The Role of Quorum Sensing in Survival, Biofilm Formation and Gene Expression of Listeria Monocytogenes

THE ROLE OF QUORUM SENSING IN SURVIVAL, BIOFILM FORMATION AND GENE EXPRESSION OF LISTERIA MONOCYTOGENES

Saleh Ali

A Thesis

presented to

The University of Guelph

In partial fulfilment of requirements

for the degree of

Doctor of Philosophy

Food Science

Guelph, Ontario, Canada

Abstract

THE ROLE OF QUORUM SENSING IN SURVIVAL, BIOFILM FORMATION AND GENE EXPRESSION OF LISTERIA MONOCYTOGENES

Saleh Ahmed Ali Advisor: Dr. Mansel W. Griffiths

University of Guelph, 2011

In recent years, listeriosis, the disease caused by Listeria monocytogenes has gained importance because of the increasing number of listeriosis outbreaks. Frequent reports of morbidity and mortality cases linked to infection by L. monocytogenes have concerned the food industry, and thus, the control of L. monocytogenes is a great challenge faced by the food industry. The discovery of the quorum-sensing system in bacteria and its role in cell behavior has made it a target for microbiologists to understand bacterial behavior and adaptation. The aim of the present study was to investigate the role of quorum sensing on the physiology, survival, and virulence in L. monocytogenes. In this study, the presence of quorum-sensing systems luxS and agrD in L. monocytogenes was investigated. Both systems were found to exist in L. monocytogenes and other Listeria species, suggesting a possible role of quorum sensing in this bacterium. In addition, the production of auto-inducers and the effect of environmental stress on auto-inducers production were examined. Different environment parameters appeared to have an impact on the production of quorum-sensing signaling molecules. To obtain a better understanding of the possible role of quorum sensing in L. monocytogenes, a whole-genome microarray assay was performed using the L. monocytogenes wild-type strain against 2 mutant

strains—agrD mutant and luxS mutant strains. Down- and upregulation of genes representing different functions in the cell were observed in both quorum-sensing systems at both refrigeration and optimum temperatures. Some of the important virulence genes were differentially expressed in the agrD mutant, suggesting a possible role of agrD quorum sensing in virulence of this pathogenic bacteria. The role of quorum sensing in survival and biofilm formation was subsequently investigated. Quorum sensing was found to have an impact on survival under different stress conditions, suggesting possible indirect support rather than direct control of survival and biofilm formation. Biofilm formation examined by confocal laser scanning microscopy revealed a significant difference in the formation of biofilms between wild- type, agrD, and luxS mutant strains. Thus, new insights into the role of cellular signaling mechanisms may throw light on novel mechanisms, which could be applied to control this problematic bacterium.

Acknowledgment

First and foremost I would like to express my sincerest gratitude and submission to Allah, who guided me, and showered me with his mercy to complete this work. His guidance and blessings were present at every step of my work.

To my parents for every success and achievement I have obtained. Their love and sacrifice is engraved in my heart, and I wish I can make them happy all my life. Thanks go also to my beloved wife, who supported me and provided me with all kind of help during my studies. The support and love of my brothers and sisters were very helpful and appreciated. Special thanks to my younger brother Abdulaziz, no words can express my gratitude to him. I can’t forget of course my beloved babies Amal and Othman who joined me during my studies in 2010 and 2011.

To my Advisor Dr. Mansel Griffiths: I’m so grateful to your supervision, guidance, kindness and patience all over my study. I feel lucky that I got the chance to work in your laboratory and under your supervision. Your encouragement, trust and support that you provided me and my colleagues were so helpful for all of us finding our ways in our research. I wish you continued success and achievements.

I’m also deeply thankful for my advisory committee members, Dr. Keith Warriner and Dr. Roger Johnson for their invaluable advice, support and patience. Without their efforts I wouldn’t reach this point of my research.

I would like to thank Dion Lepp from Agriculture Canada who helped me a lot in my research, especially in Microarray. His experience, kindness and support were so valuable.

I’m grateful to my friends and lab mates from Dr. Griffiths lab as they provided me with an excellent atmosphere and made my time as a graduate student very enjoyable, Hany Anay, Tarek El-Arabi, Mohammad Bayomi, Veronique Delcenserie, Markus walking-Rebeiro, Tumnoon Charaslertrangsi, and all other friends in CRIFS. I will carry their beautiful memories in my mind for the rest of my life.

There are also many friends I met in Guelph, past and present who have made my life in Guelph so happy and joyful. Tarek El-Salti, Bassim Owda, Dr. Nidal Nasser, Ahmed Elhossini, Nabil Al-rashid, Mohammed Melebari, Sufyan Chappra and all people in MSA. Thank you all for your love and friendship.

I would like to thank Kuwait University for providing me with this golden opportunity to continue my graduate study by sponsoring me and supporting me financially and academically.

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Table of contents

ACKNOWLEDGEMENTS……………………………………………………………………I LIST OF TABELS……………………………………………………………………………..VI LIST OF FIGURES………………………………………………………………………...... VII CHAPTER 1: INTRODUCTION……………………………………………………………….1 1.1 RESEARCH INTRODUCTION...... 1 1.2 FOODBORNE PATHOGENIC BACTERIA...... 2 1.3 LISTERIA...... 2 1.3.1 Listeria ssp...... 2 1.3.2 Listeria monocytogenes...... 4 1.3.2.1 Genetic diversity of L. monocytogenes...... 5 1.3.2.2 Source of infection...... 6 1.3.2.3 Mechanisms of Survival under harsh conditions...... 8 1.3.2.4 Listeriosis...... 9 1.3.2.5 Incidence of listeriosis...... 10 1.3.2.6 Pathogenisis...... 11 1.3.2.7 Biofilm formation and resistance...... 13 1.3.2.8 Current methods for controlling L. monocytogenes in food...... 15 1.4 Quorum sensing...... 17 1.4.1 Quorum sensing in Gram negative bacteria...... 19 1.4.2 Quorum sensing in Gram positive bacteria...... 21 1.4.3 AI production...... 22 1.4.4 Quorum sensing in food...... 25 1.4.5 L. monocytogenes quorum sensing...... 26 1.4.6 Quorum quenching or anti-quorum sensing...... 28 1.5 Research objectives...... 29 1.6 Overview of the proceeding chapters...... 30 1.7 Techniques...... 31 1.7.1 Lux gene reporter system...... 31 1.7.2 Microarrays and qRT-PCR...... 32

1.7.3 SHIME system...... 34 1.7.4 Confocal Laser Scanning Microscopy...... 35

CHAPTER 2: Identification of Listeria monocytogenes Quorum Sensing and Auto-Inducer production

2.1 ABSTRACT...... 36

2.2 INTRODUCTION...... 36

2.3 MATERIALS AND METHODS...... 38

2.3.1 Bacterial strains and culture media...... 38

2.3.2 LuxS and agrD genes detection and AI-2 Bioassay...... 39

2.3.2.1 DNA extraction and gene detection...... 39

2.3.2.2 AI-2 Bioassay...... 41

2.3.3 Environmental parameters affecting AI production...... 42

2.3.4 Food system and AI-2 activity……………………………….………...…………………43

2.3.5 Statistical Analysis...... 43

2.4 RESULTS...... 43

2.4.1 LuxS gene detection and AI-2 production...... 43

2.4.2 AgrD gene detection...... 44

2.4.3 AI-2 production in L. monocytogenes is growth phase dependent...... 44

2.4.4 Environmental Parameters and Food Systems Affecting AI Activity...... 45

2.4.4.1 Environmental parameters affecting AI activity…………………..……………………45

2.4.4.2 Food system affecting AI activity………………………………………………………46

2.5 DISCUSSION………………………………………………………………………………46

2.6 CONCLUSION...... 48

CHAPTER 3: THE ROLE OF QUORUM SENSING OF L. MONOCYTOGENES EGD-e IN GENERAL GENE EXPRESSION

3.1 ABSTRACT…………………………………………………………………...……..….….56

3.2 INTRODUCTION…………………………………………………………..….……..……56

3.3 METHODOLOGY...... 58

3.3.1 Microarray...... 58

3.3.1.1 Preparation conditions...... 58

3.3.1.1.1 Construction of L. monocytogenes mutant strains...... 58

3.3.1.1.2 Culture preparation conditions...... 61

3.3.1.2 Total RNA extraction...... 61

3.3.1.3 Microarray contruction...... 63

3.3.1.3.1 cDNA labeling and competitive microarray hybridization…………..….……….…63

3.3.2 qRT-PCR……………………………………………………………………….…..…….65

3.4 RESULTS…………………………………………………………………….….……..…..67

3.4.1Construction of Listeria monocytogenes mutant strain...... 67

3.4.2 Role of QS systems in L. monocytogenes-based microarrays ……………..……………67 3.4.3 Genes showing higher transcript in agrD mutant at both 4 °C and 37 °C…..….....…….68 3.4.3.1 Downregulated genes in L. monocytogenes mutants…………………….…...……….68 3.4.3.2 Upregulated genes in L. monocytogenes mutants………………………....…..………69 3.4.4 qRT-PCR confirmation of differentially expressed selected L. monocytogenes genes...... …70 3.5 DISCUSSION……………………………………………………………………..….……70 3.6 CONCLUSION…………………………………………………………………..………...72

CHAPTER 4: THE ROLE OF QUORUM SENSING IN SURVIVAL, GROWTH, AND BIOFILM FORMATION IN LISTERIA MONOCYTOGENES EGDe

4.1 ABSTRACT…………………………………………………………………………………85 4.2 INTRODUCTION…………………………………………………………………………..86 4.3 METHODOLOGY…………………………………………………………………………..88 4.3.1 General and selective media used………………………...………………………………..88

4.3.2 Bacteria grown under different environmental conditions……...…………………………89

4.3.3 SHIME system…………………………………………………..…………………………89

4.3.4 Antibiotic resistance………………………………………………………………………..91

4.3.5 Susceptibility to bacteriophages………………………………………..………………….91

4.3.6 Biofilm formation………………………………………………………………………….91 4.3.6.1 Microtiter plate assay…………………………………………………….…………….. 91

4.3.6.2 Biofilm formation assay using confocal laser scanning microscopy……..……………. 92

4.3.6.3 Attachment assay…………………………………………………………..…………….93

4.3.7 Statistical analysis……………………………………………………………...…………..93

4.4 RESULTS……………………………………………………………………………………93

4.4.1 Growth on different general and selective media………………………………………….94

4.4.2 Bacteria grown under different environmental conditions………………………………..94

4.4.3 SHIME system……………………………………………………………………………..95

4.4.4 Antibiotic resistance………………………………………………………………….…….95

4.4.5 Bacteriophage infectivity………………………………………………………….……….96

4.4.6 Biofilm formation………………………………………………………………………….96

4.4.6.1 Microtitre plate assay…………………………………………………………………….96

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4.4.6.2 Biofilm formation (confocal microscopy)………………………...………………….....97

4.4.6.3 Attachment assay……………………………………………………..……...…………97

4.5 DISCUSSION………………………………………………………….…………………….98

4.6 CONCLUSION………………………………………………………..……………………107

CHAPTER 5: CONCLUSIONS AND FUTURE DIRECTIONS

5.1 Thesis summary and general conclusions…………………………………………..………122

5.2 Future research………………………………………………………………………..…….125

REFERENCES………………………………………………………………………………..126

APPENDIX…………………………………………………………………………………….150

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LIST OF TABLES

Table 1.1 Listeria monocytogenes foodborne outbreaks...... 7

Table 1.2 lists of bacteria food spoilage influenced by QS in a certain foods...... 27

Table 2.1 list of L. monocytogenes strains and lineages used in this study.………..….……….49

Table 2.2 list of bacteria used in this study…………………………………………………….50

Table 2.3 List of primers used in this study…………………………………………………….51

Table 2.4 Environmental and food parameter and sampling time for AI-2 production………..52

Table 2.5 environmental conditions and AI-2 production in L. monocytogenes...... 52.

Table 3.1 Primers list used for qRT-PCR……………………………………………………….74

Table 3.2 10 important gene expressed in L. monocytogenes AgrD mutant…………………….75

Table 3.3 qRT-PCR analysis of Microarray identified genes…………………………………..76

Table 3.4 Gene role classifications for differentially regulated genes in agrD and luxS mutant strains at 37°C and 4°C...... 76

Table 3.5 genes commonly regulated by regulatory genes and QS systems……………………77

Table 3.6 genes commonly regulated in agrD system and L. monocytogenes biofilm both at 4°C……………………………………………………………………………………………….77

LIST OF FIGURES

Figure 1.1 structures of some representative quorum sensing signaling molecules...... 21

Figure 1.2 Synthesis of autoinducer (AI)-2 through the Pfs and LuxS pathways ...... 23

Figure 1.3 The Agr system of Listeria monocytogenes...... 25

Figure 1.4 Simulator of the Human Intestinal Microbial Ecosystem (SHIME)...... 35

Figure 2.1 Lmo1288 gene in Listeria monocytogenes strains……………………………....…...53

Figure 2.2 Autoinducer-2 (AI-2) bioassay…………………………………………………....…54

Figure 2.3 Autoinducer-2 (AI-2) bioassay for Listeria monocytogenes under different environmental and food conditions…………………………………………………………...…55

Figure 3.1 Comparison of genes down-regulated in AgrD and LuxS mutants in Stationary phase grown at 37C…………………………………………………………………………………...... 78

Figure 3.2 Comparison of genes down-regulated in AgrD mutants in Stationary phase grown at 37°C and 4°C……………………………………………………………………………....…….79

Figure 3.3 microarray slides screening for AgrD mutant of staionary phase at 37°C……..……80

Figure 3.4 microarray slides screening for luxS mutant of staionary phase at 37°C…….…..….81

Figure 3.5 Heat map for agrD mutant, stationary phase at 37°C from four arrays test…….…..82

Figure 3.6 Heat map of luxS mutant stationary phase at 37°C representing four arrays…….…83

Firgure 3.7 Clustering of technical replicate dye-flip genes………………………………..…...84

Figure 4.1 Simulator of Human Intestinal Microbial Ecosystem system…………………....…108

Figure 4.2 Growth at 4 °C and 4 °C+ pH 5.5 for Listeria monocytogenes wild-type, AgrD-, and luxS……..………………………………………………………………………………………109

Figure 4.3 Survival curve at 4 °C + pH 4.5 Listeria monocytogenes wild-type, AgrD-, and luxS- ………………………………………………………………………………………………….110

Figure 4.4 Survival curve at 4 °C + 5% NaCl Listeria monocytogenes wild-type, AgrD-, and luxS-……………………………………………………………………………………………111

Figure 4.5 Survival curve at 4 °C + 10% NaCl Listeria monocytogenes wild-type, AgrD-, and luxS-……………………………………………………………………………………………112

Figure 4.6 Listeria monocytogenes wild-type, AgrD-, and luxS- survival in the Simulator of Human Intestinal Microbial Ecosystem system………………………………………….……113

Figure 4.7 Antibiotic resistance of Listeria monocytogenes wild-type, AgrD-, and luxS-.……114

Figure 4.8 Antibiotic (erythromycin) strip with graded minimum inhibitory concentrations (representing the antibiotic test performed)……………………………………………………115

Figures 4.9 a, b, and c. Bacteriophage infectivity for Listeria monocytogenes wild-type (a), AgrD- (b), and luxS- (c)……………………………………………………………………...…116

Figure 4.10 Microtiter plate assay for detecting biofilm formation in Listeria monocytogenes wild-type, AgrD-, and luxS-……………………………………………………………….……117

Figure 4.11 Biofilm formation of Listeria monocytogenes wild-type, AgrD mutant, and luxS mutant examined under confocal laser scanning microscopy for 5 days………………….……118

Figure 4.12 Biofilm formation of Listeria monocytogenes wild-type, AgrD-, and luxS- incubated for 48 h and examined under confocal laser scanning microscopy………………………….…119

Figure 4.13 Attachment assay for Listeria monocytogenes wild-type, AgrD-, and luxS- …..…121

Introduction

1.1 Research Introduction

Recently, much concern has surrounded the increased number of foodborne illness outbreaks worldwide. One of the organisms having, arguably one of the greatest impact in terms of mortality is Listeria monocytogenes. The application of Hazard Analysis of Critical Control

Points (HACCP) and good manufacturing practice (GMP) have failed to prevent outbreaks of listeriosis. The ability of pathogens to survive and grow under processing and storage conditions, as well as the development of resistance to antibiotics and disinfectants and adaptation to stresses has made it even more difficult to control their levels in food. The need to understand bacterial behavior and virulence is important for enhancing food control, as understanding bacterial physiology is key to controlling these foodborne pathogens. In the last decade, much attention has been paid to the bacterial cell-cell communication process called quorum sensing (QS). This process was first discovered in the marine bacteria Vibrio fischeri and V. harveyi and later in many Gram-positive and Gram-negative bacteria. Studies have shown that chemical signaling molecules (autoinducers; AIs) and small peptides produced by bacteria play roles in survival, virulence gene expression, and microorganism behavior.

However, much work remains to gain a full understanding of the function of these signaling compounds in bacteria. Foodborne pathogens, such as pathogenic Escherichia coli,

Salmonella enterica, Campylobacter, and Listeria, are major food safety issues. Among the 6 species of the genus Listeria, only two are pathogenic: L. monocytogenes, which causes listeriosis, and L. ivanovii. L. monocytogenes is a Gram-positive pathogen that causes serious

illnesses in humans and animals. It is a ubiquitous organism that can be found in many foods, and can survive and grow under different environmental stresses and harsh conditions. L. monocytogenes has been found to produce AI-2 and small peptides as signaling molecules by two QS systems, luxS and accessory gene regulator (agr). Thus, the study of the role of QS in L. monocytogenes may allow us to better understand the pathogen’s behavior and provide the key to controlling L. monocytogenes levels in food and food-processing environments.

1.2 Foodborne Pathogenic Bacteria

Several types of microorganisms play major roles in the food industry—some positive, such as in the production of fermented food, and others negative, such as those causing food spoilage and human diseases (CDCP, 2008). Bacteria have been the major causative agents in many recent outbreaks such as the Maple Leaf listeriosis outbreak that hit Canada in 2008 and the multi-state listeriosis outbreak in US 2011 associated with cantaloupe melons. Foodborne pathogens are those microorganisms that are responsible for causing disease by contaminating food (Aase, 2000). E. coli O157:H7, Salmonella, Shigella and L. monocytogenes are among the foodborne bacterial pathogens that have been associated with recent outbreaks of foodborne illnesses (CDCP, 2008).

1.3 Listeria

1.3.1 Listeria ssp.

The genus Listeria belongs to the phylum Firmicutes and is closely related to Bacillus subtilis due to high similarity in 23s RNA and 1428 orthologous genes (Dovilas, 2010). It was first described by Murray through identification of listeriosis caused by L. monocytogenes, which

was named as Bacterium monocytogenes in 1924, but it later become known as L. monocytogenes (Hof, 2003). Generally, six closely related species belong to the genus Listeria, which includes gram-positive, non-spore-forming, facultative anaerobes, rod-shaped bacteria

(Gouin et al., 2004). The non-pathogenic species include L. seeligeri, L. innocua, L. welshimeri, and L. grayi (includes Listeria murrayi) and two newly discovered species L. marthii and L. rocourtiae (Graves et al., 2010; Leclercq et al., 2010), while the pathogenic species of Listeria are represented by the well-known pathogens L. monocytogenes, an animal and human pathogen, and L. ivanovii, an animal pathogen (Dancz et al, 2002; Liu, 2006) making a total of eight known species of Listeria. The presence of any Listeria species in food may be an indicator of poor hygiene and should be prevented. Coexistence of several species in the same food is common among Listeria species and the presence of one species might indicate the presence of others

(Laciar et al., 2006).

Based on DNA-DNA homology and 16S rRNA, Listeria spp. comprises 2 lines of descent: one containing L. monocytogenes, L. innocua, L. welshimeri, L. seeligeri, and L. ivanovii; and the second containing L. grayi (Hartford and Sneath, 2003). Gene sequencing has been completed for many L. monocytogenes strains (e.g., EGD-e, F2365, and 10403S) as well as for the L. innocua strain CLIP 11262 and the L. welshimeri strain SLCC 5334 (Genolist, 2011;

Broad Institute, 2011).

Among these Listeria species, sequence analysis indicates a high degree of similarity between L. monocytogenes and L. innocua, with 9.4% of the 2,853 protein genes specific to the

L. monocytogenes strain and 5% of the 2,973 protein genes specific to the L. innocua strain

(Glaser at al., 2001). Hence the major attention and concern is given to the human pathogen species L. monocytogenes.

1.3.2 Listeria monocytogenes

L. monocytogenes is a ubiquitous microorganism and widely distributed in nature. It is a

Gram positive, non-spore forming, facultative anaerobe that can survive low temperatures, high osmolarity, and low pH (Mosqueda-Melgar et al., 2008; Djordjevic et al., 2002). It was first discovered as an animal pathogen, but soon after was identified as also being a human pathogen in 1929. However, it was not recognized as a foodborne pathogen until 1981, when a L. monocytogenes outbreak was recorded in Nova Scotia, Canada that was traced back to contaminated coleslaw.

The ability of this bacterium to reproduce at <10 °C (range, 0 – 45 °C) makes it a problematic pathogen at refrigeration temperatures. Its ability to grow and survive at low pH (as low as 4.4), grow in the presence of 10% NaCl, and survive in high salt concentrations enhances its spread in various types of foods and beverages (Guenther et al., 2009), such as dairy products, meats, and vegetables (Skinner, 2006). It is most commonly found in ready-to-eat (RTE) foods including meat, fresh produce, and dairy products (Chao et al., 2006). Food is considered the most important source of infection by this microorganism (Norrong et al., 2009).

L. monocytogenes causes a serious disease, listeriosis, which is associated with high morbidity and mortality, especially among the elderly, pregnant women, infants and immuno- compromised people (Ramaswamy et al., 2007). Section (1.2.3) discusses listeriosis in details.

The ability of L. monocytogenes to cause disease is mainly due to the presence of a pathogenicity island that contains a set of virulence genes responsible for attachment, invasion and translocation inside the human body (Vázquez-Boland et al., 2001)

The ability of L. monocytogenes to form biofilms and attach to different surfaces like those of food-processing equipment, water pipes, and industrial equipment increases infection rates, using food as a transfer vehicle. Bacteria that exist as biofilms are usually more resistant to antibiotics and disinfectants than planktonic cells. They also have a lower growth rate, manifest an elevated level of gene transfer through conjugation, and exhibit much wider gene expression alteration compared to planktonic cells (Robbins, 2005; Dolan, 2002).

1.3.2.1 Genetic Diversity of L. monocytogenes

L. monocytogenes is present as different serotypes and lineages. Based on somatic (O) and flagellar (H) antigens, L. monocytogenes has been categorized into at least fourteen serotypes (i.e. 1/2a, 1/2b, 1/2c, 3a, 3b, 3c, 4a, 4ab, 4b, 4c, 4d, 4e, 7 and nt) (Liu et al., 2006;

Gianfranceschi et al., 2009). However, 3 serotypes—1/2a, 1/2b, and 4b cause the majority of clinical cases (Borucki and Call, 2003). Serotype 4b is mostly associated with illnesses while serotype 1/2a is mostly isolated from food (Gorski et al., 2006). L. monocytogenes serotypes vary in many characteristics including virulence. Jaradat and Bhunia (2003) tested different infectious steps (adhesion, invasion and translocation) of 13 serotypes of L. monocytogenes and suggested that L. monocytogenes serotypes can be classified based on adhesion or invasion into groups of high, medium and low adhesion/invasion, suggesting serotype differences in pathogenicity of L. monocytogenes.

Based on molecular typing methods like PCR and ribotyping, L. monocytogenes strains were grouped into lineages. According to Orsi et al., (2011) at least four lineages have been identified in L. monocytogenes. These lineages vary in virulence potential. For instance, lineage I strains are frequently associated with human listeriosis and are represented by 1/2b and 4b

serotypes; while lineage II is found in environmental and food samples and is represented by

1/2a and 1/2c serotypes (Milillo and Wiedmann, 2009). Lineage III and IV strains are rare and mainly associated with animals and represented by 4a and 4c serotyes (Bakker et al., 2008; Liu et al., 2006; Orsi et al., 2011). Lineage III was further separated into three groups (IIIA, IIIB and

IIIC) (Liu, 2008). Both lineage I and II cause human listeriosis, but representatives of lineage II were found to be less virulent than lineage I strains (Milillo and Wiedmann, 2009). This molecular classification is very important for epidemiology and clinical identification of listeriosis.

1.3.2.2 Sources of Infection

As a foodborne pathogen, L. monocytogenes is transmitted to humans and causes foodborne infections. RTE meat products, unpasteurized dairy products like milk and soft cheeses, and raw vegetables are among the main sources of L. monocytogenes outbreaks

(Gibbons et al., 2005). Post-processing contamination of produce, meats, and dairy products is the major cause of listeriosis outbreaks (Besse et al., 2010). The ability of L. monocytogenes to survive and grow at refrigeration temperatures and at low pH and high salinity makes it a problematic microorganism in the food industry (Donnelly, 2001). Even though the disease is not common, many outbreaks have been sourced back to L. monocytogenes; such outbreaks are usually accompanied by large recalls. Table 1.1 lists some of the major listeriosis outbreaks in

North America and Europe. In most cases, RTE meat was the source of contamination (William

et al., 2011). In 2008, a large outbreak of listeriosis spread across Canada, with 57 confirmed cases and 23 deaths according to the Canadian Food Inspection Agency, 2011.

Table 1.1 Selected Listeria monocytogenes foodborne outbreaks

Implicated Food Country Year(s) Cases, N Deaths, N Reference

Coleslaw Canada 1981 41 17 Health Canada, 2011

Pate UK, Ireland 1987–1989 355 94 FDA, 2011

Pork France 1992 279 85 Health Canada, 2011

Meat frankfurters US 1998–1999 108 14 Health Canada, 2011

Deli turkey US 2002 46 10 MMWR, 2002

RTE meat Canada 2008 57 22 PHAC, 2009

Cheese Canada 2008 38 2 Government of Canada

Cheese US 2010 14 2 MMWR, 2011

Cantaloupe US 2011 139 29 CDC, 2011

RTE, ready-to-eat

This outbreak led to a nationwide recall and a subsequent investigative review by an independent investigator, Sheila Weatherill, in 2009 that addressed the cause and problems associated with the outbreak and provided recommendations for government, industry, and consumers to follow (Government of Canada, 2011). Although L. monocytogenes is commonly found in raw foods and is distributed throughout the environment, processing plants are the main sources of contamination. They might be present for years in food plants, with persistence being defined as the ability to isolate a specific molecular subtype or strain in the same factory during

an extended period of time, typically months or years. Conditions such as damp or wet environments in food processing plants make it ideal for L. monocytogenes to grow and form biofilms that resist sanitizers and disinfectants (Kastbjerg and Gram, 2009; William et al., 2001).

1.3.2.3 Mechanisms of survival under harsh conditions:

L. monocytogenes is highly tolerant to different environmental conditions and stresses.

The stress factor Sigma B (σB) plays an important role in survival under harsh conditions

(Kazmierczak et al., 2003) Genetic expression and protein induction are other factors that help bacteria survive these conditions, and the 2-component regulatory systems are very important for

L. monocytogenes to survive osmotic stresses and low pH (Cotter et al., 1999).

In order to survive, L. monocytogenes applies different mechanisms to cope with each environmental stress, and these involve the induction of certain proteins to help protect the cell from damage. Cold shock proteins, ATP synthase and salt shock proteins are induced to protect cells exposed to low temperature, low pH and osmotic stress, respectively (Ghandi and

Chikindis, 2007). Accumulation of compatible solutes is another mechanism by which L. monocytogenes can survive. Two component regulatory systems are used by L. monocytogenes to sense environmental changes and alter gene expression accordingly (Ghandi and Chikinadis,

2007).

Many regulatory genes have been identified in L. monocytogenes that contribute to survival. The global response system (SOS) was found to play an important role in bacterial survival. SOS functions to repair DNA damage during stress (Van Der Veen et al., 2010).

Starvation survival response (SSR) is another response L. monocytogenes triggered during starvation (Herbert and Foster, 2001).

1.3.2.4 Listeriosis

L. monocytogenes causes the foodborne disease listeriosis, a severe illness associated with serious symptoms in children, elderly people, and pregnant women. Symptoms include fever, diarrhea, meningitis, septicemia, and brain abscesses (Chemaly et al., 2008; Bonaventura et al., 2008; Hanning et al., 2008). It is characterized by flu-like symptoms in healthy adults, and the risk of pregnant women contracting the disease is 17× higher than healthy non-pregnant women (Southwick and Purich, 1996). Even though listeriosis is not a common disease, being responsible for <1% of total foodborne diseases, it is accompanied by a high mortality rate of approximately 28% (Hanning et al, 2008). It is estimated that listeriosis comprises 20–40% of total foodborne pathogen mortality and 90% of foodborne-related hospitalizations (Meloni et al,

2009). The Centers for Disease Control and Prevention (CDC) estimated that in the United

States, there are approximately 2,500 cases of listeriosis yearly, leading to about 500 deaths per year (Uesugu and Moraru, 2009). A recent report by Scallan et al. (2011) indicated that L. monocytogenes is the cause of death for 19% of hospitalized cases. Listeriosis was the fourth most common cause of death by bacterial foodborne disease in England and Wales between 2000 and 2002 (Clark et al., 2009). In Europe, increased incidences of listeriosis were recorded between 2003 and 2006 and have continued to increase since (Allerberger and Wagner, 2010). In

Canada, the incidence rates were estimated to be 3.4 cases per million persons in 2008 and 2.7 cases per million persons in 2009 (Food Safety Network, 2003). In 2006, listeriosis was named

as a national notifiable disease by the Public Health Agency of Canada (PHAC) (Bortolussi,

2008). In 2008, a large L. monocytogenes outbreak occurred as a result of contamination of RTE meat at the Maple Leaf Foods, Bartor Road facility in Canada, and cases of listeriosis were recorded across the country, resulting in 57 cases and 23 deaths (PHAC, 2009). An investigative report by Sheila Weatherill and her team revealed important facts and recommendations regarding the outbreak; studied its etiology; and created a report listing 57 recommendations for food agencies, manufacturers, and consumers to follow, and help ensure safe food supplies (LIR,

2008). The latest outbreak was recorded in the US and was associated with contaminated cantaloupe. As of November 1, 2011 this outbreak was responsible for 139 cases in 28 states with 29 deaths (CDC, 2011). Such outbreaks show the importance of food safety and encourage food safety specialists to identify effective ways to control foodborne pathogens. Listeriosis outbreaks have been associated with many foods, ranging from fresh produce to dairy and meats

(Schuppler and Loessner, 2010; PHAC, 2009). Studies have shown that initial outbreaks of listeriosis are associated with a cluster of highly related strains designated Epidemic Clone I

(ECI), while a divergent clone (ECII) was found in other outbreaks (Ramaswamy et al., 2007).

While listeriosis normally causes flu like symptoms in healthy individuals, recent studies reported an invasive listeriosis associated with acute febrile gastroenteritis in healthy persons

(Ooi and Lorber, 2005). This is particularly important as it means that more virulent L. monocytogenes is emerging.

1.3.2.5 Incidence of listeriosis

Although listeriosis is not a common disease, as it represents <1% of the total cases of foodborne illnesses, it is the leading cause of death among all bacterial foodborne illnesses

(MMWR, 2011). The CDC, in 2003, reported an annual incidence of approximately 2,500 cases

of listeriosis and 500 subsequent deaths in the US, and these numbers have increased over the past 2 decades. The incidence in the United Kingdom increased from 2.1 cases/million in 1990–

2000 to 3.6 cases/million population in 2001–2009 (Mook et al, 2011). The incidence of listeriosis in the US is close to that in the UK (3 cases/million population) (Klontz et al, 2008).

The estimated annual incidence of listeriosis in Canada is approximately 4 cases/million population (Conly and Johnston, 2008).

1.3.2.6 Pathogenesis

As an intracellular parasite, L. monocytogenes represents a well-described cellular infectious process comprising 4 invasion-related steps: (1) infiltrating the host cell, (2) escaping phagocytosis, (3) growing and rapidly dividing inside the host cell, and (4) spreading from cell to cell (Gouin et al., 2004). Several genes are known to be responsible for infection: inlA/B, hly, act

A, mpl and plcA/B. The inlA (internalin A) gene is responsible for cell invasion, the first step of the process; hly (hemolysin, listeriolysin O (LLO); encoded by the hly gene) is a member of a family of sulfhydryl-activated pore-forming cytolysins that are responsible for the lysis of bacterium-containing vacuoles, as LLO-negative mutants are usually found within host vacuoles and are consequently unable to grow intracellularly (Portnoy et al., 2002). The ActA protein is known to be responsible for the actin polymerization that helps these bacteria to move and spread (Kocks et al., 2002). The mpl gene encodes zinc metaloprotease (Poyart et al., 1993).

PlcB (phospholipase B) helps L. monocytogenes escape from vacuoles by disrupting the vacuole membrane (Schmid et al., 2005). Besides the pathogenicity island mentioned above, some

virulence genes are found scattered within the L. monocytogenes genome. Multiple internalin genes have been identified, some of which are necessary for invasion of mammalian host cells

(Schmid et al., 2005). The invasion-associated protein P60 that is encoded by the iap gene is another important virulence gene required for cell invasion (Mello et al., 2008). Unlike other

Listeria species, L. monocytogenes possesses a cluster of virulence genes divided into 2 chromosomal regions: the positive regulatory factor (PrfA), which regulates a virulence gene cluster that contains most of the genetic determinants that are needed for bacterial infection in L. monocytogenes and includes PrfA, hly, mpl, plcA, plcB, and actA; and another region in which inlA and inlB are located (Ermolaeva et al., 2001; Kreft and Vazquez-Boland, 2001).

Cai and Wiedmann (2001) listed 5 features that are unique to the Prfa gene cluster: (1) compact organization into distinct genetic units; (2) association with tRNA; (3) instability; (4) presence of mobility genes, e.g., integrases and transposases; and (5) different G + C contents compared with the rest of Listeria DNA.

PrfA regulates many of the bacterial factors required for L. monocytogenes pathogenesis and PrfA-dependent genes play key roles in each step of the pathogenicity process. Studies showed that a prfA mutant failed to replicate in cytosol and was attenuated (Miner et al., 2008).

While the mechanism of prfA activation is still not fully understood, in vitro studies showed that environmental effects such as temperature changes, oxygen concentration, pH shifts and essential metal deficiency alter virulence gene expression in L. monocytogenes, suggesting a coordinated effect of environmental stresses in virulence gene expression (Greene and Freitag,

2002). Microarray studies have revealed three groups of genes regulated by the putative prfA gene, the virulence gene cluster, genes that are regulated by sigma B factor, and a set of genes responsible for the ABC transport system. Interestingly, the study found that the prfA gene can

act as an activator and a repressor depending on environmental conditions (Milohanic et al.,

2003).

