Detection of Viable Foodborne Pathogens & Spoilage

Detection of Viable Foodborne Pathogens & Spoilage Microorganisms by Nucleic Acid Amplification Based Platforms

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Linlin Xiao, M.S.

Graduate Program in Food Science & Nutrition

The Ohio State University

2011

Dissertation Committee:

Dr. Hua H. Wang, Advisor

Dr. M. Monica Giusti, Co-advisor

Dr. John Litchfield

Dr. Zhongtang Yu

Linlin Xiao

2011

Abstract

Foodborne disease outbreaks and microbial spoilage threaten public health and cause major financial loss to the food industry and the society. Proper detection of concerned microorganisms in both raw materials and final food productes is a key to control the problems associated with microbial contamination. Several platforms were used to develop accurate, rapid, quantitative, specific and sensitive detection methods for targeted, viable cells, including RNA-based amplification platforms, such as nucleic acid sequence based amplification (NASBA) and reverse transcriptase PCR (RT-PCR), as well as DNA amplification coupled with sample treatment with DNA-intercalating dye propidium monoazide (PMA).

In this study, a NASBA-molecular beacon assay targeting 18S ribosomal RNA has been established to investigate its potential to detect viable spoilage yeasts in juice products. Using the developed platform, less than 100 yeast cells per reaction was detected rapidly and specifically. In addition, significant decrease of amplification signals after lethal heat treatments indicated that this platform has the potential for rapid detection of viable spoilage yeasts if combined with quantitative analysis.

Listeria monocytogenes contamination is a serious public health issue. The second part of my project compared the suitability of using 16S rRNA, inlA mRNA and rplD

ii mRNA as indictors to detect viable L. monocytogenes cells via Taqman real-time RT-

PCR assay. Under the conditions examined, the amplification signals by all three transcripts were reduced in dead cells, while the inlA and rplD mRNA signals decreased more dramatically than 16S rRNA. However, residue signals were still detected from dead cells even after extreme heat treatments.

The ideal cell viability indictor should disappear rapidly and completely after cell inactivation. In the third part of my study, cDNA microarray analysis was conducted to select unstable mRNA targets. Using unstable transcript ornithine decarboxylase (ODC) mRNA screened by cDNA microarray assay, a Taqman real-time RT-PCR platform was established to detect viable and heat or disinfectant Pro-san® inactivated spoilage

Pseudomonas. Under the experimental condition, ODC-specific RT-PCR signals were almost undectable after Pseudomonas cells were exposed to mild heat treatments.

DNA-intercalating dye propidium monoazide (PMA) only can penetrate damaged cell membrane and form crosslinkage with DNA molecules, resulting in inhibition of amplification. PMA coupled Taqman real-time PCR was developed to examine viable

Pseudomonas spp. PMA treatment successfully minimized false positive amplification signals by dead cells after heat, acid or disinfectant Pro-san® inactivation.

Results from this study provided critical information regarding nucleic acid amplification-baed methods for viable foodborne microbial detection, and the new knowledge regarding overall RNA stability will have significant impact on data interpretation for transcriptome related studies. The rapid detection platforms developed have direct applications in both food industry and basic scientific research.

iii

Dedication

Dedicated to my son Allen Xiong, my daughter Alivia Xiong, and my husband

Qingming Xiong for their endless love and generous patience in my life

Dedicated to my parents, Xingshu Xiao and Jiayu Fu, my grandparents, Mingzhen

Wang and Mingquan Fu, for their deepest love and forever support

Acknowledgements

First and foremost, I am sincerely grateful to my adviser, Dr. Hua Wang, for her intellectual advice, encouragement and support throughout the four years of my study in

OSU. I also would like to thank her for insightful discussion and tireless effort in preparing this dissertation. Without her generous help, I could not finish my Ph. D. degree as a young mother having two little kids.

I would like to thank my committee members, Dr. John Litchfield, Dr. David

Min, Dr. Zhongtang Yu, and Dr. M. Monica Giusti for providing assistance and inspiring comments.

I wish to thank Xinhui Li, Dr. Wangyu Tong, Dr. Yingli Li, Andrew Wassinger,

Hanna Cortado, Monchaya Rattanaprasert, Dan Kinkelaar, Xiaojing Li, and Ying Huang for their wonderful teamwork and friendship.

I specially would like to thank Lu Zhang for his wonderful technical assistance.

Vita

Jan. 14, 1978 ··························· Born, Yuan‟an, Hubei, China 2000 ········································ B.S. Animal Science, Yangtze University, Jingzhou, China 2003 ········································ M.S. Food Science and Technology, Nanjing Agricultural University, Nanjing, China 2007 ~ present ························ Graduate Research Associate, The Ohio State University

Publications

Presentations at national conferences:

Xiao, L. and Wang, H. (2009) Critical issues in assessing live Listeria monocytogenes cells by real-time reverse transcription-PCR. Institute of Food Technologists (IFT) 2009 annual meeting book of abstract. Anaheim, CA.

Xiao, L. and Wang, H. (2008) Development of a real-time, NASBA-molecular beacon assaysfor rapid and specific detection of live microbes in juice products. International Association for Food Protection (IAFP) 2008 annual meeting book of abstract, Columbus, OH.

Fields of Study

Major Field: Food Science and Nutrition

Food microbiology

vii

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgements ...... v

Vita ...... vi

Chapter 1

Introduction ...... 1

Chapter 2

Literature Review ...... 6

2.1 Conventional culturing detection methods ...... 7 2.2 Biochemical identification methods ...... 9 2.3 Immunoassay ...... 10 2.4 Biosensors ...... 13 2.5 Molecular detection methods ...... 15 2.6 Main limitation of application of rapid methods for food microbial detection ...... 20 2.7 Food sample preparation ...... 21 2.8 Microbial cell viability indictors ...... 23 2.9 Reverse transcriptase polymerase chain reaction (RT-PCR) ...... 25 2.10 Nucleic acid sequence based amplification (NASBA) ...... 26 viii

2.11 Selective detection of live microorganisms by ethidium monoazide (EMA)-PCR and propidium monoazide (PMA)-PCR ...... 27 2.12 Listeria monocytogenes and challenge to food safety ...... 28 2.13 Microbial spoilage of food products ...... 30 2.14 Yeasts and juice spoilage ...... 31 2.15 Pseudomonas and food spoilage ...... 33 References ...... 35

Chapter 348

Development of a NASBA-Molecular Beacon System Targeting the 18S rRNA for Rapid and Specific Detection of Viable Spoilage Yeasts in Juice Products ...... 48

3.1 Abstract ...... 48 3.2 Introduction ...... 49 3.3 Materials and Methods ...... 53 3.4 Results ...... 59 3.5 Discussion and Conclusion ...... 62 References ...... 71

Chapter 4

Critical Issues in Detecting Viable Listeria monocytogenes Cells by Real-Time Reverse Transcriptase PCR ...... 76

4.1 Abstract ...... 76 4.2 Introduction ...... 77 4.3 Materials and Methods ...... 80 4.4 Results ...... 85 4.5 Discussion and Conclusion ...... 90 References ...... 109

Chapter 5

Detection of viable Pseudomonas spp. cells using Taqman Real-time Reverse Transcriptase PCR ...... 113

5.1 Abstract ...... 113 5.2 Introduction ...... 114 5.3 Materials and methods ...... 117 5.4 Results ...... 128 5.5 Discussion and Conclusion ...... 133 References ...... 147

Chapter 6

Detection of Viable Spoilage Pseudomonas spp. Using Propidium Monoazide Coupled Taqman Real-time PCR ...... 152

6.1 Abstract:...... 152 6.2 Introduction ...... 153 6.3 Materials and methods ...... 156 6.4 Results ...... 160 6.5 Discussion and Conclusion ...... 165 References ...... 180

Chapter 7

Summary and Conclusion ...... 152

Bibliography ...... 186

List of Tables

Tables Page

4.1 Taqman real-time PCR primers and probes used in this study ·································· 96

5.1 Potential unstable mRNA targets identified by cDNA microarray analysis ·············137

6.1 Taqman real-time PCR primers and probes used in this study ·································170

List of Figures

Figures Page

3.1 Formaldehyde agarose gel (2.0%) electrophoresis for total RNA ····························· 65

3.2 NASBA-molecular beacon detection of spoilage yeasts ··········································· 66

3.3 NASBA-molecular beacon sensitivity test in saline ·················································· 67

3.4 NASBA-molecular beacon sensitivity test in apple juice ·········································· 68

3.5 Correlation between the plate count results and the Ct values ··································· 69

3.6 NASBA-molecular beacon assay detect viable and heat-inactivated S. cerevisiae AC1 cells ·································································································································· 70

4.1 Locations of oligonucleotide primers ········································································ 97

4.2 SYBR Green real-time RT-PCR detect 72 °C inactivtated L. monocytogenes cells using primers pairs 16S(U) and 16S(D) ··········································································· 98

4.3 SYBR Green real-time RT-PCR detect 72 °C inactivtated L. monocytogenes cells using inlA(U), inlA(D) and inlA(D) primers pairs ·························································· 99

4.4 SYBR Green real-time RT-PCR detect 72 °C inactivtated L. monocytogenes cells using rplD(U) and rplD(D) primers pairs ······································································ 100

4.5 Taqman real-time RT-PCR detect 72 °C inactivtated L. monocytogenes using 16S(U) and 16S(D) primers-and-probe sets ···············································································101

4.6 Taqman real-time RT-PCR detect 72 °C inactivtated L. monocytogenes cells using inlA(U) and inlA(M) primers-and-probe sets ·································································102 xii

4.7 Taqman real-time RT-PCR detect 72 °C inactivtated L. monocytogenes cells using rplD(U) and rplD(D) primers-and-probe sets ·································································103

4.8 Taqman real-time RT-PCR detect 72 °C inactivtated L. monocytogenes cells (106 CFU ml-1) using 16S(D), inlA(M) and rplD(U) primers-and-probe sets ························104

4.9 Taqman real-time RT-PCR detect 100 °C inactivtated L. monocytogenes cells (109 CFU ml-1) using 16S(D), inlA(M) and rplD(U) primers-and-probe sets ·······················105

4.10 Taqman real-time RT-PCR detect 100 °C inactivtated L. monocytogenes cells (106 CFU ml-1) 16S(D), inlA(M) and rplD(U) primers-and-probe sets ································106

4.11 Taqman real-time RT-PCR detect autoclaved L. monocytogenes cells (109 CFU ml-1) using 16S(D), inlA(M) and rplD(U) primers-and-probe sets ········································107

4.12 Taqman real-time RT-PCR detect autoclaved L. monocytogenes cells (106 CFU ml-1) using 16S(D), inlA(M) and rplD(U) primers-and-probe sets ········································108

5.1 SYBR Green real-time RT-PCR detect 72 °C inactivated Pseudomonas llxm2 using primers pairs targeting ODC and SOD transcripts ·························································138

5.2 Specificity examination ····························································································139

5.3 Sensitivity test using Pse-16S primers-and-probe set ···············································140

5.4 Sensitivity test using Pse-ODC primers-and-probe set ·············································141

5.5 Taqman real time RT-PCR detect 72 °C inactivated Pseudomonas using 16S rRNA primers-and-probe set ·····································································································142

5.6 Taqman real time RT-PCR detect 72 °C inactivated Pseudomonas using Pse-ODC primers-and-probe set ·····································································································143

5.7 Taqman real-time RT-PCR detect autoclaved Pseudomonas ···································144

5.8 Taqman real-time RT-PCR (Ct values) detect Pro-san® inactivated Pseudomonas ········································································································································· 145

5.9 Taqman real-time RT-PCR detect Pro-san® inactivated Pseudomonas (RFU) ········146

6.1 Survival curves of heat inactivated Pseudomonas ····················································171

6.2 PMA coupled real-time PCR detect viable and heat inactivated Pseudomonas ·······172 xiii

6.3 Correlation between survived Pseudomonas cells after heat treatments and the corresponding Ct values ··································································································173

6.4 Survival curves of acid inactivated Pseudomonas ····················································174

6.5 PMA coupled real-time PCR detect viable and acid inactivated Pseudomonas ·······175

6.6 Correlation between survived Pseudomonas cells after acid treatments and the corresponding Ct values ··································································································176

6.7 Survival curves to Pro-san® inactivated Pseudomonas ············································177

6.8 PMA coupled real-time PCR detect viable and Pro-san® inactivated Pseudomonas178

6.9 Correlation between survived Pseudomonas cells after Pro-san® treatments and the corresponding Ct values ··································································································179

xiv

Chapter 1

Introduction

Food safety is a primary public health concern. In the US and worldwide, foodborne pathogens, such as Listeria monocytogenes, Escherichia coli, Salmonella spp. and Campylobacter jejuni, are widespread and closely associated with disease outbreaks

(Tribst et al., 2009). Foodborne illness outbreaks continue to occur despite current advances limiting microbial pathogenicity, improvements in food processing techniques and rigorous industrial hygiene (Newell et al., 2010; Nyachuba et al., 2010; Oliver et al.,

2005). Additionally, large scale food production and global supply changes means that contaminations may result in huge social and economic problems. The Centers for

Disease Control and Prevention (CDC) estimates 48 million cases of foodborne diseases occur each year in the US, resulting in 128,000 hospitalizations and 3,000 deaths

(http://www.cdc.gov/foodborneburden/2011-foodborne-estimates.html). The United

States Department of Agriculture (USDA) reports the cost of Salmonella outbreaks alone at over 2 billion dollars each year (http://www.ers.usda.gov/Data/FoodborneIllness/).

Food spoilage due to microbial and biochemical activities also causes significant financial damages to the food industry. It is estimated that one quarter of food supplies are wasted due to microbial growth or the release of their extracellular and intracellular

1 enzymes (Gram et al., 2002; Huis et al., 1996; McMeekin & Ross, 1996). Effective detection of disease and spoilage-causing agents in the raw materials, food-processing environments and final products, is paramount to preventing mass epidemics or huge resource losses in the food system.

Currently, the food industry employs standard microbial detection methods involving selective enrichment, cultivation and confirmation by biochemical analysis

(http://www.fda.gov/Food/ScienceResearch/LaboratoryMethods/ucm114664). These methods are problematic. They are time consuming and labor intensive. Usually, it takes a couple of days to weeks for results delivery, using these techniques thereby limiting the assurance of proper food quality and safety control. This is a particular concern for perishable food products with a short shelf life. Moreover, the culturing methods may underestimate the “real” microbial contamination levels. In fact, some microbial cells, including lethally injured cells, viable but non-culturable (VBNC) cells, and fastidious cells, cannot grow on artificial media (Barer & Harwood, 1999; Divol & Lonvaud, 2005;

Dreux et al., 2007; Wesche et al., 2009). These un-culturable cells have the potential to proliferate and produce enzymes, and other compounds, responsible for food spoilage

(Kell et al., 1998). In addition, VBNC pathogenic bacteria can re-gain pathogenicity in foods or in the human enteric system causing an illness (Cappelier et al., 2007; Lindback et al., 2010). New detection techniques are needed to expediate response time to foodborne illness, limit the damage caused by such illnesses and ultimately, prevent outbreaks from occurring.

Successful detection of foodborne microorganisms requires methods which meet below criteria: 1) high specificity for selectively recognizing target organisms from among the microflora associated with foods, 2) high sensitivity to identify foodborne pathogens even at low contamination levels, 3) expeditious for effective management, 4) quantitative so that it can provide information for hazard evaluations and shelf-life predications, 5) cost-effective, and 6) high-throughput. Many studies had been performed to investigate applicable methods which could meet these benchmarks. However, it still remains difficult to detect foodborne pathogens due to their low concentrations and complex microbial environments. An assay that is simple, rapid, specific, sensitive and cost-effective is needed.

Compared with techniques such as culturing and immunoassay, molecular detection methods are becoming powerful alternatives for monitoring and quantifying microbial contaminants in foods (Batt et al., 1997; Lauri & Mariani, 2009; Nugen &

Baeumner, 2008). Great advances have been made in foodborne microbial detection using nucleic acid amplification based methods. Real-time PCR based schemes are used as a quick, sensitive and specific tool for the detection and quantification of pathogens and spoilage organisms in food and environmental samples (McKillip & Drake, 2004).

High-throughput microarray-based techniques have attracted further attention due to their simultaneous detection of multiple target organisms (Fang et al., 2010; Uttamchandani et al., 2009; Yoo & Lee, 2008). However, molecular methods capitalizing on the presence of DNA sequences are criticized for having high false positives because of the relatively long half-life of bacterial DNA after cell death (Josephson et al., 1993; Keer & Birch,

2003). Microbial detection platforms are needed to distinguish between live and dead cells.

In contrast to DNA, RNA molecules have been proposed as liable viability indictors that can discriminate between viable and dead microorganisms, since RNA has a short half-life after cell death (Cenciarini et al., 2009; Kort et al., 2008). Targeting different ribosomal RNAs and messenger RNAs, reverse transcription PCR (RT-PCR) and nucleic acid sequence based amplification (NASBA) methods have been used to investigate the potential to discriminate live and dead foodborne pathogens (Miller et al.,

2010; Yaron & Matthews, 2002). Complicating the use of these techniques is the heterogeneity of RNA half-lives. Some RNA molecules disappear quickly after cell death; while some RNA persists for long periods after viability is lost (Cenciarini et al., 2008).

RNA stability depends on multiple factors, such as species, RNA-type, inactivation treatments and amplification regions (McKillip et al., 1998; Norton & Batt, 1999; Yaron

& Matthews, 2002). To successfully detect viable cells using RNA amplification, RNA stability should be carefully characterized and only unstable RNA targets should then be chosen as viability indictors.

DNA amplification-based methods have also advanced their ability to detect live cells using dead cell DNA-modifying dyes, such as propidium monoazide (PMA) and ethidium monoazide (EMA). Cell viability is correlated with membrane integrity in this method. PMA and EMA cross damaged cell membranes and intercalate into resident double-stranded DNA to form cross-linkages with the DNA following photo-activation

(Kobayashi et al, 2009; Nocker et al., 2006). After EMA or PMA treatment, PCR

4 amplification of dead cells is efficiently suppressed because the PMA/EMA cross-linked

DNA no long works as amplification template (Varma et al., 2009). Therefore,

PMA/EMA coupled PCR methods could be used to selectively detect viable microbial cells.

Chapter 2

Literature Review

Outgrowth of foodborne pathogens and spoilage organisms poses a great challenge to both public health authorities and the food industry. To determine the microbiological safety and quality of food products, effective detection tools are needed to monitor contaminations in raw materials, food processing environments and finals products.

Food products are complex matrices of particulate matter, proteins, fats, and other biochemical or inorganic food components with heterogeneous textures and viscosities.

Moreover, foods contain numerous microorganisms, including pathogenic bacteria, spoilage microbes, commensal microbes and fermentation starter cultures (Stevens &

Jaykus, 2004; Fukushima et al., 2007). The non-uniform nature of food samples and the low number of target microbial cells have impeded the development of practical detection methods. In addition, injured microbial cells exist in food and complicate the detection process for any assay system. Injured bacteria in food are common due to processing stresses, such as heating, freezing, antimicrobial compounds, etc. Injured cells also result from intrinsic bacteriostatic or bactericidal factors, such as aw, pH, or oxidation-reduction potential (Wesche et al., 2009). The injured cells may not be detected because of their

6 limited growth and colonization; but they can still maintain metabolic activity and pathogenicity (Wu, 2008). Under suitable recovery conditions, they may recolonize a food ecosystem or a human enteric system (Bozoglu et al., 2004; Sheen et al., 2010; Yan et al., 2006). Assessing microbial contamination in food samples is challenging because of: 1) high background microflora associated with foods; 2) low initial cocentrations of contaminats but their potential to proliferate in food environments, particularly during storage and delivery; 3) complex food matrices which can interfere with detection efficiency; and 4) presence of dormant or injured cells due to food processing conditions.

Corresponding strategies, such as recovery, pre-enrichment, cell separation, cell collection, selecting proper cell viability indictors, need to be requisite for any further foodborne microorganisms‟ detection assays.

2.1 Conventional culturing detection methods

The food industry employs conventional microbial detection assays that observe the physical growth of bacterial cells in artificial media. A pre-enrichment step is usually necessary to successfully isolate target microorganisms from complex food systems. Pre- enrichment is followed by plating samples on solid media and then incubating at selective temperatures and atmospheres. Finally, isolates are identified according to various biochemical and/or immunological characteristics (Yousef & Carlstrom, 2003).

Numerous growth media are available for the selective detection of foodborne pathogens and spoilage organisms. For example, the Food and Drug Administration (FDA) standard method for Listeria.monocytogenes detection and enumeration involves pre-enrichment

7 in a buffered Listeria enrichment broth (BLEB) and then isolating colonies on a selective agar (e.g. Oxford). The purified isolate is identified using classical biochemical analyses to determine genus or species level. The total procedure requires 5 to 7 days

(http://www.fda.gov/Food/ScienceResearch/LaboratoryMethods/BacteriologicalAnalytic alManualBAM/).

Conventional methods are labor-intensive and time-consuming. Generally, it takes

2–3 days for the initial results delivery, and up to 10 days for confirmation. This is an obvious inconvenience for many industrial applications, particularly in today‟s global distribution market. Recent advances in molecular technology make detection and identification faster, more convenient, more sensitive, and more specific than conventional plating assays. Bacterial strains in food products can enter dormancy or a injured state, and become non-culturable on detection media (Ozcakir, 2007; Wesche et al., 2009). This may lead to an under-estimation of contamination level, or a failure to isolate a pathogen from a contaminated sample (Dreux et al., 2007). Developing recovery methods to resuscitate injured foodborne microorganisms is important for accurate data analysis. Current practices spread food samples on a non-selective medium, and incubate the plates for a suitable amount of time and temperature to facilitate repair. This is followed by an overlaying of a selective agar medium, which is specific for the detection of target organisms (Ramalho et al., 2002; Wu, 2008; Yan et al., 2006). Some recovery steps utilize repair reagents to recover cell damage (Sheen et al., 2010), however, beyond additional time consumped, injured cells may not be sufficiently recovered (Bozoglu et

8 al., 2004; Yan et al., 2006; Zhao & Doyle, 2001). New methods are needed to detect both non-injured and injured organisms to prevent false negative results.

2.2 Biochemical identification methods

Rapid and accurate identification of pathogens isolated from food samples is important for quality assurance and the quick identification of point sources in food outbreaks. Automated microbial identification systems are widely being used in food microbiology laboratories (O'Hara, 2005). Automated systems decrease the wait times on identification assays, increase the volume of samples being analysed and improve the accuracy of various assays (O'Hara et al., 1997; Robinson et al., 1995). For this reason, automation has been adapted to a lot of popular diagnostic tools. Automation is being used with miniaturized biochemical kits. Such kits are designed for identification of common foodborne pathogens, such as enteric bacteria, Campylobacter, Listeria,

Salmonella, ect. The miniature sets consist of a disposable device containing 15-30 media or substrates specifically titrated to identify a particular bacterial group or species

(Kampfer et al., 1987; Sperner et al., 1999). Advances in instrumentation have enabled automated system for the kits. These automated instruments incubate bacteria, monitor biochemical changes, generate a phenotypic profile for any enriched populations and identify the bacteria by comparing profiles to known profiles on file. Another automated bacterial identifying system that analyzes compositional or metabolic properties of the bacteria, such as fatty acid profiles or carbon oxidation profiles, is exploited for pathogen detection (Swaminathan & Feng, 1994). These systems offer some important advantages

9 over conventional methods, including reduced labor, reduced human error, increased sample throughput, and faster turnaround times.

2.3 Immunoassay

The highly specific binding between antigens and antibodies, especially monoclonal antibodies, has facilitated the development a variety of immunological assays. Immunological assays are grouped based on their formats. The basic formats include enzyme linked immune-sorbent assay (ELISA), particle agglutination, immunoprecipitation, immune-fluorescent microscopy and immunosensors.

Immunoassays are simple, versatile and can be used for on-site detection. Immunoassays constitute the largest group of rapid pathogen test kits which are commercially available for food and environment testing (Bohaychuk et al., 2005; Gehring et al., 2004; Meyer et al., 2011).

The ELISA is the most established immunological technique used for pathogen detection in foods. Described as "sandwich" assays, ELISAs bind antibodies to a solid matrix that is used to capture antigens from enriched cultures and a second antigen- specific antibody is conjugated to an enzyme that is used for detection (Vidal et al.,

2002). The specific antigen and antibody reaction can be visualized because of the activated enzyme on the secondary antibody. The colored end products could be detected and quantified by spectrophotometer. Compared with conventional culturing methods, most ELISAs take an hour for toxin detection or less than 36 hours for cell detection

(Bennett, 2005; Bohaychuk et al., 2005). However, sensitivity range of ELISA is 103-105

10 bacterial cells without pre-enrichment, which is in insufficient for detecting low level contaminations as can occur with pathogenic cells.

Particle agglutination (LA) is methodologically the simplest immunoassay.

Antibody-coated, particles in a microwell are incubated with dilutions of samples, derived from serum or from pure culture isolates derived from foods (Thorns et al.,

1994). If the samples contain the marker to be screened then agglutination of the particles will be detected. A modification of LA, known as reverse passive latex agglutination

(RPLA), can test for soluble antigens. RPLA is mostly used to test for toxins in food extracts or toxin production in pure cultures (Ellis & Sobanski, 2000; Surinder et al.,

2007).

Immunoprecipitation, or lateral flow immunoprecipitation is based on the

„„sandwich‟‟ procedure. Samples are directly loaded into chambers containing attached, colloidal gold-labeled antibodies (Crowther & Holbrook, 1980; Mammerickx et al.,

1985). After samples diffuse into the solid matrix, any antibody recognitions result in detectable color changes. The assay is extremely quick and easy. Results can be read visually within 10 min without any other manipulations (Charlton et al., 2009). However, the procedure requires a long pre-enrichment step.

