Lateral Flow Assay for *Salmonella* Detection and Potential Reagents

*Dilek ÇAM*

#### **Abstract**

*Salmonella* is among the very important pathogens threating human and animal health. It is a common food pathogen transmitted from animals to humans via contaminated food, drinking water, and air. It invades the intestinal tract of hosts and causes salmonellosis leading to death. *S. enteritidis* was the most common species accounted for all salmonellosis cases. *S. typhimurium* is also another significant species causing the serious cases worldwide. To ensure public health, early detection of pathogens is crucial. Lateral flow assay (LFA), immunochromatographic assay, is a simple and rapid diagnostic test kits used in various fields and can be developed by, aptamers, antibodies (Abs), and nucleic acids. They are also being continued to develop different capture reagents coming from the recombinant technology. It has many advantages such as having mature technology, market presence, low cost, easy to use for end users without education, and stable shelf life. Gold nanoparticles (GNPs) are the most commonly used labels in the LFAs for the naked-eye analysis. Therefore, *Salmonella* detection by LFA based on GNPs in a rapid and simple way is always open to be developed by new reagents and methods.

**Keywords:** *Salmonella*, gold nanoparticles, lateral flow, food pathogens, rapid detection

#### **1. Introduction**

Most of *Salmonella* infections are typically food-borne illness. It was reported that around 15% of salmonellosis cases is caused by pork [1], turkey products, and meat [2]. Early detection of pathogens which contaminated the foods or consumption products is a crucial issue especially for the government authorities to ensure public health. Thus, many kinds of identification methods are in use, and new detection platforms are also being tried to develop for improving the sensitivity and selectivity of detection with low cost as rapid tests.

Traditionally, the *Salmonella* diagnosis in the laboratory is based on common cultural techniques [3], biochemical and serological confirmation tests. Along with immunomagnetic nanospheres as immunological tools [4], multiplex PCR [5] and real-time multiplex PCR [6–9] are other detection methods of *Salmonella* in chicken samples or other sources. However, some of those techniques require 5 or 7 days, skilled personnel, sterile working conditions, and sensitive and costly equipment, and they are inconvenient for food sector or industrial applications [10] and not

portable to perform sensitive and rapid microbial analysis. To develop the fast and sensitive method for bacterial antigens, electrochemical [11], optical [12], microfluidic [13], and magnetoelastic biosensors are also being developed for the detection of *Salmonella* species. Among those techniques lateral flow assay (LFA) is still the most practical and easy to use test and multiple detection tool as an immunosensor for end users.

#### **2. Lateral flow assay**

LFA, immunochromatographic strip test, which can be developed by Abs, aptamer, and nucleic acids, was described in the 1960s [14] and become a popular platform for rapid immunoassays since the mid-1980s [15–17]. Depending on their formats, LFAs might be expressed as dipstick assay, lateral flow device (LFD), point of care (POC) to bedside test, and lateral flow immunochromatographic assay (LFIA). LFAs are used to detect the presence or absence of a target analyte in sample and allow naked-eye analyses based on accumulation concepts [18]. LFAs have many advantages compared to other detection methods. They are established mature technology, with processes already developed, relative ease of manufacture, and stable shelf lives of 12–24 months often without refrigeration; easily scalable to high-volume production; and integrated with various systems, having high sensitivity, specificity, relatively low cost, market presence, and minimal education required for users and regulators [19]. However, test-to-test reproducibility, unclear patent situation, sensitivity issues in some systems, and integration with onboard electronics are drawbacks of LFAs. To note LFA market is expected to reach USD 8.7 billion by 2023 from an estimated USD 6.0 billion in 2018, at a compound annual growth rate (CAGR) of 7.7% [20].

The production of typical strip assay includes the preparation of colloidal gold conjugates, application of reagents onto the membrane and pads, lamination of the strip membranes onto a support backing, cutting the prepared master cards into strips of defined length and width, and strip packaging (**Figure 1A** and **B**).

Three types of pads, a sample pad, conjugate pad, and absorbent pad, and nitrocellulose membrane are used for developing the strip assay. The test sample is applied onto the sample pad. Conjugate pad contains Abs, aptamers, or nucleic acids specific to the target analyte which are usually conjugated to colored particles, gold nanoparticles (GNPs), and latex beads. Capture reagents such as anti-target Abs or aptamers are immobilized in a line across the membrane which are nitrocellulose or cellulose acetate as a test line. It has also a control line containing capture reagents such as Abs or complementary nucleic acids specific for the conjugate Abs or aptamers present on the conjugate, respectively. The strip components are usually fixed to an inert backing material and may be placed in a plastic casing with a sample port, and reaction window showing the test and control line or strip can be prepared as a simple dipstick format [22]. After soaking of sample pad with analyte, it flows through the conjugate pad and nitrocellulose membrane via capillary action and ends on an absorbent pad. When the flow is continuing, the analyte bound by gold conjugate on conjugate pad is captured and accumulated on test line. The excess conjugate is also captured by a control line, and it should always be visible. If the test strip works correctly and it is positive, both the test and control lines are seen as red. If no colored capture lines or only a red color at the test line appears, the strip is invalid, and the test should be repeated [21, 23, 24] (**Figure 2**).

Although LFAs for *Salmonella* are commonly noncompetitive, the competitive format of LFA can also be developed for the smaller analytes [25, 26]. The principle of this

**109**

**Figure 2.**

**Figure 1.**

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents*

*Preparation of LFA strip (a) and schematic representation of it in plastic case [21].*

*Schematic diagram of the immunochromatographic test strip principle for the dipstick assay. CSA Ab, common* 

*structural antigenic antibody; GNPs, gold nanoparticles [21].*

*DOI: http://dx.doi.org/10.5772/intechopen.88827*

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents DOI: http://dx.doi.org/10.5772/intechopen.88827*

*New Insight into* Brucella *Infection and Foodborne Diseases*

for end users.

**2. Lateral flow assay**

growth rate (CAGR) of 7.7% [20].

portable to perform sensitive and rapid microbial analysis. To develop the fast and sensitive method for bacterial antigens, electrochemical [11], optical [12], microfluidic [13], and magnetoelastic biosensors are also being developed for the detection of *Salmonella* species. Among those techniques lateral flow assay (LFA) is still the most practical and easy to use test and multiple detection tool as an immunosensor

LFA, immunochromatographic strip test, which can be developed by Abs, aptamer, and nucleic acids, was described in the 1960s [14] and become a popular platform for rapid immunoassays since the mid-1980s [15–17]. Depending on their formats, LFAs might be expressed as dipstick assay, lateral flow device (LFD), point of care (POC) to bedside test, and lateral flow immunochromatographic assay (LFIA). LFAs are used to detect the presence or absence of a target analyte in sample and allow naked-eye analyses based on accumulation concepts [18]. LFAs have many advantages compared to other detection methods. They are established mature technology, with processes already developed, relative ease of manufacture, and stable shelf lives of 12–24 months often without refrigeration; easily scalable to high-volume production; and integrated with various systems, having high sensitivity, specificity, relatively low cost, market presence, and minimal education required for users and regulators [19]. However, test-to-test reproducibility, unclear patent situation, sensitivity issues in some systems, and integration with onboard electronics are drawbacks of LFAs. To note LFA market is expected to reach USD 8.7 billion by 2023 from an estimated USD 6.0 billion in 2018, at a compound annual

The production of typical strip assay includes the preparation of colloidal gold conjugates, application of reagents onto the membrane and pads, lamination of the strip membranes onto a support backing, cutting the prepared master cards into strips of defined length and width, and strip packaging (**Figure 1A** and **B**). Three types of pads, a sample pad, conjugate pad, and absorbent pad, and nitrocellulose membrane are used for developing the strip assay. The test sample is applied onto the sample pad. Conjugate pad contains Abs, aptamers, or nucleic acids specific to the target analyte which are usually conjugated to colored particles, gold nanoparticles (GNPs), and latex beads. Capture reagents such as anti-target Abs or aptamers are immobilized in a line across the membrane which are nitrocellulose or cellulose acetate as a test line. It has also a control line containing capture reagents such as Abs or complementary nucleic acids specific for the conjugate Abs or aptamers present on the conjugate, respectively. The strip components are usually fixed to an inert backing material and may be placed in a plastic casing with a sample port, and reaction window showing the test and control line or strip can be prepared as a simple dipstick format [22]. After soaking of sample pad with analyte, it flows through the conjugate pad and nitrocellulose membrane via capillary action and ends on an absorbent pad. When the flow is continuing, the analyte bound by gold conjugate on conjugate pad is captured and accumulated on test line. The excess conjugate is also captured by a control line, and it should always be visible. If the test strip works correctly and it is positive, both the test and control lines are seen as red. If no colored capture lines or only a red color at the test line appears, the strip is invalid, and the test should be repeated [21, 23, 24]

Although LFAs for *Salmonella* are commonly noncompetitive, the competitive format of LFA can also be developed for the smaller analytes [25, 26]. The principle of this

**108**

(**Figure 2**).

#### **Figure 1.**

*Preparation of LFA strip (a) and schematic representation of it in plastic case [21].*

#### **Figure 2.**

*Schematic diagram of the immunochromatographic test strip principle for the dipstick assay. CSA Ab, common structural antigenic antibody; GNPs, gold nanoparticles [21].*

format is that sample extract is applied onto the sample pad and it flows through the absorbent pad. If the analyte is absent, all of the detection reagent would be trapped by capture reagent to form an easily visible test line. However, when the analyte is present, it competes with the immobilized capture reagent for the limited amount of competitive detection reagent. Thus, it means that the more analyte in the sample, the weaker the test line color.

Manufacturing of LFAs developed for *Salmonella* depends on some significant factors. First is the membrane type having suitable flow rate because it plays significant role for recognizing the whole bacteria cell. For instance, nitrocellulose Millipore membrane, Hi-Flow™ Plus 180 Membrane Card, shows good assay performance in terms of the whole-cell detection, analysis time, no background signal, and immobilization of capture reagents such as Abs and aptamers. If the flow rate of membrane is very slow, it takes a long time to see the results with the naked eye. However, if flow is very fast, capturing process cannot be completed and line intensities become unclear. Second is the application of analyte on sample pad. Both the dipstick assay and loading of sample as droplets can be preferred. The disadvantage of dipping the strip into bacterial media directly is that flow can be retarded on conjugate pad because of the media components. Thus, using the suitable running buffer, simple phosphate buffer saline (PBS), may enable conjugate to flow through the strip test, efficiently [21, 27]. Third is the immobilization of capture reagents. Generally streptavidin-biotin interaction is used for aptamer- or nucleic acid-based LFAs, and it sometimes may be required as multiple loading with consistent durations, while Abs can be directly immobilized once. The last one is the size of GNPs used for labeling. Although various studies showed that the size of GNPs between 15 and 40 nm can be used in LFAs, 35 nm GNPs are preferable. **Figure 3** displays the Ab-based LFAs for *Salmonella* when they are alone and present in bacterial mixture after optimal conditions are performed [27]. Strips were prepared by common structural Ab specific to *Salmonella enterica* species (*S. enteritidis***,** *S. typhimurium*,

#### **Figure 3.**

*Dipstick assay for live* Salmonella *strains alone and in the mixture using M180 membrane. Test line:*  Salmonella *Ab. Control line: Antigoat IgG. The strips were dipped into 200 μL of total bacteria with PBS. The strips 2–4–6–8–10–12–14–16–19–21 were prepared by naked GNPs. D. soil, dry soil bacteria; Bac,* B. cereus*; S. ent,* S. enteritidis*; S. typ,* S. typhimurium *[27].*

**111**

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents*

Therefore, sensitive detection of *Salmonella* such as 102

the monoclonal or polyclonal Abs on one assay.

**4. Aptamers for LFAs**

*S. infantis*). Bacteria isolated from the food samples were used. Dry soil bacteria, *E. coli*, *Bacillus*, and PBS were used as negative control. After culturing, test strips were dipped into the bacterial media without any treatment and showed positive results with target and nontarget samples. To highlight developed strip assays have high sensitivity and selectivity for the targets without non-specific interactions with the

The sensitivity and susceptibility of LFAs may also be improved by using highaffinity reagents including recombinant antibodies (Abs), one-step GNPs, or silver enhancement and integration of microfluidic papers with onboard electronics.

multiple recognition. Although cultural techniques associated with biochemical and serological confirmation tests and molecular methods are being developed for sensitive detection, they are time-consuming processes and not practical for end users. Thus, LFAs for *Salmonella* became attractive to make a rapid and sensitive detection for various species without nucleic acid isolation and advanced equipments. It is also open to improvement by integrating various detection systems for multiple recognition.

Abs are more common reagents used for LFAs and available from a number of commercial sources. Various kinds of Abs generated by different ways includes recombinant protein technology, phage display technology, and hybridoma techniques. Although LFAs developed by monoclonal or polyclonal Ab are commonly in use, there are highly limited sources of LFA based on single-chain variable fragments (scFvs) [28]. Generally, commercial Abs used for sandwich assay in LFA might be obtained as prequalified by the vendor in pairs. These pairs are most readily available for relatively common and high-volume assays, such as tests for pregnancy, infectious disease, cardiac markers, and malignancies. Abs specific to various antigens of *Salmonella* species are in use for the development of LFAs [27, 29–31]. The common Ab-based LFAs for *Salmonella* recognition require these steps: (i) coating of GNPs with target specific Abs (detection Abs) via chemical or physical adsorption under the optimal pH value, (ii) immobilization of capture Abs on nitrocellulose membrane, and (iii) preparation of the pad and running buffers which has the optimal releasing effect through the membrane. The first use of Abs with colloidal gold reagent for a diagnostic immunoassay was reported in 1981 [32]. The optimal concentration of Abs to cover the GNPs and preventing them from agglomeration can be changeable. The specificity and selectivity of the strip assay depends on the affinity of used Abs. Thus, using high-affinity Abs will increase the sensitivity and decrease the limit of detection (LOD) and non-specific interactions with different antigens. To achieve this goal, engineered Abs are being continued to generate and adapt to LFAs, recently [33, 34]. As it is seen on **Figure 3**, all the requirements for LFAs mentioned above were achieved by Ab-based strip assay. Therefore, it makes possible to develop the strip assay for multiple *Salmonella* detection using both

Aptamers are single-stranded DNA or RNA molecules that bind to the specific

targets. Usage of aptamers in biosensors and development of new diagnostic systems based on aptamers become popular since 2000. Because they have high

or 102 cfu/mL is achieved for

*DOI: http://dx.doi.org/10.5772/intechopen.88827*

membrane and other samples.

**3. Antibodies for LFAs**

*S. infantis*). Bacteria isolated from the food samples were used. Dry soil bacteria, *E. coli*, *Bacillus*, and PBS were used as negative control. After culturing, test strips were dipped into the bacterial media without any treatment and showed positive results with target and nontarget samples. To highlight developed strip assays have high sensitivity and selectivity for the targets without non-specific interactions with the membrane and other samples.

The sensitivity and susceptibility of LFAs may also be improved by using highaffinity reagents including recombinant antibodies (Abs), one-step GNPs, or silver enhancement and integration of microfluidic papers with onboard electronics. Therefore, sensitive detection of *Salmonella* such as 102 or 102 cfu/mL is achieved for multiple recognition. Although cultural techniques associated with biochemical and serological confirmation tests and molecular methods are being developed for sensitive detection, they are time-consuming processes and not practical for end users. Thus, LFAs for *Salmonella* became attractive to make a rapid and sensitive detection for various species without nucleic acid isolation and advanced equipments. It is also open to improvement by integrating various detection systems for multiple recognition.

#### **3. Antibodies for LFAs**

*New Insight into* Brucella *Infection and Foodborne Diseases*

the test line color.

format is that sample extract is applied onto the sample pad and it flows through the absorbent pad. If the analyte is absent, all of the detection reagent would be trapped by capture reagent to form an easily visible test line. However, when the analyte is present, it competes with the immobilized capture reagent for the limited amount of competitive detection reagent. Thus, it means that the more analyte in the sample, the weaker

Manufacturing of LFAs developed for *Salmonella* depends on some significant

factors. First is the membrane type having suitable flow rate because it plays significant role for recognizing the whole bacteria cell. For instance, nitrocellulose Millipore membrane, Hi-Flow™ Plus 180 Membrane Card, shows good assay performance in terms of the whole-cell detection, analysis time, no background signal, and immobilization of capture reagents such as Abs and aptamers. If the flow rate of membrane is very slow, it takes a long time to see the results with the naked eye. However, if flow is very fast, capturing process cannot be completed and line intensities become unclear. Second is the application of analyte on sample pad. Both the dipstick assay and loading of sample as droplets can be preferred. The disadvantage of dipping the strip into bacterial media directly is that flow can be retarded on conjugate pad because of the media components. Thus, using the suitable running buffer, simple phosphate buffer saline (PBS), may enable conjugate to flow through the strip test, efficiently [21, 27]. Third is the immobilization of capture reagents. Generally streptavidin-biotin interaction is used for aptamer- or nucleic acid-based LFAs, and it sometimes may be required as multiple loading with consistent durations, while Abs can be directly immobilized once. The last one is the size of GNPs used for labeling. Although various studies showed that the size of GNPs between 15 and 40 nm can be used in LFAs, 35 nm GNPs are preferable. **Figure 3** displays the Ab-based LFAs for *Salmonella* when they are alone and present in bacterial mixture after optimal conditions are performed [27]. Strips were prepared by common structural Ab specific to *Salmonella enterica* species (*S. enteritidis***,** *S. typhimurium*,

*Dipstick assay for live* Salmonella *strains alone and in the mixture using M180 membrane. Test line:* 

Salmonella *Ab. Control line: Antigoat IgG. The strips were dipped into 200 μL of total bacteria with PBS. The strips 2–4–6–8–10–12–14–16–19–21 were prepared by naked GNPs. D. soil, dry soil bacteria; Bac,* B. cereus*; S.* 

**110**

**Figure 3.**

*ent,* S. enteritidis*; S. typ,* S. typhimurium *[27].*

Abs are more common reagents used for LFAs and available from a number of commercial sources. Various kinds of Abs generated by different ways includes recombinant protein technology, phage display technology, and hybridoma techniques. Although LFAs developed by monoclonal or polyclonal Ab are commonly in use, there are highly limited sources of LFA based on single-chain variable fragments (scFvs) [28]. Generally, commercial Abs used for sandwich assay in LFA might be obtained as prequalified by the vendor in pairs. These pairs are most readily available for relatively common and high-volume assays, such as tests for pregnancy, infectious disease, cardiac markers, and malignancies. Abs specific to various antigens of *Salmonella* species are in use for the development of LFAs [27, 29–31]. The common Ab-based LFAs for *Salmonella* recognition require these steps: (i) coating of GNPs with target specific Abs (detection Abs) via chemical or physical adsorption under the optimal pH value, (ii) immobilization of capture Abs on nitrocellulose membrane, and (iii) preparation of the pad and running buffers which has the optimal releasing effect through the membrane. The first use of Abs with colloidal gold reagent for a diagnostic immunoassay was reported in 1981 [32]. The optimal concentration of Abs to cover the GNPs and preventing them from agglomeration can be changeable. The specificity and selectivity of the strip assay depends on the affinity of used Abs. Thus, using high-affinity Abs will increase the sensitivity and decrease the limit of detection (LOD) and non-specific interactions with different antigens. To achieve this goal, engineered Abs are being continued to generate and adapt to LFAs, recently [33, 34]. As it is seen on **Figure 3**, all the requirements for LFAs mentioned above were achieved by Ab-based strip assay. Therefore, it makes possible to develop the strip assay for multiple *Salmonella* detection using both the monoclonal or polyclonal Abs on one assay.

#### **4. Aptamers for LFAs**

Aptamers are single-stranded DNA or RNA molecules that bind to the specific targets. Usage of aptamers in biosensors and development of new diagnostic systems based on aptamers become popular since 2000. Because they have high

affinity to their targets, their generation is rapid and easy compared to the Abs, and conjugation with GNPs is chemical which is basically performed by thiol bonds. Besides, aptamer conjugates have long shelf life without degradation in comparison to Abs. Although they are used for developing LFAs, recently studies have still limited numbers in terms of the technical and application. While aptamers can be used together with Abs, they are commonly used as pairs for developing LFAs, and they should be decided carefully. Recognition aptamers present on GNPs and capture aptamers immobilized on the capture lines should have different binding sites to increase the sensitivity. Some LFAs for *Salmonella* detection based on aptamers are recorded in the literature with various reagents and techniques [35, 36]. While some aptamers show lower LOD such as 101 colony forming unit (cfu) of *S. enteritidis* [37], some of them show higher. Those variabilities can be caused by some reasons which are choosing the aptamer pairs, the distance of aptamers from the immobilization zone of membrane, affinity of aptamers [37, 38], and experimental assay conditions. Although large numbers of aptamers were recorded in the literature [39–41], there is still a lack of their adaptation to LFAs for the recognition of *Salmonella* species. Because the optimization of test parameters including immobilization procedure of aptamers on capture zones, optimal buffer ingredients, and membrane types, the exact size of GNPs has more complexity than Ab-based strip assays. Therefore, LFAs should be manufactured by high-affinity aptamers to detect whole *Salmonella* cells.

Nucleic acid-based LFAs using nucleic acid hybridization or amplification methods are also developed for *Salmonella*. However, further experimental steps including nucleic acid or genomic DNA isolation, primer design, and PCR are required. Due to the poorly suited point-of-care testing of PCR, new methods such as isothermal amplification become popular. The most common isothermal amplification methods are loop-mediated amplification (LAMP) [42, 43], nucleic acid sequence-based amplification (NASBA), rolling circle amplification (RCA), nicking enzyme-mediated amplification (NEMA), recombinase polymerase amplification (RPA), helicase-dependent amplification (HDA), multiple displacement amplification (MDA), and transcription-mediated amplification (TMA) depending on the detection techniques [44, 45]. Using this type of LFAs, lower detection limit of


**113**

tional linkers.

enhance LFAs.

**Acknowledgements**

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents*

[46] or 1.3–1.9 cfu/g or 1.3–1.9 cfu/mL of *Salmonella* in contaminated chicken products can be achieved after enrichment [47]. The assay sensitivity may also show variety according to the length of amplicon or target [48]. The commonly used reagents in this assay are biotin/fluorescein, biotin/digoxigenin tags for amplicons and gold/anti-digoxin Ab or gold/streptavidin conjugate on conjugate pad. Depending on the immobilized capture agents such as Abs, labeled nucleic acids, or aptamers on test and control line, assay is performed and results become visible for *Salmonella* [49]. **Table 1** shows some LFAs for *Salmonella* detection by using

Currently the nanoscale properties of GNPs have attracted more attention, and they are used in different fields like electronics [53], optics [54], and biosensors [55]. A common way to synthesize the nearly monodisperse spherical GNPs is the aqueous reduction of HAuCl4 by sodium citrate at boiling point [56]. Other reduc-

The nature of the surface chemistry of GNPs promotes easy and controlled

Following their biocompatibility, high stability, ease of characterization [58, 59], and the controllable morphology, GNP-based bioconjugates are found to be good candidates for biomedical applications because they are stable with their conjugated parts compared to the unbound forms. If sodium chloride is present in the solution, repulsive and attractive forces between the particles are imbalanced, due to the masking of negative charge of colloidal solution [60]. This resulted with collapsing of gold particles after adsorbing one particle onto another, and visualization of this phenomenon is seen as the color change of colloids. However, in the presence of coating molecules including proteins, nucleic acids, and aptamers, they adsorb onto gold particles and help in preventing them from aggregation by inhibiting the binding of other gold particles. To make GNP conjugates, physical interaction is the simple method, while chemical interaction is also another method including covalent conjugation [61] by using thiol derivatives and bifunc-

LFAs based on GNPs conjugates have become useful innovation in nanotechnology. Colloidal gold is the most widely used label today in commercial LFAs for many reasons. It is fairly easy and inexpensive to prepare in the laboratory. The color is intense, and no development process is needed for visualization. However, assays may have varying sensitivity with respect to their target agents [22] in LFAs.

As a conclusion LFA based on GNPs is rapid and sensitive assay for *Salmonella* detection as point-of-care tests compared to other detection methods because it is a naked-eye analysis test and does not require the skilled personnel. Once it is developed, it can be used for 1 year by the end users without advanced equipments. Adapting different reagents including Abs, aptamers, or nucleic acids onto LFAs is another advantage because of their practical immobilization and binding steps in terms of the whole-cell detection and also their potential to be adopted to

The author acknowledges Prof. Dr. Hüseyin Avni ÖKTEM for advisory.

ing agents such as borohydrides and amines have also been used [57].

attachment of other molecules especially those with thiol functionalities.

cfu of bacteria in pure culture

*Salmonella* such as 20 fg of target DNA or 1.05 × 101

*DOI: http://dx.doi.org/10.5772/intechopen.88827*

reagents mentioned above.

**5. Gold nanoparticles for LFAs**

#### **Table 1.**

*LFAs for* Salmonella *detection by Ab, aptamer, and nucleic acids.*

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents DOI: http://dx.doi.org/10.5772/intechopen.88827*

*Salmonella* such as 20 fg of target DNA or 1.05 × 101 cfu of bacteria in pure culture [46] or 1.3–1.9 cfu/g or 1.3–1.9 cfu/mL of *Salmonella* in contaminated chicken products can be achieved after enrichment [47]. The assay sensitivity may also show variety according to the length of amplicon or target [48]. The commonly used reagents in this assay are biotin/fluorescein, biotin/digoxigenin tags for amplicons and gold/anti-digoxin Ab or gold/streptavidin conjugate on conjugate pad. Depending on the immobilized capture agents such as Abs, labeled nucleic acids, or aptamers on test and control line, assay is performed and results become visible for *Salmonella* [49]. **Table 1** shows some LFAs for *Salmonella* detection by using reagents mentioned above.

