Current and emerging technologies for rapid detection and characterization of Salmonella in poultry and poultry products

Food Microbiology 38 (2014) 250e262

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Review

Current and emerging technologies for rapid detection and characterization of Salmonella in poultry and poultry products Si Hong Park a, b, Muhsin Aydin c, Anita Khatiwara d, Maureen C. Dolan e, David F. Gilmore f, Jennifer L. Bouldin g, Soohyoun Ahn h, Steven C. Ricke a, b, * a

Cell and Molecular Biology Program, Department of Food Science, University of Arkansas, Fayetteville, AR, USA Center for Food Safety, Department of Food Science, University of Arkansas, Fayetteville, AR, USA Molecular Biosciences Program, Arkansas State University, Jonesboro, AR, USA d Food & Drug Administration/Center for Food Safety and Applied Nutrition, College Park, MD, USA e Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, USA f Department of Biological Sciences, Arkansas State University, Jonesboro, AR, USA g Ecotoxicology Research Facility, Arkansas State University, Jonesboro, AR, USA h Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, USA b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 January 2013 Received in revised form 28 July 2013 Accepted 4 October 2013 Available online 14 October 2013

Salmonella is the leading cause of foodborne illnesses in the United States, and one of the main contributors to salmonellosis is the consumption of contaminated poultry and poultry products. Since deleterious effects of Salmonella on public health and the economy continue to occur, there is an ongoing need to develop more advanced detection methods that can identify Salmonella accurately and rapidly in foods before they reach consumers. Rapid detection and identification methods for Salmonella are considered to be an important component of strategies designed to prevent poultry and poultry productassociated illnesses. In the past three decades, there have been increasing efforts towards developing and improving rapid pathogen detection and characterization methodologies for application to poultry and poultry products. In this review, we discuss molecular methods for detection, identification and genetic characterization of Salmonella associated with poultry and poultry products. In addition, the advantages and disadvantages of the established and emerging rapid detection and characterization methods are addressed for Salmonella in poultry and poultry products. The methods with potential application to the industry are highlighted in this review. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Salmonella Rapid detection Characterization Poultry

1. Introduction Foodborne illnesses continue to be a serious concern as a public health issue for the food industry. The Centers for Disease Control and Prevention (CDC) have estimated that 48 million cases of foodborne illnesses occur in the United States (US) annually and approximately 128,000 cases require hospitalization and 3,000 cases result in death (Scallan et al., 2011). The CDC reported that viruses are major causative agents for foodborne illnesses (59%), followed by bacteria (39%), and parasites (2%); however, bacterial agents are associated with the more severe cases, being responsible for most of the hospitalizations (63.9%) and deaths (63.7%). In

* Corresponding author. Center for Food Safety; Department of Food Science, University of Arkansas, Fayetteville, AR 72704, USA. Tel.: þ1 479 575 4678; fax: þ1 479 575 6936. E-mail address: [email protected] (S.C. Ricke). 0740-0020/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fm.2013.10.002

particular, Salmonella species were considered as the leading cause for these more severe cases resulting in 35% of the hospitalizations and 28% of the deaths (Scallan et al., 2011). Most human salmonellosis cases are associated with consumption of contaminated egg, poultry, pork, beef and milk products (Geimba et al., 2004; Zaki et al., 2009). The CDC regularly reports Salmonella outbreaks that are associated with poultry and poultry products (Patrick et al., 2004; Altekruse et al., 2006; CDC, 2007; CDC, 2009a; CDC, 2010) and these food products are generally recognized as a primary source of salmonellosis (De Boer and Hahne, 1990; Braden, 2006; Linam and Gerber, 2007). Poultry and eggs are considered one of the most important reservoirs from which Salmonella is passed through the food chain and ultimately transmitted to humans (Oliviera et al., 2002; Ricke, 2003a; Maciorowski et al., 2004; CDC, 2009b; Finstad et al., 2012; Howard et al., 2012). With increasing consumption of poultry and poultry products, the number of salmonellosis associated with poultry continues to be a public health issue in the US. Since

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Salmonella is a major causative agent for poultry-associated foodborne illnesses, improving safety of poultry products by early detection of foodborne pathogens would be considered an important component for limiting exposure to Salmonella contamination. This monitoring of poultry and other related products for Salmonella contamination could be made significantly more effective by employing rapid and sensitive detection systems. Transmission of Salmonella to humans typically occurs when ingesting foods that are directly contaminated by animal feces or cross-contaminated by other sources (Gantois et al., 2009; Modaressi and Thong, 2010). Salmonella contamination of poultry in pre-harvest environments can usually be traced to production issues that include contaminated poultry feed or pathogen introduction to the facilities via a wide range of carriers including house pets, wild animals as well as insects (Jones et al., 1991; Singer et al., 1992; Butcher and Miles, 1995; Murray, 2000; Heyndrickx et al., 2002; Maciorowski et al., 2004; Okoli et al., 2006; Park et al., 2008). Many of these environmental sources have been reviewed extensively elsewhere but poultry feed has been discussed in more detail than most other sources (Murray, 2000; Maciorowski et al., 2004; Park et al., 2008; Dunkley et al., 2009; Jarquin et al., 2009; Davies and Wales, 2010; Jones, 2011; Ricke et al., 2013a). There are several reasons for the extensive focus on poultry feeds as a source of Salmonella. First of all, since one Salmonella organism per gram of feed can colonize in young chicks, low or undetectable numbers of Salmonella represent a high risk for infection in these birds that is further enhanced by the increased feed mixing and incorporation of individual feed ingredients from a multitude of sources (Milner and Shaffer, 1952; Schleifer et al., 1992). This becomes of particular concern if breeder flock hatchlings are exposed since they represent the starting point for all commercial flocks (Jarquin et al., 2009). In addition, Salmonella can linger in feed for extended time periods with reports of bacterial cells remaining viable for several weeks up to 16 months in dry feed stored at 25  C (Williams and Benson, 1978; Juven et al., 1984; Ha et al., 1998a, b; Petkar et al., 2011). This is further confounded when feeds are treated with antimicrobials such as organic acids where Salmonella either can become acid tolerant or their recovery and/or subsequent enumeration accuracy using conventional plating methods is influenced by carryover of antimicrobial compounds into the media (Kwon and Ricke, 1998; Ricke, 2003b; Carrique-Mas et al., 2007; Davies and Wales, 2010). Contaminated feed is also regarded as a source of infectious transmission of Salmonella among flocks (Veldman et al., 1995; Huehn et al., 2009). This is further accentuated by the larger numbers of birds housed in confinement resulting in an increase in more birds being infected simultaneously via aerosols and other routes (Nakamura et al., 1997; Murray, 2000; Maciorowski et al., 2006; Park et al., 2008). The high number of poultry-associated Salmonella outbreaks in humans highlights the need for rapid, reliable, and cost-effective high-throughput detection methods along the entire production chain from live poultry and feed to poultry products. Adoption of the microbiological testing of poultry products during production and processing could play a significant role in preventing Salmonella infection (Crump et al., 2002; De Medici et al., 2003; Mumma et al., 2004; Koyuncu and Haggblom, 2009; Koyuncu et al., 2010). In this review, the current and emerging rapid methodologies and their potential application in detecting and characterizing Salmonella in poultry production will be discussed. 2. Salmonella serovars commonly associated in poultry and poultry products The Salmonella genus has been divided into two major subspecies including 2579 serotypes: Salmonella enterica and Salmonella