Immunosensors detect antigen-antibody binding by immobilization of the reaction on to a solid surface called a transducer. After specific binding, the transducer converts surface change parameters into a detectable electric signal (Jiang et al., 2008). A number of studies have investigated the potential of using the emerging technique of immunosensors for detecting and enumerating foodborne pathogens (Tokarskyy &

Marshall, 2008). This approach delivers results within hours, which is similar to the gold standard classical ELISAs. Immunosensors are also portable, produce readable digital signals, automated and require few manual procedures (Hirst et al., 2008). However, most immunosensors require expensive equipment and laborious preparations.

There are disadvantages to immunoassays. The assays are unable to distinguish closely related species or the viability of cells. Immunoassays may not be sensitive enough to detect low concentrations of microorganisms such as those in foods. The assays often require an enrichment step to avoid false negative results which prolongs the analysis wait time.

An application that can enhance immunoassays and ameliorate the problems listed above is immunomagnetic separation (IMS). IMS couples specific antibodies to magnetic beads and has been effective at capturing target bacterial cells from a food matrix (Olsvik et al., 1994). Compared with clinic diagnostics, upstream sample separation and concentration is necessary to detect low initial contaminant concentrations in foods especially given the complex background microflora existing in foods or the food ingredients themselves (Benoit & Donahue, 2003; Ding et al., 2007; Stevens & Jaykus,

2004). In most case, IMS is combined with other detection methods, e.g. ELISIA, magnetic force microscopy, PCR, or plate counting, to improve detection sensitivity

(Garbaccio & Cataldi, 2010; Pyle et al., 1999; Seo et al., 2010; Wang et al., 2007). For instance, the pre-selection step by IMS can increase the detection sensitivity approximate

100 fold during ELISIA analysis (Ding et al., 2007).

2.4 Biosensors

In the microbial detection field, biosensors are analytical tools capable of converting biological reactions into observable signals. Generally, a biosensor consists of two main components, a bioreceptor and transducer. The bioreceptor consists of immobilized biological compounds that recognize a target analyte. The transducer converts the recognition event into measurable electrical, optical or thermal signals.

Sometimes, an additional amplifier responds to small signals from the transducer and delivers a larger output signal for the signal processor (Hall, 2002; Lazcka et al., 2007;

Velusamy et al., 2010). Biosensors for pathogen detection can be classified into three categories based on the bioreceptors they utilize. The three groupings are metabolic pattern of cells, antibody or antigen signatures or nucleic acid analysis.

Antibody-based sensors are the most popular biosensors used in microbial diagnostics. The principle of these biosensors relies on the aforementioned highly specific three-dimensional shape matching that occurs between antigens and antibodies.

There are two types of immunologically based biosensors. First type employs a captured antibody immobilized at an electrode, which captures a target antigen. Signal transduction is realized via a secondary antibody tagged with redox molecules or enzymes. Second type also utilizes an antigen immobilized at an electrode, which detects specific antibody (Tokarskyy & Marshall, 2008).

Enzyme-labeled immunoassays are increasingly popular because of their high sensitivity and use of direct visualization. Actually, potable biosensors are based on

ELISA principles but are smaller, faster, easier to interpretate and more convenient.

Electrochemical immunological biosensors using disposable screen-printed electrodes could be the future for developing inexpensive, miniaturized and portable devices used for foodborne pathogen detection. However, a major barrier in the application of biosensors is the lack of selectivity and low sensitivity (Bhunia, 2008; Seo et al., 2010).

Recent advances in bio-analytical sensors capitalize on certain enzymes ability to emit photons, bioluminescence, accompanying biochemical reaction. Adenosine

Triphosphate (ATP)-bioluminescence is widely used in the food industry to rapidly enumerate the presence of total bacteria in processing environments and to detect pathogens in drinking water or beverages (Sun et al., 2002). This technique measures light intensity based on the presence of ATP, using a light-producing enzyme, luciferase, that hydrolyzes ATP to produce light. The intensity of the light parallels the contamination level (http://www.cbbsweb.org/enf/attachments/roche_atp.pdf). Generally,

ATP-bioluminescence is used as a quick indicator of all bio-contamination, including organic debris, because ATP is basic compound of any biological material not just viable bacteria. ATP-bioluminescence analysis is therefore non-specific and has limited sensitivity.

Other tools recently developed are diagnostic biosensors utilizing nucleic acid hybridization. Classic optical detection methods use specific probes marked with fluorescent groups. A newer method introduces a DNA strand labeled with electroactive chemicals as the signaling molecules rather than fluorescent groups. DNA biosensors are highly sensitive, rapid, inexpensive, stable, environmentally insensitive and compatible with microarray-based technologies (Dwivedi & Jaykus, 2011; Koets et al., 2009).

As new technologies for pathogen detection, biosensors show great potential for onsite real-time detection; however a better characterization of how biosensors are influenced by food matrices and low bacterial numbers is needed, and how to differentiate dead and viable cells. Further research and development is essential to find cell viability indictors and to improve biosensors specificity and sensitivity before they are used a practical tool for in situ analysis.

2.5 Molecular detection methods

Molecular detection techniques are based on the recognition and amplification of nucleic acids. Over the past decade, amount of genetic information becomes available in public domains for many organisms, which provides solid support for DNA/RNA based tests (Abee et al., 2004). These types of detection methods are great alternatives to conventional culturing methods. Nucleic acid amplification methods, such as the polymerase chain reaction (PCR) and nucleic acid sequence based amplification

(NASBA) offer great advantages for rapid, specific and sensitive detection of microbial contamination in foods (Connor et al., 2005; Deiman et al., 2002; Lau et al., 2006; Lauri

& Mariani, 2009; Luo et al., 2004). In addition, using of microarrays allow simultaneous detection and identification of multiple pathogens and genes in a single assay.

2.5.1 Fluorescent in situ hybridization (FISH)

Fluorescence in situ hybridization (FISH) uses specific oligonucleotide probes to directly detect and identify specific microorganisms from clinical, food and environment

15 samples. In FISH assays, microbial cells are treated with appropriate chemical fixatives and fixed on a glass slide or in solution with oligonucleotide probes. After stringent washing to remove any unbound probe, stained cells are detected by epifluorescence microscopy (Cerqueira et al., 2008). The key to FISH specificity is choosing a unique oligonucleotide fragment, from target bacteria, to be used as the probe. Virulence genes are frequently used as the probe targets, for detecting food borne pathogens, since they are typically conserved among a specific group. FISH detection sensitivity is greatly improved by using ribosomal RNA (rRNA) as a target (Almeida et al., 2010).

2.5.2 Conventional PCR.

Developed in 1983 by Kary Mullis, polymerase chain reaction (PCR) is a technique of DNA fragment amplification. PCR can amplify a few copy of target DNA to thousands to millions copies of a particular DNA sequence. In microbial diagnostic PCR, specific primers are used to amplify the target DNA fragments. The presence of amplicons indicates the presence of the organism in tested sample (Josephson et al.,

1993). Traditional PCR methods visualize amplified product on agarose gels using electrophoresis and ethidium bromide (ErBr) staining. Sometimes, a confirmation step, such as southern-blot analysis, is used to distinguish real amplicons from non-specific end products (Campbell, 1996; Cockerill & Uhl, 2002). Since emerged, PCR is accredited with being the most important achievement in the field of molecular biology.

PCR offers the great advantages of specificity, sensitivity, rapidity, and accuracy.

However, using conventional PCR for food microbial detection is hindered by several

16 drawbacks. Firstly, false negative result obtained due to low levels of contaminating pathogens or residual food components that inhibit enzymatic reactions. Non-specific amplification of incidental DNA, just present in laboratory, or DNA debris from dead cells can result in a false positive detection. There is also the risk of labor and carry-over contamination associated with the post-amplification analyses (Hanna et al., 2005).

Finally, traditional PCR is not sufficiently quantifiable for which there is a growing demand in the food industry.

2.5.3 Real-time PCR

The development of real-time PCR is a major advancement for PCR-based detection. Real-time PCR incorporates a fluorescent probe during each replication cycle that can be monitored using an optical module. Real-time PCR permits rapid, dynamic assessment of PCR amplicons, and reduces contamination caused by extraneous nucleic acids. SYBR Green, molecular beacon, and Taqman probe are the three primary real-time

PCR formats.

SYBR Green, a highly specific, double-stranded DNA binding dye intercalates into double stranded amplicons when it is added to PCR product. SYBR green real-time

PCR is the most economical and simplest choice among all the real-time PCR assays.

However, all double stranded nucleic acid fragments, including primer dimers, could bind to SYBR green and create fluorescent signals. A melting curve analysis has to be performed to determine a specific product (Wittwer et al., 2001).

Molecular beacon real-time assays use single-strand oligo-nucleotide probes to detect amplicons. The probe forms a stem-and-loop hairpin structure, and is labeled with both a fluorophore and a quencher. The loop sequence is complementary to the amplicon, and the stem is formed by the annealing of complementary arm sequences that are located on either side of the probe sequence. The hairpin structure of the molecular beacon‟s stem is closed. Hybridizing the beacon loop with amplicons forces the stem region to open inducing the fluorescent signal which is detected. If amplicons are absent the stem remains closed and the quencher component absorbs the fluorescent signal (Tyagi &

Kramer, 1996).

In Taqman real-time PCR, a probe labeled with 5‟ fluorescent reporter dye and 3‟ quenching dye is used for signal detection. Initially, the probe is annealed to the template and the fluorescent signal, emitted by the reporter dye, is absorbed by the quencher dye nearby. As amplification progresses, complementary sequence to the oligo-probe anneal to the amplicon, the reporter dye is enzymatically cleaved by the 5' exonuclease activity of the polymerase, and the free reporter dye emits a signal. The quencher dye is too distant to absorb the fluorescent signal, allowing the signal from the reporter dye to be captured by the optical module (Batt, 1997).

Real-time PCR systems, especially Taqman real-time PCR, are leading technologies in the detection of bacterial, fungal and viral contaminates in food samples.

By hybridizing PCR end-products with oligo-nucleotide probes, real-time PCR provides high degree of speed and specificity. Real-time PCR also obtains result with limited manipulations and amplicon contaminations. Real-time PCR is capable of simultaneous

18 identification of multiple organisms or multiple genes in one reaction with the use of different primers-and-probe sets. Moreover, real-time PCR results are quantitative.

Template DNA copy numbers can be determined since relative fluorescence units (Ct values) are recorded for each amplification cycle. However, as mentioned above, one of the main limitations of the real-time PCR is the inability to distinguish between viable and non-viable cells.

2.5.4 Microarrays

DNA microarrays are either a glass or silicon chip arrayed with thousands of oligonucleotide spots or cDNA probes. The target DNA molecule to be analyzed is hybridized to recognition probes on the array. The signal generated by the bound target on the array allows identification based on the known information of the probes. This technology permits identification and characterization of thousands of oligonucleotides in parallel on a single mini-array assay (Goldschmidt, 2006). Generally, the typical steps in the design and implementation of a DNA microarray experiment include: 1) oligonucleotide probe synthesis, 2) array fabrication, 3) sample preparation, 4) assay hybridization, 5) scan and detection and 6) data analysis (Suo et al., 2010).

For food microbial analysis, the oligonucleotide microarray provides a high- throughput analysis for multiple virulent genes or simultaneous detection of multiple foodborne pathogens. The initial applications of microarrays for food microbe analysis was done by the FDA to identify enteric food pathogens based on their various virulence factors (Fang et al., 2010). In recent years, DNA microarrays have been used to detect

19 individual or multiple pathogens, such as Listeria spp., S. aureus, Campylobacter jejuni,

Shigella, E.coli, and Salmonella spp.(Cremonesi et al., 2009; Rasooly & Herold, 2008;

Suo et al., 2010). Generally, ribosomal RNA encoding genes and various virulent genes were used for bacterial identification. Typically, specific fragments of target organisms were amplified, followed by identification via hybridization to a microarray. PCR amplification is used initially to improve detection sensitivity and reducing the noise of nonspecific background DNA, enabling detection of low count pathogen cells in complex samples (Kim et al., 2007).

Microarrays require expensive equipment and are limited in their enumeration of pathogens. However, microarrays are fast, specific, capable of high sample volumes and can enable the simultaneous detection of multiple pathogens. The benefits of microarray- based detection methods make them superior to many available techniques and make them the potential future for food microbial detection.

2.6 Main limitation of application of rapid methods for food microbial detection

Compared with conventional culturing and immunological techniques, molecular detection methods have the great advantage of significantly reducing assay time. They also provide semi-quantitative information and ensure sensitive detection of specific targets in complex food matrices. Despite of those advantages, molecular methods have had a limited use in food analyses. One reason is the false negatives generated by interference of residual food components and low level contaminants that are common to food samples (Brehm-Stecher et al., 2009). For example, previous studies in milk have

20 documented the limitations of molecular detection methods: proteinase present in milk inhibit PCR secondary to degrading Taq polymerase; and calcium ions impeded amplification by changing the reaction‟s buffering capability. The presence of amplification inhibitors can be revealed by using an internal positive control (IPC) (Suo et al., 2010). An IPC is a precisely known amount of nucleic acid fragment that is added to the reaction and co-amplified along with the target sequence. Inhibition and detection efficiency can be characterized if no signal or a weak amplification signal is generated by the IPC.

Another disadvantage, as discussed before, is molecular detection methods targeted DNA molecules is unable to distinguish between viable and nonviable cells

(Wolffs et al., 2005). This is particularly relevant in food safety and quality control because only viable cells are likely to pose a potential risk. Suitable new methods or augmentations to known molecular methods are needed for high-speed detection of viable microorganisms.

2.7 Food sample preparation

The presence of amplification inhibiting compounds makes sample preparation a necessary step. Appropriate sample preparation can remove food matrix-associated inhibitors, reduce sample volume to appropriate small PCR volumes (10-50μl) and facilitate low count pathogen or sporadic contamination detection. Generally, suitable food sample preparations collect target bacterial cells from food matrices, concentrate

21 samples, produce a homogeneous sample, eliminate background biota and remove extraneous components that may inhibit detection process (Stevens & Jaykus, 2004).

In order to detect and identify the presence of a particular microorganism, sometimes a pre-enrichment step is used as a starting point. This step includes using a stomacher device to convert food matrices into an appropriate nutritional broth. The homogenized sample is incubated in proper growth conditions for 1-2 days allowing target microbial cells to grow. The pre-enrichment culturing conditions are specific to target organism(s) while inhibiting the growth of other “background” microorganisms.

This step also has the benefit of allowing injured microbial cells an opportunity to recover. Enrichment steps can create problems if not controlled for in later analysis steps.

Pre-enrichment can interfere with quantitative information regarding initial microbial numbers; it is time-consuming, and it may fail to amplify fastidious organisms or injured cells if enrichment conditions are not permissive to their growth (Brehm-Stecher et al.,

2009; Fukushima et al., 2007).

In addition, rapid methods are needed for sample preparation that can effectively separate and concentrate targets from the food. Centrifugation or filtration or a combination of both has been used to remove large food particles from the sample and concentrate microbial cells. But they are cumbersome techniques and may miss some bacteria cells entrapped by food particles. Adsorptive materials, such as resins, metal hydroxides, or lectins are also used to capture and separate bacteria from foods (Bhunia,

2008). These methods are more specific and can effectively reduce sample volume and remove inhibitors. Bio-affinity based separation and concentration methods have

22 attracted more attention as a possible method for sample preparation because of their specific recognition capabilities. Immunomagnetic separation (IMS) is another tool that can be effectively employed for sample preparation (Wang & Mustaphai, 2007).

Microbial cells are captured and concentrated in IMS by antibody-coated magnetic particles.

2.8 Microbial cells viability indictors

A standard criterion for viablity, in microbial detection, has been carried out by cultivation and enumeration of colony forming units (CFU). However, this method is time-consuming and may introduce detection biases. New indicators for viable bacterial detection are needed. For the purposes of food safety and quality control, microbial cells maintaining membrane integrity, retaining some metabolic activity or having specific transcripts are considered viable.

Based on membrane integrity, various viable bacteria detection methods have been established. Assays have been created that pair direct viablility counts with a double-staining method (e.g. Live/Dead Bacterial Viability Kit, BacLight) using fluorescent dye selectively marking membrane-compromised cells (Boulos et al., 1999).

Epifluorescence microscopy can then observe viable cells in environmental and food samples (Queric et al., 2004). Flow cytometry, a technique that can rapidly distinguish bacterium‟s physiological states, was applied to detection methods to deliver results quicker with increased sensitivity and decreased bias (Caron et al., 1998). In recent years,

PCR was coupled with DNA-intercalating dyes to selectively amplify the nucleic acid

23 templates from membrane-undamaged (considered „live‟) bacterial cells (Nocker et al.,

2006).

Using metabolic activity as a viability criterion, detection methods have recognized live bacteria by their metabolic end-product „trail‟. One tool utilized the end- products of respiratory activity by measuring the hydrolysis of 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) or reduction of 2-(piodophenyl)-3-(p-nitrophenyl)-5-phenyl tetrazolium chloride (Nocker & Camper, 2009). Sometimes viable cell contamination is measured by bioluminescent analysis correlated to the concentration of intracellular ATP.

Molecular detection methods targeting specific DNA as a measure of viability were criticized since DNA can persist in dead cells for significant periods of time

(Josephson et al., 1993; Koo & Jaykus, 2000; Wolffs et al., 2005). Attention has since turned to the use of RNA transcripts as alternative targets. Unlike DNA, RNA molecules generally have a shorter half-life after cell death. RNA should therefore provide a more accurate reflection of viability status. The stability of ribosomal RNA (rRNA) has been investigated. Previous studies positively correlated the presence of rRNA with viability under some bacterial-killing regimes, depending on the choice of amplification fragments.

Positive correlation existed especially after extremely lethal treatments, such as autoclaving (Klein & Juneja, 1997; McKillip et al., 1998). However, rRNA molecules have a longer half-life than other RNAs and variable retentions, following a variety of bacterial inactivation treatments during food processing (Yaron & Matthews, 2002). This makes the utility of rRNA as a viablility indicator, under many conditions, questionable.

Different mRNA molecules have been tested as gauges of viability in various bacterial

24 cells, including a number of foodborne pathogens, such as Listeria monocytogenes,

Salmonella, Campylobacter spp, Vibrio cholerae and E. coli (Barer & Harwood, 1999;

Bleve et al., 2003; Matsuda et al., 2007; Miller et al., 2010; Sung et al., 2004). The data reveals that various mRNA decays after different lethal treatments is heterogeneous. The mRNA decay rates depend on the nature of the mRNA, the region that is amplified, the environmental conditions and the method of cell inactivation (Cenciarini et al., 2008;

Kort et al., 2008). In order to successfully differentiate live and dead cells in food samples, the proper RNA molecules must be chosen as the amplification targets; and RT-

PCR and NASBA are RNA-specific techniques that could be employed to amplify such targets.

2.9 Reverse transcriptase polymerase chain reaction (RT-PCR)

RT-PCR is a variant of conventional PCR; but instead of DNA templates, RNA strands are reverse transcribed into complementary DNA (cDNA) by reverse transcriptase. The resulting cDNA is then amplified by conventional PCR. RT-PCR is commonly used to study RNA viruses and measure the expression of specific genes.

DNase treatment can also be used in this procedure as a pretreatment step to eliminate

DNA contamination that commonly appears in the RNA preparations. However, DNase requires a heat denaturation step to inactivate which may compromise an unacceptable amount of the RT-PCR reactions. The application of RT-PCR in the food microbial detection field began with the need to differentiate live and dead cells. Currently, RT-

PCR has been used by many researchers for detecting viable pathogenic cells originating

25 in bacteria, molds and yeast (Matsuda et al., 2007; Min & Baeumner, 2002; Sung et al.,

2004). These studies reveal several rRNA and mRNA targets as possible candidates for detecting viable microorganisms.

2.10 Nucleic acid sequence based amplification (NASBA)

NASBA is an isothermal amplification method developed in the early 1990s to amplify viral RNA. Three enzymes, a reverse transcriptase, RNaseH and T7 RNA polymerase, are used to amplify single-stranded RNA templates. Two specific primers, complementary to target RNA sequences, are incorporated in the amplification reaction.

The first primer contains a recognition sequence for T7 RNA polymerase and is able to initiate the RNA reverse-transcription reaction, which is catalyzed by reverse transcriptase. RNaseH digests the RNA template and the second primer binds to the cDNA, allowing the reverse transcriptase to form a double-stranded cDNA. This double- stranded DNA then acts as template to produce thousands of RNA transcripts. The product of NASBA is mainly single-stranded RNA, which is detected by gel electrophoresis. Recently, fluorescently labeled probes have been added to real-time detect NASBA amplification products and improve amplification specificity (Cook,

2003; Deiman et al., 2002; Lau et al., 2006).

Initially, NASBA was used for virus detection, such as rotavirus and hepatitis A.

There have been a few published studies regarding detection of pathogens, such as

Campylobacter spp., Listeria monocytogenes, Cryptosporidium parvum and Salmonella enterica, using NASBA analysis in food and environmental samples (Cook, 2003).

NASBA detection has several advantages. Similar to RT-PCR, RNA templates can be detected with this method therefore enhancing viable cell detection with improved sensitivity. NASBA reactions do not produce false positives from contaminating double- stranded DNA, as is the risk in RT-PCR. NASBA runs at 42 °C, the temperature genomic

DNA remains double-stranded and therefore not a suitable amplification substrate. For that same reason, NASBA doesn‟t require DNase as RT-PCR does, nor does it need an inactivation step for DNase inactivation. DNase and its heat induced inactivation are thought to interfere with RNA reactions (Fontaine & Guillot, 2003; Keer & Birch, 2003).

NASBA may have the analytical sensitivity that future bacterial detection methods require.

2.11 Selective detection of live microorganisms by ethidium monoazide (EMA)-PCR and propidium monoazide (PMA)-PCR

In the case of DNA amplification based detection of microorganisms, an important improvement in viable cell detection was introduced with EMA-PCR (Nocker

& Camper, 2006). Cell viability, in this approach, is based on membrane integrity. EMA is a DNA-intercalating dye with a photo-inducible azide group. EMA selectively enters membrane-damaged cells, whereas EMA fails to penetrate viable cells with intact cell membranes (Cawthorn & Witthuhn, 2008). Once inside cells, EMA intercalates into double stranded DNA molecules. After exposure to bright light, the azide group converts to a highly reactive nitrene radical and covalently links EMA with DNA. EMA-linked

DNA is insoluble and precipitates out with cell debris during the DNA extraction

27 procedure. Extracellular EMA is inactivated by water and doesn‟t interrupt the DNA isolation procedure. Therefore, the result of EMA treatment is that only unmodified DNA from intact cells can be amplified (Nocker et al., 2006). EMA-PCR has been applied to live cell detection of various bacterial species, including Zygosaccharomyces bailii

(Rawsthorne & Phister, 2009), Enterobacter (Cawthorn & Witthuhn, 2008), Salmonella

(Ukuku et al., 2008), Escherichia coli (Ukuku et al., 2008) and L. monocytogenes (Pan &

Breidt, 2007), ect.

However more recent studies suggest that EMA may penetrate some viable organisms with intact membranes. This potential confounder is addressed with the use of

PMA. PMA is another intercalating agent used in molecular methods that is identical to propidium iodide (PI) except that PMA carries an azide group. The azide group enables

PMA to crosslink with DNA. In contrast to EMA, PMA is efficiently excluded from cells with intact cell membranes, probably due to an increased positive charge (Nocker et al.,

2006; Pan & Breidt, 2007). Comparative PMA-PCR and EMA-PCR studies indicate

PMA use could lead to better viable cell detection.

2.12 Listeria monocytogenes and challenge to food safety

L. monocytogenes is one of the most virulent foodborne pathogens, causing listeriosis in sporadic cases and outbreaks. Listeriosis primarily affects older adults, immuno-compromised patients, pregnant women and neonates (Bind et al., 1996). L. monocytogenes is a facultative anaerobe that is ubiquitous in the environment, able to grow in a wide temperature range (below freezing to over 50°C), capable of surving in

28 acids and dyes, and resistant to heat, salt and nitrite. These characteristics of durability make L. monocytogenes a food safety challenge (Carpentier & Cerf, 2011; Donnelly,

2001). Confirming this, outbreak reports have associated L. monocytogenes with numerous food products, including fresh vegetables, dairy products, seafood, raw meat, ready-to-eat products (RTE), beverages and juice products (Hof et al., 2007; Lianou &

Sofos, 2007). Moreover, epidemiological data indicates certain food products, such as those containing raw ingredients or those subjected to mild processing, are more likely to be associated with listeriosis outbreaks (Kathariou, 2002).

The FDA, United States Department of Agriculture (USDA) and Europe Union

(EU) have all implemented a zero-tolerance rule for L. monocytogenes in RTE foods ,to enhance the microbiological quality and safety of such products

(http://vm.cfsan.fda.gov/~mow/fsislist.html; http://www.fsis.usda.gov/Regulations_&_Policies/index.asp). More stringent strategies have been instituted by the food industry, such as novel pathogen inactivation techniques and the Hazard Analysis Critical Control Point (HACCP) rules. As a result of these strict regulatory actions, microbial control has vastly decrease the presence of Listeria in food processing environments and final products, as evidenced by the decreasing rates of

Listeria infections. In 2006, the CDC documented the dramatic decrease in infections from Listeria contaminated food. However, the increasing automation in food processing increases the risk of contamination, which is a critical concern especially for RTE products. The consolidation of food companies in the USA and the international distribution of food products put potential outbreaks on a national and international scale.