Virulence might be induced or repressed according to external conditions or elements.

Activated charcoal for instance was found to induce virulence gene expression by inducing prfA expression. An interaction between activated charcoal and diffusible autorepressor produced by

L. monocytoegens has been suggested (Ermolaeva et al., 1999; Ermolaeva et al., 2004). This brings the attention to signaling molecules produced by microorganism in a process known as quorum sensing. Studies of the role of quorum sensing signaling molecules in Gram negative and some Gram positive bacteria revealed a direct role of quorum sensing in virulence gene expression (Smith et al., 2004).

1.3.2.7 Biofilm Formation and Resistance

A biofilm is defined as a sessile microbial community that is attached to a solid surface and embedded in a self-produced extracellular polymeric matrix (Kong et al., 2006). Biofilms are commonly heterogeneous, i.e., they contain more than one type of bacterial species, but they can be homogeneous in cases such as infections and medical implants (O'Toole et al., 2000). Bacteria are able to form biofilms on different surfaces, including on stainless steel surfaces and rubber conveyors of food processing plants, medical equipment, the human intestine, and many other environments (Sinde and Carballo, 2000; Begley et al., 2009). The ability of biofilms to resist desiccation, UV light, antibiotics, and sanitizers makes them of high importance to food safety and allows them to resist sanitization (Borucki et al., 2003). EPS production is the essential

developmental step of biofilm formation (Simoes et al., 2007; Flemming, 2003). The ability of bacteria to form biofilms is affected by different factors including strain characteristics, physical and chemical properties of the substrate for attachment, the bacterial growth phase, temperature, growth medium, energy source and the presence of other microorganisms (Pan et al., 2006).

Biofilm formation involves 4 main steps: (1) initial attachment regulation, (2) surface attachment, (3) biofilm formation and maturation, and (4) detachment (O’Toole et al., 2000).

Once the biofilm is formed, it becomes resistant to disinfectants and is difficult to remove completely (Simoes et al., 2007).

L. monocytogenes attaches and forms biofilm on different surfaces and resists different disinfectants and sanitizers (Milanov et al., 2009). L. monocytogenes has been found to adhere to different surfaces including stainless steel, rubber, and wood, but the biofilm formed on each surface is different (Adetunji and Isola, 2011). Studies have shown that biofilm formation is positively or negatively affected by the presence of some compounds. For instance, Stepanovic et al. (2004) found that the rich medium brain-heart infusion (BHI) enhanced biofilm formation, while Chang et al. (2011) found that a low concentration of ethylenediaminetetraacetic acid

(EDTA) reduced biofilm formation by inhibiting initial attachment. That said, the mechanism of biofilm formation by L. monocytogenes is not well investigated and remains unclear (Lemon et al., 2007). Regulatory genes in L. monocytogenes contribute effectively to biofilm formation.

Lemon et al. (2010) found that prfA promotes biofilm formation in L. monocytogenes and a prfA mutant was defective in surface adherence of biofilm. While Schwab et al. (2005) concluded in their study that alternative sigma B factor is not essential for biofilm formation, Van der veen and Abee (2010) indicated that sigma B factor contributed to the formation of static and continous flow biofilms and enhanced biofilm resistance to disinfectants.

To better understand the key behind biofilm formation, the role of QS has been suggested to be critical and has been well studied (Nadell et al., 2008; Cvitkovitch et al., 2003; Hammer and Bassler, 2003). QS has been found to control biofilm architecture in organisms such as

Pseudomonas aeruginosa (Davies et al., 2008), and has been shown to mediate the production of catalase and superoxide dismutase in Pseudomonas biofilms, leading to increased resistance to hydrogen peroxide (Hassett et al., 2009). Two studies (Sela et al., 2008; Belval et al., 2006) suggested a role of the luxS system in L. monocytogenes biofilm formation as they found that a luxS mutant formed denser biofilm. Reidel et al. (2009) found another correlation between QS and biofilm formation but this time with the Agr system. They found that an agrD mutant forms less biofilm than does the wild type.

1.3.2.8 Current Methods for Controlling L. monocytogenes in Food

Controlling foodborne illnesses is very challenging as there are many factors at play, such as increased susceptible population, lifestyle changes, global trade, and an increased demand for

RTE foods (MMWR, 2011). Different health and food organizations have developed strategies to help better control listeriosis both in processing plants and in homes. Health Canada, for instance, has a policy for controlling L. monocytogenes in RTE foods, and has specified the responsibilities of the government, industry, and consumers to help eliminate listeriosis (Health

Canada, 2011). For Health Canada, foods are divided into 2 categories: category 1 contains food products that support the growth of L. monocytogenes, while category 2 is divided into (A) foods that limit the growth of L. monocytogenes to <100 cfu/g (over the product shelf-life) and (B)

foods that do not support the growth of L. monocytogenes during a product’s shelf life (Health

Canada, 2011).

In addition to HACCP and GMP, high temperature, pressure, irradiation, chemical preservatives, and modified atmosphere packaging have been used to control foodborne microorganisms, including L. monocytogenes. Sodium/potassium lactate and sodium diacetate are widely used in meat industry as antimicrobial agent for L. monocytogenes inhibition (Glass et al., 2002). It is either used solely or in combination of other inhibitory techniques such as irradiation (Knight et al., 2007). The hurdle technique has been used in food by the application of

2 or more techniques for the same food to increase its safety (Williams and Golden, 2001;

Gounadaki et al., 2007). Current methods used to control L. monocytogenes involve in-package thermal pasteurization, irradiation, and high-pressure processing in a hurdle technology (Zhu et al., 2005). In 2006, the US FDA approved the use of bacteriophage as a food processing aid to control foodborne pathogens. The first bacteriophage, named LISTEX™, was approved for use against L. monocytogenes, first in cheeses and later in all foods. Europe and the US proved the effectiveness of bacteriophages in controlling foodborne pathogens (FDA, 2006). Even though many of these techniques were effective in eliminating the hazardous effect of L. monocytogenes, some of these techniques may reduce or lower the nutritional value of food and affect food freshness and texture

In addition to effectively implementing sanitation programs and new processing methods, such as post-packaging pasteurization, bacteriocins, irradiation, and high-pressure processing, new product formulations are being investigated to reduce the risk of L. monocytogenes contamination (FSIS, 2003; Tompkin, 2002). The US FDA and the CDC have identified 6 areas in which further action can be taken (FDA, 2003):

 Provide guidance for RTE food manufacturers and retail, food service, and institutional

establishments

 Provide training for industry and regulatory employees

 Enhance consumer and health care provider education

 Revisit enforcement and regulatory strategies, including microbial sampling activities

 Enhance disease surveillance and outbreak-response activities

 Coordinate research activities to refine risk assessment; enhance preventative control; and

support educational, enforcement, and regulatory activities.

1.4 Quorum Sensing

Quorum sensing (QS) is the process by which bacteria produce chemical signaling molecules in response to cell population density to control expression of various genes related to different bacterial functions (Schauder et al., 2001). Regulation of cell density-dependent gene expression (QS) was first described in the 1970s when researchers began to question the physiological and ecological significance of bioluminescence in marine planktonic bacteria

(Hastings and Greenberg, 2009). They discovered that the light output in V. fischeri is not correlated with growth and that bioluminescence does not increase until the mid-log phase, at which time there was a strong increase in light output. It was suggested that this observation was the result of autoinduction attributed to substances called autoinducers (AIs) that are produced by the bacteria themselves (Nealson et al, 1970). This idea was slow to capture attention, and it was not until the late 1990s that subsequent work showed that these pathways are a common phenomenon, and this type of bacterial cell-to-cell signaling controls a growing number of

phenotypes in a growing number of organisms (Hastings and Greenberg, 2009). Quorum sensing is playing an increasing role in our understanding of the regulation of gene expression in a wide range of bacteria, and systems have been described for Gram-negative and Gram-positive bacteria. Bacteria secrete and sense self-inducing signal molecules (pheromones) that accumulate in the media as the cells grow to high density. When a critical cell density is reached (i.e., quorum), a cascade of signals leads to gene expression changes, which often includes synthetic pheromone gene regulation (Ji et al., 2005).

Recent studies have shown that production of signaling molecules can be related to limited diffusion. Although QS was discovered and described over 25 years ago in two luminescent marine bacterial species, V. fischeri and V. harveyi, little was known about the role of QS in gene expression regulation in bacteria (Miller and Bassler, 2001). Bacteria use AIs as detectors to assess cell density (Mok et al., 2003), which reflects bacterial behavior and pathogenicity (Yuan et al., 2005). Three types of AIs are known to be produced by bacteria by the luxS (QS) system: acyl homoserine lactones (AI-1) produced by Gram-negative bacteria; a furanosyl borate diester (AI-2) produced by both Gram-negative and Gram-positive bacteria; and

AI-3, which is of unknown structure (Henke and Bassler, 2004) that is not luxS dependent

(Walters et al., 2006).

Many bacteria use AIs to control and regulate various functions that are associated with secondary metabolism, such as antibiotic production and susceptibility, conjugation, biofilm formation, and virulence gene expression (Yuan et al., 2005). In addition, AIs can enhance the survival of bacteria in unfavorable conditions (Soni et al., 2008). The marine bacterium V. harveyi is used as a reporter strain for AI detection (Waters and Bassler, 2006). Among the three

AIs, AI-2 was found to be nonspecific and widely distributed among bacterial species and is

thought to act as an interspecies bacterial communication molecule (Xavier and Bassler, 2003).

Agr (QS) is another system related to and responsible for QS in bacteria; the best understood agr

(QS) system is found in Staphylococcus aureus (Nakayama et al, 2006). The Agr locus consists of 4 genes, 2 precursor genes, agrD and agrB, and 2 sensing genes, agrC and agrA (Wuster and

Babu, 2008). The sensing molecules produced by this system are called AI peptides (AIP)

(Zhang et al., 2004). This system was found to control and mediate virulence gene expression and biofilm formation in S. aureus and recently in L. monocytogenes (Riedel et al, 2009). Agr

(QS) needs to be further investigated to gain a better understanding of its role in bacteria and its correlation with the luxS (QS) system.

1.4.1 Quorum Sensing in Gram-negative Bacteria

QS was first discovered in the Gram-negative bioluminescent V. fischeri, a marine pathogen, in 1970, and was later detected in many Gram-negative and Gram-positive bacteria

(Defoirdt et al., 2007). Vibrios exhibit a QS system that has been thoroughly studied and investigated (Schauder and Bassler, 2001). In Vibrio, three different QS systems have been discovered, each producing unique signaling molecules known as AI-1 (an acyl homoserine lactone system) produced by the luxRI system; AI-2 (a furanosyl borate diester) produced by the luxS system; and CAI-1, of unknown structure (Waters and Bassler, 2006). The first system consists of an AI synthase gene (luxI) and a transcriptional activator (LuxR) (Bassler et al, 1994).

While the second system producing AI-2 uses the protein free supernatant (Pfs) and LuxS enzymes, which are involved in the biosynthetic pathway leading to AI-2 production (Gao et al.,

2009), the third system producing CAI-1 is believed to be specific to Vibrio species as intergenera signaling molecules (Waters and Bassler, 2006). Gram-negative bacteria other than

Vibrio either have a system similar to that of Vibrio, such as the luxRI and/or luxS, or have unique QS systems. P. aeruginosa possesses 2 QS systems: Rh1RI, a homolog of LuxRI that produces N-butanoyl-L-homoserine lactone, and another homolog of the LuxRI system, LasRI, which produces N-(3-oxododecanoyl)-L-homoserine lactone as signaling molecules (Whitehead et al., 2001). Salmonella and E. coli use at least three QS systems luxS/AI-2, AI-

3/epinephrine/norepinephrine, and the LuxR homolog SdiA to achieve intercellular signaling

(Walters and Sperandio, 2006). AI-1 is known to be genera-specific, while AI-2 has been found to be an interspecies signaling molecule (Mok et al., 2003). These molecules were found to control and contribute to many genetic and physiological changes in bacteria (Miller et al.,

2002). For instance, the LasRI and LuxRI systems in P. aeruginosa were found to be involved in biofilm formation and virulence in this microorganism (Parsek and Greenberg, 1999; Rumbaugh et al., 2000). Figure 1.1 shows the structure of some quorum sensing signaling molecules.

Studying and understanding the role of QS in pathogenicity and survival of foodborne pathogens and other pathogenic bacteria will help in the identification of new alternative antimicrobial treatments and in the reduction of bacterial resistance. With the anti-QS molecules that have been discovered, we might be able to control these problematic bacteria in better and safer ways in the near future (Olivero et al., 2011; Tomlin et al., 2005).

Figure 1.1. structures of some representative quorum sensing signaling molecules.

(Adapted from Williams et al., 2007 with permission)

1.4.2 QS in Gram-positive Bacteria

Gram-positive bacteria such as S. aureus, Bacillus subtilis, Streptococcus pneumoniae, L. monocytogenes and lactic acid bacteria were found to communicate via QS molecules (Reading and Sperandio, 2006; Kleerebezem et al., 1997). Gram-positive QS systems differ from Gram- negative QS systems in that no acyl homoserine lactone (AHL) signals are produced and the systems depend on histidine kinase and a response regulator (Sturme et al., 2007). Both Gram- positive and Gram-negative bacteria produce AI-2 using the luxS system, although this system is

found more in Gram-negative bacteria (Zhao et al., 2010). The other well-studied system in S. aureus is the accessory gene regulator (Agr) system. This system was found to produce multi- component signal transduction that regulates extracellular toxins and enzyme production

(Abdelnour et al., 1993). The signal molecules produced by Gram-positive bacteria include the

AIP oligopeptides. Boles and Horsewill (2008) found that low Agr activity is important for biofilm development and that AIP plays a role in biofilm detachment. According to Fujii et al.

(2008), Agr regulates >100 genes in the S. aureus genome. The Agr system consists of 2 distinct transcripts driven by the divergent promoters P2 and P3 (Shaw et al., 2007). AgrB is involved in the processing of AgrD production and in the secretion of AIP. AgrD produces the signaling molecule autoinducing peptide (AIP), while AgrA and AgrC are involved in the 2-component regulatory system that receive the signaling molecules and process them inside the cell

(Yarwood and Schlievert, 2003).

While the luxRI and luxS systems are well supported as QS systems by many studies on

Gram-negative bacteria, arguments remain about whether the luxS system is a QS system or a metabolic pathway that produces AI-2 (Garmyn et al., 2009). The role of the Agr in Gram- positive bacteria is well established and hence, the debate might refer to the definition of the system as being QS or efficient sensing, rather than arguing about the system itself (Garmyn et al., 2009).

1.4.3 AI Production

As stated in section (1.7,1) AI-1 QS system, which is exclusive to Gram-negative bacteria, is produced by the lux regulon which is composed of and activated by 2 operons: luxR and luxI. LuxR is a 250-amino-acid polypeptide consisting of 2 domains: the C-terminal and the

N-terminal, which act as regulators of the transcription activities of the lux operon (England and

Greenberg, 2009). When high cell density is attained and sufficient AIs are produced, the response is induced (Nealson, 1977).

AI-2 is produced by both Gram-negative and Gram-positive bacteria, making it a nonspecific widespread AI. In this type of QS, the LuxS protein is required for the production of

AI-2 (Winzer et al, 2002). In V. harveyi, AI-2 is produced from S-adenosylhomocysteine (SAH) via enzymatic steps involving the nucleosidase Pfs, which converts SAH to S- ribosylhomocysteine (SRH), and from LuxS, which converts SRH to 4,5-dihydroxy-2,3- pentanedione, the immediate precursor of AI-2 that leads to AI-2 production (Zhang et al., 2008).

Figure 1.2 illustrates the AI-2 production pathway in L. monocytogenes.

Figure 1.2 Synthesis of autoinducer (AI)-2 through the Pfs and LuxS pathways (Adapted from Garmyn et al., 2009 with permission)

The AI-2 QS controls chemotaxis, flagellar synthesis, motility, virulence factors, and biofilm formation (Gonzalez Barrios et al., 2006). Others researchers (Ahmed et al., 2007;

Coulthurst et al., 2004) have found that a luxS mutant strain of Serratia sp. exhibited reduced

virulence and luxS mutant of Streptococcus anginosus showed increased susceptibility to antibiotics.

AI-3 is produced by the enteric pathogens E. coli, Shigella, and Salmonella and by other

Gram-negative bacteria (Annous et al., 2009). The mammalian hormones epinephrine and norepinephrine are believed to cross-talk and be received by the same receptors as AI-3 (Walters et al., 2006). Norepinephrine has been implicated in growth induction of the intestinal pathogen enterohemorrhagic E. coli (EHEC), and AI-3 activates transcription of the EHEC LEE virulence genes, leading to an agonistic effect of EHEC (Walters and Sperandio, 2006). AI-3 is chemically distinct from AI-2, and its synthesis is not dependent on LuxS (Annous et al., 2009). A number of commensal bacteria such as nonpathogenic E. coli and Enterobacter cloacae as well as pathogenic Shigella, Salmonella, and Klebsiella species, all produce AI-3 (Bai and Rai, 2011).

Studies are needed to investigate and reveal much of the currently missing information about the role of AI-3 in bacterial communication.

The AIP is produced by some Gram-positive bacteria such as S. aureus, Listeria,

Clostridium, Lactobacillus, and Bacillus spp. (Nakayama et al., 2006). The Agr gene cluster containing agrBDCA is responsible for AIP production and regulation. The AgrB protein is involved in the processing of AgrD, the AIP precursor, while AgrB removes the AgrD carboxyl tail leading to AIP production (Kavanaugh et al., 2007). AgrC and AgrA work as 2 regulatory component histidine kinases that sense AIP and uptake it for gene induction (Kavanaugh et al.,

2007). Figure 1.3 illustrates the AIP cycle in L. monocytogenes

Figure 1.3. The Agr system of Listeria monocytogenes (Adapted from Garmyn et al., 2009 with permission)

1.4.4 Quorum Sensing and Food

Many foodborne pathogens produce signaling molecules, which inspired interest in

determining whether food contains analogues of signaling molecules or molecules that

inhibit or disrupt QS. Teplitski et al. (2000) reported that tomatoes, peas, rice, and soybeans

contained activities similar to those of AI-1 in Gram-negative bacteria. Although the exact

composition and structure of these molecules are still unknown, many plant extracts in

organic solvents affected the behavior of AI-1-producing bacteria (Bauer and Mathesius,

2004). While most plant signaling molecules stimulate bacterial gene expression, the

furanone AHL from Delisea algae is inhibitory (Gao et al., 2003).

AI-2 activity was also found to be present in some vegetables and fruits such as tomatoes,

cantaloupe, and carrots and in tofu (Lu et al., 2004). Researchers have found that AI-1 and

AI-2 are found in different foods including milk, meats, and vegetables. They suggested that

specific spoilage organisms produce these signaling molecules that lead to food spoilage

(Ammor et al., 2008). Bai and Rai (2011) listed some food spoilage bacteria influenced by

QS-regulated phenotypes (Table 1.2).

1.4.5 L. monocytogenes QS

Few studies have focused on the role of QS on the behavior, survival, biofilm formation, and virulence of L. monocytogenes. Challan et al. (2006) found that L. monocytogenes EGD-e quorum sense by producing AI-2 via a luxS-ortholog gene named lmo1288, which encodes a protein with 41% identity and 59% similarity to the V. harveyi LuxS protein, and is closely related to the luxS gene of other bacteria. Available data suggest that the biosynthesis pathway of

AI-2 includes the Pfs and LuxS enzymes, which catalyze the 2-step conversion of S- adenosylhomocysteine (SAH) into homocysteine and 4,5-dihydroxy-2,3-pentanedione (DPD).

DPD is a precursor molecule of AI-2 that undergoes subsequent rearrangement to form furanosyl borate diester or (2R, 4S)-2-methyl-2, 3, 3, 4- tetrahydroxytetrahydrofuran, (AI-2) (Challan et al., 2006). Sela et al. (2006) confirmed the production of AI-2 in L. monocytogenes and studied the role of AI-2 in biofilm formation.

QS in L. innocua was found to support and increase the growth rate of this bacterium and shorten the lag phase by ~50% (Yang et al., 2006). An interesting study showed that a diffusible low-molecular-weight molecule produced by L. monocytogenes acts as an autorepressor of the

Table 1.2 lists of bacteria food spoilage influenced by QS in a certain foods

Signal-Dependent Organism Food Product Signaling Molecules Phenotype

C4-HSL and 3OC8- Pseudomonas fluorescens 395 Milk Proteolytic milk spoilage HSL

Lipolytic and proteolytic Serratia proteamaculans strain B5a Milk 3-oxo-C6-HSL milk spoilage

L-HSL α-amino-γ- Pseudomonas fluorescens Milk Proteolytic milk spoilage butyrolactones

Pseudomonas phosphoreum and Cod fillets Chitinolytic activity 3-hydroxy-C8-HSL Aeromonas spp.

Cellulolytic and Erwinia carotovora Vegetables 3-oxo-C6-HSL proteolytic spoilage

Pectinolytic and Pectobacterium sp. A2JM Bean sprouts 3-oxo-C6-HSL proteolytic spoilage

Chitinase and protease 3-oxo-C6-HSL and C6- Serratia plymuthica RVH1 Vegetables activity HSL

Vacuum-packed Hafnia alvei and Serratia spp. Proteolytic spoilage N-3-oxohexanoyl HSL meat

Biofilm formation and Pseudomonas spp. Meat AHLs proteolytic spoilage

Photobacterium phosphoreum Cod fillets Chitinolytic spoilage 3-hydroxy-C8-HSL

HSL, homoserine lactone; AHL, N-acyl homoserine lactone

PrfA virulence regulon, which might be related to the QS molecules of L. monocytogenes

(Ermolaeva et al., 2004). A recent study by Riedel et al. (2009) found that the agrD-dependent

QS system affects biofilm formation, invasion, and virulence gene expression in L.

monocytogenes. Garmyn et al. (2009) suggests that the Agr system is responsible for QS in L. monocytogenes and not AI-2, and looking at both systems would give us a better idea about their relative roles in L. monocytogenes.

QS was found to play an important role in many Gram-negative and Gram-positive bacteria, giving rise to a possible new way to control those pathogenic microorganisms in food by disabling QS (Gandhi and Chikindas, 2007). With many studies supporting the importance and role of QS in bacteria along with the supportive studies mentioned above, it would be interesting to study QS systems in L. monocytogenes strains and lineages and other Listeria species, and look more closely at the role QS plays in this pathogenic bacterium.

1.4.6 Quorum quenching or anti-quorum

The discovery of antibiotics in the early 20th century had an unprecedented impact on the treatment and control of infectious diseases, but the excessive misuse of these antibiotics led to bacteria developing resistance and becoming more difficult to treat. This led to the search for new preventive and treatment approaches for bacterial infections. With the development of molecular biology and detection techniques, the discovery that bacteria communicate via signaling molecules, and the role of QS in the pathogenicity of these microorganisms, QS has become a target of study to control pathogens (Dong et al., 2007). Preventing QS or inhibiting cell-to-cell communication may reduce the virulence and biofilm-forming capacity of many pathogens (Kievit and Iglewski, 2000). Defoirdt et al. (2006) found that marine algae produce brominated furanone, which acts as an AI-2 disrupter, and protects the gnotobiotic brine shrimp

Artemia franciscana from pathogenic V. harveyi, V. campbellii, and V. parahaemolyticus.

Janssens et al. (2008) found that the same anti-QS brominated furanone was able to inhibit biofilm formation in Salmonella Typhimurium. Brominated furanone affected gene expression in

E. coli in a study by Ren et al. (2004). The discovery of this algal anti-quorum sensing compound encouraged researchers to seek more inhibitory compounds. Adonizio et al. (2006) screened 50 medicinal plants from southern Florida for anti-quorum compounds and found 6 that produce anti-QS compounds. These discoveries opened new avenues for therapeutic compounds and new ways to control infectious diseases and might be the ―pills‖ of the 21st century.

1.5 Research Objectives

The purpose of this research is to study the influence and possible role of QS in the physiology, genetics, and virulence of L. monocytogenes to better understand bacterial behavior, pathogenicity, and survival. Using L. monocytogenes EGD-e and mutant strains as a model for L. monocytogenes, this doctoral project had the following main and intermediate objectives and hypothesis:

Main Objective:

Investigate the role of QS in survival, biofilm formation and genes expression in Listeria monocytogenes.

Main hypothesis:

QS control or contribute to survival and pathogenicity of Listeria monocytogenes by controlling biofilm formation and gene expression.

Intermediate objectives

 Detect the presence of the QS gene Agr and the luxS-like gene lom1288 as well as

the production of AI-2 in Listeria species and L. monocytogenes strains

 Investigate the role of luxS and Agr on gene expression in L. monocytogenes

 Study the influence of environmental conditions including foods on AI-2

production

 Study the influence of luxS and Agr on the survival of L. monocytogenes

 Investigate the role of luxS and Agr on biofilm formation in L. monocytogenes

1.6 Overview of the Proceeding Chapters

In the coming chapters, the relative roles of the two QS systems, luxS and Agr, on gene expression, physiology, survival, and biofilm formation of L. monocytogenes will be examined.

Chapter 2 describes the identification and production of QS molecules in Listeria species and L. monocytogenes strains and the point at which AI production peaks. The level of QS in both the log and the stationary phase was determined. Using microarray, the whole genome of L. monocytogenes was examined for gene expression under the effect of QS systems under 2 growth phases and under 2 different temperatures. Chapter 3 describes that this is the first study to examine the whole genome using the L. monocytogenes luxS system, and the first to examine the effect of both QS systems under refrigeration temperatures. Chapter 4 outlines the role of QS in the physiology and survival of L. monocytogenes. This discussion extends to the study of

biofilm formation in L. monocytogenes using confocal microscopy as well as the role of QS in the formation of biofilm and the resistance of biofilm to disinfectants. Chapter 5 explains the conclusions that can be drawn from the present project and proposes areas of future study.

1.7 Techniques

1.7.1 Lux Gene Reporter system

Several systems have been used to identify bacterial gene expression. For instance, the insertion of defined or random chromosomal DNA fragments upstream of promoterless reporter genes, such as genes encoding for alanine racemase, have been applied to study such bacterial promoters. These systems are limited and need extensive enzymatic assays to perform (Bron et al., 2006). Other systems applied reporter gene technology that monitors gene expression with easily measurable phenotypes. These include alkaline phosphatase, ß-galactosidase and bacterial luciferase (LuxAB) (Naylor, 1999). The bacterial luciferase (lux) system from Vibrio ssp. has been widely used as a reporter for bacteria, yeasts, plants and recently animals (Mudge et al.,

1996). The lux system is a very sensitive, real-time, non-invasive reporter used to monitor gene expression, and possess many advantages like rapid in vivo measurements of multiple samples, light production is easily measured and there is no interference in the results as most bacteria do not produce luciferase (Bron et al., 2006; Hautefort and Hinton, 2000). In Vibrio ssp. there are at least 8 essential lux genes required for lumincensce, seven genes in one operon (luxICDABEG) and luxR on the other operon. Bacterial luciferase is a heterodimeric enzyme of 77 kDa comprising α and ß subunits with approximate molecular size of 40 and 37 kDa, respectively

(Tehrani et al., 2011). The bacterial luciferase reaction is as follows (Liu et al., 1995):

FMNH2 + n-decanal + O2 FMN + n-decanoic acid + H2O + light (where FMN is

ﬂavin mononucleotide).

1.7.2 Microarrays and qRT-PCR

Microarrays and qRT-PCR are widely used in molecular biology due to the advantages they have over conventional techniques in terms of accuracy, cost, time, and data collected. DNA microarray is a high-throughput technology that works by exploiting the ability of a given mRNA molecule to bind specifically or hybridize to the DNA template from which it originated.

Using an array containing many DNA sequences, the expression levels of hundreds or thousands of genes within a cell can be determined in a single experiment by measuring the amount of mRNA bound to each site on the array (NCBI, 2007). Microarrays provide fast and cost-effective methods for identifying gene expression in a genome under certain conditions (Street, 2002).

Two microarray systems are commonly used according to the arrayed material: complementary

DNA (cDNA) and oligonucleotide microarrays. In cDNA, the arrays are PCR reaction products obtained from cDNA libraries and these products are printed onto glass slides as 100–300 µm spots at defined locations. A total of 30,000 cDNA can be fitted on the surface of a typical microscope slide using this technique (Schulze and Downward, 2001). On the other hand, oligonucleotide arrays are synthesized in situ and are spotted or ink-jetted onto the slide surface

(Moorcroft et al., 2005). The main advantage microarray has over other techniques is the numerous data that can be obtained from the transcriptional response of the whole genome to an environmental or genetic stimulus in one experiment (Ehrenreich, 2006).

The qauntitaive Real Time Polymerase Chain Reaction (qRT-PCR) is another well- established technique that studies gene expression using cDNA. qRT-PCR is a sensitive, rapid, high-throughput, and relatively easy technique that has the ability to measure PCR products as they accumulate, or in ―real time‖ (Sharkey et al., 2004). qRT-PCR depends on the measurement of fluorescence during the PCR. Two methods are applied in qRT-PCR: (1) non-specific, in which DNA-binding dye interacts with double-stranded DNA; and (2) specific qRT-PCR, in which additional fluorescence-labeled oligonucleotide probes are introduced that fluoresce upon their release after DNA hybridization (Klein, 2002).

In molecular biology, many methods measure quantities of target nucleic acid sequences, but most of them suffer from limitations such as high cost, time, and effort, as well as being insensitive and non-quantitative (Valasek and Repa, 2005). Advanced technologies like microarray and qRT-PCR have many advantages over conventional methods.

There are several advantages for using microarray, it provides high throughput as whole

genome can be screened. and it is cost effective analysis with the availability of microarray

chips. And in comparison with conventional methods, microarray is fast method in which

results can be obtained quickly (Hurd and Nelson, 2009). On the other side, microarray

suffers from some disadvantages like the need of large amount of RNA to produce adequate

signal over noise, and difficulty comparing results to those of other techniques and reliability

of performance and results is debatable and need to be confirmed by other methods (Froster

and Ghazal, 2003).

qRT-PCR on the other hand has advantages over microarray in which the technique is highly sensitive and precise (<2% coefficient of variation of cycle threshold values), carries wide

dynamic range of quantification (7-8 log decades) and doesn’t need any post-PCR steps. The disadvantages of qRT-PCR includes increasing variation with cycle number, increasing PCR product exponentially, increasing variation after transformation to linear values, limited number of simultaneous reactions and increased risk of false negative results (Klein, 2002). Combining microarray and qRT-PCR technologies would provide more accurate and reliable results.

1.7.3 SHIME system

The human gastrointestinal tract (GI) is considered as a major site of entry for pathogens to the human body. The gut represents the first contact between ingested food and the host (Marzorati et al., 2009). The presence and function of the microbial community in the large intestine is of great importance as some microbial communities are beneficial while others are harmful to the human body (Marzorati et al., 2009). Studying the human intestine is difficult in many aspects including ethics, health and cost. Animal trials may provide an alternative solution but they may not represent the conditions present in the human gut. The need for an in vitro system resembling the human GI is highly important. The Simulator of the Human Intestinal

Microbial Ecosystem (SHIME) is a dynamic and continuous model that mimics the gastrointestinal tract (GI). The model was first described by Molly et al. (1993). The unique characteristics of each segment of the GI tract are represented by individual culture vessels connected via peristaltic pumps. The SHIME utilizes a 5-vessel reactor that mimics the stomach/duodenum, jejunum/ileum, ascending colon, transverse colon, and descending colon.

This current design is an extension of the three vessel system connected consecutively, which, in turn, was developed from a single chamber bioreactor (chemostat). A six-stage culture vessel system was developed in 2001 (Figure 1.4). This system allows the study of physiochemical, enzymatic and microbial parameters in a GI in a controlled in vitro setting.

Figure (1.4) Simulator of the Human Intestinal Microbial Ecosystem (SHIME).

1.10.4 Confocal Laser Scanning Microscopy

Laser scanning microscopy has become an essential tool for a variety of biological investigations for imaging live and fixed samples of a range of thickness up to 100 micrometers

(Olympusfluoview, 2011). Confocal Laser Scanning Microscopy (CLSM) performs point by point image construction by illumination of the specimen. The image is created by either reflecting light off the specimen or stimulating fluorescence from dyes applied to the specimen

(Physics.emory.edu,2011). One of the major uses in microorganisms is the study of bacterial biofilms either as alive or fixed (Palmer and Sternberg, 1999). CLSM allows different horizontal and vertical sections to be examined, which can provide information regarding morphology and phylogeny (Morato et al., 2004).

Chapter 2: Identification of Listeria monocytogenes Quorum Sensing and Auto-Inducer production

2.1 Abstract

Six strains of Listeria species (monocytogenes, innocua, ivanovii, welshimeri, seeligeri, and grayi) and 15 strains of L. monocytogenes representing 3 lineages (I, II, and III) in addition to the model strain L. monocytogenes EGD-e were screened for the presence of lux and agr genes as well as for autoinducer (AI)-2 production. Subsequently, the effects of pH, temperature and water activity (NaCl concentration) on the production of AI-2 were studied. All L. monocytogenes strains were shown to possess both lux and agr genes, except for L. grayi, which lacks agrD, but all strains were able to produce AI-2. AI-2 production was maximal during the late exponential phase of growth and declined rapidly when cells entered the stationary phase.

The production was found to be growth phase dependent. Environmental conditions affected AI-

2 production, and low pH (≤5) was found to inhibit or destroy the signaling molecules tested; additionally, food ingredients in milk and beef inhibited AI-2 production. This information will be used to select strains and conditions for the study of the role of quorum sensing in L. monocytogenes.

2.2 Introduction

Despite the increasing importance and knowledge about quorum sensing (QS) in bacteria, the types and roles of QS systems that are present in many Gram-positive bacteria including the

pathogen L. monocytogenes need to be more invistigated. QS is known to control or mediate virulence gene expression, biofilm formation, antibiotic resistance, and other microbial functions in many Gram-negative and some Gram-positive bacteria (Gera and Srivastava, 2006). Different

QS systems exist in microorganisms. Vibrio harveyi, for instance, contains 3 different QS systems that produce 3 different signaling molecules. The synergistic and or antagonistic effect in a single species and between various species is well studied. Autoinducer (AI)-1 is known to be specific to each species, whereas AI-2 is present in bacteria from many genera and is considered to be an intraspecies signaling molecule. Gram-positive bacteria differ from Gram- negative bacteria in that the former have complicated QS systems. The lux system is common in

Gram-negative bacteria, whereas agr is more common in Gram-positive bacteria. Some bacteria possess more than one system.

Identifying the presence of possible QS systems in L. monocytogenes was the first step in the process of studying the role of cell signaling in this pathogenic microorganism.