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bongori (V). S. enterica subdivided into 6 subspecies as enterica (I), salamae (II), arizonae (IIIa), diarizonae (IIIb), houtenae (IV), and indica (VI) (Grimont and Weill, 2007) and several of these serotypes are frequently associated with poultry and poultry products (Grimont and Weill, 2007; Foley et al., 2008, 2011). This is in part due to their marked ability to persist in a wide range of varying environmental conditions. For example, Salmonella strains can grow in foods stored at low (2e4  C) and high (54  C) temperatures (Balamurugan, 2010). Several S. enterica serotypes have the ability to colonize and infect live birds, and are commonly associated with raw poultry and eggs (Ricke, 2003a; Dunkley et al., 2009; Howard et al., 2012; Ricke et al., 2013a). Serotypes such as S. Typhimurium, and S. Enteritidis can infect a wide range of hosts (De Medici et al., 2003; Seo et al., 2004; Altekruse et al., 2006; Cortez et al., 2006; Malorny et al., 2007a), whereas S. Gallinarum and S. Pullorum are avian-specific strains (Foley et al., 2011). S. Enteritidis is one of the most frequent causes of foodborne illnesses in humans, and it is most commonly implicated with egg and poultry in the US (Olsen et al., 2000). Among Salmonella serotypes, S. Enteritidis and S. Typhimurium represent two of the more prominent Salmonella serotypes associated with human infections (Foley et al., 2008). 3. Methodologies for rapid detection of Salmonella Recent advances in technology have made the detection of foodborne pathogens more rapid and convenient, while achieving improved sensitivity and specificity in comparison to conventional methods (Mandal et al., 2011). The detection methods employing these newer technologies are generally referred as “rapid methods” which include antibody- or nucleic acid-based assays that are modified or improved compared to conventional methods (Ibrahim, 1986; Dziezak, 1987; Fung, 1994; Stager and Davis, 1992; Doyle and Beuchat, 2013). These rapid detection methods can be of high value to the food industry by providing several key advantages such as speed, specificity, sensitivity, cost- and labor-efficiency. As detection technology has continued to advance not only has the identification of a particular foodborne pathogen become more rapid but the depth of information generated from the analysis has become more comprehensive. This has also led to improvements in the specificity of a rapid method to detect particular pathogens that are present in a background of non-pathogenic organisms in food matrices or other complex biological environments to the point of defining subtle genetic differences at the strain level. Finally, sensitivity has continued to be enhanced to detect ever fewer numbers of viable pathogens in food or other complex samples that could comprise the lower ranges of infectious doses for the highly susceptible individuals within the human population. In addition, rapid detection systems are now much more amendable to automation and high-throughput outcomes, thus reducing human errors as well as costs by increasing the total number of assays that can be conducted at a particular time point. Advanced molecular and immunological methods require only a few hours on average to detect the target pathogen from food samples compared to 4e5 days using conventional culture-based methods (Hadjinicolaou et al., 2009). Generally, non-selective or selective enrichment steps are employed to increase the sensitivity when detecting Salmonella in poultry and poultry products (Ukeda and Kuwabara, 2009; Mihayara et al., 2010); however, it should be noted that the addition of enrichment steps could increase the total assay time. According to the Food and Drug Administration (FDA), any rapid detection method that indicates the presence of the target foodborne pathogen (positive results) must be confirmed by traditional culture-based methods (FDA, 2001). Some rapid assays have been approved for Salmonella detection in poultry by the National Poultry Improvement Plan (NPIP) under USDA (USDA-

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APHIS, 2012). These NPIP-approved rapid assays for Salmonella include Rapid ChekÒ SelectÔ Salmonella Enteritidis (SE) (Strategic Diagnostics, Inc.), ADIAFOOD Rapid Pathogen Detection System for Salmonella spp. (AES Chemunex), DuPont Qualicon BAX PCR-based assay for Salmonella (DuPont Qualicon), Applied Biosystems TaqManÒ Salmonella Enteritidis Real-Time PCR assay, and MicroSEQ Salmonella Species Detection Kit (Life Technologies Corporation). The following sections describe current and emerging rapid detection methods for Salmonella in poultry. 3.1. Enzyme-linked immunosorbent assay (ELISA) ELISA-based approaches are the most prevalent antibody-based assay for pathogen detection in foods (Ramsay, 1998; Mandal et al., 2011). This immunological approach has been used to detect Salmonella in poultry production (poultry feed, feces, litter, carcass rinsing, and water samples) (Rigby, 1984; Maciorowski et al., 2006) and has provided higher sensitivity and shorter times frames than culture-based methods. As more advanced technologies became available, improvements have been made to the basic ELISA methodology for Salmonella detection. For example, sensitivity of the assay was enhanced with incorporation of monoclonal antibodies for the detection and quantification of Salmonella among poultry probiotic bacteria such as Veillonella which ferment lactate to propionate and acetate (Durant et al., 1997). In this study, the detection limit for S. Typhimurium in pure culture using a monoclonal antibody was determined to be 5.5  104 cells/ml. Dill et al. (1999) detected fewer than 100 S. Typhimurium cells in a chicken rinsate using the combination of mono, polyclonal antibodies and a commercial filtering system. Since ELISA methods were originally developed for Salmonella detection in foods and animal feeds, they are now widely used for detection of Salmonella in food-producing animals (Maciorowski et al., 2006; Hoorfar, 2011). The ELISA

platform offers several advantages in comparison with conventional culture-based method as listed in Table 1, but, the sensitivities of commercial ELISAs can differ widely depending on sampling times and processing methods, which in turn can often lead to false negative results (Peplow et al., 1999). 3.2. Polymerase chain reaction (PCR) Molecular-based PCR assays have emerged as some of the most commonly used techniques for detection and characterization of foodborne pathogens (Lampel et al., 1996; Bennett et al., 1998; Manzano et al., 1998; Waage et al., 1999; Boyd et al., 2000; Bhagwat, 2003; Kumar et al., 2005; Maciorowski et al., 2005). Numerous studies have been conducted to detect and characterize Salmonella in poultry, poultry products, and feeds using PCR assays to target selected antibiotic resistance or virulence genes along with genus-, species-, and/or serotype-specific genes (Cohen et al., 1993, 1996; Maciorowski et al., 2000, 2005; Oliviera et al., 2002; Löfström et al., 2004; Seo et al., 2004; Salomonsson et al., 2005; Bansal et al., 2006; Eyigor et al., 2007; Jarquin et al., 2009; Wise et al., 2009; Levin, 2010; Melendez et al., 2010). PCR methods have advanced over the years to provide improved sensitivity for Salmonella detection and identification. Aabo et al. (1995) developed a PCR assay for Salmonella detection in enrichment broths derived from minced meat, and compared this method to a routine culture-based methodology. The sensitivity of the PCR was 92% (Salmonella detected in 88 out of 96 minced meat samples) whereas the sensitivity of the culture method was estimated to be 50% (48 out of 96 samples detected). Rychlik et al. (1999) developed a series of nested PCR primers which have a higher annealing temperature than the primers used in first PCR to detect Salmonella in chicken feces and concluded that a PCR strategy using sucrose and proteinase K/Triton X-100 for DNA extraction exhibited

