novel methods of species and product authenticity and

Novel Methods of Species and Product Authenticity and Traceability Testing Using DNA Analysis for Food and Agricultural Applications

By

Amanda Madelaine Naaum

A Thesis

presented to

The University of Guelph

In partial fulfillment of requirements

for the degree of

Doctor of Philosophy in Integrative Biology

Guelph, Ontario, Canada

© Amanda Naaum, April, 2014

ABSTRACT

NOVEL METHODS OF SPECIES AND PRODUCT AUTHENTICITY AND TRACEABILITY TESTING USING DNA ANALYSIS FOR FOOD AND

AGRICULTURAL APPLICATIONS

Amanda Madelaine Naaum Advisor:

University of Guelph, 2014 Robert Hanner

Categorization and identification of biodiversity is key to global food security and

integrity. Accurate species identification is relevant to many sectors involved in food

production and processing, including for pest identification and management and for

food authenticity testing. The usual methods of visual identification of species are often

not applicable to food products as morphological characters are often removed during

processing. However, methods using DNA analysis enable identifications from

immature life stages, or fragmentary remains, and are therefore may be of particular

use in the food and agriculture industries. Food fraud is a serious socioeconomic issue

that continues to grow due to increasing global trade, and technological innovation for

detection as well as perpetrating fraud. Incorrect species labeling of food products is

one example of food adulteration that has broad impacts including economic, health,

conservation and religious consequences. Molecular techniques, like DNA barcoding,

have become part of routine methods for species identification and their implementation

in the regulation of market products has begun. Although DNA barcoding provides

several advantages over other methods, the ~650 base pair target required for analysis

is often too long for recovery from degraded samples, for example in cooked or canned

products. In these cases real-time PCR provides an alternative. Design of real-time

PCR assays requires particular attention to the haplotype coverage and sequence

quality. DNA barcode sequences from the Barcode of Life Data System (BOLD), an

online DNA barcode sequence library, are a source of quality sequences available for a

wide range of species, particularly those of commercial importance. Therefore, BOLD is

a useful resource in the design of successful real-time PCR assays for identification of

species in food products as well as other species of interest to the food industry, such

as agricultural pests. When endogenous DNA is not available, or provides limited

information, synthetic oligonucleotide “barcodes” can be employed for traceability and

authenticity testing. This thesis explores the linkages between species identification and

product authenticity in the agriculture and food industries and provides a proof of

concept for the use and detection of an oligonucleotide tag for traceability of apple juice.

iv

Acknowledgements Funding for student stipend was provided in part by a University of Guelph College of

Biological Sciences PhD Award. Financial support for oligonucleotide tagging research

was provided by the Advanced Food and Materials Network. I would like to thank Let’s

Talk Science and all the participating teachers and students for their collaboration on

the seafood market survey. The market survey was funded through a Mitacs Accelerate

internship. Aphid identification studies received funding from OMAFRA.

I am grateful to Teresa Crease, Nicholas Low and Rickey Yada for their helpful

comments and guidance. I would like to thank my advisor, Robert Hanner for the

opportunities provided over the course of my graduate studies. I am very appreciative of

the contributions from all my collaborators that made this research possible: Robert

Foottit, Eric Maw, Piotr Diakowski, Heinz Bernard Kraatz, Rosalee Rasmussen-

Hellberg, Haile Yancy, Jonathan Deeds, Sara Handy, Michael Morrissey and Nicholas

Low.

Finally I would like to thank my friends, and most of all my family, for their unconditional

support and encouragement.

v

Statement of Authorship and Contributions Chapters in this thesis represent co-authored work. The following is a statement of the

contributions of authors to each chapter. Citations are provided at the beginning of each

chapter for published work.

Chapter Two: Naaum and Hanner designed research. Hanner provided reagents and

equipment. Naaum performed research, analyzed data and wrote the paper.

Chapter Three: Rasmussen-Hellberg, Naaum and Hanner designed research, the

research reported in this thesis was performed by Naaum and analyzed by Rasmussen-

Hellberg and Naaum. Rasmussen-Hellberg wrote the original paper including this

research with assistance from other authors. A portion of the original paper has been

edited for inclusion in this thesis by Naaum, with the addition of results from further

research conducted by Naaum on the use of traditional PCR for salmonid identification.

Chapter Four: This chapter is a combination of two papers with the same co-authors,

with the addition of a direct visualization method for soybean aphid identification

developed by Naaum. In the case of both papers, Naaum, Foottit, and Hanner designed

research. Foottit and Maw provided morphological sample identification. Naaum

performed research and analyzed the data. Hanner provided reagents and equipment.

The papers were written by Naaum with assistance from Foottit, Maw and Hanner.

Chapter Five: Naaum, Hanner and Low designed the research, Naaum performed

research and analyzed data. Hanner provided reagents and equipment. Naaum wrote

the paper with assistance from Hanner and Low.

Chapter Six: Naaum, Diakowski, Bernard-Kraatz and Hanner designed the research.

Naaum and Diakowski performed the research. Bernard-Kraatz provided reagents and

equipment. Diakowski and Naaum analysed the data and wrote the paper.

vi

Table of Contents ABSTRACT ii Acknowledgements iv Statement of Authorship and Contributions v

List of Tables vii

List of Figures viii Chapter 1: General Introduction and Review of Relevant Literature 1 Species Identification and Food 1 Impact of Food Fraud 1 Methods of Species Identification 2 Methods for species identification using DNA 3 Standardization of gene region 5 Limitations of DNA analysis 7 Food Traceability 8 Summary 10

Section 1: The Use of Endogenous DNA for Species-Specific Identification for Authentication of Agricultural Pests or Food Products 29 Chapter 2: Seafood Market Survey Using DNA Barcoding 29 Chapter 3. Application of DNA Barcode Sequences for Identifying Salmonid Species of Commercial Interest 49 Chapter 4: Application of DNA Barcode Sequences for Differentiation of Pest Aphid Species Using Real-‐time and Conventional PCR 62

Section 2: The Use of Synthetic DNA for Food Product Traceability: Apple Juice Case Study 79 Introduction to Synthetic Tagging 79 Chapter 5: Real-‐time PCR Approach for Oligonucleotide Tag Detection 81 Chapter 6: Electrochemical Impedance Spectroscopy Approach 94 Synthetic Tagging Conclusions 108

References 115

vii

List of Tables Table 1.1 Studies employing PCR-RFLP for species authentication in food products

Table 1.2 Studies using species-specific PCR to authenticate species in food

products

Table 1.3 Studies using real-time PCR to authenticate species in food products

Table 1.4 DNA barcoding for species identification and authentication of food and

herbal medicines.

Table 2.1 Identification of market samples from this study based on DNA barcoding

Table 3.1 Samples analyzed in this study using real-time PCR. Market names and

cooking methods were collected from sample labels. Assigned species were those

detected using qPCR.

Table 4.1 Sequences of species-specific oligonucleotides used in this study

Table 4.2 Collection information for aphid specimens used in this study.

Table 5.1. List of oligonucleotides used in real-time PCR tag detection Table 5.2. Comparison of results of tag detection in apple juice from FTA and

Qiagen Blood and Tissue Kit extraction methods.

Table 5.3 Table 5.3. Tag detection after lab-simulated apple juice processing.

Table 5.4. Results of shelf-life testing.

Table 6.1 Values of the equivalent circuit elements for the DNA sensor electrode

estimated after different incubation steps.

13

17

20

26

35

57

72

73

86

87

88

89

101

viii

List of Figures Figure 1.1 Taqman probe chemistry.

Figure 3.1 Typical results after agarose gel electrophoresis of single-plex traditional

PCR for identification of salmonid species.

Figure 4.1 Linearity of multiplex assay for A. pomi and A. spiraecola

Figure 4.2 Standard curve for A. glycines

Figure 4.3 Direct visualization assay for identification of A. glycines

Figure 5.1 Oligonucleotide tag

Figure 5.2 Linearity curve for serial dilutions of tag recovered from apple juice using

Qiagen DNeasy Blood and Tissue Kit.

Figure 5.3 Linearity curve for serial dilutions of tag recovered from apple juice using

FTA extraction cards

Figure 5.4 Linearity curve for serial dilutions of tag recovered from apple juice

concentrate.

Figure 6.1 Overview of oligonucleotide tag recovery from beverages using

electrochemical impedance spectroscopy.

Figure 6.2 Typical Nyquist plots obtained for unmodified Au electrode, sensor

electrode modified with capture strand (strand 1), and sensor electrode after incubation

in buffer solution tagged with strand 2 for 2 hours.

Figure 6.3 Equivalent circuit model representing the apatamer sensor and used to

obtain theoretical impedance spectra.

Figure 6.4 Typical Nyquist plots obtained for sensor electrode modified with strand 1,

sensor electrode incubated for 2 hours in buffered apple juice, followed by 2 hours

sensor incubation in apple juice tagged with strand 2.

Figure 6.5 Typical Nyquist plots obtained for sensor electrode modified with strand 1,

sensor electrode incubated for 2 hours in buffer containing 3 mM Zn2+, followed by 2

hours sensor incubation apple juice solution tagged with strand 2

28

61

76

77

78

90

91

92

93

102

103

104

105

106

ix

Figure 6.6 Nyquist plots obtained for sensor electrode modified with strand 1, sensor

electrode incubated for 2 hours in Tris-buffer (pH 8.6) containing 50 mM tag, sensor

after dehybridization for 1 hour in Tris-buffer (pH 10.5), and subsequent incubation in

Tris-buffer (pH 8.6) containing 50 mM tag.

Figure 7.1. Haploytype accumulations curves representing random sampling of

individuals from the BOLD project “Salmonid Species North America

107

114

1

Chapter 1: General Introduction and Review of Relevant Literature Species Identification and Food

Categorization of biodiversity into species has many practical implications for food

production and authentication. The scientific names used by taxonomists to

characterize species are related to common names used by society. In food production,

management practices rely on accurate identification of pests due to differences in, for

example, life cyles or susceptibility to pesticides. In food authenticity, modern society

relies on grocery store labels for identification of products rather than hunting or growing

their own food. Many common names, and what species they are used for, may differ

based on geographical location or one common name may apply to several different

described species. Therefore, the authenticity of food products, or the validity of the

market name given on product packaging, is often tied directly to the species contained.

Food regulations are often based not on market names, but on scientific names. Certain

species may be more expensive and regulations are often in place as to what species

can be marketed under a given common name. For example the Canadian Food

Inspection Agency (CFIA) Fish List comprises a list of common market names and the

corresponding species that can be marketed legally under each name. In this way

species identification is integrally linked with food production and food authenticity.

Impact of Food Fraud

Food fraud has been defined as a collective term for the “deliberate and intentional

substitution, addition, tampering, or misrepresentation of food, food ingredients, or

food packaging; or false or misleading statements made about a product, for economic

gain.” (Spink and Moyer 2011). These practices have been documented throughout

history (Accum 1820) and the scope of the problem continues to grow as the

globalization of trade increases. Between 2003 and 2005 there was a 250% increase in

seizures of counterfeit food and drink by customs in Europe (OECD 2008). The

adulteration of food products is a serious economic issue and is estimated to cost

businesses between $10 and 15 billion each year (GMA 2010). High profit margins, low

risk of fraud detection and lenient penalties fuel these unethical practices. One

investigator likened olive oil adulteration to the drug trade, with similar profits to cocaine

2

trafficking, but with none of the risks (Mueller 2009). Substituting even small

percentages of products can be very lucrative, fueling continued instances of fraud.

Governments and key stakeholders in the food industry continue to develop regulations

for traceability and authenticity testing, yet these regulations are difficult to enforce

without appropriate analytical methodology and the willingness of stakeholders to

conduct product testing.

The exact terminology for economically motivated adulteration, one aspect of food

fraud, is currently being debated by regulatory bodies. For the purposes of this thesis

the term “food adulteration” is used and includes intentional mislabeling of ingredients,

species, point of origin, or processing methods as well as debasing with a cheaper

material. One common example is species mislabeling. Though economically

motivated, incorrect identification of species present in a product can also impact

lifestyle and consumer safety. For example, sheep and goat milk products are more

expensive than those made with cow’s milk. Adulteration of sheep or goat milk products

with cow’s milk may lead to an allergic reaction in consumers due to allergenic reactions

to the proteins found in cow’s milk (El-Agamy 2007). There may also be cultural or

religious reasons to avoid certain products. For example, Halal products have been

found to contain pork (Nakyinsige et al. 2012). Finally, there can be impacts to

conservation when food is sold illegally. For example, without a means to confirm

identities of fish species, there is no way to validate catches. In this way, endangered or

at risk species may be marketed as an environmentally friendly alternative.

Methods of Species Identification Normally, diagnostic morphological characters are used to identify a species. However,

most of the distinguishing characteristics used to identify species are removed in

processed food products. Since traditional taxonomy is of little use in these cases, other

methods are generally required to authenticate species. Protein-based methods were

once the primary method for food species authentication, however more recently, issues

such as heat denaturation of proteins during processing and differential expression

dependant on tissue type have spurred a move towards DNA-based techniques

(Lockley and Bardsley 2000). These issues are particularly problematic with food

3

samples, which may be heavily processed. Though protein-based analyses continue to

be used in some cases, recent tests focus on the use of DNA for species identification

in food products as it is a more stable molecule.

Methods of species identification using DNA have been extensively reviewed recently

(e.g. Sforza 2013). The techniques most commonly used for species identification in

food products are: restriction fragment length polymorphism (RFLP), species-specific

Polymerase Chain Reaction (PCR), real-time PCR and DNA sequencing. The following

is a brief summary of the major techniques for DNA authentication to determine species

content in food products and their advantages and disadvantages.

Methods for species identification using DNA

Restriction fragment length polymorphisms (RFLP)

RFLP has been the most commonly implemented method for species discrimination in

food. Table 1.1 outlines published uses of PCR-RFLP for varied foods. RFLP usually

starts with PCR amplification of a target region that will allow species discrimination.

This is followed by digestion of the amplicon with restriction enzymes. These enzymes

cut DNA at a specific sequence of nucleotides, generating fragments of DNA with

different sizes from the amplified target region. Analysis via gel electrophoresis

separates these segments on the basis of size, creating a restriction profile. These

profiles differ depending on the species, due to differences in DNA sequence, and can

be compared to a database of known profiles or to a reference standard to identify the

species present. Two major benefits of this method are that the equipment required is

easy to access and use, and that experimental design requires minimal upfront work as

previous knowledge of underlying nucleotide sequences is not always necessary if

universal primers are used for PCR. However, the process of sample preparation and

analysis is lengthy and there are several post-PCR processing steps that increase the

likelihood of error due to failure of a specific step, or sample mix up, or of contamination.

Also, issues with reproducibility call the accuracy of the method into question (Lockley

and Bardsley 2000).

4

Species-specific PCR

Species-specific PCR is another method of species authentication applied to food

products (Table 1.2). In this approach, species-specific primers are designed for a

target DNA sequence that will allow species discrimination. Various sized fragments are

generated with each species-specific primer set and used for species discrimination

after separation on an agarose gel. Compared with RFLP, prior DNA sequence

information is required, but the equipment remains easily accessible and the time for

sample analysis is reduced. Success of this type of testing is also subject to assay

design.

Real-time PCR

Real-time PCR (qPCR) has been introduced more recently as a tool for food

authentication. This method takes species-specific PCR a step further, allowing

quantification of a target. Since measurements are taken during the exponential portion

of PCR, fluorescence emission is directly proportional to the DNA copy number. Various

fluorescent reporter chemistries are available to achieve this. This is in contrast to

conventional PCR, which measures the presence of the target at the end point of the

reaction, after reagents have been depleted and amplification is no longer exponential.

Real-time PCR therefore allows quantification of the level of adulteration in addition to

species identification. Using species-specific primers and/or probes it is possible to use

this method to authenticate species content in food products, as outlined in Table 1.3.

Taqman probes (Figure 1.1) are the most commonly used probe-based chemistry. This

method is based on the use of a probe with a quencher dye on one end and a reporter

dye at the other. When in close proximity, the fluorescent signal from the reporter dye is

masked by the quencher dye. During PCR amplification the probe is cleaved, releasing

the reporter dye and emitting fluorescence which is measured by the instrument.

Different reporter dyes, emitting fluorescence at different wavelengths, can be used.

Some instruments have multiple channels of detection that each monitor a specific

range of wavelengths. By combining probes modified with dyes of different emission

spectra, simultaneous detection of multiple targets in a single reaction can be achieved.

Real-time PCR platforms, though generally more costly than traditional PCR thermal

5

cyclers, can also be portable, allowing on-site analysis. In addition, interpretation of

results is simple and analysis is very rapid in comparison to other methods. Finally,

since there is no post-PCR processing, the possibility of contamination from previous

PCR products is greatly reduced.

DNA Sequencing

DNA sequencing has also been used for species authentication in food. In this

approach, a small region of DNA is amplified using PCR, and then a sequence of the

amplicon is obtained using Sanger sequencing. The sequence is then compared to a

database of sequences to determine the species. Rather than simply authenticating a

sample from a small group of anticipated possibilities, DNA sequencing can identify the

species against all known sequences in the database being used. DNA sequencing has

been applied to identify species in food products directly; however, this method is not

generally suitable for all food products as the ability to detect species in mixtures is

limited. Additionally, the region used may be too long to recover from highly processed

samples (Sforza 2013), unless a smaller subset is targeted (e.g. Hajibabaei et al. 2006).

Despite these limitations, DNA sequencing is of key importance in the design and

success of the aforementioned methods of species identification, particularly the quality

of the reference database used for comparison, or for assay design.

Standardization of gene region

Currently, a handful of different regions have been targeted for DNA-based species

identification in animals and plants. Largely these are mitochondrial or chloroplast

markers, selected for their presence in high copy number, and more rapid rate of

molecular evolution, compared to nuclear DNA. This can be crucial for the analysis of

processed or otherwise degraded samples where only trace amounts of DNA may be

present. The DNA region selected to discriminate species directly affects the success of

the methods discussed above. It must be variable enough to discriminate between

species, yet exhibit low variability within the same species. Standardization of the region

for species identification, coupled with broad taxonomic surveys, would aid in the

development of diagnostic testing for food authentication by allowing tests to be more

broadly applicable across a wide range of taxa. In cases where molecular species

6

identification is needed, a compelling argument can be made for using DNA barcoding,

a species identification method based on sequencing of a short standardized gene

region, as the standard for food analysis.

The DNA barcode region for animals is 658 bp of the mitochondrial cytochrome c

oxidase subunit one (COI) gene (Hebert et al. 2003). For plants, a combination of the

chloroplast maturase K gene (matK) and ribulose-1,5-bisphosphate

carboxylase/oxygenase large subunit rbcL genes are used (CBOL Plant Working Group

2009) and Internal Transcribed Spacer (ITS) has been identified as a standard marker

for fungal identification (Schoch et al. 2012). These regions, like others selected for

species identification, generally exhibit low levels of variability within a species, and

higher levels of variability between species.

DNA barcode sequences available on the Barcode of Life Data System (BOLD;

www.barcodinglife.org, Ratnasingham and Hebert 2007) are accompanied by data that

can be crucial to successful design of assays for species identification. Errors in

common nucleotide databases, like GenBank (Harris 2003) can result in incorrect

identification when using sequencing, and also lead to false positives and false

negatives if used in the for development of other identification assays. The BOLD

database is a useful source of high-quality sequences and corresponding metadata for

the development of DNA-based assays for species differentiation in food products.