This is especially a concern since rates of listeriosis are on the rise in some European countries (Lianou & Sofos, 2007; Wing & Gregory, 2002). In order to minimize listeriosis and the devastating economic impact associated with those outbreaks, it is critical to have intense monitoring for L. monocytogenes in food products, processing environments and raw materials.

A sensitive and specific L. monocytogenes detection method is essential for upholding the "zero tolerance" policies for the bacterium. Currently, the FDA and USDA use a conventional detection system for L. monocytogenes, enriching samples on a selective medium, isolating colonies and confirming presence with biochemical tests.

This system is time-consuming and limits the detection of dormant or injured cells.

Similar to general pathogenic detection, molecular techniques, such as real time PCR, offer more rapid, sensitive and specific alternatives of all viable L. monocytogenes from food samples.

2.13 Microbial spoilage of food products

Food spoilage is a complex process combining microbial and (bio)-chemical activities. The USDA has estimated that at least ninety-six billion pounds of food are lost from the US food supply each year due to food spoilage

(http://fri.wisc.edu/docs/pdf/FRI_Brief_Microbial_Food_Spoilage_7_07.pdf). In fact, huge amounts of food products are lost due to microbial growth despite advances in the industry, such as chill chains, improved food preservation techniques and a better understanding of microbial behaviors during spoilage. Many microorganisms are initially

30 present on raw food materials; however, the final flora that dominates particular food products is limited to a few special groups, called special spoilage organisms (SSO)

(Gram et al., 2002). The type of SSO depends on the characteristics of products and the processing and storing conditions. The factors that influence the domination of SSO include the intrinsic nutrients, water activity, pH, antimicrobial activity of that food, mode of processing and perservation, and environments in which the products are stored and distributed (Borch et al., 1996). For examples, Pseudomonas spp. and a few other

Gram-negative psychrotrophic organisms will dominate proteinaceous foods stored aerobically at chill temperatures. However, a change in packaging to an anaerobic atmosphere will inhibit the respiratory Pseudomonads in meats, and can consequently shift the SSO to members of Enterobacteriaceae (Gram & Huss, 1996). Monitoring the occurrence and development of SSO in related food products are necessary for quality control and shelf life production.

2.14 Yeasts and juice spoilage

Yeasts persist in extreme environments (e.g. low pH, high sugar content) and can grow at refrigeration temperatures. These microorganisms are able to survive in foods containing up to 60% sugar and grow against common preservative (e.g. sorbic acid and benzoic acid), making them common spoiler of juices and beverages ( Fleet, 1992). Food- spoilage yeast including many different genera and species, including Saccharomyces,

Zygossacharomyces, Hansenula, Pichia, Hanseniospora, Trichosporon, and Candida

(Garcia et al., 2004; Loureiro & Malfeito-Ferreira, 2003). Among them, preservative-

31 resistant groups, such as xerophilic Zygossacharomyces, Candida, Saccharomyces,

Pichia, are a special concern for fruit juice and beverage industries (Fleet, 2007).

Most yeast strains are heat labile and can be controlled with standard pasteurization conditions, 75–85 °C for 1–3 min or 99 °C twice for 15 s with intermittent cooling (Jespersen & Jakobsen, 1996). However, juice spoilage problems persist due to high contamination of raw materials, failures in pasteurization, contamination occurrences after or during pasteurization, poor sanitation practices, or the presence of preservative-resistant yeast. In addition, some types of yeasts are thermally resistant and survive pasteurization. Outgrowth of spoilage yeast can cause excess carbon dioxide gas production, emission of off-flavors, enhanced turbidity, flocculation, and phase separation, which all lead to ruined product and economic losses in for the juice producer

(Fleet, 2007).

Similar to traditional bacterial detection and enumeration, traditional plating techniques, used to detect yeast, are labor intensive and time-consuming, requiring 3-7 days. The selective medium used is also limited in its ability to recover injured or stressed yeast colonies. Moreover, detecting spoilage yeasts is complicated due to their potential to enter a viable but nonculturable state (VBNC) (Divol & Lonvaud-Funel, 2005). DNA amplification based techniques address some of these issues and may be the more optimal method for yeast detection. Conventional PCR, PCR-RFLP, SYBR-Green real-time PCR and fluorescent-labeled probe based real-time PCR are established methods that provide specific and sensitive detection of spoilage yeasts (Casey & Dobson, 2004; Gray et al.,

2011; Pennacchia et al., 2009). However, false positive results occur with these tools

32 because of DNA debris from dead yeast cells (Josephson et al., 1993). Molecular techniques, including amplification based method that utilize RNAs, offer rapid, specific and sensitive detection of viable spoilage yeast.

2.15 Pseudomonas and food spoilage

Pseudomonas is aerobic gram-negative rods ubiquitously present in all environments, soil, fresh water, plants and animal biota. Pseudomonas bacteria account for only a small proportion of initial microflora contained in food products. However, given the right conditions they can quickly become dominating spoilage organisms, particularly at low temperatures. Several factors make Pseudomonas a potent spoilage organism, including their ability to grow in psychrotrophic conditions, wide distribution in the environment, ability to utilize a wide variety of metabolites, competition for ions, production of antibiotics and cyanides that inhibit other organisms, capability of forming biofilm and coordinating the expression of certain spoiling enzymes by bacterial quorum sensing. Studies have documented Pseudomonas spoiling various proteinaceous food products, particularly fresh or minimally processed foods with high water content, high pH and cold-stored products (Salvat et al., 1997). These spoilage Pseudomonas strains include: P. fragi, P. fluorescens, P. taetrolens, P. mudicolens, P. lundensis and P. putida; and they have been identified as spoilers in meat, fish, dairy products and eggs (Ercolini et al., 2007; Gunasekera et al., 2003; Reynisson et al., 2008). When these strains grow and colonize in foods they produce off-odors, slime, discoloration, and result in sensory rejection of food products.

Current detection methods for Pseudomonas in food products are mainly plating- based methods. PCR-based molecular methods have been established for rapid and specific detection of spoilage Pseudomonas. Using PCR, detection results can be delivered within few hours after receiving the samples (Ercolini et al., 2007; Martins et al., 2005). However, as previously mentioned, PCR is prey to high false positives due to

DNA debris from dead cells being amplified. Rapid detection of viable Pseudomonas contamination is a needed tool to monitor food quality and track the hygienic status of food processing systems.

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Chapter 3

Development of a NASBA-Molecular Beacon System Targeting the 18S rRNA for

Rapid and Specific Detection of Viable Spoilage Yeasts in Juice Products

3.1 Abstract

Rapid detection of spoilage and pathogenic microorganisms in food matrices is essential for safety and quality control. The aim of this project was to develop isothermal nucleic acid sequence based amplification (NASBA)-molecular beacon system for real- time detection of spoilage yeasts in juice products. Instead of amplifying DNA, NASBA assays measure only target RNA molecules, which should help eliminate false-positive amplification results due to the long half life of DNA after cell death.

A pair of NASBA primers and a molecular beacon probe was designed targeting conserved regions of yeast 18S rRNA gene. The specificity of the NASBA-molecular beacon assays was examined using representative microorganisms commonly found in the food environment; and detection sensitivity was evaluated using serially diluted samples. The results showed that two spoilage yeasts species Saccharomyces cerevisiae and Candida parapsilosis were detected by the newly developed NASBA-molecular beacon assays without cross-activity to molds, bacteria or raw food materials within 6

48 hours. In addition, detection of the presence of less than 100 yeast cells from juice products was achieved. This is a significant improvement compared to the current industrial practices, which take from at least a couple of days to weeks to identify and characterize spoilage yeasts.

To assess the feasibility of using18S rRNA as a cell viability indictor, NASBA- molecular beacon assays were performed using RNAs extracted from viable and heat- killed S. cerevisiae cells. 18S rRNA is stable. Real-time amplification signals were still observaed even in autoclaved samples stored at room temperature for 24 h. However, the

Ct values were significantly lower in heat-killed cells than in the viable samples, which indicated that the developed NASBA-molecular beacon system has a potential for rapid detection of viable yeasts if combined with quantitative analysis. This is the first reported

NASBA-molecular beacon assays using RNA as cell viability indictor for rapid and specific detection of viable spoilage yeasts in juice products. The new method has potential for industrial applications from raw material screening to final product quality control.

3.2 Introduction

Yeasts are among the most important microorganisms in food industry. They are commonly used in fermentation of bread, alcoholic beverages, cheeses and other products. However, yeasts can also cause spoilage in a wide range of foods, including preserved foods and beverages (Fleet, 1992). Generally, more than100 species of yeasts are associated with foods, while only about a few of them, such as Saccharomyces

49 cerevisiae, Candida spp., Dekkera bruxellensis, Debaryomyces hansenii, Kloeckera apiculata, , Zygosaccharomyces spp., and Pichia membranifaciens, are commonly involved in foods spoilage, especially in low pH and high solutes concentration products, mainly due to their ability to resist adverse environmental conditions (Beuchat, 1993;

Fleet, 2007; Jespersen & Jakobsen, 1996). Currently, with consumers‟ demand for more natural products free of fungal inhibitors, yeasts spoilage has gained an increasing attention in food industry due to its significant economic impact (Naito, 2008).

Monitoring the hygiene and sanitation procedures during manufacturing and detecting the presence of spoilage yeasts in both raw materials and final products are essential for product quality control and shelf life prediction.

The FDA classic approaches for detection of yeasts are culturing-based methods

(http://www.fda.gov/Food/ScienceResearch/LaboratoryMethods/BacteriologicalAnalytic alManualBAM/UCM071435). For spoilage yeasts, usually, nutrient rich media containing chemicals against bacteria (e.g. oxytretracycline or chloramphenicol) and molds (e.g. sodium propionate, rose bengal, dichloran, or the antibiotic oligomycin) are used to isolate and enumerate yeast cells in food matrices, followed by physical and biochemical identification (Beuchat, 1993; Nguyen & Carlin, 1994; Roller & Covill,

1999; Schuller et al., 2000). Using culturing methods, at least 5-7 days are needed for results delivery, which is unsuitable for products with limited shelf life. Besides being labor intensive and time consuming, more importantly, the conventional culturing methods may inappropriately measure “real” contamination level of spoilage yeasts.

Instead of detecting only spoilage yeasts, media used in conventional culturing methods

50 could support the growth of all yeasts, including many innocent types of yeasts with high growing rate. The occurrences of some slow-growing spoilage yeasts, such as Dekkera spp. and Zygosaccharomyces spp., however, may not be detected properly by most of commercial media (Jespersen & Jakobsen, 1996; Loureiro & Malfeito-Ferreira, 2003). In addition, previous studies have illustrated that some yeasts are able to enter a viable but nonculturable (VBNC) state (Divol & Lonvaud-Funel, 2005; Millet & Lonvaud-Funel,

2000). Even with recovery steps, VBNC cells may not grow in artificial media (Dreux et al., 2007). However, those un-culturable cells still maintain metabolism activity and may produce enzymes and other compounds causing for food spoilage (Kell et al., 1998; Wu,

2008). Therefore, rapid techniques with high specificity and sensitivity are becoming increasingly important for spoilage yeasts detection.

In past decade, significant progress has been made in developing, optimizing and validating molecular detection techniques, mainly nucleic acid amplification based methods, as powderful alternatives to classical methods in food microbial detection fiels

(Connor et al., 2005; Luo et al., 2004; Neeley et al., 2005; Wan et al., 2006). In fact, a number of PCR based detection systems, such as real-time PCR and quantitative PCR, were developed for rapid detection of spoilage yeasts in foods and beverages (Casey &

Dobson, 2004; Gray et al., 2011; Pennacchia et al., 2009; Rossi et al., 2010). However, one major drawback of the conventional PCR is that DNA detection could be positive from dead bacteria (Josephson, 1993; Koo, 2000). In contrast to DNA, RNA has been proposed as more liable viability indictors as RNA may disappears quickly in dead cells

(Bleve et al., 2003; Fontaine & Guillot, 2003; Hellyer et al., 1999; Keer & Birch, 2003).

Reverse transcription PCR based on actin mRNA and 18S rRNA have been used for spoilage yeasts detection (Bleve et al., 2003; Mayoral et al., 2006). In those studies, however, the PCR end-products were detected by electrophoreses analysis. The introduction of fluorescent probes into nucleic acid amplification permits rapid, sensitive, qualitative assessment of PCR amplicons, and lessens the possibility of end products contamination (McKillip & Drake, 2004). Therefore, this study aimed to establish real- time amplification based method for viable spoilage yeasts detection.

Nucleic acid sequence-based amplification (NASBA) is a transcription-based amplification system which allows the continuous amplifying of specific RNA region in a single mixture at a set temperature. The NASBA technique has been used for detection of various foodborne pathogens (Cook, 2003; Lau et al., 2006; Jean et al., 2004). It has contributed to the development of a simple, affordable, portable microbial detection kit without using a thermal cycler (Deiman et al., 2002). Compared with RT-PCR, NASBA is more sensitive (Jean et al., 2002; Noble & Weisberg, 2005). The objective of this study was to develop a real-time NASBA-molecular beacon assaystargeting the 18S rRNA for rapid, specific and sensitive detection of viable spoilage yeasts in beverage products. The primer-and-probe set used in the NASBA assay targeted a gene encoding 18S ribosomal

RNA, which is a universal eukaryotic housekeeping gene. Besides serving as liable cell viability indictor, the advantages for choosing rRNA as detection target are obvious: 1) high conservation of 18S rRNA gene and the presence of both conserved and variable regions within the gene enable the design for specific detection of target microorganisms,

2) 18S rRNA has high copies per cell, which can improve detection sensitivity (Cappa &

Cocconcelli, 2001). S. cerevisiae and Candida parapsilosis, two important spoilage yeasts responsible for juice spoilage, were chosen as model microorganisms (Beuchat,

1993; Fleet, 1992). The successful establishment of a viable spoilage yeast detection system could contribute to quality evaluation of the raw materials, the estimation of the product shelf life, and the contamination tracing in the processing lines.

3.3 Materials and Methods

3.3.1 Bacterial strains and culture conditions

Two spoilage yeast strains, S. cerevisiae AC1 and C. parapsilosis AC4 used in this study were obtained from the food industry. The strains were grown in yeast-mold medium (YM, 3g yeast extract, 3g malt extract, 5g peptone, 10g dextrose dissolved in 1 liter dH2O, Becton Dickinson, Sparks, MD, USA) statically for between 24 and 48 h at

8 -1 30 °C to A600 between 0.8 and 0.9, corresponding to cell densities around 10 CFU ml .

When required, the cultures were spread on YM agar and incubated at 30 °C. For the specificity tests, the following strains were used: Bacillus subtilis OSU 494 (Khadre &

Yousef, 2001), grown in Difco Nutrient broth (Becton Dickinson, Sparks, MD, USA) for

24 h at 37°C; Escherichia coli DH-5α (Invitrogen, Carlsbad, CA, USA), grown in Miller

LB broth (Fisher Chemicals, Fairlawn, NJ, USA) for 24 h at 37 °C; Lactobacillus plantarum ATCC 8014 (ATCC) grown in MRS broth (Becton Dickinson, Sparks, MD,

USA) for 24 h at 37 °C; Lactococcus lactis subsp. LM2301 grown in M17-G broth

(Becton Dickinson, Sparks, MD, USA ) for 24 h at 37°C; Listeria monocytogenes Scott A

(milk isolate, Yousef et al., 1988) grown in Tryptic Soy Broth (TSB; Becton Dickinson, 53

Sparks, MD, USA) for 24h at 37°C; Pseudomonas putida ATCC 49451(ATCC),

Penicillium digitatum KW3 and Byssochlamys fulva KW2 (environment isolates, our lab) grown in YM medium for 48 h at 30°C. Stock cultures of all strains were stored in their respective media plus 20% glycerol at -80 °C. All working stocks were kept at 4-6 °C, and maintained by biweekly transfers. Fresh cultures were made by inoculating 5% of working stocks into the appropriate broths and incubating overnight at the designated temperatures.

3.3.2 NASBA primers and molecular beacon probes design

NASBA specific primers and molecular beacon probe were used in this study.

The special NASBA primers and molecular beacon probe were designed based on complete 18S rRNA genes sequence of S. cerevisiae AC1, C. parapsilosis AC4 and

S.cerevisiae (NC_001147.6). Long fragments of 18S rRNA gene were amplified by conventional PCR using genomic DNA extracted from S.cerevisiae AC1 and

C.parapsilosis AC4, and the primers pair used in previous study (Wan et al., 2006). After determining the sequences of amplicons, an alignment of the 18S rRNA gene sequences was performed with ClustalV using MegAlign 5.01 (DNASTAR, Madison, WI, USA).

Beside yeasts 18S rRNA genes, the following sequences with the Genback accession numbers listed in the parentheses were used: Penicillium chrysogenum (NS_000201.1);

Aspergillus niger (AM270994); Pseudomonas fluorescens (AM181176), Escherichia coli

(NC_004431), Shigella flexneri (NC_008258), Salmonella spp.(FJ667502), Bacillus subtilis (FJ493055). Identical regions within the yeast sequence were chose to design the

18S rRNA specific primers and probe. Beside complementary to conserved sequences in the target RNA as used in conventional PCR, one of the NASBA primers must contain a recognition sequence for T7 RNA polymerase (Deiman et al., 2002). In addition, molecular beacon probe was designed to form a stem-and-loop hairpin structure. The loop contained a sequence that was complementary to amplicons and stems were formed by annealing complementary arm sequences located on either side (Monroe & Haselton,

2003; Tsourkas et al., 2002). Primers T7TWP1 and YeastP2 were synthesized by Sigma-

Genosys (The Woodlands, TX, USA). The Y18S probe was synthesized by Biosearch

Technologies (Novato, CA, USA). The probe was labeled with the reporter dye FAM on the 5‟ end, and quencher dye BHQ-1 on the 3‟ end.

3.3.3 RNA extraction

For RNA extraction, 1 ml fresh microbial cultures were collected by centrifugation at 13,200 rpm for 1 min. Collected cell pellets were lysed with enzyme in

100 μl TE buffer (20 mmol l-1 Tris-HCl, 2 mmol l-1 EDTA, pH 8.0) for 30 min at 37°C.

The enzyme used was 20 mg ml-1 of lysozyme (Sigma Chemical CO., St Louis, MO,

USA) for Gram negative bacteria lysis; 50 mg ml-1 of lysozyme (Sigma Chemical CO., St

Louis, MO, USA) for Gram positive bacteria; and 50 mg ml-1 of lyticase (Sigma

Chemical CO., St Louis, MO, USA) for yeasts and molds cells. Total RNA was extracted using QIAgen RNeasy® Mini kit (QIAgen, Valencia, CA, USA) and eluted with 30 μl of

RNase and DNase-free water following the instructions of the manufacturer. The quality of isolated RNA samples was determined by electrophoresis analysis using 2.0%

55 formaldehyde agarose gel (Invitrogen, Carlsbad, CA, USA) and the qualtitity were determined by ND-1000 UV/VIS spectrophotometer (Thermo Scientific, Nanodrop,

Waltham, MA, USA).

3.3.4 NASBA-molecular beacon reaction conditions

Each NASBA reaction was conducted in thin-wall micro-centrifuge tubes in a total volume of 20 μl containing 10 μl pre-mix, 1.0 nmol-1 each of the two primers, 0.8 nmol-1 molecular beacon probe, 3 μl of DMSO, 1 μl RNA template and 4 μl of NASBA enzymes mix. Pre-mix consisted of NASBA buffer (200 mmol-1 Tris-HCl, pH 8.5; 60

-1 -1 -1 -1 -1 mmol MgCl2; 350 mmol KCl, 2.5 mmol DTT), 5 mmol each dNTP, 10 mmol each of ATP, UTP, CTP, and GTP. In NASBA reaction, the reaction mixtures without enzymes were incubated at 65°C for 5 min firstly. Then after cooling to 41°C for 5 min, 4

μl of enzyme mix were added following incubation at 41°C for 120 min. The enzyme mix consisted of 1 μl of 12.5 U μl-1 RNase H, 1 μl of 32U μl-1 T7 RNA polymerase, 1 μl of

8U μl-1 AMV reverse transcriptase, and 1 μl of bovine serum albumin. All enzymes were purchased from Pharmacia Biotech (Piscataway, NJ, USA).

During the amplification, the fluorescence signal was measured with an interval time of 30 s for each independent reaction and the threshold and Ct values were determined by the background subtraction method using the iCycler iQ software (Bio-

Rad Laboratories, Hercules, CA, USA). In addition, negative control containing all components except RNA template was run in parallel with each testing.

3.3.5 Specificity and sensitivity analysis

NASBA-molecular beacon assays were conducted to test the specificity of the developed 18S primers-and-probe set against selected bacteria and molds commonly associated with beverage. Beside the spoilage yeasts S. cerevisiae AC1 and C. parapsilosis AC 4, the used strains also included B. subtilis OSU 494, E. coli DH-5α,

L. monocytogenes ScottA, Lactob. plantarum ATCC 8014, L. lactis subsp. lactis

LM2301, Ps. putida ATCC 49451, P. digitatum KW2 and B. fulva KW3. Total RNA was extracted and subjected to NASBA-molecular beacon analysis as described as above.

The sensitivity of the developed system was determined using S. cerevisiae

AC1 suspensions with different cell densities. Fresh S. cerevisiae AC1 cells were collected by centrifugation at 13,200 rpm for 1 min and spiked into samw volume of

0.85% saline and Minute Maid® apple juice (Coca-Cola, Atlanta, GA, USA). After

10-fold serial dilutions, cells from 1 ml of each suspension were collected by centrifugation at 1, 3200 rpm for 1 min following RNA extraction as described above.

1 μl of the eluted RNA was used as template and the NASBA-molecular beacon amplification was carried out as described above. To compare the detection sensitivities of the developed NASBA-molecular beacon assays with conventional plate count method, 100 μl of the selected 10-fold serial dilutions were spread on YM

Agar. Colonies were counted after incubation on YM agar at 30°C for 72 h. The experiments were repeated at least three times.

3.3.6 Heat inactivation treatments

Fresh suspensions containing 108 CFU ml-1 of S. cerevisiae AC1 cells (as determined by the plate count method) were subject to several heat treatments, including heating at 72.5 ± 0.5°C and 98 ± 0.5°C for 10 min, as well as autoclaved for 15 min. In all cases, glass tubes with 5 ml phosphate buffered saline (PBS) were pre-heated in water bath for 30 min to reach targeted temperatures. Exponentially growing cells were collected by centrifugation at 13,200 rpm for 1 min, and re-suspended into pre-heated

PBS solutions to reach the final cell concentrations to 108 CFU ml-1, and heated for designated period of time. A sterile thermometer swapped by 70% ethanol twice was placed in the tube to monitor the heating temperature throughout the study. In addition, S. cerevisiae AC1 fresh cultures (108 CFU ml-1) were autoclaved for 15 min. All the heat- treated samples were quickly cooled by placing in ice-water bath for 10 min and incubated at room temperature for 24 h. Samples were subjected to cell viability, RNA isolation, and NASBA-molecular beacon assessments as described above.

3.3.7 Viability assessment of heated cells

Heated S. cerevisiae AC1 samples were subjected to viability assessment by the plate count method. 4.0 ml of heat treated cells were collected by centrifugation at 13,200 rpm for 1 min and re-suspended into 200 μl of YM broth. The cell counts were determined by directly plating 100 μl of the cell suspension on YM agar plates and incubated at 37°C for 48 h. The rest of the 100 μl aliquot was mixed with 10 ml of fresh

YM broth (Donnelly, 2002), incubated at 37°C and checked for turbidity changes for up to one week.

3.4 Results

3.4.1 NASBA primers and molecular beacon probe development

The primers suitable for NASBA analysis were designed. The sequence information is: the forward primer T7TWP1 (5‟AATTCTAATACGACTCACTA

TAGGGAGAAGCACGACGGAGTTTCACAAGA 3‟), reverse primer YeastP2

(5‟CGAACGAGACCTTAACCTACTAAATA 3‟) and molecular beacon probe Y18S

(5‟GGTTCGCACTTCAGAGGGACTATCCGAACC3‟). This primer-and-probe set detects a 218 bp segment of the 18S rRNA.

3.4.2 Specificity and sensitivity evaluation

NASBA-molecular beacon assays were conducted to determine the specificity of

T7TWP1 primer, YeastP2 primer, and Y18S probe using total RNA extracted from different microorganisms as procedures described above. Fig. 3.1 illustrated electrophoresis results using 2.0% formaldehyde agarose gel analysis of the isolated RNA samples. High quality total RNAs were used in this study. Using the developed primers- and-probe set, only representative spoilage yeasts were tested positively using NASBA- molecular beacon assessments, as illustrated by one representive amplification curves

(Fig. 3.2). The Ct values for S. cerevisiae AC1 were 25.2 and for C. parapsilosis were

25.4. In addition, no cross-reactivity was found with bacteria B. subtilis, E. coli, L. monocytogenes, Lactob. plantarum, Lactoc. lactis subsp lactis, P. digitatum, B. fulva or common food ingredients. Although specificity analyses were conducted using a limited number of microbial strains, further in silico specificity search was conducted covering all the deposited DNA sequences available through the National Center for

Biotechnology Information (NCBI). The results showed that have no match with NASBA primers and molecular beacon probe in other microorganisms but yeasts. Therefore, the developed NASBA-molecular beacon assayscould be used to detect a broad range of yeasts as well but not other microorganisms.