Although both the lux and agr systems were identified earlier in L. monocytogenes, little emphasis has been placed on studying their respective roles. Since L. monocytogenes is an intracellular parasite, many concerns about the possible role of QS have been raised; (Garmyn et al. (2009) suggested naming quorum sensing in some Gram positive bacteria as ―efficiency sensing‖ or ―diffusion sensing‖ as the system might be related to limited diffusion rather than the achievement of a certain bacterial threshold, especially in intracellular parasites. The study of the environmetal effects on signaling molecules production is important as it provides an idea about

QS impact and limitations in cell behavior. Many environmetal conditions and food ingredients effect the production of signaling molecules either positively or negatively. Beeston and Surette

(2002) reported an induced production of AI-2 in Salmonella Typhimurium if first grown on

glucose containing medium and then exposed to low pH (5.0) or high osmolarity (0.4 M NaCl).

They also suggested that the metabolic status of the cell rather than cell density influenced AI-2 dependent signaling. Moslehi-Jenabian and Jesperson (2009) examined the effect of acid shock on probiotic strains of Lactobacillus ssp. and found that acidic shock increased levels of AI-2 production. DeLisa et al. (2001) also found that AI-2 production in E. coli was affected by environmental stresses and suggested a shift of metabolic activity was responsible for the change in AI-2 production. Soni et al. (2008) found that beef extract inhibited AI-2 production in E. coli by interfering with signaling molecules. These previous studies focused on instant environmental changes by applying acid, or osmolarity shocks to bacteria, the role of environmental conditions such as continued exposure to low pH or low temperature prior to growth on AI-2 production needs to be investigated.

Here we demonstrate the presence of QS systems in L. monocytogenes, AI-2 production, and the effect of environmental stresses in AI-2 production.

2.3 Materials and Methods

2.3.1 Bacterial Strains and Culture Media

Brain-heart infusion (BHI) agar and broth (Diagnostic Systems, Franklin Lakes, NJ,

USA), tryptic soy agar (TSA), tryptic soy broth (TSB), (Difco Laboratories, Detroit, MI, USA),

Rapid L ’mono (Bio-Rad, Mississauga, ON) was used as selective media for L. monocytogenes and autoinducer bioassay (AB) medium (17.5 g NaCl, 12.3 g MgSO4, 2 g casamino acids in 1 L distilled water adjusted to pH 7.5, supplemented after autoclaving with 10 mL/L 1.0 M potassium phosphate [pH 7.0], 20 mL/L 50% glycerol, and 10 mL/L 0.1 M L-arginine)

(Medellin-Pena et al., 2007) were used as culture media for the selected bacteria.

Chloramphenicol (10 μg/mL) and penicillin (10 μg/mL) were supplemented in the media as needed (Sigma-Aldrich, Oakville, ON). Fifteen L. monocytogenes strains representing lineages I,

II and III (Table 2.1), Listeria species (L. ivanovii; L. seeligeri; L. innocua; L. welshimeri;

L.grayi); E. coli DH5α; and Vibrio harveyi strains BB120, BB152, and BB170 (Table 2.2) were obtained from the Canadian Research Institute for Food Safety (CRIFS, Guelph, ON), while the

5 L. monocytogenes strains representing lineage III were kindly provided by Dr. Franco Pagotto

(Health Canada, Ottawa, ON). Pure cultures were obtained from -80°C frozen stocks and maintained at 4°C on TSB until use. Cultures were re-streaked every month to maintain viability.

2.3.2 LuxS and AgrD Gene Detection and AI-2 Bioassay

2.3.2.1 DNA extraction and gene detection

To detect the presence of luxS and agrBDCA, DNA was extracted from bacteria grown overnight using a Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA) as instructed by the manufacturer. In brief, a culture of L. monocytogenes was grown overnight.

One milliliter was removed and placed into a 1.5 mL microcentrifuge tube and centrifuged at

16000 × g for 2 min, after which the cells were collected and the supernatant was discarded.

Cells were resuspended in 480 µL of 50 mM ethylenediaminetetraacetic acid. Lysozyme (10 mg/mL; Sigma Aldrich) was added to the mixture for a total volume of 120 µL and mixed gently. This step was followed by incubation of the sample at 37°C for 60 min and then the sample was centrifuged for 2 min at 16000 × g. Nuclei lysis solution (600 μl) was added and the cells were resuspended. The sample was incubated again at 80°C for 5 min to completely lyse

the cells and the suspension was then cooled to room temperature. RNase Solution (3 μl) was added to the cell lysate and the tube was inverted 5 times to mix the solution. The sample was then incubated at 37°C for 60 min and cooled to room temperature. Protein precipitation solution

(200 μl) was added to the RNase-treated cell lysate and vortexed vigorously for 20 s. The sample was stored on ice for 5 min and then centrifuged at 16000 × g for 2 min. The supernatant was transferred to a clean 1.5 mL tube containing 600 µl isopropanol and mixed gently. The solution was centrifuged, the supernatant was poured off and the tube was drained before the addition of

600 µL of 70% ethanol. The DNA obtained was washed by multiple tube inversions. The tube was then centrifuged, drained, and air dried for 15 min. Finally, 100 µL of DNA rehydration solution were added to the tube, and the DNA was rehydrated by incubation at 65°C for 1 hr.

DNA concentration was then measured using Nanodrop 1000 spectrophotometer

(Thermoscientific, USA). 2 µl was added to lower measurement pedestal and measured at

260/280 nm.The DNA was stored at -20°C until needed. The extracted DNA was then prepared for polymerase chain reaction (PCR) (Applied Biosystems, USA) for detection of the luxS and agrBDCA genes using PCR Supermix (Invitrogen, USA) as follows: 45 µl of PCR Supermix were added to 3 µl of DNA and 2 µL of reverse and forward primers (Table 2.3). PCR was then run for 30 cycles. The amplified PCR product was analyzed by gel electrophoresis (Bio-Rad) in

5% agarose with 1/20 TAE buffer. Ethdium bromide (10 mg/ml) 0.5 µl was added to agarose gel before solidification, then 5 µl of DNA was placed in each well and run in parallel with 1 kb

DNA ladder (Invitrogen) for 1 hr at 95 volts. The resulting bands were examined under ultraviolet light using automated gel imaging and documentation system Bio-Rad Gel Doc (Bio-

Rad).

2.3.2.2 AI-2 bioassay

A bioassay was used to detect the production of AI-2 using the reporter strain V. harveyi

BB170. Bioluminescence was measured using the Victor Multilabel Counter (Wallac,

PerkinElmer Life Sciences, Woodbridge, ON).

All L. monocytogenes strains and all Listeria species were tested. The AI-2 bioassay was carried out as described by Surette and Bassler (1998). The bioluminescence response of V. harveyi BB170 (AI-1-negative and AI-2-positive) was detected. V. harveyi BB120 and BB152 were used as positive controls in the AI-2 bioassay and sterile AB medium served as a negative control. First, cell-free culture supernatants (CFCS) were prepared by growing Listeria species and L. monocytogenes strains in BHI broth at 37C with aeration overnight. The culture was diluted (1:100) in BHI and inoculated into fresh BHI medium and incubated at 37°C. Cell-free culture supernatants were prepared at different times by centrifugation at 12,000 × g for 5 min followed by filtration through 0.2 m HT Tuffryn filters (Sigma-Aldrich). The supernatants were stored at -20°C. V. harveyi CFCS was prepared in the same way but by using AB medium with

30°C as the incubation temperature. The CFCS from Listeria species and L. monocytogenes strains were tested for the presence of signaling substances that could induce luminescence in the

V. harveyi reporter strain by addition of 10 µL of CFCS to 90 µL of a 16-h culture of V. harveyi

BB170 (diluted 1:5,000 in AB medium) in 96-well microtiter plates. Finally, the microtiter plates were shaken in a rotary shaker at 175 rpm at 30°C. Every hour, light production was measured using the Victor Multilabel Counter.

To test whether AI-2 production is growth phase-dependent, the method of Zhang et al.

(2008) was followed with slight modification. The same AI-2 bioassay described above was

carried out, but the samples were collected every hour. Growth was determined by plate counting. Samples (1 ml) were taken every hour and serial dilution was performed in Phosphate

Buffered Saline (PBS, Fisher Scientific, Ottawa, ON), using spread plate, 100 µl was spread over the plate media surface for each dilution and incubated for 24 hrs at 37°C. Cells were counted as

CFU. OD measurement using the Bioscreen C Microbiology Plate Reader (Fisher Scientific) was done at 20-min time intervals for 24 h for growth curve determination from both plate counting and OD measurement. 100 wells honeycomb Bioscreen plate (Growth curves, USA) was used in which 200 µl of bacterial culture was added to each well, temperature was set at 37°C and data was recorded in Bioscreen software (Growth curves, USA).

2.3.3 Environmental Parameters Affecting AI Production

Environmental conditions including temperature (4°C, 10°C, 25°C, and 37°C), NaCl concentration (2.5%, 5%, and 10%), pH (4, 5, 6, and 7), and food types (milk and beef extract) were used to test their effect on AI-2 production and to determine whether AI-2 production is dependent only on cell density or other factors. The method of Cloak et al. (2002) was used. This involved growing the culture overnight and inoculating a fresh culture with a 1:100 dilution in

BHI broth of the grown culture and growing it to an OD595nm of 0.6 using spectrophotometer

(Beckman, USA). Then a 1:100 dilution of the inoculum was transferred to new media in each tube for the parameter being examined. Samples were taken at specific time intervals (Table 2.4).

At each time interval, 1 mL of sample was taken, centrifuged at 12,000 × g for 5 min, and the fluid was filtered through 0.2 m HT Tuffryn filters (Sigma-Aldrich) and stored at -20°C.

2.3.4 Food system and AI-2 activity

The effect of milk and ground beef extract on the AI-2 activity of L. monocytogenes was assessed using the procedure of Soni et al. (2008). Skim milk and ground beef patties (containing

15% fat) were purchased from commercial source. Ground beef extract was prepared by mixing with 0.1 M phosphate Buffer (PB) in (1:1, w/v) in plastic bag in stomacher for 2 min. After stomaching the mixture was centrifuged at 5000 x g for 5 min and filter-sterilized using 0.2 m

HT Tuffryn filters. The method for the AI-2 bioassay was then carried out as described above except that 5 L of CFCS purified from L. monocytogenes culture was mixed with 5 µL of ground beef extract and added to 90 µl freshly diluted culture (1:5000) of V. harveyi. AB medium (5 µl) and CFCS containing AI-2 (5 µl) were used as positive controls and 10 µl of AB medium added to 90 µl diluted culture was set as negative control. Viable counts were determined by plating 1 ml of inoculated food in BHI agar at each sampling time, plates were incubated at 37°C for 24 hrs.

2.2.5 Statistical analysis

Statistical analysis was performed using the Student t-test. P ≤ 0.05 was considered to be significant.

2.4 Results

2.4.1 LuxS Gene Detection and AI-2 Production

The 6 Listeria species (L. monocytogenes, L. ivanovii, L. seeligeri, L. innocua, L. welshimeri, and L. grayi) and 15 L. monocytogenes strains representing 3 lineages were tested

for the presence of the lmo1288 (luxS) gene by PCR, and all gave positive results (Figure 1), indicating that lmo1288 is a common gene in pathogenic and non-pathogenic Listeria species.

This test was followed by an AI-2 bioassay to confirm the presence and production of AI-2. All Listeria species and all L. monocytogenes strains were found to produce AI-2.

2.4.2 AgrD gene detection

All L. monocytogenes strains produced agrA, agrB, agrC and agrD gene products. L. innocua, L. ivanovii, L. welshimeri, and L. seeligeri also gave positive results, while L. grayi, did not show any bands for the genes tested.

2.4.3 AI-2 Production in Listeria is Growth Phase-Dependent

Our results showed that L. monocytogenes possesses AI-2 activity. The maximal production was found to be in intermediate to late log (exponential) phase of growth in which the log count is 7.5 cfu/ml (OD595nm = 0.035). This is followed by a rapid decrease in production and almost diminishes in stationary phase. This result suggests that AI-2 activity is growth phase- dependent as production was maximal during the exponential phase (Figure 2.2). The control strain V. harveyi BB152 showed a similar peak at the late exponential phase where log count =

7.9 (OD595nm = 0.040). The experiment was repeated at least twice with triplicate samples of 3 strains representing high, mid, and low AI-2 activity.

2.4.4 Environmental Parameters and Food Systems Affecting AI Activity

Environmental conditions may affect AI-2 activity and production, and this may in turn affect bacterial survival and pathogenicity. On the other hand, the production of AI-2 may be enhanced under unfavorable conditions, supporting bacterial survival.

2.4.4.1 Environmental parameters affecting AI activity

Different results were obtained when L. monocytogenes strains were cultured at different temperatures, pH, and salt concentrations at similar inoculation levels. For temperature,

AI-2 activity at 37°C was set as the control and AI-2 activity at 4°C, 10°C, and 25°C was examined. Results (Table 2.5) showed that AI-2 activity and production were significantly lower at 4°C and 10°C (P < 0.0002 and P < 0.0001, respectively) compared with AI-2 levels at the optimum growth temperature (37°C). Production of AI-2 at 25°C was the same as that obtained at 37°C (Figure 2.3). AI-2 was produced in the exponential phase at all temperatures. The production of AI-2 at optimal growth temperatures was at least 10 times higher than those at low temperature (4°C), which could be due to slow growth and/or environmental stresses that affected AI-2 production.

For pH, AI-2 activity at pH 7 was set as a control and activities at pH 5 and 6 were examined. Results showed no AI-2 activity was detectable at pH 5 and 6 (Figure 2.3).

For NaCl, concentrations of 2.5%, 5%, and 10% were examined. All three NaCl concentrations showed significant difference in AI-2 production in comparison with optimum condition (growth at 37°C in BHI with no additional supplements). At NaCl concentrations of

2.5% and 5%, AI-2 activity was lower than observed in the absence of NaCl (P < 0.004 and <

0.0005, respectively). The lowest AI-2 production was recorded at 10% NaCl (Figure 2.3).

2.4.4.2 Food and AI activity

The results showed that no AI-2 activity was detected in milk at all temperatures tested and no AI-2 was detected in ground beef extract (Figure 2.3). In both systems, CFCS from

V. harveyi 152 and AI-2 + milk/beef were set as positive controls, and in both cases, very low or no detection was recorded, suggesting that either AI-2 was inhibited or luminescence was impacted.

2.5 Discussion

Both lmo1288 (luxS like) and agrBDCA genes were present in Listeria species including L. monocytogenes. AI-2 production was confirmed and found to be universal in

Listeria species. While all Listeria species showed positive results for agrD, the gene was not present in L. grayi with the set of the primers used. Garmyn et al. (2009) reported a different agrD sequence in L. grayi, which may explain the negative results we obtained. The presence of the luxS gene in L. monocytogenes was detected earlier by Belval et al. (2006) and Sela et al.

(2006). The agrD system has also been detected in L. monocytogenes (Rieu et al., 2007; Reidel et al., 2009). The presence of QS systems in both virulent and avirulent species of Listeria spp. might indicate a general role of QS that is not limited to virulence. AI-2 production is affected by environmental conditions and might be inhibited under certain conditions. We found that AI-2 is inhibited at pH 5 or lower, the effect in the presence of NaCl was less with 2.5 and 5% than at higher NaCl (10%) concentrations. Also, production was low at low temperature. The reason for this low AI-2 production at different environmental conditions is probably due to luxS

repression, effect on enzymes activity or changes in metabolic activity of the cell. Few studies focused on the influence of environmental conditions on AI-2 production. Surette and Bassler

(1999) found that under certain growth conditions, AI-2 production in Salmonella Typhimurium increased at low pH (5.0) and high osmolarity (0.4 M NaCl), while signal degradation was observed at low osmolarity (0.1 M NaCl). In another study (DeLisa et al., 2001), AI-2 production in E-coli was found to be increased following pulsed addition of glucose, Fe(III), NaCl, and dithiothreitol and decreased following aerobiosis, amino acid starvation, and isopropyl-β-D- thiogalactopyranoside-induced expression of human interleukin-2 (hIL-2). This study showed that AI-2 production is affected by different kinds of elements or stresses.

This is the first study to investigate the influence of environmental conditions on AI-2 production in L. monocytogenes. The above studies and our results indicate a possible metabolic adaptation of bacteria to environmental stresses affecting AI-2 production. On the other hand the effect of food on AI-2 production showed inhibition in the presence of milk and beef extract. These results are similar to those reported by Soni et al. (2008), who also showed that beef extract inhibited AI-2 activity. They found that fatty acids present in ground beef were able to inhibit AI-2 in V. harveyi and E. coli but the mechanism of inhibition was not investigated. Cloak et al. (2002) found that AI-2 was not produced by any of 3 Gram negative bacteria in milk at 4°C and 25°C but the presence of AI-2 was detected at 37°C. These findings help to understand the role of environmental conditions or stresses in quorum sensing signaling molecules and possibly affect QS systems in L. monocytogenes. Unfavourable condition might affect quorum sensing and thus functions of cell that is controlled by QS. Bazire et al. (2009) showed that osmotic stresses delay signaling molecules production in P. aeruginosa which may block or repress QS systems and thus their function in the cell.

2.6 Conclusion

Listeria species and L. monocytogenes strains and lineages communicate by

QS using general chemical signaling molecules—the agr system and AI-2. Listeria has a luxS- like gene named lmo1288 (which initiates and produces AI-2) and the agrD gene, which produces AI peptides. AI-2 activity in L. monocytogenes was found to be growth phase- dependent; most activity was found in the exponential phase, suggesting that AI-2 plays a role in gene expression and growth. Environmental conditions affect the level and timing of AI-2 production; some conditions may inhibit AI-2 activity, but the reasons behind that require further investigation. Foods in which pathogens may be present can inhibit or reduce QS activity. The characteristics of AI-2 production will help explain the possible role of AI-2 in the survival and pathogenicity of L. monocytogenes. Future experiments may further clarify the picture in terms of the importance of AI-2 produced by bacteria.

Table 2.1 list of L. monocytogenes strains and lineages used in this study

LISTERIA STRAIN SEROTYPE LINEAGE

CRIFS-154620 4b I

CRIFS-159842 1/b I

CRIFS-158491 1/2b I

CRIFS-155712 1/2b I

CRIFS-158150 1/2a I

CRIFS-E111P3 1/2a II

CRIFS-E8304 1/2a II

CRIFS-E158490 1/2a II

CRIFS-E83012 1/2a II

CRIFS-E 1/2c II

HPB# 4707 (Health Canada) 4c III

HPB# 4863 (Health Canada) 4a III

HPB# 5051 (Health Canada) 4a III

HPB# 5059 (Health Canada) 4a III

HPB# 5246 (Health Canada) 4c III

Table 2.2 list of bacteria used in this study

LISTERIA SPECIES SOURCE luxS and AI-2 agrD gene production innocua CRIFS + + seeligeri CRIFS + + welshimeri CRIFS + + ivanovii CRIFS + + grayi CRIFS + - monocytogenes EGD-e Dr. PORTNOY + +

Vibrio harveyi BB170 CRIFS +

V. harveyi BB120 CRIFS +

Table 2.3 List of primers used in this study

Primers Primer Sequence Target Source

LUXSLM-FR ATGGCAGAAAAAATGAATGTAGAAA luxS This study

gene

LUXSLM-RE TTATTCACCAAACACATTTTTCCA luxS This study

gene

AgrA-FR CGAATGCCTACACATCAAGGTA agrA Riedel et al.,

2009

AgrA-RE TCACCACACCTTTTGTCGTATC agrA Riedel et al., 2009 AgrB-FR AAAGTCCCTTTGTCAGAAAGAATG agrB Riedel et al., 2009 AgrB-RE CACCTGAAACAAAGATCCTACCA agrB Riedel et al., 2009 AgrC-FR ATTAATACGGCAACCAACGAAC agrC Riedel et al., 2009 AgrC-RE AAATCGGTGGCATATTTACTGG agrC Riedel et al., 2009 AgrD-FR TCGCCTTAGTAACAGGGCTTT agrD This study

AgrD-RE CGTGCAATGTTTTGG agrD This study

Table 2.4 Environmental and food parameter and sampling time for AI-2 production

Parameter Incubation Sampling time

Temperature 4°C 12 h, 24 h, 3 d, 6 d, 9 d, & 12 d

10°C 12, 24, 72, 96, 120, &144 h

25°C 3, 6, 9, 16, 24, & 48 h

37°C 3, 6, 9, 16, 24, & 48 h

pH 5 12, 18, 24, 48, 72, & 96 h

6 3, 6, 9, 16, 24, 48, & 72 h

NaCl concentration 2.5% 3, 6, 9, 16, 24, & 48 h

5% 3, 6, 9, 16, 24, & 48 h

10% 12, 24, 72, 96, 120, & 144 h

Skimmed Milk 4°C 12 h, 24 h, 3 d, 6 d, 9 d, &12 d

10°C 12, 24, 72, 96, 120, & 144 h

37°C 3, 6, 9, 16, 24, & 48 h

Ground beef extract 37°C 3, 6, 9, 16, 24, & 48 h

Table 2.4 environmental conditions and AI-2 production in L. monocytogenes parameters Incubation condition Luminescence (RLU)a Plate count (Log CFU) 4°C 18000 5.8 Temperature 10°C 12000 6.0 25°C 510000 6.3 37°C 525000 6.6 4 8000 3.6 pH 5 0.0800 4.1 6 0.1500 5.9 2.5% 376000 6.5 NaCl % 5% 322000 6.6 10% 71000 5.6 (a) Relative light unit

Figure (2.1) Lmo1288 gene in Listeria monocytogenes strains

Arrow indicates position of 486 bp in the ladder. a, L. monocytogenes Lineage I; b, L. monocytogenes Lineage I; c, L. monocytogenes Lineage III; d, L. monocytogenes Lineage II; e, L. monocytogenes Lineage II; f, Negative control; g, positive control

Figure (2.2) Autoinducer-2 (AI-2) bioassay

1 2 3 4 5 6 7 8 9 10 11 12 16 18 20 24 6.00E+05

5.00E+05

4.00E+05

3.00E+05

2.00E+05

1.00E+05

AI-2 Activity Relative Light Unit (count per second/OD)per (count Unit Light Relative 0.00E+00 BB152 4.5 5.6 6.6 7.5 8.3 9 9.6 9.9 10.1 10 10.2 9.9 8.7 8.4 8.1 5.4 log

(AI-2) bioassay conducted 3 times with the same samples. Listeria monocytogenes (red) and positive control Vibrio harveyi (green). -1 Results are expressed as relative light units (RLU) defined as counts min and adjusted to OD600 (RLU/OD600). The data represent standard deviations of the results of 3 independent replicate trials.

Figure (2.3) Autoinducer-2 (AI-2) bioassay for Listeria monocytogenes under different environmental and food conditions

AI-2 Production

6.00E+05

Control (+) BB152 5.00E+05 LM37 4.00E+05 LM 25 LM 10 3.00E+05 LM 4

2.00E+05 LM 2.5% LM 5% 1.00E+05 LM 10% PH5

0.00E+00

- PH6

Relative Light Unit (count per second/OD)per (count Unit Light Relative

milk

PH6

PH5

milk

LM 4 LM

LM 25 LM

LM 5% LM LM37

LM 10 LM Beef extract

LM 2.5% LM LM 10% LM

Beef extract Beef Negative Control Negative Control Negative

Control (+) (+) ControlBB152

.Results are expressed as relative light units (RLU) defined as counts min_1and adjusted to OD600 (RLU/OD600). The data represent the standard deviations of the results of 3 independent replicate trials.

(LM= L.monocytogenes, 37, 25, 10 and 4 represent temperatures in °C, 2.5%, 5% and 10% represent NaCl concentrations)

CHAPTER 3 THE ROLE OF QUORUM SENSING OF L. MONOCYTOGENES EGD-e IN

GENE EXPRESSION

3.1 ABSTRACT

Whole-genome expression analysis using microarray provides very important information about the role of quorum sensing (QS) in gene expression and behavior of bacteria.

The lux and agr systems were mutated by in-frame deletion to test the gene expression against the wild-type strain. RNA was collected at different growth phases and temperatures. The late mid-log and mid-stationary phases were chosen for the RNA collection to compare the activity of both genes at these 2 different growth phases. Two temperatures were chosen: 37 °C, which represents infection temperature; and 4 °C, which represents refrigeration temperature. In the log phase at 37 °C, no significant differential gene expression was recorded in either the luxS or the agrD mutant. In the stationary phase, the agrD mutant had 332 upregulated genes and 14 downregulated genes. Interestingly, the luxS mutant had 1 upregulated gene and 179 downregulated genes. The results at 4 °C showed no gene expression for luxS and agrD mutants in exponential phase, while in the stationary phase: 336 genes were downregulated in agrD mutant and 8 genes were upregulated in luxS mutant.

3.2 Introduction

The development of DNA sequencing techniques using computers and mathematical analysis is highly advantageous for biologists’ understanding and study of cell behavior and

function. DNA, the center point of molecular biology, stores all of the cell’s information, which is then transcribed to messenger RNA (mRNA) and translated into proteins (Angelova and

Myers, 2008). Ribotyping, pulsed-field gel electrophoresis (PFGE), polymerase chain reaction

(PCR), real-time PCR (qRT-PCR), and microarray all have been used to study the DNA and gene expression of cells. Use of these techniques allows researchers to reveal much of the unknown secrets about gene function and the role of gene regulators. Gene sequencing of bacteria and eukaryotes has helped define these roles and functions. Many strains of Listeria,

Escherichia coli, Salmonella, and many other species of bacteria are now fully sequenced and available for analysis in a database. The Listeria monocytogenes EGDe strain genome is 2.9 Mb long and has an average G+C content of 39%. The genome is characterized by open reading frames encoding a large number of surface proteins (4.7%) and an abundance of transport proteins, particularly proteins dedicated to carbohydrate transport (11.6%), as well as an extensive regulatory repertoire (7.3%) (Buchrieser et al., 2003). Online databases are available for the whole genome of L. monocytogenes EGDe and other strains; the Comprehensive

Microbial Resource (CMR) website provides links to all completed microbial genomes, including L. monocytogenes EGDe. The Kyoto Encyclopedia of Genes and Genomes, the Pasteur

Institute, and the National Center for Biotechnology Information (NCBI) are examples of online databases for complete genome sequence and gene function.

Microarrays have been introduced recently and have allowed researchers to study whole genome expression under specific growth or environmental conditions to identify regulatory genes related to physiological functions. Chan et al. (2007) studied the characterization of the L. monocytogenes cold regulon in log- and stationary-phase cells using microarrays. The study revealed transcription of many genes at low temperature in comparison with optimal temperature

for the growth of L. monocytogenes with genes responsible for adaptation to cold stress being primarily affected. The application of microarray in studying differential gene expression resulting from QS has been used by researchers to better understand the role of QS in bacteria.

Wagner et al. (2003) examined quorum sensing in P. aeruginosa at different growth and environmental conditions using microarray, and results showed hundreds of genes being regulated by QS pointing to its important role in P. aeruginosa. Reidel et al. (2009) studied the role of agrD system in L. monocytogenes at 37°C using microarray techniques, in which many genes were found to be up and down regulated by this system. The objective of our research is to study the role of QS systems (agrD and luxS) in L. monocytogenes at infectious (37°C) and refrigeration (4°C) temperature and we hypothesised a role of both QS systems in regulating gene expression.

3.3 Methodology

3.3.1 Microarray

3.3.1.1 Preparation conditions

3.3.1.1.1 Construction of Listeria monocytogenes mutant strains

In order to study the role of QS in L. monocytogenes, deletion mutations in luxS and agrD genes were constructed. Listeria monocytogenes (luxS-) and (agrD-) were constructed by allelic replacement using the method of Sela et al., (2006) and Riedel et al., (2009). Plasmid pKSV7, which can replicate in both L. monocytogenes and E. coli was used for in-frame deletion construction. For the luxS- mutant, a fragment from the luxS gene (containing 303 bp) was generated by colony PCR (Geneamp PCR 9700, ABsystems) using two primers

LUXMU-FR: CTGCAGAGTTTAGAAGGTGCAAAAGC,

LUXMU-RE: CTGCAGAGTTTAGAAGGTGCAAAAGC).

Nucleotides in bold represent PstI site added to the 5´ region of the primer. Both DNA fragment and pKSV7 (kindly provided by Dr. Portnoy, University of Pennsylvania, USA) were digested with PstI (New England BioLab, Pickering, ON) using the manufacturer’s protocol. Briefly, a mixture of 2µl DNA, 1µl 10x Buffer, 6.5µl water and 0.5µl PstI was prepared and incubated at

37°C for 1 hour. Digestion was confirmed by gel electrophoresis. Next, ligation of the luxS DNA fragment in pKSV7 Plasmid was performed. Fifty ng of plasmid were mixed with 150 ng of

DNA fragment and the volume was adjusted to 10µl by water. Ten µl of Quick Ligation Buffer were added to the mixture along with 1µl T4 ligation buffer and were mixed thoroughly. This was followed by centrifugation at 6000 rpm for 5 seconds and incubation of mixture at room temperature for 5 min. The ligation for psKV7:luxS was confirmed by gel electrophoresis using

M13 forward and reverse primers (FR: GTAAAACGACGGCCAGTG, RE:

GGAAACAGCTATGACCATG). Recombinant plasmid was introduced into E. coli DH5α by heat shock treatment protocol (New England BioLab). Basically, E. coli DH5α competent cells

(Invitrogen) were thawed on ice, then 5ng (2µl) of recombinant plasmid was chilled in a 1.5 ml microcentrifuge tube and 50 µl of the competent cells were mixed gently with the plasmid and incubated on ice for 30 min. The mixture was then heat shocked at 37°C for 2 min and immediately transferred to ice and incubated for 5 min. Super Optimal Broth (950µl) with catabolite repression (SOC) medium was then added to the competent cells and the suspension was incubated for 1 hr at 37°C. The competent cells were spread plated on LB medium containing 50µg ampicillin. E. coli DH5α was kept at -80°C until further use.

Listeria monocytogenes competent cells: competent cells of L. monocytogenes were prepared according to a protocol posted online by Mathilde (The Science Advisory Board, 2003).

An overnight culture of L. monocytogenes was grown on BHI containing 0.5M sucrose at 37°C.

The overnight culture was subcultured (1/20) in 250ml of BHI-0.5M sucrose and grown to

OD595nm = 0.4. Penicillin G sample of 1 ml from stock solution (12.5 mg/ml PG + 20 ml 2N

NaOH) was then added to the culture to weaken the cell membrane. The growth was continued to OD595nm = 0.7 and then the culture was placed on ice for 10 min in sterile centrifuge tubes.

The cells were centrifuged at 12,000 x g at 4°C for 5 min (Beckman Avanti J-20 XPI, Beckman

Coulter Inc., Mississauga, ON). Cells were washed with ice cold 0.5M sucrose and resuspended by swirling in 200 ml of same washing solution. The centrifuge/washing steps were repeated twice. After centrifugation, the cells were resuspended in 2.5ml 0.5M sucrose and were distributed in 100µl aliquots and kept at -80°C.

pKSV7:luxS plasmid was then introduced to L. monocytogenes competent cells by electroporation using the Gene Pulser Xcell (Bio-Rad). L. monocytogenes competent cells

(100µl) were added to a 0.1cm electroporation cuvette (Bio-Rad) and mixed with 2µl pKSV7:luxS plasmid. Electroporation conditions were set up according to Alexander et al.

(1990). Conditions were set as follows: field strength of 8·5 kV/cm, 200 Ohms resistance, 25 μF capacitor with a time constant of 5 ms. Immediately after electroporation, 2x BHI broth was added to L. momocytogenes and the suspension was incubated at 37°C for 30 min. Cells were spread plated on BHI containing 50µg/ml chloramphenicol and incubated at 37°C for up to 48 hrs. Grown cells were transferred to BHI medium with no antibiotics and grown for 48 hrs.

Finally, the presence of the required in-frame deletion was confirmed by PCR amplification of

DNA using primers (Sela et al., 2006) from flanking chromosomal DNA regions:

FR: ATTCACCACATCTTGGCTTTCT,

RE: AAGGCGAACCAACTTCTATTTG and by AI-2 bioassay as described in Chapter 2.

The AgrD mutant strain was kindly provided by Dr. Colin Hill (University College Cork,

Ireland).

3.3.1.1.2 Culture preparation conditions

For culture preparation, the method of Chan et al. (2007) was used with slight modification. The L. monocytogenes EGDe wild type and the luxS- and agrD- mutants were used throughout this study. L. monocytogenes EGDe, luxS-, and agrD- were streaked onto BHI medium from -80 °C glycerol stock culture and incubated at 37 °C for 24 hrs. A single colony was picked up and inoculated into BHI broth and the culture was incubated overnight with shaking (250 rpm) at the same temperature. A 1-mL aliquot of the overnight culture was diluted

1:100 in fresh BHI broth and again incubated at 37 °C until an OD595nm of 0.6 was reached. The

L. monocytogenes suspension was used to inoculate 75 mL of pre-chilled (4 °C) or pre-warmed

(37 °C) BHI broth (in a 300-mL Erlenmeyer flask). Cells were then incubated either at 4 °C for

12 days or at 37 °C for 24 hrs without shaking. A sample (100µl) was taken daily for plate count and OD measurement during the incubation period. At both temperatures, cells in the log phase were collected at 8 h for the culture incubated at 37 °C and 8 days for the culture growing at 4

°C. Stationary phase cells were obtained at 15 h and 12 days from cultures at 37 °C and 4 °C, respectively. Cells were centrifuged with RNAprotect Bacteria Reagent (QIAGEN, Valencia,

CA, USA) for total RNA isolation.

3.3.1.2 Total RNA isolation

RNA was extracted from L. monocytogenes using a RiboPure-Bacteria Kit (Applied

Biosystems). Cultures were grown overnight in BHI broth at 37°C to reach cell count of approximately 0.5 x 109 then, 5 ml of the broth culture was mixed with 5 ml RNA protect

(Qiagen) and incubated at room temperature for 5 min. Mixture was then centrifuged at 5000 x g for 10 min to precipitate cells (Beckman Coulter Inc.). Cells were re-suspended in 350 µl

RNAwiz solution by vortexing for 30 sec. Then, the re-suspended cells were transferred to a screw cap tube containing 250 µg of zirconia beads and cells were beaten at the highest speed using a vortex adapter (Applied Biosystems) for 10 min followed by centrifugation at 14000 x g for 5 min to pellet the zirconia beads. The mixture was then added to 0.2 volume of chloroform and incubated for 10 min. After incubation, the mixture was spun at 14000 x g for 5 min to separate the phases as part of the partial purification process. The aqueous phase was then transferred to a new tube by micropipette and a 0.5 volume of 100% ethanol was added, mixed, and then transferred to another tube containing glass fiber filter cartridges (Applied Biosystems).