Many of these sequences are generated from specimens identified by expert

taxonomists, reducing the likelihood or erroneous identifications. A voucher held in a

collection also makes it possible to confirm identifications at a later date. Additional

information such as geographic location from which the sample was collected, is also

included with each entry. This can help provide both an adequate number of sequences

in general for assay development, and also the option to include individuals from varied

geographic range when selecting sequences from which to develop an assay.

For sequences to be considered a true “BARCODE” sequence, BOLD requires raw

trace files to be submitted. When comparing sequences for species identification using

genetic distance, one or two errors in a DNA sequence may not make a difference in

accurate identification. However, when developing species-specific primers or probes, a

7

single nucleotide difference can affect results, causing false positives or false negatives.

The ability to view trace files avoids inclusion of artificial haplotypes generated from

sequencing error. Standardization of the gene region used allows a wider range of taxa,

and a larger number of target individuals from a wider geographic range to be included

in assay design without upfront sequencing costs associated with obtaining specimens

and generating new sequences each time an assay is developed.

DNA barcoding has been applied across a wide range of taxa and results are publicly

accessible in BOLD. Many of the most commercially important species have already

been targeted, and the continued growth of the database of DNA barcode sequences

will increase the method’s robustness to the identification of adulterated foods. In fact,

the use of DNA barcoding for food authenticity has been recently reviewed (Galimberti

et al. 2013), and the method has been adopted as the official regulatory test for seafood

in the United States (Handy et al. 2011). Additionally, this method can be used to

authenticate herbal products (Newmaster et al. 2013), an area where product

authenticity is often related to the species of plant present. Table 1.4 outlines examples

where DNA barcoding has been used to determine product authenticity.

DNA barcoding has also been applied to the identification of agricultural pests, as

reviewed by Frewin et al. (2013). Pest identification can be very difficult to accomplish

based on morphology, yet accurate classification is critical information for quarantine

purposes and crop management. Therefore, assays designed from DNA barcode

sequences can be used for the identification of species relevant to the complete food

supply chain from crop management to retail products.

Limitations of DNA analysis

Although outside the scope of this review, other DNA-based methods for species

authentication that have been used to a lesser extent include biosensors and

microarrays in addition to next generation sequencing techniques, which may begin to

be applied more commonly to issues relating to food authenticity. DNA can be used for

more than authenticating species as some instances of food adulteration relate to the

geographic origin of the product. In these instances methods such as Amplified

Fragment Length Polymorphism (AFLP) and microsatellites, mainly used to differentiate

8

populations, may be used. Several DNA-based methods, including those discussed

above, are commonly used for the identification of breeds/varieties/cultivars and

Genetically Modified Organisms (GMOs). These are not questions of authenticity at the

species-level, but still benefit from DNA analysis for authentication. These methods

often require a large amount of upfront work to locate and select regions of DNA that

will allow differentiation between populations or breeds/varieties/cultivars.

There are other instances of adulteration that cannot be detected by DNA analysis.

Authentication of organic products, for example cannot be carried out using DNA. Other

analytical methods have been suggested for a small number of these applications,

however they are often unable to clearly identify these attributes (Sforza 2013).

Currently, the best approach for establishing authenticity of these types of products is

rigorous supply chain traceability.

Food Traceability

Regulation EC/178/2002 of the EU General Food Law defines traceability as the ability

to trace and follow food, feed, and ingredients through all stages of production,

processing and distribution. In light of consumer expectations for safe, quality products,

traceability is a major concern for food companies (Hobbs 2006). The ability to track a

product from harvest through transport, storage, processing, distribution and sale is

essential to trace problematic batches to their origins. By swiftly identifying and

removing problematic batches, companies can minimize their impact on consumer

safety as well as minimize costs related to recalls (Golan et al. 2003). Effective

traceability measures also allow companies to identify credence attributes, such as

environmentally friendly production, to the public (Golan et al. 2003, Smith and Furness

2006).

Some governments have implemented traceability regulations to address food safety

concerns. These may pertain to a specific sector (e.g. Japan mandates traceability of

domestic beef) or the regulation of food traceability more generally. For example, the

European Union (Article 18 of the General Food Law), United States (Title III, subtitle A,

section 301 of the Bioterrorism Act) and Canada (Federal-Provincial-Territorial

Framework Agreement on Agricultural and Agri-food Policy for the Twenty-first Century)

9

all have legislation in place regarding food traceability. Guidelines exist to assist in

developing traceability systems. The International Article Number (EAN) has developed

the following documents: Traceability of Fish – Application of EAN.UCC Standards,

Traceability of Beef Guidelines, Fresh Produce Traceability Guidelines, Traceability in

the Supply Chain and Traceability Implementation. The Codex Alimentarius, established

by the World Health Organization and Food and Agricultural Organization of the United

Nations, also has international guidelines for the development of traceability systems.

Currently, these guidelines are provided for voluntary implementation of traceability

within a supply chain. However, there is increasing pressure on exporters to meet

traceability requirements of the receiving country, and as such there is a growing push

towards global standards for food traceability.

The most widely used coding system for product traceability is the International

Numbering Association Universal Code Council (EAN.UCC) system (Smith and Furness

2006). This system provides a sequence of numbers designated to identify a variety of

items, locations and services and includes the familiar Global Trade Identification

Number (GTIN) used to identify trade items. A combination of 8, 12, 13 or 14 numbers

is used for product identification, including a company prefix number, an item reference

number and a check digit (Smith and Furness 2006). This code can be readily

incorporated into different types of data carrier technologies.

Current data carrier technologies include 1D and 2D data barcodes, as well as

radiofrequency identification (RFID) tags (Smith and Furness 2006), as well as external

labeling of information such as product origin on packaging. Barcodes are the most cost

efficient form of storing traceability coding, however they are limited because they

require line of sight for identification (Smith and Furness 2006). RFID tags can be

applied inside any packaging through which radio waves can penetrate. This provides

advantages over barcode tags, but RFID chips are not cost-effective in comparison,

especially for use on low-cost items (Smith and Furness 2006). Additionally, RFID

frequencies are not harmonized worldwide, so tags are not universally readable (Smith

and Furness 2006). The main application of RFID tags is for tracing livestock, like cattle

10

(Smith and Furness 2006). Laser labeling directly onto fruit products has been a recent

technological advancement in food labeling (http://www.laserfood.es).

The above data carrier technologies focus on labeling packages rather than the

products themselves. The main issue with this type of external tagging is the ease with

which a label, or even RFID tag, can be tampered with. This leaves open the

opportunity for fraud. Additionally, it is not possible to track separate ingredients in

mixed products using these methods, as only the final product is tagged. Traceability

could be improved with the use of internal tags used directly in food products.

Summary

For consumers, adulteration can cause economic losses, but also can affect lifestyle

religious choices, and even safety. For cases where adulteration is based on species

substitution, DNA analysis can often provide a means to authenticate samples. The

adoption of standard gene regions from which to design these tests will improve their

development and success at accurately discerning market fraud. The DNA barcode

regions for plants and animals are an ideal choice due to the existence of BOLD, which

provides crucial information including photographs, geographical location and raw trace

files that assist in assay development. Many sequences also have voucher specimens

that can be used for confirmation of identification. Additionally, the range of species

represented in BOLD continues to grow. Notably, the DNA barcode region is ideal for

development of tests to identify species at all stages of the food production and

distribution chain – from agricultural pests to retail stores and restaurants.

When species-level identification is not the concern, other methods that focus on

population differences may be adopted to provide finer identification below the species

level using DNA. In other cases DNA analysis is not helpful in determining authenticity.

These cases include very highly processed foods where endogenous DNA is too

degraded to detect, or cases like determining the authenticity of organic foods, where it

is not applicable. In these situations, proper traceability is the best defense against

adulteration.

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Current traceability systems still allow opportunities for fraud, mainly due to the use of

external labels which track and authenticate only the package, not the product itself.

Internal traceability tags may improve traceability systems, especially in foods where a

high level of fraud is suspected.

The following chapters will explore DNA-based species identification and product

authenticity methods in the agriculture and food industries. Section one contains results

from studies in which DNA barcoding, and tests derived from DNA barcode sequences,

are used for species identification of agricultural pests and retail products. Identification

of seafood products using DNA barcoding confirms the utility of DNA barcoding in this

application, as well as revealing a continued presence of mislabeling in the North

American market. To explore more cost-effective, portable and rapid methods than

Sanger sequencing, real-time PCR assays designed from DNA barcode sequences for

market sample identification, existing primers and probes were evaluated for salmonid

identification. A more rapid traditional PCR protocol is also proposed. Finally, two real-

time PCR assays targeting pest aphids are developed, exhibiting the potential for field

applications using DNA barcode sequences. This method of identification could lead to

more rapid and accurate implementation of control measures for agricultural pests that

threaten perishable commodities. While section one showcases the applicability of

species identification to food production and authenticity testing, section two

demonstrates a method for traceability and authenticity testing where endogenous DNA

is not available. A proof of concept is presented for the use of an oligonucleotide tags

(ONTs), comprised short synthetic single-stranded DNA fragment, as an internal

traceability tag for food products, including two different methods for tag detection.

This research represents a significant contribution to new knowledge concerning global

food security and integrity as it relates to species identification. By exploring the link

between molecular species recognition and food security, the importance of continued

efforts to understand biodiversity is underscored. Continued research into the patterns

of genetic and geographic diversity within species is crucial not only to understanding

the processes of biological diversification, but also to applied questions of

socioeconomic importance. Moreover, novel use of an oligonucleotide traceability

12

system for food products represents an important evolution in how food products may

be traced and will be of interest to the food industry as a tool that, in combination with

authenticity testing, will help ensure a safe and unadulterated food supply.

13

Table 1.1 Studies employing PCR-RFLP for species authentication in food products.

Target(s) Genetic Marker References

Cattle, sheep, goat, domestic pig, horse, buffalo, chicken, turkey, red deer, moose, antelope, chamois, mouflon, wild boar, and kangaroo

Cytochrome b Meyer et al. 1995

Cattle, sheep, goat, domestic pig, red deer, and sika deer

Cytochrome b Matsunga et al. 1998b

Red deer, fallow deer, roe deer, bison, and hare

Cytochrome b Zimmermann et al. 1998

Cattle, sheep, goat, buffalo, red deer, fallow deer, moose, antelope, gazelle, wildebeest, chamois, Pyrenean ibex, kangaroo, and hare

Cytochrome b Wolf et al. 1999

Domestic pig and wild boar D-loop region Montiel-Sosa et al. 2000

Cattle, sheep, goat, domestic pig, buffalo, duck, chicken, turkey, rabbit, red deer, kangaroo, horse, emu, crocodile, barramundi, cat, dog, salmon, tuna, Nile perch, and John dory

Cytochrome b Partis et al. 2000

Ostrich Cytochrome b Abdulmawjood and Buelte 2002

Wild boar and domestic pig D-loop region Krkoska et al. 2003

Cattle, domestic pig, and goat

12S rDNA Sun and Lin 2003

Cattle, water buffalo, Cytochrome b and Verkaar et al. 2002

14

African buffalo, bison, banteng, gayal, yak, wisent, and zebu

Cytochrome oxidase II

Cattle, goat, sheep, domestic pig, horse, rabbit, ostrich, duck, chicken, turkey, partridge, red deer, roe deer, and wild deer

Cytochrome b Pascoal et al. 2004

Cattle, sheep, goat, red deer, and roe deer

Cytochrome b Pfeiffer et al. 2004

Domestic pig, cattle, sheep, and chicken

Cytochrome b Aida et al. 2005

Cattle, sheep, goat, and buffalo

12S rRNA Girish et al. 2005

Horse, donkey, and their hybrids

Protamine P1 and Cytochrome b

Zhao et al. 2005

Cattle, sheep, goat, red deer, fallow deer, and roe deer

12S rRNA Fajardo et al. 2006

Bovines, porcines, equines, cervids, and birds

Cytochrome b Maede 2006

Buffalo, wildebeest, zebra, gazelle, impala, reedbuck, kongoni, oryx, warthog, and hippopotamus

D-loop region Malisa et al. 2006

Cattle, sheep, goat, chamois, Pyrenean ibex, and mouflon

12S rRNA and D-loop region

Fajardo et al. 2007a

Chicken, duck, turkey, guinea fowl, and quail

12S rRNA Girish et al. 2007

Cattle, domestic pig, horse, chicken, duck, turkey, and red deer

12S rRNA Park et al. 2007

15

Peacock, chicken, and turkey

12S rRNA Saini et al. 2007

Wild boar and domestic pig MC1R Fajardo et al. 2008a

Spotted deer, hog deer, barking deer, sika deer, musk deer, and sambar deer

12S rRNA Gupta et al. 2008

Domestic pig and chicken 12S rRNA Sharma et al. 2008

Chicken, turkey, Muscovy duck, goose, quail, pheasant, red-legged partridge, chukar partridge, guinea fowl, capercaillie, Eurasian woodcock, woodpigeon, and song thrush

12S rRNA and

D-loop region

Rojas et al. 2008, 2009a

Carrle, sheep, and domestic pig

ATP synthase subunit B Natonek-Wisniewska et al. 2009

Red brocket deer, pygmy brocket deer, and gray brocket deer

Cytochrome b Gonzalez et al. 2009

Buffalo, cattle, goat, domestic pig, rabbit chicken, and quail

Cytochrome b Murugaiah et al. 2009

Cattle, goat, sheep, domestic pig, red deer, roe deer, and fallow deer

12S rRNA Bielikova et al. 2010

Beef and pork Cytochrome b Chandrika et al. 2010

Cattle, yak, buffalo, goat, and domestic pig

12S rRNA Chen et al. 2010

Cattle, sheep, goat, domestic pig, horse, rabbit, duck, chicken, pheasant, falcon, wild boar, Chinese

Zinc nuclear finger 238 Kim et al. 2010a

16

water deer, roe deer, Formosan deer, kangaroo, wolf, dog, raccoon, vulture, elephant, and hippopotamus

Chicken, turkey, duck, goose, pheasant, partridge, woodcock, ostrich, quail, and song thrush

Cytochrome b and

12S rRNA

Stamoulis et al. 2010

Cattle, sheep, goat, domestic pig, horse, chicken, turkey, buffalo, deer, dog, sturgeon, and salmon

12S rRNA Wang et al. 2010

Cattle, sheep, goat, and buffalo

12S rRNA Mahajan et al. 2011

Cattle, sheep, domestic pig, chicken, donkey, and horse

Cytochrome b Doosti et al. 2012

Cattle, sheep, domestic pig, chicken, turkey, buffalo, camel, and donkey

Cytochrome oxidase I, COI Haider et al. 2012

Domestic pig Cytochrome b Ali et al. 2011

Anchovies Cytochrome b Santaclara et al. 2006

Anglerfish Cytochrome oxidase I, COI Espineira et al. 2008

Salmonids Cytochrome b Espineira et al. 2009

Salmonids 5S rRNA Carrera et al. 1998,1999

Gadoids 12S and 16S rRNA DiFinizio et al. 2007

Mussels 18s rDNA Santaclara et al. 2006

Cephalopods Cytochrome b Sanataclara et al. 2007

17

Table 1.2. Studies using species-specific PCR to authenticate species in food products.

Target(s) Genetic Marker Reference

Cattle, sheep, goat, domestic pig, horse, and chicken

Cytochrome b Matsunaga et al. 1999

Ostrich and emu Cytochrome b Colombo et al. 2000

Cervid species (spotted deer, Ceylon hogdeer, Ceylon sambhur, and barking deer)

Cytochrome b Rajapaksha et al. 2002

Chicken and turkey Cytochrome b Hird et al. 2003

Buffalo Cytochrome b Rajapaksha et al. 2003

Duck, goose, chicken, turkey, and domestic pig

12S rRNA Rodriguez et al. 2003

Cattle, sheep, goat, and domestic pig

12S rRNA Rodriguez et al. 2004a

Red deer, roe deer, and fallow deer

12S rRNA Fajardo et al. 2007b

Chamois, Pyrenean ibex, and mouflon

D-loop region Fajardo et al. 2007c

Pheasant, quail, guinea fowl, chicken, turkey, duck, and goose

Cytochrome b Stirtzel et al. 2007

Domestic big 12S rRNA Che Man et al. 2007

Bovine, sheep, goat, domestic pig, horse, dog, and cat

Cytochrome b Ilhak and Arslan 2007

Domestic pig, horse, and donkey

ATP synthase subunits 8/6 and NADH hydrogenase subunits 2/5

Kesman et al. 2007

Ducks and Muscovy duck 12S rRNA Martin et al. 2007

18

Cattle, sheep, goat, domestic pig, horse, donkey, red deer, cat, dog, fox, guinea pig, hedgehog, badger, harvest mouse, house mouse, rat, and rabbit

Cytochrome b Tobe et al. 2008

Cattle, domestic pig, horse, and chicken

Cytochrome b Bai et al. 2009

Ruminant, poultry, and pork 16S rRNA and 12S rRNA Ghovvati et al. 2009

Quail, pheasant, partridge, and guinea fowl

12S rRNA Rojas et al. 2009b

Cattle and yak 12S rRNA Yin et al. 2009

Cattle, domestic pig, and chicken

Cytochrome b Velebit et al. 2009

Quail, pheasant, partridge, quinea fowl, pigeon, Eurasian woodcock, and song thrush

D-loop region Rojas et al. 2010a

Domestic pig and poultry Cytochrome b and 12S rRNA

Soares et al. 2010

Buffalo D-loop region Girish et al. 2011

Water buffalo, goat, and sheep D-loop region Karabasanavar et al. 2011a, b, c

Goat D-loop region Kumar et al. 2011

Red-legged partridge, gray partridge, and genus Alectoris (red-legged partridge, chukar partridge, and Barbary partridge species)

12S rRNA Rojas et al. 2011a

Cattle, domestic pig, chicken, and crocodile

Cytochrome b and HADH hydrogenase subunits 5/6

Unajak et al. 2011

Cattle, sheep, goat, and buffalo Cytochrome b Zarringhabaie et al. 2011

19

Buffalo D-loop region Mane et al. 2012

Barley, bread and durum wheat, oat, rye, maize, and rice

Multiple targets Alary et al. 2007

20

Table 1.3. Studies using real-time PCR to authenticate species in food products.