Table 1 Advantages and disadvantages of rapid detection methods. Method

Advantages

Disadvantages

ELISA

-

More rapid than culture-based methods (2 d vs. 5e7 d) Can be automated to reduce assay time and manual labor input Able to handle large numbers of samples More specific than cultural methods

Single and multiplex PCR

-

Rapid than culture-based methods (4e24 h vs. 5e7 d) High specific and sensitive Multiplex-PCR (several pathogens at a time) Automated Precise and accurate results from specific genetics-based detection Differentiation of several Salmonella serotypes (5e6) in single reaction Specific and sensitive than cultural methods Not influenced by non-specific amplification; amplification can be monitored at real-time No post-PCR processing of products (gel electrophoresis) Rapid cycling (25 min) Confirmation of specific amplification by melting curve Specific, sensitive, and reproducible Rapid than culture-based methods (2-4 h vs. 5-7 d) Multiplex analysis (up to 100 different beads commercially available) High sensitive and specific (probe design to genome), can characterize strains Labor effective; can be applied to 96-well format (in principle, 9,600 samples can be assayed) If an additional target has to be included into the assay, a new type of probe-attached bead can simply be added Sensitive, specific and accurate Sequencing without electrophoresis Rapid (7e10 h)

-

Low sensitivity Limited multiplexing ability False negative results Difficulty in detecting damaged or stressed cells Pre-enrichment for production of cell surface antigens Cross-reactivity with antigens of closely related bacteria Costs more than culture-based methods and ELISA Difficulty in distinguishing live and dead cells Technically can be challenging (optimzed PCR condition) Enrichment to detect viable cells Requires post-PCR processing of products (electrophoresis) PCR inhibitors

-

Difficulty in multiplex assay Need skilled person and support High equipment cost mRNA lability Possibility of cross contamination,

-

Difficulty in distinguishing live and dead cells High cost (kits, equipment) Requires kits or PCR for labeling target genes Need skilled person and support

-

Data storage and data release Need skilled person for data analysis High cost No detection of viable cells without pre-enrichment Samples need to be prepared and amplified

Real-time PCR

Microarray

-

NGS

-

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improved sensitivity for detecting Salmonella when compared to the standard culture methods. An enrichment step is commonly combined with PCR-based methods in an effort to enhance assay sensitivity by ensuring the detection of viable pathogens. Ferretti et al. (2001) demonstrated that PCR with a 6 h nonselective enrichment could detect various Salmonella serotypes in Italian salami reproducibly at the level of as low as 1 colony forming unit (CFU) in 100 ml of food homogenate. Soumet et al. (1994) evaluated six different DNA extraction procedures for Salmonella detection from chicken products by PCR. Although Salmonella could not be detected directly from poultry tissue homogenates by PCR (mostly due to the presence of various PCR inhibitors and DNases), PCR combined with a 10 h enrichment step was able to detect the presence of Salmonella. Similarly, Myint et al. (2006) evaluated a PCR method for Salmonella detection in naturally contaminated poultry tissue samples, all of which generated negative results for Salmonella without enrichment. However, Salmonella was detected in all 90 samples after the enrichment step. Culture enrichment is also advisable in order to distinguish live cells from dead cells when detecting Salmonella using PCR (Keer and Birch, 2003; Cocolin et al., 2011). Maciorowski et al. (2000) evaluated different enrichment times to detect indigenous Salmonella present in poultry dietary samples using PCR. They could not detect Salmonella with 7 h enrichment however, 2 out of 8 samples (25%) that included at least 30 CFU/g of Salmonella were positive after 13 h enrichment and 4 out of 8 samples (50%) were Salmonella positive after 24 h enrichment. The quality of the target DNA serving as a PCR template is a critical factor in the design of a PCR assay for pathogen detection. However, while optimized PCR primer construction can ensure specificity of the target pathogen detection, this is still generally not sufficient to overcome the effects of PCR inhibitors present in samples (Wilson, 1997). Food products inherently contain PCR-inhibiting components such as organic chemicals, sucrose and denatured proteins that may bind the DNA template or interfere with DNA polymerase activity (Wilson, 1997). In addition, the presence of DNA and cells other than those from the targeted organism can affect the efficiency of the PCR method (Wegener et al., 2003). To overcome this requires some form of sample preparation to either remove or neutralize potential inhibitors prior to conducting the PCR assay and the preparation methodology may vary depending on the type of sample matrix (Maciorowski et al., 2005). Improvements have also been made on the basic PCR technology as well. In particular, two primary PCR-based methods that have emerged over the past several years for Salmonella detection namely, multiplex PCR and real-time quantitative PCR (qPCR) (Wittwer et al., 2001; Uyttendaele et al., 2003; Yan et al., 2004). The current status of the optimization and development of these PCR applications for Salmonella detection in poultry are reviewed in the following sections. 3.2.1. Multiplex PCR Multiplex PCR is a modified PCR method that allows for multiple sequence targets to be simultaneously detected within a single reaction. This method has proven useful for the rapid identification of multiple pathogens simultaneously in a given sample, analysis of mutations (e.g. detection of deletions or duplications in a large gene), and screening of single nucleotide polymorphisms (SNPs) (Chamberlain et al., 1988; Edwards and Gibbs, 1994; Ballabio et al., 1990; Hayden et al., 2008). In general, multiplex PCR amplifies the target DNA samples using multiple primers in a standard thermal cycler. This method has been used to detect and identify Salmonella in poultry samples (Malkawi and Gharaibeh, 2003; Li et al., 2005; Kim et al., 2006; Cortez et al., 2006; O’Regan et al., 2008; Park et al., 2009, 2011; Anbazhagan et al., 2010; Salem et al., 2010).