Experiments were conducted to determine the level of detection. The sensitivity was determined by amplifying RNA templates isolated from serial diluted S. cerevisiae

AC1cells. Fig. 3.3 and Fig. 3.4 represented one of the typical NASBA-molecular beacon detection charts from at least triplicate assays in saline and apple juice, respectively. The real-time Ct values varied with the cell density; a greater Ct values were obtained from samples containing low yeast cells than from those with high cell density. For saline diluted

S. cerevisiae suspensions, the Ct values were 35.2, 38.3, 42.7, 46.8, 53.8, corresponding to 4.3 ×105, 4.3×104, 4.3×103, 4.3×102, 4.3 ×101 CFU ml-1 live cells, respectively (Fig.

3.3). The similar results were obtained using apple juice as media to suspend S. cerevisiae AC1 cells. The corresponding Ct values were 35.2, 38.3, 42.7, 46.8, 53.8, corresponding to 8.7 ×105, 8.7×104, 8.7×103, 8.7×102, 8.7 ×101 CFU ml-1 yeasts cells, respectively (Fig. 3.4). In both cases, the reagent controls were below the detection baseline indicating no contamination. For all repeats, the confident detection level for the

60 newly established NASBA-molecular beacon assayswas less than 100 viable cells per reaction indicated by colony count on YM agar plate.

Fig. 3.5 showed the correlation between logarithm of the plate count results and the corresponding Ct valuevs obtained by NASBA-molecular beacon sensitivity assessments. Good linear corelations were observed between the amplification Ct values and the plate count results, particularly for yeasts cells spiked into saline (R2 = 0.9783).

3.4.3 NASBA-molecular beacon assaysassessments of S. cerevisiae exposed to lethal heat treatments

The ability of the developed NASBA-molecular beacon assay to distinguish viable and dead yeasts cells was examined by performing NASBA-molecular beacon assays using

RNA isolated from both live and heat-killed cells. After S. cerevisiae samples were inactivated at 72°C, 98°C and autoclaving for the designated periods, none of the treated samples exhibited growth on either YM agar plates or in YM broth. The NASBA-molecular beacon analyses results, illustrated by Ct values, suggested that the RNA level changed corresponding to various heat treatments. Fig. 3.6 represented one of the typical real-time

NASBA-molecular amplification charts for S. cerevisiae exposing to different heat treatments. The Ct value obtained from viable cells was 15.9; while the Ct values increased to 33.0 for 72 °C heating, 35.7 for 98 °C and 40.8 for autoclaving. The significant difference in Ct values was observed between viable and heat inactivated samples (P<0.05).

3.5 Discussion and Conclusion

In the food industry, rapid detection techniques with high sensitivity and specificity are necessary for food safety and quality control. Real-time amplification based methods represent the new trend for food borne microorganisms‟ detection. However, false positive result associated with DNA molecules from died cells is a concern for conventionally DNA detection (Josephson et al., 1993; Keer & Birch, 2003). Instead of DNA, RNA molecules could serve as cell viability indictors to distinguish viable and dead microbial cells because of the shorter half life (Cenciarini-Borde et al., 2009; Keer & Birch, 2003; Kort et al., 2008;

Mayoral et al., 2006; Yaron & Matthews, 2002).

NASBA is an isothermal amplification of RNA template using the concurrent activity of AMV reverse transcriptase (RT), RNase H and T7 RNA polymerase, together with two primers to produce antisense, single-stranded RNA as the major end product (Cook,

2003). It has been mostly used to detect RNA viruses (Jean et al, 2002; Lau et al, 2006), and its most recent application includes detection of live pathogenic bacterial cells (Simpkins et al., 2000). Compared with conventional PCR, NASBA could be used in the development of a simple, affordable, portable kit for microbial detection. Adding the fluorescent-labeled molecular beacon probe to the system enabled real-time monitoring of the amplification process using an optical module. In this study, a real-time NASBA-molecular beacon assaysusing 18S rRNA as cell viability indictor was developed for rapid, specific and sensitive detection of viable spoilage yeasts in juice products.

Specific analysis showed the presence of spoilage yeasts can be detected without cross reactivity with spoilage bacteria, molds or food ingredients using the newly

62 developed NASBA-molecular beacon primers-and-probe set. Although only two spoilage yeasts S. cerevisiae AC1 and C. parapsilosis AC4 were used in the specificity analyses, the primers-and-probe set were evaluated by comparing DNA sequence homology among a wider range of yeasts, molds, and other eukaryotes. Therefore the system derived could be used to detect other yeasts as well.

Using the newly developed system, the confident detection limit is the presence of less than 100 viable cells without involving other pre-enrichment or pre-extraction steps indicating the effectiveness of the system in real food sample analysis. In addition, good linear relationships were observed between the real-time Ct values and the cell count results.

Similar detection results were obtained when yeast cells were suspended in apple juice and saline, which indicated that ingredients from apple juice did not cause significant inhibition to the real-time amplification. The total detection procedures from microbial cell collection to NASBA signal display can be completed within 6 hrs, which is a significant improvement compare to conventional methods for yeasts detection, normally taking from 2 days to a couple of weeks.

In this study, based on 18S rRNA, a NASBA-molecular beacon primers-and-probe set was designed to investigate the potential of using RNA molecule as cell viability indictor to discriminate viable and dead yeasts cells. Correlations between the RNA stability and real-time NASBA-molecular beacon assessment signals were made on S. cerevisiae cells subjected to different heat treatments. The 18S rRNA was persisted in all heat inactivated samples, including autoclaving, which agrees with previous reports that rRNA molecules are stable (Hierro et al., 2006; Yaron & Matthews, 2002). While after yeast cells subjected

63 to lethal heat treatments, the decrease of the 18S rRNA copy numbers was reflected by the increase of Ct values in real-time NASBA assays. This study showed that significant increase of Ct values was observed after yeasts cells exposed to all lethal heat treatments, with the levels of change correlated to the intensity of the treatments. Two-tailed students‟s

T-tests showed that the significant changes of Ct values was observed between viable and heat inactivated samples (P<0.05), which indicated the dramatically decreasing of 18S rRNA copy numbers after killing cells. Therefore, even though a “clear”correlation between

18S rRNA stability and S. cerevisiae viability could not be established due to difficulty to

“absolutely” eliminate all RNA debris, the developed NASBA-molecular beacon assays have great potential to detect viable yeasts cells by illustrating the relationship between losing cell viability and the decreasing of copy numbers.

Figure 3.1. Formaldehyde agarose gel (2.0%) electrophoretic analysis for total RNAs.

Lane A, B, C, 1 kb plus RNA ladder; Lane 1, B. subtilis OSU 494; Lane 2, E. coli DH-5α; Lane 3, P. putida ATCC 4945; Lane 4, Lactob. plantarum ATCC 8014; Lane 5 Lactoc. lactis subsp. lactis LM2301; Lane 6, L. monocytogenes Scott A; Lane 7 and 11, B. fulva KW3; Lane 8, S. cerevisiae AC1, Lane 9, C. parapsilosis AC4; lane 10, apple ingredient; Lane 12, P. digitatum KW2.

Figure 3.2. NASB-molecular beacon detection of spoilage yeasts S. cerevisiae AC1 (▲) and C. parapsilosis AC4 (▼). Curves below the amplification baseline include the B. subtilis OSU 494, E. coli DH-5α, L. monocytogenes Scott A, Lactob. plantarum ATCC 8014, Lactoc. lactis subsp. LM2301, Ps. Putida 49451, P. digitatum KW2, B. fulva KW3 and blank control, CF RFU: curve fit relative fluorescence units.

Figure 3.3. NASBA-molecular beacon sensitivity test of S. cerevisiae AC1 in saline. Cells concentration: 4.3 ×105 CFU ml-1(●), 4.3×104 (■), 4.3×103 (▲), 4.3×102 (♦), 4.3 ×101 (▼) with the responding Ct values 35.2, 38.3, 42.7, 46.8, 53.8 appear above the threshold baseline. The black control is shown below the baseline with no amplification. CF RFU: curve fit relative fluorescence units.

Figure 3.4. NASBA-molecular beacon sensitivity test of S. cerevisiae AC1 in apple juice. Cells concentration: 8.7 ×105 CFU ml-1(●), 8.7×104 (■), 8.7×103 (▲), 8.7×102 (♦), 8.7×101 (▼) with the responding Ct values were 35.2, 38.3, 42.7, 46.8, 53.8, appear above the threshold baseline. The apple juice control and reagent black control are shown below the baseline with no amplification. CF RFU: curve fit relative fluorescence units.

60 55 50 45 R² = 0.9784 saline suspensions

Ct values Ct 40 juice 35 suspensions 30 R² = 0.8924 25 20 1 2 3 4 5 6

log10 cfu/ml

Figure 3.5. Correlation between the natural logarithm of the plate count results of serial diluted suspension and the corresponding Ct values obtained by NASBA-molecular beacon analysis. The linear regression coefficient factor (R2) is indicated.

Figure 3.6. NASBA-molecular beacon assay detection of viable and heat-inactivated S. cerevisiae AC1 cells (cell density: 2.6 ×108 CFU ml-1): viable sample (●), 72 °C inactivated sample (▲), 98 °C inactivated sample (■) and autoclaved sample (▼).The reagent black control is shown below the baseline with no amplification. CF RFU: curve fit relative fluorescence units.

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Chapter 4

Critical Issues in Detecting Viable Listeria monocytogenes Cells by Real-Time

Reverse Transcriptase PCR

4.1 Abstract

Listeria monocytogenes is one of the most important foodborne pathogens widely distributed in foods. Rapid and specific detection of viable L. monocytogenes cells, particularly in processed foods is a major challenge in the food industry. To assess the suitability of using RNA amplification-based detection methods to detect viable cells, several sets of PCR primers and florescent probes were designed targeting the 16S rRNA, internalin A, and the ribosomal protein L4 encoding genes. One-step real-time RT-PCR assays were conducted using RNAs extracted from control and heat-treated L. monocytogenes samples. The Ct values were significantly lower in heat-treated cells than that of the controls. However, real-time RT-PCR amplification signals were still detected even in samples stored at room temperature for 24 h following heat treatments, and the intensity of the signals was correlated to the cell concentration. The results revealed that the 16S rRNA molecules were the most stable among the 3 targets evaluated, and the impact of the relative positions of the PCR primers within the target genes on detection

76 efficacy was limited under the experimental conditions. The data suggested that real-time

RT-PCR assays have advantages compared to conventional PCR in assessing viable L. monocytogenes cells, but the results are affected by the stability of the RNA molecules targeted. The findings have a major impact on proper data interpretation of RNA-based detection and gene expression studies.

4.2 Introduction

L. monocytogenes is an important pathogenic bacterium ubiquitous in nature and food processing environment (Wing & Gregory, 2002). It causes listeriosis characterized by meningoencephalitis (brain infection), septicemia (bacteria in the bloodstream), abortion and stillbirth in susceptible population with a 30% motility rate, among the highest in foodborne illnesses (Kathariou, 2002). In the past decades, outbreaks and sporadic cases of listeriosis were associated to various contaminated foods, from fresh products to processed meat, dairy, and beverage products (Carpentier & Cerf, 2011). In the United States, there were approximately 2500 L. monocytogenes related illnesses and

500 deaths annually, causing significant financial loss and social burden to the food industry and the society

(http://www.fda.gov/Food/ScienceResearch/ResearchAreas/RiskAssessmentSafetyAssess ment/ucm185291.htm). The "zero tolerance" (no detectable level permitted) policy have been applied by the food and drug administration (FDA) and the Food Safety and

Inspection Service (FSIS) to regulate L. monocytogenes in all ready-to-eat foods due to the potential high-case fatality rate associated with this bacterium

(http://vm.cfsan.fda.gov/~mow/fsislist.html; http://www.fsis.usda.gov/Regulations_&_Policies/index.asp).

Despite the overall decreased incidences in recent years as a result of efforts from both the government and industry, elimination of L. monocytogenes is still difficult due to its ability to survive at various harsh environments, including hurdle factors (such as acid, heat, salt, nitrite, etc.) commonly used in the food system (Donnelly, 2001; Donnelly,

2002). Furthermore, it is able to grow at refrigeration temperature, which is particularly problematic for processed foods susceptible for pathogens from secondary contamination or those survived the processing treatments (Carpentier & Cerf, 2011). Effective control of the problem thus heavily relies on proper detection of viable L. monocytogenes in food products and raw materials, and validation of pathogen interventions around critical control points in food processing.

Standard detection for L. monocytogenes follows conventional microbiological culturing by the FDA bacteriological and analytical method (BAM) or the International

Organization of Standards (ISO) 11290 method

(http://www.fda.gov/Food/ScienceResearch/LaboratoryMethods/BacteriologicalAnalytic alManualBAM/UCM071400; http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=1926

8 ). Besides being time-consuming and labor intensive, the methods may under-estimate the number of injured or viable but non-culturable (VBNC) cells unable to grow in selective media, particularly after processing treatments, but with the potential to recover and cause further damage in hosts (Besnard et al., 2000; Cappelier et al., 2007; Dreux et

78 al., 2007; Foong & Dickson, 2004; Lindback et al., 2010; Wesche et al., 2009). Various culture independent methods, particularly nucleic acid amplification-based methods such as PCR and real-time (quantitative) PCR, offered rapid and sensitive options for microbial detection (Gasanov et al., 2005). However, due to false positive results from the DNA molecules from dead cells, DNA-based amplification methods are unsuitable for viability assessment (Josephson et al., 1993; Koo & Jaykus, 2000). Alternatively, reverse transcriptase (RT) PCR methods targeting rRNA and mRNA were reported for the detection of viable foodborne pathogens, such as E. coli (Sheridan et al., 1999; Yaron

& Matthews, 2002), Salmonella (Gonzalez-Escalona et al., 2009; Miller et al., 2010;

Techathuvanan et al., 2010), Campylobacter spp.(Sung et al., 2004), L. monocytogenes

(Klein & Juneja, 1997; Wang et al., 1992), as RNAs may disappear quickly in dead cells

(Keer & Birch, 2003; Kobayashi et al., 2009; Kort et al., 2008). However, with technology improvement in extraction and detection, a more sophisticated picture of

RNA stability emerged. While several studies found that rRNA might not be a good target for viable cell detection due to the long half life (Marois et al., 2002; McKillip et al., 1998), others reported that rRNA disappeared relatively rapidly when subjected to extreme lethal treatments, and its decay was related to the molecules targeted (Aellen et al., 2006). Heterogeneous stability of mRNA molecules was also reported. Some mRNA persisted long time, while others disappeared rather quickly depending on the lethal treatment and holding conditions (Cenciarini et al., 2008; Keer & Birch, 2003; Sung et al., 2004; Yaron & Matthews, 2002). It is worth noting that Norton and Batt (1999) reported that the detection of hlyA transcript after heat-killing was effected by the

79 location of the primers (Norton & Batt, 1999). Due to the inconsistency in presented data and interpretation, more detailed evaluations of the impact of RNA stability on cell viability assessments are greatly needed.

The objective of this study was to investigate the potential of using 16S rRNA, as well as mRNA molecules for the virulence factor InlA encoding gene inlA and the housekeeping protein ribosome protein L4 encoding gene rplD, as viability indictors for

L. monocytogenes detection by Taqman real-time RT-PCR. The impact of the location of the amplification fragment on the intensity of real-time RT-PCR signals in dead cells was also examined in this study.

4.3 Materials and Methods

4.3.1 Bacterial strain and culture conditions

L. monocytogenes Scott A, a well-characterized virulent strain of 1/2 serotype isolated from milk (Yousef et al., 1988), was used in this study. For real-time RT-PCR assays, fresh cultures were obtained by growing L. monocytogenes in tryptic soy broth supplemented with 0.6% yeast extract (TSB-YE, Becton Dickinson and Company,

Sparks, MD, USA) for 20-24 h at 30°C (109-1010 CFU ml-1). For bacterial cell counts, serially diluted samples were spread-plated on TSB-YE agar plates and incubated at 37°C for 48 h. The frozen stock was stored in TSB-YE medium supplemented with 20% glycerol and kept at -80°C. Working culture were kept at 4-6°C and maintained by biweekly transferring.

4.3.2 Taqman real-time primers-and-probe sets

Six Taqman primers-and-probe sets (Table 1) and one inlA(D) primers pair were used in this study. Beside primes-and probe-sets for inlA(M) and rplD(U) (Hanna &

Wang, 2006), the other four Taqman primers-and-probe sets, including 16S(U), 16S(D), inlA(U), and rplD(D) were designed following the strategies by Connor et al (Connor,

Luo, Gardener, & Wang, 2005).

Individual alignments of the 16S rRNA, inlA mRNA and rplD mRNA gene sequences were performed using software ClustalV by MegAlign 5.01 (DNASTAR,

Madison, WI, USA). L. monocytogenes specific primers sets for 16S(U), 16S(D) were designed based on the DNA sequences S55472, FM211688.1, CP001175.1, AE017262.2,

AM181176, NC_004431, and FJ667502 for 16S rRNA-encoding gene. The primers sets for inlA(U) and inlA(D) were designed based on L. monocytogenes surface protain internalin A encoding gene (accession number: AGU735675.1 and CP001604.1). The location of the primers was showed in Fig.1. The specificity of the designed primers-and- probe sets were checked by submitting the candidate primer sequences to the primer-blast program of NCBI nucleotide Database (Data no shown). The primers were synthesized by Sigma-Genosys (The Woodlands, Tex, USA). The probes were synthesized by

Biosearch Technologies (Novato, CA, USA). The probes were labeled with the reporter dye Quasar 670 on the 5′ end, and quencher dye BHQ-2 on the 3′ end. The compatibility of the primers and probes were evaluated by the Oligo Analysis and Plotting Tool

(available at: http://www.operon.com/oligos/toolkit.php). The RT-PCR efficiency of new

81 primer sets were first evaluated by SYBR-Green based real-time RT PCR before the fluorescent probe-based assessments.

4.3.3 Heat inactivation treatments

Fresh suspensions containing 109 and 106 CFU ml-1 of L. monocytogenes ScottA cells (as determined by the plate count method) were subject to several heat treatments, including heating at 72.5 ± 0.5°C for 0, 5 and 30 min, as well as at 98 ± 0.5°C for 0, 5 and 30 min. In all cases, glass tubes with 5 ml phosphate buffered saline (PBS) were pre- heated in water bath for 30 min to reach targeted temperatures. Exponentially growing cells were collected by centrifugation at 13,200 rpm for 1 min, and re-suspended into pre- heated PBS solutions to reach the final cell concentrations of 109 or106 CFU ml-1, and heated for the designated period of time. A sterile thermometer swapped by 70% ethanol twice was placed in the tube to monitor the heating temperature throughout the study. In addition, collected L. monocytogenes cultures diluted in PBS at room temperature containing 109 and 106 CFU ml-1 cells were subject to autoclave for 15 min. All the heat- treated samples were quickly cooled by placing in ice-water bath for 10 min and incubated at room temperature for 24 h. Samples were subjected to cell viability and real- time RT-PCR assessments as describes below.

4.3.4 Viability assessment of heated cells

Heated L. monocytogenes samples were subjected to viability assessment by the plate count method. 4.0 ml of control or heat treated cells were collected by

82 centrifugation at 13,200 rpm for 1 min and re-suspended into 200 μl of TSB-YE broth.

The cell counts were determined by directly plating 100 μl of the cell suspension on TSB-

YE agar and incubated at 37°C for 48 h. The rest of the 100 μl aliquot was mixed with

10 ml of fresh Listeria repair broth (Donnelly, 2002), incubated at 37°C and checked for turbidity changes for up to one week.

4.3.5 RNA isolation

L. monocytogenes cells from 1 ml of culture were harvested by centrifugation at

13,200 rpm for 1 min and lysed with 20 mmol l-1 of lysozyme (Sigma Chemical CO., St

Louis, MO, USA) in 100 μl TE buffer (20 mmol l-1 Tris-HCl, 2 mmol l-1 EDTA, pH 8.0) for 30 min at 37°C. Total RNA was extracted using QIAGEN RNeasy® Mini kit

(QIAGEN, CA, USA) and eluted with 30 μl of RNase and DNase-free water following manufacturer‟s instruction. The quanitity of the RNA samples was determined by the

ND-1000 UV/VIS spectrophotometer (Thermo Scientific, Nanodrop, Waltham, MA,

USA).

The DNA remnants present in the extracted RNA samples were digested by treating with Deoxyribonuclease I. Briefly, reaction mixtures containing 2.0 μl of RNA elute, 2.5 units of DNase I (invirrogen, Carlsbad, CA), 1.0 μl of Reaction buffer (200 mmol l-1 Tris-HCl, 20 mmol l-1 MgCl2, 500 mmol l-1 KCl, pH 8.4), as well as RNase and

DNase-free water to a final volume of 10 μl were incubated at 37°C for 15 min. The

DNase was then inactivated by adding 1.5 μl of 25 mmol l-1 EDTA following heat at 65

°C for 10 min.

4.3.6 Real-time RT-PCR analysis

SYBR-Green based real-time RT-PCR was conducted to evaluate the RT-PCR efficiency of new primer sets before the fluorescent probe-based assessments using

“iScript One-Step RT-PCR Kit with SYBR® Green” (Bio-Rad Laboratories, Hercules,

CA, USA). After heating 108 CFU ml-1 L. monocytogenes at 72.5 ± 0.5°C for 5 min and

30 min, RNA was isolated as described above. 25 μl RT-PCR reaction mixture contained

12.5 μl of 2×SuperScript buffer (0.4 mmol l-1 of each dNTP, 2.4 mmol l-1 MgSO4, 3.0 mmol SYBR Green), 0.5 nmol l-1 each of the corresponding two primers, 0.5 μl of iscriptreverse transcriptase, 1.0 μl of RNA template, as well as RNase DNase free water up to the final volume. The reactions were conducted using an iCycler (Bio-Rad

Laboratories, Hercules, USA). For reverse transcription, mixtures were incubated at 50°C for 10 min, followed with real-time PCR settings: one cycle for 3 min at 95°C and 40 cycles of 15 s at 95°C, 30 s at 51°C, and 20 s at 68°C. To test the specificity of the designed primers pairs, the PCR was immediately followed by a melting curve analysis to determine the melting point of the double-stranded DNA product produced. This cycle consisted of heating from 50°C at 0.5°C/20sec to 95°C. The threshold and Ct values were determined by the background subtraction method using the iCycler iQ software (Bio-

Rad Laboratories, Hercules, USA). For each RNA sample, a control that only use the

Platinum® Taq DNA Polymerase (Invitrogen, Carlsbad, CA, USA) instead of iScript reverse transcriptase to monitor contaminating genomic DNA was run in parallel with each experimental sample. One reagent blank control which contained all reaction agents without RNA template was also run in parallel with each testing.

Fluorescent probe-based real-time RT-PCR assessment was conducted using the

“iScript One-Step RT-PCR Kit for Probes” (Bio-Rad Laboratories, Hercules, CA, USA) and the corresponding primers-and-probe sets. The set up for RT-PCR in 25 μl reaction mixture contained 12.5 μl of 2×SuperScript one-step RT-PCR reaction buffer (0.4 mmol l-1 of each dNTP, 2.4 mmol l-1 MgSO4); 0.5 nmol l-1 each of the two primers, 0.4 mmol l-

1 of Taqman probe and 0.5 μl of iscriptreverse transcriptase, 1.0 μl of RNA template, as well as RNase DNase free water up to the final volume. The reactions were conducted using an iCycler (BioRad). For RT-PCR, mixtures were incubated at 50°C 10 min for reverse transcription. Real-time PCR settings after the RT step included: one cycle at

95°C for 3 min and 40 cycles of 15 s at 95°C, 30 s at 51°C, and 20 s at 68°C, followed by

68°C for 5 min and holding at 4°C. The threshold and Ct values were determined by the background subtraction method using the iCycler iQ software (BioRad). For each RNA sample, a control using the Platinum® Taq DNA Polymerase (Invitrogen, Carlsbad, CA,

USA) instead of iScript reverse transcriptase (Bio-Rad) to monitor contaminating genomic DNA was run in parallel with each experimental sample. One reagent blank control which contained all reaction agents without RNA template also was run in parallel with each testing.

4.4 Results

4.4.1 Primers-and-probe sets evaluation.

All seven primers pairs used in this study, including 16S(U), 16S(D), inlA(U), inlA(M), inlA(D) , rplD(U) and rplD(D), successfully detected target gene transcripts by 85

SRBR Green based real-time RT-PCR. Melting curves showed that only one clear peak was observed corresponding to one primers pair (data not shown), no contaminating products are present in those reactions.

Fig. 4.2-4.4 illustrated the real-time SYBR Green based RT-PCR results by all seven primers sets using L. monocytogenes RNAs isolated from control and heat-treated cells. The Ct values were 8.8 ± 0.95, 16.1 ± 1.15 and 17.1 ± 0.88 by 16S(U), 12.5 ± 1.18,

20.5 ± 0.86 and 21.4 ± 1.74 by 16S(U) primers sets; 17.1 ± 1.40, 25.9 ± 0.41 and 26.1 ±

1.45 by rplD(U), 17.2 ± 0.82, 27.1 ± 0.75 and 27.5 ± 1.13 by rplD(D) primers sets; corresponding to treatments at 72°C for 0, 5 and 30 min. For the same heat treatment, the

Ct values were 21.7 ± 0.54, 28.3 ± 0.49 and 29.0 ± 1.37 by inlA(U) and 24.5 ± 0.77, 31.5

± 1.23 and 32.7 ± 1.37 by inlA(M). However, using inlA(D), the Ct values for SYBR

Green real-time RT-PCR were 37.16 ± 0.94 in control samples without heat treatment; while the RT-PCR signal disappeared completely after 5 min and 30 min heat treatments.