#### **5. Gold nanoparticles for LFAs**

*New Insight into* Brucella *Infection and Foodborne Diseases*

36]. While some aptamers show lower LOD such as 101

*S. typhimurium* Whole cell Ab 102

RNA and DNA

gene

*enterica* yfiR(375 bp) gene

*S. enteritidis*

*LFAs for* Salmonella *detection by Ab, aptamer, and nucleic acids.*

*S. typhimurium* 16S ribosomal

*S. pullorum* Salmonella invA

*S. typhimurium Salmonella* 

*S. enteriditis* Out membrane of

*Salmonella typhi* Whole cell Ab 3 × 108

whole *Salmonella* cells.

affinity to their targets, their generation is rapid and easy compared to the Abs, and conjugation with GNPs is chemical which is basically performed by thiol bonds. Besides, aptamer conjugates have long shelf life without degradation in comparison to Abs. Although they are used for developing LFAs, recently studies have still limited numbers in terms of the technical and application. While aptamers can be used together with Abs, they are commonly used as pairs for developing LFAs, and they should be decided carefully. Recognition aptamers present on GNPs and capture aptamers immobilized on the capture lines should have different binding sites to increase the sensitivity. Some LFAs for *Salmonella* detection based on aptamers are recorded in the literature with various reagents and techniques [35,

of *S. enteritidis* [37], some of them show higher. Those variabilities can be caused by some reasons which are choosing the aptamer pairs, the distance of aptamers from the immobilization zone of membrane, affinity of aptamers [37, 38], and experimental assay conditions. Although large numbers of aptamers were recorded in the literature [39–41], there is still a lack of their adaptation to LFAs for the recognition of *Salmonella* species. Because the optimization of test parameters including immobilization procedure of aptamers on capture zones, optimal buffer ingredients, and membrane types, the exact size of GNPs has more complexity than Ab-based strip assays. Therefore, LFAs should be manufactured by high-affinity aptamers to detect

Nucleic acid-based LFAs using nucleic acid hybridization or amplification methods are also developed for *Salmonella*. However, further experimental steps including nucleic acid or genomic DNA isolation, primer design, and PCR are required. Due to the poorly suited point-of-care testing of PCR, new methods such as isothermal amplification become popular. The most common isothermal amplification methods are loop-mediated amplification (LAMP) [42, 43], nucleic acid sequence-based amplification (NASBA), rolling circle amplification (RCA), nicking enzyme-mediated amplification (NEMA), recombinase polymerase amplification (RPA), helicase-dependent amplification (HDA), multiple displacement amplification (MDA), and transcription-mediated amplification (TMA) depending on the detection techniques [44, 45]. Using this type of LFAs, lower detection limit of

*S. typhimurium* LPS Ab - Multiple [29]

*S. typhimurium* Whole cell Aptamer 85 cfu mL<sup>−</sup><sup>1</sup> Multiplex [36] *S. typhimurium* Whole cell Aptamer - Single [35]

Nucleic acid-Ab 104

**Detection Detection reagent Detection limit Detection type Refs.**

Nucleic acid-Ab 89 fg/μl Single [52]

Nucleic acid-Ab 0.75 pgμL<sup>−</sup><sup>1</sup> Single [49]

Aptamer 10 cfu mL<sup>−</sup><sup>1</sup> Single [37]

colony forming unit (cfu)

cfu/mL<sup>−</sup><sup>1</sup> Single [50]

cfu Single [30]

cells Single [51]

**112**

**Table 1.**

Currently the nanoscale properties of GNPs have attracted more attention, and they are used in different fields like electronics [53], optics [54], and biosensors [55]. A common way to synthesize the nearly monodisperse spherical GNPs is the aqueous reduction of HAuCl4 by sodium citrate at boiling point [56]. Other reducing agents such as borohydrides and amines have also been used [57].

The nature of the surface chemistry of GNPs promotes easy and controlled attachment of other molecules especially those with thiol functionalities. Following their biocompatibility, high stability, ease of characterization [58, 59], and the controllable morphology, GNP-based bioconjugates are found to be good candidates for biomedical applications because they are stable with their conjugated parts compared to the unbound forms. If sodium chloride is present in the solution, repulsive and attractive forces between the particles are imbalanced, due to the masking of negative charge of colloidal solution [60]. This resulted with collapsing of gold particles after adsorbing one particle onto another, and visualization of this phenomenon is seen as the color change of colloids. However, in the presence of coating molecules including proteins, nucleic acids, and aptamers, they adsorb onto gold particles and help in preventing them from aggregation by inhibiting the binding of other gold particles. To make GNP conjugates, physical interaction is the simple method, while chemical interaction is also another method including covalent conjugation [61] by using thiol derivatives and bifunctional linkers.

LFAs based on GNPs conjugates have become useful innovation in nanotechnology. Colloidal gold is the most widely used label today in commercial LFAs for many reasons. It is fairly easy and inexpensive to prepare in the laboratory. The color is intense, and no development process is needed for visualization. However, assays may have varying sensitivity with respect to their target agents [22] in LFAs.

As a conclusion LFA based on GNPs is rapid and sensitive assay for *Salmonella* detection as point-of-care tests compared to other detection methods because it is a naked-eye analysis test and does not require the skilled personnel. Once it is developed, it can be used for 1 year by the end users without advanced equipments. Adapting different reagents including Abs, aptamers, or nucleic acids onto LFAs is another advantage because of their practical immobilization and binding steps in terms of the whole-cell detection and also their potential to be adopted to enhance LFAs.

#### **Acknowledgements**

The author acknowledges Prof. Dr. Hüseyin Avni ÖKTEM for advisory.

## **Conflict of interest**

The author has read and approved the paper. The author agrees to the contents of the paper and has no conflict of interest.

#### **Author details**

Dilek ÇAM Department of Biology, Faculty of Science, Çankırı Karatekin University, Çankırı, Turkey

\*Address all correspondence to: cam.dilek@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**115**

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents*

[7] Kim S, Frye JG, Hu J, Fedorka-Cray PJ, Gautom R, Boyle DS. Multiplex PCR-based method for identification of common clinical serotypes of *Salmonella enterica* subsp. enterica. Journal of Clinical Microbiology. 2006;**44**(10):3608-3615. DOI: 10.1128/

[8] O'Regan E, McCabe E, Burgess C, McGuinness S, Barry T, Duffy G, et al. Development of a real-time multiplex PCR assay for the detection of multiple salmonella serotypes in chicken samples. BMC Microbiology. 2008;**8**(156):1-11.

DOI: 10.1186/1471-2180-8-156

10.1089/fpd.2010.0768

of Florida; 2003

JSEN.2008.923037

10.1039/c3lc50654a

[10] Sapparapu G. Development of immunological reagents for detecting *Salmonella enterica* Serovar Typhimurium [thesis]. USA: University

[11] Zhang D, Alocilja EC. Characterization of nanoporous silicon-based DNA biosensor for the detection of

*Salmonella enteritidis*. Sensors Journal, IEEE. 2008;**8**(6):775-780. DOI: 10.1109/

[12] Zuo P, Li X, Dominguez DC, Ye BC. A PDMS/paper/glass hybrid microfluidic biochip integrated with aptamer-functionalized graphene oxide nano-biosensors for one-step multiplexed pathogen detection. Lab on a Chip. 2013;**13**(19):3921-3928. DOI:

[13] Kim G, Moon JH, Moh CY,

for the detection of pathogenic

Lim JG. A microfluidic nano-biosensor

[9] Rajtak U, Leonard N, Bolton D, Fanning S. A real-time multiplex SYBR green I polymerase chain reaction assay for rapid screening of *Salmonella* serotypes prevalent in the European Union. Foodborne Pathogens and Disease. 2011;**8**(7):769-780. DOI:

JCM.00701-06

*DOI: http://dx.doi.org/10.5772/intechopen.88827*

[1] Borch E, Nesbakken T, Christensen H. Hazard identification in swine slaughter with respect to foodborne bacteria. International Journal of Food Microbiology. 1996;**30**(1):9-25. DOI: 10.1016/0168-1605(96)00988-9

[2] Scientific Report of EFSA and ECDC. The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2010. EFSA Journal. 2012;**10**(3):2597. DOI: 10.2903/j.

[3] Stone GG, Oberst RD, Hays MP, McVey S, Galland JC, Curtiss R, et al. Detection of *Salmonella typhimurium* from rectal swabs of experimentally infected beagles by short cultivation and PCR-hybridization. Journal of Clinical Microbiology. 1995;**33**(5):1292-1295.

[4] Wen CY, Hu J, Zhang ZL, Tian ZQ, Ou GP, Liao YL, et al. One-step sensitive detection of *Salmonella typhimurium* by coupling magnetic capture and fluorescence identification with functional nanospheres. Analytical Chemistry. 2013;**85**(2):1223-1230. DOI:

[5] Wang L, Li Y, Mustapha A. Rapid and simultaneous quantitation of *Escherichia coli* O157: H7, *Salmonella*, and *Shigella* in ground beef by multiplex real-time PCR and immunomagnetic separation. Journal of Food Protection.

2007;**70**(6):1366-1372. DOI: 10.4315/0362-028X-70.6.1366

[6] Woods DF, Reen FJ, Gilroy D, Buckley J, Frye JG, Boyd EF. Rapid multiplex PCR and real-time TaqMan PCR assays for detection of *Salmonella enterica* and the highly virulent serovars Choleraesuis and Paratyphi C. Journal of Clinical

Microbiology. 2008;**46**(12):4018-4022.

DOI: 10.1128/JCM.01229-08

**References**

efsa.2012.2597

PMID: 7615744

10.1021/ac303204q

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents DOI: http://dx.doi.org/10.5772/intechopen.88827*

#### **References**

*New Insight into* Brucella *Infection and Foodborne Diseases*

of the paper and has no conflict of interest.

The author has read and approved the paper. The author agrees to the contents

**Conflict of interest**

**114**

**Author details**

Department of Biology, Faculty of Science, Çankırı Karatekin University, Çankırı,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: cam.dilek@gmail.com

provided the original work is properly cited.

Dilek ÇAM

Turkey

[1] Borch E, Nesbakken T, Christensen H. Hazard identification in swine slaughter with respect to foodborne bacteria. International Journal of Food Microbiology. 1996;**30**(1):9-25. DOI: 10.1016/0168-1605(96)00988-9

[2] Scientific Report of EFSA and ECDC. The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2010. EFSA Journal. 2012;**10**(3):2597. DOI: 10.2903/j. efsa.2012.2597

[3] Stone GG, Oberst RD, Hays MP, McVey S, Galland JC, Curtiss R, et al. Detection of *Salmonella typhimurium* from rectal swabs of experimentally infected beagles by short cultivation and PCR-hybridization. Journal of Clinical Microbiology. 1995;**33**(5):1292-1295. PMID: 7615744

[4] Wen CY, Hu J, Zhang ZL, Tian ZQ, Ou GP, Liao YL, et al. One-step sensitive detection of *Salmonella typhimurium* by coupling magnetic capture and fluorescence identification with functional nanospheres. Analytical Chemistry. 2013;**85**(2):1223-1230. DOI: 10.1021/ac303204q

[5] Wang L, Li Y, Mustapha A. Rapid and simultaneous quantitation of *Escherichia coli* O157: H7, *Salmonella*, and *Shigella* in ground beef by multiplex real-time PCR and immunomagnetic separation. Journal of Food Protection. 2007;**70**(6):1366-1372. DOI: 10.4315/0362-028X-70.6.1366

[6] Woods DF, Reen FJ, Gilroy D, Buckley J, Frye JG, Boyd EF. Rapid multiplex PCR and real-time TaqMan PCR assays for detection of *Salmonella enterica* and the highly virulent serovars Choleraesuis and Paratyphi C. Journal of Clinical Microbiology. 2008;**46**(12):4018-4022. DOI: 10.1128/JCM.01229-08

[7] Kim S, Frye JG, Hu J, Fedorka-Cray PJ, Gautom R, Boyle DS. Multiplex PCR-based method for identification of common clinical serotypes of *Salmonella enterica* subsp. enterica. Journal of Clinical Microbiology. 2006;**44**(10):3608-3615. DOI: 10.1128/ JCM.00701-06

[8] O'Regan E, McCabe E, Burgess C, McGuinness S, Barry T, Duffy G, et al. Development of a real-time multiplex PCR assay for the detection of multiple salmonella serotypes in chicken samples. BMC Microbiology. 2008;**8**(156):1-11. DOI: 10.1186/1471-2180-8-156

[9] Rajtak U, Leonard N, Bolton D, Fanning S. A real-time multiplex SYBR green I polymerase chain reaction assay for rapid screening of *Salmonella* serotypes prevalent in the European Union. Foodborne Pathogens and Disease. 2011;**8**(7):769-780. DOI: 10.1089/fpd.2010.0768

[10] Sapparapu G. Development of immunological reagents for detecting *Salmonella enterica* Serovar Typhimurium [thesis]. USA: University of Florida; 2003

[11] Zhang D, Alocilja EC. Characterization of nanoporous silicon-based DNA biosensor for the detection of *Salmonella enteritidis*. Sensors Journal, IEEE. 2008;**8**(6):775-780. DOI: 10.1109/ JSEN.2008.923037

[12] Zuo P, Li X, Dominguez DC, Ye BC. A PDMS/paper/glass hybrid microfluidic biochip integrated with aptamer-functionalized graphene oxide nano-biosensors for one-step multiplexed pathogen detection. Lab on a Chip. 2013;**13**(19):3921-3928. DOI: 10.1039/c3lc50654a

[13] Kim G, Moon JH, Moh CY, Lim JG. A microfluidic nano-biosensor for the detection of pathogenic

salmonella. Biosensors and Bioelectronics. 2015;**67**:243-247. DOI: 10.1016/j.bios.2014.08.023

[14] Kohn J. An immunochromatographic technique. Immunology. 1968;**15**(6): 863-865. PMID: 4974484

[15] Laderman EI, Whitworth E, Dumaual E, Jones M, Hudak A, Hogrefe W, et al. Rapid, sensitive, and specific lateral-flow immunochromatographic point-of-care device for detection of herpes simplex virus type 2-specific immunoglobulin G antibodies in serum and whole blood. Clinical and Vaccine Immunology. 2008;**15**(1):159-163. DOI: 10.1128/CVI.00218-07

[16] He Y, Zhang S, Zhang X, Baloda M, Gurung AS, Xu H, et al. Ultrasensitive nucleic acid biosensor based on enzyme-gold nanoparticle dual label and lateral flow strip biosensor. Biosensors and Bioelectronics. 2011;**26**(5):2018-2024. DOI: 10.1016/j. bios.2010.08.079

[17] Ang GY, Yu CY, Yean CY. Ambient temperature detection of PCR amplicons with a novel sequence-specific nucleic acid lateral flow biosensor. Biosensors and Bioelectronics. 2012;**38**(1):151-156. DOI: 10.1016/j.bios.2012.05.019

[18] Ijeh M. Covalent gold nanoparticle—Antibody conjugates for sensitivity improvement in LFIA [thesis]. Germany: Hamburg University; 2011

[19] O'Farrell B. Evolution in lateral flow-based immunoassay systems. In: Wong R, Tse H, editors. Lateral Flow Immunoassay. USA: Humana Press; 2009. pp. 1-33. DOI: 10.1007/978-1-59745-240-3\_1

[20] Lateral Flow Assay Market by Application (Clinical Testing (Pregnancy, Infectious Disease (Mosquito , Influenza, STI, Hepatitis, TB), Cardiac Marker, Lipid Test)

Veterinary, Food Safety) Product (Reader, Kits) Technique, End User—Global Forecast to 2023 [Internet]. 2017. Available from: https://www.marketsandmarkets.com/ Market-Reports/lateral-flow-assaymarket-167205133.html

[21] Çam D, Öktem HA. Optimizations needed for lateral flow assay for rapid detection of pathogenic *E. coli*. Turkish Journal of Biology. 2017;**41**:954-968. DOI: 10.3906/biy-1705-50

[22] Peruski AH, Peruski LF. Immunological methods for detection and identification of infectious disease and biological warfare agents. Clinical and Diagnostic Laboratory Immunology. 2003;**10**(4):506-513. DOI: 10.1128/CDLI.10.4.506-513.2003

[23] Song C, Liu Q, Zhi A, Yang J, Zhi Y, Li Q, et al. Development of a lateral flow colloidal gold immunoassay strip for the rapid detection of olaquindox residues. Journal of Agricultural and Food Chemistry. 2011;**59**(17):9319-9326. DOI: 10.1021/jf202213m

[24] Tripathi V, Nara S, Singh K, Singh H, Shrivastav TG. A competitive immunochromatographic strip assay for 17-α-hydroxy progesterone using colloidal gold nanoparticles. Clinica Chimica Acta. 2012;**413**(1):262-268. DOI: 10.1016/j.cca.2011.10.016

[25] Zhang GP, Wang XN, Yang JF, Yang YY, Xing GX, Li QM, et al.Development of an immunochromatographic lateral flow test strip for detection of β-adrenergic agonist clenbuterol residues. Journal of Immunological Methods. 2006;**312**(1):27-33. DOI: 10.1016/j. jim.2006.02.017

[26] Molinelli A, Grossalber K, Krska R. A rapid lateral flow test for the determination of total type B fumonisins in maize. Analytical and Bioanalytical Chemistry.

**117**

srep21342

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents*

[33] Melnik S, Neumann AC, Karongo R, Dirndorfer S, Stübler M, Ibl V, et al. Cloning and plant-based production of antibody MC10E7 for a lateral flow immunoassay to detect [4-arginine] microcystin in freshwater. Plant Biotechnology Journal. 2018;**16**(1): 27-38. DOI: 10.1111/pbi.12746

[34] Gandhi S, Banga I, Maurya PK, Eremin SA. A gold nanoparticle-singlechain fragment variable antibody as an immunoprobe for rapid detection of morphine by dipstick. RSC Advances. 2018;**8**(3):1511-1518. DOI: 10.1039/

[35] Bruno JG. Application of DNA aptamers and quantum dots to lateral flow test strips for detection of foodborne pathogens with improved sensitivity versus colloidal gold. Pathogens. 2014;**3**(2):341-355. DOI:

[36] Jin B, Yang Y, He R, Park YI, Leee A, Bai D, et al. Lateral flow aptamer assay integrated smartphone-based portable device for simultaneous detection of multiple targets using upconversion nanoparticles. Sensors and Actuators B: Chemical. 2018;**276**:48-56. DOI:

10.3390/pathogens3020341

10.1016/j.snb.2018.08.074

[37] Fang Z, Wu W, Lu X, Zeng L. Lateral flow biosensor for DNA extraction-free detection of *Salmonella* based on aptamer mediated strand displacement amplification. Biosensors and Bioelectronics. 2014;**56**:192-197. DOI: 10.1016/j.bios.2014.01.015

[38] Park HC, Baig IA, Lee SC, Moon JY, Yoon MY. Development of ssDNA aptamers for the sensitive detection of *Salmonella typhimurium* and *Salmonella enteritidis*. Applied Biochemistry and Biotechnology. 2014;**174**(2):793-802. DOI: 10.1007/s12010-014-1103-z

[39] Book B, Chen J, Irudayaraj J. Quantification of receptor targeting aptamer binding characteristics

c7ra12810j

*DOI: http://dx.doi.org/10.5772/intechopen.88827*

[27] Çam D, Öktem HA. Development of rapid dipstick assay for food pathogens, *Salmonella*, by optimized parameters. Journal of Food Science and Technology. 2019;**56**(1):140-148. DOI: 10.1007/

[29] Schenk F, Weber P, Vogler J, Hecht L, Dietzel A, Gauglitz G. Development of a paper-based lateral flow immunoassay

[30] Lukman YM, Dyana ZN, Rahmah N, Khairunisak AR. Development and evaluation of colloidal gold lateral flow immunoassays for detection of *Escherichia Coli* O157 and *Salmonella Typhi*. IOP Conference Series: Journal of Physics: Conference Series. 2018;**1082**:012049. DOI: 10.1088/1742-6596/1082/1/012049

2009;**395**(5):1309-1316. DOI: 0.1007/

s00216-009-3082-4

s13197-018-3467-5

[28] Wemmer S, Mashau C, Fehrsen J, Van Wyngaardt W, Du Plessis DH. Chicken scFvs and bivalent scFv-C H fusions directed against HSP65 of *Mycobacterium bovis*. Biologicals. 2010;**38**(3):407-414. DOI: 10.1016/j.biologicals.2010.02.002

for simultaneous detection of lipopolysaccharides of *Salmonella* serovars. Analytical and Bioanalytical Chemistry. 2018;**410**:863-868. DOI:

10.1007/s00216-017-0643-9

[31] Zhao Y, Wang H, Zhang P, Sun C, Wang X, Wang X, et al. Rapid multiplex detection of 10 foodborne pathogens with an up-converting phosphor technology-based

[32] Leuvering JH, Thal PJ, Van der Waart M, Schuurs AH. A sol particle agglutination assay for human chorionic gonadotrophin.

Methods. 1981;**45**(2):183-194. DOI: 10.1016/0022-1759(81)90212-X

Journal of Immunological

10-channel lateral flow assay. Scientific Reports. 2016;**6**:1-8. DOI: 10.1038/

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents DOI: http://dx.doi.org/10.5772/intechopen.88827*

2009;**395**(5):1309-1316. DOI: 0.1007/ s00216-009-3082-4

*New Insight into* Brucella *Infection and Foodborne Diseases*

Veterinary, Food Safety) Product (Reader, Kits) Technique, End User—Global Forecast to 2023 [Internet]. 2017. Available from: https://www.marketsandmarkets.com/ Market-Reports/lateral-flow-assay-

[21] Çam D, Öktem HA. Optimizations needed for lateral flow assay for rapid detection of pathogenic *E. coli*. Turkish Journal of Biology. 2017;**41**:954-968.

Immunological methods for detection and identification of infectious disease and biological warfare agents. Clinical and Diagnostic Laboratory Immunology. 2003;**10**(4):506-513. DOI:

[23] Song C, Liu Q, Zhi A, Yang J, Zhi Y, Li Q, et al. Development of a lateral flow colloidal gold immunoassay strip for the rapid detection of olaquindox residues. Journal of Agricultural and Food Chemistry. 2011;**59**(17):9319-9326.

market-167205133.html

DOI: 10.3906/biy-1705-50

[22] Peruski AH, Peruski LF.

10.1128/CDLI.10.4.506-513.2003

DOI: 10.1021/jf202213m

[24] Tripathi V, Nara S, Singh K, Singh H, Shrivastav TG. A competitive immunochromatographic strip assay for 17-α-hydroxy progesterone using colloidal gold nanoparticles. Clinica Chimica Acta. 2012;**413**(1):262-268. DOI: 10.1016/j.cca.2011.10.016

[25] Zhang GP, Wang XN, Yang JF,

immunochromatographic lateral flow test strip for detection of β-adrenergic

Yang YY, Xing GX, Li QM, et al.Development of an

agonist clenbuterol residues. Journal of Immunological Methods. 2006;**312**(1):27-33. DOI: 10.1016/j.

[26] Molinelli A, Grossalber K, Krska R. A rapid lateral flow test for the determination of total type B fumonisins in maize. Analytical and Bioanalytical Chemistry.

jim.2006.02.017

salmonella. Biosensors and

10.1016/j.bios.2014.08.023

863-865. PMID: 4974484

10.1128/CVI.00218-07

bios.2010.08.079

[15] Laderman EI, Whitworth E,

Bioelectronics. 2015;**67**:243-247. DOI:

[14] Kohn J. An immunochromatographic technique. Immunology. 1968;**15**(6):

Dumaual E, Jones M, Hudak A, Hogrefe W, et al. Rapid, sensitive, and specific lateral-flow immunochromatographic point-of-care device for detection of herpes simplex virus type 2-specific immunoglobulin G antibodies in serum and whole blood. Clinical and Vaccine Immunology. 2008;**15**(1):159-163. DOI:

[16] He Y, Zhang S, Zhang X, Baloda M, Gurung AS, Xu H, et al. Ultrasensitive nucleic acid biosensor based on enzyme-gold nanoparticle dual label and lateral flow strip biosensor. Biosensors and Bioelectronics. 2011;**26**(5):2018-2024. DOI: 10.1016/j.

[17] Ang GY, Yu CY, Yean CY. Ambient temperature detection of PCR amplicons with a novel sequence-specific nucleic acid lateral flow biosensor. Biosensors and Bioelectronics. 2012;**38**(1):151-156.