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Mahon et al. (1994) developed a multiplex PCR to detect Salmonella on chicken skin and compared this molecular-based method with standard culture-based techniques. They concluded that the multiplex PCR was more sensitive than culture-based methods, with 10 of the 68 samples (15%) testing positive for Salmonella with multiplex PCR compared to only five (7.4%) detected using culture-based methods. Furthermore, the multiplex PCR method delivered results in less than 24 h compared to 4e5 days for standard culture-based techniques. Sharma and Carlson (2000) employed a multiplex fluorogenic PCR assay for simultaneous detection of Salmonella and Escherichia coli O157:H7 which was capable of detecting as low as 10 CFU/g in meat. Similarly, Kawasaki et al. (2005) have detected multiple Salmonella serotypes, Listeria monocytogenes, and E. coli O157:H7 simultaneously in enriched meat samples using multiplex PCR. Development of PCR reagents that included DNA polymerase with high yield fidelity has enabled an increased number of targets to be detected in a single reaction. Cortez et al. (2006) identified Salmonella from chicken abattoirs by multiplex PCR using three primer pairs. From these data, 29 (10%) out of 288 samples were positive for Salmonella spp. and 16 (5.6%) and 7 (2.4%) samples were identified as S. Typhimurium and S. Enteritidis, respectively. Kim et al. (2006) developed two five-plex PCR assays to differentiate the 30 most prevalent Salmonella serotypes in the US. In this study, primer pairs targeting six genetic loci from S. Typhimurium and four from S. Typhi were designed as well as evaluated for various Salmonella serotypes. More recently, Salem et al. (2010) also established two five-plex assays for the detection of the most common Salmonella in Tunisia as well. However, despite the application success of these multiple primer sets it is important to note that incorporating more than five to six primer pairs in a single reaction remains quite challenging. The primary difficulties include optimization of reaction conditions such as annealing temperature, cross-reaction among primer pairs, high amounts of DNA compared to single PCR-based assays, and increasing difficulty in discrimination between prominent PCR product sizes on traditional agarose gel electrophoresis (Edwards and Gibbs, 1994; Xu et al., 2012). Unquestionably, multiplex PCR is a powerful technology for Salmonella detection; however, in addition to the optimization of reaction conditions and primer construction, a number of issues need to be addressed when employing the multiplex PCR platform listed in Table 1 (Edwards and Gibbs, 1994; Biswas et al., 2008). In practice, cross-reactivity of primer pairs, sensitivity limitations associated with the procedure, and a high number (up to 12 (USDA-FSIS, 2010)) of Salmonella serotypes associated with poultry and poultry products make it still quite challenging to routinely use multiplex PCR for reliable simultaneous Salmonella serovar detection. 3.2.2. Real-time PCR (qPCR) With the emergence of fluorescence technology that allows for detection of targets with increased sensitivity (e.g. intercalating dyes such as SYBR Green or labeled probes such as TaqMan or molecular beacon), limitations of conventional PCR such as the errors associated with end-point analyses and lack of quantification can be overcome (Wittwer et al., 2001; De Medici et al., 2003; Bohaychuk et al., 2007). The “real-time” aspect of real-time PCR, also referred to as qPCR, technology is linked to its ability to label and cumulatively quantify the generated PCR products at each cycle throughout the ongoing amplification process (Corless et al., 2001). However, the enhanced sensitivity of the qPCR assay makes it more susceptible to inhibitors typically present in complex food matrices, thus it requires particularly effective DNA extraction methods to prevent contamination from PCR inhibitors (Pontiroli et al., 2011). Chua and Bhagwat (2009) compared three commercially available DNA isolation kits, BAX cell lysis reagent, Bio-Rad iQ-Check, and a

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filter-based DNA isolation method (FTA), in conjunction with qPCR for the detection of Salmonella and L. monocytogenes. When the authors isolated DNA with either the BAX or iQ-Check method, several samples yielded false-negative results although those samples were positive when analyzed using selective media. When they followed an FTA-based protocol, they were able to detect both pathogens with improved sensitivity (100%) compared to BAX (78%) and iQ-Check (95%) methods. The qPCR has been widely used to quantify Salmonella from poultry samples (Chen et al., 1997; Eyigor et al., 2002; De Medici et al., 2003; Catarame et al., 2006; Wolffs et al., 2006; Malorny et al., 2007b; Kundinger et al., 2008; Taha et al., 2010). Daum et al. (2002) screened nine food items associated with a Salmonella outbreak in Texas using qPCR and reported that barbecued chicken was the only food item testing positive for Salmonella. Wang et al. (2007) utilized qPCR to detect Salmonella in raw sausage meat with a reported detection limit of 4 CFU/g. A qPCR assay was also assessed for its ability to detect and quantify Salmonella in widely differing food matrices and resulted in detection limits of 2.5 CFU/25 g for salmon and minced meat, 5 CFU/25 g of chicken meat, and 5 CFU/25 ml for raw milk, respectively (Hein et al., 2006). The use of immunomagnetic beads (IMB) accompanying a qPCR assay represents a rapid and potentially reliable sampling approach for enhancing detection capacity of Salmonella molecular assays in poultry matrices (Wang et al., 2007). Antibodies coupled to magnetic particles or beads are have been utilized as part of an immunomagnetic separation (IMS) technology to capture pathogens from pre-enrichment media (Oggel et al., 1990). However, the availability of commercial sources of IMB is limited, and they are subject to high cross-reactivity when used for multiplexing. IMS is typically combined with PCR for the detection and identification of Salmonella. Mercanoglu-Taban et al. (2009) demonstrated that the combination of IMS and PCR could detect approximately 1e10 CFU/ ml of Salmonella in milk and it has also been utilized as a rapid detection assay for Salmonella in meat, vegetables, milk, water, and poultry (Lynch et al., 2004; Kumar et al., 2005; Mercanoglu-Taban et al., 2009; Taha et al., 2010). IMS not only reduces the need for selective enrichment of target cells with low numbers of DNA copies, but it also improves the sensitivity of PCR assays by removing target cells away from possible PCR inhibitory substances (Rijpens et al., 1999; Hsih and Tsen, 2001; Mercanoglu-Taban et al., 2009). Interestingly, IMB-qPCR has been shown to achieve the same sensitivity as a standard culture method, detecting 10 CFU/25 g of meat. Josefsen et al. (2007) developed a 12-h real-time PCR based assay to detect Salmonella in meat and poultry, and compared its sensitivity and specificity to a reference culture method from the Nordic Committee on Food Analysis (NMKL no. 71; reference 3). The established method was validated with 100 minced meat and poultry samples with artificially inoculated reference samples, yielding an accuracy of 99%, a relative sensitivity of 98%, and a relative specificity of 100%. The accuracy as defined in these results is the level of agreement between the PCR method and the reference culture method with identical samples, and the relative sensitivity can be used to infer the quantitative ability of the PCR method for detecting the target compared to the reference culture method (Josefsen et al., 2007). Malorny et al. (2007b) developed a duplex qPCR assay to detect S. Enteritidis in whole chicken carcass rinses and eggs. Their assay was able to detect S. Enteritidis below 3 CFU/50 ml of chicken carcass rinses and below 3 CFU/10 ml of homogenized egg content. Bohaychuk et al. (2007) also used qPCR for detection of Salmonella in various food and food-animal matrices including poultry cecal contents and carcasses with reported sensitivities ranging from 97 to 100% for the respective matrices. Although qPCR is an effective tool to detect and identify