The significant increase of Ct values between reactions by inlA(D) and inlA(M) indicated that the 3‟ end of the inlA mRNA started to decay even in untreated cells. Therefore, this pair of primers targeting the 3‟ region of inlA mRNA was not used for further study.

Only six Taqman probes were designed (Tab.4.1. and Fig.4.1).

To further evaluate the impact of the location of the primers-and-probe sets on the results of the selection of RNA targets as cell viability indictors, primers-and-probe sets with different locations were used in Taqman real-time RT-PCR assays on RNA templates extracted from cells prior to 72°C heating for 5 min or 30 min. Locations of different primers-and-probe sets were showed by Fig. 4.1. Using the primers-and-probe

86 set 16S(U) targeting the 5‟ region of 16S rRNA, the Ct values for the control samples were 10.9 ± 0.56, and increased to 20.6 ± 0.20 and 21.0 ± 0.21 after 5 min and 30 min heat treatments, respectively. The similar tendency was observed using the 16S(D) primers-and-probe set with Ct values being 10.4 ± 1.63, 19.1 ± 0.42 and 19.7 ± 0.66, respectively (Fig. 4.5). Two trial student t-test analyses showed that no significant difference was observed between the Ct values obtained by primers-probe sets targeting the 16S 5‟ and 3‟ regions, whether RNA was extracted from viable cells or heated samples (P > 0.05). Fig. 4.6 illustrated the real-time RT-PCR results by inlA(U) or inlA(M) primers-and-probes sets using L. monocytogenes RNAs isolated from control and heat-treated cells. The Ct values were 22.1 ± 1.47, 28.5 ± 0.68 and 29.0 ± 0.66 by inlA(U), and 23.0 ± 0.96, 31.2 ± 0.40 and 31.7 ± 0.55 by inlA(M) primers-and-probe sets, corresponding to treatments at 72°C for 0, 5 and 30 min. For the same heat treatment, there was no significant variation (P > 0.05) in real-time RT-PCR data using the inlA(U) and inlA(M) primers-and-probe sets. As illustrated in Fig. 4.7, the Ct values of real-time

RT-PCR for the unheated control samples were 20.1 ± 1.27, and increased to 27.8 ± 0.25 and 28.1 ± 0.25 after heat treatment at 72°C for 5 and 30 min using primers-and-probe set rplD (U). Under the same treatment conditions, the corresponding Ct values were 15.8 ±

1.44, 23.5 ± 1.01 and 24.3 ± 0.45 respectively by rplD (D) primers-and-probe sets. The data demonstrated that under same treatments, Ct value increases, corresponding to decreases of copy numbers of the target RNA molecules, indicating that the measurement of rplD mRNA reduction after same heat treatments was similar by primers targeting the

5‟ and the 3‟ regions. Therefore, Taqman primers-and-probe sets 16S(D), inlA(M) and rplD (U) were used for further study.

The detection limits of the developed Taqman real-time RT-PCR system were further examined using RNA extracted from serially diluted L. monocytogenes cells.

Strong amplification signals were detected using RNA extracted from suspensions containing 103-104 CFU ml-1(data no shown).

4.4.2 Real-time RT-PCR assessments of L. monocytogenes cells exposed to lethal heat treatments.

After L. monocytogenes samples were inactivated at 72°C, 98°C and autoclaving for designated periods, none of the treated samples exhibited growth on either TSB-YE agar plates or in TSB-YE repair broth. However, using the 16S(D), inlA(M) and rplD(U) primers-and-probe sets for the three targeted genes, the real-time RT-PCR results, illustrated by the mean Ct values (n=3, values were mean ± S.D.), suggested the amount of and the difference in the corresponding RNA molecules survived from various inactivation treatments.

As illustrated in Fig. 4.5, while the Ct values for the control samples with concentrated ( 109 CFU ml-1) L. monocytogenes cells were 10.4 ± 1.63, after treating at

72 °C for 5 min and 30 min, the Ct values for inactivated samples increased to 19.1 ±

0.42 and 19.7 ± 0.66, respectively, using the 16S(D) primers-and-probe set. Likewise, the corresponding Ct values increased from 23.0 ± 0.96 to 31.2 ± 0.40 and 31.7 ± 0.55 (Fig.

4.6.), as well as 20.1 ± 1.27 to 27.8 ± 0.25 and 28.1 ± 0.25 (Fig. 4.7.) using the inlA(M)

88 and rplD(U) primers-and-probe sets. When diluted cells (106 CFU ml-1) were used, the Ct values were 17.3 ± 1.45, 28.5 ± 2.36, 32.7 ± 2.26 by the 16S(D), 27.7 ± 1.15, 34.8 ± 1.99,

36.2 ± 1.64 by the inlA(M), and 26.0 ± 1.89, 33.4 ± 0.94, 34.9 ± 1.90 by the rplD(U) primers-probe sets for the control and samples treated at 72 °C for 5 min and 30 min, respectively (Fig. 4.8.).

Fig. 4.9. illustrated that after 109 CFU ml-1 L. monocytogenes cells were exposed to 98 °C for 5 and 30 min, the Ct values increased from 13.0 ± 1.93 to 23.8 ± 2.29

(ΔCt=10.8) and 28.6 ± 2.42 (ΔCt=15.3) using the 16S(D) primers-probe set. The corresponding ΔCt values for inlA(M) and rplD(U) primers were 9.7, 12.0, as well as 9.6,

12.1, respectively. After 30 min treatment, while the 16S rRNA molecules were readily detected (Ct =28.6± 2.42), only trace amount of the mRNA transcripts of inlA and rplD genes were found (Ct around 35) by real-time RT-PCR. This tendency was even more obvious when diluted (106 CFU ml-1) L. monocytogenes cells was used (Fig. 4.10). The corresponding ΔCt values were 13.3 (Ct 29.3 ± 2.37), 11.6 (Ct 37.0 ± 1.50), and 12.2 (Ct

36.1 ± 2.40) for 16S rRNA, inlA and rplD transcripts after 5 min treatments, respectively.

The ΔCt values was 24.7 (Ct 31.7 ± 1.97) for 16S rRNA molecules after 30 min treatments, while the inlA and rplD transcripts were disappeared.

The same observation was made using samples inactivated by autoclave treatments. While trace amount of mRNA molecules for inlA and rplD (Ct around 35) were detected with concentrated cells (Fig. 4.11.), in this case no mRNA molecules were found by real-time RT-PCR from samples containing 106 cells after autoclaving (Fig.

4.12).

In all the above cases, all negative controls using the Platinum® Taq DNA polymerase instead of iScript reverse transcriptase had negative signals, suggesting all amplifications were performed from RNA template without DNA contamination.

Meanwhile, reagent blank control without RNA template also showed negative signals.

4.5 Discussion and Conclusion

The ideal cell viability indictor should disappear rapidly after cell inactivation.

RNA molecules had been a popular choice due to their short half life and multiple copies of transcripts for increased detection sensitivity (Hellyer et al., 1999; Kort et al., 2008).

However, previously data showed that mRNA degradation is a complex process, affected by various factors such as bacterial species, susceptibility of transcripts, location of amplification fragments, bactericidal treatment conditions et al (Aellen et al., 2006;

Cenciarini et al., 2008; Hellyer et al., 1999; Sheridan et al., 1998; Sheridan et al., 1999).

To establish the real-time RT-PCR platform for viable food borne pathogens detection, the RNA target must be carefully evaluated (Cenciarini et al., 2008; Sung et al., 2004;

Yaron & Matthews, 2002). To our knowledge, systematic studies have not been conducted to select proper RNA targets or evaluate possible effect of primer locations for detection of viable L. monocytogenes cells.

In this study, the stability of 16S rRNA and mRNAs of the inlA and rplD genes were first assessed by SYBR-Green real-time PCR. The significant increase of Ct values between reactions by inlA(D) and inlA(M) indicated that the 3‟ end of the inlA mRNA started to decay even in untreated cells. In addition, inlA sequences with truncated 3‟

90 regions were reported in L. monocytogenes strains (Norton & Batt, 1999). Therefore, only inlA(U) and inlA(M) primers-and-probe sets were examined in further studies. Results by two pairs of primers for each of the three target RNA molecules were very similar, indicating that there were no significant differences in stability measurements by using these primer pairs under the experimental conditions.

After lethal heat treatments, the decrease of the targeted RNA copy numbers was reflected by the change of Ct values in real-time RT-PCR assays. In all cases, significant increase of Ct values was observed after samples exposed to lethal heat activation, with the levels of change correlated to the intensity of the treatments. In agreement with previous data (Yaron & Matthews, 2002), 16S rRNA was found the most heat-resistant transcript, with strong real-time RT-PCR signals detected even after L. monocytogenes cells exposed to extreme lethal treatment such as autoclaving. Thus, it is unsuitable for cell viability assessment. On the other hand, mRNA molecules were detected by real-time

RT-PCR using inlA and rplD-specific primers-and-probe sets with 109 CFU ml-1 cells after lethal treatments, suggesting the stability issue even with mRNA molecules.

However, once the cell concentration decreased to 106 CFU ml-1, a level practically found in contaminated foods, only trace amount of mRNA molecules were detected (Ct around

35). The data suggested the potential application of the detection template and the selected mRNA targets in assessing cell viability in the food system. Nevertheless, further studies need to be conducted to identify proper amplification cutoff limit for practical application.

While this study reported the stability assessment of the three transcripts selected, we have further used L. monocytogenes genome microarray (TIGR/PFGRC) to systematically assess the stability of the whole genome transcripts using RNAs from control and heat-treated cells. In the case of L. monocytogenes, no other gene transcripts were found more susceptible to degradation than the two mRNAs examined in this study

(data not shown). Our data indicated that RNA molecules, including mRNAs are not as fragile as previously thought, can be detected even after 24 h of incubation at room temperature, and the RNA transcripts vary in stability. The information is very important for both the development of detection platforms, and proper data interpretation involving

RNA-based detection approaches. Furthermore, the finding may also have significant impact on data interpretation for transcriptome studies, as the hybridization results (such as microarray) likely are affected not simply by gene transcription corresponding to particular environmental conditions, but also by the varied stability among transcripts.

Even though the potential of using mRNA as cell viability for detection of viable

L. monocytogenes had been investigated and the authors concluded that the detection of hylA transcript after heat-killing was effected by the location of primers (Norton & Batt,

1999), our results were different from Norton and Batt. Our data showed that the differences in Ct values did not significantly change using primers-and-probe sets located on different positions, as illustrated by real-time RT-PCR data using two primers-and- probe pairs each, for 16S rRNA and mRNA of inlA and rplD. It is worth noting that the

RT-PCR results in Norton and Batt‟s study was detected by electrophoresis, instead of the sensitive and quantitative fluorescent probe-based real-time RT-PCR. In our study,

92 we further found that the 3‟ region was extremely unstable, even in viable cells. In another word, viable cells might give false negative result. Therefore it is not suitable as viability indicator. But the same phenomenon was not observed using primers-and-probe sets targeting 3‟ of the 16S rRNA and rplD mRNA, suggesting again that stability varies among targets and needs to be evaluated carefully (Cenciarini et al., 2008).

In this study, based on different fragments of 16S rRNA and inlA, rplD mRNA,

Taqman real-time primers-and-probe sets were designed to investigate the potential of using different RNA molecules as cell viability indictors to discriminate viable and dead

L. monocytogenes. The fact that 16S rRNA and inlA, rplD mRNA are highly conserved and well expressed allowed us to design primers-and-probe sets suitable for specific and sensitive detection of L. monocytogenes (Hanna & Wang, 2006; Jacquet et al., 2004;

Wang et al., 1992).

Correlations between the RNA stability and the real-time RT-PCR signals were made on L. monocytogenes cells subjected to different heat treatments. Firstly, the data showed that in samples containing levels practically found in the food system (i.e. 106

CFU ml-1), inlA and rplD mRNA were undetectable after extreme heat treatments by real-time RT-PCR (Fig. 4.10. and Fig. 4.12.), although both persisted in samples containing 109 CFU ml-1 cells after similar extreme heating (Fig. 4.9. and Fig. 11.). On the other hand, strong amplification signals were detected by real-time RT-PCR using

16S rRNA primers-and-probe sets, even for autoclaved low density of cells (Fig. 4. 12.).

The observations suggested that 16S rRNA was the most stable transcripts, as previously reported (Cenciarini et al., 2008; Kobayashi et al., 2009; Sheridan et al., 1998). However,

93 different from some previous data (Coutard et al., 2005; Sheridan et al., 1998; Yaron &

Matthews, 2002), inlA mRNA and rplD mRNA were found more stable than expected even though their levels were significantly reduced in cells with heat treatments. It is possible that certain conditions, such as binding to proteins or trapping in the cell debris might help these molecules survive during intensive heat treatments, while naked RNA molecules would be destroyed (Condon, 2003; Deutscher, 2006; Persson et al., 2000).

Secondly, the results demonstrated that, by using all three primers-and-probe sets, dramatic reduction of real-time RT-PCR signals was observed under the heat-treatment conditions used in this study, particularly at contamination level practically found in the food system (i.e., 106 CFU). Therefore, even though a clear correlation between the three transcripts and cell viability in L. monocytogenes could not be established due to difficulty to “absolutely” eliminate all RNA debris, 16S rRNA and inlA, rplD mRNA have great potential to serve as cell viability indictors by illustrating the relationship between losing cell viability and the decreasing of copy numbers. With proper handling, this goal could be achieved by using quantitative real-time RT-PCR with high specificity and sensitivity. However, real time RT-PCR result interpretation must be carefully evaluated because degradation of RNA after cell dead is a complex process. Multiple factors, such as physiological state (e.g. cell density), inactivation conditions

(temperatures), and environmental stresses (e.g. pH and nutritional state), could influence the RNA copy number as showed by this study as well as previous data (Hanna & Wang,

2006; Kort et al., 2008; Mckillip et al., 1998; Sheridan et al., 1999).

In conclusion, using unstable RNA molecules as cell viability indictors, real-time

RT-PCR assays have advantages compared to conventional PCR in assessing viable L. monocytogenes cells, especially as contamination level practically found in the food system. The established assays will be a very useful tool for industrial application to eliminate the false positive results from dead bacterial cells. The technology being developed by the Ohio State University is patent pending.

Primers and probes Sequence Product size Tm(°C) (bp)

16S(U) F TTCGCGACCCTTTGTAC 119 54.5

16S(U) R GGCACTCTAAAGTGACTG b 53.5

16S(U) probe c CAGGTCATAAGGGGCATG 63.8

16S(D) F GAGTTATCCCCAACTTACAG 149 55.7

16S(D) R AGAGAGTTTGATCCTGGC b 55.4

16S(D) probe c CTTGCATGTATTAGGCACG 64.5

inlA(U) F GATTAACACGAGTAACGG 153 53.8

inlA(U) R TAGATCTGTTTGCGAGAC b 54.7

inlA(U) probe c CAGGCAGCTACAATTACACAAG 61.0

inlA(M) F CCACTTAAGGCAATTTTTAATG 86 56.3

inlA(M) R TCAGTCAATAAATTCCCAGC b 56.1

inlA(M) probe c CTTTGCCGTCCACATGAAAC 60.3

inlA(D) F GAAGCAACACATCTAACAC 121 52.8

inlA(D) R TAACAATCCTATCAACAGG 53.2

rplD(U) F CTTGTAAGTTGCGTGC 132 55.2

rplD(U) R GTTTGACTTTCGATGC b 56.7

rplD(U) probe c CGATTAGTGCCTTAGTATCTACAG 62.6

rplD(D) F CCACGTACTTCTGAACG 152 55.3

rplD(D) R CAAGATGGAACAAACG b 54.3

rplD(D) probe c TAGGGATGCACGTTGG 61.9

Table 4.1. Taqman real-time PCR primers and probes used in this study.

a Tm, melting temperature, calculated by the manufacturer. b Sequence shown is the reverse complement of the 5‟ to 3‟ sequence. c The probes were labeled with the reporter dye Quasar 670 on the 5′ end, and quencher dye BHQ-2 on the 3′ end.

Figure 4.1. Locations of oligonucleotide primers used for real-time RT-PCR detection of L. monocytogenes. Solid arrows represent the position and orientation of the primers. Positions are based upon GenBank accession FM211688.1, CP001604.1and GU735675.1. The figure is not drawn to scale.

25 0 min 20 5 min

Ct values values Ct 15 30 min 10

0 16S rRNA (up) 16S rRNA (down)

Figure 4.2. SYBR Green real-time RT-PCR detection of L. monocytogenes (108 CFU ml-1) exposed to 72 °C for various periods using primers pairs 16S(U) and 16S(D). Error bars represent standard deviation from three independent replicates.

40 35 30 25

20 0 min

Ct values values Ct 15 5 min 10 30 min 5 0 inlA mRNA (up) inlA mRNA inlA mRNA (middle) (down)

Figure 4.3. SYBR Green real-time RT-PCR detection of L. monocytogene (108 CFU ml-1) exposed to 72 °C for various period of time using inlA(U), inlA(D) and inlA(D) primers pairs. Error bars represent standard deviation from three independent replicates.

25 0 min 20 5 min

Ct values values Ct 15 30 min 10

0 rplD mRNA (up) rplD mRNA (down)

Figure 4.4. SYBR Green real-time RT-PCR detection of L. monocytogenes (108 CFU ml-1) exposed to 72 °C for various period of time using rplD(U) and rplD(D) primers pairs. Error bars represent standard deviation from three independent replicates.

100

40 35 30

25 0 min

20 5 min Ct values values Ct 15 30 min 10 5 0 16S rRNA(up) 16S rRNA(down)

Figure 4.5. Taqman real-time RT-PCR detection of L. monocytogenes (109 CFU ml-1) exposed to 72 °C for various period of time using 16S(U) and 16S(D) primers-and-probe set. Error bars represent standard deviation from three independent replicates.

101

40 35 30 25 0 min

20 5 min values

Ct Ct 15 30 min 10 5 0 inlA mRNA(up) inlA mRNA(middle)

Figure 4.6. Taqman real-time RT-PCR detection of L. monocytogenes (109 CFU ml-1) exposed to 72 °C for various period of time using inlA(U) and inlA(M) primers-and- probe set. Error bars represent standard deviation from three independent replicates.

102

40 35 30 25 0 min 20 5 min

Ct values values Ct 15 30 min 10 5 0 rplD mRNA(up) rplD mRNA(down)

Figure 4.7. Taqman real-time RT-PCR detection of L. monocytogenes (109 CFU ml-1) exposed to 72 °C for various period of time using rplD(U) and rplD(D) primers-and- probe set. Error bars represent standard deviation from three independent replicates.

103

40 35 30 25 0 min 20 5 min

Ct values values Ct 15 30 min 10 5 0 16S rRNA inlA mRNA rplD mRNA

Figure 4.8. Taqman real-time RT-PCR detection of low density of L. monocytogenes (106 CFU ml-1) exposed to 72 °C for various period of time using 16S(D), inlA(M) and rplD(U) primers-and-probe sets. Error bars represent standard deviation from three independent replicates.

104

40 35 30 25 0 min 20 5 min

Ct values values Ct 15 30 min 10 5 0 16S rRNA inlA mRNA rplD mRNA

Figure 4.9. Taqman real-time RT-PCR detection of concentrated L. monocytogenes (109 CFU ml-1) exposed to 100 °C for various period of time using 16S(D), inlA(M) and rplD(U) primers-and-probe sets. Error bars represent standard deviation from three independent replicates.

105

40 35 30 25 0 min 20 5 min

Ct values values Ct 15 30 min 10 5 0 16S rRNA inlA mRNA rplD mRNA

Figure 4.10. Taqman real-time RT-PCR detection of diluted L. monocytogenes (106 CFU ml-1) exposed to 100 °C for various period of time using 16S(D), inlA(M) and rplD(U) primers-and-probe sets. Error bars represent standard deviation from three independent replicates.

106

25 0 min 20 autoclaved

Ct values values Ct 15

0 16S rRNA inlA mRNA rplD mRNA

Figure 4.11. Taqman real-time RT-PCR detection of autoclaved L. monocytogenes cells (109 CFU ml-1) using 16S(D), inlA(M) and rplD(U) primers-and-probe sets. Error bars represent standard deviation from three independent replicates.

107

40 35

25 0 min 20 autoclaved

Ct values values Ct 15

0 16S rRNA inlA mRNA rplD mRNA

Figure 4.12. Taqman real-time RT-PCR detection of autoclaved L. monocytogenes (106 CFU ml-1) using 16S(D), inlA(M) and rplD(U) primers-and-probe sets. Error bars represent standard deviation from three independent replicates.

108

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Besnard, V., Federighi, M., & Cappelier, J. M. (2000). Development of a direct viable count procedure for the investigation of VBNC state in Listeria monocytogenes. Letters in Applied Microbiology, 31(1), 77-81.

Carpentier, B., & Cerf, O. (2011). Review - persistence of Listeria monocytogenes in food industry equipment and premises. International Journal of Food Microbiology,

Condon, C. (2003). RNA processing and degradation in bacillus subtilis. Microbiology and Molecular Biology Reviews:MMBR, 67(2), 157-74.

Coutard, F., Pommepuy, M., Loaec, S., & Hervio-Heath, D. (2005). mRNA detection by reverse transcription-PCR for monitoring viability and potential virulence in a pathogenic strain of Vibrio parahaemolyticus in viable but nonculturable state. Journal of Applied Microbiology, 98(4), 951-961.

Deutscher, M. P. (2006). Degradation of RNA in bacteria: Comparison of mRNA and stable RNA. Nucleic Acids Research, 34(2), 659-666.

Donnelly, C. W. (2001). Listeria monocytogenes: A continuing challenge. Nutrition Reviews, 59(6), 183-194.

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Foong, S. C., & Dickson, J. S. (2004). Survival and recovery of viable but nonculturable Listeria monocytogenes cells in a nutritionally depleted medium. Journal of Food Protection, 67(8), 1641-1645.

Gasanov, U., Hughes, D., & Hansbro, P. M. (2005). Methods for the isolation and identification of Listeria spp. and Listeria monocytogenes: A review. FEMS Microbiology Reviews, 29(5), 851-875.

Gonzalez-Escalona, N., Hammack, T. S., Russell, M., Jacobson, A. P., De Jesus, A. J., Brown, E. W., & Lampel, K. A. (2009). Detection of live Salmonella sp. cells in produce by a TaqMan-based quantitative reverse transcriptase real-time PCR targeting invA mRNA. Applied and Environmental Microbiology, 75(11), 3714-3720.

Hanna, S. E., & Wang, H. H. (2006). Assessment of environmental factors on Listeria monocytogenes scott A inlA gene expression by relative quantitative taqman real-time reverse transcriptase PCR. Journal of Food Protection, 69(11), 2754-2757.

Jacquet, C., Doumith, M., Gordon, J. I., Martin, P. M., Cossart, P., & Lecuit, M. (2004). A molecular marker for evaluating the pathogenic potential of foodborne Listeria monocytogenes. The Journal of Infectious Diseases, 189(11), 2094-2100.

Josephson, K. L., Gerba, C. P., & Pepper, I. L. (1993). Polymerase chain reaction detection of nonviable bacterial pathogens. Applied and Environmental Microbiology, 59(10), 3513-3515.

Kathariou, S. (2002). Listeria monocytogenes virulence and pathogenicity, a food safety perspective. Journal of Food Protection, 65(11), 1811-1829.

Keer, J. T., & Birch, L. (2003). Molecular methods for the assessment of bacterial viability. Journal of Microbiological Methods, 53(2), 175-183.

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Klein, P. G., & Juneja, V. K. (1997). Sensitive detection of viable Listeria monocytogenes by reverse transcription-PCR. Applied and Environmental Microbiology, 63(11), 4441-4448.

Kobayashi, H., Oethinger, M., Tuohy, M. J., Procop, G. W., Hall, G. S., & Bauer, T. W. (2009). Limiting false-positive polymerase chain reaction results: Detection of DNA and mRNA to differentiate viable from dead bacteria. Diagnostic Microbiology and Infectious Disease, 64(4), 445-447.

Kort, R., Keijser, B. J., Caspers, M. P., Schuren, F. H., & Montijn, R. (2008a). Transcriptional activity around bacterial cell death reveals molecular biomarkers for cell viability. BMC Genomics, 9, 590.

Marois, C., Savoye, C., Kobisch, M., & Kempf, I. (2002). A reverse transcription-PCR assay to detect viable Mycoplasma synoviae in poultry environmental samples. Veterinary Microbiology, 89(1), 17-28.

Norton, D. M., & Batt, C. A. (1999). Detection of viable Listeria monocytogenes with a 5' nuclease PCR assay. Applied and Environmental Microbiology, 65(5), 2122-2127.

Persson, M., Glatz, E., & Rutberg, B. (2000). Different processing of an mRNA species in Bacillus subtilis and Escherichia coli. Journal of Bacteriology, 182(3), 689-695.

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Sheridan, G. E., Masters, C. I., Shallcross, J. A., & MacKey, B. M. (1998). Detection of mRNA by reverse transcription-PCR as an indicator of viability in Escherichia coli cells. Applied and Environmental Microbiology, 64(4), 1313-1318.

Sheridan, G. E., Szabo, E. A., & Mackey, B. M. (1999a). Effect of post-treatment holding conditions on detection of tufA mRNA in ethanol-treated Escherichia coli: Implications for RT-PCR-based indirect viability tests. Letters in Applied Microbiology, 29(6), 375- 379.