Species Genetic Marker Real-time Chemistry

References

Cattle Bovine growth hormone TaqMan® probes Brodmann and Moor 2003

Pork Cytochrome b TaqMan® probes Hird et al. 2004

Beef Mt ATPase 8 subunit LighCycler 2-hybridization probe system

Tasara et al. 2005

Beef, pork, chicken, and ruminants

SINE – short interspersed elements

Sybr Green Walker et al. 2003

Beef, pork, chicken, and turkey

B-Actin, Transforming growth factor, Prolactin receptor

TaqMan® probes Köppel et al. 2008

Horse and donkey Cytochrome b TaqMan® probes Chisholm et al. 2005

Beef Cytochrome b TaqMan® probes Lopparelli et al. 2007

Beef 12S rRNA TaqMan® probes Lopez-Calleja et al. 2007b

Goat 12S rRNA TaqMan® probes Lopez-Calleja et al. 2007a

Tuna Cytochrome b TaqMan® probes Lopez and Pardo 2005

Cod ATPase 6 Sybr Green Bertoja et al. 2009

21

Hake Mt control region Taq man MGB Sanchez et al. 2009

Grouper, Nile perch, and wreck fish

16s rRNA Sybr Green Trotta et al. 2005

Haddock Transferrin TaqMan® probes Hird et al. 2005b

Eel 16S rRNA Taq Man MGB Itoi et al. 2005

Flying Fish 16S rRNA Sybr Green Nagase et al. 2010

Wheat, Rye, Barley, and Oat

w-Gliadin Sybr Green Sandberg et al. 2003

Rye, triticale w-Secalin Sybr Green, Taq Man

Terzi et al. 2004

Soft wheat Microsatellite DNA Sybr Green Pasqualone et al. 2007

Soft wheat Microsatellite DNA TaqMan® probes Sonnante et al. 2009

Bread wheat and total wheat

Gliadin TaqMan® probes Terzi et al. 2003

Bread wheat Aluminium-activated malate transporter

TaqMan® probes Vautrin and Zhang 2007

Wheat, barley, and rye Chloroplast trnL Sybr Green, LighCycler 2-hybridization probe-system

Dahinden et al. 2000

Barley, rice, sunflower, and wheat

y-Hordein, gos9, Helianthinin, Acetyl-CoA-carboxylase (acc1)

TaqMan® probes Hernandez et al. 2005

Wheat, kamut, spelt, rye, barley, and oat

High molecular weight glutenin, Hor3, 12S

TaqMan® probes Zeltner et al. 2009

22

seed storage protein

Hazelnut cora1, cora8, cora14 Sybr Green D’Andrea et al. 2011

Hazelnut hsp1 Real-time PCR Piknova et al. 2008

Peanut ara h 2 Real-time PCR Hird et al. 2003b

Peanut ara h 2 Real-time PCR Stephan and Veiths 2004

Peanut ara h 3/4 TaqMan® probes Scaravelli et al. 2008

Walnut jug r2 TaqMan® probes Brezna et al. 2006b

Celery Mannitol Dehydrogenase

TaqMan® probes Hupfer et al. 2007

Celery, mustard, and sesame

Mannitol Dehydrogenase, 2S albumin, sinA

TaqMan® probes Mustorp et al. 2008

White mustard MADS D TaqMan® probes Fuchs et al. 2010

Buckwheat ITS-1 and 5.8S rRNA Sybr Green Hirao et al. 2005

Lupin a-Conglutin, o-Conglutin TaqMan® probes Galan et al. 2010

Lupin and soy tRNA-Met TaqMan® probes Galan et al. 2011

Lupin y-Conglutin Sybr Green Scarafoni et al. 2009

Lupin rDNA 18s-5.8s internal transcribed spacer

TaqMan® probes Demmel et al. 2008

23

Olive cultivars Microsatellite DNA Sybr Green Breton et al. 2004

Orange, blueberry, strawberry, and pineapple

Anthocyanidine synthase and/or 5s rRNA

Sybr Green, Eva Green, Taq Man

Palmieri et al. 2009

Tomato, Potato Metallo carboxy-peptidase inhibitor

TaqMan® probes Hernandez et al. 2003

Acacia, broom, citrus, clover, heather, eucalyptus, lavender, linden, oak, olive, rape, rockrose, rosemary, sunflower, and sweet chestnut

adh1, Actin, adh1, Actin, Leafy/Floricauly, hmg, Nitrate reductase, Phenylalanine ammonia-lyase, ole e 10, lipase 1, DXR, adh1, Profilin, ypr 10; PAL

TaqMan® probes Laube et al. 2010

Pea legS TaqMan® probes Ramos-Gomez et al. 2008

Pea Chloroplast trnL-trnF spacer

TaqMan® probes Brezna et al. 2006a

Wasabi Myrosinase TaqMan® probes Eugster et al. 2011

Non-basmati Rice bad2 TaqMan® probes Lopez 2008

Bovine and swine GMP phosphodiesterase and ryanodin


Cattle Control region SYBR® green Sawyer et al. 2003

Cattle, sheep, domestic pig, chicken, and turkey

Cytochrome b TaqMan® probes Dooley et al. 2004

Horse and donkey Cytochrome b TaqMan® probes Chisholm et al. 2005

Cattle, sheep, Cytochrome b TaqMan® probes Lopez-

24

domestic pig, chicken, turkey, and ostrich

Andreo et al. 2005

Duck 12S rRNA TaqMan® probes Rodriguez et al. 2004b

Domestic pig 12S rRNA TaqMan® probes Rodriguez et al. 2005

Cattle, domestic pig, horse, and kangaroo

Cytochrome b SYBR® green Lopez-Andreo et al. 2006

Mallard and Muscovory duck

Cytochrome b TaqMan® probes Hird et al. 2005a

Cattle, sheep, domestic pig, chicken, goat, duck, and turkey

GMP phosphodiesterase


Cattle, sheep, domestic pig, horse, and chicken

Cytochrome b TaqMan® probes Tanabe et al. 2007

Cattle, sheep, domestic pig, horse, chicken, and turkey

Cytochrome b TaqMan® probes Jonker et al. 2008

Red deer, fallow deer, and roe deer

12S rRNA SYBR® green Fajardo et al. 2008b

Chamois and Pyrenean ibex

D-loop SYBR® green Fajardo et al. 2008c

Pheasant and quail Cytochrome b TaqMan® probes Chisholm et al. 2008a

Chamois and Pyrenean ibex

D-loop TaqMan® probes Fajardo et al. 2009b

Quail, pheasant, partridge, guinea fowl, pigeon, Eurasian woodcock, and song thrush

12S rRNA TaqMan® probes Rojas et al. 2010b

25

Domestic pig Cytochome b SYBR® green Farrokhi and Jafari 2011

Cattle, sheep, domestic pig, and horse

Beta-actin, growth hormone receptor, and prolactin receptor

TaqMan® probes Koppel et al. 2011

Domestic pig and wild boar

MC1R TaqMan® probes Mayer and Hochegge 2011

Ostrich 12S rRNA SYBR® green TaqMan® probes

Rojas et al. 2011b

Capercaillie 12S rRNA SYBR® green

TaqMan® probes

Rojas et al. 2011c

Domestic pig Cytochrome b Molecular beacon Yusop et al. 2011

Domestic pig Cytochrome b TaqMan® probes Ali et al. 2012b

Cattle, sheep, domestic pig, chicken, and turkey

Cytochrome b and 16S rRNA

TaqMan® probes Camma et al. 2012

Common pigeon, woodpigeon, and stock pigeon

12S rRNA TaqMan® probes Rojas et al. 2012

Ruminant and poultry 16S rRNA-tRNA and 12S rRNA

SYBR® green Sakalr and Abasiyanik 2012

Lepus species Cytochrome b EVA® green Santos et al. 2012

Durum wheat Puroindoline-b TaqMan® probes Alary et al. 2002

26

Table 1.4. DNA barcoding for species identification and authentication of food and herbal medicines.

Application Reference

Seafood market survey Wong and Hanner 2008

Catfish identification Carvalho et al. 2011

Catfish identification Wong et al. 2011

Seafood market survey Ardura et al. 2010

Pufferfish identification Cohen et al. 2009

Shark market survey Barbuto et al. 2010

Seafood market survey Filonzi et al. 2010

Seafood market survey Hanner et al. 2011

Tuna sushi Lowenstein et al. 2010

Indian marine fishes Lakra et al. 2011

Canadian Pacific fishes Steinke et al. 2009

Commercial fishes of South Africa Cawthorn et al. 2011

Nigerian freshwater fish Nwani et al. 2011

Catfish Santos and Quilang 2011

Salmonids Rasmussen et al. 2009

Billfishes Hanner et al. 2011

Bushmeat Eaton et al. 2010

Antelope Bityanyi et al. 2011

Bushmeat Dalton and Kotze 2011

27

Olive oil and adulerants Kumar et al. 2011

Tea market survey Stoeckle et al. 2011

Lamiacaea and adulterants Guo et al. 2011

Dendrobium species Asahina et al. 2010

Astragalus Guo et al. 2010

Lamiacaea De Mattia et al. 2011

Poisonous plants Bruni et al. 2010

Sabia parviflora and its adulterants Sui et al. 2010.

Fabacae Gao et al. 2010

Herbal product market survey Newmaster et al. 2013

28

Figure 1.1. Taqman probe chemistry. A: After denaturation, the temperature is lowered and the Taqman probe binds to the complimentary segment of DNA on the target. At this point, the presence of the quencher dye (red) masks the fluorescence emission from the reporter dye (green). Primers (blue) then bind as cooling continues. B. During the extension step, the temperature is raised again to 72 ˚C and DNA polymerase (yellow) extends the primer sequence to form a segment of DNA complimentary to the target. C. As DNA polymerase encounters the Taqman probe, the probe is cleaved, separating the quencher from the reporter dye. The reporter dye emits fluorescence that can be measured by the instrument. In this way, a signal is only generated when target DNA is present in a sample and fluorescence is also proportional to DNA concentration.

A

B

C

29

Section 1: The Use of Endogenous DNA for Species-Specific Identification for Authentication of Agricultural Pests or Food Products Chapter 2: Seafood Market Survey Using DNA Barcoding

Building on existing methods, a seafood market survey was conducted using DNA barcoding to identify market samples. High school students across Ontario participated in this large-scale citizen science project. Introduction

Food fraud is an issue of socioeconomic concern globally. Substitution or mislabeling of

species is one form of fraud. It has obvious economic implications to consumers when a

lower-cost product is labeled as one with a higher value. There can also be health

implications as different species have varying levels of heavy metals (Lowenstein et al.

2010) and nutritional value (Weaver et al. 2008), or may even be toxic (Cohen et al.

2009). Consumers may also make choices to purchase sustainable species of seafood,

however, at-risk species can be marketed as sustainable alternatives in some cases

(e.g.. Wong and Hanner 2008; Barbuto et al. 2010). Public awareness of these issues is

growing. Consumers demand high-quality, authentic and environmentally sustainable

products. Seafood consumption is also on the rise. With globalization of trade, it can be

difficult to track and authenticate seafood products, creating the possibility for

misrepresentation of products, both intentionally and unintentionally. To combat this,

regulatory bodies must utilize new methods for authenticity testing.

DNA barcoding is a method for species identification that takes advantage of

differences in the DNA sequence of a standard gene region in order to identify species

(Hebert et al. 2003). By genetically profiling expert-identified reference specimens, the

resulting “look-up table” can be used to identify an unknown sample by its DNA barcode

(via the Barcode of Life Data System or BOLD; Ratnasingham and Hebert (2007). This

approach is particularly useful when diagnostic morphological characters are removed,

for example during seafood processing. Since 2008, this method has been gaining in

popularity as a rapid and accurate method for species identification of seafood products.

30

In 2011, it was adopted by the United States Food and Drug Administration (FDA) as

the primary method of regulatory control of seafood products (Handy et al. 2011).

DNA barcoding has been used in several market studies (Wong and Hanner 2008,

Hanner 2011, Miller and Marianai 2010, Cline 2012, Barbuto et al. 2012, Alba et al.

2010, Filonzi et al. 2010, Carvalho et al. 2011). These studies have continued to shed

light on instances of fraud found in the seafood industry around the world. Additionally,

the media coverage of instances of food fraud has further empowered citizens to make

informed decisions about their seafood consumption. To further encourage citizen

involvement, a large-scale seafood market survey was developed. In partnership with

Let’s Talk Science, a national not-for-profit organization dedicated to science outreach,

high school teachers in Ontario, Canada, were provided with the tools to use DNA

barcoding to identify seafood products gathered by high school students from their local

grocery stores to determine if any mislabeling had occurred.

Methods

Sample Collection

High school teachers from the Let’s Talk Science network were invited to participate in

the market survey. After signing up, teachers were given instructions on sample

collection in the form of a lesson plan that could be shared with students. To increase

interest, supplementary lessons covering aspects of the seafood supply chain and DNA

barcoding were also provided online at

http://www.explorecuriocity.org/Community/ActionProjects/MarketSurvey.aspx. This

included a list of market names to limit the types of seafood purchased in order to

streamline collection and analysis. The following market names were suggested:

salmon, bass, snapper, tilapia, basa, shark, halibut, haddock, cod, yellowtail, catfish,

pickerel, whitefish, perch, orange roughy, sole and pollock. We also received samples

with the following market names: swordfish, rockfish, mussel, yellowtail, walleye, flying

fish roe, artificial crab, Alaskan snow crab, eel, rainbow trout, and monkfish. Products

were required to be fresh or fresh frozen only rather than processed.

31

After creating a sampling plan in class to minimize overlap in the species collected and

stores visited, students went to a local store to purchase their seafood products.

Teachers provided, from the lesson plan materials, data collection sheets for students to

fill out for each sample. We provided each teacher with 1.5mL microcentrifuge tubes,

each with a unique sample ID number. Small (~ 2 cm3) tissue subsamples of each

product were collected by students, and deposited into a unique pre-labeled vial and

preserved using 95% ethanol.

DNA Barcoding

Samples were shipped to the Biodiversity Institute of Ontario for analysis using standard

DNA barcoding protocols (http://ccdb.ca/resources.php) and C_FishF1t1/C_FishR1t1

primers (Ivanova et al. 2007). Failures, those sequences where a DNA barcode could

not be obtained due to failed PCR, were amplified and sequenced again using AquaF2

(Prosser unpublished)/C_FishR1T1 primers. Results of DNA barcoding, along with

collection data sent by students, were entered into the Barcode of Life Data System

(BOLD; www.barcodinglife.org) online database.

Species Identification

DNA barcodes were entered into the BOLD identification tool to determine a species-

level match. Sequence similarity of 98% or higher was determined to be a match. If the

sequence could not be identified using BOLD, an NCBI BLAST search of GenBank was

used.

The species name determined from the DNA barcode was then compared to the market

name using the Canadian Food Inspection Agency (CFIA) fish list. If the market name

from the sample package was listed under the species name obtained from BOLD on

the CFIA fish list, the sample was considered correctly labeled. If not, or if the species

name was not found on the CFIA Fish List, this was considered mislabeling. In cases

where the common name could not be identified for a scientific name using the CFIA

Fish List, Fish Base global database (http://www.fishbase.org) was used to assign a

common name.

32

Results and Discussion

A total of 322 samples with complete data sheets were submitted for sequencing. DNA

barcodes were obtained from 294/322samples (91%). Full-length barcodes were

obtained from 265 samples and a further 29 samples yielded shorter mini-barcodes. All

but one barcode sequence had a match in BOLD of at least 98%. The closest match to

LTSMS619 from GenBank was Shewanella baltica at 86%.

In total, 70/295 samples (24%) were identified as potentially mislabeled using the CFIA

Fish List as a guide (Table 2.1). This level of mislabeling is similar to that found in

previous market surveys (Wong and Hanner 2008, Hanner 2011, Marko et al. 2004,

Miller and Marianai 2010, Cline 2012, Barcuto et al. 2012, Alba et al. 2010, Filonzi et al.

2010, Carvalho et al. 2011). High levels of mislabeling were found in red snapper

(80%), whitefish (75%) and shark (100%) samples. Snapper (71%) and bass (53%) also

had high levels of mislabeling. We found between 25% and 50% mislabeling in sole,

perch and tuna and those samples listed in the “other” category. No specific species

seem to be consistently substituted for any of these market names.

Of the 70 cases of suspected mislabeling, 32 were straightforward examples where the

species determined with DNA barcoding was found on the CFIA Fish List and did not

match the market name listed on the product. Tilapia (Oreochromis sp.) was

substituted for red snapper and cod. Tilapia is cheaper than both cod and red snapper,

and substitution might be considered a case of economic fraud. However, substitution

with farmed fish, such as tilapia, may have other unintended consequences to human

health. These may also contain environmental contaminants such as heavy metals and

carcinogenic chemicals from the fish farming process (Sapkota et al. 2008). This type of

contamination can be monitored, but only if the species is known and the product is

labeled accurately, emphasizing the link between species authentication and food

safety.

Another example in this category was labeled bass but was identified as Dissostichus

mawsoni, Antarctic toothfish. Although this species has not yet been assessed by the

International Union for Conservation of Nature (ICUN), there has been concern about

the effect of industrial fishing on the Antarctic toothfish in the Ross Sea on not only

33

populations of D. mawsoni (Parker et al. 2002), but also other species that prey on them

(Ainley et al. 2009). In this study, various species of rockfish were found substituted for

perch, bass, cod, and snapper samples. Previous studies have focused on the impacts

of incorrect labeling of snapper species, which are slow to reproduce and may be

overfished, on conservation and consumer choice (Logan et al. 2008)

The species determined with DNA barcoding for the remaining 38 mislabeled samples

were not on the CFIA fish list. These were considered mislabeled due to the CFIA

regulations for food labeling in Canada. In 10 of these cases, identification of the

common name using Fish Base showed that the actual sample did not match the

market name. For example, one sample was labeled halibut, but was determined to be

Hypothordus flavolimbatus, or yellowedge grouper, which is listed as vulnerable by the

International Union for Conservation of Nature (IUCN). The other 28 were cases where

the common name of the species as determined by Fish Base did match the market

name. For example all eight shark samples fall into this category. Although none of the

three species identified from the shark samples using DNA barcoding (Carcharhinus

brevipinna, Carcharhinus limbatus, and Carcharhinus tilstoni) were listed on the CFIA

Fish List, they are all species of shark. Although this may seem legitimate, C. brevipinna

and C. limbatus are listed as near threatened by the IUCN, illustrating possible

conservation implications of non-compliant seafood labels. All but one mislabeled sole

sample also fell into this category. These samples were identified as Lepidopsetta

polyxsystera (northern rock sole), Solea solea (common sole) and Hippoglossoides

elassodon (flathead sole).

These examples illustrate the need for labeling seafood products with scientific names,

which has been advocated recently (Hanner et al., 2011) as well as improved labeling of

seafood sources. This would not only aid in the detection of market substitution, but

could impact consumer choice on products that are not mislabeled. For example, there

were 31 samples in this study labeled only as “cod”. Of the 25 that were not mislabeled,

seven were Gadus morhua, Atlantic Cod, which is considered a less sustainable fish

option than the 18 Pacific cod, Gadus macrocephalus, samples. Differentiating these

two species on the label could assist consumers in making a more informed choice on

34

their seafood consumption. This possibility is made even easier with the use of mobile

apps for iPhone and Android designed to help consumers determine the sustainability of

a given species (e.g. Seafood Watch; seafoodwatch.org).

Conclusion

DNA barcoding continues to be a useful tool in detection of market substitution in

seafood. This survey revealed instances of possible economic fraud in addition to

occurrences affecting conservation and health. These substitutions affect consumers

economically as well as socially by impacting health and lifestyle choices. Increased

consumer awareness of these practices, in combination with accurate means of

identifying fraud, such as DNA barcoding, may help discourage seafood mislabeling.

Citizen science projects like this one serve to improve public awareness of not only the

incidence and impact of food fraud, but also improve understanding of the scientific

tools being employed to combat it. We encourage continued involvement of

communities in similar studies, particularly in partnership with the scientific community.

35

Table 2.1. Identification of market samples from this study based on DNA barcoding.