Salmonella with high sensitivity and specificity, it does have several limitations which are listed in Table 1. 3.3. DNA microarrays Microarray analysis is a more recent development in molecularbased technology which is now being applied for foodborne pathogen detection and characterization. The concept of a DNAbased array involves the use of selected single stranded oligonucleotide probes attached to a solid surface of glass slides or fluorescently encoded beads. The microarray is subsequently hybridized with target DNA isolated from samples that are labeled with a fluorophore (Ojha and Kostrzynska, 2008; Uttamchandani et al., 2009; Bai et al., 2010). This platform offers some significant advantages over the other previously discussed platforms as it can be readily adapted to high-throughput multiplex formatting in providing rapid detection and/or identification of multiple foodborne pathogens in a single reaction (Sergeev et al., 2004). There are two main types of microarrays that have been used for detection and/or identification of Salmonella namely, DNA microarrays and protein microarrays (Al-Khaldi et al., 2002; Call, 2005; Hwang and Cha, 2008; Uttamchandani et al., 2009; Ricke et al., 2013b). In this review, we will focus on DNA microarrays since they are more commonly used to detect Salmonella and identify transcriptomic responses in various food matrices. DNA microarrays have also been regarded as ideal for broad-spectrum microbial surveillance and considered one of the more convenient formats for genomic screening and characterizing pathogen transcriptomic responses (Sirsat et al., 2010, 2011a, b; McLoughlin, 2011; Milillo et al., 2011; Chalova et al., 2012; Ricke et al., 2013b). Multiple serotypes of Salmonella can be detected at the same time using DNA microarrays (Alvarez et al., 2003; Pelludat et al., 2005; Majtan et al., 2007; Malorny et al., 2007a; Scaria et al., 2008). Concurrently, screening for the presence of specific genes of particular public health importance such as the presence of antibiotic resistance or virulence genes can also be conducted (Liu and Fratamico, 2006; Malorny et al., 2007a). The various strategies used in developing microarray platforms for Salmonella serotype identification to date are listed on Table 2. As the technical aspects have become better known and more commercial reagents and microarray slide preparations become more readily available, the routine application of microarrays for foodborne pathogen detection has gained significant momentum over the past few years (Goldschmidt, 2006; Sirsat et al., 2010; Ricke et al., 2013b). Kim et al. (2008) applied microarrays for detection of 10 foodborne pathogens including Salmonella by constructing custom arrays. They constructed 70 mer oligonucleotides specific for each pathogen via comparative genomics and the corresponding results yielded high specificity as well as sensitivity. Frye et al. (2006) used a microarray to detect antimicrobial resistance genes in diverse bacteria including Salmonella, E. coli, Enterococcus spp. and Campylobacter jejuni. The microarray results were confirmed with PCR and Southern blot analysis which demonstrated that the DNA microarray could be used to detect all known antimicrobial genes for nearly all bacteria analyzed. The microarrays have also proven to be a highly useful tool for molecular characterization and genotyping of Salmonella (Majtan et al., 2007). Majtan et al. (2007) identified and subtyped multidrug-resistant Salmonella using a microarray based on virulence factors, prophage sequences, and antimicrobial resistance genes. Huehn et al. (2009) subtyped poultry associated S. enterica serovar 4,12:d:- using a pulsed field gel electrophoresis (PFGE) and microarray. They accomplished this by utilizing 281 oligonucleotide probes for the molecular typing of serovar 4,12:d- and the resulting microarray analysis suggested that the serovar was highly

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Table 2 List of representative microarrays used for detection and/or characterization of Salmonella. Method

Salmonella serotype (serogroups)

Reference

Gene expression profiling followed by in silico motif detection A glass based microarray

Typhimurium Typhimurium, Enteritidis, Choleraesuis, Derby, Typhisuis Typhimurium, Typhi, Choleraesuis Typhimurium, Typhi, Kentucky

De Keersmaecker et al. (2005) Ma et al. (2007)

Typhi Typhimurium Typhimurium, Typhi Typhimurium Salmonella spp. Typhimurium California

Leski et al. (2009) Harrington et al. (2008) Frye et al. (2006) Hwang and Cha (2008) Ahn and Walt (2005) Pelludat et al. (2005) Alvarez et al. (2003)

Enteritidis, Typhimurium Six common serogroups (B, C1, C2, D, E, O13), and serotype Paratyphi A Salmonella spp. O and H serogroups

Ikeda et al. (2006) Fitzgerald et al. (2007)

Spotted DNA microarray Microarray involved genotypic characterization and localization of monitored genetic markers Resequencing microarray Short-oligonucleotide microarray based on 16S rDNA probes Spotted oligonucleotide microarray Microarray based on 16S rDNA Microsphere based fiber-optic microarray Oligonucleotide microarray DNA microarray used to characterize genetic repertoire of S. California DNA microarray based on 16S rDNA Bead-based suspension array Multiprobe microarray DNA based microarray

clonal and expressed a distinctive pathogenicity. Likewise, Zou et al. (2011) analyzed virulence gene profiles in Salmonella serovars from food and food animal environments including poultry farms and egg houses using a microarray which was spotted with 69 probes based on Salmonella pathogenicity islands (SPI-1 to SPI-5). They concluded that the microarray was not only effective but fairly straightforward for single-step screening of virulence-associated genes among multiple S. enterica isolates. Based on this initial screen they speculated that the presence of these genes across isolates may account for the prevalence of these pathogens in poultry environments and the potential risk that their presence represents. Similarly, Anjum et al. (2011) constructed microtubebased oligonucleotide arrays to identify antimicrobial resistance genes among Salmonella isolated from food animals including poultry in Great Britain. Approximately 77.1% of Salmonella from pigs, 49.1% of isolates from poultry, 41.6% of isolates from cattle, and 6.6% of isolates from sheep were resistant to at least one antimicrobial. The authors concluded that the microtube-based oligonucleotide array could be applied for simultaneously screening large numbers of antimicrobial resistance genes in Salmonella isolates. Improvements have also been made on the basic platform that supports the array. For example, bead-based suspension arrays, a modification of the slide-based microarray platform, use microspheres attached with either oligonucleotide probes or antibodies in place of a solid support that serves to capture targeted molecules from a sample (Dunbar and Jacobson, 2007). In DNA-based bead suspension arrays for Salmonella detection, microbeads that are internally pre-encoded with a fluorescent dye are used. The presence of a specific pathogen can be determined by reading signals from direct hybridization of biotinylated target DNA to complementary oligonucleotide probes on microbeads and the signal from the bead’s encoding dye. Bead-based microarrays have been used to perform detection assays for multiple pathogens or various Salmonella serotypes from a single reaction (Call, 2005; GonzalezBuitrago, 2006). Ahn and Walt (2010) constructed a microspherebased, fiber optic DNA microarray to detect Salmonella. Using this array, the authors were able to detect 103e104 CFU/ml of Salmonella in pure culture or 104e105 CFU/ml from a mock sample in the presence of interfering non-target organisms. Fitzgerald et al. (2007) used a multiplexed, bead-based suspension array for molecular identification of common Salmonella serogroups (B, C1, C2, D, E, and O13) in the US and with this assay they identified 362 out of 385 (94.3%) isolates correctly compared to the traditional

Scaria et al. (2008) Majtan et al. (2007)

Malorny et al. (2007a) Yoshida et al. (2007)

serotyping method. Hwang and Cha (2008) developed an artificial probe strategy for quantitative detection of pathogens including S. Typhimurium using an oligonucleotide chip as a model system. These authors concluded that the artificial standard probe strategy could be used in quantitative oligonucleotide microarray data analysis with a simple, efficient one-color labeled approach. Scaria et al. (2008) developed a spotted microarray (70 mers probes) for molecular subtyping of 14 disease-causing Salmonella serotypes. The authors were able to identify a unique gene presence/absence profile for each of the targeted serotypes when used as the serotype differentiating criteria. The bead-based DNA microarrays offer the ability to be easily refined and expanded from existing DNA microarray probe sets. This ability of these types of arrays increases flexibility and hence improves assay performance by detecting diverse individual sequences and reliability by minimizing any false positive or negative results due to bead redundancy (Uttamchandani et al., 2009; Walt, 2010). As refinements and improvements continue to be made to augment more commercialized version of recently developed customized array matrices, microarray analysis has the potential to become a leading routine detection method for more in-depth bacterial identification in food and feed environments (Al-Khaldi et al., 2002; Sirsat et al., 2010; Koyuncu et al., 2011; McLoughlin, 2011). 3.4. Next generation sequencing The ultimate goal in foodborne detection is the ability to not only detect specific pathogens occurring in low numbers on foods but be able to distinguish subtle strain genetic differences for improved tracking to original sources either during outbreak investigations or more routine analysis during food processing. Certainly microarrays offer whole genomic assessment of pathogens but are limited by availability of known sequences for particular pathogen strains of the respective bacterial species. Ideally, the ability to sequence pathogens upon initial isolation represents the ultimate flexibility for assigning not only the identity of a particular pathogen but establishing the genomic fingerprint that can be systematically compared with already known strains. As improvements have been made in sequencing technologies, these newly developed Next Generation Sequencing (NGS) methods have started to be applied more routinely to microbial detection (Kwon and Ricke, 2011; Singh et al., 2011). These

