtman et al., 2012; Ben-Darif et al., 2010; Liu et al., 2010). Overall serovar-prediction accuracy for MLST ranged from 88 to 100% (Table 1). Several 7-gene MLST studies using the same housekeeping genes (i.e. aroC-dnaN-hemD-hisD-purE-sucAthrA) (Achtman et al., 2012; Kidgell et al., 2002; Ranieri et al., 2013) have been reported, this facilitates comparison among these studies. One study involving 25 isolates that represented 7 serovars, obtained a serovar-prediction accuracy of 92% (23/25) (Liu et al., 2010) (Suppl. Table 1), and only two exceptions were found (i.e. S. Pullorum and S. Heidelberg). In another study with 110 isolates, representing 25 serovars, a serovar-prediction accuracy of 98% was obtained (108/110); exceptions in this study included S. Goettingen and S.9,12:l,v:-, which shared the same sequence type (ST) (Torpdahl et al., 2005). In a different study, 52 strains representing 33 serovars were MLST typed, with a serovar-prediction accuracy of 100% (Ben-Darif et al., 2010). One recent publication that investigated 46 isolates representing 40 serovars, found a serovar-prediction accuracy of 91% (42/46) (Ranieri et al., 2013). Importantly, a very comprehensive recent study with 4257 isolates representing 554 serovars obtained a serovar- prediction accuracy of 88% (3753/4257). This study identified a number of serovars that fell into multiple unrelated groups (e.g. S. Newport, S. Paratyphi B and S. Oranienburg (Achtman et al., 2012)), consistent with previous reports that identified at least some of these serovars as polyphyletic. Limitations of MLST as serovar predictor could arise in serovars that shared a common ancestor (and hence show identical MLST types for different serovars) and in polyphyletic serovars. Advantages of MLST include: it is highly reproducible, the MLST Salmonella databases allow for sequence comparisons and serovar prediction, and MLST can provide insight for phylogenetic inferences. Whereas serotyping could misinterpreted phylogenetically unrelated isolates as the same (same serovar, but different evolutionary origin); MLST distinguishes evolutionary groups


(Achtman et al., 2012). Importantly, many of the observed discrepancies between traditional serotyping results and MLST-based serovar prediction are due to discrimination, by MLST, of isolates with the same serovar into distinct phylogenetic groups. Clustered regularly interspaced short palindromic repeats (CRISPRs) typing Clustered regularly interspaced short palindromic repeat (CRISPR) loci are found in many prokaryotes (Jansen et al., 2002). In Salmonella two CRISPR loci, CRISPR1 and CRISPR2, have been reported (Fricke et al., 2011; Touchon & Rocha, 2010). The application of CRISPR typing is based on the high degree of polymorphism of the spacers in these loci, which can differentiate subtypes by the content of the spacers (Fabre et al., 2012). For subtyping, these loci are amplified by PCR, followed by sequencing of the PCR products, and analysis of the CRISPR spacers (Fabre et al., 2012; Liu et al., 2011). Only few studies have investigated the ability of CRISPRs typing to predict Salmonella serovars (Fabre et al., 2012; Liu et al., 2011). Overall serovar-prediction accuracy for CRISPRs typing ranged from 78 to 98% (Table 1). In one study (Liu et al., 2011), 171 isolates representing 10 serovars were characterized based on sequencing and clustering analysis of fimH, sseL and CRISPR spacers, with the overall serovar-prediction accuracy of 78% (133/171), exceptions were found with some of the isolates representing serovars Saintpaul, Montevideo and Muenchen, which occupied unique branches; and with S. Typhimurium, S. Typhimurium var. Copenhagen, and S. 4,5,12:i:-, which clustered together (Suppl. Table 1). In the other study (Fabre et al., 2012), 744 isolates representing 130 serovars were typed based on CRISPR spacer content comparison, with the overall serovar-prediction accuracy of 98% (730/744). Exceptions included S. Urbana, S. Johannesburg, S. Reading, S. Pomona, S. Gueuletapee, S. Rubislaw, S. Goettingen and S. Sandiego (Fabre et al., 2012), which had a few spacers shared among isolates of the same serovars (Suppl. Table 1). While CRISPR typing has been reported and optimized for different organisms (Gomgnimbou et al., 2012), limited studies have been conducted in Salmonella. Although one of the studies involved a large number of isolates and serovars, more studies are needed to evaluate the effectiveness of CRISPR typing as Salmonella serovar predictor. Due to the variability of CRISPR it is likely though that rapid diversification of these regions can lead to problems with regard to CRISPR-based serovar classification.