DOI: 10.1016/j.bios.2012.05.019

nanoparticle—Antibody conjugates for sensitivity improvement in LFIA [thesis]. Germany: Hamburg

[18] Ijeh M. Covalent gold

[19] O'Farrell B. Evolution in lateral flow-based immunoassay systems. In: Wong R, Tse H, editors. Lateral Flow Immunoassay. USA: Humana Press; 2009. pp. 1-33. DOI: 10.1007/978-1-59745-240-3\_1

[20] Lateral Flow Assay Market by Application (Clinical Testing (Pregnancy, Infectious Disease (Mosquito , Influenza, STI, Hepatitis, TB), Cardiac Marker, Lipid Test)

University; 2011

**116**

[27] Çam D, Öktem HA. Development of rapid dipstick assay for food pathogens, *Salmonella*, by optimized parameters. Journal of Food Science and Technology. 2019;**56**(1):140-148. DOI: 10.1007/ s13197-018-3467-5

[28] Wemmer S, Mashau C, Fehrsen J, Van Wyngaardt W, Du Plessis DH. Chicken scFvs and bivalent scFv-C H fusions directed against HSP65 of *Mycobacterium bovis*. Biologicals. 2010;**38**(3):407-414. DOI: 10.1016/j.biologicals.2010.02.002

[29] Schenk F, Weber P, Vogler J, Hecht L, Dietzel A, Gauglitz G. Development of a paper-based lateral flow immunoassay for simultaneous detection of lipopolysaccharides of *Salmonella* serovars. Analytical and Bioanalytical Chemistry. 2018;**410**:863-868. DOI: 10.1007/s00216-017-0643-9

[30] Lukman YM, Dyana ZN, Rahmah N, Khairunisak AR. Development and evaluation of colloidal gold lateral flow immunoassays for detection of *Escherichia Coli* O157 and *Salmonella Typhi*. IOP Conference Series: Journal of Physics: Conference Series. 2018;**1082**:012049. DOI: 10.1088/1742-6596/1082/1/012049

[31] Zhao Y, Wang H, Zhang P, Sun C, Wang X, Wang X, et al. Rapid multiplex detection of 10 foodborne pathogens with an up-converting phosphor technology-based 10-channel lateral flow assay. Scientific Reports. 2016;**6**:1-8. DOI: 10.1038/ srep21342

[32] Leuvering JH, Thal PJ, Van der Waart M, Schuurs AH. A sol particle agglutination assay for human chorionic gonadotrophin. Journal of Immunological Methods. 1981;**45**(2):183-194. DOI: 10.1016/0022-1759(81)90212-X

[33] Melnik S, Neumann AC, Karongo R, Dirndorfer S, Stübler M, Ibl V, et al. Cloning and plant-based production of antibody MC10E7 for a lateral flow immunoassay to detect [4-arginine] microcystin in freshwater. Plant Biotechnology Journal. 2018;**16**(1): 27-38. DOI: 10.1111/pbi.12746

[34] Gandhi S, Banga I, Maurya PK, Eremin SA. A gold nanoparticle-singlechain fragment variable antibody as an immunoprobe for rapid detection of morphine by dipstick. RSC Advances. 2018;**8**(3):1511-1518. DOI: 10.1039/ c7ra12810j

[35] Bruno JG. Application of DNA aptamers and quantum dots to lateral flow test strips for detection of foodborne pathogens with improved sensitivity versus colloidal gold. Pathogens. 2014;**3**(2):341-355. DOI: 10.3390/pathogens3020341

[36] Jin B, Yang Y, He R, Park YI, Leee A, Bai D, et al. Lateral flow aptamer assay integrated smartphone-based portable device for simultaneous detection of multiple targets using upconversion nanoparticles. Sensors and Actuators B: Chemical. 2018;**276**:48-56. DOI: 10.1016/j.snb.2018.08.074

[37] Fang Z, Wu W, Lu X, Zeng L. Lateral flow biosensor for DNA extraction-free detection of *Salmonella* based on aptamer mediated strand displacement amplification. Biosensors and Bioelectronics. 2014;**56**:192-197. DOI: 10.1016/j.bios.2014.01.015

[38] Park HC, Baig IA, Lee SC, Moon JY, Yoon MY. Development of ssDNA aptamers for the sensitive detection of *Salmonella typhimurium* and *Salmonella enteritidis*. Applied Biochemistry and Biotechnology. 2014;**174**(2):793-802. DOI: 10.1007/s12010-014-1103-z

[39] Book B, Chen J, Irudayaraj J. Quantification of receptor targeting aptamer binding characteristics

using single-molecule spectroscopy. Biotechnology and Bioengineering. 2011;**108**(5):1222-1227. DOI: 10.1002/ bit.23043

[40] Duan N, Wu S, Chen X, Huang Y, Xia Y, Ma X, et al. Selection and characterization of aptamers against *Salmonella typhimurium* using whole-bacterium systemic evolution of ligands by exponential enrichment (SELEX). Journal of Agricultural and Food Chemistry. 2013;**61**(13):3229-3234. DOI: 10.1021/jf400767d

[41] Ning Y, Li W, Duan Y, Yang M, Deng L. High specific DNAzymeaptamer sensor for *Salmonella paratyphi* a using single-walled nanotubes-based dual fluorescence-spectrophotometric methods. Journal of Biomolecular Screening. 2014;**19**(7):1099-1106. DOI: 10.1177/1087057114528538

[42] Perera RS, Ding XC, Tully F, Oliver J, Bright N, et al. Development and clinical performance of high throughput loop-mediated isothermal amplification for detection of malaria. PLoS One. 2017;**12**(2):e0171126. DOI: 10.1371/journal.pone.0171126

[43] Kokkinos PA, Ziros PG, Bellou M, Vantarakis A. Loop-mediated isothermal amplification (LAMP) for the detection of *Salmonella* in food. Food Analytical Methods. 2014;**7**(2):512-526. DOI: 10.1007/ s12161-013-9748-8

[44] Kaur A, Kapil A, Elangovan R, Jha S, Kalyanasundaram D. Highly sensitive detection of *Salmonella typhi* in clinical blood samples by magnetic nanoparticle-based enrichment and in-situ measurement of isothermal amplification of nucleic acids. PLoS One. 2018;**13**(3):e0194817. DOI: 10.1371/ journal.pone.0194817

[45] Zanoli L, Spoto G. Isothermal amplification methods for the detection of nucleic acids in microfluidic devices.

Biosensors. 2013;**3**(1):18-43. DOI: 10.3390/bios3010018

[46] Liu H, Zang YX, Du XJ, Li P, Wang S. Development of an isothermal amplification-based assay for the rapid visual detection of salmonella bacteria. Journal of Dairy Science. 2017;**100**(9):7016-7025. DOI: 10.3168/ jds.2017-12566

[47] Du XJ, Zhou TJ, Li P, Wang S. A rapid salmonella detection method involving thermophilic helicasedependent amplification and a lateral flow assay. Molecular and Cellular Probes. 2017;**34**:37-44. DOI: 10.1016/j. mcp.2017.05.004

[48] Bulut O. Development of nucleic acid based lateral flow immunochromatographic test platform for *Salmonella* detection [thesis]. Turkey: Middle East Technical University; 2014

[49] Aissa AB, Jara JJ, Sebastián RM, Vallribera A, Campoy S, Pividori MI. Comparing nucleic acid lateral flow and electrochemical genosensing for the simultaneous detection of foodborne pathogens. Biosensors and Bioelectronics. 2017;**88**:265-272. DOI: 10.1016/j.bios.2016.08.046

[50] Park TS, Li W, McCracken KE, Yoon JY. Smartphone quantifies *Salmonella* from paper microfluidics. Lab on a Chip. 2013;**13**(24):4832-4840. DOI: 10.1039/c3lc50976a

[51] Liu CC, Yeung CY, Chen PH, Yeh MK, Hou SY. Salmonella detection using 16S ribosomal DNA/RNA probe-gold nanoparticles and lateral flow immunoassay. Food Chemistry. 2013;**141**:2526-2532. DOI: 10.1016/j. foodchem.2013.05.089

[52] Liu ZK, Zhang QY, Yang NN, Xu MG, Xu JF, Jing ML, et al. Rapid and sensitive detection of salmonella in chickens using loop-mediated isothermal amplification combined

**119**

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents*

[60] Derjaguin BV, Landau L. Theory of the stability of strongly charged lyophobic sols and of the adhesion of strongly charged particles in solutions of electrolytes. Acta

Physicochim URSS. 1941;**14**(6):633-662.

[61] Caruso F. Nanoengineering of particle surfaces. Advanced Materials. 2001;**13**(1):11-22. DOI: 10.1002/ 1521-4095(200101)13:1<11: AID-ADMA11>3.0.CO;2-N

NAID: 10016600848

*DOI: http://dx.doi.org/10.5772/intechopen.88827*

Shaviv E, Banin U. Electric field induced switching of the fluorescence of single semiconductor quantum rods. Nano Letters. 2005;**5**(8):1581-1586. DOI:

[54] Yao JL, Pan GP, Xue KH, Wu DY, Ren B, Sun DM, et al. A complementary study of surface-enhanced Raman scattering and metal nanorod arrays. Pure and Applied Chemistry. 2000;**72**(1):221-228. DOI: 10.1351/

[55] Aslan K, Zhang J, Lakowicz JR, Geddes CD. Saccharide sensing using gold and silver nanoparticles-a review. Journal of Fluorescence. 2004;**14**(4):391-400. DOI: 10.1023/B:J

OFL.0000031820.17358.28

[56] Narayanan R, El-Sayed MA. Catalysis with transition metal nanoparticles in colloidal solution: Nanoparticle shape dependence and stability. The Journal of Physical Chemistry B. 2005;**109**(26):

12663-12676. DOI: 10.1021/jp051066p

[57] Wilson OM, Hu X, Cahill DG, Braun PV. Colloidal metal particles as probes of nanoscale thermal transport in fluids. Physical Review B. 2002;**66**(22):224301. DOI: 10.1103/

[58] Nie S, Emory SR. Probing single molecules and single nanoparticles by surface-enhanced Raman scattering. Science. 1997;**275**(5303):1102-1106. DOI: 10.1126/science.275.5303.1102

[59] Sperling RA, Gil PR, Zhang F, Zanella M, Parak WJ. Biological applications of gold nanoparticles. Chemical Society Reviews. 2008;**37**(9):1896-1908. DOI:

PhysRevB.66.224301

10.1039/b712170a

with a lateral flow dipstick. Journal of Microbiology and Biotechnology. 2019;**29**(3):454-464. DOI: 10.4014/

[53] Rothenberg E, Kazes M,

jmb.1712.12010

10.1021/nl051007n

pac200072010221

*Lateral Flow Assay for* Salmonella *Detection and Potential Reagents DOI: http://dx.doi.org/10.5772/intechopen.88827*

with a lateral flow dipstick. Journal of Microbiology and Biotechnology. 2019;**29**(3):454-464. DOI: 10.4014/ jmb.1712.12010

*New Insight into* Brucella *Infection and Foodborne Diseases*

Biosensors. 2013;**3**(1):18-43. DOI:

[46] Liu H, Zang YX, Du XJ, Li P, Wang S. Development of an isothermal amplification-based assay for the rapid visual detection of salmonella bacteria. Journal of Dairy Science. 2017;**100**(9):7016-7025. DOI: 10.3168/

[47] Du XJ, Zhou TJ, Li P, Wang S. A rapid salmonella detection method involving thermophilic helicasedependent amplification and a lateral flow assay. Molecular and Cellular Probes. 2017;**34**:37-44. DOI: 10.1016/j.

10.3390/bios3010018

jds.2017-12566

mcp.2017.05.004

[48] Bulut O. Development of nucleic acid based lateral flow

10.1016/j.bios.2016.08.046

DOI: 10.1039/c3lc50976a

foodchem.2013.05.089

[50] Park TS, Li W, McCracken KE, Yoon JY. Smartphone quantifies *Salmonella* from paper microfluidics. Lab on a Chip. 2013;**13**(24):4832-4840.

[51] Liu CC, Yeung CY, Chen PH, Yeh MK, Hou SY. Salmonella detection using 16S ribosomal DNA/RNA probe-gold nanoparticles and lateral flow immunoassay. Food Chemistry. 2013;**141**:2526-2532. DOI: 10.1016/j.

[52] Liu ZK, Zhang QY, Yang NN, Xu MG, Xu JF, Jing ML, et al. Rapid and sensitive detection of salmonella in chickens using loop-mediated isothermal amplification combined

immunochromatographic test platform for *Salmonella* detection [thesis]. Turkey: Middle East Technical University; 2014

[49] Aissa AB, Jara JJ, Sebastián RM, Vallribera A, Campoy S, Pividori MI. Comparing nucleic acid lateral flow and electrochemical genosensing for the simultaneous detection of foodborne pathogens. Biosensors and Bioelectronics. 2017;**88**:265-272. DOI:

using single-molecule spectroscopy. Biotechnology and Bioengineering. 2011;**108**(5):1222-1227. DOI: 10.1002/

Huang Y, Xia Y, Ma X, et al. Selection and characterization of aptamers against *Salmonella typhimurium* using whole-bacterium systemic evolution of ligands by exponential enrichment (SELEX). Journal of Agricultural and Food Chemistry. 2013;**61**(13):3229-3234.

[41] Ning Y, Li W, Duan Y, Yang M, Deng L. High specific DNAzymeaptamer sensor for *Salmonella paratyphi* a using single-walled nanotubes-based dual fluorescence-spectrophotometric methods. Journal of Biomolecular Screening. 2014;**19**(7):1099-1106. DOI:

[40] Duan N, Wu S, Chen X,

DOI: 10.1021/jf400767d

10.1177/1087057114528538

[42] Perera RS, Ding XC, Tully F, Oliver J, Bright N, et al. Development and clinical performance of high throughput loop-mediated isothermal amplification for detection of malaria. PLoS One. 2017;**12**(2):e0171126. DOI:

10.1371/journal.pone.0171126

[43] Kokkinos PA, Ziros PG,

s12161-013-9748-8

journal.pone.0194817

Bellou M, Vantarakis A. Loop-mediated isothermal amplification (LAMP) for the detection of *Salmonella* in food. Food Analytical Methods. 2014;**7**(2):512-526. DOI: 10.1007/

[44] Kaur A, Kapil A, Elangovan R, Jha S, Kalyanasundaram D. Highly sensitive detection of *Salmonella typhi* in clinical blood samples by magnetic nanoparticle-based enrichment and in-situ measurement of isothermal amplification of nucleic acids. PLoS One. 2018;**13**(3):e0194817. DOI: 10.1371/

[45] Zanoli L, Spoto G. Isothermal amplification methods for the detection of nucleic acids in microfluidic devices.

bit.23043

**118**

[53] Rothenberg E, Kazes M, Shaviv E, Banin U. Electric field induced switching of the fluorescence of single semiconductor quantum rods. Nano Letters. 2005;**5**(8):1581-1586. DOI: 10.1021/nl051007n

[54] Yao JL, Pan GP, Xue KH, Wu DY, Ren B, Sun DM, et al. A complementary study of surface-enhanced Raman scattering and metal nanorod arrays. Pure and Applied Chemistry. 2000;**72**(1):221-228. DOI: 10.1351/ pac200072010221

[55] Aslan K, Zhang J, Lakowicz JR, Geddes CD. Saccharide sensing using gold and silver nanoparticles-a review. Journal of Fluorescence. 2004;**14**(4):391-400. DOI: 10.1023/B:J OFL.0000031820.17358.28

[56] Narayanan R, El-Sayed MA. Catalysis with transition metal nanoparticles in colloidal solution: Nanoparticle shape dependence and stability. The Journal of Physical Chemistry B. 2005;**109**(26): 12663-12676. DOI: 10.1021/jp051066p

[57] Wilson OM, Hu X, Cahill DG, Braun PV. Colloidal metal particles as probes of nanoscale thermal transport in fluids. Physical Review B. 2002;**66**(22):224301. DOI: 10.1103/ PhysRevB.66.224301

[58] Nie S, Emory SR. Probing single molecules and single nanoparticles by surface-enhanced Raman scattering. Science. 1997;**275**(5303):1102-1106. DOI: 10.1126/science.275.5303.1102

[59] Sperling RA, Gil PR, Zhang F, Zanella M, Parak WJ. Biological applications of gold nanoparticles. Chemical Society Reviews. 2008;**37**(9):1896-1908. DOI: 10.1039/b712170a

[60] Derjaguin BV, Landau L. Theory of the stability of strongly charged lyophobic sols and of the adhesion of strongly charged particles in solutions of electrolytes. Acta Physicochim URSS. 1941;**14**(6):633-662. NAID: 10016600848

[61] Caruso F. Nanoengineering of particle surfaces. Advanced Materials. 2001;**13**(1):11-22. DOI: 10.1002/ 1521-4095(200101)13:1<11: AID-ADMA11>3.0.CO;2-N

**121**

**Chapter 10**

Canada

**Abstract**

*Listeria*

**1. Introduction**

*and Burton W. Blais*

Applications of Genomics in

*Catherine D. Carrillo, Adam Koziol, Neil Vary* 

Regulatory Food Safety Testing in

Recent developments in the field of pathogen genomics herald a new paradigm for analytical food microbiology in which pathogenic bacteria will be characterized on the basis of their genetic profile rather than traditional approaches relying on phenotypic properties. The ability to identify gene markers associated with virulence, antimicrobial resistance, and other properties relevant to the identification, risk profiling, and typing of foodborne bacterial isolates will play a critical role in informing regulatory decisions and tracing sources of food contamination. Here we present several scenarios illustrating current and prospective roles for pathogen genomics in food inspection.

**Keywords:** pathogen genomics, virulence, foodborne pathogens, whole-genome sequencing, typing, bioinformatics, food inspection, *Escherichia coli*, *Salmonella*,

The food microbiology testing laboratory has a key role in supporting regulatory food safety investigations, whether stemming from a contamination incident identified through routine monitoring food inspection programs or a foodborne illness outbreak event where human lives and well-being are at risk. While such investigations typically involve the concerted actions of food inspection and public health authorities at different levels of government, the main role of the regulatory testing laboratory is to confirm the presence of a specified hazard in a food vehicle and provide data indicating the scope and source of a contamination event. The extent to which the laboratory can contribute critical information to an investigation will to a large degree depend on the application of leading-edge technologies for detection and characterization of foodborne pathogens. Approaches capable of maximizing the amount of information obtained in the course of conducting laboratory testing of inspection samples will foster the most appropriate regulatory responses, for example, by informing the health risk assessment process undertaken

The impact of analytical service delivery on public health outcomes depends on the ability to process large numbers of investigative samples and produce accurate test results in the shortest timeframe possible. While cultural enrichment of food samples to amplify pathogens to detectable levels is generally necessary for their

to categorize the degree of risk attending a contamination incident.

#### **Chapter 10**

## Applications of Genomics in Regulatory Food Safety Testing in Canada

*Catherine D. Carrillo, Adam Koziol, Neil Vary and Burton W. Blais*

#### **Abstract**

Recent developments in the field of pathogen genomics herald a new paradigm for analytical food microbiology in which pathogenic bacteria will be characterized on the basis of their genetic profile rather than traditional approaches relying on phenotypic properties. The ability to identify gene markers associated with virulence, antimicrobial resistance, and other properties relevant to the identification, risk profiling, and typing of foodborne bacterial isolates will play a critical role in informing regulatory decisions and tracing sources of food contamination. Here we present several scenarios illustrating current and prospective roles for pathogen genomics in food inspection.

**Keywords:** pathogen genomics, virulence, foodborne pathogens, whole-genome sequencing, typing, bioinformatics, food inspection, *Escherichia coli*, *Salmonella*, *Listeria*

#### **1. Introduction**

The food microbiology testing laboratory has a key role in supporting regulatory food safety investigations, whether stemming from a contamination incident identified through routine monitoring food inspection programs or a foodborne illness outbreak event where human lives and well-being are at risk. While such investigations typically involve the concerted actions of food inspection and public health authorities at different levels of government, the main role of the regulatory testing laboratory is to confirm the presence of a specified hazard in a food vehicle and provide data indicating the scope and source of a contamination event. The extent to which the laboratory can contribute critical information to an investigation will to a large degree depend on the application of leading-edge technologies for detection and characterization of foodborne pathogens. Approaches capable of maximizing the amount of information obtained in the course of conducting laboratory testing of inspection samples will foster the most appropriate regulatory responses, for example, by informing the health risk assessment process undertaken to categorize the degree of risk attending a contamination incident.

The impact of analytical service delivery on public health outcomes depends on the ability to process large numbers of investigative samples and produce accurate test results in the shortest timeframe possible. While cultural enrichment of food samples to amplify pathogens to detectable levels is generally necessary for their

recovery, current approaches often use protracted identification processes relying on phenotypic characteristics elucidated by time-consuming cultivation, biochemical and serological techniques. While effective under certain circumstances, there are shortcomings to such a limited approach, when dealing with novel pathogens for which analytical parameters may not have been comprehensively worked out or in trying to attribute contamination sources.

The exploitation of the genetic blueprint, or known parts thereof, associated with a targeted bacterial pathogen is now widely accepted as an effective means for the identification of food pathogens. Polymerase chain reaction (PCR) technology is now well established as an analytical tool in the regulatory food laboratory [1–4]. Its implementation in regulatory testing programs underscores the growing acceptance of redefining the terms for the identification and characterization of bacteria from a phenotypic to a genotypic basis. Indeed, the advent of leading-edge genomics technologies opens new possibilities for comprehensive analyses of microbial isolates recovered from food inspection samples; for example, next-generation sequencing technologies (whole-genome sequencing, WGS) can now render a bacterial genome much faster (i.e., within 1–3 days) and at a significantly lower cost (about 100 dollars) than previously possible, making it feasible to sequence foodborne isolates within the timeframes of food safety investigations [5–7].

Currently available bioinformatics tools are sufficiently advanced to enable the rapid processing of raw sequence data into a usable form for many purposes. Sequencing pathogenic bacteria, whether in the context of outbreak investigations or information gathering in the course of research, can yield an unprecedented level of information regarding the presence of virulence and other marker genes relevant to the identification and risk characterization of food isolates [6–10]. WGS data can provide an exquisite degree of resolution capable of ascertaining differences between strains and determining phylogenetic relationships among different bacterial isolates for pinpoint precision in the attribution of contamination sources [6, 11]. Finally, the identification of strain-specific features such as unique DNA sequences, metabolic properties, and antimicrobial resistance will enable testing labs to implement customized tests addressing specific strains of interest in determining the scope of contamination events.

While genomics, including WGS technology, already plays a significant role in the clinical sciences, its role in regulatory food microbiology inspection programs remains to be fully delineated. Currently, methods used to characterize foodborne pathogens recovered in regulatory food testing programs aim to answer three questions: (1) what is it? (2) have we seen it before? and (3) is it dangerous? This work describes some of the ways in which characterization of bacterial pathogens using genomics technologies has provided or may contribute to faster, more reliable and cost-effective results addressing these questions. Our purpose is to present different scenarios to illustrate impacts of leading-edge genomics technologies, some imagined, and others already achieved, on food inspection programs.

#### **2. Impacts of the implementation of genomics in regulatory food microbiology**

#### **2.1 What is it? Definitive identification of pathogenic bacteria based on genomics techniques**

#### *2.1.1 In the beginning: detection of genomic markers by PCR*

*Escherichia coli* O157:H7 have been implicated in outbreaks of foodborne illness associated with the consumption of contaminated foods such as ground beef and

**123**

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

produce [12, 13]. In the event of an outbreak, it is imperative that production lots associated with the primary food vehicle are identified as quickly as possible in order to recall all contaminated products from the marketplace [14]. Traditional techniques for the detection of *E. coli* O157:H7 in foods rely on a multistep process involving pre-enrichment in a selective broth followed by plating to reveal the presence of sorbitol-negative colonies, which are then purified and subjected to a battery of biochemical and serological tests to confirm their identity [15, 16]. This process can take up to 1 week to complete before the contaminant can be definitively identified because of the requirement for growth and phenotypic expression

As an alternative to classic phenotypic techniques, the identification of foodborne colony isolates can be achieved on the basis of detection of defining gene markers. Detection platforms incorporating PCR techniques are particularly well suited for same-day analysis of a primary colony isolate. The CFIA microbiology laboratory network has undertaken a program of method development aimed at the rapid identification of colonies isolated on plating media at an early stage during the enrichment process. A key technology platform adopted by CFIA for this purpose is the cloth-based hybridization array system (CHAS) providing for identification of pathogens through amplification of key target genes by multiplex PCR, followed by rapid hybridization of the amplicons with an array of immobilized capture probes on a polyester cloth support [2–4, 17]. This approach enables facile detection of many gene markers in a single reaction, with specificity assured through the hybrid-

A CHAS method for the identification of *E. coli* O157:H7 [17] has been validated following the guidelines of the Health Canada *Compendium of Analytical Methods* (CAM) (available at [18]). This method has been published in the CAM (MFLP-22: Characterization of verotoxigenic *Escherichia coli* O157:H7 colonies by polymerase chain reaction and cloth-based hybridization array system, [2]), enabling its implementation for regulatory testing purposes in Canada. It is notable as the first instance of a genetic marker-based approach for the definitive identification of a foodborne pathogen isolate in our laboratories. The *E. coli* O157:H7 CHAS was used by CFIA laboratories on two separate occasions in 2013 to provide critical evidence supporting health risk assessments in connection with foodborne illness outbreaks implicating ground beef distributed in Canada. This method enabled the testing laboratories to issue official results of analysis a full 2 days ahead of the traditional approach, leading to more timely interventions minimizing public exposure to the contaminated product. The CHAS technology has now been incorporated into

Non-O157 STEC, particularly strains bearing certain O antigenic determinants (O26, O45, O103, O111, O121, O145, and O157), are emerging as a serious foodborne public health concern [19]. Unlike *E. coli* O157:H7, there are no biochemical features by which these so-called priority STEC strains can be differentiated from commensal *E. coli* or other STEC which are not a public health concern. However, it is universally recognized that foodborne STEC posing a public health risk can be defined on the basis of possession of certain gene markers, including the Shiga toxin genes *st1* or *st2*, the intimin-coding gene *eae*, and markers for the specific serogroups of concern [1, 19, 20]. Thus, priority STEC constitute a striking example illustrating the benefit of the use of gene markers for pinpointing pathogens otherwise not readily amenable to identification by classic means. This in turn has enabled practical strategies for multiplex gene detection methods for detection of such pathogens

routine diagnostic testing schemes in CFIA food testing laboratories.

*2.1.2 Enter the next generation: whole-genome sequencing*

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

of the organism.

ization process.