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transformative and novel sequencing technologies have led to the rapid assembly of microbial sequences encoding complete genomes within a day rather than weeks to months with the conventional Sanger sequencing methodology (Medini et al., 2005). NGS has proven to be a versatile and comprehensive approach in a variety of applications ranging from characterizing the genomic diversity to detecting pathogens such as Bacillus anthracis and Yersinia pestis (Cumming et al., 2010) and scanning S. Typhimurium genome for conditionally essential genes (Khatiwara et al., 2012). Allard et al. (2012) used NGS technology to identify the phylogenetic diversity of S. Montevideo via performing NGS on 47 strains of S. Montevideo, and concluded that NGS technology was effective in delineating strain-specific polymorphisms associated with the targeted bacteria. The advancement of DNA sequencing technologies has been characterized by fairly constant incremental introduction of more rapid methodology improvements at each step of the sequencing process. These advances in sequencing technologies have in turn accelerated applications of genomics with the corresponding introduction of next generation and more advanced hybrids of sequencing technologies that have been extensively reviewed elsewhere (Kwon and Ricke, 2011; Sirsat et al., 2010; Diaz-Sanchez et al., 2013). It is now becoming possible to much more systematically assess gene sequence with phenotype identification and assign the corresponding roles to overall biological function. NGS technologies allow an extensive parallel sequencing approach of a large number of template fragments. There are different platforms of NGS technologies based on the same flow cell sequencing, yet utilizing different sequencing chemistries. Various NGS platforms and their workflow are briefly described as follows. For more extensive coverage of protocols and specific experimental details of specific methods see Kwon and Ricke (2011). 3.4.1. The 454 pyrosequencing platform In 2005, Roche 454 Life Sciences (Branford, CT) was the first company to commercialize a next generation sequencing platform (Margulies et al., 2005). Each individual molecule of sheared template DNA is captured on a separate bead, and each bead is compartmentalized as an individual droplet of aqueous PCR reaction mixture within an oil emulsion. The template is clonally amplified on the bead surface by emulsion PCR, and the template-loaded beads are subsequently loaded into the wells of the picotiter plate. The sequence information is obtained by pyrosequencing, the corresponding wells are loaded with bead-tethered sequencing enzymes (polymerase, sulfurylase, and luciferase), and buffer containing one of four dNTPs is distributed across the plate wells. If a match occurs to the primed template, the polymerase enzyme incorporates the nucleotide and releases a pyrophosphate molecule which, when converted to ATP by sulfurylase, generates a luciferase-catalyzed chemiluminescence signal that is imaged and recorded (Holt and Jones, 2008). The residual nucleotides are removed and the cycle is repeated with the next dNTP. The current Genome Sequencer (GS) FLX Titanium chemistry platform features long reads of 300e500 bp with optimal accuracy and high throughput. 3.4.2. Illumina sequencing The second next sequencing platform referred to as the 1G Analyzer, now better known as the Illumina Genome Analyzer was developed by Solexa which in turn was purchased and subsequently marketed in 2006 by Illumina Inc. (San Diego, CA). This technology is the first of the massive parallel short-read platforms and today is one of the more successful and extensively-adopted next-generation sequencing platforms. The distinguishing features of this system include the in-situ template

amplification and use of four-color Sanger-like but reversible terminators. The Illumina flow cell is a planar optically transparent surface comparable to a microscope slide containing an array of oligonucleotide anchors attached to its respective surface (Holt and Jones, 2008). Adapters complimentary to oligonucleotides on the flow cell surface are ligated to the ends of size-selected DNA and these adapted single-stranded DNAs are bound to the flow cell and amplified by solid-phase bridge PCR. During each bridge PCR cycle, priming occurs by arching of the template molecule such that the adapter at its free end hybridizes to and subsequently becomes primed by a free oligonucleotide on the flow cell surface resulting in what has been referred to as a raindrop pattern of clonally amplified templates. Sequencing proceeds by synthesis using reversible four-color fluorescence where a combination of the four bases each labeled with a different cleavable fluorophore used simultaneously determines a given nucleotide position in the template. Labeled terminators, primer, and polymerase are applied to the flow cell. Each base incorporation step is followed by imaging and recording of the fluorescent signal at each cluster, the sequencing reagents are washed away, labels are cleaved, and the 30 end of the incorporated base is unblocked in preparation for the next nucleotide addition. This results in a very accurate base-bybase sequencing data set for a broad range of applications. 3.4.3. Sequencing by oligo sequencing by oligo ligation and detection (SOLiD) The third next generation platform commonly referred to as a SOLiD platform system was developed by Applied Biosystems (Foster City, CA) in 2008. It combines elements of various approaches to sequence clonally amplified DNA fragments linked to beads. The amplification and the attachment to the beads are similar to the Roche and Illumina systems previously discussed but the SOLiD platform relies on a unique sequence-by-ligation approach using dye-labeled oligonucleotides. The method provides essentially two base redundancies in sequence identification that enables an additional quality check of final reading accuracy. Briefly, oligonucleotides adaptor-linked DNA fragments are coupled with 1-mm magnetic beads displaying complementary oligonucleotides and each beadeDNA complex is amplified by emulsion PCR. The beads are subsequently attached covalently to the surface of a glass slide that is placed into a fluidics cassette within sequencer. The annealing reaction of the universal sequencing primer that is complementary to the SOLiD specific adapters on the library fragments actually initiates the ligation based sequencing process. The addition of a limited set of semidegenerate 8mer oligonucleotides and DNA ligase is automatically done by the instrument. When a matching 8mer hybridizes to the DNA fragment sequence adjacent to the universal primer 30 end, DNA ligase seals the phosphate backbone. After the ligation step, a fluorescent readout identifies the fixed base of the 8mer, which corresponds to either the fifth position or the second position, depending on the cycle number. Subsequent chemical cleavage removes the sixth through eighth base of the ligated 8mer by attacking the linkage between bases 5 and 6, thereby removing the fluorescent group and thus enabling a subsequent round of ligation. The process occurs in steps that identify the sequence of each fragment at five nucleotide intervals, and the synthesized fragments that end at base 25 (or 35 if more cycles are performed) are removed by denaturation and washed away. A second round of sequencing can occur with initiation of hybridization by an n-1 positioned universal primer followed by subsequent rounds of ligation-mediated sequencing. The SOLiD system applications include mutation discovery, metagenomic characterization, noncoding RNA and DNA-protein interaction discovery.