Comparison of different banding pattern and sequence-based methods for serovar prediction As part of this review, we compiled a comparison of the serovar-prediction methods discussed above (Table 2). This analysis included: analysis time of each step on a given method, database availability, necessary equipment, cost per isolate, and serovar-prediction accuracy. The biggest limitation for many of these methods is the analysis time, which is up to 43 h for AFLP, and up to 52 h for PFGE (Table 2). Conversely, for Rep-PCR, MLST and CRISPRs typing the

PFGE electrophoresis system, gel imaging system and analysis software 115

Necessary equipment2

83.8–98.8%

Serovar prediction accuracy

38.5–100%

Available with RiboPrinter7 0–100%

28 (one reaction/ isolate) Not available

PCR thermal cycler, electrophoresis unit, and gel imaging system

RiboPrinterÕ System and analysis software3 170

8h 2 h DNA extraction, 3 h PCR, 3 h electrophoresis

RAPD-PCR

10 h 2 h preparation, 8 h run time

Automated Ribotyping

Available with DiversyLab for DiversiLab7 0–100%

2.4–100%

40 (one reaction/ isolate) Not available

PCR thermal cycler, electrophoresis unit, and gel imaging system

DiversiLabÕ system3

105

28 h 2 h DNA extraction, 5 h PCR, 3 h restriction, 18 h electrophoresis

PCR-RFLP

5h 2 h DNA extraction, 2 h PCR, 1 h Agilent Bioanalyzer 2100

Automated Rep-PCR

96.4–100%

Not available

21–43 h 2 h DNA extraction, 3 h restriction, 3 h adaptor ligation, 2 h PCR, 1 h electrophoresis, 2 h selective amplification, 4 to 6 h polyacrylamide electrophoresis PCR thermal cycler, automated sequencer or access to sequencing facility4,5 NA

AFLP

CRISPR typing9

Several databases publicly available8 88.0–100%

280

77.8–98.1%

100 (two loci/ isolate) Not available

PCR thermal cycler, electrophoresis unit, gel imaging system and access to a sequencing facility5

8–10 h 2 h DNA extraction, 2 h PCR, 1 h PCR purification 3–5 h sequencing

MLST9

2

Analysis time starting from a single bacterial colony on a plate, representing a pure culture. This only lists specialized equipment necessary; basic equipment typically available in a clinical microbiology laboratory (e.g. incubator, pipettes, assay tubes, etc.) is not listed. 3 Technique can be performed without the commercial system, but reproducibility is improved with the system. 4 Equipment needed for AFLP with fluorophore-tagged primers. 5 Times provided assume a sequencing facility in the same laboratory or nearby (to avoid time delays associated with shipping). 6 These are cost estimates per isolate based on the current charges (USD), as of July 2013, by the Laboratory for Molecular Typing (LMT) at Cornell University; true costs may vary considerably based on number of isolates tested per year, labor costs etc.; NA ¼ not available. 7 Database is the limited access. 8 Databases accessible at: http://pubmlst.org/, www.pasteur.fr/mlst/, http://mlst.ucc.ie/mlst/dbs/, http://www.mlst.net/ 9 Analysis time, procedures, and equipment are similar for CRISPR typing and MLST.

1

PulseNet (not publicly available)7

Network/database

Cost per isolate6

26–52 h 3–16 h cell lysis, 3–16 h restriction, 20 h electrophoresis

Analysis time Procedures (time)

1

PFGE

Table 2. Comparison among subtyping methods that can be used to predict Salmonella serovars.