#### *Applications of Genomics in Regulatory Food Safety Testing in Canada DOI: http://dx.doi.org/10.5772/intechopen.86063*

*New Insight into* Brucella *Infection and Foodborne Diseases*

in trying to attribute contamination sources.

recovery, current approaches often use protracted identification processes relying on phenotypic characteristics elucidated by time-consuming cultivation, biochemical and serological techniques. While effective under certain circumstances, there are shortcomings to such a limited approach, when dealing with novel pathogens for which analytical parameters may not have been comprehensively worked out or

The exploitation of the genetic blueprint, or known parts thereof, associated with a targeted bacterial pathogen is now widely accepted as an effective means for the identification of food pathogens. Polymerase chain reaction (PCR) technology is now well established as an analytical tool in the regulatory food laboratory [1–4]. Its implementation in regulatory testing programs underscores the growing acceptance of redefining the terms for the identification and characterization of bacteria from a phenotypic to a genotypic basis. Indeed, the advent of leading-edge genomics technologies opens new possibilities for comprehensive analyses of microbial isolates recovered from food inspection samples; for example, next-generation sequencing technologies (whole-genome sequencing, WGS) can now render a bacterial genome much faster (i.e., within 1–3 days) and at a significantly lower cost (about 100 dollars) than previously possible, making it feasible to sequence food-

borne isolates within the timeframes of food safety investigations [5–7].

Currently available bioinformatics tools are sufficiently advanced to enable the rapid

While genomics, including WGS technology, already plays a significant role in the clinical sciences, its role in regulatory food microbiology inspection programs remains to be fully delineated. Currently, methods used to characterize foodborne pathogens recovered in regulatory food testing programs aim to answer three questions: (1) what is it? (2) have we seen it before? and (3) is it dangerous? This work describes some of the ways in which characterization of bacterial pathogens using genomics technologies has provided or may contribute to faster, more reliable and cost-effective results addressing these questions. Our purpose is to present different scenarios to illustrate impacts of leading-edge genomics technologies, some imag-

ined, and others already achieved, on food inspection programs.

*2.1.1 In the beginning: detection of genomic markers by PCR*

**2. Impacts of the implementation of genomics in regulatory food** 

**2.1 What is it? Definitive identification of pathogenic bacteria based on genomics** 

*Escherichia coli* O157:H7 have been implicated in outbreaks of foodborne illness associated with the consumption of contaminated foods such as ground beef and

processing of raw sequence data into a usable form for many purposes. Sequencing pathogenic bacteria, whether in the context of outbreak investigations or information gathering in the course of research, can yield an unprecedented level of information regarding the presence of virulence and other marker genes relevant to the identification and risk characterization of food isolates [6–10]. WGS data can provide an exquisite degree of resolution capable of ascertaining differences between strains and determining phylogenetic relationships among different bacterial isolates for pinpoint precision in the attribution of contamination sources [6, 11]. Finally, the identification of strain-specific features such as unique DNA sequences, metabolic properties, and antimicrobial resistance will enable testing labs to implement customized tests addressing specific strains of interest in determining the scope of contamination events.

**122**

**microbiology**

**techniques**

produce [12, 13]. In the event of an outbreak, it is imperative that production lots associated with the primary food vehicle are identified as quickly as possible in order to recall all contaminated products from the marketplace [14]. Traditional techniques for the detection of *E. coli* O157:H7 in foods rely on a multistep process involving pre-enrichment in a selective broth followed by plating to reveal the presence of sorbitol-negative colonies, which are then purified and subjected to a battery of biochemical and serological tests to confirm their identity [15, 16]. This process can take up to 1 week to complete before the contaminant can be definitively identified because of the requirement for growth and phenotypic expression of the organism.

As an alternative to classic phenotypic techniques, the identification of foodborne colony isolates can be achieved on the basis of detection of defining gene markers. Detection platforms incorporating PCR techniques are particularly well suited for same-day analysis of a primary colony isolate. The CFIA microbiology laboratory network has undertaken a program of method development aimed at the rapid identification of colonies isolated on plating media at an early stage during the enrichment process. A key technology platform adopted by CFIA for this purpose is the cloth-based hybridization array system (CHAS) providing for identification of pathogens through amplification of key target genes by multiplex PCR, followed by rapid hybridization of the amplicons with an array of immobilized capture probes on a polyester cloth support [2–4, 17]. This approach enables facile detection of many gene markers in a single reaction, with specificity assured through the hybridization process.

A CHAS method for the identification of *E. coli* O157:H7 [17] has been validated following the guidelines of the Health Canada *Compendium of Analytical Methods* (CAM) (available at [18]). This method has been published in the CAM (MFLP-22: Characterization of verotoxigenic *Escherichia coli* O157:H7 colonies by polymerase chain reaction and cloth-based hybridization array system, [2]), enabling its implementation for regulatory testing purposes in Canada. It is notable as the first instance of a genetic marker-based approach for the definitive identification of a foodborne pathogen isolate in our laboratories. The *E. coli* O157:H7 CHAS was used by CFIA laboratories on two separate occasions in 2013 to provide critical evidence supporting health risk assessments in connection with foodborne illness outbreaks implicating ground beef distributed in Canada. This method enabled the testing laboratories to issue official results of analysis a full 2 days ahead of the traditional approach, leading to more timely interventions minimizing public exposure to the contaminated product. The CHAS technology has now been incorporated into routine diagnostic testing schemes in CFIA food testing laboratories.

#### *2.1.2 Enter the next generation: whole-genome sequencing*

Non-O157 STEC, particularly strains bearing certain O antigenic determinants (O26, O45, O103, O111, O121, O145, and O157), are emerging as a serious foodborne public health concern [19]. Unlike *E. coli* O157:H7, there are no biochemical features by which these so-called priority STEC strains can be differentiated from commensal *E. coli* or other STEC which are not a public health concern. However, it is universally recognized that foodborne STEC posing a public health risk can be defined on the basis of possession of certain gene markers, including the Shiga toxin genes *st1* or *st2*, the intimin-coding gene *eae*, and markers for the specific serogroups of concern [1, 19, 20]. Thus, priority STEC constitute a striking example illustrating the benefit of the use of gene markers for pinpointing pathogens otherwise not readily amenable to identification by classic means. This in turn has enabled practical strategies for multiplex gene detection methods for detection of such pathogens

during routine monitoring of the food supply to verify industry compliance with food safety regulations. In fact, such an approach is the basis for the Canadian STEC Method which was developed jointly by CFIA and Health Canada for the detection of this family of pathogens in meats and produce [3, 4].

The potential of PCR technology to provide informative test results is limited by its rather fragmentary nature, that is, the fact that only a relatively small number of different DNA markers can be assessed in a single analytical procedure. A considerable effort is required to optimize and validate the performance of PCR systems, particularly those in which multiple primer pairs are combined (e.g., identification of priority Shiga toxigenic *E. coli* (STEC) using a combination of 11 primer pairs; [1]). For all intents and purposes, validated assays are locked into their original configurations, and it is not possible to modify them (e.g., add or alter primers) on an ad hoc basis without first having to undertake a laborious and time-consuming re-evaluation of assay performance characteristics. Thus, novel queries cannot be undertaken if, in the course of an active food safety investigation, new questions arise regarding the occurrence of an unanticipated assay target. This was the case in a 2011 German outbreak involving STEC with an unusual virulence profile [21, 22].

The use of WGS technologies for STEC characterization can provide a more complete picture [8]; however, completion of WGS analyses typically requires 3–5 days. As this timeline may be too long in an outbreak situation, we have developed practical processes in which genomic DNA isolated from a single STEC colony is sequenced using the Illumina MiSeq platform, followed by analysis of the sequence data during the course of the sequencing run for the determination of genomic markers for phylogenetic identity, virulence profile, serotyping, as well as biological metrics serving as quality control features supporting the validity of the process [5]. Identification and characterization of isolates can be completed within 9 h, comparable to the current method used for characterization of STEC. This real-time WGS approach produces high-resolution characterization of bacterial pathogens at a cost and within a timeframe that are similar to standard microbiological techniques and has the potential to replace lengthy biochemical characterization and molecular and serological typing procedures widely used in food testing laboratories. Our laboratory is currently studying strategies for broad implementation of WGS technology in support of regulatory food inspection objectives through the detection, identification, and characterization of priority bacterial pathogens such as STEC, *Salmonella enterica,* and *Listeria monocytogenes* [5, 23–25]. We have developed guidelines and implemented validated methods and bioinformatics tools for automated analyses of sequence data to ensure reliability and reproducibility of WGS-based analyses [24, 26].

#### **2.2 Have we seen it before? Impact of high-resolution molecular typing by WGS for distinguishing new isolates from control and historical laboratory isolates**

Following detection of a pathogen in foods, (sub)typing methods are often used to generate a profile of the isolated organism to determine similarity to previously characterized isolates from clinical or food sources (reviewed in [27–29]). Typing of isolates recovered from food samples can provide important information regarding the complexity and source(s) of a given contamination incident, enables tracking of foodborne bacterial strains, and is frequently used to support regulatory decisions.

Early methods of typing were based on phenotypic properties, for example, serotyping based on proteins expressed by the organism on surface structures [30–32] and phage typing based on susceptibility to reference panels of bacteriophage for distinguishing closely related isolates [33]. The development of methods based on DNA sequences followed [29]. Methods such as multilocus sequence typing

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*Applications of Genomics in Regulatory Food Safety Testing in Canada*

(MLST), involving sequencing of PCR-amplified fragments of a small number (i.e., 6–9) of housekeeping genes, can be used to infer evolutionary relationships among organisms [34, 35]. This portable and highly reproducible method of typing has been widely deployed, and MLST schema have been developed for all of the priority foodborne pathogens [34]. For public health surveillance in Canada, as in other jurisdictions, the standard approach for typing of foodborne pathogens for cluster identification has been pulsed-field gel electrophoresis (PFGE) and multilocus variable-number tandem repeat analysis (MLVA) [27, 28, 36, 37]. In North America, data is shared among public health agencies through PulseNet [27, 38]. The selection of a typing method depends on a number of factors, including proven utility of the method for the pathogen being investigated. Each method requires costly training of lab personnel and in many cases the purchase of specialized equipment [39]. Furthermore, comparisons of typing data among different strains can only be done in cases where the same method has been applied. In some cases, variability in the execution of methods by different analysts or different labs significantly impacts the

These typing methods are based on a limited subset of genomic sequences and often lack the discriminatory power to differentiate among closely related organisms [39]. DNA typing profiles from two isolates appearing indistinguishable might be interpreted as evidence that the bacteria have a common source. However, the strength of this type of evidence rests on the extent to which the DNA profile consists of a combination of rare traits. When the traits defining a DNA profile are not rare, there is the possibility that two isolates are in fact unrelated and that matches are mere chance occurrences. In highly clonal strains (e.g., *Salmonella*) serovar Enteritidis, where only a few single-nucleotide changes may be observed among epidemiologically unrelated strains, most methods are not sufficiently discriminatory [40]. WGS provides a high-resolution molecular typing platform that can be universally applied to bacterial pathogens [6, 38, 41]. In principle, strains differing by even a single nucleotide can be distinguished [42, 43]. Furthermore, WGS can now be done more cheaply than lower resolution methods such as MLST and is backward-compatible with previous methods since, in some cases, typing data can be generated from minimally processed genomic data in silico [8, 9, 44]. Strains characterized by WGS can be compared to strains characterized by any other DNAbased subtyping method, enabling optimal use of historical data. Molecular typing data have generally been developed as a surrogate measure of the genetic similarity between bacterial strains. Using databases of WGS information, the utility of existing subtyping methods can be rigorously assessed, and improved subtyping

schemes that reflect true strain relationships can be developed [45].

cable to bacteria typically encountered in a food testing laboratory [46].

The highest-resolution WGS-based analyses such as wgMLST and singlenucleotide variant (SNV) analyses provide unrivaled DNA fingerprinting capability and offer tremendous potential for food safety applications [41, 42, 47]. Still, the use of these analyses in a food safety context is in its infancy, and the interpretation of genomic data from foodborne pathogens in support of regulatory interventions

One advantage of the availability of WGS for typing bacterial isolates is the ability to evaluate datasets at different levels of resolution as needed to resolve biological questions. In this regard, MLST continues to be a valuable approach for tracking foodborne pathogens in the genomic era [41, 44]. WGS data can be matched to current and historical databases at different levels of resolution including pathogenspecific MLST (described above), core genome MLST (cgMLST) which uses hundreds of genes that are conserved within a species, and whole-genome MLST (wgMLST) which considers all genes within a species [38]. Similarly, ribosomal MLST (rMLST) is a 53-gene scheme with the advantage of being universally appli-

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

comparability of molecular typing data [36].

#### *Applications of Genomics in Regulatory Food Safety Testing in Canada DOI: http://dx.doi.org/10.5772/intechopen.86063*

*New Insight into* Brucella *Infection and Foodborne Diseases*

of this family of pathogens in meats and produce [3, 4].

during routine monitoring of the food supply to verify industry compliance with food safety regulations. In fact, such an approach is the basis for the Canadian STEC Method which was developed jointly by CFIA and Health Canada for the detection

to ensure reliability and reproducibility of WGS-based analyses [24, 26].

**2.2 Have we seen it before? Impact of high-resolution molecular typing by** 

**WGS for distinguishing new isolates from control and historical laboratory** 

Following detection of a pathogen in foods, (sub)typing methods are often used to generate a profile of the isolated organism to determine similarity to previously characterized isolates from clinical or food sources (reviewed in [27–29]). Typing of isolates recovered from food samples can provide important information regarding the complexity and source(s) of a given contamination incident, enables tracking of foodborne bacterial strains, and is frequently used to support regulatory decisions. Early methods of typing were based on phenotypic properties, for example, serotyping based on proteins expressed by the organism on surface structures [30–32] and phage typing based on susceptibility to reference panels of bacteriophage for distinguishing closely related isolates [33]. The development of methods based on DNA sequences followed [29]. Methods such as multilocus sequence typing

The potential of PCR technology to provide informative test results is limited by its rather fragmentary nature, that is, the fact that only a relatively small number of different DNA markers can be assessed in a single analytical procedure. A considerable effort is required to optimize and validate the performance of PCR systems, particularly those in which multiple primer pairs are combined (e.g., identification of priority Shiga toxigenic *E. coli* (STEC) using a combination of 11 primer pairs; [1]). For all intents and purposes, validated assays are locked into their original configurations, and it is not possible to modify them (e.g., add or alter primers) on an ad hoc basis without first having to undertake a laborious and time-consuming re-evaluation of assay performance characteristics. Thus, novel queries cannot be undertaken if, in the course of an active food safety investigation, new questions arise regarding the occurrence of an unanticipated assay target. This was the case in a 2011 German outbreak involving STEC with an unusual virulence profile [21, 22]. The use of WGS technologies for STEC characterization can provide a more complete picture [8]; however, completion of WGS analyses typically requires 3–5 days. As this timeline may be too long in an outbreak situation, we have developed practical processes in which genomic DNA isolated from a single STEC colony is sequenced using the Illumina MiSeq platform, followed by analysis of the sequence data during the course of the sequencing run for the determination of genomic markers for phylogenetic identity, virulence profile, serotyping, as well as biological metrics serving as quality control features supporting the validity of the process [5]. Identification and characterization of isolates can be completed within 9 h, comparable to the current method used for characterization of STEC. This real-time WGS approach produces high-resolution characterization of bacterial pathogens at a cost and within a timeframe that are similar to standard microbiological techniques and has the potential to replace lengthy biochemical characterization and molecular and serological typing procedures widely used in food testing laboratories. Our laboratory is currently studying strategies for broad implementation of WGS technology in support of regulatory food inspection objectives through the detection, identification, and characterization of priority bacterial pathogens such as STEC, *Salmonella enterica,* and *Listeria monocytogenes* [5, 23–25]. We have developed guidelines and implemented validated methods and bioinformatics tools for automated analyses of sequence data

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**isolates**

(MLST), involving sequencing of PCR-amplified fragments of a small number (i.e., 6–9) of housekeeping genes, can be used to infer evolutionary relationships among organisms [34, 35]. This portable and highly reproducible method of typing has been widely deployed, and MLST schema have been developed for all of the priority foodborne pathogens [34]. For public health surveillance in Canada, as in other jurisdictions, the standard approach for typing of foodborne pathogens for cluster identification has been pulsed-field gel electrophoresis (PFGE) and multilocus variable-number tandem repeat analysis (MLVA) [27, 28, 36, 37]. In North America, data is shared among public health agencies through PulseNet [27, 38]. The selection of a typing method depends on a number of factors, including proven utility of the method for the pathogen being investigated. Each method requires costly training of lab personnel and in many cases the purchase of specialized equipment [39]. Furthermore, comparisons of typing data among different strains can only be done in cases where the same method has been applied. In some cases, variability in the execution of methods by different analysts or different labs significantly impacts the comparability of molecular typing data [36].

These typing methods are based on a limited subset of genomic sequences and often lack the discriminatory power to differentiate among closely related organisms [39]. DNA typing profiles from two isolates appearing indistinguishable might be interpreted as evidence that the bacteria have a common source. However, the strength of this type of evidence rests on the extent to which the DNA profile consists of a combination of rare traits. When the traits defining a DNA profile are not rare, there is the possibility that two isolates are in fact unrelated and that matches are mere chance occurrences. In highly clonal strains (e.g., *Salmonella*) serovar Enteritidis, where only a few single-nucleotide changes may be observed among epidemiologically unrelated strains, most methods are not sufficiently discriminatory [40].

WGS provides a high-resolution molecular typing platform that can be universally applied to bacterial pathogens [6, 38, 41]. In principle, strains differing by even a single nucleotide can be distinguished [42, 43]. Furthermore, WGS can now be done more cheaply than lower resolution methods such as MLST and is backward-compatible with previous methods since, in some cases, typing data can be generated from minimally processed genomic data in silico [8, 9, 44]. Strains characterized by WGS can be compared to strains characterized by any other DNAbased subtyping method, enabling optimal use of historical data. Molecular typing data have generally been developed as a surrogate measure of the genetic similarity between bacterial strains. Using databases of WGS information, the utility of existing subtyping methods can be rigorously assessed, and improved subtyping schemes that reflect true strain relationships can be developed [45].

One advantage of the availability of WGS for typing bacterial isolates is the ability to evaluate datasets at different levels of resolution as needed to resolve biological questions. In this regard, MLST continues to be a valuable approach for tracking foodborne pathogens in the genomic era [41, 44]. WGS data can be matched to current and historical databases at different levels of resolution including pathogenspecific MLST (described above), core genome MLST (cgMLST) which uses hundreds of genes that are conserved within a species, and whole-genome MLST (wgMLST) which considers all genes within a species [38]. Similarly, ribosomal MLST (rMLST) is a 53-gene scheme with the advantage of being universally applicable to bacteria typically encountered in a food testing laboratory [46].

The highest-resolution WGS-based analyses such as wgMLST and singlenucleotide variant (SNV) analyses provide unrivaled DNA fingerprinting capability and offer tremendous potential for food safety applications [41, 42, 47]. Still, the use of these analyses in a food safety context is in its infancy, and the interpretation of genomic data from foodborne pathogens in support of regulatory interventions

remains challenging [48]. For example, how many SNVs are required to exclude a sample from a food safety investigation? This question remains difficult to answer, in part because of differences in rates at which DNA accumulates changes within a species or among strains within a species. Bacterial strains with a mutator phenotype have an elevated mutation rate, typically due to mutations in genes encoding components of DNA replication and repair pathways [49]. Mutator phenotypes are commonly found in clinical bacterial populations and may contribute significantly to the acquisition of antimicrobial resistance [50–52]. For example, the mutator phenotype has been attributed to the development of multidrug-resistant *S*. *typhimurium* [50]. In *E. coli*, this phenotype has recently been shown to be induced in response to stress conditions [53]. The possibility that environmental stress could lead to the development of the mutator phenotype may be highly relevant to analyses of foodborne pathogens.

#### *2.2.1 Identification of persistent contamination*

Persistent contamination of food manufacturing environments with bacteria such as *Listeria monocytogenes* poses significant public health risks as these events serve as a source for the continual contamination of food products, primarily because (by definition) persistent contaminators defy attempts to sanitize the manufacturing environment using standard protocols [54–56]. *Listeria monocytogenes* is the causative agent of listeriosis, a potentially fatal foodborne illness in susceptible populations such as the very young, the elderly, and the immunocompromised [57]. The ability of this organism to contaminate food contact surfaces (e.g., conveyors, saws, etc.) and survive in the manufacturing environment increases the risk of food product contamination, with possible serious public health consequences. Routinely monitoring the food manufacturing environment for the presence of pathogenic bacteria is a key to preventing contamination of food products [56]. The presence of environmental contamination is usually ascertained through swabbing and testing of food manufacturing environment surfaces using standard microbiology techniques. Transient contamination is of a sporadic nature and may be effectively managed through the application of standard sanitation regimens followed by testing to ensure treatment efficacy. Persistent contamination occurs when a specific strain becomes a permanent resident of specific niches in the manufacturing environment. Persistence may be attributable to the incorporation of *L. monocytogenes* into biofilms occurring on equipment surfaces, resistance to commonly used sanitizers such as benzalkonium chloride, or through mechanical sequestration in hard-to-clean areas (e.g., meat cutting saw arbors, as was the case in the 2008 Canadian listeriosis outbreak resulting in many fatalities associated with the consumption of contaminated ready-to-eat meats [58]).

The ability to distinguish the two modes of contamination in the analysis of environmental isolates recovered during routine monitoring activities would constitute an important element to inform the best approach for the management of microbial hazards in food manufacturing plants [54, 55]. For example, while regular sanitation procedures may be effective in dealing with removal of sporadic surface contaminants, a more comprehensive approach requiring equipment teardown and aggressive sanitation would be required to deal with persistent contaminants, which are by nature highly resistant to sanitizers and cleaning procedures. The traditional approach to identify the occurrence of persistent contamination in food manufacturing environments involves the characterization of successive isolates using typing procedures such as PFGE to determine their relatedness [54]. However, PFGE is of limited value for this purpose because it is not sufficiently discriminatory to unequivocally establish whether two strains are clonally related

**127**

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

*2.2.2 Contribution to surveillance programs and outbreak investigations*

Although numerous methods are used by food safety and public health agencies to support regulatory decisions during outbreak investigations, demonstrating that food and clinical isolates originated from the same source can be challenging. As the results generated by WGS make their way into situation rooms to guide decisionmakers, concise metrics for the interpretation and contextualization of genomicsderived data will be required to achieve more precise assessments [64]. The value of WGS-based typing for cluster identification has already been demonstrated through

global surveillance networks such as GenomeTrakr and PulseNet [6, 38, 41]. Smaller clusters of cases can be linked through WGS analyses and investigated [65], and conversely, unrelated cases can be excluded from an epidemiological investigation leading to improved outcomes for rapid identification of foods implicated in outbreaks [66]. Nonetheless, WGS results should not be interpreted in the absence of epidemiological context as some lineage rates of mutation are low and strains

from different sources may appear to be linked [67].

(i.e., one being descendant from the other). Depending on the scope of the contamination, there may be multiple related populations within the food production

Whole-genome sequencing approaches offer the prospect of determining the degree of relatedness among isolates on the basis of very fine base sequence differences, because more closely or clonally related isolates have fewer SNV differences. Therefore, it should be possible to compare two isolates (e.g., recovered on successive sampling incursions in the same plant) using high-resolution WGS typing methods to ascertain whether they are clonally related or different [55]. In the former case, this would be a strong indication that there is either an unresolved source of contamination in the plant, or more likely, a case of persistent contamination, whereas the latter case would suggest two independent contamination incidents. Each scenario would warrant a different approach to decontamination, and the ability to differentiate persistent and sporadic strains on the basis of the relatedness of successive isolates would constitute a powerful risk assessment and risk management tool for the use in the most highly proactive food safety programs. There is one caveat in the use of SNV-based typing, and that is the temporal drift which naturally occurs in bacteria, resulting in the accumulation of SNVs among the progeny derived from a single parent. The question remains under which conditions this occurs for bacterial strains in a food manufacturing environment and how many SNV differences constitute a real difference in terms of the provenance of isolates under comparison. One cause of *L. monocytogenes* persistence has been identified as resistance to sanitizers such as quaternary ammonium compounds (QACs) [59–61]. An important determinant of QAC resistance is efflux systems such as those encoded by the *bcrABC* gene cassette [62] or *emrE* [63]. In a recent study to characterize the genomes of *L. monocytogenes* isolates collected at a pig slaughterhouse to determine the molecular basis for their persistence, we found that successive environmental isolates (persistent types) linked on the basis of SNV analyses all harbored the *bcrABC* and concluded that high-resolution typing and determination of the cassette may serve to distinguish between persistent and sporadic *L. monocytogenes* isolates [59]. This in turn may have important ramifications for risk management actions when *L. monocytogenes* is recovered from a food manufacturing environment, since the ability of a strain to persist casts doubt on the efficacy of standard sanitization protocols, and more intensive cleaning procedures (e.g., equipment teardown, use of alternative sanitizers) may be warranted. These types of analyses of food inspection isolates are greatly facilitated by WGS technology [55, 59].

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

environment.

*New Insight into* Brucella *Infection and Foodborne Diseases*

analyses of foodborne pathogens.