RNA purification was performed using the kit solutions (Applied Biosystems). Simply, filter cartridge was washed with Solution 1 by adding 700 µl to the filter followed by centrifugation at

14000 x g for 1 min. the flow-through was discarded and filter was placed in same collection tube. Second washing took place with 500 µl of solution 2/3 followed by centrifugation at 14000 x g for 1 min. Flow-through was discarded and filter was placed in same collection tube. This step was repeated once and followed by a centrifugation one more time and filter was transferred to new collection tube. RNA was eluted by applying 25 μL Elution Solution, preheated to 95–

100°C, to the center of the filter, centrifuged for 1 min. elution step was repeated one more time and total RNA volume was 48 µl. RNA was then treated with DNAse I by adding 1/9th volume of 10X DNase buffer and 4 µl DNase I (2U/µl). Mixed gently by vortexing and incubated at

37°C for 30 min. after incubation, DNase Inactivation Reagent was added at volume equals to

20% of total volume of mixture to inactivate DNase and vortexed vigorously and incubated at room temperature for 2 min. lastly, mixture was centrifuged at 12000 x g for 1 min to pellet the

DNase Inactivation Reagent, then RNA solution was transferred to a new RNase-free tube. The concentration of which was detected using a Nanodrop 1000 spectrophotometer

(Thermoscientific) by applying 2 µl to the lower measurement pedestal and reading was taken using nanodrop software (Thermoscientific) at 260/280 nm.

3.3.1.3 Microarray construction

Whole-genome microarray slides were obtained from Dr Martin Wiedmann (Cornell University,

USA) and included 70-mer oligonucleotides representing 2,857 open reading frames that were identified based on the annotated L. monocytogenes EGDe genome and the methodology established by Chan et al. (2007).

3.3.1.3.1 cDNA labeling and competitive microarray hybridization

Total RNA was extracted as previously described and used for cDNA synthesis. For each group, a total of six arrays were hybridized. A dye-swap design was used to control for dye- specific effects. For each reaction, 3 µg of random primers (Invitrogen) were added to the RNA

(15 μg) in a 0.2 mL thin-walled PCR tube to a total volume of 18.5 µL in water. The reaction was incubated in a thermal cycler at 65 °C for 5 min, followed by 42 °C for 5 min. A total of 8

μL 5× first-strand buffer (Invitrogen), 4 μL dithiothreitol (DTT) (Sigma-Aldrich), 9 μL aa-dNTP mix consisting of 2.22 µM dATP, dCTP and dGTP and 0.67 µM dTTP and aminoallyl-dUTP

(Sigma-Aldrich), 0.5 μL RNasin (Promega, Madison, WI, USA), and 2 μL SuperScript II (SSII)

(Invitrogen) were added to the reaction for a total volume of 40.5 µL. The reaction was

incubated at 42 °C for 3 h, followed by enzyme inactivation at 95 °C for 5 min. Sodium hydroxide (1 M; 8 μL) was added and the reaction mixture was heated to 65 °C for 15 min to hydrolyze the RNA. This step was followed by the addition of 4 μl of 1 M TRIS, pH 7.5 and 8 µl

1M hydrochloric acid to neutralize the solution.

cDNA purification: Molecular grade water (500 μl) was added to an Amicon Ultra-0.5

30 kDa centrifugal filter tube (Millipore, Bellirica, MA) and spun at 14,000 × g for 5 min. The cDNA reaction was added to the filter containing 440 µl water and was spun again as above.

Following one additional wash with 400 µl water, approximately 35 μL was collected and DNA concentration was measured using the NanoDrop spectrophotometer at an absorbance of 260nm.

Cy-dye coupling was carried out as follows. The sample was concentrated by evaporation in vacuum concentrator for approximately 25 min and resuspended in 10 μL of H2O with the addition of 5 μL of 0.3 M sodium bicarbonate (pH 9.0). From the original stock of Cy-dye, 4.5 µl were suspended in DMSO and added to the sample, vortexed vigorously. The reaction mixture was incubated in the dark for 2 hrs to overnight at room temperature.

Cy-labeled cDNA purification was the last step before hybridization. Sigma water (45

μL) and NaOAc (35 μL; 100 mM) were added to the sample to bring the total volume to approximately 100 μL. Buffer PB (500 μL) was added and mixed with the sample. A Qiagen column (Qiagen, Toronto, ON) was used to spin the sample at 10,000 × g for 30 sec, followed by two washes with 750 µl buffer PE. The Cy-labelled cDNA was then eluted in 35 µl Buffer EB by centrifuging at maximum speed for 1 min. The eluted sample was collected and scanned using the NanoDrop spectrophotometer. Samples were used immediately for hybridization or kept in the freezer at -20 °C until use.

DNA hybridization: The fluorescently labeled cDNAs, Cy-3 and Cy-5, were concentrated to several µl in a vacuum concentrator for approximately 25 min and pooled by resuspending both in a total of 29 µl of hybridization buffer (Ocimum Biosolutions,

Gaithersburg, MD, USA). The labeled cDNA was denatured at 95 °C for 3 min, cooled on ice for

3 min, and applied to the microarray slide. . Competitive hybridization was performed in a humidified slide chamber at 42 °C overnight. The microarray slides were washed with gentle shaking at room temperature, unless otherwise indicated, to remove any unbound fluorescently labeled cDNA as follows: one wash for 5 minutes in 2x SSC, 0.1% SDS at 42°C; two washes for

5 minutes in 0.2xSSC, two washes for 2.5 minutes in 0.2x SSC; one wash for 10 sec in water.

The slides were then spun dry at 500 × g for 5 min.

Data analysis: Slide scanning was performed using a Scanarray Express Scanner (Perkin

Elmer, Waltham, MA). Fluorescence intensities were extracted using Scanarray Express software v3.0 (Perkin Elmer). Statistical analyses were performed using BRB ArrayTools v4.1, developed by Drs. Richard Simon and Amy Lam (http://linus.nci.nih.gov/BRB-

ArrayTools.html), and SAM (Statistical Analysis of Microarrays) v.3.11. Duplicate spots were averaged and data were filtered to exclude spots with raw intensities < 100 relative fluorescence units (RFU) for both channels. Log-transformed data were normalized using the Lowess function and differentially expressed genes were identified with SAM v. 3.11 using the one-class response type.

3.3.2 qRT-PCR qRT-PCR was used to confirm the expression of genes of interest in the microarray using the same culture incubation conditions. Primers were designed using Invitrogen OligoPerfect™

Designer (Table 3.1) and RNA samples were prepared and purified as described for the microarray experiments.

The purified RNA was used immediately for RT-PCR using cDNA high capacity reverse transcription kit (Applied Biosystems, Streetsville, ON). Briefly, 1 µg of RNA was reverse transcribed with 0.8 µl of dNTP (100 mM), 1 µl of Multiscribe Reverse transcriptase (50 ng/µl),

2 µl of 10 x random hexamer primers, 2 µl of 10 x RT buffer in an adjusted total volume of 20 µl using molecular-grade water. cDNA synthesis was performed in a thermal cycler (Eppendorf

Mastercycler, Hauppauge, NY) with the following conditions: 25°C for 10 min, 37°C for 120 min, 85°C for 5 sec and a cooling step to 4°C. The cDNA was stored at -20°C until further use.

Quantitative PCR amplification was performed in an ABI Prism 7900HT Sequence

Detector (Applied Biosystems,) using Power Sybr Green PCR master mix (Applied Biosystems) according to the manufacturer’s instruction. The PCR reaction was performed in a total volume of 25 µl, which contained 12.5 µl Power Sybr Green PCR master mix, 0.5 µl forward primer

(800 ng/µl), 0.5 µl reverse primer (800 ng/µl), 2 µl of 1:16 diluted cDNA in water and 9.5 µl of molecular-grade water. PCR conditions were as follows: denaturation: 95°C for 10 min; amplification and quantification repeated 35 times: 95°C for 15 sec, 55°C for 30 sec and 72°C for 30 sec for all genes of interest; melting curve program: 60°C to 95°C with a heating rate of

0.1°C per second and finally a cooling step to 40°C. Verification of each specific amplicon was performed by melting profile and the appearance of a single band of the expected size on a 1% agarose gel that was prepared by dissolving 1g of agarose in 100 ml 1x TAE buffer and left to solidify. Just before solidification, 0.5µl of ethidium bromide (10 mg/ml) (Bio-Rad) was added to the gel. After solidification, 1µl of amplicon was placed in the agarose gel well and run against 1kb plus DNA ladder at 95 volts for 1 hr. Three housekeeping genes were included in the

analysis: 16S rRNA, gap and rpoB for transcript level normalization for each sample. The relative changes in gene expression were calculated as described by Pfaffle (2001)

All of the qRT-PCR experiments were performed using the 3 RNA samples extracted from 3 different cultures under the same conditions applied to microarray RNA extracted samples. The analyses were repeated three times. A relative expression value of more than two-fold was considered as significant up- or down-regulation

3.4 Results

3.4.1Construction of Listeria monocytogenes mutant strain

Part of the lmo1288 gene (insert fragment) was successfully inserted into pKSV7 plasmid to obtain pKSV7L plasmid. Nucleotide sequence of the luxS gene confirms the in-frame deletion.

AI-2 bioassay tests were performed to confirm the deletion of lmo1288 and absence of AI-2 production by mutant strains. The luxS mutant strain cell free culture supernatant did not generate any luminescence in the AI-2 bioassay, indicating that AI-2 was not produced by the mutant.

3.4.2 Role of QS systems in L. monocytogenes-based microarrays

To assess the role of the agr and luxS QS systems on overall gene expression in L. monocytogenes EGDe, whole-genome expression analysis was performed using mRNA from the exponential and stationary phase of the wild-type strain and 2 mutants, luxS- and AgrD-, at optimal and refrigeration temperatures. Statistical analysis was performed separately for each microarray dataset. The P value was adjusted to 0.001 and at least a 1.5 fold change was used to identify differentially expressed genes. This analysis showed that at 37 °C, 14 genes were

upregulated and 332 genes were downregulated in the agrD mutant in the stationary phase

(Appendix A and B and Figure 3.3), while 1 gene was upregulated and 179 genes were downregulated in the luxS mutant in the stationary phase (Appendix C and D and Figure 3.4).

The exponential phase cells did not seem to respond to QS under the conditions tested, as only 2 genes were found to be upregulated in the agrD mutant and no significant differences were found between the wild-type and the luxS mutant in the log phase (Appendix E). Figures 3.5 and

3.6 show the heat map for agrD and luxS mutants during stationary phase at 37°.

The agrD mutant in the stationary phase at 4 °C presented similar significantly high transcript levels as those at 37 °C, as no genes were significantly upregulated and 336 genes were downregulated (Appendix F). Interestingly, no genes were downregulated in the luxS mutant at 4 °C when compared with the wild-type, but 8 genes were upregulated at the same temperature (Appendix G). Analysis of the TIGR role (gene categorized based on functions)

(http://cmr.jcvi.org/tigr-scripts/CMR/shared/RoleList.cgi) for the genes that were differentially regulated in the stationary phase at both temperatures and in both mutants revealed that the expression of all gene categories except for signal transduction and disrupted reading frame categories was influenced by the deletion of agrD and luxS. A good proportion (24%) of all the genes expressed in the stationary phase at 37 °C were also expressed in the stationary phase at 4

°C (Figure 3.1). On the other hand, 45% of the genes expressed in the agrD mutant at 37 °C were also differentially regulated in the luxS mutant at the same temperature (Figure 3.2).

3.4.3 Genes showing higher transcription in agrD mutant at both 4 °C and 37 °C

3.4.3.1 Downregulated genes in L. monocytogenes mutants: Of the 332 genes that were down regulated in the agrD mutant at 37 °C and the 336 genes at 4 °C in the stationary phase, 80

genes were present at both temperatures in the stationary phase, meaning they can be considered part of the agrD QS regulon (Appendix H). Those 80 genes represent different functions and categories in the genome that are mainly involved in metabolism. The agrD mutant in the stationary phase at 37 °C exhibited downregulation of some important virulence genes, including hly, plcA, inlA and inlH (Table 3.2 represents 10 important genes that are differentially regulated in the agrD mutant). On the other hand, the agrD mutant at refrigeration temperature exhibited differentially regulated virulence and regulatory genes that differed from those at the infectious temperature, except for plcA, which was differentially regulated at both temperatures. These include inlE, anti-sigma-B factor antagonist, P45 (peptidoglycan lytic protein P45) precursor, and the heat-inducible transcription repressor hrcA. The luxS mutant at 37 °C differentially expressed genes related to metabolism, ribosomal proteins, cold shock protein (CspD), inlC for pathogenesis, and other hypothetical proteins. At 4 °C, the 8 downregulated genes represented different functions, including amino acid biosynthesis, DNA metabolism, and protein transport and binding.

3.4.3.2 Upregulated genes in L. monocytogenes mutants

The percentage of upregulated genes compared to downregulated genes in the mutant strains was

<5%. The agrD mutant in the stationary phase at 37 °C exhibited 14 upregulated genes, including those for multi-drug resistance transporter and regulatory protein RecX. These are in addition to the genes involved in cell division that were upregulated in the log phase. No upregulated genes were found for the agrD mutant at 4 °C. For the luxS mutant at 37 °C, one gene was upregulated in the stationary phase only and was described as a hypothetical protein.

3.4.4 qRT-PCR confirmation of differentially expressed selected L. monocytogenes genes

Six genes were chosen to confirm the results obtained from microarray of the agrD mutant in the stationary phase at the infectious temperature: Gap, 16S rRNA, and rpoB were used as housekeeping genes (Chan et al., 2007; Tasara and Stephan, 2007), while agrD, hly, inlA,

CspD, plcA, and the fosfomycin resistance gene were tested for microarray validation. We tested

1 gene (lmo1849) in the agrD mutant in the stationary phase at refrigeration temperature.

Another 4 genes (luxS, lmo0943, lmo1683, and CspD) were tested for the luxS mutant in the stationary phase at 37 °C. After analysis, 16S rRNA was chosen as the housekeeping gene since it was the most stale housekeeping gene. Regarding the tested genes, agrD gene that was not expressed in microarrays due to poorly printed spots in the slide (a few genes were missing in the chip) was expressed in the wild type using qRT-PCR; most of the genes had similar findings on microarray except for lmo1849, which was not expressed in qRT-PCR. This finding validated the microarray results obtained earlier in this study. Table 3.3 represents the microarray-identified gene qRT-PCR analysis results.

3.5 Discussion

Results show a significant number of differentially expressed genes for both agrD and luxS QS systems. While agrD showed differentially expressed genes at both examined temperatures, luxS exhibited differential gene expression only at 37°C. It was obvious that most of the genes were downregulated with few upregulated genes. Technical replicates show consistency in the results, which gives support to data accuracy (figure 3.7). The importance of this study can be seen in the category of genes being expressed. Virulence genes, which are

essential for bacterial pathogenicity, were regulated by the agrD system. As well as stress genes like cold shock proteins and DNA protection during starvation genes regulated by both the agrD and luxS systems, genes associated with metabolism, DNA synthesis and replication were also found to be differentially expressed in the mutants. As we expected for quorum sensing, stationary phase cells of the agrD mutant grown at 37°C showed differentially regulated genes for 26 regulatory genes, 7 adaptation to atypical condition genes, and 3 genes for each pathogenicity, toxin production and resistance genes. Similar results were obtained for stationary phase cells of the agrD mutant at 4°C with 19 regulatory genes being differentially expressed, 10 genes included in cell division, and higher number of genes function in toxin production and resistance were regulated by agrD mutant at refrigeration temperature (6 genes). LuxS system regulates 16 regulatory genes and 3 adaptation to atypical conditions genes. One gene involved in pathogenesis was regulated by luxS (Table 3.4). In comparison between the virulence genes regulated by PrfA/sigmaB genes in a study done by Melohanic et al. (2003) with our study, we found that agrD system at 37°C regulated 11 genes that were also regulated by PrfA/sigmaB regulatory genes. Five genes were also regulated by agrD at 4°C while luxS system regulated only 1 gene (Table 3.5). This would give us a better understanding for a possible role of QS in L. monocytogenes. The study by Riedel et al. (2009) found 345 upregulated genes and 325 downregulated genes in the agrD mutant in the stationary phase, while 121 genes were differentially expressed in the exponential phase in contrast to the 2 genes that were differentially expressed in our study. Although the results display similar numbers of differentially expressed downregulated genes in the stationary phase between our study and the above study (332 vs. 325 genes), we were not able to compare the similarity of genes expressed in both studies, as the full list of differentially expressed genes were not published by the authors. The differences in the

other results may be related to growth conditions, the growth phase at which the samples were collected, and/or technical differences in the microarray experiments. Chan et al (2007) studied the cold regulon in L. monocytogenes by comparing gene expression between 2 temperatures 4°C and 37°C. He found different gene expression between the two temperatures and growth phases, suggesting that bacteria regulate the genes according to environmental and growth conditions, the same outcome that we found. Another study (Lina, 2010) on the role of biofilm formation at low temperature in gene expression in both exponential and stationary phases showed significant differences in gene expression between planktonic cells and biofilm. We compared the genes expressed in biofilm at stationary phase in a study performed by Dovalis (2010) at 4°C and agrD mutant strains grown under similar condition and found 10 genes that were commonly downregulated in both conditions (Table 3.6). This might suggest a possible contribution of agrD in biofim formation at low temperature. Overall, our data show that (1) a large number of L. monocytogenes genes are differentially regulated by both QS systems at 37°C, (2) a large number of L. monocytogenes genes are deferentially expressed in agrD system at 4°C, and (3) genes expressed in agrD and luxS mutants represented all major gene category and functions.

Results also show that agrD is involved in more cell functions, particularly virulence, than luxS, nevertheless, the results show the importance of the luxS gene in L. monocytogenes specifically during growth at its optimum temperature. At low temperature, only 8 genes were found to be differentially expressed by luxS gene, in Chapter 2, we demonstrated that AI-2 production was significantly lower at low temperature, here the result supports the finding as the role of luxS in overall gene expression seems to be limited.

3.6 Conclusion

This is the first study of the transcriptome of a luxS mutant strain of L. monocytogenes.

The aim of this study was to determine if luxS as a gene or as a QS system plays a role in, or contributes to cell growth, pathogenicity, or other functions. It is obvious from the results that under conditions tested QS contributes to cell metabolism and growth in the stationary phase at an optimal temperature, with 180 differentially expressed genes representing different gene categories. These findings need to be supported by physiological examination to observe the direct effect of luxS in L. monocytogenes. As for the agrD mutant, this is the first study to demonstrate the role of the agrD QS system at refrigeration temperature versus infectious temperature, and the similarity of the genes that were differentially expressed at both temperatures suggests that the agrD QS system directly regulates them, while the other expressed genes may have specific roles at different temperatures. A general role for QS is suggested as we found that both QS systems differentially expressed genes belonging to different functions and categories.

Table 3.1 Primers list used for qRT-PCR

Primer Sequence Source 16S rRNA FW agcgttgtccggatttattg this study 16S rRNA RE ctacgcatttcaccgctaca this study gap FW cagcaactggcgatatgaaa this study Gap RE ggtcaccagtgtaagcgtga this study rpoB FW gaagttttgcgcgaatcagt this study rpoB RE cgaatatttcggctctccaa this study AgrD FW tcgccttagtaacagggcttt this study LUX FW actggcgggaacgaaagt this study hly FW ctggtttagcttgggaatgg this study hly RE atttcggataaagcgtggtg this study CSP RE attcaacgctttgaccttcg this study AgrD RE cgtgcaattcaatgttttgg this study plcA FW cccagaactgacacgagca this study fosfo RE tccttccataatgcaaatcca this study fosfo FW tgatttcaggattaagccatatca this study inlA RE aagtggcgttatgtccgtaag this study inlA FW ttcaggcggatagattaggg this study plcA RE ttgttttcacactcggacca this study CSP FW gaaaaaggcttcggtttcatc this study Lmo1849 FR gcgtgaacaaggtcaagtca this study Lmo1849 RE ctagcgcgagttgcttttct this study Lmo0943 FR ggcgaacaaatggatgaagt this study Lmo0943 RE cattgtcgccttctttgtca this study Lmo1683 FR cactcctcagcgtcatgcta this study Lmo1683 RE atcacggaaaacgcgtaaat this study

Table 3.2 10 important gene expressed in L. monocytogenes AgrD mutant

Gene Name

lmo0202_Listeriolysin O precursor (hly) lmo0201_1-phosphatidylinositol phosphodiesterase precursor (plcA) Lmo1580 universal stress protein

lmo0433_Internalin-A precursor (InlA) lmo1288_S-ribosylhomocysteine lyase (luxS) lmo2016_Cold shock-like protein cspLB

lmo1879_CspD protein

lmo1702_Fosfomycin resistance protein fosX (antibiotc resistance) lmo1454_RNA polymerase sigma factor rpoD lmo1379_Membrane protein oxaA 2 precursor (similar to B. subtilis SpoIIIJ)

lmo0263_Internalin H

lmo1409_Multidrug resistance transporter

Protein functions are based on annotations provided by ListiList (http://genolist.pasteur.fr/ListiList/),

TIGR (http://cmr.tigr.org/tigr-scripts/CMR/CmrHomePage.cgi), the NCBI

(http://www.ncbi.nlm.nih.gov/), and the KEGG Sequence Similarity Database

(http://www.genome.jp/kegg/ssdb/).

Table 3.3 qRT-PCR analysis of Microarray identified genes

Primer Microarray qRT-PCR Hly 10.2 10.6 CspD 6.8 6.7 InlA 10.8 12.7 plcA 10.3 9.9 Fosfomycing RS 4.1 2.5 lmo1849 4.5 - lmo0943 4.2 5.1 AgrD - 20.6 luxS 6.9 6.0 lmo2467 5.2 5.2 lmo1683 6.4 4.8

Table 3.4 Gene role classifications for differentially regulated genes in agrD and luxS mutant strains at 37°C and 4°C

luxS stat agrD stat Gene role Type agrD stat 37°C 37°C 4°C regulatory DNA 3 3 3 function RNA 1 0 1 protein 1 1 1

other 21 12 14 Regulatory function Total 26 16 19 cell division 5 2 10 Detoxification 4 2 2 toxin production and resistance 3 1 6 Pathogenisis 3 1 2 adaptation to atypical condition 7 3 4 Motility 0 0 4

Table (3.5) genes commonly regulated by regulatory genes and QS systems gene regulation agrD 4 agrD 37 luxS 37 Virulence 1 3 1 prfa controlled 1 3 1 prfA + sigma factor 5 11 1

Table (3.6) genes commonly regulated in agrD system and L. monocytogenes biofilm both at 4°C

Gene Function Cell membrane function lmo0264 Internalin E Cell metabolic pathways/Biosynthesis lmo2336 Fructose-1-phosphate kinase lmo1187 hypothetical protein lmo0611 FMN-dependent NADH-azoreductase 1 Replication, Transcription, Translation lmo2334 hypothetical protein lmo1041 hypothetical protein lmo2145 hypothetical protein lmo1965 hypothetical protein lmo0479 Putative secreted protein

lmo1171 PduQ protein

Figure 3.1 Comparison of genes down-regulated in AgrD and LuxS mutants in Stationary phase grown at 37C

Figure 3.2 Comparison of genes down-regulated in AgrD mutants in Stationary phase grown at

37°C and 4°C

Figure 3.3 microarray slides screening for AgrD mutant of staionary phase at 37°C

Figure 3.4 microarray slides screening for luxS mutant of staionary phase at 37°C

Figure 3.5 Heat map for agrD mutant, stationary phase at 37°C from four arrays test

stat_27 stat_28 stat_29 stat_30

- - - -

Agr Agr Agr Agr

Includes genes with >1.5 fold change in >20% of samples

Figure 3.6 Heat map of luxS mutant stationary phase at 37°C representing four arrays

Includes genes with >1.5 fold change in >20% of samples

Firgure 3.7 Clustering of technical replicate dye-flip genes

Clustering of technical rep dye-flip genes

Includes genes with >1.5 fold change in >20% of samples

-technical replicates are consistent

CHAPTER 4: THE ROLE OF QUORUM SENSING IN SURVIVAL, GROWTH, AND BIOFILM FORMATION IN LISTERIA MONOCYTOGENES EGDe

4.1 ABSTRACT

This study compared Listeria monocytogenes wild-type and 2 quorum-sensing (QS) mutant strains AgrD- and luxS- with respect to the response to different environmental stresses, antibiotic resistance, bacteriophage infectivity, and biofilm formation First, environmental stresses like low temperature, low pH, high salinity, and a combination of 2 stresses were examined. L. monocytogenes QS mutant strains were found to have different growth/survival patterns when a combination of 2 stresses was tested, but behaved in a similar manner when only

1 stress was applied. Five antibiotics were chosen to study a potential role for QS in antibiotic resistance. The AgrD mutant was more susceptible to antibiotics than the wild-type strain. The luxS mutant behaved differently; being more resistant to the antibiotics tested. Using a simulator of the human intestinal microbial ecosystem, the three L. monocytogenes EGDe strains were examined for survival in the human digestive system and behaved similarly. All the strains tested were susceptible to the tested bacteriophages at the same multiplicity of infection. Biofilm formation, an important factor pertaining to food safety, is defined as the formation of a surface- attached sessile bacterial community embedded in extra polymeric substance (EPS). We studied biofilm formation in the L. monocytogenes wild-type strain and the QS mutant strains AgrD−- and luxS−. Confocal microscopy showed that the luxS mutant formed denser biofilms than the

WT and that the AgrD mutant forms a sparse biofilm. When the ability of the mutants to attach to glass was determined, as expected, the AgrD− mutant had fewer attached cells than wild-type or luxS− strains, which exhibited similar numbers of attached cells. These results indicate a

possible role for QS in cell behavior and function of L. monocytogenes. Biofilm of wild type and

QS mutant strains agrD and luxS were resistant to benzalkonium chloride and ethanol, but were susceptible to sodium hypochlorite.

4.2 INTRODUCTION

Listeria monocytogenes is a facultative intracellular pathogen that can survive in a wide range of ecological niches (Chaturongakul and Boor, 2006). This characteristic makes it very difficult to control. L. monocytogenes can survive or grow across a wide range of temperatures

(1–50 °C), pH (4–9.5), as well as at a water activity as low as 0.90, and high salinity (Kim et al.,

2005; Moltz and Martin, 2005). Thus, the organism can contaminate a wide range of foods primarily due to its ability to survive a variety of environmental stresses, thereby leading to diseases caused by ingesting food contaminated by this food-borne pathogen.

Recent molecular studies revealed an important role of certain regulatory genes in bacterial survival under harsh conditions. Li et al. (2011) studied the role of σB factor in antibiotic resistance and found that a σB mutant was more susceptible to antibiotics than the parent strain. Kazmeirczak et al. (2003) found that σB regulates stress responses and virulence in

L. monocytogenes. While much of the focus was given to σB factor as a stress regulator and prfA as a virulence regulator in L. monocytogenes, the role of QS as a stress regulator in some Gram negative bacteria has been demonstrated. Joelsson et al. (2007) found that QS enhances stress responses in the marine pathogen V. rholerae by up regulating the RNA polymerase sigma factor rpoS. Another study by Lumjiaktase et al. (2006) detected a role for QS in the oxidative stress response of Burkholderia psuedomallei.

The formation of biofilm, which is defined as a structured community of cells enclosed in a self-produced polymeric matrix that is adherent to an inert or living surface, is another factor that challenges food safety (Moretro and Langsrud, 2004). Cells present in biofilms are resistant to disinfectants and sanitizers and persist in food processing and production environments

(Jordan et al., 2008) and consequently cross contaminate foods. L. monocytogenes can form biofilms on different surfaces, including rubber, stainless steel, polypropylene, and glass (Molt and Martin, 2005). Gene expression and regulation studies revealed an important role of some gene regulons in biofilm formation. For example, the Lm.G_1771 gene (encoding a putative

ABC-transporter permease) was found to regulate biofilm formation in L. monocytogenes and

(Zhu et al., 2011). Belval et al. (2006) and Sela et al. (2006) both found the luxS mutant in L. monocytogenes formed denser biofilm than the parent strain. Belval et al. (2006) suggested a metabolic role rather than a QS function for luxS in biofilm formation. Ruei et al (2007) and

Reidel et al. (2009) found a different role for the agrD system in biofilm formation as they found that an agrD mutant formed less biofilm than the parent strain. They suggest a role in cell detachment for the agrD system in L. monocytogenes. In the previous chapter, we investigated the role of quorum sensing (QS) on gene expression in L. monocytogenes and found that both agrD and luxS systems contribute to general gene expression. In the current chapter, we aim to study the role of QS systems in cell behavior and function by examining cell physiology and survival. Growth under unfavorable conditions, antibiotic resistance, biofilm formation, and the effect of disinfectants are covered in this chapter.

4.3 METHODOLOGY

L. monocytogenes EGDe wild-type, luxS− mutant, and agrD− mutant were examined under different conditions by using tryptic soy agar (TSA) or broth (TSB) as the growth media.

Cultures were prepared as described in Chapter 2 (section 2.2.1).

4.3.1 General and selective media used

L. monocytogenes is usually cultured in a complex media like TSB and BHI, and isolated with a selective medium like Oxford or selective enrichment Fraser medium. To study whether the growth of each strain is affected by medium composition, the bacteria were grown in 4 different general and selective media: brain-heart infusion (BHI), a rich medium for Listeria; a general medium (TSB); Luria–Bertani (LB) medium; and a selective medium (Fraser medium) at

37 °C for 24 h. The growth was assessed using Bioscreen C Microbiology Plate Reader

(LabSystems, Helsinki, Finland). The following parameters were used: a single wide-band (wb) wavelength; incubation temperature, 37 °C; preheating time, 30-min; and kinetic measurement, measured every 60 min for 24 h. A diluted overnight bacterial culture (200 μL; 103 CFU/mL) was transferred to each of the 100 wells of the sterile honeycomb plates of the Bioscreen Reader

(Fisher Scientific, Mississauga, ON). The optical density (OD) data were analyzed using

Bioscreen data processing software version 5.26 (Labsystems). All samples were tested in triplicate and replicated at least 2 times. Samples were collected at regular intervals of 3 h to determine the bacterial count using the plate count method. A 1ml aliquot was taken and serial dilution was performed with Phosphate Buffered Saline (PBS) and 100µl were plated onto TSA medium and incubated at 37°C for 24hrs. The objective of this experiment is to ensure that

media used for L. monocytogenes wild type and mutant strains will not affect the experiment or results obtained by affecting growth of wild type or mutant strains.

4.3.2 Bacteria grown under different environmental conditions

pH, temperature, and osmolarity were set as the stress factors/conditions to examine the possible role of the luxS and agrD QS systems in growth or survival. Bacterial cells were grown overnight and diluted to 103 CFU/mL in TSB, transferred to TSB at pH 4, 5 or 6, and incubated for 24 h. Hourly samples were collected to determine the bacterial count using the plate count method explained above (section 4.3.1). For studies relating to low temperature, the cells were incubated at 4 °C for 30 days and a sample was collected every 2 days to determine the bacterial count and OD. Osmolarity test was performed inoculating the diluted overnight culture in 5% and 10% NaCl. The effect of combination of 2 stresses (low temperature and low pH) was studied at 4 °C and pH 5.5 and 4.5 as well as low temperature with 5% and 10% NaCl. A sample was collected every 2 days for the first 12 days, and then at day 15, 20, 25, and 30. All samples were taken in triplicate and the experiments were repeated at least 2 times.

4.3.3 SHIME system

The present study sought to evaluate the differential survival of L. monocytogenes wild type and mutant strains (luxS- and agrD-) through a simulation of the GI tract using the SHIME system. To study survival, we employed the upper three vessels of the SHIME that represent stomach, duodenum/jejunum and ileum, respectively.

The SHIME is a dynamic and continuous model that mimics the gastrointestinal tract

(GI). The model was first described by Molly et al. (1993). The unique characteristics of each segment of the GI tract are represented by individual culture vessels connected via peristaltic

pumps. The SHIME utilizes a 5-vessel reactor that mimics the stomach, duodenum and jejunum, the ileum, ascending colon, transverse colon, and descending colon. A six-stage culture vessel system was developed in 2001 (Figure 4.1).

In the present study, the SHIME was set up according to Abbeele et al. (2010), but with six vessels as described by De Boever et al. (2001). The SHIME feed consisted of (in g/L) arabinogalactan (1.0), pectin (2.0), xylan (1.0), starch (4.0), glucose (0.4), yeast extract (3.0), peptone (3.0), mucin (1.0) and cystein (0.5). The pancreatic juice contained 150 mmol/L

NaHCO3, bile salts (6.0 g/L) and pancreatin (0.9 g/L). Three times per day, 160 mL of SHIME feed and pancreatic juice were added to the stomach and small intestinal compartment, respectively. Table 1 shows the working volume, retention time, and pH conditions in Vessels 1,

2, and 3. The retention time and working volume were modified according to ICRP Publication

89 (2002).

After setting up the SHIME, a study of the survival of L. monocytogenes was conducted.

An overnight culture of L. monocytogenes (wild type, agrD and luxS mutants each on separate days) grown in BHI broth was delivered into the SHIME after dilution to 103 CFU/ml and allowing the growth to reach 106 CFU/ml. In Vessel 1, the bacteria were exposed to acidic conditions (pH 2.0-2.5) for one hour. They then were transferred to Vessel 2 where addition of pancreatic juice and elevation of pH to neutral took place. After another one hour of retention time, the bacteria were transferred to Vessel 3, which was retained at neutral pH 6.0-7.0 for three hours.

To determine the differential survival of L. monocytogenes, samples were collected from each vessel after 45 min, 2, and 5 h of treatment. Enumeration of bacteria was done on BHI agar

at 37°C for 24 h. all vessels were autoclaved and tested for absence of any possible presence of microorganisms by plating samples from each vessel in Nutrient Agar.

4.3.4 Antibiotic resistance

The aim of this experiment was to examine the possible role of QS systems in bacterial antibiotic resistance by using 5 different antibiotics known to be effective against L. monocytogenes, including amoxicillin, ampicillin, erythromycin, gentamicin, and vancomycin

(Oxoid, Hants, UK). Antibiotic strips (M.I.C.E. strips; Oxoid) containing graded antibiotic concentrations were used to determine differences in the minimal inhibitory concentration

(MIC), if any. The strips were used according to the manufacturer’s protocol. Simply, Muller

Hinton agar (MH) + 5% sheep’s blood (Diagnostic Systems, Franklin Lakes, NJ, USA) was used as a culture medium and 0.5 McFarland inoculum of the L. monocytogenes wild-type strain, luxS mutant, and AgrD mutant was spread over the plate, and the M.I.C.E. strips were applied to the dried surface within 15 min. The plates were incubated at 37 °C for 24 h. Each test was performed in triplicate for each organism and repeated at least 2 times.