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Molecular methods for serovar determination of Salmonella 9


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total procedures is just a few hours (5–8 h). In addition, whereas some methods described here require equipment that could be used in other application within a laboratory (e.g. PCR thermal cycler, electrophoresis unit, and gel imaging system), other methods require specialized equipment with limited or no use in other applications (e.g. PFGE electrophoresis system). Among methods based on banding patterns, PFGE and AFLP were the only methods showing similar serovar-prediction accuracy among the studies. The large range in serovar-prediction accuracy (ranging from 0 to 100%) for RAPD-PCR, Rep-PCR and PCR-RFLP suggests some limitations of these methods. In contrast, sequence based methods are typically reproducible; in addition, the serovar-prediction accuracy of sequenced based serotyping methods was relatively high (77.8% to 100%). Finally, the database used to interrogate the isolates is an important limitation; as described above, in a number of studies the serovar could not been assigned because it did not match with any pattern or ST in the database used. Overall, the presence of optimized protocols, and available databases for comparisons make PFGE and MLST practical methods for serovar prediction, even though the fact that the PulseNet PFGE database is not publicly available reduces the attractiveness of this approach for some users.

Genomic marker based methods not targeting O and H antigen alleles With the development of genomics, numerous genome sequences of Salmonella serovars have been completed. Comparative genomics analyses of these data provided opportunities to identify regions in the genome that are unique for a given serovar, or ‘‘serovar-specific genomic markers’’ (Arrach et al., 2008). A number of schemes, including PCR (multiplex and real-time PCR), and probebased schemes (Akiba et al., 2011; Alvarez et al., 2004; Kim et al., 2006; Lee et al., 2009; Liu et al., 2012; Peterson et al., 2010) have been developed to target these regions for serovar prediction (Table 1). The overall serovar-prediction accuracy of genomic marker based methods ranged from 51 to 100% (Table 1). Most of the studies using genomic marker based methods only tested serovars Enteritidis, Typhimurium (Lee et al., 2009; Liu et al., 2012), and 4,5,12:i:- (Alvarez et al., 2004). In 2011, Akiba et al. used serovar-specific genomic regions (SSGRs) based multiplex PCR to determine seven serovars including S. Typhimurium, S. Choleraesuis, S. Infantis, S. Hadar, S. Enteritidis, S. Dublin, and S. Gallinarum, with 100% serovar-prediction accuracy (Akiba et al., 2011). In order to allow for the correct identification of more serovars by multiplex PCR, Kim et al. (2006) developed a double 5-plex PCR scheme to identify 30 common clinical serovars, targeting the unique genomic regions in Typhimurium LT2 (STM) and Typhi CT18 (STY) (Porwollik et al., 2004). The overall serovar-prediction accuracy of this assay was 99% (271/273); in only two isolates (i.e. one S. Chester and one S. Infantis), results were not consistent with traditional serotyping (Kim et al., 2006) (Suppl. Table 1). Later, Peterson et al. (2010) added an additional 5-plex PCR targeting Salmonella Typing Virulence (STV) that determines