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3.4.4. Third generation sequencing The third generation sequencing platforms based on single molecule sequencing have been proposed and these technologies are beginning to emerge in commercial market settings. The advantage of single molecule sequencing offers the singular value of being able to sequence without amplifying the template thus permitting the accurate quantification of specific RNA or DNA molecules rapidly from what would be considered a very minimal initial amount of template (Podolak, 2010). With the aid of these sequencing technologies, it is now possible to acquire gigabases of sequence information within a fairly short time frame of just a few days. 3.4.5. Application of NGS to Salmonella In general, DNA-based NGS approaches have been utilized to investigate epidemiological analysis and classify Salmonella serotypes isolated from outbreak samples while RNA-based NGS (RNAseq) techniques have been applied to define mechanisms among virulence genes in transcription levels. As NGS becomes more routine it is anticipated that it will have utility for a more comprehensive delineation of the identity of specific Salmonella serovars and even strains within the same serovar which would greatly enhance tracking capabilities throughout the poultry production system. Likewise, a more detailed understanding of virulence mechanisms might help to develop more precise and effective control measures such as more optimal genetic targets for Salmonella vaccine genetic constructs. Some of the more recent applications of NGS for Salmonella serovars are listed in Table 3 and discussed in the following sections. As NGS methodology has become a more routine methodology the possibility of determining minor differences in genome sequences to differentiate subtle differences among Salmonella strains has led to insight on the evolution patterns and the epidemiological significance of these strains (Jarvik et al., 2010; Allard et al., 2012; Singh et al., 2012, 2013; Williams et al., 2013). In early work, Holt et al. (2008) used NGS to differentiate isolates of the human host specific S. Typhi which traditionally is known for limited genetic variation and concluded that evolution of this serovar was via gene function loss with minimal antigenic variation. When comparing the S. Typhi isolates they also suggested that these conclusions supported the importance of asymptomatic carriers as primary reservoirs and therefore making their identification critical. After sequencing a S. Typhi isolated from a Malaysian

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typhoid fever outbreak human carrier, Baddam et al. (2012) suggested that identification of diagnostic signatures was possible but comparative genomics of a much broader isolate base from a range of endemic countries and carriers of different socio-economical status would be needed to delineate truly defining carrier state microbial pathogen characteristics. More recently, Barquist et al. (2013) combined NGS with high density transposon mutagenesis (transposon-directed insertion-site sequencing or TraDIS) to compare the host specific S. Typhi with non-host specific S. Typhimurium genome to differentiate in their respective host niche preferences. Based on the comparison of TraDIS patterns they concluded that even gene products that were identical between the two serovars might not result in the same phenotypic responses in the two serovars and that there may be differences in sRNA regulatory networks between the two serovars. Consequently, the ability to delineate RNA sequences would appear to be a critical component to develop a better understanding of regulatory responses of Salmonella species and to predict which serovars have competitive advantages in specific environments such as those associated with poultry production. Although NGS has revealed considerable detailed information on Salmonella species and strains at the genomic DNA level much further advancement in a comprehensive understanding Salmonella global regulation is now becoming possible through RNA sequencing (RNA-seq). Currently, RNA-seq involves initial isolation of the RNA and reverse transcribed to cDNA and sequencing the resulting DNA (Wang et al., 2009; Mutz et al., 2013; Ricke et al., 2013b). Since the initial application of RNA-seq several refinements and applications have been implemented to differentiate transcriptomic roles of the RNA recovered and sequenced from Salmonella serovars grown in a variety of environmental conditions (Perkins et al., 2009; Patterson et al., 2012; Kröger et al., 2012; Wang et al., 2013). Kröger et al. (2012) utilized multiple RNA-seq approaches on the RNA transcriptomic analysis of S. Typhimurium to identify and locate transcriptional start sites (TSSs) on the chromosome. To specifically identify TSSs they employed differential RNA-seq (dRNA-seq) which uses 50 -monophosphatedependent terminator exonuclease to target already formed monophosphorylated RNA such as rRNA and tRNA but not triphosphosphorylated primary transcripts. Using this exonuclease allowed them to enrich for primary transcripts and comparison of treated versus untreated libraries allowed for identification of TSSs. In addition to identifying the TSSs of several virulence regulators

Table 3 Various NGS applications on Salmonella serovars. Sequencing type

Sequencer

Species

Analysis

References

DNA sequencing

454 454 454 454 and Illumina

S. Heidelberg 10 Salmonella serovars S. Montevideo S. Typhi

Williams et al., 2013 Singh et al., 2012, 2013 Allard et al., 2012 Holt et al., 2008

454, Illumina, Sanger and SOLiD Illumina Illumina

S. Typhimurium

Evolution of serovar specific virulence plasmids Salmonella typing Epidemiological analysis among 34 S. Montevideo isolates Comparison of genome variation and evolution among 19 S. Typhi isolates Comparison of evolutionary change in S. Typhimurium LT2 and 14028s Genome sequencing from outbreak isolates Identification of transposon mutant library

RNA-seq

454 and Illumina

S. S. S. S.

Typhi Typhimurium and Typhi Typhimurium

Illumina

S. Typhi

Illumina Illumina

S. Typhimurium S. Typhimurium

Illumina

S. Typhimurium

Transcriptomic analysis of ppGpp roles during invasion gene expression Definition of S. Typhi OmpR regulon and transcript regulated by OmpR Investigation of small RNAs in S. Typhimurium Investigation of Fis which is the most important nucleoid-associated gene on Salmonella pathogenecity islands (SPI) gene regulation Differential gene expression between two phenotypic phase variant strains

Jarvik et al., 2010 Baddam et al., 2012 Barquist et al., 2013 Ramachandran et al., 2012 Perkins et al., 2009 Kröger et al., 2012 Wang et al., 2013 Patterson et al., 2012

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they discovered several-fold more promoters than had been previously identified and numerous small regulatory RNA (sRNA) many of which were unique to the Salmonella genus. Ramachandran et al. (2012) also used dRNA-seq to elucidate the extent of involvement of the bacterial alarmone, guanosine tetraphosphate (ppGpp) on environmental regulation of S. Typhimurium virulence genes at the transcription level. Based on mapping of TSSs new candidate sRNAs and antisense RNAs (asRNAs) were identified as well as several ppGpp dependent alternative TSSs that were linked to metabolism. As more becomes known and some of the intricate interactions between metabolism and pathogenesis are revealed the metabolicpathogenesis linkages may be the critical point to understanding emerging patterns of Salmonella strain variants. Rohmer et al. (2011) have proposed that nutrient requirements may have been the primary evolutionary influence on microbial pathogens to develop virulence traits that now define them as pathogens. This linkage may become more understood as the possibility of conducting dual RNA-seq to assess gene expression levels of both the pathogen and the host simultaneously becomes a reality (Westermann et al., 2012). 4. Conclusions According to the US Food and Drug Administration (FDA), any rapid detection method that indicates the presence of the targeted bacteria (positive results) must be confirmed by traditional culturebased methods (FDA, 2001). Currently, FDA follows the Bacteriological Analytical Manual (BAM) which describes selective media and biochemical tests to detect and isolate Salmonella from various foodstuffs. Some rapid assays have been approved for Salmonella detection in poultry by National Poultry Improvement Plan (NPIP) under USDA (USDA-APHIS, 2012). Although traditional culturebased methods detect “viable” bacterial cells, they do not detect potentially infectious non-culturable cells, and these techniques are time consuming, labor intensive, and not specific enough to detect and characterize Salmonella at the strain level, particularly when a large number of samples are involved (Maciorowski et al., 2006). While culture-based methods require five to seven days to yield positive results for the presence of Salmonella, most rapid methods reviewed here are able to provide results within two days. Although most rapid methods still involve some type of culturing (usually with two types of enrichment), total assay time of most molecular techniques is much less than culture-based methods and they provide a level of sensitivity needed to detect the low number of Salmonella that may be present in poultry and poultry products (Maciorowski et al., 2005). Among various detection methods based on nucleic acids and antigens, appropriate detection methods for Salmonella need to be chosen based on food matrices, convenience, time, and cost. Although emerging technologies such as microarrays and NGS offer intriguing possibilities for providing even more rapid and accurate detection and genetic characterization of various Salmonella serotypes isolated from poultry and poultry products, the costs will remain relatively higher than currently available rapid methods until further technological and commercialization development occurs and may still require skilled technical and computer support to conduct analysis of the massive data sets. Therefore, multiplex PCR and qPCR combined with an enrichment step are still highly practical for quantitative and qualitative analysis of Salmonella in most foodstuffs and should provide sufficient specificity and sensitivity. However, as data generated from NGS-based studies become available and more comparative genomics among serovars at the strain level are conducted the identification of signature characteristics the more precisely identify the defining features of these isolates will