*2.2.1 Identification of persistent contamination*

remains challenging [48]. For example, how many SNVs are required to exclude a sample from a food safety investigation? This question remains difficult to answer, in part because of differences in rates at which DNA accumulates changes within a species or among strains within a species. Bacterial strains with a mutator phenotype have an elevated mutation rate, typically due to mutations in genes encoding components of DNA replication and repair pathways [49]. Mutator phenotypes are commonly found in clinical bacterial populations and may contribute significantly to the acquisition of antimicrobial resistance [50–52]. For example, the mutator phenotype has been attributed to the development of multidrug-resistant *S*. *typhimurium* [50]. In *E. coli*, this phenotype has recently been shown to be induced in response to stress conditions [53]. The possibility that environmental stress could lead to the development of the mutator phenotype may be highly relevant to

Persistent contamination of food manufacturing environments with bacteria such as *Listeria monocytogenes* poses significant public health risks as these events serve as a source for the continual contamination of food products, primarily because (by definition) persistent contaminators defy attempts to sanitize the manufacturing environment using standard protocols [54–56]. *Listeria monocytogenes* is the causative agent of listeriosis, a potentially fatal foodborne illness in susceptible populations such as the very young, the elderly, and the immunocompromised [57]. The ability of this organism to contaminate food contact surfaces (e.g., conveyors, saws, etc.) and survive in the manufacturing environment increases the risk of food product contamination, with possible serious public health consequences. Routinely monitoring the food manufacturing environment for the presence of pathogenic bacteria is a key to preventing contamination of food products [56]. The presence of environmental contamination is usually ascertained through swabbing and testing of food manufacturing environment surfaces using standard microbiology techniques. Transient contamination is of a sporadic nature and may be effectively managed through the application of standard sanitation regimens followed by testing to ensure treatment efficacy. Persistent contamination occurs when a specific strain becomes a permanent resident of specific niches in the manufacturing environment. Persistence may be attributable to the incorporation of *L. monocytogenes* into biofilms occurring on equipment surfaces, resistance to commonly used sanitizers such as benzalkonium chloride, or through mechanical sequestration in hard-to-clean areas (e.g., meat cutting saw arbors, as was the case in the 2008 Canadian listeriosis outbreak resulting in many fatalities associated

with the consumption of contaminated ready-to-eat meats [58]).

The ability to distinguish the two modes of contamination in the analysis of environmental isolates recovered during routine monitoring activities would constitute an important element to inform the best approach for the management of microbial hazards in food manufacturing plants [54, 55]. For example, while regular sanitation procedures may be effective in dealing with removal of sporadic surface contaminants, a more comprehensive approach requiring equipment teardown and aggressive sanitation would be required to deal with persistent contaminants, which are by nature highly resistant to sanitizers and cleaning procedures. The traditional approach to identify the occurrence of persistent contamination in food manufacturing environments involves the characterization of successive isolates using typing procedures such as PFGE to determine their relatedness [54]. However, PFGE is of limited value for this purpose because it is not sufficiently discriminatory to unequivocally establish whether two strains are clonally related

**126**

(i.e., one being descendant from the other). Depending on the scope of the contamination, there may be multiple related populations within the food production environment.

Whole-genome sequencing approaches offer the prospect of determining the degree of relatedness among isolates on the basis of very fine base sequence differences, because more closely or clonally related isolates have fewer SNV differences. Therefore, it should be possible to compare two isolates (e.g., recovered on successive sampling incursions in the same plant) using high-resolution WGS typing methods to ascertain whether they are clonally related or different [55]. In the former case, this would be a strong indication that there is either an unresolved source of contamination in the plant, or more likely, a case of persistent contamination, whereas the latter case would suggest two independent contamination incidents. Each scenario would warrant a different approach to decontamination, and the ability to differentiate persistent and sporadic strains on the basis of the relatedness of successive isolates would constitute a powerful risk assessment and risk management tool for the use in the most highly proactive food safety programs. There is one caveat in the use of SNV-based typing, and that is the temporal drift which naturally occurs in bacteria, resulting in the accumulation of SNVs among the progeny derived from a single parent. The question remains under which conditions this occurs for bacterial strains in a food manufacturing environment and how many SNV differences constitute a real difference in terms of the provenance of isolates under comparison.

One cause of *L. monocytogenes* persistence has been identified as resistance to sanitizers such as quaternary ammonium compounds (QACs) [59–61]. An important determinant of QAC resistance is efflux systems such as those encoded by the *bcrABC* gene cassette [62] or *emrE* [63]. In a recent study to characterize the genomes of *L. monocytogenes* isolates collected at a pig slaughterhouse to determine the molecular basis for their persistence, we found that successive environmental isolates (persistent types) linked on the basis of SNV analyses all harbored the *bcrABC* and concluded that high-resolution typing and determination of the cassette may serve to distinguish between persistent and sporadic *L. monocytogenes* isolates [59]. This in turn may have important ramifications for risk management actions when *L. monocytogenes* is recovered from a food manufacturing environment, since the ability of a strain to persist casts doubt on the efficacy of standard sanitization protocols, and more intensive cleaning procedures (e.g., equipment teardown, use of alternative sanitizers) may be warranted. These types of analyses of food inspection isolates are greatly facilitated by WGS technology [55, 59].

#### *2.2.2 Contribution to surveillance programs and outbreak investigations*

Although numerous methods are used by food safety and public health agencies to support regulatory decisions during outbreak investigations, demonstrating that food and clinical isolates originated from the same source can be challenging. As the results generated by WGS make their way into situation rooms to guide decisionmakers, concise metrics for the interpretation and contextualization of genomicsderived data will be required to achieve more precise assessments [64]. The value of WGS-based typing for cluster identification has already been demonstrated through global surveillance networks such as GenomeTrakr and PulseNet [6, 38, 41]. Smaller clusters of cases can be linked through WGS analyses and investigated [65], and conversely, unrelated cases can be excluded from an epidemiological investigation leading to improved outcomes for rapid identification of foods implicated in outbreaks [66]. Nonetheless, WGS results should not be interpreted in the absence of epidemiological context as some lineage rates of mutation are low and strains from different sources may appear to be linked [67].

The concepts of "match probability" and "likelihood ratios" are well known in human forensic sciences where they facilitate the interpretation of DNA profiles in matching individuals to a crime scene [68]. For example, when the DNA profile found on a crime scene matches that of a suspect and there is only a one-in-one million probability that this DNA profile might be found in another individual, there is a strong case linking the suspect to the crime scene. Food inspectors face a similar situation during outbreak investigations when trying to establish causal links between isolates from different sources [69]. Bacteria may undergo subtle changes in their genomes during the course of a foodborne illness outbreak event, with possible impacts on the typing profiles of clonally related isolates recovered over time. The question arises as to how much change in a genome constitutes a significant difference between individual isolates (i.e., different origins or strains). Through statistical analyses of comprehensive pathogen genome databases, it should be possible to develop a likelihood ratio approach to determine the probability of finding a given profile in a defined population and, hence, develop criteria to measure sequence diversity between isolates with different degrees of relatedness and even among clonally related isolates recovered over the course of an outbreak event [69]. This in turn would provide a greater degree of confidence in attributing the origins of isolates, identifying clusters of foodborne illness and their food vehicles, and the scope of contamination. This information can also be used to revise and adjust detection tools (e.g., PCR primers) to ensure their effectiveness in identifying "moving" genomic targets. The development of a forensic likelihood ratio approach would provide a valuable tool to assess the reliability of genomic information underlying regulatory decision-making.

#### *2.2.3 Attribution of food vehicles through genomic surveillance*

The advent of genomic typing augurs well for the creation of highly refined databases of bacterial isolates from various sources (foods, production facilities, farms, environmental and clinical strains) providing high-resolution characterization of individual strains with established linkages to their geographic and temporal origins. Historically, the use of low-resolution typing approaches such as MLST profiles or serotypes has been valuable for the association of specific lineages with a given food type, production environment, or country [70, 71]. Initiatives such as the GenomeTrakr and the PulseNet WGS networks represent rich resources from which to draw valuable information linking isolates to their origin in the food production continuum [6, 38, 72]. For example, an analysis of *E. coli* O157:H7 identified SNVs associated with country of origin [73]. Similarly, an outbreak involving *S*. Bareilly in the United States was tracked to a food originating in India based on high-resolution SNV typing [74].

With the aid of bioinformatics tools, databases can be queried to identify genomic signatures that are overrepresented in particular head sources for bacterial isolates. For example, a study by Thépault et al. [75] identified 15 host-associated *C. jejuni* markers and demonstrated utility of these markers for identifying host association of strains with 80% accuracy. The ability to discern this type of information would be a tremendous boon for foodborne illness investigations: WGS data could be used to determine the "source signature" of clinical isolates, enabling a highly proactive approach in rapidly narrowing the field for the attribution of food vehicles. Regulatory food inspection agencies such as the CFIA would have an important role to play in such a scheme. Ongoing, extensive sampling plans will be required to ensure adequate representation of different food production elements, such as food types and geographic provenance. Given that most cases of foodborne illness occur sporadically, this approach would enable public health authorities to track the causes

**129**

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

**and epidemiological markers through WGS**

and the attendant severity of illness [77, 78].

of a larger proportion of cases of foodborne illness. This would ultimately lead to a better understanding of foods commonly implicated in disease and the implementation of more effective interventions to reduce the burden of foodborne illness.

**2.3 Is it dangerous? Rapid identification of virulence, antimicrobial resistance,** 

Genomic information is highly complex, and there are many knowledge gaps with respect to the significance of various marker genes to public health [76]. Nonetheless, there is a growing body of evidence linking certain well-defined gene markers to virulence characteristics of bacteria, for example, the role of intimin (coded by the *eae* gene) in the pathogenesis of STEC, epidemiological associations between certain serotype features and outbreaks of serious foodborne illness (e.g., *L. monocytogenes* serogroups 1/2a, 1/2b, and 4b, STEC serogroups O26, O45, O103, O111, O121, O145, and O157), and even the type of toxin secreted (e.g., Shiga toxin 2)

In the case of STEC, regulatory food testing programs currently define priority target strains as bearing markers for Shiga toxin genes and intimin, in addition to markers associated with a narrow family of O serogroups [2]. However, the question arises whether in the course of conducting routine monitoring of food inspection samples the occurrence of an isolate bearing markers for Shiga toxin and intimin, but none of the so-called priority serogroups would be actionable. There are varying subjective opinions on the matter, ranging from a narrow interpretation of test results in which only isolates bearing all the designated factors are considered hazardous to the more precautionary approach whereby any isolate bearing both Shiga toxin and intimin factors, regardless of O serogroup, constitutes a public health risk. There is also evidence suggesting that severity or likelihood of foodborne illness varies with Shiga toxin type and subtype (e.g., STEC strains possessing st2a tend to be more frequently implicated in cases of severe foodborne illness [79]) and that this should be a factor in determining the appropriate response to the presence of a food contaminant. These properties are readily discoverable through the analysis of WGS data. For instance, the Shiga toxin subtype can be reliably determined using the V-typer tool, which is an automated assembly-independent subtyping module that can be integrated in a bioinformatics pipeline for the analysis of foodborne STEC isolates [23]. Yet another possibility would be to define priority STEC on the basis of contemporary public health data (reviewed periodically) identifying STEC serogroups most frequently associated with illness in a given jurisdiction. The serogroup of an *E. coli* isolate can be determined using tools such as SeroTypeFinder [8] which can be freely accessed through the Center for Genomic Epidemiology website [80]. Such considerations raise problems for health risk assessment specialists who must interpret laboratory results (among other factors) to determine the degree of risk informing the course of regulatory interventions [76]. It is tempting to speculate that it may be possible to devise an objective scheme for rating the degree of hazard associated with a given isolate on the basis of genomic analyses. For instance, the public health and food inspection communities could agree on a list of key factors relevant to the characterization of a given pathogen (**Table 1**). For organisms such as *E. coli*, acquired virulence gene databases have been established, and isolates may be profiled using tools such as VirulenceFinder [7], accessible through the Center for Genomic Epidemiology website [80]. Since not all factors have the same impact, it should be possible to develop a weighted index approach in which each constituent factor determined by genomic analysis makes up a fraction of a final index value which is proportional to the degree of hazard. Such an index value (hazard characterization or HazChar Score), used in conjunction with

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

*New Insight into* Brucella *Infection and Foodborne Diseases*

information underlying regulatory decision-making.

high-resolution SNV typing [74].

*2.2.3 Attribution of food vehicles through genomic surveillance*

The advent of genomic typing augurs well for the creation of highly refined databases of bacterial isolates from various sources (foods, production facilities, farms, environmental and clinical strains) providing high-resolution characterization of individual strains with established linkages to their geographic and temporal origins. Historically, the use of low-resolution typing approaches such as MLST profiles or serotypes has been valuable for the association of specific lineages with a given food type, production environment, or country [70, 71]. Initiatives such as the GenomeTrakr and the PulseNet WGS networks represent rich resources from which to draw valuable information linking isolates to their origin in the food production continuum [6, 38, 72]. For example, an analysis of *E. coli* O157:H7 identified SNVs associated with country of origin [73]. Similarly, an outbreak involving *S*. Bareilly in the United States was tracked to a food originating in India based on

With the aid of bioinformatics tools, databases can be queried to identify genomic signatures that are overrepresented in particular head sources for bacterial isolates. For example, a study by Thépault et al. [75] identified 15 host-associated *C. jejuni* markers and demonstrated utility of these markers for identifying host association of strains with 80% accuracy. The ability to discern this type of information would be a tremendous boon for foodborne illness investigations: WGS data could be used to determine the "source signature" of clinical isolates, enabling a highly proactive approach in rapidly narrowing the field for the attribution of food vehicles. Regulatory food inspection agencies such as the CFIA would have an important role to play in such a scheme. Ongoing, extensive sampling plans will be required to ensure adequate representation of different food production elements, such as food types and geographic provenance. Given that most cases of foodborne illness occur sporadically, this approach would enable public health authorities to track the causes

The concepts of "match probability" and "likelihood ratios" are well known in human forensic sciences where they facilitate the interpretation of DNA profiles in matching individuals to a crime scene [68]. For example, when the DNA profile found on a crime scene matches that of a suspect and there is only a one-in-one million probability that this DNA profile might be found in another individual, there is a strong case linking the suspect to the crime scene. Food inspectors face a similar situation during outbreak investigations when trying to establish causal links between isolates from different sources [69]. Bacteria may undergo subtle changes in their genomes during the course of a foodborne illness outbreak event, with possible impacts on the typing profiles of clonally related isolates recovered over time. The question arises as to how much change in a genome constitutes a significant difference between individual isolates (i.e., different origins or strains). Through statistical analyses of comprehensive pathogen genome databases, it should be possible to develop a likelihood ratio approach to determine the probability of finding a given profile in a defined population and, hence, develop criteria to measure sequence diversity between isolates with different degrees of relatedness and even among clonally related isolates recovered over the course of an outbreak event [69]. This in turn would provide a greater degree of confidence in attributing the origins of isolates, identifying clusters of foodborne illness and their food vehicles, and the scope of contamination. This information can also be used to revise and adjust detection tools (e.g., PCR primers) to ensure their effectiveness in identifying "moving" genomic targets. The development of a forensic likelihood ratio approach would provide a valuable tool to assess the reliability of genomic

**128**

of a larger proportion of cases of foodborne illness. This would ultimately lead to a better understanding of foods commonly implicated in disease and the implementation of more effective interventions to reduce the burden of foodborne illness.

#### **2.3 Is it dangerous? Rapid identification of virulence, antimicrobial resistance, and epidemiological markers through WGS**

Genomic information is highly complex, and there are many knowledge gaps with respect to the significance of various marker genes to public health [76]. Nonetheless, there is a growing body of evidence linking certain well-defined gene markers to virulence characteristics of bacteria, for example, the role of intimin (coded by the *eae* gene) in the pathogenesis of STEC, epidemiological associations between certain serotype features and outbreaks of serious foodborne illness (e.g., *L. monocytogenes* serogroups 1/2a, 1/2b, and 4b, STEC serogroups O26, O45, O103, O111, O121, O145, and O157), and even the type of toxin secreted (e.g., Shiga toxin 2) and the attendant severity of illness [77, 78].

In the case of STEC, regulatory food testing programs currently define priority target strains as bearing markers for Shiga toxin genes and intimin, in addition to markers associated with a narrow family of O serogroups [2]. However, the question arises whether in the course of conducting routine monitoring of food inspection samples the occurrence of an isolate bearing markers for Shiga toxin and intimin, but none of the so-called priority serogroups would be actionable. There are varying subjective opinions on the matter, ranging from a narrow interpretation of test results in which only isolates bearing all the designated factors are considered hazardous to the more precautionary approach whereby any isolate bearing both Shiga toxin and intimin factors, regardless of O serogroup, constitutes a public health risk. There is also evidence suggesting that severity or likelihood of foodborne illness varies with Shiga toxin type and subtype (e.g., STEC strains possessing st2a tend to be more frequently implicated in cases of severe foodborne illness [79]) and that this should be a factor in determining the appropriate response to the presence of a food contaminant. These properties are readily discoverable through the analysis of WGS data. For instance, the Shiga toxin subtype can be reliably determined using the V-typer tool, which is an automated assembly-independent subtyping module that can be integrated in a bioinformatics pipeline for the analysis of foodborne STEC isolates [23]. Yet another possibility would be to define priority STEC on the basis of contemporary public health data (reviewed periodically) identifying STEC serogroups most frequently associated with illness in a given jurisdiction. The serogroup of an *E. coli* isolate can be determined using tools such as SeroTypeFinder [8] which can be freely accessed through the Center for Genomic Epidemiology website [80].

Such considerations raise problems for health risk assessment specialists who must interpret laboratory results (among other factors) to determine the degree of risk informing the course of regulatory interventions [76]. It is tempting to speculate that it may be possible to devise an objective scheme for rating the degree of hazard associated with a given isolate on the basis of genomic analyses. For instance, the public health and food inspection communities could agree on a list of key factors relevant to the characterization of a given pathogen (**Table 1**). For organisms such as *E. coli*, acquired virulence gene databases have been established, and isolates may be profiled using tools such as VirulenceFinder [7], accessible through the Center for Genomic Epidemiology website [80]. Since not all factors have the same impact, it should be possible to develop a weighted index approach in which each constituent factor determined by genomic analysis makes up a fraction of a final index value which is proportional to the degree of hazard. Such an index value (hazard characterization or HazChar Score), used in conjunction with


*a A list of key factors is developed for a given pathogen, and each element is assigned a weighted value based on its significance in human illness. Genomic analysis of a foodborne isolate by whole-genome sequencing with the application of bioinformatics tools to determine the presence of targeted features. The individual weighted values are summed giving the HazChar Score, which is then compared against a set of predetermined criteria to categorize the degree of risk.*

#### **Table 1.**

*Proposed concept for hazard characterization: HazChar Scorea .*

numerical criteria derived from historical data, could form a basis for attributing the degree of hazard associated with a particular isolate, which in turn would enable an objective categorization of risk to inform the appropriate regulatory response.

The antimicrobial resistance profile of pathogenic bacteria, while not a virulence attribute per se, remains an important factor in the ultimate public health outcomes of foodborne illness events, since a significant fraction of the affected population (e.g., the elderly and the immunocompromised) may critically require antibiotic therapy to recover. Furthermore, antimicrobial use at sub-therapeutic levels for growth promotion in food animal production has been implicated in the development of antimicrobial resistance (AMR) in animals and humans [81, 82], though there is paucity of data to support this claim. Food testing laboratories can play an important role in contributing data on the occurrence of AMR bacteria to national and international surveillance initiatives seeking to understand the role of production practices in the emergence of these microorganisms. As an alternative to labor- and time-consuming phenotypic testing, AMR profiles can be predicted from WGS data through the identification of genetic markers by querying the subject genome using DNA sequence information deposited in curated AMR gene databases, such as well-cataloged AMR gene markers. A number of tools are currently available to predict AMR from bacterial WGS data (e.g., ResFinder [10, 83], SEAR [84], Resistance Gene Identifier [85], and ARMI [86]). These AMR marker prediction tools rely on curated international AMR gene databases such as CARD [85], ARDB [87], and ARG-ANNOT [88]. WGS-based methods for prediction of AMR phenotype have been shown to be highly accurate [86, 89–91].

**131**

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

**3.1 Deployment of ad hoc methods in support of outbreak investigations**

Despite recent efforts of regulatory food safety agencies to implement test methods targeting defined serogroups of so-called priority STEC, the history of foodborne disease outbreaks is rife with examples of causative strains with unexpected characteristics (e.g., the 2011 German outbreak in which the etiologic agent belonged to serogroup O104, not a designated priority serogroup, and lacked the definitive virulence marker *eae* [21]), making it difficult to anticipate detection methods suiting all contingencies. Detection is further complicated by variability among non-O157 STEC strains in resistance to selective agents commonly used in enrichment culture techniques, hindering their recovery from foods bearing high levels of background microbiota [92–94]. Genomic information garnered from clinical bacterial isolates implicated in outbreaks of foodborne illness will be very useful in the customization of selective recovery and identification procedures to facilitate their detection in food samples during outbreak investigations [86]. The state of the art in WGS technology has reached the point where clinical isolates implicated in foodborne disease outbreaks are routinely sequenced in public health laboratories at an early stage during these events [38]. With the application of appropriate bioinformatics tools to analyze the ensuing data, it should be possible to develop customized strain-specific test methods that can be rapidly deployed to food testing labs conducting analyses in support of outbreak investigations. The availability of WGS information for these strains should make it possible to ascertain the presence of traits conferring resistance to antimicrobial agents such as antibiotics, quaternary ammonium compounds, and tellurite, suggesting an avenue for the formulation of customized selective enrichment media enabling recovery of specific outbreak strains [86]. This would be a particular advantage in instances where a food matrix (e.g., meats, sprouts, etc.) contains high levels of background microbiota, which might otherwise interfere with recovery of the target organism. Genomic AMR prediction tools can be used to discern the AMR marker profile of a strain of interest (e.g., outbreak strain) to identify an antibiotic resistance trait which can be exploited for customization of selective enrichment media favoring its recovery from samples with high background bacteria loads [86, 95]. In addition, WGS data can be analyzed using a pipeline such as SigSeekr [96] designed to identify DNA sequences associated with a particular strain for its rapid identification by PCR. By combining strain-specific selective enrichment and PCR detection tools, it should be possible to deploy custom recovery and identification tools for the efficient detection of STEC outbreak strains within the timeframe of an active investigation. The feasibility of such an approach has been demonstrated using laboratory STEC strains as models, with resistance to a variety of antibiotic classes used as the basis for their selective recovery against high backgrounds of commensal

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

*E. coli* bacteria in ground beef samples [86].

**development**

**3.2 Characterization of food microbiomes in support of improved method** 

Metagenomic analysis of enrichment dynamics can be used to inform the development of improved methods for cultural enrichment of pathogens [97–99]. A practical approach for this is to selectively amplify hypervariable regions within the 16 s rDNA and to sequence amplicons using WGS technologies. Sequences are then mapped to databases to determine composition of microbial communities using bioinformatics tools such as QIIME [100] or mothur [101]. Samples from

**3. Future applications**

### **3. Future applications**

*New Insight into* Brucella *Infection and Foodborne Diseases*

*Proposed concept for hazard characterization: HazChar Scorea*

**Key factors Element Relevance**

Severity modulator Type *st*1 vs. *st2*

Primary virulence Toxin Presence or absence

Accessory functions Antibiotic resistance Therapeutic impact

Attachment and colonization *eae*, enteroaggregative factors Pathogenicity Pathogenesis mechanisms (e.g.,

Pathogenicity islands Signatures for novel pathogens

Phage type Reservoirs, illness outbreaks

Subtype *st2a* vs. *st2f*

Epidemiological markers Serotype Outbreak vs. sporadic vs. nil association

Phylogenetic markers Genus/species Identity (e.g., *Salmonella, E. coli*) Family or group STEC

*A list of key factors is developed for a given pathogen, and each element is assigned a weighted value based on its significance in human illness. Genomic analysis of a foodborne isolate by whole-genome sequencing with the application of bioinformatics tools to determine the presence of targeted features. The individual weighted values are summed giving the HazChar Score, which is then compared against a set of predetermined criteria to categorize* 

Antimicrobial resistance Sanitizer efficacy Biofilm formation Persistence

Molecular type PFGE/SNV cluster

*.*

hemolysin)

numerical criteria derived from historical data, could form a basis for attributing the degree of hazard associated with a particular isolate, which in turn would enable an objective categorization of risk to inform the appropriate regulatory

The antimicrobial resistance profile of pathogenic bacteria, while not a virulence attribute per se, remains an important factor in the ultimate public health outcomes of foodborne illness events, since a significant fraction of the affected population (e.g., the elderly and the immunocompromised) may critically require antibiotic therapy to recover. Furthermore, antimicrobial use at sub-therapeutic levels for growth promotion in food animal production has been implicated in the development of antimicrobial resistance (AMR) in animals and humans [81, 82], though there is paucity of data to support this claim. Food testing laboratories can play an important role in contributing data on the occurrence of AMR bacteria to national and international surveillance initiatives seeking to understand the role of production practices in the emergence of these microorganisms. As an alternative to labor- and time-consuming phenotypic testing, AMR profiles can be predicted from WGS data through the identification of genetic markers by querying the subject genome using DNA sequence information deposited in curated AMR gene databases, such as well-cataloged AMR gene markers. A number of tools are currently available to predict AMR from bacterial WGS data (e.g., ResFinder [10, 83], SEAR [84], Resistance Gene Identifier [85], and ARMI [86]). These AMR marker prediction tools rely on curated international AMR gene databases such as CARD [85], ARDB [87], and ARG-ANNOT [88]. WGS-based methods for prediction of AMR phenotype have been shown to

**130**

be highly accurate [86, 89–91].

response.

*the degree of risk.*

**Table 1.**

*a*

#### **3.1 Deployment of ad hoc methods in support of outbreak investigations**

Despite recent efforts of regulatory food safety agencies to implement test methods targeting defined serogroups of so-called priority STEC, the history of foodborne disease outbreaks is rife with examples of causative strains with unexpected characteristics (e.g., the 2011 German outbreak in which the etiologic agent belonged to serogroup O104, not a designated priority serogroup, and lacked the definitive virulence marker *eae* [21]), making it difficult to anticipate detection methods suiting all contingencies. Detection is further complicated by variability among non-O157 STEC strains in resistance to selective agents commonly used in enrichment culture techniques, hindering their recovery from foods bearing high levels of background microbiota [92–94]. Genomic information garnered from clinical bacterial isolates implicated in outbreaks of foodborne illness will be very useful in the customization of selective recovery and identification procedures to facilitate their detection in food samples during outbreak investigations [86].