the presence or absence of the genes spvC, invA, sseL, PT4 and STM7 into Kim’s scheme; this modified approach allowed for identification of 42 serovars with an overall serovar-prediction accuracy of 95% (135/142); the exceptions were found with isolates representing serovars Anatum (n ¼ 2), Kentucky (n ¼ 1), Saintpaul (n ¼ 1), Weltevreden (n ¼ 1) and Westhampton (n ¼ 2) (Peterson et al., 2010) (Suppl. Table 1). Real-time PCR has also been used to predict Salmonella serovars (Arrach et al., 2008; O’Regan et al., 2008; Rajtak et al., 2011). Rajtak et al. (2011) developed an approach that included three real-time PCR reactions targeting fliC, fljB, sdr, spv, and floR; with this approach, a serovar prediction accuracy of 51% (49/97) was obtained. While only 19/35 Salmonella serovars were correctly predicted; these 19 serovars represent serovars commonly found in the European Union (Rajtak et al., 2011). In another study, a real-time PCR targeting four genes (sefA, sdf, fliC, aceK) was tested with 60 isolates, representing 26 serovars (O’Regan et al., 2008). This approach allowed for a serovarprediction accuracy of 65% (39/60). This assay allowed for correct serovar prediction of all isolates representing selected Salmonella serovars associated with poultry (Enteritidis, Gallinarum, Typhimurium, Kentucky) as well as serovar Dublin (associated with cattle). An improved serovar prediction accuracy of 100% (24/24), using real-time PCR, was obtained by Arrach et al. (2008). Upon investigation of gene content in 291 strains, representing 32 serovars, these authors developed a 146-gene real-time PCR approach that correctly predicted the 24 isolates tested; these isolates represented 12 serovars (e.g. Dublin, Infantis, Typhimurium, Enteritidis (Table 1)) (Arrach et al., 2008). Some high throughput genomic based methods for serovar prediction have been also developed. In a study that identified Salmonella serovars using a Ligation Detection Reaction (LDR) microarray assay, Aarts et al. (2011) designed 62 probes targeting 44 genes (related to pathogenicity, fimbriae, antibiotic resistance, and serovar-specific genes); this array identified isolates representing 23 serovars with an overall serovar-prediction accuracy of 90% (37/41); the exceptions represented (i) S. Enteritidis and S. Moscow, as well as (ii) S. Hadar and S. Istanbul; each of these respective pairs produced identical hybridization pattern (Aarts et al., 2011). Another study determined Salmonella serovars using the Universal Probe Salmonella Serotyping (UPSS) nanoPCR chip (Kim et al., 2006), the overall serovar-prediction accuracy was 73% (62/85). There are also some commercial assay products currently available, e.g. PremiTestÕ (PT; DSM Nutritional Products, Switzerland); in a comparison study between traditional serotyping and PremiTest assay (Wattiau et al., 2008), 754 strains representing 58 serovars were tested, and the overall serovar-prediction accuracy was 95% (714/754) (Table 1). The exceptions occurred as three types: (i) yielding discrepant results with traditional serotyping; (ii) not recognized as defined serovars; and (iii) generating a numeric code corresponding to two possible serovars (Suppl. Table 1). The widely used genomic markers for serovar determination are mainly flagellar and somatic antigen-encoding genes, virulence, phage-associated, and antibiotic resistance genes (Huehn & Malorny, 2009; Malorny et al., 2007;


DOI: 10.3109/1040841X.2013.837862

Porwollik et al., 2004). Since the genomic marker based methods do not require complex information on a variety of O and H antigen alleles, the design and manipulation are relatively easier, and the result evaluation is simply based on the presence or absence of certain amplicon bands or hybridization signals. On the other hand, most of these systems only allow for reliable serovar identification of a limited number of serovars, typically the most common serovars and/or those serovars for which genome sequence data was available when a given assay was designed. The rapid improvement in sequencing technologies has increased the availability to obtain whole genome sequences of Salmonella serovars. These sequences can be used for the identification of further serovar-specific genomic markers (Huehn & Malorny, 2009; Malorny et al., 2007; Porwollik et al., 2004), which will likely further improve these methods in the near future.

Molecular serotyping based on direct identification and characterization of the genes encoding O biosynthesis pathways and H-antigens We used the term ‘‘direct methods’’ here to refer to molecular methods that target (i) genes that encode the enzymes involved in somatic antigen (O) synthesis (typically found in the rfb cluster (Jiang et al., 1991)) as well as (ii) genes that encode the flagellar antigens (H1 and H2) (Braun et al., 2012; Franklin et al., 2011; Yoshida et al., 2007), i.e. fliC (Smith & Selander, 1990) and fljB (Vanegas & Joys, 1995). For serotyping, molecular methods such as PCR, microarray or sequencing-based strategies can be utilized to identify and characterize these targets. Genes responsible for somatic (O) antigen synthesis are located within the rfb cluster in the Salmonella chromosome, typically located between galF and gnd (Figure 1) (Samuel & Reeves, 2003). Three types of genes are found within the rfb cluster: (i) genes encoding proteins that facilitate the synthesis of nucleotide sugars (e.g. rmlBDAC, ddhDABC, tyv), (ii) genes encoding sugar transferases (e.g. wbaVUN, wbaP, wbaBCD), and (iii) genes encoding O antigen polymerase and transporters (e.g. wzx, wzy, and wzz) (Fitzgerald et al., 2003; Samuel & Reeves, 2003). Importantly, the antigenic differences in the 46 Salmonella O serogroups are mainly due to the genetic variation in the gene content (Fitzgerald et al., 2007) and not due to individual gene sequence variation Figure 1. Linear representation of rfb gene cluster for nine Salmonella serogroups (i.e. A, B, C1, C2, D1, E1, F and G). Arrows indicate genes within the clusters. Potential serogroup-specific targets are circled (Fitzgerald et al., 2003, 2006, 2007).