Sample ID Market Name BOLD Species Match mislabeled (Y/N)

Full Length Barcodes

LTSMS819 Alaskan halibut Hippoglossus stenolepis Y

LTSMS163 artificial crab Salmo salar N

LTSMS800 atlantic cod Gadus macrocephalus Y

LTSMS380 atlantic salmon Salmo salar N






LTSMS542 basa Pangasius hypophthalmus N





LTSMS778 bass Micropterus salmoides N

LTSMS822 bass Morone chrysops N



LTSMS813 bass Dissostichus mawsoni Y

36

LTSMS703 bass Stenotomus chrysops Y

LTSMS762 bass Dissostichus eleginoides Y

LTSMS219 blue cod Micromesistius australis N

LTSMS845 catfish Ictalurus punctatus N

















LTSMS809 catfish Ameiurus nebulosus Y

LTSMS301 catfish Ictalurus furcatus Y

37

LSMS401 catfish Ameiurus melas Y

LTSMS325 Chilean sea bass Hippoglossus stenolepis Y

LTSMS332 Chilean sea bass Hippoglossus stenolepis Y

LTSMS895 cod Gadus morhua N

LTSMS157 cod Gadus macrocephalus N

















LSMS854 cod Gadus macrocephalus N

38







LTSMS313 cod Oreochromis sp. Y

LTSMS892 cod Oreochromis mossambicus Y

LTSMS330 cod Epinephelus morio Y

LTSMS770 cod Pollachius virens Y

LTSMS619 cod no match

LTSMS258 Coho salmon Oncorynchus mykiss Y

LTSMS752 eel Anguilla rostrata N

LTSMS478 eel Anguilla anguilla Y

LTSMS652 fake crab Gadus chalcogrammus N

LTSMS648 farmed catfish Ictalurus punctatus N

LTSMS521 flying fish roe Cheilopogon unicolor Y

LTSMS754 fresh pacific salmon Lutjanus campechanus Y

LTSMS866 fresh sea bass Dicentrarchus labrax N

LTSMS848 fresh water bass Morone chrysops Y

LTSMS873 haddock Melanogrammus aeglefinnus N


39























40




LTSMS602 halibut Hippoglossus stenolepis N











LTSMS590 halibut Hyporthodus flavolimbatus Y

LTSMS844 live water bass Morone chrysops Y

LTSMS023 mackerel Scomber scombrus N



LTSMS255 monkfish Lophius americanus N

LTSMS273 mussel Mytilus trossulus Y

LTSMS761 north atlantic haddock

Melanogrammus aeglefinnus N

41

LTSMS262 orange roughy Hoplostethus atlanticus N



LTSMS885 pacific cod Gadus macrocephalus N

LTSMS821 pacific halibut Hippoglossus stenolepis N

LTSMS883 pacific halibut Melanogrammus aeglefinnus Y

LTSMS555 pacific salmon Oncorynchus gorbuscha N

LTSMS758 pacific salmon Oncorynchus gorbuscha N

LTSMS792 pacific snapper Sebastes melanops Y

LTSMS187 pacific snapper Sebastes serranoides Y

LTSMS874 perch Perca flavescens N

LTSMS331 perch Sebastes viviparus Y

LTSMS108 pickerel Sander vitreus N










42



LTSMS887 pollock Gadus chalcogrammus N








LTSMS899 pollock Pollachius virens N


LTSMS418 pollock pollachius pollachius Y

LTSMS688 rainbow trout Oncorynchus mykiss N

LTSMS252 red snapper Lutjanus campechanus N

LTSMS875 red snapper Rhomboplites aurorubens Y

LTSMS305 red snapper Lutjanus synagris Y

LTSMS285 red snapper Oreochromis sp. Y

LTSMS842 red snapper Lutjanus synagris Y

LTSMS857 red snapper Oreochromis sp. Y

LTSMS781 red snapper Sebastes viviparus Y

LTSMS423 rockfish Sebastes flavidus N

43

LTSMS461 salmon Salmo salar N





LTSMS624 seabass Dicentrarchus labrax N

LTSMS817 shark Carcharhinus brevipinna Y


LTSMS528 shark Carcharhinus limbatus Y

LTSMS826 shark Carcharhinus limbatus Y

LTSMS814 shark Carcharhinus tilstoni Y



LTSMS748 snapper Lutjanus campechanus N

LTSMS784 snapper Lutjanus campechanus N

LTSMS670 snapper Sebases aleutianus Y

LTSMS552 snapper Sebastes melanops Y

LTSMS751 sole Limanda aspera N





44



LTSMS871 sole Lepidopsetta bilineata N


LTSMS697 sole Microstommus pacificus N






LTSMS856 sole Parophrys vetulus N


LTSMS307 sole Lepidopsetta polyxystera Y



LTSMS474 sole Solea solea Y



LTSMS859 sole Hippoglossus platessoides Y

LTSMS888 sole Solea solea Y

LTSMS730 swordfish Xiphias gladius N

LTSMS365 swordfish Xiphias gladius N

45

LTSMS765 tilapia Oreochromis sp. N

LTSMS816 tilapia Sarotherodon galilaeus N


LTSMS796 tilapia Oreochromis niloticus N

LTSMS064 tilapia Oreochromis mossambicus N








LTSMS034 tuna Thunnus alalunga N

LTSMS491 tuna Thunnus albacares N

LTSMS585 tuna Thunnus alalunga N

LTSMS556 tuna Thunnus obesus N

LTSMS581 walleye Sander vitreus N

LTSMS847 water bass Morone chrysops Y

LTSMS832 white bass Morone chrysops N

LTSMS679 white snapper Pagrus pagrus Y

LTSMS756 white tuna Thunnus tonggol Y

LTSMS890 whitefish Coregonus clupeaformis N

46

LTSMS728 whitefish Morone chrysops Y

LTSMS489 whitefish Gadus macrocephalus Y

LTSMS849 whitefish Sebastes proriger Y

LTSMS128 wild pacific salmon

Ochorhynchus keta Y

LTSMS898 wild pollock Gadus chalcogrammus N

LTSMS900 wild sole Limanda aspera N

LTSMS621 yellow perch Perca flavescens N



LTSMS698 yellowtail Seriola quinqueradiata Y

LTSMS215 haddock Melanogrammus aeglefinus N


LTSMS295 sole Lepidopsetta polyxystra Y



LTSMS468 Alaskan pollock Gadus chalcogrammus N

LTSMS506 sockeye salmon Oncorhynchus nerka N


LTSMS543 Alaskan pollock fillet

Gadus chalcogrammus N


LTSMS593 black tilapia Oreochromis urolepis Y

47

LTSMS606 sockeye salmon Oncorhynchus nerka N

LTSMS653 wild sockeye salmon

Oncorhynchus nerka N


LTSMS840 Alaskan snow crab

Gadus chalcogrammus Y

LTSMS851 pacific pink salmon

Oncorhynchus gorbuscha N

Mini Barcodes

LTSMS471 sole Hippoglossoides elassodon Y

LTSMS166 sole Lepidopsetta polyxystra Y







LTSMS434 cod Sebastes viviparus Y

LTSMS599 halibut Hippoglossus hippoglossus N





LTSMS659 pacific halibut Hippoglossus stenolepis N

48



LTSMS098 red snapper Sebastes melanops Y

LTSMS855 red snapper Lutjanus campechanus N

LTSMS368 haddock Hippoglossus stenolepis Y



LTSMS289 ocean perch Sebastes viviparus Y

LTSMS642 ocean perch Sebastes viviparus Y

LTSMS111 tuna Sebastes flavidus Y





49

Chapter 3. Application of DNA Barcode Sequences for Identifying Salmonid Species of Commercial Interest

Though the DNA barcoding market survey outlined in Chapter 2 represents one method

of accurate seafood identification, DNA barcoding is not well suited to detect multiple

species in mixtures, and is not always suitable for processed products. Additionally, the

process may be lengthy. Using DNA barcode sequences to develop real-time PCR and

rapid conventional PCR protocols capable of identifying individual species within

mixtures has been proposed as an alternative to DNA barcoding for certain applications.

Two such methods are evaluated in this chapter.

This chapter consists of edited excerpts representing the work completed primarily by Naaum at the University of Guelph from: Rasmussen Hellberg, R.S., Naaum, A.M., Handy, S.M., Hanner, R.H, Deeds, J.R., Yancy HF and Morrissey MT. 2011. Interlaboratory Evaluation of a Real-Time Multiplex Polymerase Chain Reaction Method for Identification of Salmon and Trout Species in Commercial Products. J. Ag. Food Chem. 59: 876-884, as well as original work completed by Naaum.

Introduction

Salmonids represent an important commercial commodity for both the United States

(Voorhees 2008) and Canada (DFO 2009). Seven species are commonly sold; two

farmed species: Atlantic salmon (Salmo salar) and rainbow (steelhead) trout

(Oncorhynchus mykiss) and five wild species, commonly referred to as Pacific salmon:

Chinook salmon (Oncorhynchus tshawytscha), sockeye salmon (Oncorhynchus nerka),

coho salmon (Oncorhynchus kisutch), chum salmon (Oncorhynchus keta), and pink

salmon (Oncorhynchus gorbuscha). Though these species look similar, especially after

processing, they are sold at different market rates due to varying supply and demand,

as well as competition between farmed and wild species (Knapp et al. 2007). This

difference in market price provides incentive for species substitution.

DNA barcoding can be a powerful tool in detecting market substitution, however, it still

requires relatively time-consuming methods using costly equipment. Furthermore, it is

not suitable for detection of multiple species in mixed samples and the 650 base pair

fragment required can be difficult to recover from highly processed samples.

Using the extensive DNA barcode sequence data available on BOLD, multiplex primers

50

and probes were developed to differentiate the seven commercially important salmonid

species (Rasmussen Hellberg et al. 2010). Primers were designed for traditional PCR,

and primer and probes sets were designed for real-time PCR. These assays targeted a

shorter species-specific region of the 658bp DNA barcode sequence for each species,

and allowed for multiplex detection of species in mixtures either by real-time PCR or

post-PCR gel electrophoresis in the case of traditional PCR primers. The shorter target

region allowed better identification for processed products where DNA may be

degraded (e.g. Pardo and Perez-Villareal 2004).

The objective of this study was two-fold. First, although Rasmussen Hellberg et al.

(2010) included a small selection of market samples, further evaluation of the published

primers and probes for real-time PCR was warranted. In this study, 80 market samples

collected in both the US and Canada were analyzed using the existing primer and probe

sets to determine their efficacy in identifying market samples. Two other labs also

analyzed the same samples, forming a small inter-laboratory evaluation, published in

2011 (Rasmussen Hellberg et al. 2011). Secondly, the conventional PCR primers were

repurposed to develop a more rapid protocol in single-plex, allowing easier

interpretation of results.

Methods

Real-time PCR Evaluation

Sample Collection and Preparation

A total of 80 commercial salmon and trout products representing a variety of species

and processing methods were obtained from retail outlets in Canada and the United

States in November 2009. Forty products were collected from 9 locations in Oregon and

Washington (U.S.A) and the other 40 products were collected from 8 locations in

Ontario (Canada). Each product was assigned a random three-digit number, and only

the collecting laboratory was aware of the species declaration linked to each product.

Tissue samples from each product were subsampled into 2 mL tubes and preserved in

95% ethanol. Products with increased susceptibility to species mixing (e.g. canned and

51

retort-packed salmon, salmon burgers, and frozen salmon dinners) were individually

blended with up to 30 mL of sterile water for at least 2 min prior to subsampling.

Reference DNA of known concentrations and the corresponding expected cycle

threshold values (Ct) as determined previously (Rasmussen Hellberg et al. 2010) for

each of the target species were obtained from the Food Innovation Center, Oregon

State University in order to optimize the baseline and threshold settings for use on a

new instrument. Reference tissue samples of the seven commercial salmon and trout

species were also obtained as positive controls for DNA extraction and real-time PCR.

These reference samples were verified for accuracy of species identification in a

previous study with DNA barcoding (Rasmussen Hellberg et al. 2009).

DNA Extraction and PCR Preparation

DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA) with

overnight lysis and elution in 60-100 µL of elution buffer. A negative control with no

tissue was included in each DNA extraction and subsequent PCR. Nucleic acid

concentrations were determined using a NanoDrop spectrophotometer. Extracted DNA

samples were adjusted to 20-30 ng/µL for use in real-time PCR. TaqMan minor groove

binder (MGB) probes and PCR primers used in this study are described in Rasmussen

Hellberg et al. (2010). These primers and probes target species-specific regions of the

DNA barcode in all seven commercially important salmon and trout species in North

America, as well as a universal region of cytochrome b for a positive control. All probes

and degenerate primers were synthesized by Applied Biosystems (Foster City, CA), and

nondegenerate primers were synthesized by TriLink Biotechnologies (San Diego, CA).

Real-Time Multiplex PCR

Real-time PCR was conducted on a StepOnePlus Real-Time PCR System (Applied

Biosystems). All reactions contained 12.5 µL of 2X QuantiTectMultiplex PCR No ROX

Master Mix (Qiagen), 0.10-0.60 µM final concentration of primers, 0.10-0.60 µM final

concentration of TaqMan MGB probes (Applied Biosystems), 40-60 ng of template

DNA, and sterile water for a total reaction volume of 25 µL. Each sample was amplified

with all seven species-specific primers and probes, along with the universal primers and

52

probe set in three different multiplex reactions: one triplex set targeting S. salar, O. keta,

and O. tshawytscha; one duplex set targeting O. nerka and O. kisutch; and one duplex

set targeting O. gorbuscha and O. mykiss. The universal set was run separately for

each sample. Cycling conditions, primer and probe details, and multiplex sets are

described in Rasmussen Hellberg et al. (2010).

All real-time PCR settings were optimized for species identification using reference DNA

from each of the target species. The established baseline setting was 3-15 cycles and

established thresholds were 5.5 x 103 (FAM), 3.5 x 103 (VIC), and 1.5 x 103 (NED)

fluorescent units. The cycle threshold (Ct) value, defined as the cycle at which the

fluorescence signal crosses the threshold, was recorded for each sample. A Ct value

below 25 in fresh/frozen (uncooked) or heat-treated (partially or fully cooked) samples

or a Ct value below 30 in heat-sterilized (canned or retort-packed) samples was used to

identify species in food products, as recommended previously (Rasmussen Hellberg et

al. 2010). A no-template PCR control was included alongside all reactions.

Any samples found to contain substituted species or exhibiting secondary signals of Ct

< 30 were examined further to verify the results. Fresh/frozen or heat-treated samples

that exhibited a Ct value between 25 and 30 were subjected to repeat DNA extraction

and real-time PCR. These tests were carried out in duplicate, and a Q-test was used for

the rejection of outliers, as described in Smith et al. (2003).

Conventional PCR

Samples of sockeye, Atlantic and pink salmon were collected from markets and

restaurants. Species identifications were verified using DNA barcoding. The Xytogen

Animal Extraction Kit was used according to manufacturer guidelines.

Reactions were carried out in 20 µL volumes with 0.4 µL Phire Taq polymerase (Fisher

Scientific), 0.4 µL dNTPs (Bio Basics Inc., Markham, Ontario, Canada), 4 µL reaction

buffer, 1 µL DNA, and primer concentrations as outlined in (Rasmussen Hellberg et al.

2010). Primer sequences were described in (Rasmussen Hellberg et al. 2010), and

synthesized by Integrated DNA Technologies (Iowa, U.S.A). Optimized cycling

conditions were 98 ˚C for 30 seconds followed by 30 cycles of 98 ˚C for 5s, 59 ˚C for 5s

53

and 72 ˚C for 10s, and finally 72 ˚C for 1min. Eight PCR reactions were run for each

individual sample – one for each species-specific set of primers and one for the

universal primer set. Agarose gel electrophoresis and visualization of the gel was

carried out as described in Rasmussen Hellberg et al. 2010, with SYBR Safe dye (Life

Technologies) substituted for ethidium bromide.


Real-time PCR

Reference Samples

The real-time multiplex PCR method allowed for detection of each target species in

reference DNA samples. The use of reference DNA samples was found to be essential

for the initial optimization of threshold and baseline settings. Reference tissue samples

that were provided as positive controls for DNA extraction and real-time multiplex PCR

were also detected below expected cutoff values, with one exception; the O. keta

reference sample showed an elevated Ct value (Ct=27.7). The universal Ct value for

this sample was also high (27.3). Elevated Ct values (Ct =25-30) were previously found

more frequently for reference tissue samples of O. keta compared to other species

(Rasmussen Hellberg et al. 2010).

Analysis of Commercial Products

A total of 80 salmon and trout products were tested using real-time multiplex PCR

(Table 3.1). The items chosen represent many of the product types available in North

America, including fresh/frozen fillets, frozen salmon dinners and burgers, smoked

salmon and trout, salmon jerky, canned salmon, and retort-packed smoked salmon.

Overall, there were 28 fresh/frozen (uncooked) products, 29 heat-treated (partially or

fully cooked) products, and 23 heat-sterilized (canned or retort-packed) products. The

species present could be identified in 79 of the 80 products (98.8%).

The one product that could not be identified was hot-smoked and retort-packed Alaskan

salmon (sample 991). A Ct value below 30 cycles could not be obtained for this sample

from any of the multiplex sets. The universal Ct value for this product was also above

54

30, indicating the sample may be heavily degraded, or the possible presence of PCR

inhibitors. Two cases of species substitution (samples 958 and 752) and two cases of

mixed-species samples (samples 266 and 733) were identified. These products are

discussed in detail in later sections. The majority (75%) of fresh/frozen products labeled

“O. keta or O. gorbuscha” were identified as O. gorbuscha, and the remaining products

were identified as O. keta. There were 10 fresh/frozen or heat-treated products

purchased in the United States that were labeled “salmon” with no species declaration,

and testing with real-time multiplex PCR indicated that 80% contained either O. keta or

O. gorbuscha. Oncorhynchus keta and O. gorbuscha have the lowest values among the

commercial salmon species, with average 2008 ex-vessel prices of U.S. $1.17 and U.S.

$0.64 per kg, respectively (Voorhees et al. 2008). Thus, it is not surprising that they are

the main species present in generically labeled salmon products.

Species Substitution

Species substitution was detected in 2 of the 79 samples (2.5%) that could be identified

at the species level. One product was a cold-smoked item sold at a seafood counter

(sample 958) that did not have a species label but was verbally declared to be farmed

O. tshawytscha. This product was determined to be S. salar. The other substituted

product (sample 752) was a smoked and canned item labeled O. kisutch that was

determined to be O. keta. Both of these cases represent substitution of a lower value

species for a higher value species. For example, in 2008 the average ex-vessel price for

O. tshawytscha was U.S. $7.19/kg compared to an average import price for S. salar of

U.S. $5.57/kg, and the average ex-vessel price of O. kisutch (U.S. $2.66/kg) was more

than twice that for O. keta (Voorhees et al. 2008, NMFS 2009).

Mixed-Species Samples

Two products were found to contain multiple species on the basis of the real-time

multiplex PCR results. Sample 266, a cold-smoked product labeled “salmon”, was found

to contain O. keta as the primary species and O. tshawytscha as the secondary

species, whereas sample 733, a canned product labeled O. gorbuscha, was found to

contain O. gorbuscha as the primary species and O. keta as the secondary species.

55

Delayed Secondary Signals

There were twelve samples that originally exhibited secondary signals between Ct = 25-

30. Although acceptable based on a Ct = 25 cutoff for a primary signal, a secondary

signal of Ct <30 had not previously been observed in lightly processed samples

(Rasmussen Hellberg et al. 2010). These signals were eliminated after repeat real-time

PCR or after repeat DNA extraction. These secondary signals were likely caused by the

presence of a low level of secondary species present due to trace contamination during

real-time PCR reaction setup or DNA extraction. This suggests that a cutoff value of Ct

< 25 for fresh/frozen and heat-treated samples is appropriate, allowing for species

identifications that are consistent with previously established methods while minimizing

false-positive signals from trace contamination.