4.3.5 Susceptibility to bacteriophages

In this experiment, the infectivity of L. monocytogenes phages was examined for the wild-type strain, luxS mutant, and agrD mutant by spotting 20 μL of the phage suspension (1 x

109 pfu/ml) over a lawn of the bacterial host on TSA plates. Three L. monocytogenes phages were used: V8, V13, and V20 (Culture Collection, CRIFS, University of Guelph, Guelph,

ON). The plates were examined for the zones of lysis as sensitive/resistant after 18 h at 25 °C.

4.3.6 Biofilm formation

4.3.6.1 Microtiter plate assay

The L. monocytogenes strains tested (wild-type strain, luxS mutant, and agrD mutant) were grown in TSB and incubated for 18 h at 30 ºC. Culture (20 µL) was then inoculated into 5 mL of TSB and incubated for 18 h at 20 ºC, followed by inoculation of 125 µL into 5 mL of fresh TSB. Microtiter plate assay for biofilm formation was conducted as described by Harvey et al. (2006). Culture suspensions were vortexed for 1 min and 100-µL volumes were transferred into 6 wells of a sterile polystyrene plate. Plates were incubated at 20 °C for 24, 48, or 72 h. In each plate, 8 wells containing 100 µL of sterile medium were set as controls. Using a spectrophotometer at 595 nm, the turbidity of the aliquots collected post incubation was determined. The cells attached to the wells were then washed three times with 150 µL of sterile water to remove loosely attached bacteria, and then dried at 30 °C for 30 min. Biofilms were quantitatively assessed by staining the wells with 1% aqueous crystal violet (CV) solution (150

µL) and incubating at 20 °C for 45 min. The CV solution was removed, and the wells were washed three times with sterile water (150 µL) and air dried at 30 °C for 30 min. In order to de- stain the biofilm, alcohol (95%; 100 μL) was added to each well, and the concentration of CV was determined by measuring the OD at 530 nm (CV-OD530 value). The mean CV-OD530 value was adjusted with the mean CV-OD530 of the controls included in each microtiter plate. Assays were performed in triplicate for each L. monocytogenes test strain.

4.3.6.2 Biofilm formation assay using confocal laser scanning microscopy

Confocal laser scanning microscopy (CLSM; Leica Microsystems Inc., Concord, ON,

CANADA) was used to examine the wild-type and mutant strains. The methodology of Sela et al. (2006) was followed with some modifications. Briefly, biofilms were grown overnight in BHI broth at 37 °C. The culture was diluted 1:100 in fresh BHI medium and transferred to glass cover slips submerged (horizontally) in a 35mm glass bottom dish of 20mm well size (In Vitro

Scientific, Sunnyvale, CA, USA) and then incubated up to 5 days at 25°C. Every day, plates were removed from incubators and were washed with sterile distilled water (DW), fixed with 4% glutaraldehyde for 5 min, and stained with 0.1% acridine orange, followed by CLSM examination. Parameters used included no treatment, formed biofilm treated with benzalkonium chloride (BC), formed biofilm treated with sodium hypochlorite (SHC), and formed biofilm treated with ethanol. This was carried out as follows: bacteria were grown in glass bottom dishes for 48 hrs to form biofilm, then the growth medium was removed and each disinfectant was added to the biofilm for 30 min except ethanol, which was left for 5 min. All were incubated at

25°C. Disinfectants were added at a concentration of 1 mg/ml for BC, 800 p.p.m. for SHC and ethanol at a concentration of 70%.

4.3.6.3 Attachment assay

This experiment was carried out as described by Sela et al. (2006). An overnight culture of the L. monocytogenes wild-type strain, luxS mutant, or agrD mutant was adjusted with BHI broth to an OD600nm of 1.0, and 2 mL of the resulting suspension were added to glass coverslips

(25 × 25 cm) submerged (horizontally) in a polystyrene micro-plate. The plate was incubated for

0.5 h at 37 °C, washed three times with double distilled water (DDW), and stained with CV as described above. Using a conventional microscope, stained bacteria were counted under 40× magnification. Ten fields per cover slip were counted.

4.3.7 Statistical analysis

Statistical analysis was performed using the Student’s t-test. Data were analyzed using pair wise comparison. A P value of ≤ 0.05 was considered significant.

4.4 Results

4.4.1 Growth on different general and selective media

Results of OD measurement and bacterial count determination for the wild-type and mutant strains showed no significant difference in the growth rate or cell count with respect to

BHI, Fraser, and TSB media (P > 0.05). LB medium showed a low cell count with no significant difference (P > 0.05) between strains. Thus, QS mutant strains are not affected by the media used in our experiments.

4.4.2 Bacteria grown under different environmental conditions

At pH 4, the cells of wild type, agrD and luxS mutants were incubated under static conditions for the first 24 h, and a slightly increased but non-significant growth rate was observed among the mutants in comparison with wild type. Bacterial cells were also incubated for 12 days at 4 °C, and no significant difference was found between the wild-type strain and the agrD mutant and a slight difference was found between the wild-type strain and the luxS mutant

(P > 0.05 and P < 0.04 for agrD and luxS, respectively) (Figure 4.2). Although the AgrD mutant showed downregulation of cold shock proteins in the microarray experiment, low temperature alone did not result in changes to growth rates at 4 °C. However, when the organism was subjected to a temperature of 4 °C and pH 5.5, the AgrD mutant showed the best growth rate, followed by the wild-type strain and the luxS mutant (P < 0.0007 and P < 0.007 for AgrD and luxS; Figure 4.2). At 4°C and pH 4.5, no significant difference was found between the wild-type strain and mutant strains (P > 0.05). When low temperature and salinity were used as the 2 stressors, both AgrD and luxS mutants showed tolerance at 5% compared with the wild-type strain, which showed reduced survival (Figure 4.4). At 4 °C and 5% NaCl, a 0.5 log reduction was observed for the wild-type strain at day 30, while the AgrD mutant exhibited a 0.2 log

reduction and the luxS mutant a 0.1 log reduction. The difference was significant between the wild-type strain and the AgrD mutant (P < 0.008). The luxS mutant survival curve can be split into 2 phases, first phase from day 0 to day 25 in which the survival was insignificant in comparison with wild type survival rate (P > 0.05); and second phase from day 25 to day 30 in which luxS and AgrD mutants showed tolerance and started to grow, while the cell number of the wild-type strain continued to decline. On the other hand, the cells number of all strains showed a decline during the 30-day incubation at 4 °C with 10% salinity (Figure 4.5). Thus, the mutant strains were more tolerant than the wild-type strain (AgrD, 0.3-log reduction; luxS, 0.6-log reduction; wild-type, 0.9-log reduction) with a significant difference of P < 0.002 and P < 0.01 for the AgrD and luxS mutants, respectively.

4.4.3 SHIME system: the survival of L. monocytogenes wild type and both QS mutant strains showed no significant difference (P > 0.05). A sharp decrease in cell number was recorded after 45 min in comparison with 0 time (starting log was 8-9 for the three strains tested) and after 45 min cell log number was from 0-2. The cell number started to increase again in the second and third vessels but with no significant difference between wild type and agrD and luxS mutants (Figure 4.6).

4.4.4 Antibiotic resistance: To study the effect of QS on antibiotic resistance, 5 antibiotics were chosen using the antibiotic strips. Table 4.1 represents the list of antibiotics and the minimum inhibitory concentration (MIC) of each L. monocytogenes strain. Figure 4.7 is the graphical representation of the results of antibiotic resistance test, while Figure 4.8 shows MH agar with antibiotic strip. In general, the AgrD mutant showed maximum susceptibility to antibiotic (4 of 5 antibiotics), while the luxS mutant and the wild-type strain were most resistant

(2 of 5 antibiotics); in 1 case, the MIC of the wild-type strain and the luxS mutant was identical.

No siginificant differences were found between the three strains tested with ampicillin and amoxicillin, but the luxS mutant showed a significant difference in resistance when compared to the wild type for vancomycin (P < 0.01), erythromycin (P < 0.03) and gentamycin (P < 0.004). In the case of vancomycin and gentamycin, the luxS mutant was more resistant than wild-type and for erythromycin the luxS mutant was more susceptible than wild type. With the AgrD mutant the difference was only significant with erythromycin for which the mutant was more susceptible than the wild type. This finding provides evidence of a possible role for QS in bacterial antibiotic resistance and susceptibility.

4.4.5 Bacteriophage infectivity: Three different L. monocytogenes bacteriophages were tested in duplicate on each plate containing a lawn of L. monocytogenes wild-type strain, luxS mutant, or AgrD mutant. The test was repeated three times. Cell lysis was found in all tested strains. Re-infectivity test and free bacteriophage count need to be investigated to look further in the role of QS in bacteriophage infectivity (Figure 4.9 a, b and c).

4.4.6 Biofilm formation:

4.4.6.1 Microtitre plate assay: Different parameters were examined. BHI medium was

compared with TSB and the formed biofim was treated with BC, ethanol, and sodium

hypochlorite (SHC). Biofilm formation was better in BHI medium than in TSB for all the

3 strains tested (wild-type strain, AgrD mutant, and luxS mutant). SHC was the most

effective at removing biofilms. BC and ethanol had no effect on biofilms. The results also

showed that the AgrD mutant formed less biofilm than the luxS mutant or the wild-type

strain (P < 0.0004). The luxS mutant formed denser biofilm than the wild-type strain (P =

0.05) in BHI medium. A similar result was obtained in TSB, but a slight yet significant

difference was found between the wild-type strain and agrD and LuxS mutant (P < 0.001

and P < 0.047, respectively). No significant difference was found among other

parameters (BC and Ethanol (P > 0.05; Figure 4.10).

4.4.6.2 Biofilm formation (confocal microscopy): Biofilm formation by L. monocytogenes wild-type and QS mutant strains was assessed using CLSM. First, biofilm formation was detected at days 1, 3, and 5. Micrographs (Figure 4.11) show that at day 1, slightly different numbers of dispersed cells were found on the slide. At day 3, the luxS mutant formed a thicker biofilm in contrast to the wild-type strain. The AgrD formed sparser biofilm than the wild-type strain. An increase in biofilm in agrD mutant was observed on day 5. The agrD mutant had denser biofilm on day 5 than on day 3. The second experiment was performed by incubating the L. monocytogenes wild-type and mutant strains for 48 h alone or with 1mg/ml BC, 800 p.p.m. SHC, or 70% ethanol as a disinfectant. After 48 h, SHC removed the biofilms from the QS mutant strains, while the wild-type strain was resistant and had less biofilm than biofilm formed in absence of sanitizer. Interestingly, BC induced biofilm formation in the wild-type strain and the

AgrD mutant and had a lesser effect on the LuxS mutant. Ethanol had no effect on biofilms in the strains tested. The other notable finding was the effect of these disinfectants on biofilm structure as the cells seem to be more aggregated and elongated than cells in biofilm with no treatment (Figure 4.12).

4.4.6.3 Attachment assay: Attachment assays demonstrated that significantly fewer

AgrD mutant cells (140 ± 23 cells per field) attached to a glass surface compared with

wild-type cells (223 ± 26 cells per field; P < 0.01). On the other hand, cellular attachment

of the luxS mutant was significant (255 ± 20 cells per field; P < 0.001), as more cells

attached compared with the wild-type cells (Figure 4.13). Thus, repression of bioﬁlm

development in the AgrD mutant might be related, at least partially, to poor attachment

capacity, possibly due to repression of the genes encoding the structures necessary for

adhesion.

4.5 Discussion

The results of this study showed that QS did not induce any significant difference or play a role in bacterial survival/growth at low pH. Early results from Chapter 2 showed that AI-2 production was not detected at low pH. Yoon and Sofos (2010) examined the role of luxS system

(AI-2) in Salmonella at low pH (3 and 3.5) and concluded that an AI-2-based QS system did not appear to be associated with acid resistance. As for low temperature, our microarray results found that the AgrD system contributes to the expression of cold shock proteins; but, we did not see any difference in growth between the wild-type and mutant strains at low temperature (4°C).

However, since L. monocytogenes is capable of growing at this temperature, a possible role might be relevant at lower temperatures (<0°C). The application of 2 stresses (low temperature and low pH) showed a significant difference between the wild-type and the QS mutant strains.

This finding suggests a difference in the role of QS between the AgrD mutant and the luxS mutant. The AgrD mutant showed differential gene expression at low temperature, while the

LuxS mutant might be affected by lower pH (5.5), and thereby influence gene expression, since this condition was not examined in the experiment on genetic regulation. To our knowledge, no other studies used similar conditions for the purpose of comparison. For the luxS system, the studies of the effect of environmental stresses showed environmental tolerance in mutant cells.

Moslehi-Jenabian et al. (2009) suggested a clear role of luxS system in acidic stress response in probiotic lactobacilli when bacteria was exposed to acidic shock of pH (5, 4 and 3). Joelsson et

al. (2006) found that HapR (QS regulator protein) mutant cells in vibrio cholerae survived better as planktonic cells when exposed to low temperature (4°C) and suggested a loss or alteration of

QS functions that gives the advantage for planktonic cells to survive better in such conditions. As a Gram positive bacteria possessing luxS system, Streptococcus mutants luxS system was found to be involved in acid tolerance and oxidative stresses (Wen and Burne, 2004). These studies showed similar affect of luxS system to what we found, studying gene expression is recommended for further clarification of role of luxS in L. monocytogenes in stress conditions.

L. monocytogenes is a well-known halotolerant bacterium. Shabala et al. (2008) determined that the maximum NaCl concentration allowing bacterial growth was 13%.

Application of another stress might have inhibited the growth of or killed the bacterium. In our present study using NaCl concentrations of 5% and 10%, the mutant strains were found to be more resistant than the wild-type strain. In the presence of 5% NaCl, wild type strain showed tolerance for the first 25 days, and then cell number started to decline while for the mutant strains

(agrD and luxS) growth was detected at the same point (25 days). And in the case of 10% NaCl, wild type strain started to decline after 10 days of incubation while luxS mutant showed a resistance and a decline started 10 days later than wild type decline (in day 20), agrD mutant showed more tolerance and started to decline after day 25. The resistance of mutant strains may be due to salt shock proteins up regulations or other stress regulatory proteins. According to our microarrays data, in agrD mutant, KdpB protein (responsible for potassium transport) was found to be down regulated and in luxS mutant, lmo1422 (osmoprotectant transport system) was found to be down regulated. Interestingly, a down regulation of lexA repressor enzyme that represses the regulatory stress response system SOS was found in both QS mutant strains. SOS system was found to be involved in stress resistance (Van der Veen et al., 2010). This can explain the

tolerance of mutant strains to environmental stress in contrast to wild type. Up to the best of our knowledge no other studies linked between survival in osmotic stress and QS. Combining 2 or more stresses might induce different regulatory genes that interfere with each other, including

QS genes. Some studies found that QS regulates stress responses and enhances survival in some bacteria. Joelsson et al. (2007) found that the QS signaling system acting through HapR (the central regulatory protein in the V. cholerae quorum-sensing system) enhances the expression of

RNA polymerase sigma factor (rpoS), an essential gene for adaptation to environmental and nutritional stresses; and resistance to stress factors like oxidative stress; interestingly, the study found that hapR mutants survived better than wild-type strains after being incubated in seawater at 4°C for 5 days; similar to our finding with agrD and luxS QS systems in L. monocytogenes.

The study suggested a different mechanism that may be involved in survival in this extreme environment. In another study by (Lumjiaktase et al., 2006), Burkholderia pseudomallei Quorum sensing was found to regulate the oxidative stress response by regulating the expression of dpsA, a DNA-binding protein from starved cells, which protects the DNA from acid and oxidative stresses. A QS mutant strain was more susceptible to oxidative stress than wild type suggesting a role of QS in stress response. Brackman et al. (2009) found that AI-2 inhibitors in Vibrio species affect some stress responses, like starvation, by interfering with the QS system in this microorganism. These studies support the theory that QS plays a role in stress responses.

The SHIME system was used to investigate the role of QS in survival in digestive systems, and we did not find any difference between the wild-type and mutant strains. We tried to change the setting and model where we faced difficulties in many aspects, e.g., incubating the cells in the first tank (acidic) for more than 1 h led to cell death, and we were at times unable to obtain any cell count after the first tank. We assume that either complete cell death happened, as

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the cells where in direct contact with acids not like the case of being in food, or possibly L. monocytogenes entered a viable but non-culturable state. Few studies recorded a VBNC state for

L. monocytogenes. Cunningham et al. (2009) studied the effect of weak acids on survival of L. monocytogenes and concluded that L. monocytogenes may remain viable at low pH but nonculturable and might regain culturablility when growth conditions changes. In our case we were not able to observe any growth even after 24 hrs in the vessel, which indicates that the cells were killed. In comparison between SHIME experiment and above experiments applying environmental stresses, we found that no significant difference between wild type and mutant strains under one stress, but combining more than one stress revealed significant differences. So we expected to have a difference in SHIME result as more than one factor is applied, but probably, the direct contact of culture to acidic environment lead to cell death, as usually cells inter the stomach attached or embedded in foods which can reduce the direct effect on cells.

Sperandio (2010) reported that sdiA sensing of acyl-homoserine-lactone by the enterohemorrhagic Escherichia coli serotype O157:H7 was responsible for cell survival in the gastrointestinal tract of cattle. This was an important finding, as it provides a key to understanding bacterial survival and a way to prevent diseases.

The study of the role of QS in antibiotic resistance is very important, as it is related to disease treatment and prevention. L. monocytogenes acquires resistant to antibiotic either by conjugation (Charpentier and Courvalin, 1999) or by active efflux pumps (Mata et al., 2000).

Microarrays study in Chapter 3 showed a down regulation for fosfomycin resistance protein

(fosX) and an up regulation of multidrug resistance transporter gene in agrD mutant strain. A common regulation of some genes found between agrD and sigmaB factor, which might give a possibility of antibiotic susceptibility as a sigmaB factor mutant strain was found to be more

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susceptible to antibiotics than the parent strain (Li et al, 2011). Skindersoe et al. (2008) found that some antibiotics like azithromycin help in treatment of Cystic Fibrosis (CF) patients chronically infected by P. aeruginosa by inhibiting QS, which regulates virulence genes in this pathogenic bacterium. Shih and Huang (2001) found that a QS mutant strain of P. aeruginosa is more susceptible to kanamycin than wild-type. Yarwood et al. (2004) examined antibiotic resistance of planktonic cells and biofilm in Staphylococcus aureus and the role of agrD QS system in antibiotic resistance. Results showed that the parent strain was more resistant to one antibiotic while planktonic strains were all susceptible at the same concentrations. Here, we found that the AgrD mutant was significantly more susceptible to antibiotics than the wild-type strain for 1of the 5 antibiotics tested. The antibiotic susceptibility of the LuxS mutant was found to be significantly different for 3 out of 5 antibiotics tested. These results suggest a drug-specific role in antibiotic resistance since no significant differences were found with amoxicillin and ampicillin (aminopenicillin) but a significant difference was found with erythromycin

(macrolides), gentamycin (aminoglycoside), and vancomycin (glycopeptides). Aminopenicillin targets the cross-linkage between linear peptidoglycan polymer chains, while erythromycin targets the 50S subunit in 23S rRNA leading to protein synthesis inhibition. Gentamycin inhibits protein synthesis by targeting ribosomal RNA, while vancomycin targets peptidoglycan synthesis and inhibits cell wall synthesis. Microarray results showed down-regulation of certain ribosomal protein subunits (30S and 50S), as well as genes associated with cell membrane synthesis. These findings are supported by the studies on other microorganisms. Shih and Huang

(2002) found that a P. aeruginosa QS mutant strain was more susceptible to kanamycin than the wild-type strain. In another study of the role of AgrD in S aureus, Yarwood et al. (2004) found that the AgrD mutant strain was more susceptible to rifampin than the wild-type strain in biofilm.

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They also found no difference in resistance to oxacillin. It was not clear why this happened as planktonic cells were found to be affected insignificantly by the antibiotic, thus, they suggested a possible role of agrD in biofilm resistance. A possible indirect mechanism such as the regulation of genes responsible for antiobiotic resistance might exist.

Biofilm formation has gained attention because cells present in biofilms are more resistant to antibiotics and disinfectants and hard to remove (Yarwood et al., 2004). We examined the biofilm formation by L. monocytogenes EGDe wild-type and QS mutant strains by using MPA and confocal microscopy. Since MPA results are criticized for measuring biofilms treated with disinfectant (Romanova et al., 2007), we compared the MPA results with confocal microscopy results. As seen by the results (Figure 4.11), the luxS mutant formed a denser biofilm at day 3 than the wild-type strain and the AgrD mutant; at day 5, detachment was observed in the wild- type strain and the LuxS mutant, while the AgrD mutant continued to form biofilm. Few studies have focused on the role of QS in biofilm formation. Both Bleval et al. (2006) and Sela et al.

(2006) found that a LuxS mutant formed denser biofilm than the AgrD mutant. On the other hand, Riedel et al. (2009) found that the AgrD mutant had impaired biofilm formation compared with the wild-type strain after 16-h incubation. Rieu et al. (2007) found similar results, but also learned that extending the incubation time results in an insignificant difference in biofilm formation. Those findings were similar to the present findings using CLSM, while MPA showed similar results for biofilm formation among the 3 strains after 24-h incubation and treatment with disinfectants.

Study of the relation between quorum sensing and bacteriophage infectivity is rare and no published articles were found that address this subject. An abstract submitted to the Genereal

Meeting of American Society for Microbiology (2011) by Hoyland-Kroghsbo presented a role of

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signaling molecules (AI-1) and (AI-2) in E. coli resistance mechanisms to bacteriophage. The role of QS in cell membrane synthesis found in microarrays data may suggest an impact for L. monocytogenes resistance/reduction of bacteriophage infectivity. Our results showed no significant difference in infectivity between wild type and mutant strains, further investigation for re-infectivity and assessment of amount of free bactriophage present in medium after infection need to be tested for better understanding of possible contribution of QS in bacteriophage infectivity.

We also tested the effect of 3 disinfectants on biofilm formation and found that BC and

70% ethanol had no effect on biofilm removal, but that biofilm shape altered with increased cell aggregation. SHC was effective against the mutant strains, but less effective against the wild- type strain, suggesting a possible role of QS in biofilm resistance. Romanova et al. (2007) obtained similar results when the wild-type strain was treated with BC. Biofilm resistance may refer to extracellular polymeric substance or efflux pumps. Two efflux pumps have been identified in L. monocytogenes (Mdrl and Lde). Romanova et al. (2006) studied the role of these efflux pumps on biofilm resistance to disinfectants and found a significant increase in mdrl expression but not lde and suggested that mdrl efflux pump is at least partially responsible for the adaptation to BC resistance. On the other hand, Pan et al. (2006) examined the resistance of biofilm in simulated food processing environment against different sanitizers including quaternary ammonium compounds, chlorine and peroxides. They suggested an attribution of the extracellular polymeric substance of the biofilm to resistance rather than intrinsic attribute of the cells. While an important study for the role of σB factor on biofilm formation and resistance provided an important impact to understanding of this phenomenon, as σB factor was found to be involved in not only biofilm formation but also biofilm resistance to disinfectants (Van Der Veen

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and Abee, 2010). As a stress response gene, σB factor might contribute to biofilm formation and resistance in presence of stress factors like oxidative stresses. a transcriptomic analysis of L. monocytogenes EGD-e σB regulon showed an up regulation of stress responses genes, specifically the three members of universal stress proteins (USP) of L. monocytogenes

lmo0515, lmo1580 and lmo2673 (Hain et al., 2008). The relation between this study and our study, is that agrD QS system was found to regulate two of the three USP proteins, 5 genes were down regulated in both mutants (σB and agrD) and in total of 38 genes were found to be differentially regulated in both mutants in which they were up regulated in σB mutant and down regulated in agrD. This suggests a role of these systems in resistance by regulating stress proteins and other factors like DNA repairing and cell membrane synthesis and repair. The luxS mutant was found in our microarray analysis to regulate the HtrA gene, which is necessary for resistance to cellular stress and virulence as well as superoxide dismutase production, which contributes to survival and virulence in L. monocytogenes (Wilson et al., 2006; Vasconcelos and

Deneer, 1994).

Houari and Martino (2007) found that BC induced biofilm formation in S. epidermidis.

As for SHC, Rodrigues et al. (2011) found an effect similar to our finding in L. monocytogenes, suggesting the effectiveness of SHC as a reliable disinfectant for L. monocytogenes biofilm.

Ethanol had an effect similar to BC, suggesting similar mechanisms of resistance. BC is thought to cause dissociation of cellular membrane bilayer leading to leakage of cell contents (Singh et al., 2008) and ethanol is believed to cause membrane damage and protein denaturation

(McDonnell and Russell, 1999). SHC is an oxidizing agent that inhibits enzymatic activity and has bactericidal effect by generating superoxide anions (oxygen singlets) and hydroxyl radicals.

Microarrays data show that agrD mutant down regulated some of the oxidative stress genes like

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qoxD gene, lmo0015 (quinol oxidase subunit III), msrB and superoxide dismutase. luxS gene down regulated superoxide dismutase gene which is an important stress genes involved in oxidation stress and virulence (Vasconcelos and Deneer, 1994). This explains the resistance of wild type as this was expressed in it and explain the involvement of QS in such stress resistance.

Finally, the attachment assays showed a significant difference in attachment between the wild- type strain and the LuxS mutant after 30-min incubation, with more luxS mutant attached cells while a slight but significant difference was found between the wild-type strain and the AgrD mutant with agrD mutant showing the lowest number of attached cells. This finding is different from the significant 10-fold difference (P < 0.0001) in attachment of cells of luxS mutant than wild type as reported by Sela et al. (2006). While the luxS system was found to increase cell attachment and biofilm formation, agrD was found to impede this formation as found by Boles and Horswill (2008). Microarray data showed a down regulation for inlA gene in agrD mutant, inlA along with inlB were found to correlate to attachment strength in L. monocytogenes (Chen et al., 2009). And down regulation of genes contribute to cell adhesion like: lmo1462, an era like gene that contribute to L. monocytogenes adhesion to surfaces (Auvray et al., 2007); ClpC

ATPase required for cell adhesion and invasion (Nair et al., 2000). A comparison between gene expression of L. monocytogenes biofilm carried out by (Lina, 2010) and agrD mutant obtained from our microarray results showed a down regulation of 10 genes in both experiment, suggesting a contribution in attachment and biofilm formation.

4.5 Conclusion

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In this study, under the conditions examined, QS contributed to the behavior of L. monocytogenes with respect to antibiotic resistance, biofilm formation, and environmental stresses. Some experiments in this chapter constitute new investigations of the role of QS in bacteria, including bacteriophage infectivity, application of 2 stresses, and growth at low temperatures. These experiments provide a background for further investigation and an improved understanding of the role of QS in bacterial behavior and physiology. Understanding this role can help us find a new method to better control this pathogen in more efficient ways to prevent the diseases caused by L. monocytogenes.

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Figure 4.1 Simulator of Human Intestinal Microbial Ecosystem system (adapted from Boever et al., 2000 with permission)

Vessel 1: stomach, vessel 2: small interstine, vessel 3: colon ascendans, vessel 4: colon transversum and vessel 5: colon desendans. Culture will be pumed to vessel 1, where incubation time and condition is set up, then transferred automatically to vessel 2 all the way to vessel 5 simulating human intestinal ecosystem.

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Figure 4.2 Growth at 4 °C and 4 °C+ pH 5.5 for Listeria monocytogenes wild-type, AgrD-, and luxS-

6.5

5.5

4.5

4 wt-4C agr-4C

3.5

cfu lux-4C

3 log WT-5.5 2.5 AGR-5.5 LUX-5.5 2

1.5

0.5

0 0 d 2 d 4 d 6 d 8 d 10 d 12 d 15 d 20 d 25 D 30 D Time

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Figure 4.3 Survival curve at 4 °C + pH 4.5 Listeria monocytogenes wild-type, AgrD-, and luxS-

5.3

5.2

5.1

4.9 WT-PH4.5

log/cfu AGR-PH4.5 4.8 LUX-PH4.5

4.7

4.6

4.5

4.4 0D 5D 10D 15D 20D 25D 30D days

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Figure 4.4 Survival curve at 4 °C + 5% NaCl Listeria monocytogenes wild-type, AgrD-, and luxS-

5.2

5.1

4.9

4.8

4.7 WT-5% log/cfu Agr-5% 4.6 lux-5%

4.5

4.4

4.3

4.2

4.1 days 0D 5D 10D 15D 20D 25D 30D

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Figure 4.5 Survival curve at 4 °C + 10% NaCl Listeria monocytogenes wild-type, AgrD-, and luxS-

5.3

4.7

AGR-10%

log/cfu LUX-10% 4.4 WT-10%

4.1

3.8

3.5 days 0D 5D 10D 15D 20D 25D 30D

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Figure 4.6 Listeria monocytogenes wild-type, AgrD-, and luxS- survival in the Simulator of Human Intestinal Microbial Ecosystem system (45 min = vessel 1, 2 hours = vessel 2 and 5 hours = vessel 3)

WT 5

log cfu log AGR 4 LUX

0 0H 45 min 2H 5H

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Figure 4.7 Antibiotic resistance of Listeria monocytogenes wild-type, AgrD-, and luxS-

1.2

0.8

WT 0.6

agrD MIC luxS

0.4

0.2

0 Amoxicillin Ampicillin Erythromycin Gentamycin Vancomycin

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Figure 4.8 Antibiotic (erythromycin) strip with graded minimum inhibitory concentrations (representing the antibiotic test performed)

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Figures 4.9 a, b, and c. Bacteriophage infectivity for Listeria monocytogenes wild-type (a), AgrD- (b), and luxS- (c)

a b c

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Figure 4.10 Microtiter plate assay for detecting biofilm formation in Listeria monocytogenes wild-type, AgrD-, and luxS-. BC = benzalkonium chloride, SHC = sodium hypochlorite, ETHA = ethanol, TSB = tryptic soy broth (standard deviation represents a maximum difference of 10%). SHC and BC was added at 800 p.p.m. and 1mg/ml respectively for 30 min, ethanol was used at a concentration of 70% and incubated for 5 min. (disinfectants were added after 48 hrs of bacterial biofilm).

0.6

0.5

0.4

0.3

OD = 595 nm 595 = OD 0.2

0.1

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Figure 4.11. Biofilm formation of Listeria monocytogenes wild-type, AgrD mutant, and luxS mutant examined under confocal laser scanning microscopy for 5 days

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Figure 4.12. Biofilm formation of Listeria monocytogenes wild-type, AgrD-, and luxS- incubated for 48 h and examined under confocal laser scanning microscopy

WT NO TREATMENT NO WT BENZALKONIUM WT

AGR BENZALKONIUM AGR TREATMENT NO AGR

LUX BENZALKONIUM LUX LUX NO TREATMENT LUX

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WT ETHANOL WT

WT SODIUMHYPO WT

AGR ETHANOL AGR

AGR SODIUM HYPO SODIUM AGR

LUX ETHANOL LUX LUX SODIUM HYPO SODIUM LUX

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Figure 4.13 Attachment assay for Listeria monocytogenes wild-type, AgrD-, and luxS-

WT (223 ± 26), AgrD- (140 ± 23), and luxS- (255 ± 20)

300

250

200

WT 150 AGR- LUX- 100

number of cells attached cells of number 50

0 attachment assay

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CHAPTER 5: CONCLUSIONS AND FUTURE DIRECTIONS

5.1 Thesis summary and general conclusions

L. monocytogenes contamination continues to be a major concern in the food industry.

Listeriosis outbreaks found to be associated with different types of foods including fresh produce, meat and dairy. The call for new better method to control this microorganism, is of great need. This study investigated the quorum sensing systems in L. monocytogenes and their possible role in bacterial survival, adaptation, and gene expression. We performed PCR sequence detection for quorum sensing system genes—luxS-like gene (lmo1228), and agrBDCA genes—in

L. monocytogenes as well as in other Listeria species. We found that all Listeria species possess both QS systems except for L. grayi, which has a specific agrD gene that differs in sequence from the rest of the species. An AI-2 bioassay was then performed to examine AI-2 production in

Listeria. The results showed that all Listeria species produce AI-2 signaling molecules.

Furthermore, AI-2 production was found to be growth phase-dependent and affected by environmental conditions like temperature and pH.

The major finding of this work, as described in Chapters 3 and 4, is that quorum sensing in L. monocytogenes contributes to expression of genes of different functions including virulence, regulatory functions, DNA metabolism, and cellular processes. For microarray analyses, AgrD and luxS mutants were constructed, grown at 37°C and 4°C, and RNA was collected at 2 phases of growth—exponential and stationary. The stationary phase was found to be important for quorum sensing since most of the differentially regulated genes were expressed during this phase for both QS systems. The wide range of categorized genes suggests an

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important impact of QS in overall cell growth and behavior. Our results showed that 332 genes were down-regulated while 14 genes were up-regulated in the agrD mutant grown until the stationary phase was reached at 37°C (based on ≥1.5-fold expression and P ≤ 0.001). A total of

336 genes were down-regulated in cells grown in the stationary phase at 4°C. In addition, our results showed that 180 genes were differentially expressed (1 up-regulated and 179 downregulated) in the luxS mutant, which highlights the role of luxS in Gram-positive bacteria and particularly in L. monocytogenes. The effects of luxS may not be AI-2-dependent, but rather this gene may encode a regulatory protein. Nevertheless, Chapters 2, 3, and 4 showed a different effect of AI-2/luxS on bacterial behavior. The agrD system was found to regulate a wider set of genes in comparison to luxS; most importantly, the majority of these were related to virulence and adaptation to stress. Therefore, this QS system, by being active at different temperatures, confirms its importance as a regulatory mechanism along with prfA, the virulence regulator, and sigmaB, the stress regulator gene.

By changing different environmental parameters, the effects of quorum sensing systems were found to be condition-specific. Some differences were found in growth/survival at different osmolarity, while no significant difference was found in growth at the different temperatures tested. Applying lower temperature (≤ 0 °C) or higher temperature (≥ 45 °C) could be important for studying the effect of quorum sensing on survival under extreme temperature conditions.

Combining two stress factors, low temperature and low pH or low temperature and low water activity, revealed a significant difference between cellular responses of the wild type and that of the agrD/luxS mutant strains.

Furthermore, our results indicated a role for QS in antibiotic resistance. We found that the luxS mutant was more resistant than the wild type, and in 1 case, the agrD mutant was more

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susceptible to antibiotics than the wild type. These results also suggest a drug-specific role in antibiotic resistance since no significant differences were found with amoxicillin and ampicillin

(aminopenicillin) but a significant difference was found with erythromycin (macrolides), gentamycin (aminoglycoside), and vancomycin (glycopeptides). This difference in susceptibility refer to antibiotics mode of action and QS gene expression as QS is involved in many cellular functions including cell membrane synthesis and DNA repair.