Sung, K. D., Stern, N. J., & Hiett, K. L. (2004). Relationship of messenger RNA reverse transcriptase-polymerase chain reaction signal to Campylobacter spp. viability. Avian Diseases, 48(2), 254-262.

Techathuvanan, C., Draughon, F. A., & D'Souza, D. H. (2010). Real-time reverse transcriptase PCR for the rapid and sensitive detection of Salmonella typhimurium from pork. Journal of Food Protection, 73(3), 507-514.

Wang, R. F., Cao, W. W., & Johnson, M. G. (1992). 16S rRNA-based probes and polymerase chain reaction method to detect Listeria monocytogenes cells added to foods. Applied and Environmental Microbiology, 58(9), 2827-2831.

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Chapter 5

Detection of viable Pseudomonas spp. cells using Taqman Real-time

Reverse Transcriptase PCR

5.1 Abstract

Pseudomonas spp. are ubiquitous in the environment, various foods and hosts as commensal bacteria or opportunistic pathogens, and the psychrotrophic Pseudomonas spp. are common spoilage agents in dairy and other food products. Viable Pseudomonas spp. detection is critical for food safety and quality control. Instead of stable DNA molecules, utilization of labile RNAs as bacterial viability indicators could circumvent false-positive results due to detection of DNA from dead cells, and overcome the disadvantages of cultivation. In this study, a Taqman real-time RT-PCR was developed for rapid and specific detection of viable Pseudomonas spp.

To select proper RNA as cell viability indictor, cDNA microarray analyses were conducted to investigate RNAs stability by comparing the RNA level change before and after lethal heat treatment. The messenger RNA molecules for ornithine decarboxylase

(ODC) encoding gene was chosen as labile cell viability indictor. PCR primers and

Taqman probes were designed targeting conserved areas of Pseudomonas spp. ODC gene

113 as well as 16S rRNA for comparison. Using designed primers pairs, specific detection of

Pseudomonas spp. was achieved without cross-reaction to other bacteria and fungi commonly found in food environment. In addition, the presence of 103 CFU ml-1 viable cells could be detected without pre-enrichment step by established one step real-time RT-

PCR assays.

Taqman real-time RT-PCR assays were conducted using RNAs extracted from control and heat-inactivated or disinfectant Pro-san® inactivated Pseudomonas spp. samples. ODC mRNA disappeared rapidly and almost became undectable after lethal heat treatment and 24 h holding; wheras strong amplification signals still presented in dead samples subjected to Pro-san® treatments. For 16S rRNA, strong real-time signals were observed for both of the treatments. The result indicated that ODC mRNA is promising candidate as a viability indicator of spoilage Pseudomonas spp., but its persistence in dead cells depends on the inactivating conditions.

5.2 Introduction

Pseudomonas spp. strains are ubiquitously present in various natural, food and host environments as commensal bacteria or opportunistic pathogens. Psychrotrophic

Pseudomonas spp., such as P. fragi, P. fluorescens, and P. putida, are involved in spoilage of many food products, including dairy, meat, fish, and vegetables, particularly after introduction of chill chain storage and distribution system (Borch et al., 1996; Gram

& Huss, 1996; Huis, 1996). Currently, increasing consumption of unprocessed or minimally processed foods made microbial spoilage an increasing concern in food

114 industry (Spiess et al., 2003). Surviving and post-processing contaminations of spoilage

Pseudomonas spp. represent a serious problem despite this group could be minimized by many preserving methods (Lado & Yousef, 2002). In order to avoid unnecessary financial lost, screening of spoilage agents in raw materials and monitoring their occurrences in final food products are crucial for quality control and shelf life predication.

Conventionally, plate count methods are used to detect psychrotrophic

Pseudomonas spp. in food products. Usually, plates are incubated for 7 to 10 days around

8 °C, followed by biochemical confirmation. Alternative culturing methods involve incubation of plates for two days, if the temperature is kept at above 20 °C

(Chandrasekaran et al., 1985; Salvat et al., 1997). These traditional microbiological detection methods are not only labor intensive and time consuming, but also do not reliably identify the spoilage potential of the bacteria. Previous studies showed that artificial media are insufficient supporting the growth of all bacterial cells, especially injured microbial cells, which may recover during storage or be able to release enzymes or metabolites associated with later spoilage (Makdesi & Beuchat, 1996; Wesche et al.,

2009; Wu, 2008).

In recent years, besides plate counting, molecular methods, such as real-time quantitative PCR, have been established for rapid and specific detection of spoilage

Pseudomonas spp. (He et al., 2009; Martins et al., 2005; Reynisson, et al., 2008). Using

PCR, detection results could be expected within 4-5 hours after receiving the samples.

However, the application of those platforms was limited because of false positive results due to DNA amplification of dead cells (Fontaine & Guillot, 2003; Josephson et al., 1993;

115

Koo & Jaykus, 2000). Therefore, reliable amplification-based methods for detection of viable Pseudomonas spp. contamination is needed to monitor the food quality and assess the hygienic status.

In contrast to DNA, RNA has been proposed as more liable viability indictors

(Barer & Harwood, 1999; Keer & Birch, 2003). Reverse transcriptase (RT)-PCR methods targeting ribosomal RNA and messenger RNA had been used to detect a wide broad of foodborne pathogens (Cenciarini et al., 2009; D'Souza et al., 2009; Dzieciol et al., 2010;

Matsuda et al., 2007; Min & Baeumner, 2002). However, previous research suggested that decay of various RNAs after different lethal treatments is heterogeneous and unpredictable, as showed by study in our lab (Cenciarini et al., 2008; Kort et al., 2008;

Sheridan et al., 1998; Yaron & Matthews, 2002). In order to successfully detect viable cells, therefore, the RNA stability must be carefully evaluated and only unstable targets should be chose as viability indictors.

cDNA microarray is a technique which can be used to study the presence or expression of hundreds to thousands of genes simultaneously (Dhiman et al., 2001;

Miller & Tang, 2009; Schoolnik, 2002). The RNA stability of dead bacterial cells could be systematically investigated using cDNA microarray analysis to comparison RNA disappearing profile. The aim of this project was to develop a Taqman real-time RT-PCR based detection platform targeting the unstable mRNA molecules for rapid and specific detection of viable spoilage Pseudomonas spp. The global stability of RNA molecules after heat treatments was evaluated by cDNA microarray, and RNA targets with dramatic decay during heat inactivation was chosen as cell viability indictors. This is the first

116 report using unstable mRNA as cell viability indictor to detect viable spoilage

Pseudomonas spp. The developed method has great potential as rapid alternative for

Pseudomonas detection from raw material screening to final product quality control in food industry.

5.3 Materials and Methods

5.3.1 Bacterial strains and culture conditions

Pseudomonas spp. strains llxm1, llxm2 and llxm4, isolated from refrigerated milk at the end of shelf life (purchased at local grocery store), were used in this study.

Identification of the isolates was performed by 16S rRNA gene sequencing. Bacterial genomic DNA extraction, PCR amplification, and DNA sequencing of the 16S rRNA gene were performed as described in previous studies (Weisburg et al., 1991; Connor et al., 2005). Primers PS-G-16S-F (5′-GGTCTGAGAGGATGATCAGT-3′) and PS-G-16S-

R (5′-TTAGCTCCACCTCGCGGC-3′) were used to amplify approximately 1500 bp 16S rRNA gene fragments. The sequence of the PCR products were compared with published

16S rRNA gene sequences in GenBank by multiple-sequence alignment using nucleotide blast program (http://blast.ncbi.nlm.nih.gov/Blast.cgi).

For cDNA microarray analysis and real-time RT-PCR assays, Pseudomonas llxm2 strain was grown in Tryptic Soy Broth (TSB, Becton Dickinson and Company,

Sparks, MD, USA) at 30°C in a 200 rpm shaker to reach final OD600 between 0.8 and 0.9, corresponding to cell densities around 108-109 colony forming unit (CFU) ml-1.

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For the specificity tests, the following strains were used: Bacillus subtilis OSU

494 (Khadre & Yousef, 2001), grown in Difco Nutrient broth (Becton Dickinson, Sparks,

MD, USA) for 24 h at 37°C; Escherichia coli DH-5α (Invitrogen, Carlsbad, CA, USA), grown in Miller LB broth (Fisher Chemicals, Fairlawn, NJ, USA) for 24 h at 37 °C;

Lactobacillus plantarum ATCC 8014 (ATCC) grown in Lactobacilli MRS broth (Becton

Dickinson, Sparks, MD, USA) for 24 h at 37 °C; Lactococcus lactis subsp. LM2301 grown in M17-G broth (Becton Dickinson, Sparks, MD, USA) for 24 h at 37 °C; Listeria monocytogenes Scott A (ATCC) grown in TSB broth for 24 h at 37 °C; Streptococcus thermophilus P25BTG13 (cheese manufacture isolate, our lab) and Carbobacterium

(seafood isolate, our lab) grown in M17-L broth (Becton Dickinson, Sparks, MD, USA) for 24 h at 37 °C; Micrococcus spp. M1T8 (farm isolate, our lab), Pectobacterium spp.

M2ST14 (farm isolate, our lab), Pseudomonas putida ATCC 49451(ATCC),

Pseudomonas llxm1, Pseudomonas llxm4 grown in TSB for 24 h at 30°C; Streptococcus mutant UA159 (cheese isolate, our lab) grown un-aerobically in Brain Heart Infusion

(BHI, Becton Dickinson, Sparks, MD, USA) for 24 h at 37 °C. Stock cultures of all strains were stored in their respective media plus 20% glycerol at -80 °C. All working stocks were kept at 4-6 °C, and maintained by biweekly transfer. Fresh cultures were made by inoculating 5% of working stocks into the appropriate broths and incubating overnight at the designated temperatures.

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5.3.2 RNA isolation

Pseudomonas cells from each samples were harvested by centrifugation at 13,200 rpm for 1 min and lysed with 20 mmol l-1 of lysozyme (Sigma Chemical CO., St Louis, MO,

USA) in 100 μl TE buffer (20 mmol l-1 Tris-HCl, 2 mmol l-1 EDTA, pH 8.0) for 30 min at 37°C. Total RNA was extracted using QIAGEN RNeasy® Mini kit (QIAGEN, CA,

USA) and eluted with 30 μl of RNase and DNase-free water following manufacturer‟s instruction. The quantity of the RNA samples was determined by the ND-1000 UV/VIS spectrophotometer (Thermo Scientific, Nanodrop, Waltham, MA, USA).

For real-time RT-PCR assays, the DNA remnants present in the extracted RNA samples were digested by Deoxyribonuclease I. Briefly, reaction mixtures containing 2.0

μl of RNA elute, 2.5 units of DNase I (invirrogen, Carlsbad, CA), 1.0 μl of Reaction buffer (200 mmol l-1 Tris-HCl, 20 mmol l-1 MgCl2, 500 mmol l-1 KCl, pH 8.4), as well as

RNase and DNase-free water to a final volume of 10 μl were incubated at 37°C for 15 min. The DNase was then inactivated by adding 1.5 μl of 25 mmol l-1 EDTA followed by heating at 65 °C for 10 min.

5.3.3 cDNA microarray analysis

Pseudomonas spp. RNAs disappearing after cells death was measured by

Pseudomonas aeruginosa 70-mer glass slide obtained from Pathogen Functional

Genomics Resource Center (PFGR, the J. Craig Venter Institute, Rockville, MD, USA) using viable cultures grown under same conditions as a reference.

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Late log-phase Fresh Pseudomonas llxm2 cultures (108 CFU ml-1) were collected by centrifugation at 13,200 rpm for 1 min and re-suspended into the same volume of pre- heated PBS solutions, and heated at 98 ± 0.5 °C for 10 min. The heat-treated samples were quickly cooled down and incubated at room temperature for 24 h. Total RNA was isolated using the procedure describes above and the quantity of extracted RNA samples were determined by the ND-1000 UV/VIS spectrophotometer (Thermo Scientific,

Nanodrop, Waltham, MA, USA). Fluorescent dye labeled cDNA was synthesized by coupling of Cyanine 3 or Cyanine 5 dyes to the amino-allyl groups of cDNA molecules using standard protocol distributed by PFGR. And Cy-dye cDNA probes (mix of Cy3 and

Cy5) were hybridized onto amino-silane coated slide spotted with oligonucleotide probes following standard steps: prehybridization to extremely clean slides, washing and dying prehybridized slides, preparing Cy3/Cy5 probes, applying labeled probes mixture to slide, slide incubation and post-hybirdization washing (PFGR).

A GenePix Scanner 4000B (BioMicro® Systems, Inc., Salt Lake City, UT, USA) was used to scan the hybridized microarray slides. Cy3 and Cy5 were simultaneously excited by dual lasers at 532 nm and 635nm respectively, and the emitted fluorescence of each dye was measured independently. After scanning, data are extracted through

„„gridding‟‟ to define the location of the spots. Images were scanned and saved as a 16-bit

TIFF file and and optimized using GenePix software (V6.0). Three independent replicates, including 1 flip-dye duplicate, were performed throughout the study. After gridding, images files were saved as .gps files and the raw data were exported as .gpr files for next step data analysis.

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The influence of heat treatment on the stability of mRNA was determined by comparing mRNA level before and after the treatment. Change of mRNA level in each gene was measured on microarray by introducing Log2 (mean intensity of untreated sample/mean intensity of treated sample) value. The value on each microarray slide was averaged among four in-slide replicates. The experiment was repeated at least three times, including one set of flip-dye experiment. Criteria of potential targets evaluating the performance of heat treatment includes: 1) the value of log2 is equal or greater than 4 (a sixteen fold change of mRNA level after treatment); 2) the result was consistent among the 3 experimental replicates. 3) the array-determined unstable transcripts were confirmed by Syber Green-based RT-PCR.

5.3.4 Validation of RNA stability using SYBR Green based real-time RT-PCR

SYBR Green-based real-time RT-PCR was used to verify the un-stability of ornithine decarboxylase (ODC) mRNA and superoxide dismutase (SOD) mRNA selected by cDNA microarray analysis. To confirm array results by real-time RT-PCR, firstly, specific primer pairs were designed according to complete sequence of Pseudomonas

ODC gene (AM181176) and SOD gene (NC_010322.1). The designed primers were Pse-

ODC-F 5′ ATCAGCTACGGCAACACCAT 3′, Pse-ODC-R 5′ TCGTC

CATGGTTTCGATCAG3′, Pse-SOD-F 5′ ACCACGACAAGCACCACAAC 3′ and Pse-

SOD-R 5′ CCAGTTGACCAGGTTCCAG 3′.

Real-time RT-PCR was performed using the “iScript One-Step RT-PCR Kit with

SYBR® Green” (Bio-Rad Laboratories, Hercules, CA, USA), the corresponding primers

121 pairs and RNA template extracted from viable or 72.5 ± 0.5°C 10 min inactivated

Pseudomonas llxm2 samples. 25 μl RT-PCR reaction mixture contained: 12.5 μl of 2 ×

SuperScript buffer (containing 0.4 mmol l-1 of each dNTP, 2.4 mmol l-1 MgSO4), 0.5 nmol l-1 each of the two primers, 0.5 μl of iscriptreverse transcriptase, 1.0 μl of DNase treated RNA template, as well as RNase DNase free water up to the final volume. The reactions were conducted using an iCycler (Bio-Rad Laboratories, Hercules, CA, USA).

Mixtures were incubated at 50°C for 10 min for reverse transcription, following amplification settings: one cycle at 3 min at 95°C and 40 cycles of 15 s at 95°C, 30 s at

60°C, and 30 s at 72°C, followed by 72°C for 5 minutes and holding at 10°C. To test the amplification specificity, melting curve analysis was used to to determine the melting point of the double-stranded DNA product produced. This cycle consisted of heating from 50°C at 0.5°C per 20sec to 95°C. The threshold and Ct values were determined by the background subtraction method using the iCycler iQ software (Bio-Rad Laboratories,

Hercules, USA). For each RNA sample, a control that only uses the Platinum® Taq DNA polymerase (Invitrogen, Carlsbad, CA, USA) instead of iScript reverse transcriptase to monitor possible genomic DNA contamination was included in parallel with each experimental sample. One reagent blank control which contained all reaction agents but the RNA template also was run in parallel with each testing.

In real-time PCR, ΔCt (Ct treated samples – Ct control) were used to measure the RNA molecule copy number changes. Statistical analyses were performed with unpaired, two- tailed student‟s t-tests. P < 0.05 was considered to be significant.

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5.3.5 Taqman real-time primers-and-probe sets

Two Taqman real-time primers-and-probe sets used in this study were designed following the strategies by Connor (Connor et al, 2005; Wan et al., 2006). Firstly, alignment of the 16S rRNA gene sequences was performed with ClustalV using

MegAlign 5.01 (DNASTAR, Madison, WI, USA). Beside Pseudomonas llxm1, llxm2 and llxm4 16S rRNA gene sequences, the following 16 sRNA sequences with the

Genback accession numbers listed in the parentheses were used: Pseudomonas fluorescens (AM181176, AF336349, A3F36352), Pseudomonas aeruginosa (EF515832,

DQ777836), Escherichia coli (NC_004431), Shigella flexneri (NC_008258),

Enterococcus faecalis (v583), Salmonella (FJ667502), Bacillus subtilis (FJ493055).

Identical regions within the Pseudomonas spp. were chosen to design the 16S rRNA specific primers-and-probe. Two pairs of possible primers were derived based on the primer design guideline and additional specifications for the TaqMan® system

(http://www.appliedbiosystems.com/support/apptech/#). Finally, primers Pse16S real- time (2) (forward: 5′ GCGTAGATATAGGAAGGAAC 3′; reverse: 5′

ACTAAGAGCTCAAGGCTC 3′) was chose according to specificity analyses; and

Pse16S real-time (2)-probe (5′ AACGATGTCAACTAGCCGTTG 3′) was designed. The designed primers-and-probe set amplifies a 157-bp segment of 16S rRNA gene.

Similarly, using the primers pair Pse-ODC (same as SYBR Green-based real-time

RT-PCR assays) and the genomic DNA from two representative strains Pseudomonas llxm1 and Pseudomonas llxm2, approximately 650-bp ODC-encoding gene fragments were amplified by conventional PCR. After determining the DNA sequences, an

123 alignment of ODC gene sequences was performed using the same tool described above and the following sequences published in Genback: P. fluorescens (AM181176,

CP000094), Escherichia coli (NC_008253), Lactobacillus casei (NC_010999),

Salmonella enterica serovar Typhimurium (CP001363). Four sets of possible primer pairs were designed. After the specificity test, primers Pse ODC real-time (3) (Forward: 5′

CTSAAGCTGATCAACATGG 3′; Reverse: 5′ GAAGTCTTCCTTGAGGAAG 3′) and

Pse ODC real-time (3) probe 5′ CAACAGCCTGGAAACCTACG 3′ was chosen for next step testing. This primers pair amplifies a 108 bp segment of ODC gene.

All the primers were synthesized by Sigma-Genosys (The Woodlands, TX, USA).

The probes were synthesized by Biosearch Technologies (Novato, CA, USA). The probes were labeled with the reporter dye Quasar 670 at the 5′ end, and quencher dye BHQ-2 at the 3′ end. The compatibility of the primers and probes was evaluated using the Oligo

Analysis and Plotting Tool (available at: http://www.operon.com/oligos/toolkit.php).

5.3.6 Specificity and sensitivity analysis

The specificity of the developed Pse16S real-time (2) and Pse ODC real-time (3) primers pairs was examined using Pseudomonas strains and other representative bacterial strains commonly associated with food. These strains included Ps. Putida ATCC 49451,

Pseudomonas llxm1, llxm2, llxm4, B. subtilis osu 494, Carbobacterium, E. coli, L. monocytogenes Scott A, S. mutant UA159, S. thermophilus P25BTG13, Lactob. plantarum ACTT 8014, Lactoc. lactis subsp. LM2301, Micrococcus spp. M1T8 and

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Pectobacterium M2ST14. Genomic DNA was extracted for 1 ml overnight fresh cultures and subjected to conventional PCR assay following gel electrophoreses analysis.

The sensitivity of the Taqman real-time RT-PCR system was determined using

Pse16S real-time (2) and Pse ODC real-time (3) primers-and-probe sets to amplify RNA isolated from viable Pseudomonas llxm2 suspensions with different cell densities. 1 ml fresh Pseudomonas culture was spiked in a 0.85% NaCl solution. After 10 fold serial dilutions, cells from 1 ml of each sample were collected by centrifugation at 13,200 rpm for 1 min followed by RNA extraction as described above. 1 μl of the eluted RNA was subjected to DNase treatment and used as template. Taqman real-time RT-PCR amplification was carried out as described below. To compare the detection sensitivities of the developed RT-PCR system and the plate count method, 100 μl of the selected 10- fold serial dilutions were spread on TSB Agar. Colonies were counted after incubation on

TSA agar at 30°C for 48 h. The experiments were repeated at least three times.

5.3.7 Taqman Real time RT-PCR

Taqman real-time RT-PCR was conducted using the “iScript One-Step RT-PCR

Kit for Probes” (Bio-Rad Laboratories, Hercules, CA, USA) and the corresponding primers-and-probe set. The set up for RT-PCR in 25 μl reaction mixture contained: 12.5

μl of 2 × SuperScript one-step RT-PCR reaction buffer (a buffer containing 0.4 mmol l-1 of each dNTP, 2.4 mmol l-1 MgSO4), 0.5 nmol l-1 each primers, 0.4 mmol l-1 of Taqman probe and 0.5 μl of iscriptreverse transcriptase, 1.0 μl of RNA template, as well as RNase

DNase free water up to a final volume. The reactions were conducted using an iCycler.

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For RT-PCR, mixtures were incubated at 50°C 10 min for reverse transcription. Real- time PCR settings after the RT step includeds: one cycle at 3 min at 95°C and 40 cycles of 15 s at 95°C, 30 s at 53°C, and 30 s at 72°C, followed by 72°C for 5 min and holding at 10°C. The threshold and Ct values were determined by the background subtraction method using the iCycler software. For each RNA sample, a control that only use the

Platinum® Taq DNA Polymerase instead of iScript reverse transcriptase to monitor contaminating genomic DNA was run in parallel with each experimental sample. One reagent blank control which contained all reaction agents without RNA template was also run in parallel with each testing. All experiments were at least triplicate. Besides Ct value, RFU (relative fluorescent units) was used to measure the fluorescent intensity of amplification signals.

5.3.8 Heat inactivation

Fresh cultures containing 109 and 107 CFU ml-1 of Pseudomonas llxm2 (as determined by the plate count method) were heated at 72.5 ± 0.5°C for 1 min, 3 min and

10min. In all the cases, glass tubes with phosphate buffered saline (PBS) were pre-heated in water bath for 30 min to reach targeted temperatures. Exponential stage (108 CFU ml-1) cells were collected by centrifugation at 13,200 rpm for 1 min, then re-suspended into pre-heated PBS solutions to reach the final cell concentrations of 109 or 107 CFU ml-1, and heated for the designated period of time. A sterile thermometer swapped by 70% ethanol twice was placed in the tube to monitor the heating temperature throughout the study. All the heat-treated samples were quickly cooled by placing in ice-water bath for

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10 min and incubation at room temperature for 24 h. Then each sample was subjected to cell viability test and RNA isolation as describes above.

5.3.9 Pro-san® inactivation

Fresh Pseudomonas llxm2 suspensions were incubated with disinfectant Pro-san®

(Maintenance Supply, Inc. Durham, NC, USA). Pro-san® 1 ×, 2 ×, and 4 × working solution were prepared by adding stock solution into sterile distill water according to the instruction of the manufacturer. 1 ml fresh Pseudomonas llxm2 cells were harvested by centrifugation at 13,200 rpm for 1 min, and then the cell pellets were spiked into Pro-san working solutionsto reach a final concentration 109 CFU ml-, and kept at room temperature for 10 min. After Pro-san® treatments, cell pellets were spun down by centrifugation at 13,200 rpm for 1 min and carefully washed twice with PBS, and then re- suspended in an equal volume of PBS. Treated sampes were incubated at room temperature for 24 h. Each sample was subjected to cell viability test and RNA isolation as describes below after holding at room temperature for 24 h. The experiments were repeated three times.

5.3.10 The viability test of treated cells

Heated or Pro-san® treated Pseudomonas spp. samples were subjected to viability assessment by the plate count method. Except using 1 ml treated samples for RNA extraction, other cell pellets were collected by centrifugation at 13,200 rpm for 1.0 min and re-suspended into 200 μl of TSB broth. 100 μl aliquot was directly spread plated on

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TSA agar plates and incubated at 30°C for 48 h followed by cell count; another 100 μl aliquot was mixed with 10 ml of fresh TSB broth, incubated at 30°C and to check turbidity change for up to 3 days.

5.4 Results

5.4.1 DNA microarray analysis

Various labile mRNA targets were identified in microarray replicates and summarized in table following screening criterion: 1) expression was high before treatment and minimized after treatment; 2) log2 difference >3; 3) was consistent at least among three array replicates.

National Center for Biotechnology Information (NCBI) Genbank search results showed that ornithine decarboxylase (ODC) and superoxide dismutase (SOD) gene are universial and conserved enough for design primers for specific detection. Therefore,

ODC mRNA and SOD mRNA were chosen as the potential targets for the next step un- stability confirmation by real-time RT-PCR.