Conventional PCR

Results from conventional PCR using the newly optimized protocol successfully

identified all three species tested. Agarose gel electrophoresis showed bands of 205

base pairs for each species in the positive control lane, 143 base pairs for pink salmon,

219 base pairs for Atlantic salmon, and 183 base pairs for sockeye salmon, as expected

based on Rasmussen Hellberg et al. (2010). Typical results are shown in Figure 3.1. No

bands were observed for any non-target primer sets in any of the samples tested.

Splitting each primer pair into a single reaction, although increasing the cost per sample

slightly, allows for more rapid interpretation. These primer sets can be run on pre-cast

gels to observe presence/absence of bands, rather than having to interpret band sizes.

Conclusions

The real-time multiplex PCR assay tested in this study is a rapid, sensitive, and reliable

method for identification of salmon and trout species in commercial products. Test

results revealed the ability of this assay to identify species in a range of product types,

including 96% of the heat sterilized products tested. The results of this study are also

promising with regard to the low level of species substitution detected in salmon and

trout products (2.5%). However, these products were collected from retail stores, and

substitution of salmon products has been found to be more common in restaurant

56

samples (Cline 2012). This method may be applied in regulatory settings as a rapid and

high-throughput screening tool for testing commercial salmon and trout products. The

traditional PCR primers developed by Rasmussen Hellberg et al. (2010) were also

shown to be successful in a more rapid single-plex analysis, allowing for more rapid

screening than the original multi-plex assay. This study also illustrates the high potential

to make use of the large volume of reference sequences in the Barcode of Life

database for the development of more rapid, simple and cost-effective assays targeting

commonly substituted seafood species.

57

Table 3.1. Samples analyzed in this study using real-time PCR. Market names and cooking methods were collected from sample labels. Assigned species were those detected using qPCR.

ID Declared market name Cooking method Species assigned

803 Pink salmon canned Pink


589 Wild pink salmon canned Pink

741 Sockeye salmon canned Sockeye

590 Red pacific sockeye salmon canned Sockeye

408 Atlantic salmon canned Atlantic


890 Sockeye salmon smoked Sockeye

363 Wild pacific salmon (pink or chum)

frozen raw Pink

424 Pink or chum salmon other [frozen dinner (raw frozen)]

Pink



609 Atlantic salmon fresh/frozen Atlantic

922 Rainbow trout fresh/frozen Rainbow

789 Atlantic salmon smoked Atlantic


445 Alaskan pink salmon or alaskan chum salmon

frozen raw Pink

192 Pink salmon frozen raw Pink

58

426 Wild alaskan sockeye salmon frozen raw Sockeye

501 Atlantic salmon other [frozen dinner (frozen

raw)] Atlantic

176 Wild sockeye salmon smoked Sockeye


frozen raw Chum

842 Chum salmon frozen raw Chum

903 Rainbow trout fresh/frozen (whole) Rainbow


frozen raw Chum


frozen raw Pink

373 Wild pacific salmon (chum) frozen raw Chum



871 Pacific coho salmon smoked Coho

260 Alaskan sockeye salmon smoked Sockeye

255 Pacific salmon (pink or chum) frozen raw Pink





536 Sockeye salmon smoked Sockeye


59

212 Coho salmon smoked Coho

117 Rainbow trout fresh/frozen Rainbow

135 Sockeye salmon Canned Sockeye

752 Coho salmon Smoked (can-packed) Chum

407 Pink salmon Canned Pink

568 King salmon Other (jerky) Chinook

873 King salmon Other (jerky) Chinook

244 Atlantic salmon Hot-smoked Atlantic

583 Chum salmon fresh/frozen Chum

266 "salmon" Cold-smoked (nova) Chum and Chinook

718 Sockeye salmon Canned Sockeye

733 Pink salmon Canned Pink and Chum

218 Sockeye salmon Hot-smoked (packed in retort

pouch) Sockeye

958

Unlabeled, but when asked, the salespersion said it was "farmed Chinook salmon"

Cold-smoked Atlantic

636 "salmon" Other (jerky) Chum

894 n/a Other (jerky) Chum

382 Chinook salmon Canned Chinook

715 Coho salmon fresh/frozen Coho

980 "salmon" Frozen raw Pink

833 Coho salmon Hot-smoked Coho

991 Pink salmon Hot-smoked, packed in retort Undiagnosable

60

pouch

168 Sockeye salmon fresh/frozen Sockeye

147 Coho salmon fresh/frozen Coho

578 Steelhead trout Fresh/frozen Rainbow

517 Atlantic salmon Frozen raw Atlantic

948 Pink and or chum salmon "Parfried in soybean or canola

oil" Pink

717 Sockeye salmon Frozen raw Sockeye

412 Atlantic salmon Canned Atlantic

383 "salmon" Frozen raw Pink

808 "salmon" Hot-smoked Chum

422 "salmon" cold-smoked Atlantic

888 Atlantic salmon Cold-smoked Atlantic

822 King salmon Hot-smoked Chinook

855 Keta salmon Frozen raw Chum

836 Keta salmon Hot-smoked Chum


843 Sockeye salmon Hot-smoked Sockeye

978 "salmon" Hot-smoked Coho

279 Sockeye salmon cold-smoked Sockeye

911 "salmon" Hot-smoked Chum

856 "salmon" Cold-smoked (nova) Chum

196 Pink salmon Grilled, packed in retort-pouch Pink

61

Figure 3.1. Typical results after agarose gel electrophoresis of single-plex traditional PCR for identification of salmonid species. Results from sockeye salmon are shown. All eight species-specific primers were tested. Bands were present for sockeye salmon primers (lane 9, 183bp) and the universal primer set (lane 2, 205bp). No bands were present for non-specific primers (lanes 3-8).

Universal

Sockeye

Ladder

200bp

62

Chapter 4: Application of DNA Barcode Sequences for Differentiation of Pest Aphid Species Using Real-time and Conventional PCR The development of other tests using DNA barcode sequences has several benefits for

certain applications in the food and agricultural sectors. Real-time PCR was

successfully employed for salmonid identification in Chapter 3. Chapter 4 focuses on

another application where rapid identification is crucial. This chapter consists of a

combination of the following two published papers dealing with development of real-time

PCR assays for identification of pest aphids, with the addition of a direct visualization

method for soybean aphid identification.

Naaum, A.M., Foottit, R.G., Maw, H.E.L. and Hanner, R. 2012. Differentiation between Aphis pomi and Aphis spiraecola using multiplex real-time PCR based on DNA barcode sequences. J. Appl. Entomol. 136, 704-710.

and

Naaum, A.M., Foottit, R.G., Maw, H.E.L. and Hanner, R. 2012. Real-time PCR for identification of the soybean aphid Aphis glycines Matsumara. J. Appl. Entomol. In press.

Apple Aphid Identification

The green apple aphid (Aphis pomi De Geer 1783) and the spirea aphid (Aphis

spiraecola Patch 1914) are pests of apples in North America (Foottit et al. 2006). They

play a role in disease transmission (Blackman and Eastop 2000), and affect healthy

growth of their hosts (Kaakeh et al 1993). Existing management regimes to effectively

control these pests differ significantly due to varying susceptibility of each species to

common pesticides (Lowery et al. 2005, 2006), and differences in their life-cycles

(Foottit et al. 2009). Therefore, accurate identification of the species present is essential

to pest control. However, the identification process is complicated due to the

morphological similarity between these two species. As a result, confusion between A.

pomi and A. spiraecola often occurs (Blackman and Eastop 2000; Foottit et al. 2009).

Soybean Aphid Identification

The soybean aphid, Aphis glycines Matsumura, is an invasive pest of soybean crops.

Since its arrival in North America in 2000 to 2003, A. glycines had spread to 20 states

63

and three provinces (Ragsdale et al. 2004) and by 2009 was found in 30 states

(Ragsdale et al. 2011). There is a risk of further spread through the U.S and South

America (Venette and Ragsdale 2004; Ragsdale et al. 2011). Populations can change

quickly, doubling in less than two days under ideal conditions (McCornack et al. 2004).

Economic loss from this pest can occur due to direct feeding damage and disease

transmission (Blackman and Eastop 2000). Evidence suggests that effects on

photosynthesis in soybean can occur even at low densities of A. glycines (Macedo et al.

2003).

Due to the potential economic impact of this pest, including continued spread, early

identification and management is critical for crop protection. However, morphological

identification can be complicated due to complex life cycles, morphological plasticity and

similarity to other species that share the same hosts. In North America, A. glycines and

Aphis nasturtii Kaltenbach are difficult to distinguish on the shared primary

(overwintering) hosts, Rhamnus species (Voegtlin et al. 2004). A polyphagous aphid,

Aphis gossypii Glover, is morphologically similar to A. glycines and may also occur on

both soybean and buckthorn. Other species related to A. gossypii occur on Rhamnus in

Asia and Europe (Blackman and Eastop 1994).

PCR Approaches

Molecular techniques, such as DNA barcoding, may compliment visual identification in

cases where species are morphologically similar. DNA barcoding of the 5’ end of the

mitochondrial cytochrome C oxidase I (COI) gene for species identification (Hebert et al.

2003) has been shown to differentiate species of Aphididae (Foottit et al. 2008; Lee et

al. 2011). This method also allows for identification of immature individuals, including

eggs (Shufran and Puterka 2011). DNA sequence data has been used to distinguish

soybean aphid from related aphids on Rhamnus (Kim et al 2010). In addition, the ability

to identify immature forms is especially important for soybean aphids, as adults are not

always found upon initial colonization of a soybean field (Ragsdale et al. 2004).

Molecular methods also allow detection of eggs on overwintering hosts. Although DNA

barcoding represents an important step towards improved pest identification, the lack of

64

portability can still present an obstacle to rapid implementation of pest management. A

simpler, field-ready form of molecular identification is becoming increasingly desirable.

Real-time PCR has been used for pest insect identification (Huang et al. 2010; Walsh et

al. 2005; Yu et al. 2005), including aphids (Naaum et al. 2012). This method is quicker,

and yields results that are easier to interpret than DNA barcoding. An additional benefit

is that portable platforms exist, allowing these assays to be run in the field. Paired with

simple extraction techniques, it has been demonstrated that the entire process of

molecular identification, including DNA extraction, can be performed in the field

(Tomlinson et al. 2005). Rapid, non-destructive DNA extraction techniques for

arthropods (Castalanelli et al. 2010) allow for complete analysis from sample to

identification in the field for insect pests, while preserving the sample as a voucher for

further morphological analysis.

This study describes two real-time PCR assays for pest aphid identification in cases

where morphological identification can be augmented with molecular techniques. A

method of direct visualization of conventional PCR product with primers developed for

A. glycines is also demonstrated.

Materials and Methods

Multiplex PCR Primer and Probe Design

Species-specific primers and TaqMan probes were developed using 690 COI barcode

sequences from 338 species in the publically available BOLD project “Barcoding the

Aphididae”. These sequences were derived from individuals collected over a wide

geographical range. Primer and probe design was based on all unique haplotypes from

this project. Haplotype analysis was carried out using the haplotype variation tool at

ibarcode.org. In total, 209 haplotypes were used for primer and probe design. Primers

and probes (Table 4.1) were developed using AlleleID (version 7.7, Premier Biosoft

International, Palo Alto, California, U.S.A). The A. pomi –specific probe was tagged at

the 5’ end with the fluorescent reporter tetrachloro-6-carboxyfluorescein (TET) and at

the 3’ end with Black Hole Quencher-1 (BHQ-1) quenching dye. The A. spiraecola-

specific probe was tagged with the fluorescent reporter 6-carboxyfluorescein (FAM) at

65

the 5’ end, and BHQ-1 at the 3’ end. The A. glycines probe was tagged with FAM at the

5’ and BHQ-1 at the 3’ end. Optimization of probe and primer concentrations and

cycling conditions was carried out according to the guidelines set by Cepheid for the

Smart Cycler II System (SmartNote 6.2; http://www.cepheid.com/systems-and-

software/smartcycler-system/).

Species Selection and Sample Collection

A number of species of various degrees of relatedness to the target species were

selected for specificity testing, following the classification scheme of Remaudière and

Remaudière (1997). The selected species are: other species of genus Aphis (A.

coreopsidis (Thomas), A. craccivora Koch, A. fabae Scopoli, A. farinose Gmelin, A.

glycines Matsumura, A. gossypii Glover, A. helianthi Monell in Riley and Monell, A.

hyperici Monell in Riley and Monell, A. illinoisensis Shimer, A. impatientis Thomas, A.

lugentis Williams, A. maculatae Oestlund, A. nasturtii Kaltenbach, A. neilliae Oestlund,

A. neogillettei Palmer, A. nerii Boyer de Fonscolombe, A. oestlund Gillettei, A. rubicola

Oestlund, A. varians Patch); other members of the tribe, Aphidini (Braggia eriogoni

(Cowen ex Gillette and Baker), Sanbornia juniperi Pergande ex Baker); representatives

of the related tribe Macrosiphini of the subfamily, Aphidinae (Acyrthosiphon pisum

(Harris), Aulacorthum solani (Kaltenbach), Lipaphis pseudobrassicae (Davis),

Macrosiphum rosae (Linnaeus), Myzus ascalonicus Doncaster, Myzus persicae

(Sulzer), and Sitobion avenae (Fabricius)); and a representative of the distantly related

subfamily, Eriosomatinae (Eriosoma lanigerum (Hausmann), which shares the same

host plant as the target species). All specimens collected were stored in 95% ethanol.

Detailed collection data is given in Table 4.2. Voucher specimens for these collections

were deposited in the Canadian National Collection of Insects (Agriculture and Agri-

Food Canada, Ottawa).

DNA Extraction

For the apple aphid assay, DNA extraction was carried out on individual aphids using

the DNeasy Blood and Tissue Kit (Qiagen, Mississauga, Ontario, Canada) according to

manufacturer guidelines with the following exception: elutions were done in 100 µL of

elution buffer, and passed through the extraction column twice. Concentrations were

66

determined using a Nanodrop 8000 spectrophotometer (Thermo Scientific, Wilmington,

Delaware, U.S.A) and ranged from 4.28 ng/µL – 112.3 ng/µL depending on the species

of aphid. For the soybean aphid assay, DNA extraction was carried out on individual

aphids using the XytXtract Insect (ANDE) extraction kit (Xytogen, Perth, Australia)

according to manufacturer guidelines. DNA concentration was determined using a

Nanodrop 8000 spectrophotometer and ranged from 65 ng/µL to 475 ng/µL. Template

DNA was first adjusted to 50 ng/µL for optimization of the assays. Subsequent tests

utilized DNA directly from the reaction in order to test the ability to successfully identify

target species successfully without first measuring DNA concentration.

Apple Aphid Real-time Multiplex PCR

Primer and probe sets for A. pomi and A. spiraecola (Integrated DNA Technologies,

Coralville, Iowa, U.S.A) were combined in a multiplex assay. Reactions were carried out

in 25 µL volumes containing sterile water, 5 µL template DNA, each forward primer at a

concentration of 0.3 µM, each reverse primer at a concentration of 0.5 µM and each

probe at a concentration of 0.1 µM. OmniMix HS lyophilized master mix (Cepheid,

Sunnyvale, California, U.S.A) was used according to manufacturer guidelines. PCR

cycling conditions were as follows: an initial step of 95°C for 120s, followed by 40 cycles

of 94°C for 11s, 60°C for 40s and 72°C for 25s. Fluorescence readings were taken at

the annealing step of each cycle. All reactions were carried out on the SmartCycler II

platform (Cepheid, Sunnyvale, California, U.S.A). A no-template control (NTC) was

included with each run. The cycle threshold (Ct) value was determined as the number of

cycles at which the fluorescence reading exceeded the detection threshold. If no signal

was observed, a Ct value of 40 was assigned. The multiplexed set was tested using

DNA from 10 individuals of each of the two target species and DNA from 3 individuals of

each of the species selected for background testing. To determine the ability of the

multiplex assay to detect both species simultaneously, DNA admixtures containing the

following ratios of A. pomi: A spiraecola were tested: 50:50, 80:20, 90:10, 20:80 and

10:90.

67

Soybean Aphid Real-time PCR

Reactions were carried out in 25 µL volumes containing sterile water, 5 µL template

DNA, forward and reverse primers at a concentration of 0.3 µM and 0.5 µM

respectively, probe at a concentration of 0.1 µM, and OmniMix HS lyophilized master

mix (Cepheid, Sunnyvale, California, U.S.A.) according to manufacturer guidelines.

PCR cycling conditions were as follows: an initial step of 95 ˚C for 120s, followed by 40

cycles of 94 ˚C for 11s, 60 ˚C for 40s and 72 ˚C for 25s. Fluorescence readings were

taken at the annealing step of each cycle. All reactions were carried out on the

SmartCyclerII platform. The number of cycles required for fluorescence to exceed the

threshold (cycle threshold, Ct) was recorded for each sample. The default threshold of

30 was used. If no signal was observed, a Ct of 40 was recorded. Ten individuals from

the target species were tested. Three individuals from each of the other species

selected were also tested.

Sensitivity and Linearity

Standard curves were generated from 10-fold serial dilutions of target species DNA

from 10 ng/µL to 1 pg/µL to determine the linear range, detection limits, and efficiency of

the assays. Efficiency was calculated based on the slope of the standard curve

according to the formula Efficiency = -1+10(-1/slope).

Direct Visualization

Traditional PCR was optimized for detection of A. glycines using the same primers used

in real-time PCR. Each 20 µL reaction contained forward and reverse primers at a

concentration of 0.4 µM. The remaining master mix components were as suggested in

the manufacturer instructions for Phire Taq (Thermo Scientific). Thermocycling

conditions were 98 ˚C for 30s, followed by 30 cycles of 98 ˚C for 5s, 60 ˚C for 5s and 72

˚C for 5s, followed by a final hold of 72 ˚C for 1min. Rather than visualizing PCR product

using gel electrophoresis, a method where presence of product could be visually

confirmed was employed. Immediately after PCR, 1 µL of Gel Green (Biotium, Hayward,

CA), diluted 10X, was added to each tube. PCR tubes were placed in a GelDoc imager

68

for visualization. Three individuals of the target species, A. glycines, and two other

species commonly occurring on soybeans, A. gossypii and A. nasturtii, were tested.

Results

Apple Aphid Differentiation

Standard curves of the TET and FAM fluorescence channels for the multiplexed primer

and probe set showed linear range from 10ng/µL to 1pg/µL for both target species, with

a detection limit of 1pg/µL (Figure 4.1). Efficiencies were over 99% for each

fluorescence channel in the multiplex assay, and r2 values for standard curves were

0.999. The FAM fluorescence channel showed a signal for A. spiraecola between

Ct=17.21-19.7 and the TET fluorescence channel showed a signal for A. pomi between

Ct=17.66-21.16. Signals were only observed in either fluorescence channel for 3 of the

29 non-target species: In the TET fluorescence channel a signal for Aphis rubicola

between Ct = 26.66-40 was observed and in the FAM fluorescence channel signals

were observed at Ct = 36.86 – 40 for Aphis neogillettei and Ct = 38.45-40 for Aphis

coreopsidis. No false-negatives were observed in this study, and no signal was

observed for any no-template controls. The assay was also able to successfully detect

both species in DNA admixtures with as low as 10% of the DNA belonging to one of the

two targets.