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(Verma et al., 1998). In rare instances, antigenic factors (e.g. O:24 and O:25) are encoded by genes outside the rfb cluster (Fitzgerald et al., 2003). Currently, sequences are available for the rfb cluster of 28 of the 46 serogroups, representing the serogroups covering the most common Salmonella serovars. Genes related to sugar biosynthesis and sugar transferases present a high level of similarity, within serogroups (Fitzgerald et al., 2007; Samuel & Reeves, 2003). Importantly, among all genes, wzx is almost ubiquitous in rfb clusters sequenced to date (Fitzgerald et al., 2003). Targets to identify Salmonella O antigens of serovars commonly associated with human infection have been identified (Tennant et al., 2010). However, to allow for identification of the complete diversity of Salmonella serogroups, sequence data for all O-antigen rfb clusters needs to be determined. Salmonella has two flagellar antigens, designated as phase I (H1) and phase II (H2), but only one of them is expressed at a time (Yamamoto & Kutsukake, 2006), a phenomenon known as phase variation (Silverman et al., 1979). Phase I and phase II are encoded by fliC and fljB, respectively; these genes are located on different locations on the chromosome, and their expression is regulated by the fljBA operon (Aldridge et al., 2006). fliC and fljB are highly conserved at their 50 and 30 ends; an alignment of 280 fliC and fljB alleles, showed only 6 and 8 nucleotide substitutions, respectively, within the first 37 and the last 30 nucleotides (McQuiston et al., 2004). The middle region, corresponding approximately to amino acids 181 to 390, is quite variable. This variable region encodes the exposed surface and variable portion of the flagellar filament (Joys, 1985; Kanto et al., 1991). Some H antigens are composed of multiple factors which are a group of antigens; for example, flagellar antigen H2:e,n,x is composed of three distinct factors: e, n and x (Echeita et al., 2002; McQuiston et al., 2004). The 114 distinct flagellar antigens are classified according to immunological relation into complexes (McQuiston et al., 2004). For example, whereas flagellar antigens containing antigenic factors ‘‘g’’, ‘‘m’’ or ‘‘t’’ are members of the g complex; antigens not containing these antigenic factors are members of non-g complex (Mortimer et al., 2004). Within each complex, amino acid sequences in the conserved regions have more than 95% identity, whereas between different complexes only 75 to 85% amino acid identity is observed; this indicates that

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immunologically related antigens are encoded by closely related alleles (McQuiston et al., 2004). Consequently, these flagellar genes can be used as targets for molecular determination of Salmonella serovars, as is described below. Serotyping methods recognize 63 different Salmonella phase I antigenic factors and 37 different phase II antigenic factors (Mortimer et al., 2004). As of January 2013, the National Center for Biotechnology Information (NCBI) includes 994 and 607 complete or partial Salmonella fliC and fljB allele sequences, respectively (Suppl. Table 2).