5.4.2 Validation microarray results by SYBR Green based real-time RT-PCR

To further validate the transcripts level change of ODC and SOD transcripts,

SYBR Green-based real-time RT-PCR assays were performed using Pseudomonas specific primer pairs and RNAs extracted from viable and heat inactivated cells. As showed in Fig. 5.1., the Ct values for the unheated control samples were 23.3 ± 1.04, and

128 increased to 34.1 ± 1.73 (ΔCt=10.6) after 72.5°C 10 min heat treatments when RT-PCR were performed using primrs pair Pse-ODC. While for the similar heat treatment, the corresponding Ct values were increased from 28.1 ± 1.31 to 32.5 ± 1.05 (ΔCt =4.4) when

Pse-SOD primers set were used. Under same treatments, the ΔCt was 10.6 by ODC primers pair; and the ΔCt was 4.4 by SOD primers pairs, even though corresponding Ct values have similar increasing tendency. RT-PCR assessments demonstrated that ODC transcript disappeared more quickly after cell death. Therefore, and Pseudomonas ODC gene was selected as a cell viability indictor to establish Taqman real-time RT-PCR detection platform.

5.4.3 Primers-and-probe sets evaluation

Using developed Pse16S real-time (2) and Pse ODC real-time (3) primers pairs, all three Pseudomonas spp. strains llxm1, llxm2, llxm4 and the Ps. Putida ATCC 49451 were tested positive by conventional PCR. And no cross-reactivity was found with B. subtilis, E. coli, L. monocytogenes, S. mutant, S. thermophilus, Lactob.plantarum,

Lactoc.lactis subsp., Micrococcus spp., Carbobacterium and Pectobacterium spp. The typical specificity evaluation results were showed in Fig. 5.2 (partially results were showed). Although only four representative strains were used in the laboratory specificity studies, further in silico specificity search was conducted covering all the deposited DNA sequences available through the NCBI. The results showed that no combinations three oligonucleotides fragments used in primers-and-probe sets were found in any other microorganisms but Pseudomonas spp..

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The sensitivity of developed Taqman real-time RT-PCR systems was examined by amplifying RNA isolated from serial diluted Pseudomonas spp. llxm2 suspensions. A typical real-time RT-PCR amplification graph from a replicate by 16S rRNA is shown in

Fig. 5.3. Minimum confident detection level for 16S rRNA was the 104 CFU ml-1 viable cells. Fig. 5.4 represented one of the typical Taqman real-time RT-PCR amplification graph using Pse- ODC primers-and-probe sets. The confident detection level was the 10-5 dilution, which corresponds to 103-104 living cells indicated by colony counts on TSA plate.

5.4.4 RT-PCR assessments of Pseudomonas spp. exposed to heat treatments

After high concentration Pseudomonas spp. llxm2 cells were inactivated at 72°C for 1 min, the log values of survived cells decreased from 8.97 ± 0.19 to 0.15 ± 0.62. For other treated sample, no exhibited growth was observed on either TSB agar plates or in

TSB broth. The potential of the developed RT-PCR assay to distinguish viable and nonviable Pseudomonas cells was examined using RNA isolated from live and heat- treated cells as amplifying templates. Using the Pse16S real-time (2) and Pse ODC real- time (3) primers-and-probe sets, both transcripts were detected by Taqman real time RT-

PCR in all samples heating at 72.5 °C for 1 min, 3 min and 10 min. However, the real- time RT-PCR results, illustrated by the mean Ct values and relative fluorescence units

(RFU), suggested the difference in the corresponding RNA molecules survived from heat treatments. Fig. 5.5 illustrated the RT-PCR assay results obtained by Pse16S real-time (2) primers-and-probe set. The Ct values were 18.2 ± 0.56 for viable cultures with high

130 concentration (109 CFU ml-1) Pseudomonas cells; while the Ct values increased to 22.4 ±

0.70, 23.1 ± 0.42 and 23.6 ± 0.60 respectively after treating at 72.5 °C for 1 min, 3 min and 10 min (Fig. 5.5-A). The similar tendency was observed when diluted Pseudomonas cells (107 CFU ml-1) were used. The corresponding Ct values were 21.1 ± 0.61, 23.1 ±

0.82, 25.9 ± 0.72 and 26.1 ± 0.40 individually for 0 min 1 min, 3 min and 10 min (Fig.

5.5-A). Two trial student t-test analyses showed that, the Ct values of heated samples was significantly higher than unheated viable cells when low density Pseudomonas cells subjected to pasterurization (P > 0.05). These results suggested that 16 rRNA molecules degraded after heating; and with the extension of heating time, more 16 rRNA disappeared. However, strong amplification signals after 10 min heat treatments indicted that 16 rRNA molecules was stable. Fig. 6.6 illustrated the RT-PCR assay results obtained by ODC primers-and-probe set. The Ct values of viable samples containing 109

CFU ml-1 viable cells were 26.7 ± 0.47; and the Ct values increased linearly to 33.9 ±

0.53 (ΔCt=7.2), 34.7 ± 0.68 (ΔCt=8.0) and 35.3 ± 0.87 (ΔCt=8.6) after 1 min, 3 min and

10 min heating. Similarly, the responding Ct values were increased from 27.9 ± 0.55 to

34.7 ± 0.62 (ΔCt=6.8), 35.7 ± 0.75 (ΔCt=7.8) and 37.3 ± 0.40 (ΔCt=9.4) when 100 fold diluted cells were subjected to 1 min, 3 min and 10 min heat treatments. The results suggested that ODC mRNA molecule are unstable than 16S rRNA. Under 72°C heat treatment, the great difference of amplification signals obtained by the 16S rRNA primers-and-probe set and the ODC primers-and-probe set was reflected by relative fluorescence units (RFU). Fig. 5.5-B showed that 16S rRNA have strong amplification signal. Even though with the slight decrease, RFUs were more than 900 after 10 min

131 heating in diluted samples, which suggested that 16S rRNA was stable after lethal heat treatments. For ODC primers-and-probe set, the positive amplification signals could be observed, however, the RFU dropped below 200 in all heat-treated cells (Fig. 5.6-B). The results showed that ODC mRNA was almost disappeared after 10 min heat treatment.

The Pseudomonas spp. llxm2 cells (109 CFU ml-1) also subjected to extremely killing condition: autoclave. As illustrated in Fig. 6.7, the Ct values increased form 18.1 ±

1.23 to 31.8 ± 1.12 after autoclave using 16S rRNA primers-and-probe set. While ODC transcripts disappeared completely after same treatment.

In above cases, all negative controls using the Platinum® Taq DNA polymerase instead of iScript reverse transcriptase had negative signals, suggesting all amplifications were performed from RNA template without DNA contamination. Meanwhile, reagent blank control without RNA template also showed negative signals.

5.4.5 RT-PCR assessments of Pseudomonas spp. exposed to Pro-san® treatments

The cell viability analysis showed that treating Pseudomonas spp. with 1 × Pro- san® 10 min causes viable cell density dropped sharply from 9.03 ± 0.12 to 1.74 ± 0.43

(log values), while complete loss of cultivability was observed after exposing same cultures to 2 × Pro-san® 10 min. The Ct values of RT-PCR assay were illustrated by Fig.

5.8. Using 16S rRNA primers-and-probe set, the Ct values for were 12.9 ± 0.85 for viable cell, and increased to 13.8 ± 0.20, 14.3 ± 0.40 and 15.2 ± 0.25 after 1 ×, 2 ×, and 4 × Pro- san® inactivation. Two trial student t-test analyses showed that no significant difference was observed between the Ct values obtained from viable cells and 1 ×, 2 × Pro-san®

132 inactivated (P > 0.05). Under the same Pro-san® inactiving conditions, the corresponding

Ct values were increased form 26.4 ± 1.00 to 29.0 ± 0.62, 29.7 ± 0.68, and 30.5 ± 1.06 respectively by ODC primers-and-probe set. Even thoght significant increasing of Ct values (P < 0.05) was observed after 1 × Pro-san® treatment, strong amplification signals were obtained as showed by Fig. 5.9.

5.5 Discussion and Conclusion

Currently, the conventional culture methods are used for monitoring spoilage

Pseudomonas occurrences in the food industry. However, beside labor intensive and time consuming, these methods may miss some injured or unculturable cells. Rapid, specific and detection of all viable Pseudomonas cells is important for raw material screening and shelf life prediction. In principle, RNA can serve as cell viability indictors if the RNA is present only in viable bacterial cells and disappeared quickly after cell death (Cenciarini et al., 2009, McKillip & Drake, 2004). In fact, a number of studies have focused on using mRNA to inhibit the false positive amplification signals of DNA amplification based detection methods (Gonzalez-Escalona et al., 2009; Hellyer et al., 1999; Min &

Baeumner, 2002; Sung et al., 2005). Previous research suggested that general disappearing kinetics of different transcripts after bacterial death is not uniform and unpredictable (Cenciarini et al., 2008; Vaitilingom et al., 1998; Yaron & Matthews, 2002).

It is necessary to systeminally study the disappearing rate of large scale of RNAs to identify proper targets as viability indictors.

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As a high throughput, highly parallel technique, cDNA microarray analysis is able to quantitatively investigate the presence or expression of hundreds to thousands of genes simultaneously (Dhiman et al., 2001; Miller & Tang, 2009). In this study, the presence of a whole spectrum of RNA molecules in Pseudomonas spp. before and after lethal heat treatments was compared by using cDNA microarray analysis. The microarray analysis results identified a few potential targets, including ODC gene, SOD gene, ribosomal protein L34 encoding gene, and ribosomal protein L28 encoding gene, ect. This was the cutting-edge attempt using microarray to systematical assess RNA stability.

When developing a real-time PCR assay, the selection of target gene is a critical step. It has to be present in all strains and conserved enough to enable the design of primers set specific for the genus or species targeted (Casey & Dobson, 2004; Luo et al.,

2004; Rawsthorne & Phister, 2006). ODC is the first enzyme in urea cycle which catalyzes the decarboxylation of ornithine to synthesize polyamines, a functional molecule that is essential for DNA structure stabilization, the DNA double strand-break repairing and as antioxidants, ODC is an essential enzyme. Moreover, gene alignment result also proven that ODC-encoding gene is universal in genus of Pseudomonas and conserved for specific detection (Kern et al., 1999; Zhang et al., 2003). So, ODC mRNA was selected as cell viability indictor for further propose. In addition, 16S rRNA, a stable

RNA molecule after heat treatment, was chosen for comparison.

In this study, Taqman real-time primers-and-probes based on ODC-encoding gene and 16S rRNA gene were designed and the specificity analysis results demonstrated that only Pseudomonas spp. could be detected without cross reaction with other

134 microorganisms commonly associated with foods (showed in figure 5.2). Using the newly establish RT-PCR platform, the presence of 103 viable cells could be consisted detected with strong amplification signals. For practical usage, a few hours pre- enrichment step could improve the detection limits.

The potential of using ODC mRNA as cell viability indictor was investigated by performing Taqman real-time RT-PCR on RNA extracted from viable and heat (or Pro- san®) treated Pseudomonas spp. cells. The 16S rRNA primers-and-probe set was run in parallel for comparing. The results demonstrated that ODC mRNA almost completed disappeared after 72.5 °C 1 min heating, only weak amplification signeds were observed eventhogh high density cells were used for analysis. After Pro-san® treatments, the degrading of Pseudomonas spp. ODC mRNA was different from heat inactivations. Even though increase of Ct values was showed by real-time RT-PCR, strong amplification signals indictaed that the transcript was relative stable when cell death was caused by disinfentant Pro-san®. In living bacterial cells, most mRNA turns over rapidly, reflecting a balance between the synthesis of mRNA and its degradation by RNases (Deutscher,

2006). In dead cells, mRNA synthesis is may be inhihited or slow and nuclease activity will continue to degrade any mRNA present (Sheridan et al., 1998). The activity ingredients of Pro-san® are citric acid and sodium dodecylbenzene sulfonate, may these two compounds do not inactivate the degradative RNase enzymes comparing with heating.

Compared with ODC mRNA, using 16S rRNA molecules as amplification template, strong amplification signals could be obtained when cells suffered both heat

135 and Pro-san® treatment. Several previous studies chose 16S rRNA as target for RT-PCR detection of viable bacterial cells (Bleve et al., 2003; Fontaine & Guillot, 2003; Matsuda et al., 2007). Our data indicated that 16S rRNA is not a good choice for detection of viable bacterial cells, which was in agreed with my previous study.

In conclusion, this study demonstrated the great potential of using Taqman real- time RT-PCR platform to distinguish between viable and heat-killed Pseudomonas spp. via unstable ODC mRNA target. With rapid, sensitivity and high specificity, this rapid detection was able to be applied in food industry for quality control and shelf life prediction of pasteurized food. However, the disappearing rate of mRNA transcripts after different lethal inactivation was not uniform; the universal viability indictor suitable for broad environments should be investigated via more detailed microarray study in the further.

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Locus name Gene name

PA4366 Superoxide dismutase

PA5316 50S ribosomal protein L28

PA4519 Ornithine decarboxylase

PA5570 50S ribosomal protein L34

PA3553 Probable serine protease

Table 5.1. Potential unstable mRNA targets identified by cDNA microarray analysis.

mean intensity of untreated sample The unstable targets were selected by criterion: Log2 ( /mean intensity of treated sample) >4.

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20 0 min

Ct values Ct 15 10 min 10

0 ODC SOD

Figure 5.1. SYBR Green real-time RT-PCR detection of Pseudomonas llxm2 (109 CFU ml-1) exposing to 72 °C 10 min using primers pairs target to ODC and SOD transcripts.

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M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

200bp 100bp

Figure 5.2. Specificity examination of Pse16S real-time (2) and Pse ODC real-time (3) primers-and-probe sets demonstrated by 2.0% agrose gel electrophoresis for PCR end- products. Lane 1-4: Pse16S real-time (2) primers-and-probe set, Lane 5-8: Pse ODC real- time (3) primers-and-probe set. Lane 1 and 5 Pseudomonas llxm1; Lane 2 and 6, Pseudomonas llxm2; Lane 3 and 7, Pseudomonas llxm4; Lane 4 and 8, Ps. Putida 49451; Lane 9, B. subtilis US494; Lane 10, E. coli DH-5α; Lane 11, L. monocytogenes Scott A; Lane 12, Lactob.plantarum ATCC 8014; Lane 13, Lactoc.lactis subsp. lactis LM2301; Lane 14, Micrococcus spp. M1T8; Lane 15, S. thermophilus P25BTG13.

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Figure 5.3. TaqMan real-time PCR sensitivity test of Pseudomonas llxm2 using the Pse16S real-time (2) primers-and-probe set. Dilutions with cell concentrations 2.6×109 CFU ml-1(▲), 2.6×108 CFU ml-1 (▼), 2.6×107 CFU ml-1 (■), 2.6×106 CFU ml-1 (●), 2.6×105 CFU ml-1 (♦) responding to Ct values 17.5, 18.2, 20.4, 21.8, 24.3 appear above the threshold baseline. The reagent blank control and non reverse transcriptase controls are showed below the baseline without amplification (horizontal line).

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Figure 5.4. TaqMan real-time PCR sensitivity test using the Pse ODC real-time (3) primers-and-probe set. Cell concentration 1.9×109 CFU ml-1(▲), 1.9×108 CFU ml-1 (▼), 1.9×107 CFU ml-1 (■), 1.9×106 CFU ml-1 (●), 1.9×105 CFU ml-1 (♦) with the responding Ct values 26.3, 26.7, 27.9, 28.7, 30.6 appear above the threshold baseline. The reagent blank control and non reverse transcriptase controls are showed below the baseline without amplification (horizontal line).

141

A 40 35

25 0 min 20 1 min

Ct value Ct 15 3 min 10 10 min

0 high concentration low concentration

1600 B 1400 1200 1000 0 min

800 1 min RFU 600 3 min 400 10 min 200 0 high concentration low concentration

Figure 5.5. Taqman real time RT-PCR detection of Pseudomonas llxm2 exposing to 72 °C for various period of time using Pse16S real-time (2) primers-and-probe set. Error bars represent standard deviation from three independent replicates.

A) Ct difference; B) RFU (relative fluorescence units)

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A 40 35

25 0 min 20 1 min

Ct value Ct 15 3 min

10 10 min

0 high concentration low concentration

900 800 700 600 0 min 500 1 min RFU 400 300 3 min 200 10 min 100 0 high concentration low concentration

Figure 5.6. Taqman real time RT-PCR detection of Pseudomonas llxm2 exposing to 72 °C for various period of time the Pse ODC real-time (3) primers-and-probe set. Error bars represent standard deviation from three independent replicates.

A) Ct difference; B) RFU (relative fluorescence units)

143

20 0 min

Ct values values Ct 15 autoclaved 10

0 16S rRNA ODC

Figure 5.7. Taqman real-time RT-PCR detection of autoclaved Pseudomonas llxm2 (cell concentration: 109 CFU ml-1) using Pse16S real-time (2) and Pse ODC real-time (3) primers-and-probe sets. Error bars represent standard deviation from three independent replicates.

144

25 viable 20 1 x pro-san®

Ct values values Ct 15 2 x pro-san®

10 4 x pro-san®

0 16S rRNA ODC mRNA

Figure 5.8. Taqman real-time RT-PCR assays (Ct values) detection of Pseudomonas llxm2 (cell concentration: 109 CFU ml-1) subjected to Pro-san® treatment using Pse16S real-time (2) and Pse ODC real-time (3) primers-and-probe sets. Error bars represent standard deviation from three independent replicates.

145

16S rRNA gene

ODC mRNA gene

Figure 5.9. Taqman real-time RT-PCR assays (RFU, relative fluorescence units) detection of Pseudomonas llxm2 (cell concentration: 109 CFU ml-1) subjected to Pro-san® treatment using Pse16S real-time (2) and Pse ODC real-time (3) primers-and-probe sets.

146

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Chapter 6

Detection of Viable Spoilage Pseudomonas spp. Using Propidium Monoazide

Coupled Taqman Real-time PCR

6.1 Abstract:

The limited shelf life of chill foods is generally due to growth and metabolism of psychrotrophic Pseudomonas strains. Rapid and specific detection of viable

Pseudomonas spp. cells is important for food industry. However, conventional culturing methods are labor intensive and time consuming. On the other hand, DNA detection methods are critized because DNA molecules from dead cells cause false positive results.

Propidium monoazide (PMA) can penetrate damaged cell membrane and form crosslinkage with DNA molecules resulting in DNA loss during subsequent genomic

DNA extraction. Previous studies illustrated that PMA treatment prior to PCR analysis could successfully prevent the amplification of DNA from many inactivated foodborne pathogens cells. However, little is understood about the effectiveness of this assay for detecting viable population of spoilage Pseudomonas spp. subjected to common applied microbial inhibiting conditions. The purpose of this study was to evaluate the efficacy of

PMA coupled Taqman real-time PCR assay in selective detection of viable Pseudomonas

152 spp. cells from dead ones exposed to several bacteria inactivation treatments. After heat, acid and disinfectant inactivations, Pseudomonas cells were treated with PMA, followed by DNA extraction and real-time PCR assessment using primers-and-probe sets targeting conserved regions of the 16S rRNA gene and Ornithine decarboxylase (ODC) gene. PMA treatment successfully minimized false positive amplification signals by dead cells from different inactivation treatments. The good linear correlations were observed between the

PMA real-time PCR threshold cycles (Ct values) and the number of viable cells obtained by plate counts. The results suggested that the established PMA-coupled Taqman real- time assays can be used for rapid and specific detection of viable spoilage Pseudomonas spp. cells. Application of the developed detection system can enhance quality control and minimize spoilage incidence.

6.2 Introduction

The reliable methods for detection of foodborne microorganisms are great challenge in food microbiology areas. Currently, increasing number of molecular detection methods promise rapid, specific and sensitive detection of foodborne microorganisms (McKillip & Drake, 2004; Nugen & Baeumner, 2008). However, DNA- detection can lead to false-positive results because of the persistence of DNA after cell death (Keer & Birch, 2003; Koo & Jaykus, 2000). RNA molecules, on the other hands, may disappear more rapidly when cells loss viability (Keer & Birth, 2003; Cenciarini et al., 2009). In my third project, RT-PCR assays were successfully developed to detect viable Pseudomonas spp.. However, RNA cell viability markers must be selected

153 carefully due to heterogeneous stability of transcripts associated with RNA intrinsic properties, bacteria species, killing conditions, ect (Cenciarini et al., 2008; sung et al,

2004; Yaron & Matthews, 2002). And intrinsic instability of RNA results in technical challenges. Therefore, simple molecular detection methods using universal viability markers are needed for viable microbial cells detection.

The bacterial membrane integrity is a well-accepted criterion characterizing viable bacterial cells (Caron et al., 1998; Nocker et al., 2006). Live cells with intact membranes are able to exclude DNA-binding dyes that easily penetrate dead or membrane compromised cells. This principle is increasingly applied for viable bacterial cells detection. By staining of intact cells with one florescent stain and counter-staining of membrane-compromised cells with another stain, microscopy (LIVE-DEAD staining kits) and analytical flow cytometry (FCM) were used to distinguish viable and dead bacterial cells (Boulos et al., 1999; Veal et al., 2000). However, separate staining counts do not enable detection of specific microorganisms. PCR amplification in conjunction with the DNA-intercalating dyes could be easy-to-use alternatives which facilitate rapid, specific, sensitive and quantitative detection of viable cells (Rudi et al., 2005; Soejima et al., 2008; Nocker et al., 2006).

Propidium monoazide (PMA) is a DNA-intercalating dye with the azide group, which allows covalent cross-linkage of the dye to the DNA upon light-exposure when

PMA penetrates into cells. This modification results in PCR inhibition either by strongly inhibiting PCR amplification or by inducing DNA loss with cell debris in the subsequent

DNA extraction. In contrast, PMA is unable to penetrate though intact membrane of

154 viable cells (Nocker et al., 2009; Pan & Breidt, 2007). The free dyes are inactivated by reacting with water and don‟t interrupt the DNA isolation. Therefore, treating bacterial samples with PMA prior to DNA-extraction allows selective detection of only viable bacterial cells with intact membrane.

PMA coupled PCR methods have been developed for detection broad range foodborne microorganisms, such as Campylobacter (Josefsen et al., 2010), Enterobacter

(Cawthorn & Witthuhn, 2008), Escherichia coli O157:H7 (Nocker et al., 2006), Listeria monocytogenes (Pan & Breidt, 2007) and Staphylococcus aureus (Kobayashi et al., 2009).

However, the application efficiency must be carefully evaluated because the viability criterion is based on the membrane integrity. Bacterial membrane damages caused by different treatments are varied during food processing (Wu, 2008). To our knowledge, there is still no available data regarding the detection of viable Pseudomonas using PMA coupled PCR assays, particially under different inactivation conditions. The aim of this study was to evaluate the applicability of using PMA coupled Taqman real-time PCR assays for rapid and specific detection of live spoilage Pseudomonas spp. cells subjected to typical inactivation methods in food industrial processes, including heat, acid and sanitizer Pro-san® treatments. In addition, the PMA-PCR results were compared with conventional plate counting data.

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6.3 Materials and Methods

6.3.1 Bacterial strain and culture conditions

Pseudomonas spp. llxm2, a strain isolated from refrigerated milk at the end of shelf life (purchased at local grocery store), was used in this study. For PMA coupled real-time PCR assays, fresh Pseudomonas spp. llxm2 cultures were obtained by growing in Tryptic Soy Broth (TSB, Becton Dickinson and Company, Sparks, MD, USA) overnight at 30°C in a shaker at 200 rpm to reach an OD600 between 1.0 and 1.2, corresponding to cell densities around 109 colony forming unit (CFU) ml-1. For bacterial cell counts, serially diluted samples were spread-plated on TSA agar plates and incubated at 30°C for 48 h. The frozen stock was stored in TSB medium supplemented with 20% glycerol and kept at -80°C. Working culture were kept at 4-6°C and maintained by biweekly transferring.

6.3.2 Pseudomonas spp. inactivation treatments

6.3.2.1 Heat inactivation

Fresh Pseudomonas spp. llxm2 suspensions (109 CFU ml-1; plate count determination) were heated at 72.5 ± 0.5°C for 15 s, 1 min and 3 min, as well as autoclaving 15 min, respectively. In all cases, glass tubes with 5 ml phosphate buffered saline (PBS) were pre-heated in water bath for 30 min to reach targeted temperatures.

Fresh bacterial cells were collected by centrifugation at 13,200 rpm for 1 min. cell pellets

156 were re-suspended into pre-heated PBS solutions to reach the final cell concentrations of

109 CFU ml-1, and heated for designated periods of time. A sterile thermometer swapped by 70% ethanol twice was placed in the tube to monitor the heating temperature throughout the study. In addition, fresh Pseudomonas spp. cultures were autoclaved for

15 min. All the heat-treated samples were quickly cooled by placing in ice-water bath for

10 min. Then 0.5 ml cell suspension was subjected to PMA treatment and DNA extraction procedures as described below. Another 0.5 ml aliquot was spun downby centrifugation at 13,200 rpm for 1 min and re-suspended into 100 μl PBS solution following cell viability test by streaking on the TSA agar plates and incubating at 30°C for 48 h. The experiments were repeated at least three times.

6.3.2.2 Acid inactivation

Fresh Pseudomonas spp. llxm2 suspensions (109 CFU ml-1; plate count determination) were incubated with acidified PBS solution (pH = 4.0, 3.5, 3.0 and 2.5), respectively. In all cases, the pH value of PBS solution was adjusted to 4.0, 3.5, 3.0 and

2.5 by 10% acetic acid stock solution (Fisher Scientific, Fair Lawn, NJ, USA). Then cell pellets of 1 ml fresh Pseudomonas spp. llxm2 working cultures were collected by centrifugation at 13,200 rpm for 1 min. The cells pellets were spiked into 1 ml acidified

PBS solutions and incubated at room temperature for 15 min. After acid treatment, cell pellets were spun downby centrifugation at 13,200 rpm for 1 min and carefully washed twice with PBS, and then re-suspended in equal volume of PBS solution. 0.5 ml acid treated samples were subjected to further PMA treatment following DNA extraction as

157 described below. Another 0.5 ml aliquot was used for viability test same as heat treated samples. The experiments were repeated at least three times.