We also used different techniques, including microtitre plate assay, confocal laser scanning microscopy, and attachment assays, to assess biofilm formation, and found that the luxS mutant could form denser biofilms, while agrD formed lighter biofilms, compared to the wild type. In addition, biofilms formed by the mutants were resistant to disinfectants, including benzalkonium chloride and ethanol, but were susceptible to sodium hypochlorite. Biofilms formed by the wild type were more resistant to sodium hypochlorite than those formed by the QS mutant strains. As an oxidizing agent, the oxidative stress response genes are very important factor in resisting oxidation damage, microarrays results indicated that some oxidative stress genes like qoxD, lmo0015 (quinol oxidase subunit) and msrB protein were down regulated in agrD mutant and superoxide dismutase was down regulated in luxS mutant which makes the mutant strains more susceptible to sodium hypochlorite than wild type.

During our study, we noted that media composition may affect L. monocytogenes cell physiology or shape. We noticed an elongation in the cell shape of up to 4 times the regular length of the bacteria in the Oxford or Fraser medium. The selective media ―Rapid L ’mono‖ did not have this effect, and the cell shape was similar to that of cells grown on TSA medium or

BHI. Bacteria subcultured in TSA from Oxford or Fraser medium regained their regular shape.

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In conclusion, the results of this study provide information on the presence and role of quorum sensing in L. monocytogenes. Moreover, our data show the importance of cell-to-cell signaling in the behavior and gene expression of the microorganism. This research shows that targeting quorum sensing in L. monocytogenes may be useful for reducing the resistance and risks of this pathogen for food safety and industry.

5.2 Future research

Microarray analysis revealed a mass expression of genes related to QS systems. By classifying these genes and studying their functions, specific experiments could be performed to investigate the effects of QS on different classes of genes. Applying two or more stresses to L. monocytogenes cells would also be an important approach in this area; for example, investigating gene expression during biofilm formation by wild type and QS mutant strains may elucidate the role of QS in biofilm attachment, formation, and detachment. Double mutant strains missing both

QS would also be interesting to study since both systems were found to control expression of different sets of genes. Anti-quorum sensing is another important criterion that should be addressed and investigated in L. monocytogenes. The discovery of anti-quorum sensing targeting

AI-2 molecules is well established and examined in many Gram-negative bacteria, including E. coli, and has been found to reduce virulence and biofilm formation. Lastly, AIP inhibitors could be investigated as anti-quorum targets. Studying these factors along with our present findings may help us to understand and therapeutically control this pathogen.

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APPENDIX

Appndix (A): differentially regulated genes (up-regulation) of AgrD mutant strain of L. monocytogenes at stationary phase at 37°C

q- Gene Name Score(d) Numerator(r) Denominator(s+s0) value(%)

lmo1806_Acyl carrier protein -8.373393225 -0.632918626 0.075587 1.331486

lmo1018_Lmo1018 protein -7.156610348 -0.849130016 0.11865 1.331486

lmo2732_Lmo2732 protein -7.024424207 -0.689459671 0.098152 1.331486

lmo1693_Regulatory protein recX -6.64239222 -0.955249663 0.143811 1.331486

lmo1238_Ribonuclease PH -6.374824106 -0.593099549 0.093038 1.331486

lmo0854_Lmo0854 protein -6.167585505 -1.57443035 0.255275 1.331486

lmo1961_Lmo1961 protein -6.149429864 -1.210285008 0.196813 1.331486

lmo0777_Lmo0777 protein -6.007673187 -0.342718221 0.057047 1.331486

lmo1409_Multidrug resistance transporter -5.965970269 -1.695409481 0.28418 1.331486

lmo1911_Lmo1911 protein -5.884979897 -1.647104948 0.279883 1.331486

lmo0849_Lmo0849 protein -5.85336091 -1.506961203 0.257452 1.331486

lmo0112_Lmo0112 protein -5.778955871 -0.962038239 0.166473 1.331486

lmo2240_Lmo2240 protein -5.698189468 -0.536270842 0.094112 1.331486

lmo2318_Lmo2318 protein -5.480224748 -1.23323764 0.225034 1.561318

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Appendix (B): differentially regulated genes (down-regulation) of AgrD mutant strain of L. monocytogenes at stationary phase at 37°C

Denominator q- Fold- Gene Name Score(d) Numerator(r) (s+s0) value(%) change

lmo0341_Lmo0341 protein 21.59579666 4.548305154 0.210611 0 23.39787

lmo1702_Fosfomycin resistance protein fosX 20.18001245 2.038754433 0.101028 0 4.108906

lmo0348_Lmo0348 protein 19.06357937 4.322697699 0.226752 0 20.01067

lmo2132_Lmo2132 protein 18.79523868 2.740234971 0.145794 0 6.681792

lmo0346_Triosephosphate isomerase 2 18.46158273 4.753102899 0.257459 0 26.96662

lmo0343_Probable transaldolase 2 18.0742242 4.91914022 0.272163 0 30.25581

lmo1539_Lmo1539 protein 17.83907534 2.889425099 0.161972 0 7.409751

lmo1580_Lmo1580 protein 13.36329646 3.913393021 0.292846 0 15.06776

lmo0880_Lmo0880 protein 12.63397396 3.417073011 0.270467 0 10.68173

lmo0943_DNA protection during starvation protein 11.97792016 3.585873365 0.299374 0 12.00758

lmo0345_Lmo0345 protein 10.68470722 5.272955894 0.493505 0 38.66499

lmo0344_Lmo0344 protein 10.53855075 4.543545663 0.431136 0 23.3208

lmo2748_Lmo2748 protein 10.35609953 3.813656092 0.368252 0 14.06128 lmo1350_Probable glycine dehydrogenase [decarboxylating]

subunit 2 10.00895027 4.691751003 0.468756 0 25.84388

lmo2056_Lmo2056 protein 9.378078282 0.582561612 0.06212 0 1.497506

lmo0994_Lmo0994 protein 9.3734308 4.519439518 0.482154 0 22.93437

lmo1683_Lmo1683 protein 9.14125595 3.698328793 0.404576 0 12.98099

lmo0347_Lmo0347 protein 9.052335397 1.218449801 0.134601 0 2.326965 lmo1349_Probable glycine dehydrogenase [decarboxylating]

subunit 1 8.79982144 4.104126811 0.466388 0 17.1975

lmo2675_Lmo2675 protein 8.760710727 1.157501638 0.132124 0 2.230708

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lmo0911_Lmo0911 protein 8.664283338 3.218012393 0.371411 0 9.30504 lmo1572_Acetyl-coenzyme A carboxylase carboxyl

transferase subunit alpha 8.659308121 2.194222352 0.253395 0 4.576429

lmo1057_Lmo1057 protein 8.540467178 3.003203034 0.351644 0 8.017781

lmo0349_Lmo0349 protein 8.445653123 2.237351954 0.264912 0 4.715308

lmo1704_Lmo1704 protein 7.938673598 1.771657348 0.223168 0.224622 3.41446

lmo1701_Lmo1701 protein 7.884870545 1.507837087 0.191232 0.224622 2.843834

lmo2696_Lmo2696 protein 7.87216125 3.04228878 0.386462 0.224622 8.237969

lmo2565_Lmo2565 protein 7.814912448 2.517956645 0.322199 0.224622 5.727703

lmo1171_PduQ protein 7.639923389 3.89322108 0.509589 0.224622 14.85855

lmo0479_Putative secreted protein 7.569523288 3.671967626 0.485099 0.224622 12.74596

lmo2474_UPF0042 protein lmo2474 7.551243713 2.611721545 0.345866 0.224622 6.112326

lmo0945_Lmo0945 protein 7.539371823 0.717532665 0.095171 0.224622 1.644367

lmo1859_Peptide methionine sulfoxide reductase msrB 7.47696787 2.442011058 0.326604 0.224622 5.433987

lmo1921_UPF0302 protein lmo1921 7.472352958 1.065406933 0.14258 0.224622 2.09276

lmo0342_Lmo0342 protein 7.467387598 2.727848709 0.365302 0.224622 6.624671

lmo0608_Lmo0608 protein 7.395810164 3.593257785 0.48585 0.224622 12.0692

lmo0626_Lmo0626 protein 7.31889025 1.24877651 0.170624 0.224622 2.376398

lmo2572_Lmo2572 protein 7.219679572 1.062055647 0.147106 0.224622 2.087904

lmo1860_Peptide methionine sulfoxide reductase msrA 7.193595052 2.499521524 0.347465 0.224622 5.654978

lmo2383_Lmo2383 protein 7.184211862 0.540137455 0.075184 0.224622 1.454111

lmo0688_Lmo0688 protein 7.149220258 0.570788525 0.079839 0.224622 1.485335

lmo2250_ArpJ protein 7.085028016 1.018551365 0.143761 0.224622 2.025884

lmo1888_Cell cycle protein gpsB 7.025316503 3.154713571 0.449049 0.224622 8.905605

lmo0782_Lmo0782 protein 7.001604123 4.120754957 0.588544 0.224622 17.39686

lmo1468_Lmo1468 protein 6.994128763 2.51414904 0.359466 0.224622 5.712606

lmo2603_Lmo2603 protein 6.86775482 0.474052332 0.069026 0.224622 1.389006

lmo0210_L-lactate dehydrogenase 1 6.773714214 2.420429379 0.357327 0.224622 5.353303

lmo1262_Lmo1262 protein 6.739473201 1.67633003 0.248733 0.224622 3.196139

lmo1454_RNA polymerase sigma factor rpoD 6.738774129 3.077612638 0.456702 0.224622 8.442163

lmo2697_Lmo2697 protein 6.723903578 2.293221176 0.341055 0.224622 4.901493

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lmo0770_Lmo0770 protein 6.665093248 2.72288686 0.408529 0.224622 6.601926

lmo2511_Lmo2511 protein 6.596361397 4.468617022 0.677437 0.224622 22.14052

lmo1219_Lmo1219 protein 6.593535322 3.314490795 0.502688 0.224622 9.948581

lmo2570_Lmo2570 protein 6.586950598 2.829488575 0.42956 0.224622 7.108221

lmo0539_Tagatose 1,6-diphosphate aldolase 6.58620063 4.842778444 0.735292 0.224622 28.69601

lmo1077_Lmo1077 protein 6.580609392 0.610550279 0.09278 0.224622 1.526841

lmo0653_Lmo0653 protein 6.55665339 3.773811936 0.57557 0.224622 13.67825

lmo2460_Lmo2460 protein 6.550989629 1.223091424 0.186703 0.224622 2.334464

lmo1301_Lmo1301 protein 6.526370266 3.9188959 0.600471 0.224622 15.12534

lmo1288_S-ribosylhomocysteine lyase 6.522290476 2.791837697 0.428046 0.224622 6.925113

lmo1794_Lmo1794 protein 6.417084804 1.743131655 0.271639 0.224622 3.34761

lmo2274_Protein gp29 6.383278709 1.614170074 0.252875 0.224622 3.061354

lmo2373_Lmo2373 protein 6.370695356 3.876934826 0.608558 0.224622 14.69175

lmo2462_Lmo2462 protein 6.339646062 0.638131246 0.100657 0.224622 1.556312

lmo2681_Potassium-transporting ATPase B chain 6.220399464 1.279936433 0.205764 0.224622 2.428283

lmo1379_Membrane protein oxaA 2 precursor 6.2058061 4.566295683 0.73581 0.224622 23.69147

lmo1058_UPF0223 protein lmo1058 6.20401201 5.249551296 0.846154 0.224622 38.04279

lmo1140_Lmo1140 protein 6.201864472 3.197171032 0.515518 0.224622 9.171585

lmo0584_Lmo0584 protein 6.19110342 4.253321826 0.687005 0.224622 19.07117

lmo2792_Lmo2792 protein 6.172225037 4.101804674 0.664559 0.224622 17.16984

lmo1178_Lmo1178 protein 6.171247432 3.153499603 0.510999 0.224622 8.898114

lmo2644_Lmo2644 protein 6.170121712 3.571724908 0.578874 0.224622 11.8904

lmo2337_Lmo2337 protein 6.074163978 0.252463553 0.041564 0.224622 1.19124

lmo0386_Lmo0386 protein 6.043814772 0.317830317 0.052588 0.224622 1.246455

lmo1602_Lmo1602 protein 6.02295908 3.411735535 0.566455 0.224622 10.64228

lmo1612_Lmo1612 protein 6.000097113 1.845049322 0.307503 0.224622 3.592652

lmo2484_Lmo2484 protein 5.940372533 2.470423639 0.41587 0.224622 5.542065

lmo0964_UPF0413 protein lmo0964 5.871997506 4.548241973 0.774565 0.224622 23.39684

lmo1119_Lmo1119 protein 5.86534132 1.613543212 0.275098 0.224622 3.060025

lmo1469_30S ribosomal protein S21 5.854753358 3.746537209 0.639914 0.224622 13.42209

lmo1538_Glycerol kinase 5.832478534 1.680093691 0.288058 0.224622 3.204488

153

lmo1295_Lmo1295 protein 5.814135527 2.803342402 0.48216 0.224622 6.980558

lmo2220_3'-5' exoribonuclease yhaM 5.800681897 2.048797309 0.353199 0.224622 4.137609

lmo1501_UPF0473 protein lmo1501 5.786375287 4.266501367 0.737336 0.224622 19.2462

lmo2714_Peptidoglycan anchored protein 5.779739621 3.489350259 0.603721 0.224622 11.2305

lmo2378_Lmo2378 protein 5.776456674 1.905931503 0.329948 0.224622 3.747508

lmo0933_Lmo0933 protein 5.768872268 3.501838723 0.607023 0.224622 11.32814

lmo1274_Lmo1274 protein 5.755338077 3.226661265 0.560638 0.224622 9.360991

lmo0002_DNA polymerase III, beta chain 5.661418241 1.652237466 0.291842 0.224622 3.143207

lmo0202_Listeriolysin O precursor 5.633834046 3.349146485 0.59447 0.224622 10.19045

lmo2701_Lmo2701 protein 5.629441744 2.305249696 0.409499 0.224622 4.94253

lmo1071_Lmo1071 protein 5.615321838 1.135206291 0.202162 0.224622 2.1965

lmo2468_ATP-dependent Clp protease proteolytic subunit 5.566014664 4.005742729 0.719679 0.224622 16.06382

lmo0524_Lmo0524 protein 5.547818369 1.627267003 0.293317 0.224622 3.089272

lmo1502_Putative Holliday junction resolvase 5.539393973 3.414399385 0.616385 0.224622 10.66195

lmo1059_Lmo1059 protein 5.520950278 2.668580562 0.483355 0.224622 6.358033

lmo0101_Lmo0101 protein 5.512437528 2.401722336 0.435692 0.224622 5.284336

lmo2571_Lmo2571 protein 5.511653257 0.770481393 0.139791 0.224622 1.705839

lmo2665_Lmo2665 protein 5.50095372 1.082974941 0.19687 0.224622 2.1184

lmo1100_Probable cadmium-transporting ATPase 5.492668899 4.235137582 0.771053 0.224622 18.8323

lmo0930_Lmo0930 protein 5.481065529 3.41310817 0.622709 0.224622 10.65241 lmo2398_Low temperature requirement C protein, also similar

to B. subtilis YutG protein 5.432754109 2.392819673 0.440443 0.224622 5.251828

lmo1690_Lmo1690 protein 5.425640146 3.376261175 0.622279 0.224622 10.38379

lmo1237_Glutamate racemase 5.373348347 1.678290755 0.312336 0.224622 3.200485

lmo1051_Peptide deformylase 5.368297835 2.86235261 0.533196 0.224622 7.272002

lmo2670_Lmo2670 protein 5.365092977 2.188556761 0.407925 0.224622 4.558492

lmo0769_Lmo0769 protein 5.362959033 2.028258711 0.378198 0.224622 4.079122

lmo2722_Lmo2722 protein 5.331516591 2.103974094 0.39463 0.224622 4.298919

lmo2707_Lmo2707 protein 5.319423414 2.756579787 0.51821 0.224622 6.757922

lmo2790_Partition protein ParB homolg 5.317531389 2.106971562 0.396231 0.224622 4.307861

lmo2067_Lmo2067 protein 5.310694168 1.217309028 0.229218 0.224622 2.325126

154

lmo1685_Glutamate-1-semialdehyde 2,1-aminomutase 2 5.287692297 0.647885047 0.122527 0.224622 1.56687

lmo2069_10 kDa chaperonin 5.25852712 2.368875712 0.450483 0.224622 5.165384

lmo2472_Lmo2472 protein 5.257023943 1.988914073 0.378335 0.224622 3.969381

lmo1423_Lmo1423 protein 5.250220167 2.631233841 0.501166 0.224622 6.195556

lmo1332_Putative ribosome biogenesis GTPase rsgA 1 5.246816127 2.852490634 0.543661 0.224622 7.222462

lmo0052_Lmo0052 protein 5.215559997 0.244073518 0.046797 0.224622 1.184332

lmo2386_Lmo2386 protein 5.210407137 1.979914933 0.379992 0.224622 3.944698

lmo1566_Isocitrate dehydrogenase 5.201492856 2.722153187 0.523341 0.224622 6.598569

lmo1097_Lmo1097 protein 5.197897286 3.083236039 0.59317 0.224622 8.475133

lmo0575_Lmo0575 protein 5.190818121 1.592074856 0.30671 0.224622 3.014826

lmo1068_Lmo1068 protein 5.177772978 1.842014432 0.355754 0.224622 3.585103

lmo0596_Lmo0596 protein 5.173896886 3.249726534 0.6281 0.224622 9.511854

lmo1326_Lmo1326 protein 5.159880926 0.557403311 0.108026 0.224622 1.471618

lmo1963_Lmo1963 protein 5.141586936 0.592304736 0.115199 0.224622 1.507653

lmo2471_NADPH dehydrogenase 5.132603648 2.775631905 0.540784 0.224622 6.847759 lmo2205_2,3-bisphosphoglycerate-dependent

phosphoglycerate mutase 5.125655143 3.301347077 0.644083 0.224622 9.858356

lmo0408_Lmo0408 protein 5.121972193 1.326984242 0.259077 0.224622 2.508777

lmo2676_Lmo2676 protein 5.117788892 1.555987805 0.304035 0.224622 2.94035

lmo0114_AX protein 5.102632924 3.708781561 0.726837 0.224622 13.07539

lmo0610_Lmo0610 protein 5.068733013 1.934991837 0.381751 0.224622 3.82376

lmo1536_Lmo1536 protein 5.050846099 2.193416387 0.434267 0.224622 4.573873

lmo2057_Protoheme IX farnesyltransferase 5.025466229 2.197121471 0.437198 0.224622 4.585635

lmo0898_Lmo0898 protein 5.015901796 1.445329875 0.28815 0.224622 2.723251

lmo2079_Lmo2079 protein 5.013376182 0.435629938 0.086894 0.224622 1.352501

lmo2397_Lmo2397 protein 4.943645043 1.108774617 0.224283 0.420156 2.156624 lmo1276_Methylenetetrahydrofolate--tRNA-(uracil-5-)-

methyltransferase trmFO 4.939261417 2.9782717 0.602979 0.420156 7.880416

lmo2529_ATP synthase subunit beta 2 4.92797713 2.479597867 0.503167 0.420156 5.57742

lmo2673_Lmo2673 protein 4.919873582 1.112798043 0.226184 0.420156 2.162647 lmo1279_ATP-dependent hsl protease ATP-binding subunit 4.841250171 2.149083908 0.443911 0.420156 4.435461

155

hslU

lin0769_ 4.838275624 1.47795324 0.305471 0.420156 2.785533

lmo2136_Lmo2136 protein 4.800393778 3.101705629 0.646136 0.587937 8.584331

lmo1340_Lmo1340 protein 4.792607035 1.083868235 0.226154 0.587937 2.119712

lmo0536_Lmo0536 protein 4.789991932 0.539437458 0.112618 0.587937 1.453406

lmo1503_UPF0297 protein lmo1503 4.773132707 4.005422294 0.83916 0.587937 16.06025

lmo1254_Lmo1254 protein 4.770024249 4.011125267 0.840902 0.587937 16.12386

lmo1381_Acylphosphatase 4.763120584 2.687842846 0.564303 0.587937 6.443492

lmo0794_Lmo0794 protein 4.748634388 3.241191924 0.682552 0.587937 9.45575

lmo1751_Uncharacterized RNA methyltransferase lmo1751 4.741369132 1.202370262 0.253591 0.587937 2.301174

lmo2695_Lmo2695 protein 4.705658202 0.744530678 0.15822 0.587937 1.675429

lmo0015_AA3-600 quinol oxidase subunit III 4.698444078 2.591123819 0.551486 0.587937 6.025679

lmo1257_Lmo1257 protein 4.692916714 4.294558418 0.915115 0.587937 19.62415

lmo0392_UPF0365 protein lmo0392 4.683751381 2.518525153 0.537715 0.587937 5.72996

lmo0553_Lmo0553 protein 4.682353172 1.260277078 0.269155 0.587937 2.395417

lmo1302_LexA repressor 4.654895632 3.251902044 0.698598 0.587937 9.526208

lmo1322_NusA protein 4.62075107 0.727735125 0.157493 0.638769 1.656037

lmo2068_60 kDa chaperonin 4.584603263 1.196294993 0.260938 0.638769 2.291504

lmo2417_Lmo2417 protein 4.560635307 0.982570663 0.215446 0.638769 1.975983

lmo2391_Lmo2391 protein 4.560285114 0.564878523 0.123869 0.638769 1.479263

lmo1569_Lmo1569 protein 4.555551278 3.116934687 0.684206 0.638769 8.675427

lmo1855_Lmo1855 protein 4.540093061 0.996385165 0.219464 0.638769 1.994995

lmo0356_Lmo0356 protein 4.537924904 2.632202691 0.580045 0.638769 6.199718

lmo0208_UPF0145 protein lmo0208 4.524018236 0.498919922 0.110282 0.638769 1.413155

lmo1941_Lmo1941 protein 4.508807524 1.880515143 0.417076 0.638769 3.682065

lmo1457_Putative phosphotransferase lmo1457 4.485309182 3.613726076 0.80568 0.638769 12.24165

lmo1189_Lmo1189 protein 4.437287915 1.655697793 0.373133 0.777538 3.150755

lmo2437_Lmo2437 protein 4.420874353 0.852319539 0.192794 0.777538 1.805401

lmo2791_Partition protein, ParA homolog 4.419765977 1.829793662 0.414002 0.777538 3.554862

lmo2191_Regulatory protein spx 4.392394887 2.565633059 0.584108 0.777538 5.920147

lmo1750_Lmo1750 protein 4.383257868 2.848509832 0.649861 0.777538 7.20256

156

lmo1253_Lmo1253 protein 4.376758072 1.260432852 0.287983 0.777538 2.395676

lmo2109_Lmo2109 protein 4.351620424 1.428841568 0.328347 0.777538 2.692304

lmo1489_Lmo1489 protein 4.342694965 0.27043315 0.062273 0.777538 1.20617

lmo0016_QoxD protein 4.342130223 3.020530492 0.695633 0.777538 8.114659

lmo0560_Lmo0560 protein 4.33174515 2.166367025 0.500114 0.777538 4.488916

lmo1917_PflA protein 4.32464643 2.226875372 0.514927 0.777538 4.68119

lmo0054_DnaC protein 4.311318133 1.323426515 0.306966 0.777538 2.502598

lmo2653_Elongation factor Tu 4.300689559 2.926509738 0.680475 0.777538 7.602689

lmo1577_UPF0173 metal-dependent hydrolase lmo1577 4.299987052 2.670327112 0.621008 0.777538 6.365735

lmo0360_Lmo0360 protein 4.288229928 2.124920754 0.495524 0.912527 4.361791

lmo0997_ATP-dependent protease 4.287293749 2.777982861 0.647957 0.912527 6.858927

lmo0029_Lmo0029 protein 4.28053939 2.333324701 0.545101 0.912527 5.039654

lmo0161_Lmo0161 protein 4.268360228 1.353195935 0.317029 0.912527 2.554774

lmo2213_Lmo2213 protein 4.267328594 4.21914649 0.988709 0.912527 18.62472

lmo0201_1-phosphatidylinositol phosphodiesterase precursor 4.243511331 3.36066736 0.791954 0.912527 10.27216

lmo2715_CydD protein 4.231905234 2.64882949 0.625919 0.912527 6.271582

lmo2530_ATP synthase gamma chain 4.221711299 2.58190256 0.611577 0.912527 5.987288

lmo1596_30S ribosomal protein S4 4.218327138 2.660912037 0.630798 0.912527 6.324327

lmo0508_Lmo0508 protein 4.217628565 1.347724289 0.319546 0.912527 2.545103

lmo1293_GlpD protein 4.213052401 0.533696674 0.126677 0.912527 1.447634

lmo2713_Secreted protein with 1 GW repeat 4.198699687 3.200663745 0.762299 0.912527 9.193816

lmo2654_Elongation factor G 4.19372648 2.319302827 0.553041 0.912527 4.99091

lmo1526_Lmo1526 protein 4.186333557 1.689704791 0.403624 0.912527 3.225907

lmo0931_Lmo0931 protein 4.176215028 2.714304179 0.649944 0.912527 6.562767

lmo1393_Lmo1393 protein 4.160081978 2.88990581 0.694675 0.912527 7.412221

lmo2473_UPF0052 protein lmo2473 4.114901821 0.620525464 0.1508 1.08419 1.537435

lmo2358_Lmo2358 protein 4.095463783 2.31815358 0.56603 1.08419 4.986936

lmo0944_Lmo0944 protein 4.069986696 2.074633718 0.50974 1.08419 4.212375

lmo1213_Lmo1213 protein 4.061860243 1.141926199 0.281134 1.08419 2.206755

lmo0804_Lmo0804 protein 4.055570751 1.665298074 0.41062 1.08419 3.171792

lmo0370_Lmo0370 protein 4.042622202 1.964332208 0.485905 1.08419 3.90232

157

lmo0187_Lmo0187 protein 4.040568566 0.176435057 0.043666 1.08419 1.130088

lmo0606_Lmo0606 protein 4.022654118 1.239605233 0.308156 1.08419 2.361339

lmo1220_Lmo1220 protein 3.97794423 1.143356875 0.287424 1.08419 2.208944

lmo1797_30S ribosomal protein S16 3.977297252 2.312321037 0.58138 1.08419 4.966815

lmo2335_FruA protein 3.968278279 2.121133178 0.534522 1.08419 4.350355

lmo2796_Lmo2796 protein 3.965622118 0.892379299 0.225029 1.08419 1.856235

lmo1222_Phenylalanyl-tRNA synthetase beta chain 3.964995833 1.283816442 0.323788 1.08419 2.434822

lmo1908_Lmo1908 protein 3.962796155 1.684248209 0.425015 1.090489 3.213729

lmo2006_AlsS protein 3.961429469 1.648418665 0.416117 1.090489 3.134898

lmo1284_UPF0078 membrane protein lmo1284 3.959998106 2.134854302 0.539105 1.090489 4.391928

lmo1552_Valyl-tRNA synthetase 3.958644752 0.482813731 0.121964 1.090489 1.397467

lmo2706_Lmo2706 protein 3.950013541 1.522798106 0.385517 1.090489 2.873478

lmo0296_Lmo0296 protein 3.943579864 1.147327244 0.290935 1.090489 2.215032

lmo1962_Lmo1962 protein 3.938010004 1.891557753 0.480333 1.090489 3.710356

lmo1462_GTP-binding protein era homolog 3.919654424 2.079727471 0.530589 1.090489 4.227274

lmo1283_Lmo1283 protein 3.901426767 3.443778872 0.882697 1.090489 10.8813

lmo0517_Lmo0517 protein 3.895742671 0.142815303 0.036659 1.090489 1.104057

lmo1252_Lmo1252 protein 3.888056578 1.652932866 0.425131 1.090489 3.144723

lmo1439_Superoxide dismutase 3.8666731 2.328709394 0.602251 1.090489 5.023558

lmo0113_Lmo0113 protein 3.856433173 1.721091926 0.446291 1.090489 3.296858

lmo2206_Chaperone protein clpB 3.855190236 1.938799292 0.502906 1.090489 3.833864

lmo2370_Lmo2370 protein 3.842779865 1.634590909 0.425367 1.090489 3.104995

lmo0557_Lmo0557 protein 3.841580797 2.436909795 0.634351 1.090489 5.414807

lmo2230_Lmo2230 protein 3.826516302 1.120725974 0.292884 1.090489 2.174564

lmo2479_Lmo2479 protein 3.824244631 1.265969677 0.331038 1.090489 2.404888

lmo2704_DnaX protein 3.8224785 1.511384972 0.395394 1.090489 2.850836

lmo0647_Lmo0647 protein 3.818765399 3.60453397 0.9439 1.090489 12.1639

lmo1416_Lmo1416 protein 3.818494599 1.325420916 0.347106 1.090489 2.50606

lmo1934_Hup protein 3.814964567 0.768816963 0.201527 1.090489 1.703872

lmo0287_Lmo0287 protein 3.805525019 1.103158806 0.289883 1.090489 2.148245

lmo2204_Lmo2204 protein 3.790839346 3.195951074 0.843072 1.090489 9.163832

158

lmo2029_Lmo2029 protein 3.776536294 2.20225355 0.583141 1.090489 4.601976

lmo1275_DNA topoisomerase 3.773165514 2.511275427 0.665562 1.090489 5.701239

lmo0819_Lmo0819 protein 3.771981054 1.820583791 0.48266 1.090489 3.532241

lmo1281_Lmo1281 protein 3.751483502 2.736385665 0.729414 1.090489 6.663987

lmo1796_UPF0109 protein lmo1796 3.748601317 1.123680174 0.29976 1.090489 2.179021

lmo0927_Lmo0927 protein 3.747798555 1.769507602 0.472146 1.090489 3.409376

lmo0922_Pantothenate kinase 3.728412689 2.386649248 0.640125 1.090489 5.229414

lmo1225_Lmo1225 protein 3.717194426 2.697937101 0.725799 1.090489 6.488734

lmo1570_Pyruvate kinase 3.707433702 2.490008727 0.671626 1.090489 5.617813

lmo1069_Lmo1069 protein 3.682414184 1.852192195 0.502983 1.090489 3.610484

lmo0378_Lmo0378 protein 3.681206205 0.972077504 0.264065 1.090489 1.961663

lmo1460_DNA repair protein recO 3.678414534 3.395278126 0.923028 1.090489 10.52157

lmo1370_Probable butyrate kinase 3.67639001 2.083054125 0.566603 1.090489 4.237032

lmo1405_Lmo1405 protein 3.662460848 1.743085757 0.475933 1.090489 3.347504

lmo1474_Protein grpE 3.661632552 2.604751021 0.711363 1.090489 6.082865

lmo1967_Uncharacterized protein Lmo1967 3.632360544 2.270749014 0.625144 1.090489 4.825736

lmo2241_Lmo2241 protein 3.62839932 1.343661845 0.370318 1.090489 2.537947

lmo1732_Lmo1732 protein 3.623950913 0.243096273 0.06708 1.110902 1.18353

lmo0955_Lmo0955 protein 3.618751984 0.600803226 0.166025 1.110902 1.516561

lmo0829_NifJ protein 3.59436747 1.064696565 0.296212 1.110902 2.09173

lmo0784_Lmo0784 protein 3.594105206 1.446727023 0.402528 1.110902 2.725889

lmo2085_Putative peptidoglycan bound protein 3.591103759 3.753558159 1.045238 1.110902 13.48757

lmo2702_Recombination protein recR 3.572657687 1.316401124 0.368466 1.110902 2.490441

lmo1055_Dihydrolipoyl dehydrogenase 3.558173876 3.308708757 0.92989 1.110902 9.908789

lmo1233_Thioredoxin 3.552654387 2.634454519 0.741545 1.110902 6.209403

lmo2453_Lmo2453 protein 3.550465158 3.561448827 1.003094 1.110902 11.806

lmo1094_Lmo1094 protein 3.544575669 0.664543983 0.187482 1.110902 1.585067

lmo0259_DNA-directed RNA polymerase subunit beta' 3.528448097 3.449563831 0.977643 1.110902 10.92502

lmo2264_Lmo2264 protein 3.515111601 1.502892413 0.427552 1.110902 2.834103

lmo2655_30S ribosomal protein S7 3.513419454 1.809460193 0.515014 1.110902 3.505111

lmo2064_Large-conductance mechanosensitive channel 3.499743866 2.993894219 0.855461 1.110902 7.966214

159

lmo0258_DNA-directed RNA polymerase subunit beta 3.498200122 2.222442433 0.63531 1.110902 4.666828

lmo1313_Uridylate kinase 3.483328161 1.439640078 0.413294 1.110902 2.712532

lmo0263_Internalin H 3.482918863 1.185518518 0.340381 1.110902 2.274451

lmo0351_Lmo0351 protein 3.468178601 0.862882875 0.2488 1.110902 1.818669

lmo1583_Probable thiol peroxidase 3.466429044 3.475275725 1.002552 1.110902 11.12147

lmo0836_Protein psiE homolog 3.46530598 1.593762368 0.45992 1.110902 3.018355

lmo2177_Lmo2177 protein 3.459397265 2.790707827 0.806703 1.110902 6.919692

lmo0045_Single-stranded DNA-binding protein 1 3.441398441 1.39331691 0.404869 1.110902 2.626819

lmo0956_Lmo0956 protein 3.438397617 0.672912167 0.195705 1.110902 1.594288

lmo0624_Lmo0624 protein 3.421865628 0.38364741 0.112116 1.110902 1.304636

lmo2528_ATP synthase epsilon chain 3.415668779 1.500032052 0.439162 1.110902 2.82849

lmo2638_Lmo2638 protein 3.404298833 1.205481559 0.354106 1.110902 2.306142

lmo2612_Preprotein translocase secY subunit 3.404284342 1.718697041 0.504863 1.110902 3.29139

lmo2152_Lmo2152 protein 3.391119468 3.778671503 1.114284 1.110902 13.7244

lmo2412_Lmo2412 protein 3.38471674 1.908943325 0.563989 1.110902 3.755339

lmo0196_Putative septation protein spoVG 1 3.375527233 1.902528644 0.563624 1.110902 3.738679

lmo1993_Pdp protein 3.366471574 1.385482396 0.411553 1.110902 2.612593

lmo1466_Lmo1466 protein 3.344136577 2.098883522 0.627631 1.110902 4.283777

lmo1378_Sensor protein 3.341267952 0.804167546 0.240677 1.161972 1.746138

lmo0271_Lmo0271 protein 3.32589103 0.366707116 0.110258 1.161972 1.289406

lmo1215_Lmo1215 protein 3.320798806 1.845404446 0.555711 1.161972 3.593537

lmo1578_Lmo1578 protein 3.31917726 2.837754726 0.854957 1.161972 7.149066

lmo2148_Lmo2148 protein 3.301111147 1.133591088 0.343397 1.161972 2.194042

lmo2032_Cell division protein ftsZ 3.298472353 1.036629234 0.314276 1.161972 2.051429

lmo0007_DNA gyrase subunit A 3.287246913 2.215756729 0.674046 1.161972 4.645252

lmo1966_Lmo1966 protein 3.282392154 2.806625158 0.855055 1.161972 6.99646

lmo1493_Lmo1493 protein 3.251282828 1.535687063 0.472333 1.161972 2.899265

lmo2256_Lmo2256 protein 3.247044054 1.702402771 0.524293 1.161972 3.254425 lmo1788_Putative mercury resistance operon regulatory

protein 3.235605938 0.940693043 0.290732 1.161972 1.91945

lmo0232_Endopeptidase Clp ATP-binding chain C 3.203856426 2.137657553 0.667214 1.161972 4.40047