inevitably lead to better diagnostic tools for regulatory and monitoring needs of the food industry. Acknowledgments We thank the University of Arkansas, Fayetteville, Cell and Molecular Biology (CEMB) program for supporting a graduate student assistantship to SHP. We would also like to acknowledge partial support of SHP by Vivione Biosciences, Pine Bluff, AR. References Aabo, S., Andersen, J.K., Olsen, J.E., 1995. Research note: detection of Salmonella in minced meat by the polymerase chain reaction. Lett. Appl. Microbiol. 21, 180e 182. Ahn, S., Walt, D.R., 2005. Detection of Salmonella spp. using microsphere-based, fiber-optic DNA microarrays. Analyt. Chem. 77, 5041e5047. Al-Khaldi, S.F., Martin, S.A., Rasooly, A., Evans, J.D., 2002. DNA microarray technology used for studying foodborne pathogens and microbial habitats: minireview. J. AOAC Int. 85, 906e910. Allard, M.W., Luo, Y., Strain, E., Li, C., Keys, C.E., Son, I., Stones, R., Musser, S.M., Brown, E.W., 2012. High resolution clustering of Salmonella enterica serovar Montevideo strains using a next-generation sequencing approach. BMC Genom. 13, 1e46. Altekruse, S.F., Bauer, N., Chanlongbutra, A., De Sagun, R., Naugle, A., Schlosser, W., Umholtz, R., Whit, P., 2006. Salmonella Enteritidis in broiler chicken, United States, 2000-2005. Emerg. Infect. Dis. 12, 1848e1852. Alvarez, J., Porwollik, S., Laconcha, I., Gisakis, V., Vivanco, A.B., Gonzalez, I., Echenagusia, S., Zabala, N., Blackmer, F., McClelland, M., Rementeria, A., Garaizar, J., 2003. Detection of a Salmonella enterica serovar California strain spreading in Spanish feed mills and genetic characterization with DNA microarrays. Appl. Environ. Microbiol. 69, 7531e7534. Anbazhagan, D., Kathirvalu, G.G., Mansor, M., Yan, G.O.S., Yusof, M.Y., Sekaran, S.D., 2010. Multiplex polymerase chain reaction (PCR) assays for the detection of Enterobacteriaceae in clinical samples. Afr. J. Microbiol. Res. 4, 1186e1191. Anjum, M.F., Choudhary, S., Morrison, V., Snow, L.C., Mafura, M., Slickers, P., Ehricht, R., Woodard, M.J., 2011. Identifying antimicrobial resistance genes of human clinical relevance within Salmonella isolated from food animals in Great Britain. J. Antimicrob. Chemother. 66, 550e559. Baddam, R., Kumar, N., Shaik, S., Suma, T., Ngoi, S.T., Thong, K.-L., Ahmed, N., 2012. Genome sequencing and analysis of Salmonella enterica serovar Typhi strain CR0063 representing a carrier individual during an outbreak of typhoid fever in Kelantan, Malaysia. Gut Pathog. 4, 20. Bai, S., Zhao, J., Zhang, Y., Huang, W., Xu, S., Chen, H., Fan, L.M., Chen, Y., Deng, X.W., 2010. Rapid and reliable detection of 11 food-borne pathogens using thin-film biosensor chips. Appl. Microbiol. Biotechnol. 86, 983e990. Balamurugan, S., 2010. Growth temperature associated protein expression and membrane fatty acid composition profiles of Salmonella enterica serovar Typhimurium. J. Basic Microbiol. 50, 507e518. Ballabio, A., Anier, J.E., Chamberlain, J.S., Zollo, M., Caskey, C.T., 1990. Screening for steroid sulfatase gene deletions by multiplex DNA amplification. Human Genet. 84, 571e573. Bansal, N., Gray, V., Mcdonell, F., 2006. Validated PCR assay for the routine detection of Salmonella in food. J. Food Prot. 69, 282e287. Barquist, L., Langridge, G.C., Turner, D.J., Phan, M.-D., Turner, A.K., Bateman, A., Parkhill, J., Wain, J., Gardner, P.P., 2013. A comparison of dense transposon insertion libraries in the Salmonella serovars Typhi and Typhimurium. Nucl. Acids Res. 41, 4549e4563. Bennett, A.R., Greenwood, D., Tennant, C., Banks, J.G., Betts, R.P., 1998. Rapid and definitive detection of Salmonella in foods by PCR. Lett. Appl. Microbiol. 26, 437e441. Bhagwat, A.A., 2003. Simultaneous detection of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella strains by real-time PCR. Int. J. Food Microbiol. 84, 217e224. Biswas, A.K., Kondaiah, K.N., Bheilegaonkar, A.S., Anjaneyulu, S.K., Mendiratta, C.H., Jana, Singh, R.R., 2008. Microbial profiles of frozen trimmings and silver sides prepared at Indian buffalo meat packing plants. Meat Sci. 80, 151e154. Bohaychuk, V., Gensler, G., McFall, M., King, R., Renter, D., 2007. A real-time PCR assay for the detection of Salmonella in a wide variety of food and food-animal matrices. J. Food Prot. 70, 1080e1087. Boyd, D.A., Peters, G.A., Ng, L.K., Mulvey, M.R., 2000. Partial characterization of a genomic island associated with the multidrug resistance region of Salmonella enterica Typhimurium. FEMS Microbiol. Lett. 189, 285e291. Braden, C.R., 2006. Salmonella enterica serotype Enteritidis and eggs: a national epidemic in the United States. Clin. Infect. Dis. 43, 512e517. Butcher, G.D., Miles, R.D., 1995. Minimizing Microbial Contamination in Feed Mills Producing Poultry Feed. University of Florida, Institute of Food and Agricultural Science, pp. 1e3. VM93. Call, D.R., 2005. Challenges and opportunities for pathogen detection using DNA microarrays. Crit. Rev. Microbiol. 31, 91e99.

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