Soybean Aphid Identification

Signals were observed in the FAM fluorescence channel for A. glycines between

Ct=19.81-25.72, and for Aphis oestlundi between Ct=30.75-32.51. None of the other

non-target species produced a signal. No false negatives were observed in this study

and no signal was observed for any no-template “blank” controls. The standard curve

(r2=0.994) showed a linear range of 120 ng/µL-0.12 ng/µL, a detection limit of 0.012

ng/µL and efficiency of 110% (Figure 4.2).

The direct visualization method clearly differentiated between A. glycines and other

aphid species found on soybean (A. gosypii and A. nasturtii). The fluorescence

observed was similar between NTC and non-target species, with the target species

clearly differentiated (Figure 4.3).

69

Discussion

Using aphid sequences available in BOLD, real-time multiplex PCR assays were

designed to: 1) successfully differentiated Aphis pomi from Aphis spiraecola and 2)

accurately identify A. glycines. A method of direct visualization for soybean aphid

identification was also described.

The apple aphid assay was specific to the target species. Though some cross-

amplification was observed with a small percentage of non-target species in the apple

aphid assay, these were all observed at Ct >35. Only one species out of the 29 tested

gave a signal under Ct = 35, and therefore could cause a false positive in routine

analysis. The use of a cutoff value of Ct = 25 to indicate a positive result from target

species would circumvent this issue. The highest Ct value observed for Aphis pomi was

below this suggested cutoff (Ct = 21.16) and the lowest Ct value for Aphis rubicola

observed in this study (Ct = 26.6) still remained above the suggested cutoff. The

suitability of the cutoff could be confirmed with analysis of more individuals of Aphis

rubicola during validation of this assay. A Ct cutoff value of 25 is unlikely to cause

confusion between weak false-positive signals and weak signals due to low DNA

concentration of targets, as this value corresponds to a DNA concentration of the target

species of less than 0.1 pg/µL according to the standard curve, which is over 40 times

less concentrated than the lowest concentration obtained from DNA extraction from a

single aphid observed in this study. Furthermore, in practice this deficiency is not likely

to be a problem since such a test is likely to be applied only in situations in which aphid

species that are likely to give a false positive result do not occur. For example, in the

current context, only other aphid species that are likely to be found on apple need be

considered.

For the target species, a 100% correlation between the morphologically-assigned

species and the species identified using real-time PCR was observed indicating the

method could replace or confirm morphological identification of adults, with the added

benefit of being applicable to egg and immature forms. The wide range of DNA

concentrations used in this study did not appear to impact the ability to identify the

target species from single individuals.

70

It was possible to detect both species simultaneously, suggesting that extraction of

multiple aphids at once will not affect the ability to detect one or both of these species in

the resulting mixture, reducing the cost per individual aphid identification.

Though the PCR protocol takes under 1 hour, the lysis step in the extraction protocol

adds time to the identification process. However, this processing time could be greatly

reduced with the adoption of more rapid extraction methods (e.g.. Hunter et al. 2008,

Castalanelli et al. 2010) to obtain results much more quickly. These methods would

reduce extraction time from several hours including lysis, to approximately 30 minutes.

The soybean aphid real-time PCR assay proved to be an accurate method of identifying

A. glycines, correlating with the morphological identification in 100% of trials. This

indicates that this method could be used to augment morphological identification of this

species. Cross-amplification was only observed for one non-target species, with Ct

values always exceeding 30. The largest Ct for any A. glycines individual was 25.72.

Therefore the use of a Ct=30 cutoff for positive identification of A. glycines can be used

to avoid incidence of false positives. Given that no DNA amplification was observed for

any of the other 26 non-target species, we also conclude that the assay is specific

enough to differentiate A. glycines from closely related species, including two species

with which it shares hosts: A. gossypii and A. nasturtii. Therefore, rapid identification of

the soybean aphid is possible on either it’s primary or secondary host.

The ANDE DNA extraction method takes just thirty minutes to complete, and requires

only that the solution be heated to 95 ˚C to obtain DNA (Castalanelli et al. 2010). This

simple, rapid method can therefore be carried out in the field using a heating block of

any kind, including the real-time PCR platform itself. This improves on previous studies

where DNA extraction proved to be a practical limitation to field identification of pests

with real-time PCR (e.g. Naaum et al. 2012). Additionally, the non-destructive nature of

the extraction provides a means for adult specimens to be recovered for independent

morphological analysis and/or archiving.

The A. glycines primers were also successfully used to develop a method of directly

visualizing traditional PCR product for identification of this species. For those individuals

tested, this was able to clearly differentiate this species from other species commonly

71

found on soybean. This method is suitable for use with a portable UV light for direct

identification in the field with very little equipment or expertise required.

Conclusion

All known haplotypes of the target species, as well as sequences from other aphid

species known to feed on apples and soybeans in North America were used in the in

silico design of primers and probes published here. However it will be important for

future studies to focus on the practical confirmation of the specificity of these primers

and probes. Though further optimization and validation of these results is necessary,

the data presented here show that these real-time PCR assays are an effective

approach to complement current methods of aphid species identification on apple and

soybean. This study also confirms the utility of DNA barcode sequences as a resource

for designing probes and primers for real-time PCR assays to distinguish between

morphologically similar species. As BOLD continues to grow, and more pest species

and their close relatives are referenced, comparable assays could be designed to do

the same for other morphologically similar pairs or small groups of species.

With rapid identification available, appropriate pest management regimes can be put

into place almost immediately. This also allows on-site identification of pests of potential

quarantine significance, for example at border crossings, which help mitigate the spread

of pests like the soybean aphid. The use of the portable Smart Cycler II system, or

direct visualization, makes on-site analysis a possibility. Due to the simplicity of assay

and the ease with which results can be interpreted, identification of specimens can be

carried out by non-specialists after limited training with high confidence in the results.

Lyophilizing all reagents in the appropriate concentrations for sale in commercial kits

would further reduce the likelihood of human error.

72

Table 4.1. Sequences of species-specific oligonucleotides used in this study and amplicon length 5’-3’. All primers and probes target portions of the Aphis glycines COI DNA barcode region.

Aphis pomi Aphis spiraecola Aphis glycines

Forward TGCCCAGATATATCTTTCC GAGCAATTAATTTTATTTGTACA GGATGTCCAGATATATCTTTTC

Reverse CCTGTTCCTGTTCCATTA GCTAGAACTGGTAGAGA CCTGTTCCTGTTCCATTA

Probe TET-AGATTCTGATTATTACCGCCTTCACT -BHQ1

6FAM-TTAAATCAAATCCCACTATTTCCATG-BHQ1

6FAM-AGATTCTGATTATTGCCACCTTCATT-BHQ1

Amplicon length 110 base pairs 130 base pairs 113 base pairs

73

Table 4.2. Collection information for aphid specimens used in this study.

Species Sample Identifier Locality Latitude Longitude Date Host Aphidinae: Aphidini

Aphis coreopsidis (Thomas)

CNC#HEM049398 USA: Georgia: Sirman

31.100 -82.961 2002/06/04 Baccharis halimifolia

Aphis craccivora Koch

CNC#HEM050467 Guam: Barrigada

13.509 144.832 2003/02/26 Spondias purpurea

Aphis fabae Scopoli

CNC#HEM057623 Canada: British Columbia: Sutton Pass

49.280 -125.355 2007/08/22 Rumex sp.

Aphis farinosa J.F. Gmelin

CNC#HEM057573 Canada: British Columbia: Kitamaat Village

53.981 -128.650 2007/08/18 Salix discolor

Aphis glycines Matsumura

CNC#HEM052292 Canada: Quebec: Sainte-Anne-des-Plaines

45.775 -73.828 2004/07/21 Glycine max

Aphis gossypii Glover

CNC#HEM007420 Canada: Ontario: Wolf Island

44.208 -76.259 1993/05/22 Hibiscus sp.

Aphis helianthi Monell

CNC#HEM057205 Canada: Manitoba: Burntwood River

55.755 -97.843 2007/07/21 Cicuta sp.

Aphis hyperici Monell

CNC#HEM049278 USA: North Carolina: Rainbow Springs

35.093 -83.556 2002/05/30 Hypericum prolificum

Aphis illinoisensis Shimer

CNC#HEM049338 USA: Tennessee: Tallassee

35.552 -84.091 2002/06/01 Vitis sp.

Aphis impatientis Thomas

CNC#HEM070495 Canada: Ontario: Ottawa

45.386 -75.704 2010/08/31 Impatiens capensis

Aphis lugentis Williams

CNC#HEM049241 USA: North Carolina: Buck Creek

35.086 -83.616 2002/05/30 Senecio plattensis

Aphis maculatae Oestlund

CNC#HEM057479 Canada: Ontario:

47.863 -83.906 2007/07/30 Populus tremuloides

74

Prairie Bee River

Aphis nasturtii Kaltenbach

CNC#HEM007709 Canada: Ontario: Packenham

45.341 -76.291 1994/06/05 Rhamnus cathartica

Aphis neilliae Oestlund

CNC#HEM070027 Canada: Ontario: Pitopiko River

49.769 -84.773 2010/07/09 Physocarpus opulifolius

Aphis neogillettei Palmer

CNC#HEM070051 Canada: Ontario: Dryden

49.786 -92.810 2010/07/10 Cornus stolonifera

Aphis nerii Boyer de Fonscolombe


45.377 -75.655 1994/10/08 Asclepias syriaca

Aphis oestlundi Gillette


45.371 -75.733 2010/08/20 Oenothera biennis

Aphis pomi DeGeer

CNC#HEM039309 Canada: Ontario: Yonge Mills

44.506 -75.835 2001/07/12 Malus domestica

Aphis rubicola Oestlund


45.386 -75.704 2010/08/31 Rubus idaeus ssp. strigosus

Aphis sedi Kaltenbach

n/a Canada: Ontario: Guelph

43.532 -80.225 2012/06/08 Hyotelephium telephium

Aphis spiraecola Patch

CNC#HEM033926 Canada: British Columbia: Penticton

49.473 -119.583 2000/05/27 Spiraea sp.(ornamental)

Aphis varians Patch

CNC#HEM057244 Canada: Manitoba: Odei River

55.994 -97.357 2007/07/22 Epilobium angustifolium

Braggia eriogoni Cowen

CNC#HEM033259 Canada: British Columbia: Williams Lake

51.973 -122.666 2000/07/20 Eriogonum heracloides

Hyalopterus pruni (Geoffroy)

CNC#HEM061299 Canada: Ontario: Port Colbourne

42.930 -79.252 2008/06/26 Phragmites australis

Sanbornia juniperi Pergande

CNC#HEM069879 USA: Colorado: Drake

40.450 -105.386 2010/06/23 Juniperus scopularum

75

Aphidinae: Macrosiphini

Acyrthosiphon pisum (Harris)


45.033 -75.902 2010/09/05 Coronilla varia

Aulacorthum solani (Kaltenbach)

CNC#HEM056959 Canada: Ontario: Malakoff

45.111 -75.788 2007/07/10 Thalictrum sp.

Lipaphis pseudobrassicae (Davis)


45.033 -75.902 2010/09/04 Erucastrum gallicum

Macrosiphum rosae (Linnaeus)


45.389 -75.708 2010/08/10 Rosa sp.

Myzus ascalonicus Donscaster

CNC#HEM061938 Canada: British Columbia: Langley

49.100 -122.600 2008/05/07 Agoseris aurantiaca

Myzus persicae (Sulzer)

KSP 24 USA: Washington: Prosser

46.257 -119.729 Solanum tuberosum

Sitobion avenae (Fabricius)

CEF colony Canada: Manitoba: Winnipeg

49.883 -97.150 1995/08/15 Poaceae

Eriosomatinae Eriosoma lanigerum Hausmann

CNC#HEM007886 Canada: Nova Scotia: Wentworth

45.633 -63.432 1994/08/07 Malus domestica

76

Figure 4.1. Linearity of multiplex assay for A. pomi (TET; r2 = 0.999, slope = -3.319) and A. spiraecola (FAM; r2 = 0.999, slope = -3.331). Standard curves are generated from 10-fold serial dilutions of A.pomi or A. spiraecola DNA from 10 ng/µL to 1 pg/µL. Ct values shown are an average of three replicates per concentration.

77

Figure 4.2. Standard curve for A. glycines (r2 = 0.994, slope = -0.325), generated from 10-fold serial dilutions of target DNA from 120 ng/µL-0.12 ng/µL. Ct values shown are an average of three replicates per concentration.

-‐1.5

-‐1

-‐0.5

0

0.5

1

1.5

2

2.5

26 29 32 36

Log DNA Concentration

FAM Ct Value

78

Figure 4.3. Direct visualization assay for identification of A. glycines. Tube one contains A. glycines DNA, tube 2 A. gossypii, and tube 3 A. nasturtii. Tube 4 is a no-template control.

1 2 3 4

79

Section 2: The Use of Synthetic DNA for Food Product Traceability: Apple Juice Case Study At times, endogenous species-specific DNA cannot provide the information required for

authenticity or traceability purposes. In these cases, methods like those in Section 1

cannot be employed. Instead, traceability can provide important information about the

origins of a product and its ingredients. This section shows proof of concept for the use

of an oligonucleotide traceability tag for food products and two possible detection

methods.

Introduction to Synthetic Tagging Food fraud is a serious socio-economic issue evolving new dimensions of complexity as

industrialization and globalization of trade increases. It has been estimated that

adulteration costs businesses $10-15 billion each year (Grocery Manufacturers

Association 2010). The high profit margin, and low risk of detection fuel the continuation

of these unethical practices. In addition to economic loss, adulteration can impact the

lifestyle and health of consumers. For example lifestyle and religious choices can be

affected when products are mislabeled as vegetarian, organic, or Kosher. Adulteration

also raises health concerns when adulterants may contain allergens or other harmful

substances (e.g. sulfites).

Consumers expect that their purchases are safe, and that they are getting an authentic

product, and are willing to pay for that assurance (Hobbs 2003, Hobbs et al. 2005,

Hobbs 2006). Traceability is a key factor for food authentication and safety. The ability

to track a product throughout the supply chain allows swift removal of problematic

batches. Effective traceability measures also allow companies to better market the

credence attributes, such as environmental sustainability, or geographical origin, to

consumers. Governments and key stakeholders in the food industry continue to develop

regulations for traceability and authenticity testing (Hobbs 2006), yet these regulations

are difficult to enforce due to supply chain complexity.

Current traceability methods rely on external tags such as barcode labels and Radio

Frequency Identification (RFID) tags. Although they enhance supply chain

80

management, these external tags can be tampered with, and allow whole-product

substitution to go unnoticed. An additional complication is that individual ingredients in

manufactured goods cannot be traced independently with conventional tags, as only the

final product is labeled. This limitation also applies to stealth product tagging

approaches, like those offered by Microtrace (http://www.microtracesolutions.com/),

which tags a final product rather than individual ingredients.

The use of an internal traceability marker in the product itself would make tampering

more difficult, allowing better assurance that consumers are getting a safe, authentic

product. Individual ingredients in a manufactured product can also be tagged. Tagging

products with a synthetic oligonucleotide marker for traceability purposes is one method

that has been used to track luxury goods and electronics. Applied DNA Sciences

(http://www.adnas.com) is one company based in New York that tags products with

unique DNA for downstream authenticity testing. Oligonucleotide tagging has not,

however, been applied to food products.

Custom oligonucleotides of any sequence can be easily manufactured synthetically.

The individual nucleotides can be coded, and that proprietary and unique code linked to

represent a particular company, date, or batch, much as a numerical barcode would be.

Short fragments of DNA, like these oligonucleotides, are stable over time in many pH

and temperature conditions, making them an ideal traceability marker for food products.

Another benefit to oligonucleotide tags is that they can be detected by a variety of

methods that may be tailored to industry demands.

The following two chapters describe the conceptual development of oligonucleotide

traceability tags and their addition to food products, in addition to tag detection using

two different methods: real-time PCR and electrochemical impedance spectroscopy,

demonstrating the feasibility of using oligonucleotide tags for enhanced supply chain

traceability of foods and ingredients. In addition to the development of two methods of

tag detection, the stability of the tag over time, and its ability to withstand processing

were investigated.

81

Chapter 5: Real-time PCR Approach for Oligonucleotide Tag Detection Introduction Broadly applied for detection of food-borne pathogens, real-time PCR has been

introduced relatively recently as a tool for authentication of food by identifying the

species present in a product. As reviewed recently (Rodríguez-Lázaro 2013), this

method has been used successfully to authenticate a variety of foods including but not

limited to, meat, dairy and cereals. Real-time PCR allows monitoring of DNA

concentration at each amplification cycle via fluorescence detection that is directly

proportional to the DNA copy number. This allows for quantification of a target molecule.

In this case, quantification of the oligonucleotide tag would allow for traceability as well

as simultaneous detection of debasing of a tagged product with an adulterant.

Methods

Tag Design and Synthesis

Random oligonucleotides were generated and then screened using GenBank for

homology to known sequences that may occur in food or beverages. From those with

no match, a 76 base pair sequence was selected. M13 tails were added to this

sequence to facilitate generation of double stranded tag from the single stranded

oligonucleotide using universal primers. The complete tag (Figure 5.1) was 102 base

pairs long, representing the upper limit of synthetic oligonucleotide synthesis. The

oligonucleotide was purchased from IDT (Integrated DNA Technologies, Coralville, IA,

U.S.A). A double stranded oligonucleotide tag (ONT) was generated from the single

stranded oligonucleotide using traditional PCR. The thermocyler program was as

follows: 94 ˚C for 2 min, 34 cycles of 30s at 94 ˚C, 40s at 52 ˚C and 1 min at 72 ˚C and

a final extension of 72 ˚C for 10 min. The concentration of double stranded tag was

determined using a Nanodrop 8000 spectrophotometer (Thermo Scientific, Wilmington,

Delaware, U.S.A).

Detection in Apple Juice

The ONT was added to apple juice (no name®, Loblaws Inc., Toronto, Canada), and

molecular grade water in serial dilutions between concentrations of 1200 pg/µL –

82

0.00012 pg/µL. Subsequent detection was attempted after DNA extraction using real-

time PCR, as described in detail below, on three different days using three different

batches of ONT. Negative controls with no ONT added were included as well as no-

template controls. Two different extraction methods were tested, as detailed below.

Simulated Laboratory Scale Apple Juice Processing

The ability of the tag to withstand complex food processing conditions of time,

temperature, multiple enzymatic steps and filtration was estimated using a simulated

apple juice processing protocol, developed in partnership with SunRype Products Ltd

(Kelowna, BC). This laboratory scale process included approximations of all the

commercial steps employed in apple juice production. Two golden delicious apples

were blended together with 10 mL of sterile molecular grade water in a blender on

puree setting. The ONT was added in concentrations of 1200 pg/µL – 12 pg/µL to 15 g

of the apple puree. No ONT was added to the control samples. Mash enzyme was

added and was mixed using a stir bar and plate for 90 minutes. The mash was then

vacuum filtered through VWR 417 filter paper. The first aliquot was taken for analysis at

this stage (stage 1). The filtered juice was then heated to 90 ˚C and then held at this

temperature for 1 minute. Another aliquot was taken for analysis at this stage (stage 2).