160

lmo2828_Lmo2828 protein 3.192015065 1.922800839 0.602378 1.331486 3.791584

lmo2414_Lmo2414 protein 3.190732561 1.144083142 0.358564 1.331486 2.210056

lmo0729_Lmo0729 protein 3.185636005 1.02827606 0.322785 1.331486 2.039586

lmo0521_Lmo0521 protein 3.183126487 2.123013929 0.666959 1.331486 4.35603

lmo2411_Lmo2411 protein 3.177627919 2.634378099 0.829039 1.331486 6.209074

lmo2452_Lmo2452 protein 3.15590604 0.233338159 0.073937 1.331486 1.175552

lmo2652_Lmo2652 protein 3.151372295 1.295248404 0.411011 1.331486 2.454192

lmo2016_Cold shock-like protein cspLB 3.141474958 2.775292441 0.883436 1.331486 6.846148

lmo1560_Primosome component (Helicase loader) DnaI 3.140806765 0.573999748 0.182756 1.331486 1.488645

lmo2190_Adapter protein mecA 3.138134405 1.510228202 0.48125 1.331486 2.848551

lmo0046_30S ribosomal protein S18 3.126964889 2.767586052 0.885071 1.331486 6.809676

lmo0433_Internalin-A precursor 3.121710275 3.42997697 1.098749 1.331486 10.7777

lmo0959_Lmo0959 protein 3.110403448 2.26327914 0.727648 1.331486 4.800814

lmo1742_Adenine deaminase 3.107510218 1.170458221 0.376655 1.331486 2.250832

lmo0607_Lmo0607 protein 3.100138795 2.112088021 0.681288 1.331486 4.323165

lmo2716_CydC protein 3.097069121 1.101128072 0.355539 1.331486 2.145224

lmo0932_Lmo0932 protein 3.093988857 1.922382746 0.621328 1.331486 3.790486

lmo0982_Lmo0982 protein 3.093487513 2.67673555 0.865281 1.331486 6.394074

lmo0822_Lmo0822 protein 3.088931402 2.012397692 0.651487 1.331486 4.034522

lmo2478_Thioredoxin reductase 3.087095454 1.60352008 0.519427 1.331486 3.038839

lmo0051_Lmo0051 protein 3.082184909 0.618503506 0.20067 1.331486 1.535282

lmo0388_Lmo0388 protein 3.072587643 2.034947082 0.662291 1.331486 4.098077

lmo1589_Acetylglutamate kinase 3.072300627 2.947273538 0.959305 1.331486 7.712901

lmo2804_Lmo2804 protein 3.067597718 2.543172844 0.829044 1.331486 5.828695

lmo2659_Lmo2659 protein 3.05795236 2.129295915 0.696314 1.331486 4.375039

lmo1450_Lmo1450 protein 3.046445771 0.537746727 0.176516 1.561318 1.451703

lmo2031_Lmo2031 protein 3.042268686 1.49372526 0.490991 1.561318 2.816152

lmo1879_CspD protein 3.037417994 1.864702642 0.61391 1.561318 3.641929

lmo2193_ATPase OppD 3.027845815 2.603730026 0.859928 1.561318 6.078562

lmo1953_Pnp protein 3.027463898 0.926256195 0.305951 1.561318 1.900338

lmo2360_Transmembrane protein 3.026571607 1.420544594 0.469358 1.561318 2.676865

161

lmo0044_30S ribosomal protein S6 3.0092306 2.096902072 0.696823 1.561318 4.277898

lmo2406_Lmo2406 protein 3.005585791 0.943491206 0.313913 1.561318 1.923177

lmo1981_Lmo1981 protein 2.998727001 0.232874699 0.077658 1.561318 1.175174

lmo2742_Lmo2742 protein 2.994043956 1.325252622 0.44263 1.561318 2.505768

lmo2555_Lmo2555 protein 2.99298296 0.893135274 0.29841 1.561318 1.857208

lmo1488_Probable nicotinate-nucleotide adenylyltransferase 2.99171019 2.462287635 0.823037 1.561318 5.510899

lmo1893_Lmo1893 protein 2.9826888 0.623472814 0.20903 1.561318 1.540579

lmo1410_Lmo1410 protein 2.973297799 1.447972596 0.486992 1.561318 2.728244

162

Appendix (C): differentially regulated genes (up-regulation) of LuxS mutant strain of L. monocytogenes at stationary phase at 37°C

Gene ID Gene Name Score(d) Numerator(r) Denominator(s+s0) q-value(%) fold-change

LM00002440 lmo1113_Lmo1113 protein 4.194412 0.613241 0.146204 0 1.529691

163

Appendix (D): differentially regulated genes (Down-regulation) of LuxS mutant strain of L. monocytogenes at stationary phase at 37°C

q- Score(d Numerator(r Denominator(s+s value(% fold- Gene Name ) ) 0) ) change - 4.1506

lmo0943_DNA protection during starvation protein -3.3209 -2.05334 0.61831 3.796704 7 lmo1350_Probable glycine dehydrogenase [decarboxylating]

subunit 2 -3.31914 -2.44101 0.735433 3.796704 -5.4302 - 2.3235

lmo2667_Lmo2667 protein -3.27759 -1.21634 0.371107 3.796704 6 - 2.3391

lmo2637_Lmo2637 protein -3.02187 -1.22601 0.405711 3.796704 9 - 2.9475

lmo0647_Lmo0647 protein -2.98641 -1.55953 0.522209 3.796704 8

lmo2029_Lmo2029 protein -2.97113 -0.88393 0.297507 3.796704 -1.8454 - 1.5162

lmo0703_Lmo0703 protein -2.94556 -0.60053 0.203875 3.796704 7

lmo1097_Lmo1097 protein -2.86647 -2.16014 0.75359 3.796704 -4.4696 - 6.3579

lmo1683_Lmo1683 protein -2.80024 -2.66857 0.952978 3.796704 7 - 1.3990

lmo2716_CydC protein -2.79544 -0.48447 0.173308 3.796704 7 - 3.0520

lmo1254_Lmo1254 protein -2.7793 -1.60976 0.579197 3.796704 2

164

- 2.8403

lmo1762_Lmo1762 protein -2.75505 -1.50607 0.546658 3.796704 5 - 3.2305

lmo1257_Lmo1257 protein -2.74842 -1.69176 0.615539 3.796704 1 - 2.7174

lmo2715_CydD protein -2.74043 -1.44223 0.52628 3.796704 1 - 2.6204

lmo1469_30S ribosomal protein S21 -2.73977 -1.38982 0.507277 3.796704 7

lmo1053_PdhB protein -2.71922 -1.41927 0.521941 3.796704 -2.6745 - 2.0314

lmo1237_Glutamate racemase -2.71912 -1.02248 0.376033 3.796704 1 - 3.5031

lmo1275_DNA topoisomerase -2.71583 -1.80866 0.665972 3.796704 8 - 2.0953

lmo0414_Lmo0414 protein -2.71001 -1.06717 0.393788 3.796704 1

lmo0606_Lmo0606 protein -2.70089 -1.72557 0.638888 3.796704 -3.3071 - 1.7821

lmo2706_Lmo2706 protein -2.68914 -0.8336 0.309988 3.796704 3 - 2.1987

lmo1596_30S ribosomal protein S4 -2.67717 -1.13668 0.424582 3.796704 4 - 4.8398

lmo0007_DNA gyrase subunit A -2.67015 -2.27496 0.851998 3.796704 5

lmo2666_Lmo2666 protein -2.66623 -0.78879 0.295845 3.796704 -

165

1.7276 3 - lmo1268_ATP-dependent Clp protease ATP-binding subunit 4.4148

clpX -2.66319 -2.14236 0.804433 3.796704 3 - 2.4354

lmo1302_LexA repressor -2.65758 -1.28417 0.483211 3.796704 2 - 2.8179

lmo1502_Putative Holliday junction resolvase -2.63522 -1.49467 0.567188 3.796704 9 - 2.3874

lmo2530_ATP synthase gamma chain -2.63088 -1.25546 0.477203 3.796704 4 - 2.4735

lmo1301_Lmo1301 protein -2.62795 -1.30656 0.49718 3.796704 2

lmo2330_Lmo2330 protein -2.62423 -0.47944 0.182697 3.796704 -1.3942 - 1.6337

lmo0553_Lmo0553 protein -2.60492 -0.70816 0.271857 3.796704 2 - 1.8538

lmo2407_Lmo2407 protein -2.60181 -0.89049 0.34226 3.796704 1 - 1.7309

lmo2191_Regulatory protein spx -2.59805 -0.79154 0.304668 3.796704 2 - 3.4260

lmo1539_Lmo1539 protein -2.59452 -1.77656 0.684736 3.796704 9 - 2.3836

lmo1599_Catabolite control protein A -2.57936 -1.25315 0.48584 3.796704 2

166

- 1.9301

lmo2665_Lmo2665 protein -2.57626 -0.94872 0.368253 3.796704 5 - 3.2158

lmo1888_Cell cycle protein gpsB -2.56928 -1.6852 0.655903 3.796704 4 - 1.9708

lmo1213_Lmo1213 protein -2.56281 -0.97882 0.381932 3.796704 5 - 8.4475

lmo0964_UPF0413 protein lmo0964 -2.54792 -3.07854 1.208255 3.796704 9 - 2.2995

lmo0044_30S ribosomal protein S6 -2.53898 -1.20135 0.473165 3.796704 5 - 1.7082

lmo1367_Arginine repressor -2.53665 -0.77251 0.304541 3.796704 4 - 2.1375

lmo1406_Pyruvate formate-lyase -2.53528 -1.09598 0.43229 3.796704 8 - 3.2684

lmo1962_Lmo1962 protein -2.52882 -1.70862 0.675659 3.796704 8 - 1.2913

lmo0769_Lmo0769 protein -2.52815 -0.36887 0.145904 3.796704 4 - 9.1497

lmo1379_Membrane protein oxaA 2 precursor -2.52776 -3.19374 1.263466 3.796704 8 - 2.3259

lmo1604_Lmo1604 protein -2.51451 -1.21783 0.484322 3.796704 7

167

- 3.1274

lmo1381_Acylphosphatase -2.50575 -1.64499 0.656488 3.796704 6

lmo2717_CydB protein -2.4861 -0.54439 0.218974 3.796704 -1.4584 - 1.9309

lmo2386_Lmo2386 protein -2.47816 -0.94932 0.383075 3.796704 6 - 2.0971

lmo1853_Lmo1853 protein -2.46504 -1.06845 0.433443 3.796704 8 - 1.2330

lmo1422_Lmo1422 protein -2.46165 -0.30226 0.122786 3.796704 7 - 3.5206

lmo2069_10 kDa chaperonin -2.45898 -1.81585 0.738456 3.796704 7 - 5.4400

lmo1054_PdhC protein -2.44785 -2.44363 0.998277 3.796704 8 - 3.9452

lmo0930_Lmo0930 protein -2.43741 -1.98011 0.812381 3.796704 2 - 2.0364

lmo1474_Protein grpE -2.43733 -1.02603 0.420967 3.796704 2

lmo1215_Lmo1215 protein -2.43198 -1.44329 0.593462 3.796704 -2.7194 - 2.2601

lmo2718_CydA protein -2.42943 -1.17639 0.484225 3.796704 1 - 3.4975

lmo2792_Lmo2792 protein -2.42603 -1.80635 0.744567 3.796704 5

lmo1273_Ribonuclease HII -2.41402 -1.97754 0.819192 3.796704 -

168

3.9382 2 - 4.7538

lmo1569_Lmo1569 protein -2.39864 -2.24909 0.937653 3.796704 4 - 2.2275

lmo0508_Lmo0508 protein -2.38888 -1.15544 0.483676 3.796704 3 - 2.1307

lmo2714_Peptidoglycan anchored protein -2.38739 -1.09133 0.457124 3.796704 1 - 2.2475

lmo1963_Lmo1963 protein -2.37922 -1.16834 0.491061 3.796704 3 - 2.8268

lmo0607_Lmo0607 protein -2.37589 -1.49918 0.630997 3.796704 2 - 2.4169

lmo1416_Lmo1416 protein -2.37588 -1.2732 0.535884 3.796704 7 - 3.3278

lmo1323_Lmo1323 protein -2.37085 -1.7346 0.731636 3.796704 6 - 1.8015

lmo2337_Lmo2337 protein -2.36415 -0.84926 0.359224 3.796704 8 - 2.0446

lmo1748_Lmo1748 protein -2.36406 -1.03184 0.43647 3.796704 3 - lmo2205_2,3-bisphosphoglycerate-dependent 1.9383

phosphoglycerate mutase -2.36259 -0.9548 0.404133 3.796704 1

lmo2040_FtsL protein -2.36061 -0.4893 0.207278 3.796704 -

169

1.4037 6 - 1.6396

lmo0046_30S ribosomal protein S18 -2.35771 -0.71336 0.302565 3.796704 2 - 3.1255

lmo1658_30S ribosomal protein S2 -2.35674 -1.64412 0.697623 3.796704 7 - 2.1611

lmo1405_Lmo1405 protein -2.35349 -1.11182 0.472412 3.796704 7 - 1.2870

lmo1252_Lmo1252 protein -2.35204 -0.36406 0.154784 3.796704 4 - 2.0557

lmo1274_Lmo1274 protein -2.34991 -1.03967 0.442431 3.796704 6 - 4.0395

lmo2412_Lmo2412 protein -2.34912 -2.01419 0.857422 3.796704 2 - 2.5453

lmo1351_Lmo1351 protein -2.34837 -1.34788 0.573964 3.796704 7 - 3.1842

lmo0608_Lmo0608 protein -2.34277 -1.67096 0.713242 3.796704 7 - lmo1349_Probable glycine dehydrogenase [decarboxylating] 3.4799

subunit 1 -2.33783 -1.79905 0.769538 3.796704 1 - 4.0327

lmo1055_Dihydrolipoyl dehydrogenase -2.33762 -2.01177 0.860606 3.796704 6

lmo1058_UPF0223 protein lmo1058 -2.33231 -1.61425 0.692123 3.796704 -

170

3.0615 2 - 1.9528

lmo2668_Lmo2668 protein -2.33045 -0.96558 0.414332 3.796704 5 - 1.3566

lmo1322_NusA protein -2.3293 -0.44009 0.188935 3.796704 9 - 2.1215

lmo2474_UPF0042 protein lmo2474 -2.32909 -1.08513 0.465903 3.796704 7 - 1.7345

lmo1096_GMP synthase -2.32584 -0.79454 0.341614 3.796704 3 - 1.9756

lmo0464_Lmo0464 protein -2.32493 -0.98235 0.422527 3.796704 8 - 1.6552

lmo0391_Lmo0391 protein -2.31141 -0.72705 0.314547 3.796704 5 - lmo1279_ATP-dependent hsl protease ATP-binding subunit 1.9943

hslU -2.30813 -0.99593 0.431487 3.796704 6 - 1.5462

lmo1664_S-adenosylmethionine synthetase -2.30708 -0.62876 0.272537 3.796704 4 - 2.4174

lmo0822_Lmo0822 protein -2.30676 -1.27349 0.552068 3.796704 5 - 1.7459

lmo1027_Lmo1027 protein -2.2926 -0.80399 0.350689 3.796704 2

lmo0045_Single-stranded DNA-binding protein 1 -2.29244 -0.41074 0.179169 3.796704 -

171

1.3293 6 - 2.0887

lmo1783_50S ribosomal protein L20 -2.28916 -1.06261 0.464193 3.796704 1

lmo0488_Lmo0488 protein -2.2829 -0.73976 0.324043 3.796704 -1.6699 - 4.1114

lmo1797_30S ribosomal protein S16 -2.27331 -2.03964 0.897211 3.796704 2 - 2.4135

lmo2596_30S ribosomal protein S9 -2.27197 -1.27114 0.559489 3.796704 2 - 2.4367

lmo2713_Secreted protein with 1 GW repeat -2.27086 -1.28495 0.56584 3.796704 3 - 1.5377

lmo2204_Lmo2204 protein -2.26817 -0.62083 0.273713 3.796704 6

lmo1068_Lmo1068 protein -2.26317 -0.79668 0.35202 3.796704 -1.7371 - 2.8726

lmo0114_AX protein -2.25736 -1.52239 0.674414 3.796704 7 - 1.9266

lmo2654_Elongation factor G -2.25623 -0.94611 0.419332 3.796704 7 - 4.4968

lmo1225_Lmo1225 protein -2.25509 -2.16892 0.961792 3.796704 8 - 4.1495

lmo1439_Superoxide dismutase -2.25059 -2.05294 0.912179 3.796704 1 -

lmo1059_Lmo1059 protein -2.25045 -2.0003 0.888845 3.796704 4.0008

172

3 - 3.3769

lmo1879_CspD protein -2.24656 -1.75572 0.781515 3.796704 5 - 2.5541

lmo1468_Lmo1468 protein -2.24505 -1.35285 0.602592 3.796704 7 - 2.3092

lmo2511_Lmo2511 protein -2.24407 -1.20741 0.538046 3.796704 3 - 1.3502

lmo1178_Lmo1178 protein -2.24104 -0.43327 0.193335 3.796704 9 - 1.4624

lmo0161_Lmo0161 protein -2.23868 -0.54843 0.244979 3.796704 9 - 2.7380

lmo1488_Probable nicotinate-nucleotide adenylyltransferase -2.2383 -1.45317 0.649226 3.796704 8 - 2.3691

lmo2358_Lmo2358 protein -2.23608 -1.24437 0.556496 3.796704 5 - 3.6566

lmo1002_Phosphocarrier protein HPr -2.23515 -1.87052 0.836865 3.796704 5 - 2.6628

lmo0933_Lmo0933 protein -2.23373 -1.41297 0.632561 3.796704 5 - 1.5307

lmo0994_Lmo0994 protein -2.23077 -0.61422 0.275342 3.796704 3 -

lmo2405_Lmo2405 protein -2.22427 -0.38155 0.17154 3.796704 1.3027

173

4 - 3.2081

lmo0443_Lmo0443 protein -2.22398 -1.68176 0.756191 3.796704 8 - 2.9431

lmo1690_Lmo1690 protein -2.22241 -1.55738 0.70076 3.796704 8 - 3.0852

lmo2468_ATP-dependent Clp protease proteolytic subunit -2.22171 -1.6254 0.731599 3.796704 8 - 1.4653

lmo1330_30S ribosomal protein S15 -2.22113 -0.55121 0.248168 3.796704 2 - 3.5277

lmo1233_Thioredoxin -2.21721 -1.81876 0.820295 3.796704 9 - 1.3412

lmo1828_Lmo1828 protein -2.21244 -0.42355 0.191439 3.796704 2 - 1.8245

lmo1179_Lmo1179 protein -2.20881 -0.86756 0.392773 3.796704 8

lmo2479_Lmo2479 protein -2.20605 -0.77045 0.349244 3.796704 -1.7058 - 2.6333

lmo2335_FruA protein -2.19932 -1.39688 0.635141 3.796704 1 - 2.3664

lmo0054_DnaC protein -2.19411 -1.24271 0.566384 3.796704 3 - 4.0072

lmo0982_Lmo0982 protein -2.19288 -2.0026 0.913228 3.796704 1

lmo1582_Lmo1582 protein -2.18664 -0.60243 0.275504 3.796704 -

174

1.5182 7

lmo1665_Lmo1665 protein -2.17823 -0.37584 0.172545 3.796704 -1.2976 - lmo1376_6-phosphogluconate dehydrogenase, 2.4239

decarboxylating -2.16765 -1.27738 0.58929 3.796704 8

lmo1730_Lmo1730 protein -2.16549 -0.80859 0.373399 3.796704 -1.7515 - 2.1111

lmo2789_Lmo2789 protein -2.16496 -1.07804 0.497951 3.796704 7 - 2.8177

lmo2206_Chaperone protein clpB -2.1648 -1.49452 0.690373 3.796704 1 - 2.9916

lmo2016_Cold shock-like protein cspLB -2.15643 -1.58093 0.733122 3.796704 2 - 3.2399

lmo1187_Lmo1187 protein -2.15485 -1.69596 0.787044 3.796704 3 - 1.6025

lmo0966_Lmo0966 protein -2.15484 -0.6804 0.315752 3.796704 8 - 4.2485

lmo2556_Fructose-bisphosphate aldolase -2.15255 -2.08698 0.969539 3.796704 9 - 1.3673

lmo0941_Lmo0941 protein -2.15138 -0.45138 0.209809 3.796704 5 - 1.5019

lmo1340_Lmo1340 protein -2.15123 -0.58682 0.272785 3.796704 4 -

lmo2760_Lmo2760 protein -2.14846 -0.45425 0.211432 3.796704 1.3700

175

7 - 2.1230

lmo0584_Lmo0584 protein -2.13984 -1.08613 0.507575 3.796704 3 - 1.9951

lmo1403_DNA mismatch repair protein mutS -2.13939 -0.99651 0.465791 3.796704 7 - 1.7688

lmo2381_Lmo2381 protein -2.13665 -0.82279 0.385085 3.796704 3 - 1.7272

lmo1691_Lmo1691 protein -2.13248 -0.78844 0.36973 3.796704 1

lmo2414_Lmo2414 protein -2.13189 -0.83196 0.390244 3.796704 -1.7801 - 3.5222

lmo2528_ATP synthase epsilon chain -2.13074 -1.8165 0.85252 3.796704 5 - 2.3464

lmo2075_Probable O-sialoglycoprotein endopeptidase -2.12231 -1.23049 0.579785 4.988655 6 - lmo1586_Probable inorganic polyphosphate/ATP-NAD 1.9198

kinase 2 -2.11531 -0.941 0.444851 4.988655 6 - 2.1915

lmo1503_UPF0297 protein lmo1503 -2.11359 -1.13196 0.535564 4.988655 6 - 2.6901

lmo1731_Lmo1731 protein -2.10922 -1.42771 0.67689 4.988655 9 - 1.8356

lmo1733_Lmo1733 protein -2.10245 -0.87633 0.416812 4.988655 9

lmo0258_DNA-directed RNA polymerase subunit beta -2.10074 -0.80508 0.383238 4.988655 -

176

1.7472 5 - 1.9166

lmo1612_Lmo1612 protein -2.10014 -0.93861 0.446928 4.988655 8 - 1.6130

lmo1695_Lmo1695 protein -2.09843 -0.68978 0.328711 4.988655 3 - lmo1849_Manganese transport system ATP-binding protein 5.1908

mntB -2.09559 -2.37597 1.133792 4.988655 3 - 1.6366

lmo0179_Lmo0179 protein -2.09292 -0.71074 0.339594 4.988655 5 - 3.1166

lmo1567_CitZ protein -2.09183 -1.63998 0.783993 4.988655 1

lmo1825_Lmo1825 protein -2.09117 -1.67016 0.79867 4.988655 -3.1825

lmo1454_RNA polymerase sigma factor rpoD -2.08032 -1.50848 0.72512 4.988655 -2.8451 - 2.4345

lmo0428_Lmo0428 protein -2.07881 -1.28365 0.617493 4.988655 4 - 1.5306

lmo2796_Lmo2796 protein -2.07565 -0.6141 0.29586 4.988655 1 - 1.8339

lmo1704_Lmo1704 protein -2.07532 -0.87499 0.421616 4.988655 9 - 3.1038

lmo2653_Elongation factor Tu -2.07268 -1.63406 0.78838 4.988655 5 -

lmo2196_Peptide binding protein OppA -2.07157 -2.4616 1.188278 4.988655 5.5082

177

8 - 1.9759

lmo2473_UPF0052 protein lmo2473 -2.07064 -0.98253 0.474506 4.988655 3 - 1.4310

lmo1201_Lmo1201 protein -2.07062 -0.51709 0.249727 4.988655 7 - 1.6741

lmo1566_Isocitrate dehydrogenase -2.06651 -0.74341 0.359744 4.988655 3 - 1.4087

lmo1876_ -2.06125 -0.49438 0.239845 4.988655 2 - 3.5470

lmo1423_Lmo1423 protein -2.06031 -1.82662 0.886577 4.988655 5 - 3.7327

lmo2529_ATP synthase subunit beta 2 -2.05165 -1.90023 0.926196 4.988655 2 - 2.5128

lmo1786_Internalin C -2.04904 -1.32932 0.648753 4.988655 5 - 1.9735

lmo1896_Asparaginyl-tRNA synthetase -2.04661 -0.98081 0.479238 4.988655 7

lmo2791_Partition protein, ParA homolog -2.04508 -1.36714 0.668503 4.988655 -2.5796 - 1.6997

lmo2199_Lmo2199 protein -2.04474 -0.76529 0.37427 4.988655 1

lmo0513_Lmo0513 protein -2.03924 -0.70373 0.345093 5.441454 -1.6287 - 3.5200

lmo2797_Lmo2797 protein -2.03853 -1.81558 0.890633 5.441454 1

178

lmo0196_Putative septation protein spoVG 1 -2.03736 -1.39336 0.683906 5.441454 -2.6269 - 1.8851

lmo1978_Glucose-6-phosphate 1-dehydrogenase -2.03688 -0.91469 0.449067 5.441454 7 - 2.2897

lmo2597_50S ribosomal protein L13 -2.03668 -1.19522 0.586845 5.441454 9 - 2.6773

lmo2453_Lmo2453 protein -2.03583 -1.4208 0.697898 5.441454 4

179

Appendix (E) differentially regulated genes (up-regulation) of AgrD mutant strain of L. monocytogenes at exponential phase at 37°C

q- Gene ID Gene Name Score(d) Numerator(r) Denominator(s+s0) value(%)

LM00000666 lmo2506_FtsX protein -1.70639 -2.14198 1.25527 0

LM00000667 lmo2507_FtsE protein -1.65443 -2.3861 1.442248 0

180

Appendix (F): differentially regulated genes (Down-regulation) of AgrD mutant strain of L. monocytogenes at stationary phase at 4°C

Numerator(r Denominator(s+s0 q- fold- Gene Name Score(d) ) ) value(%) change - 4.8706

lmo0582_Protein p60 precursor -3.30326 -2.28411 0.691471 0 2 - 5.1816

lmo2467_Lmo2467 protein -3.11301 -2.37342 0.762419 0 8 - lmo1849_Manganese transport system ATP- 4.4806

binding protein mntB -3.02563 -2.1637 0.715125 0 2 - 5.0499

lmo1677_Lmo1677 protein -2.83798 -2.33627 0.823215 0 6 - 3.7816

lmo1041_Lmo1041 protein -2.69124 -1.91902 0.713063 0 7 - 3.5656

lmo2169_Lmo2169 protein -2.61589 -1.83417 0.701164 0 6 - 3.9268

lmo2113_UPF0447 protein lmo2113 -2.54443 -1.97339 0.775573 0 9 - 3.1600

lmo0522_Lmo0522 protein -2.48693 -1.65995 0.667472 0 6 - 2.6409

lmo0414_Lmo0414 protein -2.45041 -1.40104 0.57176 0 3 -

lmo2527_Lmo2527 protein -2.39676 -1.80167 0.751711 0 3.4862

181

4 - 2.9322

lmo1273_Ribonuclease HII -2.38648 -1.55202 0.65034 0 8 - 3.1425

lmo1713_Lmo1713 protein -2.38347 -1.65194 0.693083 0 6 - 2.5583

lmo0724_Lmo0724 protein -2.33637 -1.35519 0.58004 0 1 - 4.3653

lmo2212_Uroporphyrinogen decarboxylase -2.31013 -2.12611 0.92034 0 8 - lmo1086_Putative 2-C-methyl-D-erythritol 4- 3.1034

phosphate cytidylyltransferase 2 -2.26228 -1.63389 0.722231 0 8 - 3.7559

lmo1941_Lmo1941 protein -2.21806 -1.90919 0.860746 0 8 - 3.0062

lmo1355_Elongation factor P -2.21104 -1.58795 0.71819 0 2 - 3.3240

lmo2154_Lmo2154 protein -2.2017 -1.73296 0.787101 0 9 - 2.6638

lmo1452_UPF0135 protein lmo1452 -2.19187 -1.41351 0.64489 0 5 - 2.2742

lmo1352_Lmo1352 protein -2.16831 -1.18539 0.546686 0 4 lmo1376_6-phosphogluconate dehydrogenase, -

decarboxylating -2.15131 -1.70546 0.792757 0 3.2613

182

4 - lmo0943_DNA protection during starvation 2.7985

protein -2.14796 -1.4847 0.691213 0 8 - 3.1340

lmo0196_Putative septation protein spoVG 1 -2.13292 -1.64803 0.772664 0 5

lmo2854_Membrane protein oxaA 1 precursor -2.12921 -1.65983 0.779554 0 -3.1598 - 2.9272

lmo2718_CydA protein -2.08666 -1.54953 0.742589 0 1 - 3.2276

lmo2428_Lmo2428 protein -2.04492 -1.6905 0.826684 0 9 - 3.7743

lmo0596_Lmo0596 protein -2.03188 -1.91624 0.943089 0 8 - 3.5529

lmo2376_Peptidyl-prolyl cis-trans isomerase -1.94468 -1.82902 0.940524 0 6

lmo1439_Superoxide dismutase -1.9246 -1.65324 0.859005 0 -3.1454 - 2.2159

lmo0933_Lmo0933 protein -1.9155 -1.14793 0.599288 0 6 - 2.6812

lmo0893_Anti-sigma-B factor antagonist -1.89643 -1.42289 0.750301 0 3 - 3.2382

lmo1002_Phosphocarrier protein HPr -1.89186 -1.69521 0.896058 0 5 - 3.0956

lmo2032_Cell division protein ftsZ -1.88497 -1.63026 0.864874 0 8

183

- 2.4517

lmo2211_Ferrochelatase -1.88215 -1.29381 0.687409 0 4 - 2.8074

lmo2686_Lmo2686 protein -1.87165 -1.48927 0.795696 0 6 - 2.9258

lmo1187_Lmo1187 protein -1.86392 -1.54886 0.830971 0 6 - 2.7997

lmo1353_Lmo1353 protein -1.85289 -1.48528 0.801604 0 2 - 2.6597

lmo1230_Lmo1230 protein -1.85242 -1.41131 0.761872 0 8 - 2.4547

lmo0578_Putative conserved membrane protein -1.84179 -1.29558 0.703435 0 5 - 3.3047

lmo1171_PduQ protein -1.82878 -1.72455 0.943001 0 6 - 2.0965

lmo1637_Lmo1637 protein -1.79725 -1.06803 0.594258 0 7 - 2.3050

lmo1252_Lmo1252 protein -1.79715 -1.20477 0.67038 0 1 - 2.3781

lmo2427_Lmo2427 protein -1.79133 -1.24984 0.697716 0 5 - 2.4107

lmo1633_TrpE protein -1.78828 -1.26948 0.709886 0 4

184

- 2.1242

lmo1367_Arginine repressor -1.78318 -1.08692 0.609542 0 1 - 2.6829

lmo1855_Lmo1855 protein -1.77517 -1.42381 0.802067 0 2 - 2.1676

lmo1381_Acylphosphatase -1.77007 -1.1161 0.63054 0 1 - 2.6817

lmo1295_Lmo1295 protein -1.76768 -1.42319 0.805114 0 7 - 2.8864

lmo2196_Peptide binding protein OppA -1.76645 -1.52931 0.865754 0 8 - 2.3992

lin1695_ -1.76162 -1.26258 0.716719 0 5 lmo2482_Prolipoprotein diacylglyceryl

transferase -1.76107 -1.10064 0.624985 0 -2.1445 - 2.8890

lmo1748_Lmo1748 protein -1.75918 -1.53059 0.870059 0 4 - 3.1699

lmo1864_Lmo1864 protein -1.75543 -1.66445 0.948175 0 3 - 3.7488

lmo1857_UPF0346 protein lmo1857 -1.74689 -1.90644 1.091329 0 2 - lmo1675_2-succinyl-5-enolpyruvyl-6-hydroxy-3- 2.6063

cyclohexene- 1-carboxylate synthase -1.74411 -1.38204 0.792402 0 6

lmo2195_Lmo2195 protein -1.73814 -1.70956 0.983555 0 -

185

3.2706 1 - 4.1895

lmo0995_Lmo0995 protein -1.73506 -2.0668 1.191197 0 6 - 2.8876

lmo2532_AtpH protein -1.70311 -1.5299 0.898298 0 5 - 2.2288

lmo1571_6-phosphofructokinase -1.70192 -1.15629 0.679404 0 3 - 2.4438

lmo1845_Lmo1845 protein -1.69772 -1.28917 0.759355 0 7 - 3.1324

lmo2029_Lmo2029 protein -1.69206 -1.64731 0.973553 0.473714 8

lmo2232_Lmo2232 protein -1.6821 -1.22694 0.729408 0.473714 -2.3407 - 2.5094

lmo0606_Lmo0606 protein -1.67865 -1.32736 0.790728 0.473714 3 - 3.6807

lmo2637_Lmo2637 protein -1.67635 -1.88001 1.121493 0.473714 8 - 2.3646

lmo2659_Lmo2659 protein -1.67164 -1.24162 0.742756 0.473714 4 - 2.1586

lmo1919_Lmo1919 protein -1.6696 -1.11011 0.664898 0.473714 3 - 2.9074

lmo1570_Pyruvate kinase -1.6586 -1.53976 0.928353 0.473714 7

186

- 2.4216

lmo0404_Lmo0404 protein -1.65259 -1.276 0.772121 0.473714 7 - 3.3053

lmo0539_Tagatose 1,6-diphosphate aldolase -1.6447 -1.72479 1.048692 0.473714 1 - 2.2813

lmo1924_TyrA protein -1.63352 -1.18986 0.728403 0.473714 1 - 2.4231

lmo0891_RsbT protein -1.6289 -1.2769 0.783907 0.473714 8 - 2.0527

lmo1275_DNA topoisomerase -1.62791 -1.03759 0.637376 0.473714 9 - 3.2165

lmo0611_FMN-dependent NADH-azoreductase 1 -1.61144 -1.68552 1.045967 0.473714 5 - 2.4939