PCR based methods targeting O and H antigen alleles Traditional Salmonella serotyping requires identification of variable O and H antigens; with molecular techniques, serotyping can be done effectively and rapidly by identifying the unique gene sequences associated with these antigens (Luk et al., 1993; Mortimer et al., 2004). Current molecular schemes include an initial multiplex PCR, which is conducted to identify the serogroup of an isolate (Fitzgerald et al., 2007; Herrera-Leoń et al., 2007). Primers targeting serovars commonly associated with human salmonellosis cases have been developed. In addition, separate PCRs or PCR-sequencing approaches have been designed to determine H1 and H2 antigens (Garaizar et al., 2002; Herrera-Leoń et al., 2004; Mortimer et al., 2004; Ranieri et al., 2013). Some studies have tested the ability to determine Salmonella serovars by multiplex PCR (Cardona-Castro et al., 2009; Herrera-Leoń et al., 2007; Hirose et al., 2002; Hong et al., 2008; Munõz et al., 2010; Rajtak et al., 2011). Examining up to 500 strains and 43 serovars, the overall serovar-determination accuracy ranged from 33% to 97% (Table 1). In one initial study, Hirose et al. (2002) attempted to discriminate S. Typhi, S. Paratyphi A and 19 other Salmonella serovars (Suppl. Table 1). This study showed the lowest serovar-determination accuracy (33%) (Hirose et al., 2002). Later, with more sequences available for the rfb cluster, as well as fliC and fljB genes for different serovars (Fitzgerald et al., 2003; Garaizar et al., 2002; Herrera-Leoń et al., 2004), several multiplex PCRs were designed to determine O, H1 and H2 antigens. The study that involved the most variety of antigen combinations targeted, through multiplex PCRs: (i) five O-antigens (O:4; O:7; O:8; O:9; O:3,10), (ii) eight H1-antigens (i; r; l,v; e,h; z10; b; d; g complex), and (iii) seven H2-antigens (1,2; 1,5; 1,6; 1,7; l,w; e,n,x; e,n,z15) (Herrera-Leoń et al., 2007). This combination of multiplex PCRs was tested with 500 isolates representing 30 serovars; for 423/500 isolates the serovars identified matched traditional serotyping, yielding a serovar-determination accuracy of 85% (Table 1 and Suppl. Table 1). While multiplex PCR is a rapid and cost effective alternative to the traditional serotyping approach, this technique also has some disadvantages. For example, this method does not differentiate serovar variants due to phage conversion, which results in some antigenic alterations, or subtle point mutation in H1/H2 antigenic genes responsible for loss of flagellar expression (Hong et al., 2008). Recently, a new method based on sequence variation was reported (Ranieri et al., 2013). In this study a multiplex PCR was used to


identify the serogroup, and PCR and subsequent sequencing of partial fliC and fljB, was used to identify H1 and H2 antigens. This scheme allows for identification of point mutations in fliC and fljB (Table 1). However, sequence data for the complete diversity of O and H antigens are not yet available, making it virtually impossible to yet design a scheme that could correctly identify the whole diversity of Salmonella serovars. Probe based methods targeting O and H antigen alleles To allow for identification of Salmonella serovars, probe based serotyping methods targeting O and H antigen alleles have also been developed, including a multiplex bead-based suspension array (Bio-Plex array) (Fitzgerald et al., 2007), a microsphere-based liquid array (McQuiston et al., 2011), a DNA-based microarray (Yoshida et al., 2007), a Salmonella genoserotyping array (SGSA) (Franklin et al., 2011) and a ‘‘fast DNA serotyping’’ microarray (Braun et al., 2012) (Table 1). In studies with these probe-based methods, the overall serovar-determination accuracy ranged from 75% to 95%, based on evaluation of isolates representing up to 100 serovars (see Table 1). Two studies used probe-based methods to determine either O-antigen or H-antigens. Fitzgerald et al. (2007) used array-based method to differentiate ten O-antigens among 200 isolates, obtaining an antigen determination accuracy of 95%; and McQuiston et al. (2011) used a probe-based method to differentiate 36 different H-antigens among 500 isolates, obtaining an antigen determination accuracy of 80%. Yoshida et al. (2007) developed a serotyping microarray targeting four O-antigen and eight H-antigen alleles; the serovar-determination accuracy with this array was 75% (12/16) with 16 strains representing 16 serovars (Yoshida et al., 2007). Franklin et al. (2011) and Braun et al. (2012) added more O- and H-antigen alleles into the assay to increase the number of Salmonella serovars that could be identified; these assays showed overall serovardetermination accuracies of 87% and 89%, respectively (Braun et al., 2012; Franklin et al., 2011). The array reported by Franklin et al. (2011) differentiated 18 somatic serogroups and 41 flagellar antigens, while the array reported by Braun et al. (2012) discriminated 28 O-antigens and 86 H-antigens. Limitations of this array included crossreaction of highly similar serogroups (e.g. O:2 with O:9). For example, identical microarray patterns were generated for S. Enteritidis, S. Nitra and S. Blegdam (Braun et al., 2012) (Suppl. Table 1). With the methods targeting O and H antigen encoding genes, as described above (PCR-based and probe-based), problems and discrepancies with serovar identification of isolates usually represent three issues: (i) rare serovars are not covered in the assay, (ii) results are not congruent with the traditional serotyping (e.g. cross-reaction of highly similar serogroups), and (iii) monophasic, rough, mucoid, and nonmotile samples are positively identified (Braun et al., 2012; Fitzgerald et al., 2007; Franklin et al., 2011; McQuiston et al., 2011). The first and second exceptions can be revised through technical improvements, which include the design