The state of the art in WGS technology has reached the point where clinical isolates implicated in foodborne disease outbreaks are routinely sequenced in public health laboratories at an early stage during these events [38]. With the application of appropriate bioinformatics tools to analyze the ensuing data, it should be possible to develop customized strain-specific test methods that can be rapidly deployed to food testing labs conducting analyses in support of outbreak investigations. The availability of WGS information for these strains should make it possible to ascertain the presence of traits conferring resistance to antimicrobial agents such as antibiotics, quaternary ammonium compounds, and tellurite, suggesting an avenue for the formulation of customized selective enrichment media enabling recovery of specific outbreak strains [86]. This would be a particular advantage in instances where a food matrix (e.g., meats, sprouts, etc.) contains high levels of background microbiota, which might otherwise interfere with recovery of the target organism. Genomic AMR prediction tools can be used to discern the AMR marker profile of a strain of interest (e.g., outbreak strain) to identify an antibiotic resistance trait which can be exploited for customization of selective enrichment media favoring its recovery from samples with high background bacteria loads [86, 95]. In addition, WGS data can be analyzed using a pipeline such as SigSeekr [96] designed to identify DNA sequences associated with a particular strain for its rapid identification by PCR. By combining strain-specific selective enrichment and PCR detection tools, it should be possible to deploy custom recovery and identification tools for the efficient detection of STEC outbreak strains within the timeframe of an active investigation. The feasibility of such an approach has been demonstrated using laboratory STEC strains as models, with resistance to a variety of antibiotic classes used as the basis for their selective recovery against high backgrounds of commensal *E. coli* bacteria in ground beef samples [86].

#### **3.2 Characterization of food microbiomes in support of improved method development**

Metagenomic analysis of enrichment dynamics can be used to inform the development of improved methods for cultural enrichment of pathogens [97–99]. A practical approach for this is to selectively amplify hypervariable regions within the 16 s rDNA and to sequence amplicons using WGS technologies. Sequences are then mapped to databases to determine composition of microbial communities using bioinformatics tools such as QIIME [100] or mothur [101]. Samples from

enrichment cultures can be used to evaluate growth of target pathogens relative to background food microbiota over time [97, 99]. Such studies can provide valuable insight into species that could potentially interfere with target pathogens that can be applied to the development of improved methodology.

A modern concept in the study of pathogenic bacteria is the emergence of novel pathogens among commensals in a given environment through the acquisition of virulence factors by horizontal gene transfers from other bacteria [102–104]. The evolutionary trail of the STEC family suggests a priori transformations of benign *E. coli* strains into virulent STEC having acquired exogenous DNA segments such as bacteriophage carrying Shiga toxin genes and pathogenicity islands harboring host colonization factors [105]. There is evidence that other food pathogens such as *L. monocytogenes* strains implicated in serious outbreaks of foodborne illness may have acquired enhanced virulence characteristics through horizontal gene transfer processes [106]. This is believed to occur on a relatively short time scale, perhaps on the order of weeks or months, making the emergence of novel pathogens in food production environments or animal reservoirs in near real time a significant possibility. Furthermore, food-acquired coinfections may arise in which two or more bacterial strains complement each another, for example, a toxigenic strain lacking adherence factors might colonize a host by cross-utilizing a factor secreted by another strain [107]. It may be possible to predict the probability of emergence of novel pathogens with enhanced virulence or antibiotic resistance characteristics in the food supply through periodic microbial metagenomic analyses to ascertain the presence of key indicators in the background microflora of food commodities (e.g., ground beef, trim), food manufacturing environments (e.g., floors, food contact surfaces), and animal reservoirs (e.g., cattle, poultry) [108, 109]. A weighted index approach much like that described for the HazChar Score above could be employed here, with possible inclusion of a more comprehensive catalog of known virulence, AMR, and other critical factors relevant to public health.

#### **4. Conclusions**

Modern food microbiology research has generated a critical understanding of the epidemiology, pathogenic mechanisms, virulence factors, and other salient characteristics of the major food pathogens. The convergence of expanded scientific knowledge and sophisticated technological capability create exciting new opportunities for the refinement of food microbiology testing programs to meet the needs of a comprehensive risk-based inspection approach. Advances in nextgeneration sequencing technologies have made it possible for investigators to carry out sequencing and processing of bacterial genomes within the time course of a typical foodborne illness outbreak investigation. It may reasonably be expected that in the near future, analysts will be moving from traditional DNA hybridization approaches (e.g., PCR and microarrays) toward rapid whole-genome sequencing allowing a much more comprehensive examination of the isolate at hand. This new approach will require broad access to leading-edge bioinformatics capability for analysis of complex genomics data in silico to ascertain the presence of key genetic markers (e.g., presence of virulence genes in bacterial pathogens, completeness and functionality of gene products, markers for molecular typing, etc.). The generation and analysis of WGS information requires the migration of large packets of information between laboratory sites involved in the exploitation of this information, remote computing sites, and internet databases for data manipulation and comparative analyses. There are many ways in which the high-tech needs of the future can be met, even for relatively small laboratories with low operating budgets.

**133**

**Author details**

provided the original work is properly cited.

Inspection Agency, Ottawa, Ontario, Canada

\*Address all correspondence to: burton.blais@canada.ca

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

ing citizens from preventable food-acquired illness.

Opportunities abound for food inspection, public health, and academic laboratories to pool their resources and serve one another in the common purpose of safeguard-

The authors thank Paul Manninger, Mylène Deschênes, and Martine Dixon for providing laboratory support throughout many years in developing the food pathogen genomics program at the Canadian Food Inspection Agency and Andrew Low for providing bioinformatics support and for critical review of this manuscript.

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

**Acknowledgements**

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Catherine D. Carrillo, Adam Koziol, Neil Vary and Burton W. Blais\* Research and Development, Ottawa Laboratory (Carling), Canadian Food Opportunities abound for food inspection, public health, and academic laboratories to pool their resources and serve one another in the common purpose of safeguarding citizens from preventable food-acquired illness.

### **Acknowledgements**

*New Insight into* Brucella *Infection and Foodborne Diseases*

applied to the development of improved methodology.

AMR, and other critical factors relevant to public health.

Modern food microbiology research has generated a critical understanding of the epidemiology, pathogenic mechanisms, virulence factors, and other salient characteristics of the major food pathogens. The convergence of expanded scientific knowledge and sophisticated technological capability create exciting new opportunities for the refinement of food microbiology testing programs to meet the needs of a comprehensive risk-based inspection approach. Advances in nextgeneration sequencing technologies have made it possible for investigators to carry out sequencing and processing of bacterial genomes within the time course of a typical foodborne illness outbreak investigation. It may reasonably be expected that in the near future, analysts will be moving from traditional DNA hybridization approaches (e.g., PCR and microarrays) toward rapid whole-genome sequencing allowing a much more comprehensive examination of the isolate at hand. This new approach will require broad access to leading-edge bioinformatics capability for analysis of complex genomics data in silico to ascertain the presence of key genetic markers (e.g., presence of virulence genes in bacterial pathogens, completeness and functionality of gene products, markers for molecular typing, etc.). The generation and analysis of WGS information requires the migration of large packets of information between laboratory sites involved in the exploitation of this information, remote computing sites, and internet databases for data manipulation and comparative analyses. There are many ways in which the high-tech needs of the future can be met, even for relatively small laboratories with low operating budgets.

enrichment cultures can be used to evaluate growth of target pathogens relative to background food microbiota over time [97, 99]. Such studies can provide valuable insight into species that could potentially interfere with target pathogens that can be

A modern concept in the study of pathogenic bacteria is the emergence of novel pathogens among commensals in a given environment through the acquisition of virulence factors by horizontal gene transfers from other bacteria [102–104]. The evolutionary trail of the STEC family suggests a priori transformations of benign *E. coli* strains into virulent STEC having acquired exogenous DNA segments such as bacteriophage carrying Shiga toxin genes and pathogenicity islands harboring host colonization factors [105]. There is evidence that other food pathogens such as *L. monocytogenes* strains implicated in serious outbreaks of foodborne illness may have acquired enhanced virulence characteristics through horizontal gene transfer processes [106]. This is believed to occur on a relatively short time scale, perhaps on the order of weeks or months, making the emergence of novel pathogens in food production environments or animal reservoirs in near real time a significant possibility. Furthermore, food-acquired coinfections may arise in which two or more bacterial strains complement each another, for example, a toxigenic strain lacking adherence factors might colonize a host by cross-utilizing a factor secreted by another strain [107]. It may be possible to predict the probability of emergence of novel pathogens with enhanced virulence or antibiotic resistance characteristics in the food supply through periodic microbial metagenomic analyses to ascertain the presence of key indicators in the background microflora of food commodities (e.g., ground beef, trim), food manufacturing environments (e.g., floors, food contact surfaces), and animal reservoirs (e.g., cattle, poultry) [108, 109]. A weighted index approach much like that described for the HazChar Score above could be employed here, with possible inclusion of a more comprehensive catalog of known virulence,

**132**

**4. Conclusions**

The authors thank Paul Manninger, Mylène Deschênes, and Martine Dixon for providing laboratory support throughout many years in developing the food pathogen genomics program at the Canadian Food Inspection Agency and Andrew Low for providing bioinformatics support and for critical review of this manuscript.

### **Author details**

Catherine D. Carrillo, Adam Koziol, Neil Vary and Burton W. Blais\* Research and Development, Ottawa Laboratory (Carling), Canadian Food Inspection Agency, Ottawa, Ontario, Canada

\*Address all correspondence to: burton.blais@canada.ca

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Blais BW, Gauthier M, Deschênes M, Huszczynski G. Polyester clothbased hybridization array system for identification of enterohemorrhagic *Escherichia coli* serogroups O26, O45, O103, O111, O121, O145, and O157. Journal of Food Protection. 2012;**75**:1691-1697. DOI: 10.4315/0362- 028X.JFP-12-116

[2] Blais BW, Gauthier M, Deschenes M, Huszczynski G. Characterization of verotoxigenic *Escherichia coli* O157:H7 colonies by polymerase chain reaction (PCR) and cloth-based hybridization array system (CHAS) (MFLP-22). In: Compendium of Analytical Methods, Laboratory Procedures for the Microbiological Analysis of Foods. Vol. 3. Ottawa, Canada: Health Canada; 2013. https://www.canada.ca/en/ health-canada/services/food-nutrition/ research-programs-analyticalmethods/analytical-methods/ compendium-methods.html [Accessed: February 20, 2019]

[3] Blais BW, Martinez A, Gill A, McIlwham S, Mohajer S, Gauthier M. Isolation and identification of priority verotoxigenic *Escherichia coli* (VTEC) in meat and vegetable products (MFLP-52). In: Compendium of Methods, Laboratory Procedures for the Microbiological Analysis of Foods. Vol. 3. Ottawa, Canada: Health Canada; 2014. Available from: https:// www.canada.ca/en/health-canada/ services/food-nutrition/ researchprograms-analytical-methods/ analytical-methods/ compendiummethods.html [Accessed: February 20, 2019]

[4] Huszczynski G, Gauthier M, Mohajer S, Gill A, Blais B. Method for the detection of priority Shiga toxin-producing *Escherichia coli* in beef trim. Journal of Food Protection. 2013;**76**:1689-1696. DOI: 10.4315/0362- 028X.JFP-13-059

[5] Lambert D, Carrillo CD, Koziol AG, Manninger P, Blais BW. GeneSippr: A rapid whole-genome approach for the identification and characterization of foodborne pathogens such as priority Shiga toxigenic *Escherichia coli*. PLoS One. 2015;**10**:e0122928. DOI: 10.1371/ journal.pone.0122928

[6] Allard MW, Strain E, Melka D, Bunning K, Musser SM, Brown EW, et al. Practical value of food pathogen traceability through building a whole-genome sequencing network and database. Journal of Clinical Microbiology. 2016;**54**:1975-1983. DOI: 10.1128/JCM.00081-16

[7] Joensen KG, Scheutz F, Lund O, Hasman H, Kaas RS, Nielsen EM, et al. Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic *Escherichia coli*. Journal of Clinical Microbiology. 2014;**52**:1501-1510. DOI: 10.1128/JCM.03617-13

[8] Joensen KG, Tetzschner AMM, Iguchi A, Aarestrup FM, Scheutz F. Rapid and easy *in silico* serotyping of *Escherichia coli* isolates by use of wholegenome sequencing data. Journal of Clinical Microbiology. 2015;**53**:2410-2426. DOI: 10.1128/JCM.00008-15

[9] Yoshida CE, Kruczkiewicz P, Laing CR, Lingohr EJ, Gannon VPJ, Nash JHE, et al. The *Salmonella* In Silico Typing Resource (SISTR): An open web-accessible tool for rapidly typing and subtyping draft *Salmonella* genome assemblies. PLoS One. 2016;**11**:e0147101. DOI: 10.1371/journal. pone.0147101

[10] Kleinheinz KA, Joensen KG, Larsen MV. Applying the ResFinder and VirulenceFinder web-services for easy identification of acquired antibiotic resistance and *E. coli* virulence genes in bacteriophage and prophage

**135**

2019]

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

Shiga toxin-producing *Escherichia coli* (STECs) from meat products and carcass and environmental sponges. United States Department of Agriculture, Athens, GA, 2019. Available from: https://www.fsis.usda.gov/wps/ wcm/connect/fsis-content/internet/ main/topics/science/laboratories-andprocedures/guidebooks-and-methods/ microbiology-laboratory-guidebook/ microbiology-laboratory-guidebook

[Accessed April 18, 2019]

[17] Martinez-Perez A, Blais BW. Clothbased hybridization array system for the identification of *Escherichia coli* O157:H7. Food Control. 2010;**21**:1354-1359. DOI:

[18] Health Canada. The Compendium of Analytical Methods. n.d. Available from: https://www.canada.ca/en/ health-canada/services/food-nutrition/

compendium-methods.html [Accessed:

[19] Catford A, Kouamé V, Martinez-Perez A, Gill A, Buenaventura E, Couture H, et al. Risk profile on non-O157 verotoxin-producing *Escherichia coli* in produce, beef, milk and dairy products in Canada. International Food Risk Analysis Journal. 2014;**4**:1-25. DOI:

[20] EFSA Panel on Biological Hazards (BIOHAZ). Scientific opinion on VTECseropathotype and scientific criteria regarding pathogenicity assessment. EFSA Journal. 2013;**11**:3138. DOI:

10.1016/j.foodcont.2010.04.009

research-programs-analyticalmethods/analytical-methods/

February 20, 2019]

10.5772/59208

10.2903/j.efsa.2013.3138

[21] Burger R. EHEC O104:H4 in

Germany 2011: Large Outbreak of Bloody Diarrhea and Haemolytic Uraemic Syndrome by Shiga Toxin-producing E. coli via Contaminated Food. In: Institute of Medicine (US). Improving Food Safety Through a One Health Approach: Workshop Summary.Washington DC: National Academies Press (US); 2012.

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

[11] Ronholm J, Nasheri N, Petronella N, Pagotto F. Navigating microbiological food safety in the era of whole-genome sequencing. Clinical Microbiology Reviews. 2016;**29**:837-857. DOI: 10.1128/

[12] Sharapov UM, Wendel AM, Davis JP, Keene WE, Farrar J, Sodha S, et al. Multistate outbreak of *Escherichia coli* O157:H7 infections associated with consumption of fresh spinach: United States, 2006. Journal of Food Protection. 2016;**79**:2024-2030. DOI: 10.4315/0362-

[13] Berenger BM, Berry C, Peterson T, Fach P, Delannoy S, Li V, et al. The utility of multiple molecular methods including whole genome sequencing as tools to differentiate *Escherichia coli* O157:H7 outbreaks. Eurosurveillance. 2015;**20**:30073. DOI: 10.2807/1560-7917.

[14] Seys SA, Sampedro F, Hedberg CW. Assessment of Shiga toxin–

[15] Microbiological Methods Committee. Isolation of *Escherichia coli* O157:H7/NM from foods and environmental surface samples (MFHPB-10). In: Compend. Anal Methods. Ottawa, Canada: Health Canada. Vol. 2. Ottawa, Canada: Health Canada; 2017. Available from: https:// www.canada.ca/en/health-canada/ services/food-nutrition/ researchprograms-analytical-methods/ analytical-methods/compendiummethods.html [Accessed: February 20,

[16] USDA-FSIS. Detection, isolation and identification of top seven

producing *Escherichia coli* O157 illnesses prevented by recalls of beef products. Foodborne Pathogens and Disease. 2015;**12**:800-805. DOI: 10.1089/

CMR.00056-16

028X.JFP-15-556

ES.2015.20.47.30073

fpd.2015.1968

nucleotide sequences. Bacteriophage. 2014;**4**:e27943. DOI: 10.4161/bact.27943 *Applications of Genomics in Regulatory Food Safety Testing in Canada DOI: http://dx.doi.org/10.5772/intechopen.86063*

nucleotide sequences. Bacteriophage. 2014;**4**:e27943. DOI: 10.4161/bact.27943

[11] Ronholm J, Nasheri N, Petronella N, Pagotto F. Navigating microbiological food safety in the era of whole-genome sequencing. Clinical Microbiology Reviews. 2016;**29**:837-857. DOI: 10.1128/ CMR.00056-16

[12] Sharapov UM, Wendel AM, Davis JP, Keene WE, Farrar J, Sodha S, et al. Multistate outbreak of *Escherichia coli* O157:H7 infections associated with consumption of fresh spinach: United States, 2006. Journal of Food Protection. 2016;**79**:2024-2030. DOI: 10.4315/0362- 028X.JFP-15-556

[13] Berenger BM, Berry C, Peterson T, Fach P, Delannoy S, Li V, et al. The utility of multiple molecular methods including whole genome sequencing as tools to differentiate *Escherichia coli* O157:H7 outbreaks. Eurosurveillance. 2015;**20**:30073. DOI: 10.2807/1560-7917. ES.2015.20.47.30073

[14] Seys SA, Sampedro F, Hedberg CW. Assessment of Shiga toxin– producing *Escherichia coli* O157 illnesses prevented by recalls of beef products. Foodborne Pathogens and Disease. 2015;**12**:800-805. DOI: 10.1089/ fpd.2015.1968

[15] Microbiological Methods Committee. Isolation of *Escherichia coli* O157:H7/NM from foods and environmental surface samples (MFHPB-10). In: Compend. Anal Methods. Ottawa, Canada: Health Canada. Vol. 2. Ottawa, Canada: Health Canada; 2017. Available from: https:// www.canada.ca/en/health-canada/ services/food-nutrition/ researchprograms-analytical-methods/ analytical-methods/compendiummethods.html [Accessed: February 20, 2019]

[16] USDA-FSIS. Detection, isolation and identification of top seven

Shiga toxin-producing *Escherichia coli* (STECs) from meat products and carcass and environmental sponges. United States Department of Agriculture, Athens, GA, 2019. Available from: https://www.fsis.usda.gov/wps/ wcm/connect/fsis-content/internet/ main/topics/science/laboratories-andprocedures/guidebooks-and-methods/ microbiology-laboratory-guidebook/ microbiology-laboratory-guidebook [Accessed April 18, 2019]

[17] Martinez-Perez A, Blais BW. Clothbased hybridization array system for the identification of *Escherichia coli* O157:H7. Food Control. 2010;**21**:1354-1359. DOI: 10.1016/j.foodcont.2010.04.009

[18] Health Canada. The Compendium of Analytical Methods. n.d. Available from: https://www.canada.ca/en/ health-canada/services/food-nutrition/ research-programs-analyticalmethods/analytical-methods/ compendium-methods.html [Accessed: February 20, 2019]

[19] Catford A, Kouamé V, Martinez-Perez A, Gill A, Buenaventura E, Couture H, et al. Risk profile on non-O157 verotoxin-producing *Escherichia coli* in produce, beef, milk and dairy products in Canada. International Food Risk Analysis Journal. 2014;**4**:1-25. DOI: 10.5772/59208

[20] EFSA Panel on Biological Hazards (BIOHAZ). Scientific opinion on VTECseropathotype and scientific criteria regarding pathogenicity assessment. EFSA Journal. 2013;**11**:3138. DOI: 10.2903/j.efsa.2013.3138

[21] Burger R. EHEC O104:H4 in Germany 2011: Large Outbreak of Bloody Diarrhea and Haemolytic Uraemic Syndrome by Shiga Toxin-producing E. coli via Contaminated Food. In: Institute of Medicine (US). Improving Food Safety Through a One Health Approach: Workshop Summary.Washington DC: National Academies Press (US); 2012.

**134**

20, 2019]

028X.JFP-13-059

*New Insight into* Brucella *Infection and Foodborne Diseases*

[5] Lambert D, Carrillo CD, Koziol AG, Manninger P, Blais BW. GeneSippr: A rapid whole-genome approach for the identification and characterization of foodborne pathogens such as priority Shiga toxigenic *Escherichia coli*. PLoS One. 2015;**10**:e0122928. DOI: 10.1371/

journal.pone.0122928

10.1128/JCM.00081-16

10.1128/JCM.03617-13

[6] Allard MW, Strain E, Melka D, Bunning K, Musser SM, Brown EW, et al. Practical value of food pathogen traceability through building a whole-genome sequencing network and database. Journal of Clinical Microbiology. 2016;**54**:1975-1983. DOI:

[7] Joensen KG, Scheutz F, Lund O, Hasman H, Kaas RS, Nielsen EM, et al. Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic *Escherichia coli*. Journal of Clinical Microbiology. 2014;**52**:1501-1510. DOI:

[8] Joensen KG, Tetzschner AMM, Iguchi A, Aarestrup FM, Scheutz F. Rapid and easy *in silico* serotyping of *Escherichia coli* isolates by use of wholegenome sequencing data. Journal of Clinical Microbiology. 2015;**53**:2410-2426.

DOI: 10.1128/JCM.00008-15

pone.0147101

[9] Yoshida CE, Kruczkiewicz P, Laing CR, Lingohr EJ, Gannon VPJ, Nash JHE, et al. The *Salmonella* In Silico Typing Resource (SISTR): An open web-accessible tool for rapidly typing and subtyping draft *Salmonella* genome assemblies. PLoS One.

2016;**11**:e0147101. DOI: 10.1371/journal.