lmo2155_Ribonucleoside-diphosphate reductase -1.58154 -1.31841 0.833626 0.846369 1 - 2.1552

lmo2127_Lmo2127 protein -1.57953 -1.10786 0.701386 0.846369 6 - 3.1851

lmo1546_MreD protein -1.57105 -1.67138 1.063857 0.846369 9 - 2.2329

lmo1283_Lmo1283 protein -1.55701 -1.15893 0.744332 0.846369 2

lmo0892_RsbU protein -1.55583 -1.43194 0.920371 0.846369 -2.6981 -

lmo0454_Lmo0454 protein -1.55573 -1.14184 0.733953 0.846369 2.2066

187

2 - lmo1003_Phosphoenolpyruvate-protein 2.2392

phosphotransferase -1.54844 -1.163 0.751081 0.846369 3 - 2.5043

lmo1443_Lmo1443 protein -1.53948 -1.32441 0.860301 0.846369 1 - 1.8998

lmo1773_Adenylosuccinate lyase -1.5371 -0.92591 0.602373 0.846369 8 - lmo1605_UDP-N-acetylmuramate--L-alanine 2.3881

ligase -1.5369 -1.25589 0.817156 0.846369 4 - 2.9111

lmo1096_GMP synthase -1.52906 -1.54158 1.008193 0.846369 4 - 2.3361

lmo1596_30S ribosomal protein S4 -1.52899 -1.22415 0.800625 0.846369 8 - lmo2505_P45 (Peptidoglycan lytic protein P45) 3.8106

precursor -1.51331 -1.93004 1.275372 1.395113 5 - 2.2710

lmo1077_Lmo1077 protein -1.51301 -1.18334 0.78211 1.395113 2 - 2.2495

lmo2531_ATP synthase subunit alpha 2 -1.50794 -1.16967 0.775671 1.395113 9 - 2.5454

lmo1580_Lmo1580 protein -1.50733 -1.34794 0.894259 1.395113 9 -

lmo2031_Lmo2031 protein -1.50296 -1.55634 1.035515 1.395113 2.9410

188

6 - 3.0803

lmo1806_Acyl carrier protein -1.50068 -1.62309 1.081566 1.395113 4 - 1.9871

lmo2153_Lmo2153 protein -1.47566 -0.9907 0.67136 1.395113 5 - 2.0126

lmo2405_Lmo2405 protein -1.46795 -1.00907 0.687403 1.395113 2 - 2.3308

lmo1215_Lmo1215 protein -1.46162 -1.22087 0.835282 1.395113 7 - 2.1812

lmo2109_Lmo2109 protein -1.45852 -1.12518 0.771454 1.395113 8 - 2.0875

lmo2165_Lmo2165 protein -1.45836 -1.06178 0.728068 1.395113 1 - 2.1503

lmo1068_Lmo1068 protein -1.4523 -1.10459 0.760584 1.525905 8

lmo2119_Lmo2119 protein -1.45164 -1.09795 0.75635 1.525905 -2.1405 - 2.3775

lmo2231_Lmo2231 protein -1.44673 -1.24945 0.863639 1.525905 1 - 2.9378

lmo2367_Glucose-6-phosphate isomerase -1.44638 -1.55476 1.074932 1.525905 4 - 2.3083

lmo0014_AA3-600 quinol oxidase subunit I -1.44531 -1.20684 0.834999 1.525905 1

lmo0403_Lmo0403 protein -1.43664 -1.2745 0.887138 1.525905 -

189

2.4191 5 - 2.8658

lmo2371_Lmo2371 protein -1.43495 -1.51895 1.058542 1.525905 3 - 1.7761

lmo1948_ResD protein -1.43384 -0.82877 0.578006 1.525905 7 - 2.3376

lmo0581_Lmo0581 protein -1.41115 -1.22505 0.86812 1.747091 4

lmo1495_Lmo1495 protein -1.40929 -1.18079 0.837856 1.747091 -2.267 - 2.0584

lmo2638_Lmo2638 protein -1.40867 -1.04157 0.7394 1.747091 6 - 2.0827

lmo1494_Lmo1494 protein -1.40409 -1.05849 0.753861 1.747091 5 - 2.2499

lmo0388_Lmo0388 protein -1.40394 -1.16988 0.833281 1.747091 2

lmo1175_EutB protein -1.39756 -1.09356 0.782478 1.747091 -2.134 - 2.6495

lmo1626_Lmo1626 protein -1.39469 -1.40577 1.007945 1.747091 9 - lmo0259_DNA-directed RNA polymerase 2.2708

subunit beta' -1.39236 -1.18325 0.849811 1.747091 7 - 1.9360

lmo1178_Lmo1178 protein -1.39106 -0.95313 0.685184 1.747091 7 -

lmo1619_D-alanine aminotransferase -1.38279 -0.9068 0.655779 1.747091 1.8748

190

9 - 1.7573

lmo1930_Lmo1930 protein -1.38 -0.81337 0.5894 1.763268 1 - 2.6776

lmo0903_Lmo0903 protein -1.37778 -1.42095 1.031333 1.763268 1 - 1.7066

lmo0152_Lmo0152 protein -1.37752 -0.77114 0.559798 1.763268 1 - 1.9873

lmo2457_Triosephosphate isomerase 1 -1.37278 -0.99087 0.721798 1.763268 9 - 2.0328

lmo2191_Regulatory protein spx -1.37197 -1.02349 0.746003 1.763268 3 - 2.5813

lmo0221_Type III pantothenate kinase -1.36981 -1.36813 0.998771 1.763268 5 - 2.6007

lmo1179_Lmo1179 protein -1.36872 -1.37891 1.007442 1.763268 1 - 2.5192

lmo0135_Lmo0135 protein -1.36414 -1.33298 0.977162 1.763268 3 - lmo1774_Phosphoribosylaminoimidazole 2.2447

carboxylase II -1.36355 -1.16655 0.855518 1.763268 4 - 2.1915

lmo2612_Preprotein translocase secY subunit -1.36043 -1.13192 0.832033 1.763268 1 lmo2468_ATP-dependent Clp protease

proteolytic subunit -1.35873 -1.34772 0.991898 1.763268 -2.5451

191

- 2.2205

lmo1978_Glucose-6-phosphate 1-dehydrogenase -1.35759 -1.15091 0.84776 1.763268 4 - 2.0057

lmo2715_CydD protein -1.35037 -1.00414 0.743601 1.763268 5 - 1.8056

lmo2120_Lmo2120 protein -1.34761 -0.8525 0.632603 1.763268 3 - 2.3397

lmo1270_Lmo1270 protein -1.34742 -1.22636 0.910152 1.763268 5 - 2.6014

lmo0524_Lmo0524 protein -1.34003 -1.3793 1.029302 1.763268 2 - 1.9822

lmo1879_CspD protein -1.33587 -0.98714 0.738947 1.763268 5 - 2.0961

lmo2358_Lmo2358 protein -1.33404 -1.06776 0.800396 1.763268 8 - 3.3004

lmo1261_Lmo1261 protein -1.33144 -1.72267 1.293837 1.763268 7 lmo1075_Teichoic acids export ATP-binding

protein tagH -1.32836 -1.26963 0.955792 1.763268 -2.411 - 2.1572

lmo1461_Lmo1461 protein -1.31641 -1.10917 0.84257 1.763268 2 - 2.0360

lmo1825_Lmo1825 protein -1.31612 -1.02574 0.779368 1.763268 1

lmo1980_Lmo1980 protein -1.30491 -1.05366 0.807462 3.043449 -2.0758

192

- 2.0276

lmo2529_ATP synthase subunit beta 2 -1.30463 -1.01979 0.781671 3.043449 3 - 1.9160

lmo0217_DivIC homolog -1.30341 -0.93811 0.719734 3.043449 1 - 2.1790

lmo1762_Lmo1762 protein -1.3029 -1.12372 0.862471 3.043449 8 - 2.6846

lmo1797_30S ribosomal protein S16 -1.29987 -1.42473 1.09606 3.043449 5 - lmo0201_1-phosphatidylinositol 1.7946

phosphodiesterase precursor -1.29688 -0.84367 0.650536 3.043449 1 - 2.3683

lmo1351_Lmo1351 protein -1.2965 -1.24386 0.959398 3.043449 2 - 2.0125

lmo1269_Signal peptidase I -1.28688 -1.00901 0.784069 3.043449 2 - 2.4824

lmo2610_Translation initiation factor IF-1 -1.28423 -1.31175 1.021428 3.043449 2 - 1.7994

lmo1763_Lmo1763 protein -1.28386 -0.84754 0.660148 3.043449 3 - 2.8664

lmo1937_GTP-binding protein engA -1.28007 -1.51926 1.186854 3.043449 3 - 3.5744

lmo2157_SepA protein -1.27523 -1.83773 1.441091 3.043449 6

193

- 2.3338

lmo0407_Lmo0407 protein -1.27401 -1.22269 0.95972 3.043449 2 - 2.3279

lmo0100_Lmo0100 protein -1.26905 -1.21905 0.960602 3.043449 4 - 1.7820

lmo0220_FtsH protein -1.2677 -0.83355 0.65753 3.043449 6 - 1.7208

lmo2261_Lmo2261 protein -1.2627 -0.78311 0.620189 3.043449 4 - lmo1756_Aspartyl/glutamyl-tRNA(Asn/Gln) 1.8577

amidotransfrase subunit C -1.26219 -0.89353 0.70792 3.043449 1 - 1.7242

lmo1354_Lmo1354 protein -1.26181 -0.78594 0.622871 3.043449 2

lmo2844_Lmo2844 protein -1.26178 -0.94126 0.745978 3.043449 -1.9202 - 2.2114

lmo1649_Lmo1649 protein -1.25818 -1.14499 0.910034 3.043449 4 - 1.7524

lmo1385_Lmo1385 protein -1.25118 -0.80936 0.646874 3.043449 3 - 1.7893

lmo1305_Tkt protein -1.251 -0.83943 0.671008 3.043449 4 - 1.7662

lmo1745_Lmo1745 protein -1.25017 -0.82072 0.656483 3.043449 8 -

lmo0663_Lmo0663 protein -1.24938 -1.00768 0.806549 3.043449 2.0106

194

8 - 2.5495

lmo0959_Lmo0959 protein -1.24668 -1.35027 1.08309 3.043449 9 - 3.6101

lmo1547_MreC protein -1.24507 -1.85207 1.487523 3.043449 7 - 1.7920

lmo1284_UPF0078 membrane protein lmo1284 -1.24479 -0.84158 0.676079 3.043449 1 - 1.9745

lmo0799_Blue-light photoreceptor -1.23924 -0.98154 0.792049 3.043449 7 - 2.0127

lmo2145_Lmo2145 protein -1.23492 -1.00919 0.817205 3.043449 7

lmo0755_Lmo0755 protein -1.23077 -1.03337 0.839614 3.547281 -2.0468 - lmo1279_ATP-dependent hsl protease ATP- 1.6311

binding subunit hslU -1.22837 -0.7059 0.574664 3.547281 6 - 2.0739

lmo0908_Lmo0908 protein -1.22651 -1.0524 0.858045 3.547281 8 - 1.9650

lmo1573_AccD protein -1.22253 -0.97459 0.797186 3.547281 8 - 1.8003

lmo2334_Lmo2334 protein -1.22148 -0.8483 0.69449 3.547281 8 - 2.4722

lmo2458_Phosphoglycerate kinase -1.21304 -1.30584 1.076502 3.547281 7

lmo2518_Lmo2518 protein -1.21188 -1.29312 1.06704 3.547281 -

195

2.4505 8 - 2.0875

lmo2193_ATPase OppD -1.21083 -1.0618 0.876924 3.547281 4 - lmo1847_Manganese-binding lipoprotein mntA 2.4161

precursor -1.20607 -1.27271 1.055255 3.547281 4

lmo2654_Elongation factor G -1.20409 -1.05859 0.879163 3.547281 -2.0829 - 2.0871

lmo1938_Lmo1938 protein -1.20345 -1.06154 0.882082 3.547281 6 - 1.9935

lmo0782_Lmo0782 protein -1.20282 -0.99532 0.827486 3.547281 2 - 2.9089

lmo2216_Lmo2216 protein -1.20173 -1.54051 1.281907 3.547281 8 - 2.2144

lmo2537_UDP-N-acetylglucosamine 2-epimerase -1.19813 -1.14697 0.957302 3.547281 8 - 2.2236

lmo1965_Lmo1965 protein -1.19453 -1.15292 0.965159 3.547281 3 - 2.2882

lmo1083_dTDP-glucose 4,6-dehydratase -1.19272 -1.19424 1.001273 3.547281 4 - 1.8655

lmo1926_Lmo1926 protein -1.19198 -0.89959 0.7547 3.547281 3 - lmo0218_Polyribonucleotide 2.2845

nucleotidyltransferase domain present -1.18742 -1.19189 1.003769 3.547281 2

196

- 1.7470

lmo1966_Lmo1966 protein -1.18624 -0.80489 0.678523 3.547281 2 - 2.1571

lmo0994_Lmo0994 protein -1.17959 -1.10914 0.940275 3.547281 8 - 1.7649

lmo0291_Lmo0291 protein -1.1772 -0.81964 0.696259 3.547281 6 - 2.0510

lmo2653_Elongation factor Tu -1.17513 -1.03633 0.881884 3.547281 1 - 1.8872

lmo2556_Fructose-bisphosphate aldolase -1.17186 -0.9163 0.781918 3.547281 7 - 1.9605

lmo0264_Internalin E -1.16734 -0.97123 0.832002 3.926453 1 - 1.6447

lmo0213_Peptidyl-tRNA hydrolase -1.16484 -0.71784 0.616258 3.926453 2 - 2.6822

lmo1555_Lmo1555 protein -1.16149 -1.42346 1.225545 3.926453 8 - 1.6805

lmo0097_Lmo0097 protein -1.15605 -0.74897 0.647868 3.926453 9 - 2.3284

lmo2051_Lmo2051 protein -1.15461 -1.21938 1.056097 3.926453 7 - 1.9246

lmo1464_Lmo1464 protein -1.15337 -0.9446 0.818986 3.926453 5

197

- 1.7686

lmo2017_Lmo2017 protein -1.14649 -0.82261 0.717505 3.926453 1 - 2.6776

lmo1364_Cold shock-like protein cspLA -1.14639 -1.42095 1.239504 3.926453 2 - 1.9838

lmo2720_Lmo2720 protein -1.14563 -0.98829 0.862657 3.926453 3 lmo1848_Manganese transport system membrane

protein mntC -1.14539 -1.02928 0.898628 3.926453 -2.041 - 1.7868

lmo0513_Lmo0513 protein -1.13932 -0.83741 0.735007 3.926453 4 - lmo1317_1-deoxy-D-xylulose 5-phosphate 1.8962

reductoisomerase -1.13592 -0.92318 0.812714 3.926453 8 - 2.7144

lmo1699_Lmo1699 protein -1.13533 -1.44065 1.268925 3.926453 3 - 1.8830

lmo0708_Lmo0708 protein -1.13225 -0.91308 0.806434 5.19093 7 - 1.7703

lmo0982_Lmo0982 protein -1.12941 -0.82403 0.729615 5.19093 5 - 1.9700

lin0434_ -1.12843 -0.97824 0.866908 5.19093 7 - 1.7104

lmo2130_Lmo2130 protein -1.12518 -0.77436 0.688206 5.19093 3

lmo2039_PbpB protein -1.12374 -1.06605 0.948664 5.19093 -2.0937

198

- 2.1545

lmo0978_Lmo0978 protein -1.12175 -1.1074 0.987209 5.19093 7 - 3.1936

lmo0279_Lmo0279 protein -1.1182 -1.67522 1.49813 5.19093 7 - lmo1923_3-phosphoshikimate 1- 2.3392

carboxyvinyltransferase -1.11818 -1.22605 1.096471 5.19093 5 - 2.4975

lmo0425_Lmo0425 protein -1.11789 -1.32052 1.181257 5.19093 5 - 1.6832

lmo0274_Lmo0274 protein -1.11634 -0.75126 0.672969 5.19093 6 - 1.8244

lmo1569_Lmo1569 protein -1.11543 -0.86747 0.777701 5.19093 6 - 1.7371

lmo0964_UPF0413 protein lmo0964 -1.11469 -0.79669 0.714722 5.19093 1 - 2.0106

lmo1415_Lmo1415 protein -1.11429 -1.00768 0.904322 5.19093 8 - lmo1572_Acetyl-coenzyme A carboxylase 1.7115

carboxyl transferase subunit alpha -1.11128 -0.77527 0.69764 5.19093 1 - 2.7071

lmo2506_FtsX protein -1.11093 -1.4368 1.293329 5.19093 9 - 1.7606

lmo1736_Lmo1736 protein -1.10665 -0.81612 0.737465 5.19093 6

199

- 1.5682

lmo2503_Cardiolipin synthetase -1.10104 -0.64912 0.589555 5.83539 1 - 1.8587

lmo2703_UPF0133 protein lmo2703 -1.09668 -0.89436 0.815515 5.83539 9 - 2.5879

lmo0099_Lmo0099 protein -1.09639 -1.37183 1.251225 5.83539 9 - 1.5569

lmo1405_Lmo1405 protein -1.09595 -0.63869 0.582774 5.83539 1 - 2.9097

lmo2605_50S ribosomal protein L17 -1.09533 -1.54087 1.406771 5.83539 1 - 1.5561

lmo1953_Pnp protein -1.09507 -0.63795 0.582571 5.83539 2 - 2.0181

lmo2040_FtsL protein -1.08604 -1.01301 0.932756 5.83539 1 - 1.9991

lmo2577_Lmo2577 protein -1.08353 -0.99942 0.922367 5.83539 9 - 1.6601

lmo1583_Probable thiol peroxidase -1.08234 -0.7313 0.675665 5.83539 3 - 1.7720

lmo1567_CitZ protein -1.08052 -0.82543 0.763913 5.83539 6 - 1.8071

lmo2555_Lmo2555 protein -1.07984 -0.85373 0.790615 5.83539 7

200

- 1.9429

lmo2533_ATP synthase B chain -1.07899 -0.95827 0.888119 5.83539 8 - 1.5965

lmo2520_Lmo2520 protein -1.07897 -0.67495 0.625547 5.83539 4

lmo0223_Cysteine synthase -1.07662 -0.78056 0.725008 5.83539 -1.7178 - 1.6957

lmo2785_Catalase -1.07586 -0.76191 0.708182 5.83539 3 - 1.8684

lmo1908_Lmo1908 protein -1.07522 -0.90182 0.838725 5.83539 2 - 1.6762

lmo1438_Lmo1438 protein -1.07301 -0.74524 0.694537 5.83539 6

lmo2372_Lmo2372 protein -1.07142 -0.96414 0.899869 5.83539 -1.9509 - 1.5566

lmo0801_Lmo0801 protein -1.06976 -0.63847 0.596836 6.910874 8 - 1.8054

lmo1599_Catabolite control protein A -1.06612 -0.85239 0.799523 6.910874 8 - 1.9871

lmo0713_Lmo0713 protein -1.06465 -0.99072 0.930566 6.910874 8 - 1.7231

lmo1823_Methionyl-tRNA formyltransferase -1.06425 -0.78508 0.737685 6.910874 9 - 1.5824

lmo1450_Lmo1450 protein -1.06386 -0.66216 0.622412 6.910874 5

lmo1557_Glutamyl-tRNA reductase -1.06147 -1.1046 1.040629 6.910874 -

201

2.1503 9 - 2.9145

lmo1696_Lmo1696 protein -1.06037 -1.5433 1.45543 6.910874 9 - 1.8116

lmo0773_Lmo0773 protein -1.05444 -0.85728 0.813012 6.910874 1 - 2.0869

lmo2129_Lmo2129 protein -1.05434 -1.06138 1.00667 6.910874 2 - 2.5210

lmo2020_DivIVA protein -1.04903 -1.33402 1.27167 6.910874 4 - lmo1268_ATP-dependent Clp protease ATP- 1.8945

binding subunit clpX -1.04638 -0.92183 0.880972 6.910874 1 - 1.8976

lmo0592_Lmo0592 protein -1.04597 -0.92422 0.883605 6.910874 6 - 1.7051

lmo0779_UPF0266 membrane protein lmo0779 -1.04535 -0.76992 0.736518 6.910874 8

lmo1322_NusA protein -1.03885 -0.80463 0.774542 6.910874 -1.7467

lmo0322_Lmo0322 protein -1.03201 -0.74674 0.723578 7.258104 -1.678

lmo0688_Lmo0688 protein -1.03095 -1.52476 1.478984 7.258104 -2.8774 - 2.0488

lmo2596_30S ribosomal protein S9 -1.02822 -1.03481 1.006409 7.258104 4

lmo0673_Lmo0673 protein -1.02555 -1.03471 1.008928 7.258104 -2.0487 - 1.5612

lmo2305_Lmo2305 protein -1.02405 -0.64267 0.627575 7.258104 1

202

- 2.0808

lmo0250_50S ribosomal protein L10 -1.02245 -1.05718 1.033971 7.258104 6 - lmo1755_Glutamyl-tRNA(Gln) amidotransferase 2.2541

subunit A -1.02189 -1.17261 1.147488 7.258104 9

lmo2413_Lmo2413 protein -1.02119 -0.59827 0.585854 7.258104 -1.5139 - 1.7075

lmo0922_Pantothenate kinase -1.02056 -0.77195 0.756398 7.258104 8 - lmo2601_Cobalt import ATP-binding protein 1.5997

cbiO 2 -1.01556 -0.67786 0.66747 7.258104 6 - 1.8207

lmo1233_Thioredoxin -1.01017 -0.86457 0.855861 7.258104 9

lmo0192_Lmo0192 protein -1.00595 -0.84245 0.837467 7.453815 -1.7931 - 1.6419

lmo2397_Lmo2397 protein -1.00111 -0.71541 0.714611 7.453815 5 - 1.6882

lmo2407_Lmo2407 protein -0.99996 -0.75554 0.755567 7.453815 6 - 1.6752

lmo2570_Lmo2570 protein -0.99966 -0.74441 0.744662 7.453815 9 - 1.5941

lmo2701_Lmo2701 protein -0.99573 -0.67278 0.675659 7.453815 4

lmo1548_MreB protein -0.99201 -1.12347 1.132517 7.453815 -2.1787 - 2.2896

lmo1358_Lmo1358 protein -0.99185 -1.19515 1.204973 7.453815 9

203

- 2.3386

lmo0707_Lmo0707 protein -0.99164 -1.22569 1.236024 7.453815 7 - 1.8477

lmo0717_Lmo0717 protein -0.99119 -0.88579 0.893655 7.453815 7 - lmo1768_Glutamine 1.5954

phosphoribosylpyrophosphate amidotransferase -0.99025 -0.67398 0.680619 7.453815 7 - 1.5051

lin1056_ -0.9875 -0.58994 0.597412 7.453815 9 - 1.7309

lmo1766_PurN protein -0.98672 -0.79153 0.802185 7.453815 1 - 2.1054

lmo2656_30S ribosomal protein S12 -0.98498 -1.07414 1.090519 7.453815 7 - 2.1383

lmo1239_Nucleoside-triphosphatase -0.98271 -1.09647 1.115766 7.453815 1 - 1.6012

lmo0012_Lmo0012 protein -0.97848 -0.67919 0.694131 7.453815 4 - 1.6764

lmo2336_Fructose-1-phosphate kinase -0.97753 -0.74539 0.762528 7.453815 3 - 1.6976

lmo2057_Protoheme IX farnesyltransferase -0.97474 -0.7635 0.78329 8.307478 1

lmo1787_50S ribosomal protein L19 -0.97383 -1.13468 1.165166 8.307478 -2.1957 -

lmo2357_Lmo2357 protein -0.97355 -0.99088 1.0178 8.307478 1.9873

204

9 - 1.6465

lmo1943_Lmo1943 protein -0.9728 -0.71947 0.739584 8.307478 8 - 2.2614

lmo1455_DNA primase -0.96727 -1.17727 1.217106 8.307478 9 - 1.8044

lmo2166_Lmo2166 protein -0.96704 -0.85158 0.880601 8.307478 7 - 1.6897

lmo0161_Lmo0161 protein -0.96655 -0.75682 0.78301 8.307478 6 - 1.6653

lmo1459_Glycyl-tRNA synthetase alpha subunit -0.96637 -0.7358 0.761412 8.307478 3 - 1.5507

lmo2417_Lmo2417 protein -0.96432 -0.63296 0.656378 8.307478 4 - 1.4920

lmo2386_Lmo2386 protein -0.96382 -0.57732 0.598986 8.307478 7 - 2.0453

lmo1072_PycA protein -0.96311 -1.03238 1.071915 8.307478 9 - 2.0655

lmo1729_Lmo1729 protein -0.96278 -1.04655 1.087015 8.307478 9 - 1.8029

lmo0479_Putative secreted protein -0.96253 -0.85036 0.883467 8.307478 5 -

lmo2471_NADPH dehydrogenase -0.96018 -0.62853 0.654599 8.307478 1.5459

205

9 - 1.9227

lmo0597_Lmo0597 protein -0.96002 -0.94316 0.982433 8.307478 3 - lmo1475_Heat-inducible transcription repressor 1.5098

hrcA -0.95998 -0.59442 0.619199 8.307478 7

lmo2685_Lmo2685 protein -0.9599 -1.20671 1.257114 8.307478 -2.3081 - 1.6544

lmo1896_Asparaginyl-tRNA synthetase -0.95844 -0.72635 0.757848 8.307478 5 - 1.7646

lmo2115_Lmo2115 protein -0.95778 -0.81939 0.855518 8.307478 7 - 1.8291

lmo1319_Prolyl-tRNA synthetase -0.95611 -0.87114 0.911129 8.307478 1 - 1.5923

lmo1867_Lmo1867 protein -0.95219 -0.67117 0.704871 8.307478 6 - lmo0001_Chromosomal replication initiator 1.6639

protein dnaA -0.95083 -0.73462 0.772613 8.307478 6 - 1.8407

lmo2194_Lmo2194 protein -0.94664 -0.88028 0.929898 8.307478 4 - lmo2549_Cell wall teichoic acid glycosylation 1.8477

protein gtcA -0.94565 -0.88574 0.936647 8.307478 1 - 1.4999

lmo0468_Lmo0468 protein -0.94415 -0.58495 0.61955 8.307478 8 lmo2526_UDP-N-acetylglucosamine 1- -0.94389 -1.01614 1.076545 8.307478 -2.0225

206

carboxyvinyltransferase 1 - 1.5587

lmo0287_Lmo0287 protein -0.94325 -0.64037 0.678898 8.307478 3 - 1.5531

lmo1813_Lmo1813 protein -0.94191 -0.63517 0.674342 8.307478 2

lmo0170_Lmo0170 protein -0.94184 -1.32314 1.404843 8.307478 -2.5021 - 1.9167

lmo2249_Lmo2249 protein -0.93988 -0.93869 0.998734 8.307478 9 - lmo1685_Glutamate-1-semialdehyde 2,1- 1.6274

aminomutase 2 -0.93967 -0.70258 0.747696 8.307478 2 - 2.0783

lmo0703_Lmo0703 protein -0.93909 -1.05544 1.123893 8.307478 5 - 2.5906

lmo1934_Hup protein -0.93561 -1.37332 1.467847 8.307478 7 - 2.3973

lmo2223_UPF0342 protein lmo2223 -0.93532 -1.26146 1.348695 8.307478 9 - 1.6221

lmo2140_Lmo2140 protein -0.93381 -0.69788 0.747344 8.307478 2 - 1.8041

lmo2640_Lmo2640 protein -0.93216 -0.85132 0.913273 9.224097 5 - 1.6615

lmo1537_Lmo1537 protein -0.9308 -0.73249 0.786951 9.224097 1 lmo1545_Probable septum site-determining -0.92864 -0.65869 0.709298 9.224097 -

207

protein minC 1.5786 4

lmo0281_Lmo0281 protein -0.92838 -0.71818 0.773585 9.224097 -1.6451

lmo1822_Lmo1822 protein -0.927 -0.67301 0.726013 9.224097 -1.5944 - 1.9660

lmo1462_GTP-binding protein era homolog -0.92581 -0.97528 1.053441 9.224097 3 - 1.9317

lmo1468_Lmo1468 protein -0.92505 -0.94994 1.026908 9.224097 9 - 1.8648

lmo0197_Putative septation protein spoVG 2 -0.92341 -0.89906 0.973634 9.224097 5

lmo2262_Lmo2262 protein -0.9167 -0.68015 0.741952 9.224097 -1.6023 - 1.4326

lmo2652_Lmo2652 protein -0.91222 -0.51871 0.568617 9.224097 7 - 1.4516

lmo0604_Lmo0604 protein -0.91212 -0.53773 0.589541 9.224097 9 - 1.7792

lmo1925_Histidinol-phosphate aminotransferase -0.90946 -0.83124 0.913998 9.224097 2 - 1.5182

lmo1623_Lmo1623 protein -0.90936 -0.60242 0.662466 9.224097 6 - 1.5482

lmo2424_Lmo2424 protein -0.90865 -0.63064 0.694043 9.224097 6 - 1.7343

lmo0665_Lmo0665 protein -0.90563 -0.79437 0.877145 9.224097 2

lmo2421_Sensor protein -0.9051 -0.61261 0.676846 9.224097 -

208

1.5290 2 - 1.9044

lmo0248_50S ribosomal protein L11 -0.90488 -0.92936 1.027055 9.224097 4

lmo1392_Lmo1392 protein -0.90471 -0.75796 0.837802 9.224097 -1.6911 - 2.0088

lmo1783_50S ribosomal protein L20 -0.90365 -1.00637 1.11368 9.224097 6 - 1.4955

lmo2768_Lmo2768 protein -0.89977 -0.58071 0.645395 10.394 8 - 1.5003

lmo0608_Lmo0608 protein -0.89705 -0.58526 0.652435 10.394 1 - 1.5108

lmo2415_Lmo2415 protein -0.8954 -0.59535 0.664904 10.394 4 - 1.5709

lmo0947_Lmo0947 protein -0.89507 -0.6516 0.727995 10.394 1

lmo1700_Lmo1700 protein -0.89476 -1.4197 1.586692 10.394 -2.6753 - 1.9082

lmo0815_Lmo0815 protein -0.89276 -0.93223 1.044209 10.394 2

209

Appendix (G): regulated genes (down-regulated-regulation) of luxS mutant strain of L. monocytogenes at exponential phase at 4°C

Numerator(r Denominator(s+s0 q- Gene Name Score(d) ) ) value(%) fold-change lmo0246_Transcription antitermination protein

nusG 3.4641 0.707272 0.204172 17.05839 -1.632714218

lmo1663_AnsB protein 3.20171 0.949257 0.296484 17.05839 -1.930877526 2.78683

lmo2113_UPF0447 protein lmo2113 2 0.998504 0.358293 17.05839 -1.99792699 2.78121

lmo2224_Lmo2224 protein 2 0.760604 0.27348 17.05839 -1.694200255 2.77458

lmo1449_Probable endonuclease 4 4 0.550828 0.198526 17.05839 -1.464926511

lmo0226_FolK protein 2.58159 0.677644 0.262491 21.32299 -1.599525144 2.54007

lmo2378_Lmo2378 protein 3 0.667468 0.262775 21.32299 -1.588282592 2.52239

lmo0478_Putative secreted protein 6 0.779994 0.309228 21.32299 -1.717124108

210

Appendix (H): genes expressed in agrD mutant stationary phase at both 37°C and 4°C (80 genes)

Gene Name

lmo1580_Lmo1580 protein

lmo0943_DNA protection during starvation protein

lmo0994_Lmo0994 protein

lmo1572_Acetyl-coenzyme A carboxylase carboxyl transferase subunit alpha

lmo1171_PduQ protein

lmo0479_Putative secreted protein

lmo0608_Lmo0608 protein

lmo0688_Lmo0688 protein

lmo0782_Lmo0782 protein

lmo1468_Lmo1468 protein

lmo2570_Lmo2570 protein

lmo0539_Tagatose 1,6-diphosphate aldolase

lmo1077_Lmo1077 protein

lmo1178_Lmo1178 protein

lmo0964_UPF0413 protein lmo0964

lmo1295_Lmo1295 protein

lmo0933_Lmo0933 protein

lmo2701_Lmo2701 protein

lmo2468_ATP-dependent Clp protease proteolytic subunit

lmo0524_Lmo0524 protein

lmo1685_Glutamate-1-semialdehyde 2,1-aminomutase 2

lmo2386_Lmo2386 protein

lmo1068_Lmo1068 protein

lmo0596_Lmo0596 protein

lmo2471_NADPH dehydrogenase

lmo2057_Protoheme IX farnesyltransferase

211

lmo2397_Lmo2397 protein

lmo2529_ATP synthase subunit beta 2

lmo1279_ATP-dependent hsl protease ATP-binding subunit hslU

lmo1381_Acylphosphatase

lmo1322_NusA protein

lmo2417_Lmo2417 protein

lmo1569_Lmo1569 protein

lmo1855_Lmo1855 protein

lmo1941_Lmo1941 protein

lmo2191_Regulatory protein spx

lmo2109_Lmo2109 protein

lmo2653_Elongation factor Tu

lmo0161_Lmo0161 protein

lmo0201_1-phosphatidylinositol phosphodiesterase precursor

lmo2715_CydD protein

lmo1596_30S ribosomal protein S4

lmo2654_Elongation factor G

lmo2358_Lmo2358 protein

lmo0606_Lmo0606 protein

lmo1797_30S ribosomal protein S16

lmo1908_Lmo1908 protein

lmo1284_UPF0078 membrane protein lmo1284

lmo1462_GTP-binding protein era homolog

lmo1283_Lmo1283 protein

lmo1252_Lmo1252 protein

lmo1439_Superoxide dismutase

lmo1934_Hup protein

lmo0287_Lmo0287 protein

lmo2029_Lmo2029 protein

lmo1275_DNA topoisomerase

lmo0922_Pantothenate kinase

lmo1570_Pyruvate kinase

212

lmo1405_Lmo1405 protein

lmo1233_Thioredoxin

lmo0259_DNA-directed RNA polymerase subunit beta'

lmo1583_Probable thiol peroxidase

lmo2638_Lmo2638 protein

lmo2612_Preprotein translocase secY subunit

lmo0196_Putative septation protein spoVG 1

lmo1215_Lmo1215 protein

lmo2032_Cell division protein ftsZ

lmo1966_Lmo1966 protein

lmo2652_Lmo2652 protein

lmo0959_Lmo0959 protein

lmo0982_Lmo0982 protein

lmo0388_Lmo0388 protein

lmo2659_Lmo2659 protein

lmo1450_Lmo1450 protein

lmo2031_Lmo2031 protein

lmo1879_CspD protein

lmo2193_ATPase OppD

lmo1953_Pnp protein

lmo2555_Lmo2555 protein

213