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of new primers or probes as more sequences become public. The third exception represents an advantage of methods targeting O and H antigen encoding genes over traditional serotyping, but makes it difficult to compare obtained results with traditional serotyping (Fitzgerald et al., 2007). Overall, PCR-based detection of O and H-antigens can be conducted in any lab with basic equipment for molecular biology (e.g. PCR thermocycler), but probe-based methods might require special equipment.


Next generation sequencing technologies as a future tool for Salmonella serotyping In the recent years, considerable improvements in DNA and genome sequencing technologies have occurred (MacLean et al., 2009), which has led to a point where we have approached affordable prices for sequencing of whole bacterial genomes (Carriço et al., 2013). A number of Salmonella serovars have been fully sequenced (e.g. den Bakker et al., 2011a; Fricke et al., 2011; Jacobsen et al., 2011; Moreno Switt et al., 2012). Importantly, this not only had led to the identification of serovar-specific genomic markers (as detailed above), but also to the application of whole genome sequencing in outbreak investigations, single nucleotide polymorphism detection and mobile elements identification (Carriço et al., 2013; den Bakker et al., 2011b; Diaz-Sanchez et al., 2013; Moreno Switt et al., 2012). Several genome sequencing platforms (e.g. Illumina, Solid, 454, Ion torrent (Diaz-Sanchez et al., 2013; MacLean et al., 2009)) are now routinely used to sequence bacterial genomes. In addition, available algorithms and pipelines for sequence assembly and comparative genomics analyses have improved considerably (Carriço et al., 2013; Darling et al., 2004; Zerbino & Birney, 2008). At the moment, one limitation for the use of whole genome sequencing to predict Salmonella serovars is the lack of a complete database that could be used to interrogate sequence data (Ranieri et al., 2013). Specifically, a comprehensive database that contains (i) the sequences data for the rfb clusters encoding the pathways for synthesis and (ii) sequence data for fliC and fljB (as well as the corresponding phase 1 and 2 antigens, as determined by traditional serotyping), as well as possibly serovar-specific genomic markers, would be extremely valuable to facilitate rapid full genome sequencing based serotyping. While the application of whole genome sequencing for Salmonella serovar prediction has not yet been comprehensively investigated, the scientific community has recognized that in the near future (5–10 years), whole genome sequencing could become a widely used tool for Salmonella serovar prediction and subtyping (Carriço et al., 2013; The National Food Institute, 2011). We may see, in the future, a whole genome sequencing approach for Salmonella serotyping that includes (i) genome sequencing, (ii) genome assembly, (iii) extraction of the ‘‘genes of interest’’ (e.g. serovar markers), and (iv) interrogation of these genes against a comprehensive database to predict serovars. In addition, along with serovar prediction, DNA sequences could be used for epidemiological investigations or to predict the antimicrobial resistance pattern of the sequenced isolates.