[10] Kleinheinz KA, Joensen KG, Larsen MV. Applying the ResFinder and VirulenceFinder web-services for easy identification of acquired antibiotic resistance and *E. coli* virulence genes in bacteriophage and prophage

[1] Blais BW, Gauthier M, Deschênes M,

[2] Blais BW, Gauthier M, Deschenes M, Huszczynski G. Characterization of verotoxigenic *Escherichia coli* O157:H7 colonies by polymerase chain reaction (PCR) and cloth-based hybridization array system (CHAS) (MFLP-22). In: Compendium of Analytical Methods,

Laboratory Procedures for the

research-programs-analyticalmethods/analytical-methods/

February 20, 2019]

Microbiological Analysis of Foods. Vol. 3. Ottawa, Canada: Health Canada; 2013. https://www.canada.ca/en/ health-canada/services/food-nutrition/

compendium-methods.html [Accessed:

[3] Blais BW, Martinez A, Gill A, McIlwham S, Mohajer S, Gauthier M. Isolation and identification of priority verotoxigenic *Escherichia coli* (VTEC) in meat and vegetable products (MFLP-52). In: Compendium of Methods, Laboratory Procedures for the Microbiological Analysis of Foods. Vol. 3. Ottawa, Canada: Health Canada; 2014. Available from: https:// www.canada.ca/en/health-canada/ services/food-nutrition/ researchprograms-analytical-methods/ analytical-methods/ compendiummethods.html [Accessed: February

[4] Huszczynski G, Gauthier M, Mohajer S, Gill A, Blais B. Method for the detection of priority Shiga toxin-producing *Escherichia coli* in beef trim. Journal of Food Protection. 2013;**76**:1689-1696. DOI: 10.4315/0362-

Huszczynski G. Polyester clothbased hybridization array system for identification of enterohemorrhagic *Escherichia coli* serogroups O26, O45, O103, O111, O121, O145, and O157. Journal of Food Protection. 2012;**75**:1691-1697. DOI: 10.4315/0362-

028X.JFP-12-116

**References**

Available from: https://www.ncbi.nlm. nih.gov/books/NBK114499/[Accessed February 20, 2019]

[22] Frank C, Werber D, Cramer JP, Askar M, Faber M, an der Heiden M, et al. Epidemic profile of Shigatoxin–producing *Escherichia coli* O104:H4 outbreak in Germany. The New England Journal of Medicine. 2011;**365**:1771-1780. DOI: 10.1056/ NEJMoa1106483

[23] Carrillo CD, Koziol AG, Mathews A, Goji N, Lambert D, Huszczynski G, et al. Comparative evaluation of genomic and laboratory approaches for determination of Shiga toxin subtypes in *Escherichia coli*. Journal of Food Protection. 2016;**79**:2078-2085. DOI: 10.4315/0362-028X.JFP-16-228

[24] Lambert D, Pightling A, Griffiths E, Van Domselaar G, Evans P, Berthelet S, et al. Baseline practices for the application of genomic data supporting regulatory food safety. Journal of AOAC International. 2017;**100**:721-731. DOI: 10.5740/jaoacint.16-0269

[25] Low AJ, Koziol AG, Manninger PA, Blais BW, Carrillo CD. ConFindr: Rapid detection of intraspecies and cross-species contamination in bacterial whole-genome sequence data. 2019;**7**:e27499v1. DOI: 10.7287/peerj. preprints.27499v1

[26] Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan K-G, et al. The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies. F1000 Research. 2018;**7**:459. DOI: 10.12688/f1000research.14509.2

[27] Ribot EM, Hise KB. Future challenges for tracking foodborne diseases: PulseNet, a 20-year-old US surveillance system for foodborne

diseases, is expanding both globally and technologically. EMBO Reports. 2016;**17**:1499-1505. DOI: 10.15252/ embr.201643128

[28] Swaminathan B, Barrett TJ, Hunter SB, Tauxe RV, CDC PulseNet Task Force. PulseNet: The molecular subtyping network for foodborne bacterial disease surveillance, United States. Emerging Infectious Diseases. 2001;**7**:382-389. DOI: 10.3201/ eid0703.010303

[29] Taboada EN, Clark CG, Sproston EL, Carrillo CD. Current methods for molecular typing of *Campylobacter* species. Journal of Microbiological Methods. 2013;**95**:24-31. DOI: 10.1016/j. mimet.2013.07.007

[30] Herikstad H, Motarjemi Y, Tauxe RV. *Salmonella* surveillance: A global survey of public health serotyping. Epidemiology and Infection. 2002;**129**:1-8

[31] Penner JL, Hennessy JN. Passive hemagglutination technique for serotyping *Campylobacter fetus* subsp. *jejuni* on the basis of soluble heatstable antigens. Journal of Clinical Microbiology. 1980;**12**:732-737

[32] Evans DJ, Evans DG. Classification of pathogenic *Escherichia coli* according to serotype and the production of virulence factors, with special reference to colonization-factor antigens. Reviews of Infectious Diseases. 1983;**5**(Suppl 4):S692-S701

[33] Hickman-Brenner FW, Stubbs AD, Farmer JJ. Phage typing of *Salmonella* enteritidis in the United States. Journal of Clinical Microbiology. 1991;**29**:2817-2823

[34] Maiden MCJ. Multilocus sequence typing of bacteria. Annual Review of Microbiology. 2006;**60**:561-588. DOI: 10.1146/annurev.micro.59.030804. 121325

**137**

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

2014;**20**:1481-1489. DOI: 10.3201/

[41] Jagadeesan B, Gerner-Smidt P, Allard MW, Leuillet S, Winkler A, Xiao Y, et al. The use of next generation sequencing for improving food safety: Translation into practice. Food Microbiology. 2019;**79**:96-115. DOI:

[42] Petkau A, Mabon P, Sieffert C, Knox NC, Cabral J, Iskander M, et al. SNVPhyl: A single nucleotide variant phylogenomics pipeline for microbial genomic epidemiology. Microbial Genomics. 2017;**3**:e000116. DOI:

[43] Markell JA, Koziol AG, Lambert D. Draft genome sequence of *Escherichia coli* O157:H7 ATCC 35150 and a nalidixic acid-resistant mutant derivative. Genome Announcements. 2015;**3**:e00734-15. DOI: 10.1128/

[44] Maiden MCJ, Jansen van Rensburg MJ, Bray JE, Earle SG, Ford SA, Jolley KA, et al. MLST revisited: The gene-bygene approach to bacterial genomics. Nature Reviews. Microbiology. 2013;**11**:728-736. DOI: 10.1038/

[45] Carrillo CD, Kruczkiewicz P, Mutschall S, Tudor A, Clark C, Taboada EN. A framework for assessing the concordance of molecular typing methods and the true strain phylogeny of *Campylobacter jejuni* and *C. coli* using draft genome sequence data. Frontiers in Cellular and Infection Microbiology. 2012;**2**:57. DOI: 10.3389/

[46] Jolley KA, Bliss CM, Bennett JS, Bratcher HB, Brehony C, Colles FM, et al. Ribosomal multilocus sequence typing: Universal characterization of bacteria from domain to strain. Microbiology. 2012;**158**:1005-1015. DOI:

10.1016/j.fm.2018.11.005

10.1099/mgen.0.000116

genomeA.00734-15

nrmicro3093

fcimb.2012.00057

10.1099/mic.0.055459-0

eid2009.131095

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

[35] Maiden MCJ, Bygraves JA, Feil E, Morelli G, Russell JE, Urwin R, et al. Multilocus sequence typing: A portable approach to the identification

[36] Schjørring S, Niskanen T, Torpdahl

of clones within populations of pathogenicmicroorganisms. Proceedings of the National Academy of Sciences of the United States of America. 1998;**95**:3140-3145

M, Björkman JT, Nielsen EM. Evaluation of molecular typing of foodborne pathogens in European reference laboratories from 2012 to 2013. Euro Surveillance. 2016;**21**:pii=30429. DOI: 10.2807/1560-

7917.ES.2016.21.50.30429

1994;**32**:3013-3017

DOI: 10.2807/1560-7917. ES.2017.22.23.30544

[37] Barrett TJ, Lior H, Green JH, Khakhria R, Wells JG, Bell BP, et al. Laboratory investigation of a multistate food-borne outbreak of *Escherichia coli* O157:H7 by using pulsed-field gel electrophoresis and phage typing. Journal of Clinical Microbiology.

[38] Nadon C, Van Walle I, Gerner-Smidt P, Campos J, Chinen I, Concepcion-Acevedo J, et al. PulseNet international: Vision for the implementation of whole genome sequencing (WGS) for global food-borne disease surveillance. Eurosurveillance. 2017;**22**:pii=30544.

[39] Grant K, Jenkins C, Arnold C, Green J, Zambon M. Implementing Pathogen Genomics: A Case Study. Public Health England. 2018. Available from: https:// www.gov.uk/government/publications/ implementing-pathogen-genomics-acase-study [Accessed February 23, 2019]

[40] Deng X, Desai PT, den Bakker HC, Mikoleit M, Tolar B, Trees E, et al. Genomic epidemiology of *Salmonella enterica* serotype Enteritidis based on population structure of prevalent lineages. Emerging Infectious Diseases. *Applications of Genomics in Regulatory Food Safety Testing in Canada DOI: http://dx.doi.org/10.5772/intechopen.86063*

[35] Maiden MCJ, Bygraves JA, Feil E, Morelli G, Russell JE, Urwin R, et al. Multilocus sequence typing: A portable approach to the identification of clones within populations of pathogenicmicroorganisms. Proceedings of the National Academy of Sciences of the United States of America. 1998;**95**:3140-3145

*New Insight into* Brucella *Infection and Foodborne Diseases*

diseases, is expanding both globally and technologically. EMBO Reports. 2016;**17**:1499-1505. DOI: 10.15252/

[28] Swaminathan B, Barrett TJ, Hunter SB, Tauxe RV, CDC PulseNet Task Force. PulseNet: The molecular subtyping network for foodborne bacterial disease surveillance, United States. Emerging Infectious Diseases.

2001;**7**:382-389. DOI: 10.3201/

[29] Taboada EN, Clark CG, Sproston EL, Carrillo CD. Current methods for molecular typing of *Campylobacter* species. Journal of Microbiological Methods. 2013;**95**:24-31. DOI: 10.1016/j.

[30] Herikstad H, Motarjemi Y, Tauxe RV. *Salmonella* surveillance: A global survey of public health serotyping. Epidemiology and Infection.

[31] Penner JL, Hennessy JN. Passive hemagglutination technique for serotyping *Campylobacter fetus* subsp. *jejuni* on the basis of soluble heatstable antigens. Journal of Clinical Microbiology. 1980;**12**:732-737

[32] Evans DJ, Evans DG. Classification of pathogenic *Escherichia coli* according to serotype and the production of virulence factors, with special reference

to colonization-factor antigens. Reviews of Infectious Diseases. 1983;**5**(Suppl 4):S692-S701

[33] Hickman-Brenner FW, Stubbs AD, Farmer JJ. Phage typing of *Salmonella* enteritidis in the United States. Journal of Clinical Microbiology.

[34] Maiden MCJ. Multilocus sequence typing of bacteria. Annual Review of Microbiology. 2006;**60**:561-588. DOI: 10.1146/annurev.micro.59.030804.

1991;**29**:2817-2823

121325

embr.201643128

eid0703.010303

mimet.2013.07.007

2002;**129**:1-8

Available from: https://www.ncbi.nlm. nih.gov/books/NBK114499/[Accessed

[22] Frank C, Werber D, Cramer JP, Askar M, Faber M, an der Heiden M, et al. Epidemic profile of Shigatoxin–producing *Escherichia coli* O104:H4 outbreak in Germany. The New England Journal of Medicine. 2011;**365**:1771-1780. DOI: 10.1056/

[23] Carrillo CD, Koziol AG, Mathews A, Goji N, Lambert D, Huszczynski G, et al. Comparative evaluation of genomic and laboratory approaches for determination of Shiga toxin subtypes in *Escherichia coli*. Journal of Food Protection. 2016;**79**:2078-2085. DOI: 10.4315/0362-028X.JFP-16-228

[24] Lambert D, Pightling A, Griffiths E, Van Domselaar G, Evans P, Berthelet S, et al. Baseline practices for the application of genomic data supporting regulatory food safety. Journal of AOAC International. 2017;**100**:721-731. DOI:

[25] Low AJ, Koziol AG, Manninger PA, Blais BW, Carrillo CD. ConFindr: Rapid detection of intraspecies and cross-species contamination in bacterial whole-genome sequence data. 2019;**7**:e27499v1. DOI: 10.7287/peerj.

[26] Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan K-G, et al. The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies. F1000 Research. 2018;**7**:459. DOI: 10.12688/f1000research.14509.2

[27] Ribot EM, Hise KB. Future challenges for tracking foodborne diseases: PulseNet, a 20-year-old US surveillance system for foodborne

10.5740/jaoacint.16-0269

preprints.27499v1

February 20, 2019]

NEJMoa1106483

**136**

[36] Schjørring S, Niskanen T, Torpdahl M, Björkman JT, Nielsen EM. Evaluation of molecular typing of foodborne pathogens in European reference laboratories from 2012 to 2013. Euro Surveillance. 2016;**21**:pii=30429. DOI: 10.2807/1560- 7917.ES.2016.21.50.30429

[37] Barrett TJ, Lior H, Green JH, Khakhria R, Wells JG, Bell BP, et al. Laboratory investigation of a multistate food-borne outbreak of *Escherichia coli* O157:H7 by using pulsed-field gel electrophoresis and phage typing. Journal of Clinical Microbiology. 1994;**32**:3013-3017

[38] Nadon C, Van Walle I, Gerner-Smidt P, Campos J, Chinen I, Concepcion-Acevedo J, et al. PulseNet international: Vision for the implementation of whole genome sequencing (WGS) for global food-borne disease surveillance. Eurosurveillance. 2017;**22**:pii=30544. DOI: 10.2807/1560-7917. ES.2017.22.23.30544

[39] Grant K, Jenkins C, Arnold C, Green J, Zambon M. Implementing Pathogen Genomics: A Case Study. Public Health England. 2018. Available from: https:// www.gov.uk/government/publications/ implementing-pathogen-genomics-acase-study [Accessed February 23, 2019]

[40] Deng X, Desai PT, den Bakker HC, Mikoleit M, Tolar B, Trees E, et al. Genomic epidemiology of *Salmonella enterica* serotype Enteritidis based on population structure of prevalent lineages. Emerging Infectious Diseases. 2014;**20**:1481-1489. DOI: 10.3201/ eid2009.131095

[41] Jagadeesan B, Gerner-Smidt P, Allard MW, Leuillet S, Winkler A, Xiao Y, et al. The use of next generation sequencing for improving food safety: Translation into practice. Food Microbiology. 2019;**79**:96-115. DOI: 10.1016/j.fm.2018.11.005

[42] Petkau A, Mabon P, Sieffert C, Knox NC, Cabral J, Iskander M, et al. SNVPhyl: A single nucleotide variant phylogenomics pipeline for microbial genomic epidemiology. Microbial Genomics. 2017;**3**:e000116. DOI: 10.1099/mgen.0.000116

[43] Markell JA, Koziol AG, Lambert D. Draft genome sequence of *Escherichia coli* O157:H7 ATCC 35150 and a nalidixic acid-resistant mutant derivative. Genome Announcements. 2015;**3**:e00734-15. DOI: 10.1128/ genomeA.00734-15

[44] Maiden MCJ, Jansen van Rensburg MJ, Bray JE, Earle SG, Ford SA, Jolley KA, et al. MLST revisited: The gene-bygene approach to bacterial genomics. Nature Reviews. Microbiology. 2013;**11**:728-736. DOI: 10.1038/ nrmicro3093

[45] Carrillo CD, Kruczkiewicz P, Mutschall S, Tudor A, Clark C, Taboada EN. A framework for assessing the concordance of molecular typing methods and the true strain phylogeny of *Campylobacter jejuni* and *C. coli* using draft genome sequence data. Frontiers in Cellular and Infection Microbiology. 2012;**2**:57. DOI: 10.3389/ fcimb.2012.00057

[46] Jolley KA, Bliss CM, Bennett JS, Bratcher HB, Brehony C, Colles FM, et al. Ribosomal multilocus sequence typing: Universal characterization of bacteria from domain to strain. Microbiology. 2012;**158**:1005-1015. DOI: 10.1099/mic.0.055459-0

[47] Chen Y, Luo Y, Carleton H, Timme R, Melka D, Muruvanda T, et al. Whole genome and core genome multilocus sequence typing and single nucleotide polymorphism analyses of *Listeria monocytogenes* associated with an outbreak linked to cheese, United States, 2013. Applied and Environmental Microbiology. 2017;**83**:pii: e00633-17. DOI: 10.1128/AEM.00633-17

[48] Gilmour MW, Graham M, Reimer A, Van Domselaar G. Public health genomics and the new molecular epidemiology of bacterial pathogens. Public Health Genomics. 2013;**16**:25-30. DOI: 10.1159/000342709

[49] LeClerc JE, Li B, Payne WL, Cebula TA. High mutation frequencies among *Escherichia coli* and *Salmonella* pathogens. Science. 1996;**274**:1208-1211. DOI: 10.1126/ science.274.5290.1208

[50] Kozyreva VK, Ilina EN, Malakhova MV, Carattoli A, Azizov IS, Tapalski DV, et al. Long-term dissemination of CTX-M-5-producing hypermutable *Salmonella enterica* serovar typhimurium sequence type 328 strains in Russia, Belarus, and Kazakhstan. Antimicrobial Agents and Chemotherapy. 2014;**58**:5202-5210. DOI: 10.1128/AAC.02506-14

[51] Pope CF, O'Sullivan DM, McHugh TD, Gillespie SH. A practical guide to measuring mutation rates in antibiotic resistance. Antimicrobial Agents and Chemotherapy. 2008;**52**:1209-1214. DOI: 10.1128/AAC.01152-07

[52] Eliopoulos GM, Blázquez J. Hypermutation as a factor contributing to the acquisition of antimicrobial resistance. Clinical Infectious Diseases. 2003;**37**:1201-1209. DOI: 10.1086/378810

[53] Swings T, Van den Bergh B, Wuyts S, Oeyen E, Voordeckers K, Verstrepen KJ, et al. Adaptive tuning of mutation rates allows fast response to lethal stress in *Escherichia coli*. eLife. 2017;**6**:e22939. DOI: 10.7554/eLife.22939

[54] Leong D, Alvarez-Ordóñez A, Jordan K. Monitoring occurrence and persistence of *Listeria monocytogenes* in foods and food processing environments in the Republic of Ireland. Frontiers in Microbiology. 2014;**5**:436. DOI: 10.3389/ fmicb.2014.00436

[55] Nastasijevic I, Milanov D, Velebit B, Djordjevic V, Swift C, Painset A, et al. Tracking of *Listeria monocytogenes* in meat establishment using whole genome sequencing as a food safety management tool: A proof of concept. International Journal of Food Microbiology. 2017;**257**:157-164. DOI: 10.1016/j. ijfoodmicro.2017.06.015

[56] Malley TJV, Butts J, Wiedmann M. Seek and destroy process: *Listeria monocytogenes* process controls in the ready-to-eat meat and poultry industry. Journal of Food Protection. 2015;**78**:436-445. DOI: 10.4315/0362- 028X.JFP-13-507

[57] Thomas MK, Murray R, Flockhart L, Pintar K, Fazil A, Nesbitt A, et al. Estimates of foodborne illnessrelated hospitalizations and deaths in Canada for 30 specified pathogens and unspecified agents. Foodborne Pathogens and Disease. 2015;**12**:820-827. DOI: 10.1089/fpd.2015.1966

[58] Government of Canada. Report of the Independent Investigator into the 2008 listeriosis outbreak 2009. Available from: http://publications.gc.ca/site/ eng/361474/publication.html [Accessed: February 18, 2019]

[59] Cherifi T, Carrillo C, Lambert D, Miniaï I, Quessy S, Larivière-Gauthier G, et al. Genomic characterization of *Listeria monocytogenes* isolates

**139**

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

[65] Schjørring S, Lassen SG, Jensen T, Moura A, Kjeldgaard JS, Müller L, et al. Cross-border outbreak of listeriosis caused by cold-smoked salmon, revealed by integrated surveillance and whole genome sequencing (WGS), Denmark and France, 2015 to 2017.

Eurosurveillance. 2017;**22**:

ES.2017.22.50.17-00762

10.1056/NEJMc1100443

17-00762. DOI: 10.2807/1560-7917.

[66] Lienau EK, Strain E, Wang C, Zheng J, Ottesen AR, Keys CE, et al. Identification of a salmonellosis outbreak by means of molecular sequencing. The New England Journal of Medicine. 2011;**364**:981-982. DOI:

[67] Rantsiou K, Kathariou S, Winkler A, Skandamis P, Saint-Cyr MJ, Rouzeau-Szynalski K, et al. Next generation microbiological risk assessment: Opportunities of whole genome sequencing (WGS) for foodborne pathogen surveillance, source tracking and risk assessment. International Journal of Food Microbiology. 2018;**287**:3-9. DOI: 10.1016/j. ijfoodmicro.2017.11.007

[68] National Research Council (US) Committee on DNA Forensic Science: An Update. The Evaluation of Forensic DNA Evidence.5. Statistical Issues. Washington DC: National Academies Press (US); 1996. Available from: https://www.ncbi.nlm.nih.gov/books/ NBK232615/ [Accessed February 24

[69] Wilson MR, Allard MW, Brown EW. The forensic analysis of foodborne bacterial pathogens in the age of whole-genome sequencing. Cladistics. 2013;**29**:449-461. DOI: 10.1111/cla.12012

[70] Rosner BM, Schielke A, Didelot X, Kops F, Breidenbach J, Willrich N, et al. A combined case-control and molecular source attribution study of human *Campylobacter* infections

2019]

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

reveals that their persistence in a pig slaughterhouse is linked to the presence of benzalkonium chloride resistance genes. BMC Microbiology. 2018;**18**:220. DOI: 10.1186/s12866-018-1363-9

[60] Martínez-Suárez JV, Ortiz S, López-Alonso V. Potential impact of the resistance to quaternary ammonium disinfectants on the persistence of *Listeria monocytogenes* in food processing environments. Frontiers in Microbiology. 2016;**7**:638. DOI: 10.3389/

[61] Yu T, Jiang X, Zhang Y, Ji S, Gao W, Shi L. Effect of benzalkonium chloride adaptation on sensitivity to antimicrobial agents and tolerance to environmental stresses in *Listeria monocytogenes*. Frontiers in Microbiology. 2018;**9**:2906. DOI: 10.3389/fmicb.2018.02906

[62] Elhanafi D, Dutta V, Kathariou S.

[63] Kovacevic J, Ziegler J, Wałecka-Zacharska E, Reimer A, Kitts DD, Gilmour MW. Tolerance of *Listeria monocytogenes* to quaternary ammonium sanitizers is mediated by a novel efflux pump encoded by *emrE*. Applied and Environmental Microbiology. 2016;**82**:939-953. DOI: 10.1128/

[64] Franz E, Gras LM, Dallman T. Significance of whole genome sequencing for surveillance, source attribution and microbial risk assessment of foodborne pathogens. Current Opinion in Food Science. 2016;**8**:74-79. DOI: 10.1016/j.

Genetic characterization of plasmid-associated benzalkonium chloride resistance determinants in a *Listeria monocytogenes* strain from the 1998-1999 outbreak. Applied and Environmental Microbiology. 2010;**76**:8231-8238. DOI: 10.1128/

AEM.02056-10

AEM.03741-15

cofs.2016.04.004

fmicb.2016.00638

*Applications of Genomics in Regulatory Food Safety Testing in Canada DOI: http://dx.doi.org/10.5772/intechopen.86063*

reveals that their persistence in a pig slaughterhouse is linked to the presence of benzalkonium chloride resistance genes. BMC Microbiology. 2018;**18**:220. DOI: 10.1186/s12866-018-1363-9

*New Insight into* Brucella *Infection and Foodborne Diseases*

KJ, et al. Adaptive tuning of mutation rates allows fast response to lethal stress in *Escherichia coli*. eLife. 2017;**6**:e22939.

[54] Leong D, Alvarez-Ordóñez A, Jordan K. Monitoring occurrence and persistence of *Listeria monocytogenes* in foods and food processing environments in the Republic of Ireland. Frontiers in Microbiology. 2014;**5**:436. DOI: 10.3389/

[55] Nastasijevic I, Milanov D, Velebit B, Djordjevic V, Swift C, Painset A, et al. Tracking of *Listeria monocytogenes* in meat establishment using whole genome sequencing as a food safety management tool: A proof of concept. International Journal of Food Microbiology. 2017;**257**:157-164. DOI: 10.1016/j.

ijfoodmicro.2017.06.015

028X.JFP-13-507

[56] Malley TJV, Butts J, Wiedmann M. Seek and destroy process: *Listeria monocytogenes* process controls in the ready-to-eat meat and poultry industry. Journal of Food Protection. 2015;**78**:436-445. DOI: 10.4315/0362-

[57] Thomas MK, Murray R, Flockhart L, Pintar K, Fazil A, Nesbitt A, et al. Estimates of foodborne illnessrelated hospitalizations and deaths in Canada for 30 specified pathogens and unspecified agents. Foodborne Pathogens and Disease. 2015;**12**:820-827.

[58] Government of Canada. Report of the Independent Investigator into the 2008 listeriosis outbreak 2009. Available from: http://publications.gc.ca/site/ eng/361474/publication.html [Accessed:

[59] Cherifi T, Carrillo C, Lambert D, Miniaï I, Quessy S, Larivière-Gauthier G, et al. Genomic characterization of *Listeria monocytogenes* isolates

DOI: 10.1089/fpd.2015.1966

February 18, 2019]

DOI: 10.7554/eLife.22939

fmicb.2014.00436

[47] Chen Y, Luo Y, Carleton H, Timme R, Melka D, Muruvanda T, et al. Whole genome and core genome multilocus sequence typing and single nucleotide polymorphism analyses of *Listeria monocytogenes* associated with an

outbreak linked to cheese, United States, 2013. Applied and Environmental Microbiology. 2017;**83**:pii: e00633-17.

[48] Gilmour MW, Graham M, Reimer A, Van Domselaar G. Public health genomics and the new molecular epidemiology of bacterial pathogens. Public Health Genomics. 2013;**16**:25-30.

DOI: 10.1128/AEM.00633-17

DOI: 10.1159/000342709

science.274.5290.1208

10.1128/AAC.02506-14

DOI: 10.1128/AAC.01152-07

[52] Eliopoulos GM, Blázquez J.

Hypermutation as a factor contributing to the acquisition of antimicrobial resistance. Clinical Infectious Diseases. 2003;**37**:1201-1209. DOI:

[53] Swings T, Van den Bergh B, Wuyts S, Oeyen E, Voordeckers K, Verstrepen

[50] Kozyreva VK, Ilina EN,

Malakhova MV, Carattoli A, Azizov IS, Tapalski DV, et al. Long-term dissemination of CTX-M-5-producing hypermutable *Salmonella enterica* serovar typhimurium sequence type 328 strains in Russia, Belarus, and Kazakhstan. Antimicrobial Agents and Chemotherapy. 2014;**58**:5202-5210. DOI:

[51] Pope CF, O'Sullivan DM, McHugh TD, Gillespie SH. A practical guide to measuring mutation rates in antibiotic resistance. Antimicrobial Agents and Chemotherapy. 2008;**52**:1209-1214.