6.3.2.3 Pro-san® inactivation

Fresh Pseudomonas spp. llxm2 suspensions (109 CFU ml-1) were incubated with disinfectant Pro-san® (Maintenance Supply, Inc. Durham, NC, USA). Pro-san® 1 ×, 2 ×, 3

× and 4 × working solution were prepared by adding stock solution into sterile distill water according the instruction of manufacturer. 1 ml fresh Pseudomonas spp. llxm2 cells were harvested by centrifugation at 13,200 rpm for 1 min, and then the cell pellets were spiked into equal volume of Pro-san working solutions and remained in room temperature for 10 min. Then the cell pellets were spin down, rinsed and re-suspended as the same processing as acid treatments. 0.5 ml cells suspension waas subjected PMA treatment and

DNA extraction. Another 0.5 ml aliquot was used for viability test same as heat treated samples. The experiments were repeated at least three times.

6.3.3 PMA treatments and DNA extraction

PMA (phenanthridium, 3-amino-8 azido-5-[3-(diethylmethylammonio) propyl]-6- phenyldichloride, Biotium, Hayward, CA, USA) was dissolved in 20% dimethyl sulfoxide (DMSO, Sigma Chemical CO., St Louis, MO, USA) to make 20 mmol l-1 stock and stored at −20 °C in the dark. 1.25 μl PMA stock was added to 0.5 ml Pseudomonas spp. cell suspensions to final concentration of 50 μmol l-1 in light transparent glass tube.

Samples were incubated in the dark for 5 min at room temperature to allow penetration of

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PMA into membrane damaged cells. Then tubes were exposed to a 650 W halogen lamp

(GE Lighting, General Electric Co., Cleveland, OH, USA) for 2 min at distance of 20 cm.

During exposure, tubes were placed on ice to avoid excessive heating.

After photoinduced crosslinking, cells were collected by centrifugation at 13,200 rpm for 1 min. DNA was extracted using QIAgen DNeasy® blood and tissue kit

(QIAgen, Valencia, CA, USA) and eluted with 60 μl ddH2O following the instructions of the manufacturer. The quantity of the DNA isolates was determined by the ND-1000

UV/VIS spectrophotometer (Thermo Scientific, Nanodrop, Waltham, MA, USA)

6.3.4 Taqman real-time PCR assays

The Pse-16S and Pse-ODC primers-and-probe sets used in this project were derived from my previous study (Table 6.1). The real-time PCR amplification was performed using mixer containing 2.5 μl 10 × reaction buffer (100 mmol l-1 KCl, 40

-1 -1 -1 mmol l Tris-HCl, pH 8.4), 1.5 μl MgCl2 (25 mmol l ), 1.0 μl dNTPs (10 mmol l ), 0.5

μl Platinum® Taq DNA Polymerase, 0.5 μl of each primers (10 umol l-1), 1 μl probe (6

-1 mol l ), 1.0 μl of extracted DNA template, and ddH2O to the final volume of 25 μl.

Beside primers and probes, other reagents came from Invitrogen (Invitrogen, Carlsbad,

CA, USA). Thermal cycling was performed using the iCycler iQ Real-Time PCR

Detection System (Bio-Rad Laboratories, Hercules, CA, USA) with the following amplification conditions: one cycle at 3 min at 95°C and 40 cycles of 30 s at 95°C, 30 s at

51°C, and 20 s at 68°C, followed by 68°C 5 min and holding at 4°C. One blank control

159 containing all reaction agents except DNA template also was run in parallel with each testing.

6.3.5 Control samples

Beside PMA treated samples, corresponding controls used in the study were 0.5 ml viable and heat, acid or sanitizer killed cells without exposing to PMA treatment.

Real-time PCR was run using DNA extracted from control samples. The dead samples included cell suspensions subjected to 72.5 °C 3 min heating, pH 2.5 acid treat and 4 ×

Pro-san® treatment. Completely loss of viability was confirmed by plate count method.

6.3.6 Statistical analysis

Independent experiment was repeated at least in triplicate for all treatments. Error bars in diagrams represent standard deviations from three independent replicas. Statistical analyses were performed with unpaired, two-tailed student‟s t-tests. P < 0.05 was considered to be significant difference.

6.4 Results

6.4.1 PMA-PCR assessments of Pseudomonas exposed to heat treatments

As determined by TSA plate counting method, dramatic loss of cells culturability was observed after fresh Pseudomonas spp. samples were inactivated at 72°C for designated periods (Fig. 6.1). Heat treatment at 72°C for 15s resulted in around 6 log

160 reductions of cells density. And all bacterial cells were killed after 72 °C 3 min heating and autoclaving.

The real-time PCR results, illustrated by mean Ct values, (n=3, values are mean ±

S.D.), suggested the inhibition of PCR amplification signals after heat inactivation cells treated with PMA (Fig. 6.2). Fig. 6.2 illustrated that using 16S rRNA primers-and-probe, the Ct values for PMA treated viable cells were 18.7 ± 0.88; while the Ct values for PMA treated inactivated samples increased to 28.9 ± 1.56, 37.1 ± 0.93, 38.22 ± 0.94 and 39.15

± 0.60, respectively, corresponding to 72 °C heating 15 S, 1 min, 3 min, and autoclaving.

Likewise, the Ct values increased from 22.2 ± 1.01 to 31.9 ± 1.28, 37.9 ± 0.67, 39.2 ±

1.23 and 38.9 ± 1.08 for 72 °C heating and autoclaved samples by the Pse-ODC primers- and-probe set when PCR assays were coupled with PMA treatment. Statistic analysis showed that significant increases (P < 0.05) of the Ct values were observed after 72 °C

15s heating, the corresponding ΔCt values for 16S rRNA and Pse-ODC primers-and- probe sets were 8.2 and 9.7. Extending the heat time to 1 min lead to continuely increase of Ct values (P < 0.05); however, no further significant increase of Cts were observed when cells were subjected to 72 °C 3 min heating (P = 0.127 for 16S primers-and-probe set; and P = 0.135 for ODC primers-and-probe set).

The plate count results (logarithmic values) obtained by heat inactivation methods were plotted against the corresponding PMA coupled real-time PCR Ct values up to the first time no colonies were counted (72 °C 3 min heating). Fig. 6.3 illustrates the relationship between the log of survived Pseudomonas spp. cell after heat treatments and the corresponding Ct values for PMA-coupled real-time PCR. Culturability loss tendency

161 was correlated well with the corresponding increasing of Ct values. A linear relationship was observed between these two parameters. For 16S rRNA gene based amplification, the R2 value was 0.979; and for ODC primers-and probe-set, the R2 value was 0.992.

6.4.2 PMA-PCR assessments of Pseudomonas exposed to acid treatments

Pseudomonas spp. is sensitive to acid environment. After 109 CFU ml-1

Pseudomonas spp. samples exposed to acid treatment with pH 4.0, 3.5 and 3.0, the log values of survived cells decreased from 8.77 ± 0.20 to 8.05 ± 0.22, 4.98 ± 0.43 and 0.43

± 0.75 respectively. No colonies were observed on plate after exposure to pH 2.5 enviroment for 10 min (Fig. 6.4).

Fig. 6.5 showed the Taqman real-time PCR results. Increasing of amplification Ct values was observed after Pseudomonas spp. was incubated in PBS solution with decreased pH values. When 16S rRNA primers-and-probe was used, Ct values of PMA treated samples increased from 19.4 ± 0.43 (pH 7.2) to 21.2 ± 1.85, 28.8 ± 0.90, 34.2 ±

0.67 and 35.1 ± 0.53, corresponding to pH 4.0, 3.5, 3.0 and 2.5 inactivation. The same observation was made using Pse-ODC primers-and-probe set, the Ct values for PMA treated samples were 21.6 ± 0.85 (viable), 23.7 ± 1.00 (pH 4.0), 31.4 ± 1.32 (pH 3.5),

35.7 ± 0.46 (pH 3.0), and 36.7 ± 1.55 (pH 2.5), respectively (Fig. 6.5). For both primers- and-probe sets, Ct values was increased after exposing to pH 4.0 PBS environment, however, there was no significant changes (P = 0.211 for 16S primers-and-probe set; and

P = 0.053 for ODC primers-and-probe set). Exposing cell to PBS with lower pH values

162 resulted in continuing increase of amplification Ct values, and significiant difference between viable and treated samples could be observed (P < 0.05).

Fig. 6.6 illustrated the relationship between the log of survived Pseudomonas spp. cell after acid treatments and the corresponding Ct values for PMA-coupled real time

PCR. A good linear relationship also was observed between these two parameters. R2 value was 0.958 (ODC primers-and-probe set) and 0.978 (16S rRNA primers-and-probe set) respectively.

6.4.3 PMA-PCR assessments of Pseudomonas exposed to Pro-san® treatments

The activity compound of Pro-san® is citric acid and sodium dodecylbenzene sulfonate. Fresh Pseudomonas spp. was subjected to Pro-san® working solutions with increasing concentration. As show by Fig. 6.7, 1 × Pro-san® 10 min treatment causes viable cell density dropped sharply from 8.78 ± 0.08 to 2.17 ± 0.63 (log values), while complete loss of cultivability was observed after exposing viable cultures to 2 × Pro-san® work solution for same time period.

After Pseudomonas spp. were inactivated by 1 × Pro-san® work solution following PMA incubation, as illustrated in Fig. 6.8, Ct value were increased from 18.7 ±

0.89 to 26.5 ± 1.35 (16S primers-and-probe set); and from 21.1 ± 0.40 to 29.4 ± 1.14

(ODC primers-and-probe set), which is significant higher than PMA treated viable samples (P < 0.05). Increasing Pro-san® concentration led to further increase of PMA-

PCR Ct values. After treating with 2 × Pro-san® and 4 × Pro-san®, the Ct values were

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32.8 ± 0.1.15 and 33.6 ± 0.79 using 16S rRNA primers-and-probe set, while 33.7 ± 0.96 and 34.9 ± 1.40 using Pse-ODC primers-and-probe set.

Fig. 6.9 illustrated the relationship between the logarithmic values of survived

Pseudomonas spp. cell after disinfectant Pro-san® treatments and the corresponding Ct values for PMA-coupled real time PCR up to the first time no colonies were counted (2 ×

Pro-san® treatments). A good linear relationship also was observed between these two parameters using 16S rRNA (R2 = 0.963) and ODC (R2 = 0.990) primers-and-probe sets.

6.4.4 Control samples

As controls, real-time PCR was runned using DNA extracted from dead (control 1) and viable (control 2) Pseudomonas spp. cells (109 CFU ml-1) without PMA treatments in parallel with PMA coupled amplification.

The dead control samples for heat treatment were 72°C 3 min heated cell suspensions. As showed in Fig. 6.2, the Ct values obtained by direct amplifying DNA from viable cells (control 2) were 18.4 ± 0.75 (16S primers-and-probe set) and 21.9 ±

1.22 (ODC primers-and-probe set), while the corresponding Ct values of heat inactivated control 2 were 19.4 ± 1.76 and 23.0 ± 1.14 respectively. Even though Ct values increased a little after heat-killing, there was no significant difference between control 1 and control

2 (P = 0.42 using 16S primers-and probe-set; and P = 0.33 using ODC primers-and-probe set). On the other hand, no statistically significant changes obtained between Ct values

PMA treated live samples and both control.

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For acid treatments, Ct values of dead control 2 (pH 4.0) were 21.3 ± 1.44 (16S) and 23.3 ± 1.31 (ODC) while the corresponding Ct values of viable control 1 were 18.9 ±

1.12 (16S) and 21.8 ± 1.30 (ODC), respectively. Two trial t-tests proved that there were no significant difference (P > 0.05) between Ct values of control 1, control 2 and PMA coupled viable samples using both primers-and-probe sets (Fig. 6.5).

Similar results were observed when control samples were inactivated by 4 × Pro- san® (Fig. 6.8). Compared with PMA treated viable cells, there were significant changes between either the Ct values obtained from viable control samples or Pro-san® killed controls cells (P = 0.063 using 16S primers-and-probe set; and P = 0.173 using ODC primers-and-probe set).

6.5 Discussion and Conclusion

The ability to only detect viable cells by molecular techniques is critical for microbial safety and quality control. Beside metabolic and reproductive activity, another important criterion for distinguishing viable and irreversibly damaged cells is membrane integrity. The application of this criterion for viable cells detection could be achieved by direct observing. By staining intact bacterial cells and membrane-compromised ones with discriminating dyes, viable cells could be directly counted by flow cytometry or fluorescent microscopy (Boulos et al., 1999; Caron et al., 1998; Queric et al., 2004;

Swarts et al., 1998; Veal et al., 2000).

Another methods developed in recent years are treating with ethidium monoazide

(EMA) prior amplification to selectively detect viable microbial cells (Martorell et al.,

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2005; Nocker & Camper, 2006). Because EMA treatment also resulted in loss of the genomic DNA of viable cells, alternative chemical PMA was introduced, which is more selective in penetrating dead bacterial cells with compromised membrane integrity

(Cawthorn & Witthuhn, 2008; Nocker, et al., 2006; Rawsthorne & Phister, 2009). The main objective of this study was to determine the potential to use PMA in combination with Taqman real-time PCR for discrimination of viable Pseudomonas spp. In order to evaluate possible application of newly established system in food industry, Pseudomonas spp. was treated by different inactivation methods commonly used in food environments, including heat, acid and disinfectant Pro-san®.

Heat is one of the most common approaches to control the microbial growth. High temperatures destroy organic molecules such as proteins, carbohydrates, lipid and nucleic acids. And heat can disintegrate of cell walls and membrane structure (Russell & Harries,

1967). This study confirmed that heat has a direct effect on membrane permeability allowing PMA to enter the cell to inhibition PCR amplification. A good correlation between loss of culturability and increased of ΔCt after heat treatments. Complete loss of culturability was observed after 72 °C 3 min heating. After all cells dead, no significant increase of of Ct was observed when Pseudomonas spp. shujected to autoclaving, which indicated that PMA can effectively enter heat inactivated Pseudomonas spp. once cells lose culturability.

Exposing bacterial cells to organic acids cause reduction of intracellular pH through transport system (Gilbert, 1984; Johnson & Busta, 1984; Steiner, et al., 2002). In addition, low pH environment could inhibit the microbial growth by interrupting the

166 permeability of bacterial cell membrane (Barer & Harwood, 1999). The reduction of real- time PCR amplification signals after PMA treatments indicated that PMA could penetrate through the cell membrane after Pseudomonas spp. exposure to acetic acid solutions, which proved that cells permeability was changed after cells exposed to organic acids.

Pro-san® is a new type of sanitizer containing activity ingredient citric acid and sodium dodecylbenzene sulfonate. It could be used for surface sanitizer in food processing environments and products clean up. Significant increase of Ct values was observed after Pseudomonas llxm2 samples exposed to 1 × Pro-san® treatment, with the level of change correlated to the concentration of Pro-san® solution (Fig. 6.8). The results indictaed that bacterial cell membrane damages also were happened after Pro-san® treatments. And good linear relations were observed when the plate count data were plotted against the corresponding real-time PCR Ct values, which demonstrated the potential of using PMA coupled real-time PCR to monitor the sanitization processing.

Even though dramatic increase of Ct values were observed in all PMA coupled real-time PCR cases, Ct values changes varied after Pseudomonas spp. samples subjected to different lethal treatments. PMA coupled amplification signals almost disappeared when Pseudomonas spp. subjected to lethal heat treatments. For example, the

Ct values were 39.2 ± 1.23 by 16S primers-and-probe set and 38.9 ± 1.08 by ODC primers-and-probe set when Pseudomonas spp. subjected to 72 °C 3min heating (Fig.

6.2). On the other hand, corresponding Ct values of PMA-PCR were 35.1 ± 0.53 (16S),

36.7 ± 1.55 (ODC) for acid (pH 2.5) inactivation (Fig. 6.5). The similar weak amplifications were observed for 2 × Pro-san® treated samples (Fig. 6.8). The PMA-PCR

167

Ct values were 33.6 ± 0.79 (16S) and 36.7 ± 1.55 (ODC). The observations suggested that most severe membrane damage caused by heat treatments. Low acid and disinfectant

Pro-san® also causes cell membrane/wall damage, while relatively strong PMA-PCR amplification signals after cell dead indicated that membrane intact cells are still presented.

The control samples without PMA treatment were run in parallel. Firstly, the results showed that the Ct values of viable controls were similar to PMA treated viable cells. The Ct values obtained from these two groups didn‟t have significant differences

(P > 0.05) in all cases. This finding indicated that PMA could not penetrate into the membrane intact bacterial cells, and free PMA outside the cells don‟t interfere the PCR amplification. In fact, another DNA-intercalating dyes EMA was first used for inhibiting the false-positive amplification of DNA from dead bacterial cells instead of PMA

(Nocker & Camper, 2006). However, some research observed that instead of entering deal cells, EMA also readily could penetrates through membrane of viable cells resulting in partial DNA loss (Nocker et al., 2006). Comparing with EMA, PMA is more suitable for viable cells detection because PMA is high selective and only penetrated into dead cells (Pan & Breidt, 2007). This study demonstrated PMA doesn‟t interrupt the DNA isolation and following PCR amplification of viable cells, which agreed with previous reports. Dead cells without PMA treatment were settled as another control. No significant differences were observed between Ct values of viable cells and dead cells, whether the

Pseudomonas spp. was killed by heating, acid, or sanitizer Pro-san®. This study

168 confirmed that the DNA from dead cells would bring false positive signals when using conventional DNA amplification based detection platform.

In summary, PMA coupled Taqman real time PCR assays had been successfully developed to detect viable Pseudomonas spp. The amplification signals were dramatically inhibited when heat, acid and sanitizer-inactivated bacterial cells were exposed to PMA treatment. Good linear correlations were observed between culturability reductions obtained by plate counting and Ct values of real-time PCR. The established platform made DNA amplification-based molecular detection methods more suitable by selectively detect membrane intact viable cells instead of indiscriminately amplifying

DNA from all cells including dead cells.

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Primers and probe Sequence (5’ to 3’) Tm(°C) a

Pse -16S F GCGTAGATATAGGAAGGAAC 54.5

Pse-16S R ACTAAGAGCTCAAGGCTC b 53.5

Pse-16S probe c AACGATGTCAACTAGCCGTTG 61.8

Pse-ODC F CTSAAGCTGATCAACATGG 55.7

Pse-ODC R GAAGTCTTCCTTGAGGAAG 55.4

Pse-ODC probe c CAACAGCCTGGAAACCTACG 62.5

Table 6.1. Taqman real-time PCR primers-and-probe sets used in this study.

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log CFU/ml (culture) CFU/mllog 2

0 viable 72°C 15s 72°C 1min 72°C 3min autoclaved

Figure 6.1. Survival curves after exposure Pseudomonas llxm2 to 72 °C heat treatments for increased time periods and autoclaving. Error bars represent standard deviations from three independent replicates.

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40 35 30 25 20

Ct values Ct 15 10 5 0 16S rRNA gene ODC gene No-heat 72°C 15s 72°C 1min 72°C 3min autoclaved control 1 (dead No-PMA) control 2 (viable No-PMA)

Figure 6.2. PMA coupled real-time PCR detection of viable and heat inactivated Pseudomonas llxm2 using Pse-16S and Pse-ODC primers-and-probe sets. Error bars represent standard deviations from three independent replicates.

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35 R² = 0.9918

30 16S rRNA gene

Ct values Ct 25 R² = 0.9794 ODC gene 20

15 0 2 4 6 8 10 log cfu/ml 10

Figure 6.3. Correlation between the natural logarithm of the survived Pseudomonas llxm2 cells after heat treatments and the corresponding Ct values obtained using Pse-16S and Pse-ODC primers-and-probe sets. The linear regression coefficient factor (R2) is indicated.

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log CFU/ml (culture) CFU/mllog 2

0 pH 7.2 pH 4.0 pH 3.5 pH 3.0 pH 2.5 B

Figure 6.4. Survival curves after exposure Pseudomonas llxm2 to acid environments with decreasing pH values. Error bars represent standard deviations from three independent replicates.

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40 35 30 25 20

Ct values values Ct 15 10 5 0 16s rRNA gene ODC gene

pH 7.2 pH 4.0 PH 3.5 pH 3.0 pH 2.5 control 1 (dead No-PMA)

control 2 (viable No-PMA)

Figure 6.5. PMA coupled real-time PCR detection of viable and acid inactivated Pseudomonas llxm2 using Pse-16S and Pse-ODC primers-and-probe sets. Error bars represent standard deviations from three independent replicates.

175

35 R² = 0.9577

30 16S rRNA gene Ct values Ct 25 R² = 0.9756 ODC gene 20

15 0 2 4 6 8 10 log cfu/ml 10

Figure 6.6. Correlation between the natural logarithm of the plate count results exposure to acid inactivation and the corresponding Ct values obtained using Pse-16S and Pse- ODC primers-and-probe sets. The linear regression coefficient factor (R2) is indicated.

176

log CFU/ml (culture) CFU/ml log 2

0 viable 1 x pro-san® 2 x pro-san® 4 x pro-san® B

Figure 6.7. Survival curves after exposure Pseudomonas llxm2 to Pro-san® with increasing concentration. Error bars represent standard deviations from three independent replicates.

177

20 Ct values values Ct 15

0 16S rRNA gene ODC gene

No-pro-san® 1 x pro-san® 2 x pro-san® 4 x pro-san® control 1 (dead No-PMA) control 2 (viable No-PMA)

Figure 6.8. PMA coupled real-time PCR detection of viable and Pro-san® inactivated Pseudomonas llxm2 using Pse-16S and Pse-ODC primers-and-probe sets. Error bars represent standard deviations from three independent replicates.

178

30 R² = 0.9899 16S rRNA gene 25

Ct values Ct ODC gene

20 R² = 0.9625

15 0 2 4 6 8 10 log cfu/ml 10

Figure 6.9. Correlation between the natural logarithm of the plate count results exposure to Pro-san® inactivation and the corresponding Ct values obtained using Pse-16S and Pse-ODC primers-and-probe sets. The linear regression coefficient factor (R2) is indicated.

179

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181

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182

Chapter 7

Summary and Conclusion

Rapid detection and identification of foodborne microorganisms are essential for food safety and quality control. PCR and other nucleic acid based methods have been developed for rapid and specific detection of foodborne pathogens. However, in the case of DNA amplification-based detection methods, the results must be carefully interpreted because DNA can persist for a long period of time after cell death. In this study, RNA molecules were selected as liable viability indictors to detect viable foodborne pathogens and spoilage microorganisms.

rRNA is highly conserved, which is benefitial to the specific detection and identification of target microorganism; in addition, rRNA has thousands copies per cells, which improves detection sensitivity. Targeting 18S rRNA, a NASBA-molecular beacon system with high specificity and sensitivity was established to detect viable spoilage

Saccharomyces cerevisiae and Candida parapsilosis in juice products. Yeasts 18S rRNA was stable even after autoclave. However, the 18S rRNA copy numbers were significantly decreased after heat-killing indicated that the developed NASBA-molecular beacon system has a potential for rapid detection of viable yeasts if combined with quantitative analysis.

183

Instead of rRNA, mRNAs may be a better choice for viable cell detection because of its shorter half-life after cell death. One-step Taqman real-time RT-PCR platform was used for viable Listeria monocytogenes detection targeting16S rRNA and transcripts for internalin A, the ribosomal protein L4. After lethal treatments, the decrease of the targeted RNA copy numbers was correlated to the treatment intensity and bacterial cell concentration. The 16S rRNA was found the most heat-resistant transcript. The inlA and rplD mRNAs were more stable than expected. They were detected by real-time RT-PCR after 109 CFU ml-1 L. monocytogenes cells exposed to extreme lethal treatment such as autoclaving. However, once the cell concentration decreased to 106 CFU ml-1, amplification signals disappeared after extreme heating and autoclaving, which indicated the potential of using developed platform for viable L. monocytogenes detection, especiallyunder practical contamination level found in foods.

The half life of mRNAs is heterogeneous and unpredictable in dead cells. In order to choose suitable targets for viable spoilage Pseudomonas detecttion, the degradation of mRNAs after cell death was systematically evaluated by cDNA microarray analysis. The transcript of ornithine decarboxylase (ODC) was selected as a potential cell viability indictor for the Taqman real-time RT-PCR assay. Using the developed primer-and-probe set, less than 103 cells of Pseudomonas were detected within 5-6 h, without cross- reactivity with other common foodborne bacteria. ODC mRNA disappeared rapidly and almost became undectable after mild heat inactivation, which indicated that the developed platform has a great potential for rapid detection of viable spoilage

Pseudomonas.

184

Beside RNA stability, bacterial membrane integrity is used as another criterion characterizing viable bacterial cells. Treating by DNA-binding dye Propidium monoazide

(PMA), the DNA amplification of dead cells could be inhibited because PMA only penetrates into dead or membrane compromised cells. PMA coupled Taqman real-time

PCR assay was developed for live spoilage Pseudomonas detection. The amplification signals were dramatically inhibited when heat-, acid- and disinfectant Pro-san®- killed

Pseudomonas cells were exposed to PMA treatment. Good linear correlations were observed between real-time PCR Ct values and culturability reductions obtained by plate counting.

The study systematically evaluated the efficacy of viable cell detection methods.

The results will have great impact on proper method selection, data interpretation for microbial detection and transcriptome related studies. The several detection platforms developed have industrial application potential.

185

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