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Conclusions A number of studies have attempted to use molecular methods to predict or determine Salmonella serovars. However, the complexity and diversity of Salmonella serovars makes this a noteworthy challenge. Attempts to date have focused on predicting the most common serovars associated with human salmonellosis. Schemes described in this review will typically have difficulties correctly identifying rare serovars, which is crucial in the case of emerging serovars or an outbreak caused by an uncommon serovar. Errors determining the most common serovars (e.g. Typhimurium, 4,5,12:i:-, Newport, etc.) were common for a number of methods; these errors are not trivial and could seriously interfere with epidemiological investigations. Importantly, many methods described here provide better resolution than traditional serotyping and are thus valuable tools for subtyping of foodborne pathogens. Whereas subtyping methods described in this review can improve their accuracy for serovar prediction by increasing the serovar coverage of the associated subtype databases, direct methods and serovarspecific markers can improve serovar prediction accuracy as genome sequences of the remaining serogroups become available. With the increased use of whole genome sequencing, more sequences will become available, facilitating improved design of molecular methods for Salmonella serotyping in the near future.

Acknowledgements The authors would like to express their deepest gratitude to L.D. Rodriguez-Rivera for her critical reading of the manuscript.

Declaration of interest This project was supported by USDA-National Integrated Food safety initiative grant 2008-51110-04333 as well as USDA-NIFA Special Research Grants 2009-34459-19750 and 2010-34459-20756. The National Natural Science Foundation of China (NSFC 31000779) supported C. Shi.

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Supplementary material available online Supplementary Tables 1 and 2

Biochemical and molecular characterization of Salmonella enterica serovar berta, and comparison of methods for typing.

Molecular evolutionary genetics of the cattle-adapted serovar Salmonella dublin.

Evaluation of Molecular Methods for Identification of Salmonella Serovars.

Molecular Characterization of Salmonella enterica Serovar Aberdeen Negative for H2S Production in China.

Comparison of four molecular methods to type Salmonella Enteritidis strains.

An infection-relevant transcriptomic compendium for Salmonella enterica Serovar Typhimurium.

Mapping the Regulatory Network for Salmonella enterica Serovar Typhimurium Invasion.

Molecular epidemiology of Salmonella enterica serovar Kottbus isolated in Germany from humans, food and animals.

Molecular typing of Salmonella enterica subsp. enterica serovar Wien by rRNA gene restriction patterns.

MdsABC-Mediated Pathway for Pathogenicity in Salmonella enterica Serovar Typhimurium.

Antimicrobial and molecular analysis of Salmonella serovar Livingstone strains isolated from humans in Tunisia and Belgium.

Vi Capsular Polysaccharide Produced by Recombinant Salmonella enterica Serovar Paratyphi A Confers Immunoprotection against Infection by Salmonella enterica Serovar Typhi.

Development of a new molecular subtyping tool for Salmonella enterica serovar Enteritidis based on single nucleotide polymorphism genotyping using PCR.

Characteristics of Salmonella enterica serovar 4,[5],12:i:- as a monophasic variant of serovar Typhimurium.

Methods for determination of hirudin.

USP compendial methods for analysis of heparin: chromatographic determination of molecular weight distributions for heparin sodium.

Emergence of Ciprofloxacin-Resistant Salmonella enterica Serovar Typhi in Italy.

Complete Genome of Salmonella enterica Serovar Enteritidis Myophage Marshall.

Salmonella enterica serovar Typhi and the pathogenesis of typhoid fever.

Identification of HilD-regulated genes in Salmonella enterica serovar Typhimurium.

Complete Genome Sequence of Salmonella enterica Serovar Typhimurium Myophage Mushroom.

Antimicrobial Resistance of Salmonella enterica Serovar Typhimurium in Shanghai, China.

Complete Genome Sequence of Salmonella enterica Serovar Typhimurium Siphophage Shivani.

Acid exposure induces multiplication of Salmonella enterica serovar Typhi.