Samples were cooled at room temperature until they reached 50 ˚C, and pectinase and

amylase were added. Dosage rates for enzymes used are not provided due to their

proprietary nature. The sample was then incubated at 50 ˚C for 90 minutes in a water

bath. A third aliquot was taken for analysis after this step (stage 3). The resulting

mixture was then vacuum filtered through VWR 413 filter paper. A final sample was

taken for analysis (stage 4). Real-time PCR was used to determine if the tag could be

detected at each stage of the process (see details below). Laboratory scale apple juice

processing was repeated three times with three separate batches of ONT generated

from the single stranded oligonucleotide.

Shelf-life Study

To determine the stability of the ONT over time, a shelf-life study was conducted. The

ONT was added to apple juice concentrate at concentrations between 1200 pg/µL-0.12

pg/µL. The tagged concentrate was stored at 4 ˚C. Samples were removed after 2

83

weeks, 6 weeks and 10 weeks and stored at -20C for future analysis. Real-time PCR

was used after DNA extraction of samples, as described below, to determine if the tag

could still be detected after storage.

DNA Extraction

Two extraction methods were used for water and apple juice samples. The first was

Qiagen DNeasy Blood and Tissue Kit. Extractions were carried out according to the

manufacturer protocol, with the following modifications: for apple juice samples, 200 µL

of apple juice was used. The lysis step was omitted and samples were eluted in 100 µL

of elution buffer. The second extraction method involved spotting 15 µL of liquid sample

onto FTA Elute cards (GE Healthcare Biosciences, Pittsburgh, PA, U.S.A.) and allowing

the card to dry. The samples were spotted in approximately the centre of the sample

circles. After drying, a 3mm punch was taken from approximately the centre of the

sample circle. DNA extraction was then carried out on the punch according to

manufacturer instructions.

Real-time PCR

Primers and probes were designed using AlleleID (version 7.7; Premier Biosoft

International, Palo Alto, CA, U.S.A) and ordered from IDT. Sequences for all

oligonucleotides used can be found in Table 5.1. All real-time PCR reactions were

carried out in the SmartCycler II platform (Cepheid, Sunnyvale, CA, U.S.A) using default

background and threshold settings. Optimization of the assay, including primer and

probe concentrations, was carried out according to guidelines for the Smart Cycler II

System (SmartNote 6.2; http://www.cepheid.com/systems-and-software/smartcycler-

system/). The final protocol was as follows: initial denaturing step at 94 ˚C for 120s,

followed by 40 cycles of 94 ˚C for 11s, 60 ˚C for 40s and 72 ˚C for 25s. Reactions were

carried out in 25 µL volumes with a final concentration of primers and probe of 0.6 µM

and 0.2 µM respectively. OmniMix Master Mix (Cepheid) was used according to

manufacturer guidelines. A no template control (NTC) was included with each run.

84


Tag Detection in Apple Juice

Using real-time PCR, the ONT tag was successfully detected at concentrations from

1200 pg/µL – 0.0012 pg/µL in water and apple juice using both DNA extraction methods

(Table 5.2). Although the limit of detection was 0.0012 pg/µL after extraction using the

DNeasy kit, the linear range was observed to be between 1200 pg/µL – 0.12 pg/µL with

both the DNeasy kit and the FTA cards (Figure 5.2 and Figure 5.3). Standard curves

showed higher coefficients of determination of using the extractions done with DNeasy

kit than those done from samples spotted onto FTA cards (r2=0.998 and 0.993

respectively). Efficiency, calculated based on the slope of the standard curve was

92.31% after the DNeasy extraction and 98.03% with the FTA card extraction. Both

methods were shown to be suitable for detection of the tag over a broad range of

concentrations. The FTA card extraction represents a simpler method of extraction,

which may be more suitable for applications or industries where tag quantification, and

therefore detection of debasing, is not a high priority. However, based on the variability

in Ct values observed with the use of the FTA card extractions (standard deviations

from 0.09-1.74), the DNeasy extraction kit is more suitable for quantification purposes.

These instances of higher variation are likely due to the variability in sample placement

on the card, as well as the location from which the punch is taken. Standard deviations

between samples of the same concentration were 0.11-0.45 using the DNeasy kit. The

MIQE guidelines (Bustin et al. 2009) recommend that a fresh batch of template should

be prepared when a shift in Ct between 0.5-1 is detected in points on the calibration

curve, suggesting that standard deviations of less than 0.5 between different batches of

ONT indicate potential for quantification purposes. No NTC samples had a Ct below 30.

The average Ct for NTC samples in all real-time PCR experiments was 38.39. The

lowest Ct observed in a NTC was 30.60.

Simulated Apple Juice Processing

A lab-scale simulation showed that the ONT could be detected even after apples

underwent all processing steps (Table 5.3). Although some increase in Ct values was

observed from the beginning of processing to the end, suggesting some loss of target,

85

the ONT was detected at each stage of processing when added in concentrations from

1200 pg/µL - 12 pg/µL. These results show that the oligonucleotide tag used in these

experiments could be added at any stage of apple juice processing as it was still

detected in the final product. Apple juice processing involves heating, filtration,

enzymatic treatments and mechanical breakdown (e.g. shearing). The ability to detect

ONT after treatment with this wide range of processing steps suggests it is suitable for

use in most foods or natural health products. If necessary, further stability may be

achieved by ONT encapsulation which has been employed for other food ingredients

(Desai and Park 2005). The wide disparity between Ct values for samples of the same

concentration suggests that if quantification is required, the tag should be added post-

production, for example at the packing stage, if possible.

Tag Stability

Tag detection in apple juice concentrate was possible for all concentrations of ONT

tested with Ct values shown in Table 5.4. The linear range was determined to be

between 1200 pg/µL – 0.12 pg/µL (Figure 5.4). Reaction efficiency as calculated from

the slope of the standard curve was 100.46% and standard deviations between samples

of the same concentration ranged from 0.08-0.98. This higher variability is likely due to

the difficulty in accurately measuring small volumes of the viscous apple juice

concentrate. For quantification purposes, tagged concentrate may be diluted to single

strength (e.g. 11.5 ˚Brix) before tag detection is attempted. However, Ct values

remained similar over 10 weeks of incubation at 4 ˚C (Table 5.4), suggesting that the

tag was stable over time and could successfully be used for traceability of apple juice

concentrate directly, without dilution, if quantification of adulteration is important.

86

Table 5.1. List of oligonucleotides used in real-time PCR tag detection

Oligonucleotide (5’ – 3’) Sequence

Forward Primer GTGTGAATCACTTGCTC

Reverse Primer ACAGCTATGACCTTTCC

Probe FAM –CAAGCAACCATACAAGAACTTATATCG –BHQ1

M13 Forward Primer TGTAAAACGACGGCCAGT

M13 Reverse Primer CAGGAAACAGCTATGAC

Amplicon Size 82 bp

87

Table 5.2. Comparison of results of tag detection in apple juice from FTA and Qiagen Blood and Tissue Kit extraction methods.

Concentration of tag

Average Ct values (FTA)

Standard Deviation

Average Ct values (Qiagen)

Standard Deviation

1200 pg/µL 15.78 1.58 11.80 0.19

120 pg/µL 18.39 1.70 15.19 0.11

12 pg/µL 22.44 1.05 18.44 0.44

1.2 pg/µL 26.21 1.74 22.07 0.45

0.12 pg/µL 28.71 0.09 25.96 0.37

88

Table 5.3. Tag detection after lab-simulated apple juice processing.


Average Ct value stage one

Standard Deviation

Average Ct value stage two

Standard Deviation

Average Ct value stage three

Standard deviation

Average Ct value stage four

Standard Deviation

1200 pg/µL 16.01 2.50 19.33 0.98 19.05 2.57 19.44 2.65

120 pg/µL 19.72 2.21 23.03 1.29 22.01 1.56 22.20 1.94

12 pg/µL 22.26 0.79 26.35 0.55 25.01 1.84 26.86 1.55

89

Table 5.4. Results of shelf-life testing. Changes in Ct values were recorded after ten weeks of storage at 4 ˚C.


Initial Ct values

Ct values after 2 weeks



Standard Deviation

1200 pg/µL 12.16 12.22 12.11 12.04 0.08

120 pg/µL 15.17 14.59 14.33 15.60 0.57

12 pg/µL 17.83 18.07 18.11 18.85 0.44

1.2 pg/µL 22.87 21.81 21.28 23.46 0.98

0.12 pg/µL 24.51 25.83 24.88 26.39 0.86

90

Figure 5.1. Oligonucleotide tag. In blue is the 76 base-pair unique region flanked by M13 ends (yellow). In red is the Taqman probe and in green are the sequence-specific primers designed for use in real-time PCR

91

Figure 5.2. Linearity curve for serial dilutions (1200pg/µL – 0.12pg/µL) of tag recovered from apple juice using Qiagen DNeasy Blood and Tissue Kit. Slope of the curve is -3.52, r2 is 0.999. Ct values shown represent an average of values obtained from three batches of ONT, and three replicates per concentration.

0

5

10

15

20

25

30

-‐1.5 -‐1 -‐0.5 0 0.5 1 1.5 2 2.5 3 3.5

Average Ct Value

Log DNA Concentration (pg/uL)

92

Figure 5.3. Linearity curve for serial dilutions (1200pg/µL – 0.12pg/µL) of tag recovered from apple juice using FTA extraction cards. Slope of the curve is -3.37, and r2=0.993. Ct values shown represent an average of values obtained from three batches of ONT, and three replicates per concentration.

0

5

10

15

20

25

30

35

-‐1.5 -‐1 -‐0.5 0 0.5 1 1.5 2 2.5 3 3.5

Average Ct Value

Log DNA Concentration (pg/uL)

93

Figure 5.4. Linearity curve for serial dilutions (1200 pg/µL – 0.12 pg/µL) of tag recovered from apple juice concentrate. Slope of the curve is -3.51, r2 is 0.998. Ct values shown represent results of the initial baseline phase of the shelf-life testing.

0

5

10

15

20

25

30

-‐1.5 -‐1 -‐0.5 0 0.5 1 1.5 2 2.5 3 3.5

Average Ct Value

Log DNA Concentraion (pg/uL)

94

Chapter 6: Electrochemical Impedance Spectroscopy Approach When ONT quantification is not a primary concern, alternate detection methods to real-

time PCR for tag detection could be employed. For example, Electrochemical

Impedance Spectroscopy (EIS) can be used for detection of DNA in various biosensing

applications (Lidsat and Schafer 2008) but this has never been shown in food products.

In this study, EIS was used to recover a synthetic oligonucleotide from apple juice,

demonstrating a second possible method of ONT detection (Figure 6.1).

Methods

Chemicals and materials

All aqueous solutions were prepared with deionized water produced by a Millipore Milli-

Q™ water purification system. Sodium perchlorate (anhydrous) was purchased from

Alfa Aesar (Ward Hill, MA, U.S.A). Zinc perchlorate hexahydrate was obtained from

Aldrich (Milwaukee, WI, U.S.A). Potassium ferrocyanide trihydrate and EDTA were

from EM Science (Darmstadt, Germany). Tris (tris hydroxymethyl 1-aminomethane,

ultrapure) was from BMD (Indianapolis, IN, U.S.A). Sulfuric (95- 98%) and perchloric

(70%) acids were from Caledon Laboratory Chemicals (Georgetown, ON, Canada).

Prior to use, apple juice (no name®, Loblaws Inc., Toronto, Canada) was adjusted to pH

8.0 by addition of Tris buffer (0.5 M, pH 10.5).

Oligonucleotides

Oligonucleotides used in EIS tag detection were obtained from BioCorp DNA Inc.

(Dollard-des-Ormeaux, Quebec, Canada) and were of HPLC purity. Oligonucleotides

were resuspended in 20 mM Tris-ClO4 (pH 8.6) and 1 mM EDTA to a concentration of

100 µM by incubating at 65°C for 10 minutes. The following oligonucleotides were used

in this study:

1: HO-(CH2)6-S-S-(CH2)6-5’-CAAGTGATTCACACTCAAGTACTGG-3’

2: 5'-CCAGTACTTGAGTGTGAATCACTTG-3'

3: 3’-GCG-AGG-TAG-GGG-TTG-TTT-GAA-5’

95

Disulfide-labeled capture strand (1) was used to prepare DNA modified sensor

electrodes, and was designed as complimentary to the tag sequence (Strand 2). Strand

2 was the target strand used to tag the apple juice and was a 25 base pair segment of

the oligonucleotide tag designed for use with real-time PCR (Naaum et. al.,

unpublished). Strand 3 corresponds to chicken (Gallus gallus) CYTbos1 primer

sequence and was used as a negative control (Pascoal et al. 2005) to ensure that any

changes in the observed impedance were due to binding of complimentary tag, rather

than the presence of non-specific nucleotides in solution.

Sensor electrodes

Sensor electrodes used in this work were custom made. Briefly, 100 µm dia. gold

electrodes were obtained by placing 100 µm dia. Au wire (99.998%, Premion, Alfa

Aesar, MA, U.S.A) into a low melting point glass capillary 1.5/0.84 mm OD/ID (World

Precision Instruments, Inc., FL, U.S.A) that was sealed at one end. Next, the Au wire

was sealed under high vacuum (Model 17 High Vacuum Pump, Labconco, MO, U.S.A)

using a propane torch. The tip of the sealed glass capillary was then polished flat to

expose the Au disk. The surface of the electrode was polished using rotating alumina

lapping discs (3.0, 0.3 µm, World Precision Instruments, Inc., FL, U.S.A). The resulting

electrodes were then evaluated under an optical microscope and tested by performing

cyclic voltammetry. Before modification, electrodes were cleaned electrochemically by

cycling the potential applied to the electrode between the onset of hydrogen and oxygen

evolution in 1 M H2SO4. Next, electrode surface area was estimated for each individual

electrode before it was placed in the capture strand suspension. Surface area was

calculated by integration of oxide stripping peak observed in 1 M H2SO4 and assumption

that a charge of 482 µC/cm2 is required to strip the monolayer of oxygen (Oesch and

Janata 1983). Using this procedure, Au disk electrodes with roughness factor of less

than two were obtained. Immediately after cleaning, electrodes were rinsed with copious

amounts of water and incubated in 100 mM suspension of the capture strand where

they were incubated for 48 hours to facilitate formation of films consisting of dsDNA and

hydroxyalkyl groups tethered to the gold surface.

96

Instrumentation and electrochemical measurements

All electrochemical measurements were carried out under ambient conditions (without

removal of dissolved oxygen) using CompactStat - Electrochemical Interface &

Impedance Analyzer (Ivium Technologies B.V., Eindhoven, The Netherlands). A

standard three-electrode cell was used with the 100 µm Au disk electrode serving as the

working electrode, a platinum wire (5 cm length, 0.25 mm dia., 99.9%, Alfa Aesar, MA,

U.S.A) as the auxiliary electrode, and a Ag/AgCl reference electrode (CH Instruments,

TX, U.S.A) that was connected to the electrochemical cell via a homemade agar salt

bridge (1 M KNO3). The electrochemical cell was housed in a grounded Faraday cage

to minimize environmental interference. All measurements were performed in 20 mM

Tris-ClO4 (pH 8.6) containing 1 mM K4Fe(CN)6/K3Fe(CN)6 as a depolarizer and 50 mM

NaClO4 as a supporting electrolyte. The mean electrode potential, Emean, was held at the

half wave potential of the [Fe(CN)6]4+/[Fe(CN)6]3+ redox couple, Emean = 0.22 V. EIS

measurements were carried out for frequencies ranging from 10 kHz to 0.1 Hz and

excitation amplitude Vamp = 20 mV.

Model System and Tag Detection

A model system was generated by comparison of EIS values for unmodified electrodes

and electrodes modified with strand 1 before and after incubation for two hours in buffer

solution containing the target strand 2 (50 µM). Detection of the tag was determined by

comparison of EIS values for electrodes modified with strand 1 before and after

incubation for two hours in either untagged apple juice or apple juice tagged with strand

2 (50 µM). Apple juice tagged with strand 3 was used as a control to ensure changes in

EIS values were due to complementary binding of DNA.

Nature of Apple Juice-Sensor Interactions

In an attempt to explain the effects observed on impedance after sensor electrode

incubation in untagged apple juice, the effect of metal ions on tag detection was tested

by incubation of sensor electrodes modified with strand 1 before and after incubation

with zinc solution (0.3 mM), and after subsequent incubation with buffer solution tagged

with strand 2(50 µM).

97

Sensor Reuse

The ability to reuse the sensor electrodes was determined by measuring impedance of

a sensor electrode modified with strand 1 before and after incubation with buffer

solution tagged with strand 2 (50 µM) and comparing these to impedance observed

after subsequent dehybridization in Tris buffer at pH 10.5 for one hour, and

rehybridization in tagged buffer solution.


Tag detection in model system

Evaluation of the DNA sensor system was carried out under model conditions using a

series of electrochemical impedance spectroscopy measurements. Figure 6.2 shows

typical impedance spectra (Nyquist plot) observed for the unmodified gold electrode, the

sensor electrode modified with capture strand 1, and the sensor electrode following

incubation with the tagged buffer solution containing strand 2.

The impedance data (Figure 6.2) was interpreted with the help of equivalent circuit

analysis. A proposed equivalent circuit, which was used to model experimentally

observed behavior, is shown in Figure 6.3. Rs represents the solution resistance and is

determined by supporting electrolyte concentration. Qdl is the Constant Phase Element

(CPE) used to account for the frequency dispersion of the double-layer capacitance of

the DNA modified electrode. Impedance of the constant phase element is given by the

following relationship ZQ = 1/(Q(j⋅ω)n, where Q is the frequency-independent constant

relating to the redox properties of the surface, j = -11/2, ω is the angular frequency, and

the exponent n arises from the slope of logZ vs. logf (−1 ≤ n ≤ 1). For n = 0, the CPE

behaves as a pure resistance, n = 1 CPE behaves as a pure capacitor, and for n = 0.5

CPE corresponds to Warburg impedance which is associated with the mass transport

due to the diffusion of ions at the electrode/solution interface (Barsoukov & MacDonald,

2005; Brug, Van den Eeden, Sluyters-Rehbach, & Sluyters, 1984). Rf is the resistance

of the DNA film. Rct represents the charge-transfer resistance related to the faradaic

process, [Fe(CN)6]3- + e ⇄ [Fe(CN)6]4-. Qmt is the constant phase element used to

98

account for the non-ideal behavior of the mass transport impedance arising from the

diffusion of the depolarizer towards the sensor surface.

Predicted impedance spectra are shown in Figure 6.2 as solid lines and estimated

values of equivalent circuit elements are summarized in Table 6.1. Modeling was not

carried out for bare Au electrodes as this is a qualitatively different system than sensor

electrodes and therefore the equivalent circuit proposed in Figure 6.3 could not be used.

Clearly modification of the electrode surface with the DNA recognition film resulted in

noticeable increase of the system impedance in comparison to the bare Au electrode.

This is not surprising because electrostatic repulsion between surface confined ssDNA

and depolarizer hinders the [Fe(CN)6]3- + e ⇄ [Fe(CN)6]4- redox process that occurs at

the electrode surface. Importantly, incubation of the sensor electrode in the buffer

solution containing 50 mM tag results in further increase of the overall system

impedance as electrostatic repulsion between the sensor surface and redox probe

increases upon hybridization between the capture strand and the tag. This is also

reflected by an increase of Rct from 630 (88) Ω⋅cm2 to 855 (79) Ω⋅cm2 and an increase

in Rf from 1287 (117) Ω⋅cm2 to 1401 (162) Ω⋅cm2. Moreover, it is worth noting that the

character of CPE associated with mass transport changes from slightly capacitive, n =

0.58 (0.05), to almost ideal Warburg impedance behavior, n = 0.49 (0.06), while the

character of CPE associated with double layer capacitance remains unchanged upon

hybridization.