[49] LeClerc JE, Li B, Payne WL, Cebula TA. High mutation frequencies among *Escherichia coli* and *Salmonella* pathogens. Science. 1996;**274**:1208-1211. DOI: 10.1126/

**138**

10.1086/378810

[60] Martínez-Suárez JV, Ortiz S, López-Alonso V. Potential impact of the resistance to quaternary ammonium disinfectants on the persistence of *Listeria monocytogenes* in food processing environments. Frontiers in Microbiology. 2016;**7**:638. DOI: 10.3389/ fmicb.2016.00638

[61] Yu T, Jiang X, Zhang Y, Ji S, Gao W, Shi L. Effect of benzalkonium chloride adaptation on sensitivity to antimicrobial agents and tolerance to environmental stresses in *Listeria monocytogenes*. Frontiers in Microbiology. 2018;**9**:2906. DOI: 10.3389/fmicb.2018.02906

[62] Elhanafi D, Dutta V, Kathariou S. Genetic characterization of plasmid-associated benzalkonium chloride resistance determinants in a *Listeria monocytogenes* strain from the 1998-1999 outbreak. Applied and Environmental Microbiology. 2010;**76**:8231-8238. DOI: 10.1128/ AEM.02056-10

[63] Kovacevic J, Ziegler J, Wałecka-Zacharska E, Reimer A, Kitts DD, Gilmour MW. Tolerance of *Listeria monocytogenes* to quaternary ammonium sanitizers is mediated by a novel efflux pump encoded by *emrE*. Applied and Environmental Microbiology. 2016;**82**:939-953. DOI: 10.1128/ AEM.03741-15

[64] Franz E, Gras LM, Dallman T. Significance of whole genome sequencing for surveillance, source attribution and microbial risk assessment of foodborne pathogens. Current Opinion in Food Science. 2016;**8**:74-79. DOI: 10.1016/j. cofs.2016.04.004

[65] Schjørring S, Lassen SG, Jensen T, Moura A, Kjeldgaard JS, Müller L, et al. Cross-border outbreak of listeriosis caused by cold-smoked salmon, revealed by integrated surveillance and whole genome sequencing (WGS), Denmark and France, 2015 to 2017. Eurosurveillance. 2017;**22**: 17-00762. DOI: 10.2807/1560-7917. ES.2017.22.50.17-00762

[66] Lienau EK, Strain E, Wang C, Zheng J, Ottesen AR, Keys CE, et al. Identification of a salmonellosis outbreak by means of molecular sequencing. The New England Journal of Medicine. 2011;**364**:981-982. DOI: 10.1056/NEJMc1100443

[67] Rantsiou K, Kathariou S, Winkler A, Skandamis P, Saint-Cyr MJ, Rouzeau-Szynalski K, et al. Next generation microbiological risk assessment: Opportunities of whole genome sequencing (WGS) for foodborne pathogen surveillance, source tracking and risk assessment. International Journal of Food Microbiology. 2018;**287**:3-9. DOI: 10.1016/j. ijfoodmicro.2017.11.007

[68] National Research Council (US) Committee on DNA Forensic Science: An Update. The Evaluation of Forensic DNA Evidence.5. Statistical Issues. Washington DC: National Academies Press (US); 1996. Available from: https://www.ncbi.nlm.nih.gov/books/ NBK232615/ [Accessed February 24 2019]

[69] Wilson MR, Allard MW, Brown EW. The forensic analysis of foodborne bacterial pathogens in the age of whole-genome sequencing. Cladistics. 2013;**29**:449-461. DOI: 10.1111/cla.12012

[70] Rosner BM, Schielke A, Didelot X, Kops F, Breidenbach J, Willrich N, et al. A combined case-control and molecular source attribution study of human *Campylobacter* infections

in Germany, 2011-2014. Scientific Reports. 2017;**7**:5139. DOI: 10.1038/ s41598-017-05227-x

[71] Pires SM, Vieira AR, Hald T, Cole D. Source attribution of human salmonellosis: An overview of methods and estimates. Foodborne Pathogens and Disease. 2014;**11**:667-676. DOI: 10.1089/fpd.2014.1744

[72] Yachison CA, Yoshida C, Robertson J, Nash JHE, Kruczkiewicz P, Taboada EN, et al. The validation and implications of using whole genome sequencing as a replacement for traditional serotyping for a National *Salmonella* Reference Laboratory. Frontiers in Microbiology. 2017;**8**:1044. DOI: 10.3389/fmicb.2017.01044

[73] Mellor GE, Fegan N, Gobius KS, Smith HV, Jennison AV, D'Astek BA, et al. Geographically distinct *Escherichia coli* O157 isolates differ by lineage, Shiga toxin genotype, and total Shiga toxin production. Journal of Clinical Microbiology. 2015;**53**:579-586. DOI: 10.1128/JCM.01532-14

[74] Hoffmann M, Luo Y, Monday SR, Gonzalez-Escalona N, Ottesen AR, Muruvanda T, et al. Tracing origins of the *Salmonella* Bareilly Strain causing a food-borne outbreak in the United States. The Journal of Infectious Diseases. 2016;**213**:502-508. DOI: 10.1093/infdis/jiv297

[75] Thépault A, Méric G, Rivoal K, Pascoe B, Mageiros L, Touzain F, et al. Genome-wide identification of hostsegregating epidemiological markers for source attribution in *Campylobacter jejuni*. Applied and Environmental Microbiology. 2017;**83**. DOI: 10.1128/AEM.03085-16

[76] Cocolin L, Membré J-M, Zwietering MH. Editorial: Integration of omics into MRA. International Journal of Food Microbiology. 2018;**287**:1-2. DOI: 10.1016/j.ijfoodmicro.2017.11.006

[77] Farber JM, Peterkin PI. *Listeria monocytogenes*, a food-borne pathogen. Microbiological Reviews. 1991;**55**:476-511

[78] de Boer RF, Ferdous M, Ott A, Scheper HR, Wisselink GJ, Heck ME, et al. Assessing the public health risk of Shiga toxin-producing *Escherichia coli* by use of a rapid diagnostic screening algorithm. Journal of Clinical Microbiology. 2015;**53**:1588-1598. DOI: 10.1128/JCM.03590-14

[79] Ashton PM, Perry N, Ellis R, Petrovska L, Wain J, Grant KA, et al. Insight into Shiga toxin genes encoded by *Escherichia coli* O157 from whole genome sequencing. PeerJ. 2015;**3**:e739. DOI: 10.7717/peerj.739

[80] Center for Genomic Epidemiology. n.d. Available from: http://www. genomicepidemiology.org/ [Accessed: February 20, 2019]

[81] Dutil L, Irwin R, Finley R, Ng LK, Avery B, Boerlin P, et al. Ceftiofur resistance in *Salmonella enterica* serovar Heidelberg from chicken meat and humans, Canada. Emerging Infectious Diseases. 2010;**16**:48-54. DOI: 10.3201/ eid1601.090729

[82] Tang KL, Caffrey NP, Nóbrega DB, Cork SC, Ronksley PE, Barkema HW, et al. Restricting the use of antibiotics in food-producing animals and its associations with antibiotic resistance in food-producing animals and human beings: A systematic review and meta-analysis. Lancet Planetary Health. 2017;**1**:e316-e327. DOI: 10.1016/ S2542-5196(17)30141-9

[83] Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, et al. Identification of acquired antimicrobial resistance genes. The Journal of Antimicrobial Chemotherapy. 2012;**67**:2640-2644. DOI: 10.1093/jac/ dks261

**141**

dkv186

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

antimicrobial resistance in nontyphoidal *Salmonella*. Antimicrobial Agents and Chemotherapy. 2016;**60**:5515-5520. DOI:

[91] Zhao S, Tyson GH, Chen Y, Li C, Mukherjee S, Young S, et al. Whole genome sequencing analysis accurately predicts antimicrobial resistance phenotypes in *Campylobacter*. Applied and Environmental Microbiology. 2015;**82**:459-466. DOI:10.1128/

[92] Gill A, Huszczynski G, Gauthier M, Blais B. Evaluation of eight agar media for the isolation of Shiga toxinproducing *Escherichia coli*. Journal of Microbiological Methods. 2014;**96**:6-11. DOI: 10.1016/j.mimet.2013.10.022

[93] Gill A, Martinez-Perez A,

*Escherichia coli* from food: A

McIlwham S, Blais B. Development of a method for the detection of Verotoxinproducing *Escherichia coli* in food. Journal of Food Protection. 2012;**75**: 827-837. DOI: 10.4315/0362-028X.

[94] Vimont A, Delignette-Muller M-L, Vernozy-Rozand C. Supplementation of enrichment broths by novobiocin for detecting Shiga toxin-producing

controversial use. Letters in Applied Microbiology. 2007;**44**:326-331. DOI: 10.1111/j.1472-765X.2006.02059.x

[95] Blais BW, Tapp K, Dixon M, Carrillo CD. Genomically informed strain-specific recovery of Shiga toxin-producing *Escherichia coli* during foodborne illness outbreak investigations. Journal of Food Protection. 2019;**82**:39-44. DOI: 10.4315/0362-028X.JFP-18-340

[96] Knowles M, Lambert D, Huszczynski G, Gauthier M, Blais BW. PCR for the specific selection of an *Escherichia coli* O157:H7 laboratory control strain. Journal of Food

10.1128/AAC.01030-16

AEM.02873-15

JFP-11-395

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

[84] Rowe W, Baker KS, Verner-Jeffreys D, Baker-Austin C, Ryan JJ, Maskell D, et al. Search engine for antimicrobial resistance: A cloud compatible pipeline and web interface for rapidly detecting antimicrobial resistance genes directly from sequence data. PLoS One.

2015;**10**:e0133492. DOI: 10.1371/journal.

[86] Knowles M, Stinson S, Lambert D, Carrillo C, Koziol A, Gauthier M, et al. Genomic tools for customized recovery and detection of foodborne Shiga toxigenic *Escherichia coli*. Journal of Food Protection. 2016;**79**: 2066-2077. DOI: 10.4315/0362-028X.

[87] Liu B, Pop M. ARDB-antibiotic resistance genes database. Nucleic Acids Research. 2009;**37**:D443-D447. DOI:

[88] Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrobial Agents and Chemotherapy. 2014;**58**:212-220. DOI:

[89] Tyson GH, McDermott PF, Li C, Chen Y, Tadesse DA, Mukherjee S, et al. WGS accurately predicts antimicrobial resistance in *Escherichia coli*. The Journal

of Antimicrobial Chemotherapy. 2015;**70**:2763-2769. DOI: 10.1093/jac/

[90] McDermott PF, Tyson GH, Kabera C, Chen Y, Li C, Folster JP, et al. Wholegenome sequencing for detecting

[85] McArthur AG, Waglechner N, Nizam F, Yan A, Azad MA, Baylay AJ, et al. The comprehensive antibiotic resistance database. Antimicrobial Agents and Chemotherapy. 2013;**57**:3348-3357. DOI: 10.1128/

pone.0133492

AAC.00419-13

JFP-16-220

10.1093/nar/gkn656

10.1128/AAC.01310-13

*Applications of Genomics in Regulatory Food Safety Testing in Canada DOI: http://dx.doi.org/10.5772/intechopen.86063*

[84] Rowe W, Baker KS, Verner-Jeffreys D, Baker-Austin C, Ryan JJ, Maskell D, et al. Search engine for antimicrobial resistance: A cloud compatible pipeline and web interface for rapidly detecting antimicrobial resistance genes directly from sequence data. PLoS One. 2015;**10**:e0133492. DOI: 10.1371/journal. pone.0133492

*New Insight into* Brucella *Infection and Foodborne Diseases*

[77] Farber JM, Peterkin PI. *Listeria monocytogenes*, a food-borne

pathogen. Microbiological Reviews.

[78] de Boer RF, Ferdous M, Ott A, Scheper HR, Wisselink GJ, Heck ME, et al. Assessing the public health risk of Shiga toxin-producing *Escherichia coli* by use of a rapid diagnostic

screening algorithm. Journal of Clinical Microbiology. 2015;**53**:1588-1598. DOI:

[80] Center for Genomic Epidemiology. n.d. Available from: http://www. genomicepidemiology.org/ [Accessed:

[81] Dutil L, Irwin R, Finley R, Ng LK, Avery B, Boerlin P, et al. Ceftiofur resistance in *Salmonella enterica* serovar Heidelberg from chicken meat and humans, Canada. Emerging Infectious Diseases. 2010;**16**:48-54. DOI: 10.3201/

[82] Tang KL, Caffrey NP, Nóbrega DB, Cork SC, Ronksley PE, Barkema HW, et al. Restricting the use of antibiotics in food-producing animals and its associations with antibiotic resistance in food-producing animals and human beings: A systematic review and meta-analysis. Lancet Planetary Health. 2017;**1**:e316-e327. DOI: 10.1016/

[83] Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, et al. Identification of acquired antimicrobial resistance genes. The Journal of Antimicrobial Chemotherapy. 2012;**67**:2640-2644. DOI: 10.1093/jac/

[79] Ashton PM, Perry N, Ellis R, Petrovska L, Wain J, Grant KA, et al. Insight into Shiga toxin genes encoded by *Escherichia coli* O157 from whole genome sequencing. PeerJ. 2015;**3**:e739.

1991;**55**:476-511

10.1128/JCM.03590-14

DOI: 10.7717/peerj.739

February 20, 2019]

eid1601.090729

S2542-5196(17)30141-9

dks261

in Germany, 2011-2014. Scientific Reports. 2017;**7**:5139. DOI: 10.1038/

[71] Pires SM, Vieira AR, Hald T, Cole D. Source attribution of human salmonellosis: An overview of methods and estimates. Foodborne Pathogens and Disease. 2014;**11**:667-676. DOI:

[72] Yachison CA, Yoshida C, Robertson

Taboada EN, et al. The validation and implications of using whole genome sequencing as a replacement for traditional serotyping for a National *Salmonella* Reference Laboratory. Frontiers in Microbiology. 2017;**8**:1044.

s41598-017-05227-x

10.1089/fpd.2014.1744

J, Nash JHE, Kruczkiewicz P,

DOI: 10.3389/fmicb.2017.01044

10.1128/JCM.01532-14

10.1093/infdis/jiv297

[73] Mellor GE, Fegan N, Gobius KS, Smith HV, Jennison AV, D'Astek BA, et al. Geographically distinct *Escherichia coli* O157 isolates differ by lineage, Shiga toxin genotype, and total Shiga toxin production. Journal of Clinical Microbiology. 2015;**53**:579-586. DOI:

[74] Hoffmann M, Luo Y, Monday SR, Gonzalez-Escalona N, Ottesen AR, Muruvanda T, et al. Tracing origins of the *Salmonella* Bareilly Strain causing a food-borne outbreak in the United States. The Journal of Infectious Diseases. 2016;**213**:502-508. DOI:

[75] Thépault A, Méric G, Rivoal K, Pascoe B, Mageiros L, Touzain F, et al. Genome-wide identification of hostsegregating epidemiological markers for source attribution in *Campylobacter jejuni*. Applied and Environmental Microbiology. 2017;**83**. DOI: 10.1128/AEM.03085-16

[76] Cocolin L, Membré J-M, Zwietering MH. Editorial: Integration of omics into MRA. International Journal of Food Microbiology. 2018;**287**:1-2. DOI: 10.1016/j.ijfoodmicro.2017.11.006

**140**

[85] McArthur AG, Waglechner N, Nizam F, Yan A, Azad MA, Baylay AJ, et al. The comprehensive antibiotic resistance database. Antimicrobial Agents and Chemotherapy. 2013;**57**:3348-3357. DOI: 10.1128/ AAC.00419-13

[86] Knowles M, Stinson S, Lambert D, Carrillo C, Koziol A, Gauthier M, et al. Genomic tools for customized recovery and detection of foodborne Shiga toxigenic *Escherichia coli*. Journal of Food Protection. 2016;**79**: 2066-2077. DOI: 10.4315/0362-028X. JFP-16-220

[87] Liu B, Pop M. ARDB-antibiotic resistance genes database. Nucleic Acids Research. 2009;**37**:D443-D447. DOI: 10.1093/nar/gkn656

[88] Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrobial Agents and Chemotherapy. 2014;**58**:212-220. DOI: 10.1128/AAC.01310-13

[89] Tyson GH, McDermott PF, Li C, Chen Y, Tadesse DA, Mukherjee S, et al. WGS accurately predicts antimicrobial resistance in *Escherichia coli*. The Journal of Antimicrobial Chemotherapy. 2015;**70**:2763-2769. DOI: 10.1093/jac/ dkv186

[90] McDermott PF, Tyson GH, Kabera C, Chen Y, Li C, Folster JP, et al. Wholegenome sequencing for detecting

antimicrobial resistance in nontyphoidal *Salmonella*. Antimicrobial Agents and Chemotherapy. 2016;**60**:5515-5520. DOI: 10.1128/AAC.01030-16

[91] Zhao S, Tyson GH, Chen Y, Li C, Mukherjee S, Young S, et al. Whole genome sequencing analysis accurately predicts antimicrobial resistance phenotypes in *Campylobacter*. Applied and Environmental Microbiology. 2015;**82**:459-466. DOI:10.1128/ AEM.02873-15

[92] Gill A, Huszczynski G, Gauthier M, Blais B. Evaluation of eight agar media for the isolation of Shiga toxinproducing *Escherichia coli*. Journal of Microbiological Methods. 2014;**96**:6-11. DOI: 10.1016/j.mimet.2013.10.022

[93] Gill A, Martinez-Perez A, McIlwham S, Blais B. Development of a method for the detection of Verotoxinproducing *Escherichia coli* in food. Journal of Food Protection. 2012;**75**: 827-837. DOI: 10.4315/0362-028X. JFP-11-395

[94] Vimont A, Delignette-Muller M-L, Vernozy-Rozand C. Supplementation of enrichment broths by novobiocin for detecting Shiga toxin-producing *Escherichia coli* from food: A controversial use. Letters in Applied Microbiology. 2007;**44**:326-331. DOI: 10.1111/j.1472-765X.2006.02059.x

[95] Blais BW, Tapp K, Dixon M, Carrillo CD. Genomically informed strain-specific recovery of Shiga toxin-producing *Escherichia coli* during foodborne illness outbreak investigations. Journal of Food Protection. 2019;**82**:39-44. DOI: 10.4315/0362-028X.JFP-18-340

[96] Knowles M, Lambert D, Huszczynski G, Gauthier M, Blais BW. PCR for the specific selection of an *Escherichia coli* O157:H7 laboratory control strain. Journal of Food

Protection. 2015;**78**:1738-1744. DOI: 10.4315/0362-028X.JFP-15-147

[97] Margot H, Tasara T, Zwietering MH, Joosten H, Stephan R. Effects of different media on the enrichment of low numbers of Shiga toxin-producing *Escherichia coli* in mung bean sprouts and on the development of the sprout microbiome. International Journal of Food Microbiology. 2016;**232**:26-34. DOI: 10.1016/j.ijfoodmicro.2016.05.005

[98] Jarvis KG, Daquigan N, White JR, Morin PM, Howard LM, Manetas JE, et al. Microbiomes associated with foods from plant and animal sources. Frontiers in Microbiology. 2018;**9**:2540. DOI: 10.3389/fmicb.2018.02540

[99] Ottesen A, Ramachandran P, Reed E, White JR, Hasan N, Subramanian P, et al. Enrichment dynamics of *Listeria monocytogenes* and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak. BMC Microbiology. 2016;**16**:275. DOI: 10.1186/s12866-016-0894-1

[100] Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods. 2010;**7**:335-336. DOI: 10.1038/ nmeth.f.303

[101] Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-source, platform-independent, communitysupported software for describing and comparing microbial communities. Applied and Environmental Microbiology. 2009;**75**:7537-7541. DOI: 10.1128/AEM.01541-09

[102] Morschhäuser J, Köhler G, Ziebuhr W, Blum-Oehler G, Dobrindt U, Hacker J. Evolution of microbial pathogens. Philosophical Transactions of the Royal Society of London. Series B, Biological

Sciences. 2000;**355**:695-704. DOI: 10.1098/rstb.2000.0609

[103] Iraola G, Pérez R, Naya H, Paolicchi F, Pastor E, Valenzuela S, et al. Genomic evidence for the emergence and evolution of pathogenicity and niche preferences in the genus *Campylobacter*. Genome Biology and Evolution. 2014;**6**:2392-2405. DOI: 10.1093/gbe/evu195

[104] Wirth T, Falush D, Lan R, Colles F, Mensa P, Wieler LH, et al. Sex and virulence in *Escherichia coli*: An evolutionary perspective. Molecular Microbiology. 2006;**60**:1136-1151. DOI: 10.1111/j.1365-2958.2006.05172.x

[105] Lacher DW. The evolutionary model of *Escherichia coli* O157:H7. In: Walk S, Feng P, editors. Population Genetics of Bacteria. Washington, DC: ASM Press; 2011:227-239. DOI: 10.1128/9781555817114.ch13

[106] Kelly BG, Vespermann A, Bolton DJ. Horizontal gene transfer of virulence determinants in selected bacterial foodborne pathogens. Food and Chemical Toxicology: An International Journal Published for the British Industrial Biological Research Association. 2009;**47**:969-977. DOI: 10.1016/j.fct.2008.02.007

[107] Gilmour MW, Tabor H, Wang G, Clark CG, Tracz DM, Olson AB, et al. Isolation and genetic characterization of a coinfection of non-O157 Shiga toxinproducing *Escherichia coli*. Journal of Clinical Microbiology. 2007;**45**: 3771-3773. DOI: 10.1128/JCM.01125-07

[108] Oniciuc EA, Likotrafiti E, Alvarez-Molina A, Prieto M, Santos JA, Alvarez-Ordóñez A. The present and future of whole genome sequencing (WGS) and whole metagenome sequencing (WMS) for surveillance of antimicrobial resistant microorganisms and antimicrobial resistance genes across the food chain. Genes. 2018;**9**:268. DOI: 10.3390/genes9050268

**143**

*Applications of Genomics in Regulatory Food Safety Testing in Canada*

*DOI: http://dx.doi.org/10.5772/intechopen.86063*

[109] Auffret MD, Dewhurst RJ, Duthie C-A, Rooke JA, John Wallace R, Freeman TC, et al. The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle.

Microbiome. 2017;**5**:159. DOI: 10.1186/

s40168-017-0378-z

*Applications of Genomics in Regulatory Food Safety Testing in Canada DOI: http://dx.doi.org/10.5772/intechopen.86063*

[109] Auffret MD, Dewhurst RJ, Duthie C-A, Rooke JA, John Wallace R, Freeman TC, et al. The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle. Microbiome. 2017;**5**:159. DOI: 10.1186/ s40168-017-0378-z

*New Insight into* Brucella *Infection and Foodborne Diseases*

Sciences. 2000;**355**:695-704. DOI:

[103] Iraola G, Pérez R, Naya H, Paolicchi F, Pastor E, Valenzuela S, et al. Genomic evidence for the emergence and evolution of pathogenicity and niche preferences in the genus *Campylobacter*. Genome Biology and Evolution. 2014;**6**:2392-2405.

[104] Wirth T, Falush D, Lan R, Colles F, Mensa P, Wieler LH, et al. Sex and virulence in *Escherichia coli*: An evolutionary perspective. Molecular Microbiology. 2006;**60**:1136-1151. DOI: 10.1111/j.1365-2958.2006.05172.x

[105] Lacher DW. The evolutionary model of *Escherichia coli* O157:H7. In: Walk S, Feng P, editors. Population Genetics of Bacteria. Washington, DC: ASM Press; 2011:227-239. DOI:

10.1128/9781555817114.ch13

10.1016/j.fct.2008.02.007

10.3390/genes9050268

[107] Gilmour MW, Tabor H, Wang G, Clark CG, Tracz DM, Olson AB, et al. Isolation and genetic characterization of a coinfection of non-O157 Shiga toxinproducing *Escherichia coli*. Journal of Clinical Microbiology. 2007;**45**:

3771-3773. DOI: 10.1128/JCM.01125-07

[108] Oniciuc EA, Likotrafiti E, Alvarez-Molina A, Prieto M, Santos JA, Alvarez-Ordóñez A. The present and future of whole genome sequencing (WGS) and whole metagenome sequencing (WMS) for surveillance of antimicrobial resistant microorganisms and antimicrobial resistance genes across the food chain. Genes. 2018;**9**:268. DOI:

[106] Kelly BG, Vespermann A, Bolton DJ. Horizontal gene transfer of virulence determinants in selected bacterial foodborne pathogens. Food and Chemical Toxicology: An International Journal Published for the British Industrial Biological Research Association. 2009;**47**:969-977. DOI:

10.1098/rstb.2000.0609

DOI: 10.1093/gbe/evu195

Protection. 2015;**78**:1738-1744. DOI: 10.4315/0362-028X.JFP-15-147

[97] Margot H, Tasara T, Zwietering MH, Joosten H, Stephan R. Effects of different media on the enrichment of low numbers of Shiga toxin-producing *Escherichia coli* in mung bean sprouts and on the development of the sprout microbiome. International Journal of Food Microbiology. 2016;**232**:26-34. DOI: 10.1016/j.ijfoodmicro.2016.05.005

[98] Jarvis KG, Daquigan N, White JR, Morin PM, Howard LM, Manetas JE, et al. Microbiomes associated with foods from plant and animal sources. Frontiers in Microbiology. 2018;**9**:2540.

DOI: 10.3389/fmicb.2018.02540

[99] Ottesen A, Ramachandran P, Reed E, White JR, Hasan N, Subramanian P, et al. Enrichment dynamics of *Listeria monocytogenes* and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak. BMC Microbiology. 2016;**16**:275. DOI: 10.1186/s12866-016-0894-1

[100] Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods. 2010;**7**:335-336. DOI: 10.1038/

[101] Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-source, platform-independent, communitysupported software for describing and comparing microbial communities. Applied and Environmental

Microbiology. 2009;**75**:7537-7541. DOI:

[102] Morschhäuser J, Köhler G, Ziebuhr W, Blum-Oehler G, Dobrindt U, Hacker J. Evolution of microbial pathogens. Philosophical Transactions of the Royal Society of London. Series B, Biological

10.1128/AEM.01541-09

nmeth.f.303

**142**

## *Edited by Mitra Ranjbar, Marzieh Nojomi and Maria T. Mascellino*

Brucellosis is an important zoonotic disease. More than half a million new cases from 100 countries are reported annually to the World Health Organization (WHO). The majority of patients are living in developing countries. Brucellosis is a systemic infection with a broad clinical spectrum, ranging from an asymptomatic disease to a severe and fatal illness. Clinical and laboratory features vary widely. The main presentations are acute febrile illness, localized infection, and chronic infection. Laboratory tools for diagnosis of brucellosis include culture, serology, and polymerase chain reaction (PCR). The goal of brucellosis therapy is to control the illness and prevent complications, relapses, and sequelae. Important principles of brucellosis treatment include use of antibiotics with activity in the acidic intracellular environment, use of combination regimens, and prolonged duration of treatment. This book is the result of several months of outstanding efforts by the authors and the revision of the content by experts in the field of brucellosis. This book is a valid resource and is intended for everyone interested in infectious disease to learn the most important aspects of brucellosis.

Published in London, UK © 2020 IntechOpen © Dr\_Microbe / iStock

New Insight into Brucella Infection and Foodborne Diseases

New Insight into Brucella

Infection and Foodborne

Diseases

*Edited by Mitra Ranjbar, Marzieh Nojomi and Maria T. Mascellino*