Tag detection in apple juice

Sensor electrodes modified with strand 1 were employed to detect the oligonucleotide

tag (strand 2) directly in the apple juice sample. However, when the sensor electrode

was incubated in the tagged apple juice, a negligible change in the impedance

characteristics of the system was observed (data not shown). This loss of sensitivity

towards the tag was likely caused by the interference of the complex apple juice matrix.

Conditioning of the sensor electrode in buffered apple juice prior to incubation with

tagged apple juice eliminated this issue. Figure 6.4 shows a set of impedance data

obtained for the sensor electrode before incubation, after incubation in untagged

buffered apple juice and after subsequent incubation in tagged apple juice. This change

99

in impedance was not observed for electrodes incubated with strand 3, indicating that

the change in impedance was related to the binding of complimentary DNA to the

capture strand.

In order to gain a better understanding of the effect of apple juice on sensor response,

the system was evaluated in terms of the equivalent circuit shown in Figure 6.3, and the

resulting numerical values of the individual circuit elements are summarized in Table

6.1. Theoretical impedance spectra are shown in Figure 6.4 as solid lines. It is evident

that exposure of the sensor to the apple juice lowers its overall impedance in

comparison to pre-exposure values. This is also reflected by decrease in Rs from 1287

(117) to 1022 (86) and decrease in Rct from 630 (88) to 428 (75). Subsequent sensor

incubation in tagged apple juice increases the sensor impedance, Rs from 1022 (86) to

1202 (102) and Rct from 428 (75) to 583 (81). These changes in system impedance

suggest that when the DNA sensor is exposed to the tagged apple juice two

simultaneous processes take place. (1) a hybridization event, which increases the

system impedance and (2) an interaction of the apple juice constituents with the sensor

surface, which lowers the overall impedance. These results imply that for reliable

detection of the tag in apple juice, it is necessary to conduct the sample measurement

using a sensor electrode that has first been conditioned in untagged apple juice.

Nature of apple juice – sensor interactions

Additional experiments were conducted with the purpose of elucidating the nature of

interactions between apple juice and the sensor electrode. Apple juice is a complex

mixture of carbohydrates, organic acids, minerals, and other chemical compounds.

(Eisele and Drake 2005, Paredes et al. 2006; Suárez-Luque et al. 2006). For instance,

typical apple juice contains 0.26 mg/L Zn2+ (Paredes et al., 2006) and 0.89 mg/L Mg2+

(Suárez-Luque et al., 2006). It is known that certain bivalent metal ions can interact with

DNA and affect its electrical properties (Matsui et al. 2009; Oliveira et al. 2008; Yang et

al., 2009). To investigate the effect of metal ions on impedance measurements, the

sensor electrode was incubated in Tris containing 0.3 mM ZnClO4. Figure 6.5 shows

impedance data obtained for the a sensor electrode modified with strand 1 after 2 hours

100

incubation in 0.3 mM ZnClO4, and following incubation (2 hours) in buffered apple juice

tagged with strand 2 at a concentration of 50 mM.

Incubation of the sensor electrode in the presence Zn2+ (Figure 6.5) mimics the effect

observed in the case of apple juice (Figure 6.4). The presence of Zn ions results in a

decrease in system impedance. Moreover, subsequent exposure to the DNA tag results

in an increase in the overall impedance. This suggests that impedance drop caused by

sensor exposure to the apple juice is likely caused by the presence of metal ions.

Sensor reuse

Normally, each sensor electrode must be cleaned, polished and re-incubated with

modified DNA for 48 hours in between each new hybridization process. This is a time

consuming step, and reuse of the sensor electrode is therefore desirable. Similar EIS

values to those initially observed for an unmodified electrode were observed after

dehybridization (Figure 6.6). Subsequent rehybridization of the target DNA to the sensor

electrode modified with the capture strand was also demonstrated (Figure 6.6). The

resulting equivalent circuit analysis of the experimental data shown in Figure 6.6 is

summarized in Table 6.1.

These results suggest that the DNA sensor electrodes may be reused at least once, but

that the sensor recycling procedure introduces some loss of reproducibility as the

standard deviation associated with Rct circuit element increases from 79 to 132 Ω⋅cm2.

Thermal dehybridization, which was also investigated, resulted in the loss of the capture

strand from the electrode surface. Nevertheless, potential for the sensor to be reused is

demonstrated, but further optimization of the sensor recycling procedure is required.

The ability to reuse the sensor electrodes without having to clean, polish and modify in

between measurements streamlines the process and could help in the

commercialization of this method for oligonucleotide tag detection.

101

Table 6.1. Values of the equivalent circuit elements for the DNA sensor electrode

estimated after different incubation steps.

Rs

(Ω⋅cm2)

Qdl

(µS⋅sn/cm2)

ndl Rf

(Ω⋅cm2)

Rct

(Ω⋅cm2)

Qmt

(µS⋅sn/cm2)

nmt

sensa 0.8

(0.1)

4.4 (0.9)

10-5

0.90

(0.03)

1287

(117)

630

(88)

6.5 (0.9)

10-4

0.58

(0.05)

sens+tagb 0.8

(0.1)

3.7 (0.7)

10-5

0.90

(0.02)

1401

(162)

855

(79)

8.1 (0.7)

10-4

0.49

(0.06)

sens(a.j.)c 0.9

(0.1)

5.7 (1.5)

10-5

0.88

(0.02)

1022

(86)

428

(75)

8.5 (1.6)

10-4

0.53

(0.11)

sens(a.j.)+tagd 0.8

(0.1)

5.1 (1.3)

10-5

0.90

(0.02)

1202

(102)

583

(81)

8.0 (2.2)

10-4

0.49

(0.12)

sens(re)e 0.8

(0.1)

4.76 (0.8)

10-5

0.89

(0.02)

1285

(148)

659

(121)

6.7 (0.2)

10-4

0.55

(0.08)

sens(re)+tagf 0.9

(0.1)

4.2 (0.5)

10-5

0.89

(0.01)

1412

(195)

795

(132)

7.9 (1.4)

10-4

0.48

(0.18)

Values in parentheses represent SD for 5 replicates of different electrodes. a represents “fresh” sensor electrode b represents electrode after 2 hours incubation in 50 mM tag in Tris c represents electrode after 2 hours incubation in buffered apple juice d represents electrode after 2 h incubation in buffered apple juice and subsequent 2 h incubation in tagged apple juice e represents recycled electrode (2 hours incubation in 50 mM tag and subsequent dehybridization) f represents recycled electrode after 2 hours incubation in 50 mM tag in Tris

102

Figure 6.1. Overview of oligonucleotide tag recovery from beverages using electrochemical impedance spectroscopy. (a) A gold electrode is incubated in a solution containing disulfide labeled capture strands, complementary to the oligonucleotide tag. (b) The capture strands self-assemble into a film on the surface of the electrode, and the electrochemical impedance of the modified electrode is measured. The electrode is then incubated in any beverage containing the oligonucleotide tag. (c) The two complementary strands hybridize, changing the electrochemical impedance of the electrode. If no tag is present, then no change in impedance is observed.

103

Figure 6.2. Typical Nyquist plots (imaginary component of the impedance, -Z”, vs real

component of the impedance, Z’) obtained for an unmodified Au electrode (×), a sensor

electrode modified with capture strand (strand 1) (◇) and a sensor electrode after

incubation in buffer solution tagged with strand 2 for 2 hours (○). The data points

represent experimental data observed in 20 mM Tris-ClO4 (pH 8.6), 50 mM NaClO4 and

1 mM K4Fe(CN)6/K3Fe(CN)6, and the solid lines correspond to the theoretical spectra.

Experimental conditions: Emean = 0.22 V, fmin = 10 kHz, fmax = 0.1 Hz, Vamp = 20 mV.

104

Figure 6.3. Equivalent circuit model representing the apatamer sensor and used to

obtain theoretical impedance spectra. Rs is the solution resistance, Qdl is the constant

phase element accounting for frequency dispersion of the double layer capacitance, Rf

is the resistance of the recognition film, Rct is the charge transfer resistance and Qmt is

the constant phase element representing non-ideal mass transfer behavior.

105

Figure 6.4. Typical Nyquist plots obtained for a sensor electrode modified with strand 1

(◇), sensor electrode incubated for 2 hours in buffered apple juice (△), followed by 2

hours sensor incubation in apple juice tagged with strand 2 (○). The data points

represent experimental data observed in 20 mM Tris-ClO4 (pH 8.6), 50 mM NaClO4 and

1 mM K4Fe(CN)6/K3Fe(CN)6, and the solid lines correspond to the theoretical spectra.

Experimental conditions: Emean = 0.22 V, fmin = 0.1 Hz, fmax = 10 kHz, Vamp = 20 mV.

106

Figure 6.5. Typical Nyquist plots obtained for a sensor electrode modified with strand 1

(◇), sensor electrode incubated for 2 hours in buffer containing 3 mM Zn2+ (△), followed

by 2 hours sensor incubation apple juice solution tagged with strand 2 at a

concentration of 50 mM tag in apple juice (○). The data points represent experimental

data observed in 20 mM Tris-ClO4 (pH 8.6), 50 mM NaClO4 and 1 mM

K4Fe(CN)6/K3Fe(CN)6, and the solid lines correspond to the theoretical spectra.


107

Figure 6.6. Nyquist plots obtained for sensor electrode modified with strand 1 (◇),

sensor electrode incubated for 2 hours in Tris-buffer (pH 8.6) containing 50 mM tag (○),

sensor after dehybridization for 1 hour in Tris-buffer (pH 10.5) (◆) and subsequent

incubation in Tris-buffer (pH 8.6) containing 50 mM tag (●). The data points represent

experimental data recorded in 20 mM Tris-ClO4 (pH 8.6), 50 mM NaClO4 and 1 mM

K4Fe(CN)6/K3Fe(CN)6, and the solid lines correspond to the theoretical spectra.


108

Synthetic Tagging Conclusions In section two, a novel DNA based food tagging technology is described, coupled with

two different detection methods. Using real-time PCR the potential to quantify tag

concentration for detection of debasing has been shown. The various processing

methods employed in the creation of apple juice represent many of those employed by

the food industry. Therefore, the successful detection of the ONT in the laboratory-

produced tagged apple juice suggests that this tag could be used in a wide range of

food and natural health products for traceability purposes. Shelf-life studies also show

this tag was stable for a minimum of 10 weeks in apple juice concentrate. Further

studies will need to be undertaken to evaluate if and how ONTs bind to container

materials and how exposure to light may affect their stability to ensure changes in tag

concentration observed can be attributed to adulteration rather than other factors.

This research focused on ONT addition to aqueous solutions. Encapsulation of the ONT

may provide added stability in hydrophilic solution, and also allow the addition of the

ONT to hydrophobic liquids such as oils, as well as dry products. The ability to add

ONT to oils would be highly beneficial as they are often adulterated (Moore et al. 2012).

For dried products, natural health products and pharmaceuticals, ONT may be bound to

sugar spheres, which are common carriers for bioactives for pharmaceuticals. These

methods of encapsulation will be the focus of continued studies.

The use of electrochemical impedance spectroscopy for the detection of a synthetic

oligonucleotide from apple juice was also clearly demonstrated. Recent breakthroughs

in detecting single-nucleotide mismatches using EIS (e.g. Kafka et al. 2008) suggest

that this method has the potential to differentiate tags with similar sequences. Further

studies will focus on optimizing this assay for reduced hybridization time, testing for

detection of longer oligonucleotide tags and determining the lower limits of tag

concentration necessary for accurate detection. In addition further tests of the

reproducibility of this method in other types of food products will be necessary to

confirm its utility in multicomponent systems.

In both cases of detection, the use of an internal traceability tag has shown potential for

commercial use as a fraud deterrent, or to help companies communicate increased

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traceability measures to their consumers. Though traceability using ONTs will certainly

require further optimization and validation by independent laboratories before reaching

the market, it is certain to serve as a valuable tool in a move towards improving

detection of fraudulent products and consumer safety.

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Chapter 7. Summary and Recommendations Food Authenticity

The methods of species identification described here serve not only as improved tools

for crop management and food authenticity, but also illustrate the link between species

identification and the food and agriculture industries and the close relationship between

basic and applied research in this area. Species identification is of key importance in

these fields, and continued improvements using DNA analysis will provide more

accurate, rapid and cost-effective means for conducting socio-economically important

biological identifications.

DNA barcode sequences in BOLD are a logical source for design of these assays due

to the presence of vouchered specimens, metadata, and original sequence trace files.

Of particular importance when designing primers and probes based on polymorphisms

for species identification is the ability to access the original trace files, as these data are

not typically available in other databases. A full characterization of the haplotypes of a

given species is necessary for accurate primer and probe design to minimize false

positives or false negatives arising from unique haplotypes with differences in primer or

probe regions. In the case of haplotypes found from only one individual, the trace file

can be consulted to determine the accuracy of the sequence, thereby eliminating

unnecessary complications in primer/probe design arising from the inclusion of artifacts

of sequencing error rather than biological variants. The metadata available with DNA

barcode sequences can and should be used to determine the geographic coverage of

individuals to ensure the appropriate geographic regions have been adequately

represented in assay design.

The utility of BOLD for development of PCR-based methods for species identification is

clear. However, development of guidelines for sampling can only serve to improve the

design and accuracy of these tests. When using Sanger sequencing to identify a

sample the presence of all possible haplotypes is not crucial. Since matches are made

by genetic distance, a close match will still be returned even if the unknown sample is a

new haplotype of the same species, likely allowing accurate identification. However,

with PCR based methods, a haplotype that was not considered during primer/probe

111

design may result in erroneous conclusions. Therefore sampling effort to assess

geographic and genetic variants is directly linked to the success of any primer or probe

developed for species identification. The genetic and geographic diversity of the target

species, as well as closely related species, should be clearly articulated for assay

design, a feature that is omitted from most published studies summarized in Chapter 1.

Representation of individuals covering the full range of haplotypic diversity is ideal, yet

few studies dealing with the generation of species-specific real-time PCR assays

provide information on the number of individuals or geographic coverage of the

sequences used for the development of primers and probes. This is possibly due to the

lack of sampling guidelines on how to adequately ensure haplotype diversity coverage.

The issue of determining appropriate sample sizes must be addressed for better

accuracy of real-time PCR assays meant to provide biological identifications. This is

particularly important in applied cases where results must fit within existing regulatory

framework that may provide little room for error. One previous study looking at

haplotype occurrence in lake trout estimates that a sample size of 59 individuals would

be required for 95% confidence that all haplotypes from a population were represented

in a sample (Grewe et al. 1993). For regulatory purposes, a higher confidence may be

required. According to the same formula, 99% confidence would require 459 individuals.

This may provide a theoretical basis for sampling, but actual requirements may differ

between different taxa and for different applications, depending upon such factors as

gene choice, rates of molecular evolution, population structure and gene flow.

For some applications, minimal sampling may be sufficient. This is especially true for

studies where multiple gene regions are sequenced and haplotype coverage is not of

primary concern. When the number of gene regions is limited, such as with DNA

barcoding, haplotype coverage becomes more important. DNA barcoding guidelines

suggest between five and ten individuals per species be sampled (ibol.org). This is

usually sufficient to make identifications using a genetic distance based approach.

However, as discussed above, haplotype diversity is of particular concern when

developing primers and probes for real-time PCR. Continued addition of DNA barcode

sequences will improve the utility of assay developed from sequences deposited in

112

barcode libraries. A study of existing sequences is necessary to help inform guidelines

for sampling to ensure success of downstream applications of the database.

The salmonid DNA barcodes used to develop the oligonucleotides evaluated in Chapter

1 were generated from many individuals of each species. However, haplotype

accumulation curves showed a plateau in the number of haplotypes only for those

species in which more than 60 individuals were sampled (Figure 7.1). Although this has

not had a practical impact on primer or probe success for identifying species in the

market samples tested to date, adequate coverage of the population, and saturation of

haplotype accumulation, is an ideal worth exploring when developing real-time PCR

based identification tools. This example illustrates the need for standard sampling

guidelines, which are currently lacking. Although the number of individuals per species

used was much higher than numbers usually seen in DNA barcoding studies, complete

haplotype coverage may not have been achieved. These guidelines will be especially

important for assays that may be implemented in regulatory settings. In cases where

deep sampling effort is not possible, for example if the species in question is very rare,

samples should be tested via a secondary method such as DNA sequencing or RFLP,

to confirm real-time PCR identification for high-impact cases.

The socioeconomic impact of the tests outlined in this thesis is evident. Species

identification for pests and seafood products can lead to improved crop and fisheries

management. These tests, combined with adequate traceability methods help limit

impact to consumers from purchase and consumption of adulterated products and

contribute not only to detection but also to deterrence and prevention of food fraud.

Since DNA barcoding has been applied to a wide range of species found in food

products (Galimberti et al. 2013), similar tests can be developed for a wide range of

sectors of the food industry such as meat, dairy, grains, allergens and more. Using data

available in BOLD, questions of authenticity related to species identification can be

addressed for a variety of applications.

Food Traceability

External traceability tags are vulnerable to removal or tampering. External traceabilty or

authenticity features also have an inherent weakness in that they trace and/or

113

authenticate the package and not the product. Internal tagging adds a further dimension

of protection against fraud because the authentication is of the product. By using an

internal synthetic oligonucleotide tag (ONT) to aid traceability, companies can better

track products and protect their consumers and their brand from the effects of

adulterated or fraudulent products by determining presence of the tag in a manner

similar to detecting species in the above examples.

Results presented here show that an ONT is stable over time, and can still be detected

after intensive processing using a case study of apple juice. Used in fish brine, the ONT

can be used to trace smoked fish products as well (Naaum et al. unpublished results).

These data suggest that a wide variety of other food and natural health products may

make use of this tagging principle for improved traceability.

Species mislabeling has been found in natural health products (e.g. Newmaster et al.

2013), and therefore this may represent a market where oligonucleotide tagging may be

valuable. Initial measurements of response to the new traceability system for ginseng

has suggested that consumers would be willing to pay for improved traceability using

this type of tag (Lilavanichakul and Boecker, 2013). Therefore, encapsulation of the tag

for use in dried products like ginseng may expand the commercial utility of this method.

Improved traceability measures are necessary not only to comply with changing

policies, but also to ensure consumer safety and maintain brand image. The recent

horse meat scandal, in which horse meat was identified as an illegal and unannounced

adulterant in products labeled as beef, has been extensively covered by the media.

Stories like this, and other highly publicized incidences of food adulteration,

demonstrate a need to continue the advancement of traceability technology. The role of

molecular techniques to enhance knowledge of species, and the genetic variation both

within and between them, is crucial for subsequent pest management and regulation of

food ingredients.

114

0 10 20 30 40

24

68

10

Oncorhynchus gorbuscha

Specimens Sampled

Uni

que

Hap

loty

pes

0 50 100 150 200

24

68

1012

Oncorhynchus tshawytscha

Specimens Sampled

Uni

que

Hap

loty

pes

Figure 7.1. Haplotype accumulation curves representing random sampling of individuals of Oncorhynchus tshawytscha and Oncorhynchus gorbuscha from the BOLD project “Salmonid Species North America”, with the number of haplotypes counted after each sample. Error bars represent repeat sampling (n=100).

115

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